Research ArticleIdentification of Putative Molecular Markers Associated withRoot Traits in Coffea canephora Pierre ex Froehner

Devaraja Achar1 Mallikarjuana G Awati2 M Udayakumar1 and T G Prasad1

1Department of Crop Physiology University of Agriculture Sciences Gandhi Krishi Vignana Ken-dra BangaloreKarnataka 560 065 India2Division of Plant Physiology Central Coffee Research Institute Balehonnur Chikmagalur District Karnataka 577 112 India

Correspondence should be addressed to Devaraja Achar dacharrediffmailcom

Received 25 June 2014 Accepted 11 January 2015

Academic Editor Surjit Kaila S Srai

Copyright copy 2015 Devaraja Achar et alThis is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Coffea canephora exhibit poor root system and are very sensitive to drought stress that affects growth and production Deeperroot system has been largely empirical as better avoidance to soil water limitation in drought condition The present study aimedto identify molecular markers linked to high root types in Coffea canephora using molecular markers Contrasting parents L1valley with low root and S3334 with high root type were crossed and 134 F1 individuals were phenotyped for root and associatedphysiological traits (29 traits) and genotyped with 41 of the 320 RAPD and 9 of the 55 SSR polymorphic primers Single markeranalysis was deployed for detecting the association ofmarkers linked to root associated traits by SAS softwareTherewere 13 putativeRAPD markers associated with root traits such as root length secondary roots root dry weight and root to shoot ratio in whichroot length associatedmarker OPS1

850showed high phenotypic variance of 686 Twomicrosatellite markers linked to root length

(CPCM13400

) and root to shoot ratio (CM211300

) Besides 25 markers were associated with more than one trait and few of themarkers were associated with positively related physiological traits and can be used in marker assisted trait selection

1 Introduction

The worldrsquos primary source of caffeine is the coffee ldquobeanrdquowhich is the seed of the coffee plant from which coffee isbrewed and therefore it is one of the most important com-modities in the international agricultural trade representinga significant source of income to several coffee growingcountries The genus Coffea belongs to Rubiaceae familywhich has 500 genera and over 6000 species in which Coffeaarabica (2119899 = 44) and Coffea canephora (2119899 = 22) are the twocommercially cultivated species Currently the productionof arabica and robusta coffee accounts for 65 and 35respectively however arabica coffee typically contains halfthe caffeine of the robusta variety [1 2] The world coffeeproduction in 20122013 was 155140 (in 1000 60 kilogrambags) but then in 20132014 and 20142015 there is a declinein coffee production and it is predicted to be decreasedThis decreased coffee production is predominantly associatedwith variable climatic conditions particularly limited water

stress (drought) wherewater shortages are responsible for thegreatest crop losses around the world [3 4] In several coffeegrowing countries drought is considered to be themajor envi-ronmental stress affecting coffee production in particularCoffea robusta Because the robusta coffee is shallow rooted ithas largely been cultivated in water constraint experiencingthe stress at various crop phenology which impacts cropgrowth and development above ground [5ndash7]

Indeed drought-adapted plants are often characterizedby deep and vigorous root systems since root associatedtraits play a crucial role in maintaining canopy hydraulicconductance with high carbon assimilation in drought [8 9]Breeding coffee plants for root traits to enhance the pro-ductivity under water stress is very much required but littleprogress has been achieved due to quantitative nature andpoor knowledge of the genetic control of drought toleranceFurthermore the quantitative nature of root traits could beeither constitutive or adaptive but difficult to phenotypeTherefore it is not surprising that a majority of genetic

Hindawi Publishing CorporationMolecular Biology InternationalVolume 2015 Article ID 532386 11 pageshttpdxdoiorg1011552015532386

2 Molecular Biology International

research has focused on aboveground traits while the ldquohiddenhalf rdquo of the plant is much less represented in recent research[10] With the conventional breeding methods introgressionof complexquantitative traits is unfeasibleMolecularmarkertechnology and genomics serve as a tool for selecting suchcomplex traits and allow breeders to track genetic locicontrolling drought resistance traits without measuring thephenotype thus reducing the need for extensive field testingover space and time [11ndash13] With the functional genomicsit was shown that the increase in the amount of RBCS1(Rubisco small subunit) protein could contribute to theantioxidative function of photorespiration in water-stressedCoffea canephora plants [14] Furthermore it was also shownthat drought acclimation in Coffee canephora clones probablyinvolving the abscisic signalling pathway and nitric oxide aremajor molecular determinants that might explain the betterefficiency in controlling stomata closure and transpirationdisplayed by drought-tolerant clones of C canephora [15]

With an advent of Next Generation Sequencing (NGS)technologies Coffea canephora is the first coffee species fullysequenced [16] The developed crop-specific hub the CoffeeGenome Hub (CGH) (httpcoffee-genomeorg) can beexploited to identify the genesSNPmarkers conditioning fordrought and root associated secondary traits by resequencingintra- and interspecific genetic resources of robust coffeespecies [17] Therefore it is very important to identify thegenetic loci conditioning root associated traits for breedingbetter drought tolerance clones With this background ourstudy aims at (1) developing a mapping population withcontrasting parental lines for root traits and phenotyping(2) genotyping the mapping population with RAPD and SSRmarkers and (3) detecting the association ofmolecularmark-ers with complex physiological and morphological traits

2 Materials and Methods

21 Plant Materials To identify the markers linked to rootand associated physiological traits F1 mapping populationwas developed by crossing low root type L1 valley as femaleparent with high root type S3334 as male parent Aftersuccessful pollination matured 300 coffee fruits were sownin nursery and after 48 days and 135 healthy seedlings weretransplanted to carbonized rubber containers (35 kg capacity)for better establishment This experiment was conductedat Central Coffee Research Institute Chikmagalur DistrictKarnataka State India Institute is situated at 13∘2210158401015840 northLatitude and 75∘2810158401015840 east Longitude at an elevation of 824 to884 meters above mean sea level

22 Phenotypic Analysis The moisture regime of 70 fieldcapacity (FC) was imposed for 180 days after six months ofcoffee seedlings established in carbonized rubber containersGravimetric approach was followed to maintain 70 FCwhere potted plants were daily weighed to add water whichwas evapotranspired [18]

23 Observations Recorded during Treatment Period Dur-ing the treatment period (180 days) cumulative wateradded evaporation evapotranspiration were recorded After

the treatment period root traits such as root length (cm)number of secondary roots root dry weight (gplant) shootdry weight (gplant) and root to shoot ratio were recordedBesidesmorphological gravimetric and gas exchange param-eters were also recoded [19]

24 Genomic DNA Extraction Coffee leaves were frozen inliquid nitrogen and stored at minus80∘C DNA was extractedfrom frozen leaves using cetyltrimethyl ammonium bromide(CTAB) method [20] For the CTAB technique 900 120583L ofCTAB extraction buffer was added to lyophilized leaf tissuein 2mL Eppendorf tubes and then lightly vortexed Thetubes were placed in hot water bath (65∘C) for 45min andmixed with 400120583L of chloroform isoamylalcohol (24 1) andcentrifuged for 15min The aqueous layer was collected and800 120583L of isopropanol was added to precipitate the nucleicacids Nucleic acid pellets were washed with 400120583L of 70ethanol dried and resuspended in 100 120583L of Tris-EDTAbuffer (10mM Tris with pH 75 and 05mM EDTA)

25 Polymerase Chain Reaction (PCR) RAPD (RandomAmplified Polymorphic DNA Operon Technologies)primers were used to genotype mapping populationPolymerase chain reaction was carried out in 15120583L reactioncontaining 1x buffer 2mM dNTPs 25mM MgCl

2 5 120583M

primer and 1U Taq DNA polymerase (NEB) Amplificationwas performed with the following thermal cycle profile94∘C4min hot start denaturation followed by 35 cycles of94∘C for 1min primer annealing at 38∘C for 1min extensionat 72∘C for 2min and a final extension at 72∘C for 8minThe PCR was performed using Eppendorf thermocycler(Eppendorf Hamburg Germany) The PCR products wererun on 15 agarose gel at 90 volts for 1 h 30min andamplified fragments were documented using Hero Lab GelDocumentation system (Inkarp)

SSR (simple sequence repeats) primers for polymerasechain reaction were synthesized based on the informationavailable in coffee genome database and also from in-housedeveloped from S3334 coffee accession

PCR amplification was carried out with 15 120583L reactionmixture having 50 ng DNA 1x PCR buffer 100 120583M dNTPs250 120583M primers and 1 unit Taq polymerase enzyme (NEB)Amplification was performed with the following thermalcycle profile 95∘C for 5min followed by 35 cycles ofpolymerization reaction each consisting of denaturation at94∘C for 15 s annealing at 60∘C for 45 s and an extensionstep at 72∘C for 1min A final extension step was run for5min at 72∘C The PCR was performed using Eppendorfthermocycler (Eppendorf Hamburg Germany) The PCRproducts were run on 6 polyacrylamide denaturing gelsAmplified fragments were detected using a silver-stainingprocedure (Promega Madison WI USA)

26 Study of Parental Polymorphism The contrasting parents(L1 valley times S3334) for root traits were screened with 320RAPD (series from OPK1 to OPZ20) and 55 SSR markersThe polymorphic RAPD bands were visually scored for thepresence (1) or absence (0) and in SSR analysis the segregatingband from the female parent was scored as 3 male parent as 1

Molecular Biology International 3

Table 1Genotypic variation of parental accessions S3334 (high roottype) and L1 valley (low root type)

S3334 L1 valley1 Root length 45 302 Secondary roots 40 153 Root dry weight 3181 4954 Root to shoot ratio 041 0365 Total dry matter 10677 15496 Cumulative water transpired 2886 31157 water use efficiency 372 3628 Net assimilation rate 1375 9099 Mean transpiration rate 372 22310 Pngs 7839 437811 Pn119864 299 18412 Cigs 38635 33994

and heterozygous bands were scored as 2 in all individuals ofF1 mapping populationThe binary data was used for furtherstatistical analysis Based on the segregation of RAPD mark-ers in the mapping population the putative genotypic inter-pretation of the parents for the marker locus was made andtheChi-square testwas performed (httpwwwphysicscsbsjuedustatschi-square formhtml)

27 Association of Identified Polymorphic Markers with Physi-ological Traits Single point analysis [21 22] for detecting theassociation of molecular markers with complex physiologicaland morphological traits was done using SAS software Tofind the amount of variability explained by these markersregression (1198772) values were worked out by one-way analysisof variance (ANOVA) by general linear model (GLM)procedure In this analysis different traits were treated asdependent variable and the various molecular markers asindependent variables A total of 30 different physiologicaland morphological traits were used to associate with the 85polymorphic molecular markers [23]

3 Results

31 Phenotyping Root Length Secondary Roots Root DryWeight and Physiological Traits At the end of experimentperiod about 180 days water stress pots were completelysaturated with water and then in next day all plants werecarefully detached with water force to avoid root loss Thedata on root length secondary roots and root biomass dataof parental lines (Table 1) and F1 mapping population wererecorded (Table 2) There was significant genetic variabilityobserved in root length ranging from 415 cm to 830 cmwith a mean of 5708 cm Similar trend was followed indry weight ranging from 257 g to 3469 g with a mean of1381 gplant and root to shoot ratio varies from 257 to 441with mean ratio of 377 It was observed that F1s root traitswere distributed between the values of the respective parentallines without much considerable transgressive segregation(Figure 1) The quantitative nature of these traits showedcontinuous variation confirming the metric nature of thetraits

Morphological traits such as plant height number ofnodes and leaves stem girth internodal length leaf areashoot dry weight and total dry matter were recordedNonetheless all these traits showed significant and contin-uous variation (Table 2) Besides genetic variability in themapping population in net photosynthesis (Pn) stomatalconductance (gs) transpiration rate (119864) intrinsic WUE(Pngs) and mesophyll efficiency (Cigs) was observed atsingle leaf level

Significant variations were observed in water use effi-ciency (WUE) and associated physiological traits like cumu-lative water transpired (CWT) net assimilation rate (NAR)mean transpiration rate (MTR) leaf area duration (LAD)and Δ13C in the population The WUE ranges from 386to 684 gkg with an average of 540 gkg and considerabletransgressive segregation was observed The variation inCWT was observed between 250 and 2350 kgplant Theaverage transpiration rate was 1081 Lplant in the popula-tion The NAR and MTR also varied with mean values of1307mgdm2 and 244mLdm2 of leaf area respectivelySimilarly the functional leaf area (LAD) varied between106589 and 799277 dm2day with mean leaf area duration of437657 dm2dayΔ13C also varied between 1975 and 2653permilwith a mean of 2180permil (Table 2) The variation in WUECWT Δ13C and so forth showed that traits are metricnature and mapping population could be utilized for theidentification of marker linked to quantitative traits

32 Association of Root with Other Physiological Traits Theplants with the higher root biomass showed higher totalbiomass and correlation between these two traits is highlysignificant (119903 = 0968) (Figure 2) It signifies the importanceof root system in determining the total dry matter by absorb-ing the water from sub-soil layers from its better root systemcompared to parental lines Subsequently strong positivesignificant correlations were noticed between root weightand total transpiration (119903 = 0940) CWT and MTR (119903 =0524) and total dry matter and CWT (119903 = 0970) (Figure 2)suggesting that maintaining canopy hydraulic conductancewith high carbon assimilation is one of the drought tolerancemechanisms

33 Relationship between WUE with Other PhysiologicalTraits Significant inverse relationship between WUE andΔ13C (119903 = minus0413) was observed (Figure 2) It suggests

that Δ13C could be a strong surrogate measure for WUEeven in mapping populationThe relationship between CWTand WUE was poor suggesting stomatal control of WUE inmapping population and similarly between root weight andWUE However significant negative relationship betweenMTR and WUE was observed (119903 = minus0552) in mapping pop-ulation (data not shown) This signifies the genetic nature ofthe coffee plants for its heritable conductance type It suggeststhat stomatal factors regulate the WUE in these plants ratherthan photosynthetic efficiency (mesophyll factors)

34 Identification of Polymorphic RAPD and MicrosatellitePrimers in Parental Lines The contrasting parental lines L1

4 Molecular Biology International

S3334 L1valley

05

10152025303540

Freq

uenc

y

RDWt

05

10152025

S3334 L1valley

0

5

10

15

20

25

30

35

Freq

uenc

y

WUE

44485256

S3334 L1valley

2ndash6

6ndash10

10

ndash14

14

ndash18

18

ndash22

22

ndash26

26

ndash30

30

ndash34

34

ndash38

385

ndash420

420

ndash455

455

ndash490

490

ndash525

525

ndash560

560

ndash595

595

ndash630

630

ndash665

665

ndash700

Figure 1 Continued

Molecular Biology International 5

0

5

10

15

20

25

30Fr

eque

ncy

TDM

020406080

100

S3334 L1valley

05

1015202530354045

Freq

uenc

y

RSR

0250025402580262

S3334 L1valley

0

5

10

15

20

25

30

35

Freq

uenc

y

CWT

05

101520

S3334 L1valley

0

5

10

15

20

25

30

Freq

uenc

y

LAD

0200040006000

S3334 L1valley

05

10152025303540

Freq

uenc

y

MTR

262728293031

S3334 L1valley

05

101520253035404550

Freq

uenc

y

NAR

05

101520

S3334 L1valley

10

ndash22

22

ndash34

34

ndash46

46

ndash58

58

ndash70

70

ndash82

82

ndash94

94

ndash106

106

ndash118

118

ndash130

20

ndash45

45

ndash70

70

ndash95

95

ndash120

120

ndash145

145

ndash170

170

ndash195

195

ndash220

220

ndash245

022

ndash024

024

ndash026

026

ndash028

028

ndash030

030

ndash032

032

ndash034

034

ndash036

036

ndash038

038

ndash040

1000

ndash1800

1800

ndash2600

2600

ndash3400

3400

ndash4200

4200

ndash5000

5000

ndash5800

5800

ndash6600

6600

ndash7400

7400

ndash8200

15

ndash175

20

ndash225

250

ndash275

30

ndash325

350

ndash375

85

ndash99

99

ndash113

113

ndash127

127

ndash141

141

ndash155

155

ndash169

169

ndash183

183

ndash197

Figure 1 Genetic variability of root system and quantitative traits of mapping population of L1 valley times S334

valley (low root type) and S3334 (high root type) weregenotyped with 320 RAPD primers of which 41 polymor-phic primers generated 70 polymorphic loci (Table 3) SinceRAPD is dominant marker the PCR amplified DNA frag-ments were scored as dominant (AAAa) whereas absenceof bands was scored as recessive (aa) We have analyzed ourdata within the framework of these assumptions

Among them more than 85 of the primers producedsingle polymorphic locus and 15 of the primers yieldedmore than three polymorphic loci

All the RAPD markers in the F1 generation of Ccanephora segregated in ratios that were not consistent withMendelian inheritanceThe Chi-square tests were performed

for eachmarker to determine segregation distortion from theexpected allele frequency ratio of 1 1 At a significance levelof 119875 = 005 about 62 of the marker loci were in agreementwith the expected ratios and 38 of the marker loci were notfollowing the Mendelian inheritance (Table 4) It is expectedthat RAPD bands of maternal DNA origin would show non-Mendelian inheritance

Besides RAPD marker system 55 SSR markers were alsoused for genotyping however only nine primers showedpolymorphism (Table 5) but did not follow the Mendeliansegregation The low level of DNA polymorphism betweenthe two parental accessions coupled with the large number ofcommon bands implies parental lines could be less diverse

6 Molecular Biology International

Table 2 Range and mean values of the morphophysiological parameters in 134 F1 mapping population of Coffea canephora (L1 valley timesS3334)

Serial number Parameters Range Mean STDEV

Root traits

1 Root length (cm2) 415ndash830 5708 7292 Number of secondary roots 2ndash60 269 12613 Root dry weight (gplant) 257ndash3469 1377 664 Root shoot ratio 023ndash039 027 003

Morphological traits

5 Plant height (cm2) 135ndash1053 726 15696 Number of nodes 110ndash420 2408 6887 Number of leaves 2ndash70 3402 13428 Stem girth (mm) 588ndash1365 1028 1799 Internodal length (cm) 19ndash125 854 210 Specific leaf dry weight (mgdm2) 141ndash264 18 02211 Final leaf area (cm2) 113692ndash819061 449119 17101712 Specific leaf area (cm) 37899ndash71097 56244 653413 Total leaf dry weight (gplant) 578ndash4859 2602 104314 Stem dry weight (gplant) 481ndash5262 2433 10415 Shoot dry weight (gplant) 1059ndash9664 5035 206316 Total dry matter (gplant) 116ndash12143 5793 2477

Gravimetric traits

17 Leaf area duration (dm2 days) 106589ndash799277 437252 16580418 Net assimilation rate 89ndash1938 1307 17119 Cumulative water transpiration (ltplant) 25ndash235 1081 46220 Water use efficiency (gkg) 386ndash684 54 05821 Mean transpiration rate (mLdm2 days) 15ndash352 244 03822 Delta 13c (mill) 1975ndash2653 218 111

Gas exchange parameters

23 Photosynthesis (Pn) (mmolm2S) 545ndash2727 169 39124 Transpiration rate (119864) (mgm2s) 071ndash747 338 13425 Conductance (gs) (mmolm2s) 005ndash1 035 01726 Cigs 28876ndash348425 8593 405127 Ci 1865ndash29667 24798 248828 Ags 2406ndash9326 5442 155229 A119864 263ndash1407 552 176

Table 3 Polymorphic RAPD primers and their sequences used to study the segregation of markers in the mapping population

Polymorphicbands

Primername Sequence Primer

name Sequence Primername Sequence Primer

name Sequence

1

OPK18 CCTAGTCGAG OPN6 GAGACGCACA OPQ5 CCGCGTCTTG OPV5 TCCGAGAGGGOPK19 CACAGGCGGA OPO1 GGCACGTAAG OPS1 CTACTGCGCT OPW9 GTGACCGAGTOPK20 GTGTCGCGAG OPO18 CTCGCTATCC OPV11 CTCGACAGAG OPY20 AGCCGTGGAAOPM5 GGGAACGTGT OPO4 AAGTCCGCTC OPV14 AGATCCCGCC OPZ11 CTCAGTCGCAOPN19 GTCCGTACTG OPP2 TCGGCACGCA OPV2 AGTCACTCCC

2

OPK10 GTGCSSCGTG OPL18 ACCACCCACC OPO16 TCGGCGGTTC OPT7 GTCCATGCCAOPK11 AATGCCCCAG OPM11 GTCCACTGTG OPP5 CCCCGGTAAC OPU1 ACGGACGTCAOPL1 GGCATGACCT OPM17 TCAGTCCGGG OPQ20 TCGCCCAGTC OPW17 GTCCTGGGTTOPL12 GGGCGGTACT OPO10 TCAGAGCGCC OPR4 CCCGTAGCAC OPZ3 CAGCACCGCA

3 OPL19 GAGTGGTGAC OPM2 ACAACGCCTC OPP11 AACGCGTCGG OPP9 GTGGTCCGCAOPZ10 CCGACAAACC

4 OPK17 CCCAGCTGTG

Molecular Biology International 7

0 5 10 15 20 25 30 350

20

40

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120

140

1

2

3 4

5

6

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9

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1113

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15

161718

19

20

21

22

23

2425

26 2728

29

3032

33

34

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36

37

38 3940

41

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49

50

5152

5354

55

5657

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7677

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81828384

85

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9192

939495

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122123

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127128129

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131

132

133

134

135

136

137

138

139140

141

Tota

l dry

mat

ter (

gpl

ant)

Root dry weight (gplant)

r = 0968

P lt 0001 n = 135

(a)

0 5 10 15 20 25 30 350

5

10

15

20

25

1

2

3

4

5

6

7

8

910

1113

14

15

161718

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2425

262728 29

3032

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38 3940

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4647

48

49

50

5152

5354

55

5657

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6061

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7980

81

8283

84

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86

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9192 93

9495

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97

98 99

100101

102

103104

105

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108

109110

111

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115

116

117

118119

120

121

122123

124

125

126

127128

129

130

131

132

133

134

135

136

137138

139140

141

CWT

(L)

Root dry weight (gplant)

r = 0940

P lt 0001 n = 135

(b)

0 20 40 60 80 100 120 1400

5

10

15

20

25

1

2

3

4

5

6

7

8

910

1113

1415

1617

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19

20

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22

23

2425

2627 28 29

3032

33

34

35

36

37

383940

41

43

44

45

4647

48

49

50

5152

5354

55

5657

58

59

6061

62

63

64

65

6667

68

69

70

72

73

75

76

77

78

7980

81

8283

84

85

86

8788

89

90

9192 93

9495

96

97

98 99

100101

102

103104

105

106

107

108

109110

111

113

114

115

116

117

118 119

120

121

122123

124

125

126

127128

129

130

131

132

133

134

135

136

137138

139140

141

CWT

(L)

Total dry matter (gplant)

r = 0970

P lt 0001 n = 135

(c)

0 5 10 15 20 25

15

20

25

30

35

12

3

45

67

8

9

10

11 1314

1516

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1920

2122

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3233

34

35 36

37 38

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40

4143

44

45

4647

4849

50

5152 53

5455

56

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5859

60

61

62 6364 65

66

67

68

69

70

7273

75

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78

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8283

84

85 86

8788

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9091

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9798

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109110

111

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114115

116

117

118119

120

121122

123124

125

126127

128

129130

131

132133

134

135136

137

138

139

140141

CWT (L)

r = 0524

P lt 0001 n = 135

MTR

(mL

dm2)

(d)

0 5 10 15 20 25

15

20

25

30

35

12

3

45

67

8

9

10

11 1314

1516

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1920

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3233

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4143

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5859

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7273

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76

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120

121122

123124

125

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131

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140141

CWT (L)

r = 0524

P lt 0001 n = 135

MTR

(mL

dm2)

(e)

35 40 45 50 55 60 65 70

20

22

24

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28

12

345 6 7

89

10

11

131415

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121122

123124

125

126127

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129

130131

132133

134

135136 137

138139

140

141

WUE (gkg)

r = minus0413

P lt 0001 n = 135

Δ13

C

(f)

Figure 2 Relationship between the RDWTDM RDWCWT TDMCWT CWTMTR CWTMTR and WUEΔ13C in the mappingpopulation of C canephora (L1 valley times S334)

8 Molecular Biology International

Table 4 Chi-square tests (1205942) for RAPD and SSR markers in the Coffea canephoramapping population

Parents F1progenies

Markersystem

Total polymor-phic markers

Marker loci notfollowing expected ratio

Marker loci followingexpected ratio

Number of significant markers119875 = 095 119875 = 005 119875 = 0001

L1 valley timesS3334 134 RAPD 70 28 42 (1 1) 32 3 7

SSR 9 9 0 (3 1) mdash mdash mdash

Table 5 Polymorphic microsatellite primers and their sequences used to study the segregation of markers in the mapping population

Serial number Primer Sequence Tm (∘C) Size range

1 CM11 F GCTGCCAGAAAAATGTTGCAGTG 58 270ndash338R CTGCCTCGTAAAAGCTTGCGTTG

2 CM13 F GCTATGCAGCTTGTTCGCAATCC 59 350ndash411R CCAGCTATATCAGGAGCAGAACC

3 CM180 F CATGTGTAATACATTCAACAGTGA 60 300ndash350R GCAATAGTGGTTGTCATCCTT

4 CM32 F AACTCTCCATTCCCGCATTC 60 180ndash200R CTGGGTTTTCTGTGTTCTCG

5 CM46 F CAGCTAGTGTGAAGGGAAAC 55 300ndash350R GTTATCATGGTCTTACACG

6 CM20 F CTTGTTTGAGTCTGTCGCTG 60 250ndash300R TTTCCCTCCCAATGTCTGTA

7 CM171 F TTCCCCCATCTTTTTCTTTC 60 250ndash300R TTGTATACGGCTCGTCAGGT

8 CM166 F AAGAGGTGCCTATCACCGTC 60 230ndash260R CGAGGTATCAAAAAGCACCT

9 CM211 F TCATGCCAAATATGAGTGGA 60 300ndash350R GAGATGGCAAAGGCTGTTC

35 Single Marker Analysis for Root and Associated Physiolog-ical Traits Complex physiological traits have been describedby a small number of major QTL [24] but it is intricateto find useful QTL for a particular trait as their individualcontribution is smaller Therefore under such conditionSingle Marker Analysis (SMA) is generally a good choicewhen the goal is simply a detection of marker locus linkedto a trait However estimation of its position and its effectsrequires further complex analysis such as marker regression[25] or interval analysis [26]

In our study Single Marker ANOVA is used to identifymarkers showing significant association with 29 morpho-physiological traits (SAS software with 119875 le 005)

In the single marker analysis simple regression modelwas examined to study the association between marker loci(independent) and trait score (dependent variable) and alsocomputed the percent phenotypic variance explained by eachmarker Seventy polymorphic marker loci were employed forsimple regression analysis however it was discovered that 37RAPD and 5 SSR markers explained their association withmorphophysiological traits

36Markers Linked to Root Traits The singlemarker analysisrevealed fourmarkers such as OPS1

850 OPK11

780 OPY20

1200

OPZ101350

and CM13400

were significantly associated withroot length (119875 = 005) and explained phenotypic vari-ance of 441 295 356 512 and 512 respectively

A number of secondary roots linked to three markers suchas OPO16

450(440) OPK11

1000(551) and OPV14

550

(582) were significantly (119875 = 005) associated Similarlysix markers such as OPK11

1000(326) OPP9

1030(35)

OPM11600

(375) OPL19900

(561) OPL19600

(411)OPR4

600(330) and CM211

300(1198772 = 496) revealed

significant (119875 = 005) association with the root to shootratio (Table 6) This implies the root associated traits arepolygenic controlling many genes However root dry weightis associated with only one maker OPL1

1400(486)

37 Markers Linked to Morphophysiological Traits Besidesroot traits polygenic inheritance traits such as morpho-logical gravimetric and gas exchange at single leaf leveltraits were also deployed for marker trait association Singlemarker analysis revealed more than three markers linked toeach trait and 25 markers are colocalized with more thanone trait and 17 markers are associated with single trait(Table 7)ThemarkerOPP5

1800is coassociatedwith shoot dry

weight and leaf area duration (LAD) and these two traits arepositively correlated (1198772 = 093) Four markers (OPL1

1450

OPL191650

OPM171400

and OPK20350

) linked to NAR andtwo markers OPR4

600and OPL12

2000are significantly (119875 =

001) associated with cumulative water transpired (CWT)The 13ΔC is the surrogate method to quantify the water

use efficiency and it was demonstrated that there is inverse

Molecular Biology International 9

Table 6 RAPD markers linked to root traits in the mappingpopulation of C canephora (L1 valley times S3334)

Trait Marker 1198772 () 119875 lt 005

Root length

OPY201200 295 00449OPZ101350 356 00286OPK11780 441 00178OPS1850 686 00001

CPCM13400 512 00105

Number of secondary rootsOPO16450 44 00179OPK111000 551 00059OPV14550 582 00042

Root dry weight OPL11450 476 00073

Root to shoot ratio

OPK111000 326 0038OPR4600 33 00367OPP91030 35 00288OPM11600 375 00267OPL19600 411 00223OPL19900 561 00062CM211300 496 00105

relationship between these two traits Of the six associatedmarkers OPZ10

1350was found to be associatedwith 13ΔCand

WUE with phenotypic variance of 412 and 302 respec-tively This marker locus could be negative additive effectsince traits are negatively related The mesophyll efficiency(Cigs) intrinsic WUE (Ags) and photosynthetic rate (Pn)are respectively associated with CM13

400(483) CM171

210

(345) and CM171190

(372) (Table 7) Whereas positivelyrelated traits are associated with the same marker such asOPP5

1800(LAD and shoot dry weight) OPZ10

1030(MTR and

gs) and OPK20350

(Ci and NAR) alleles contribution fromS3334 The markers linked to other gas exchange parameterswere given in Table 7 which are contributed from S3334

4 Discussion

We developed a F1 mapping population by crossing contrast-ing root traits S3334 high root type as male and L1 valley lowroot type as female to identify makers linked to root traitsand water use efficient types However in annual crops F

2

recombinant inbred lines (RILs) near isogenic lines (NILs)and doubled haploids (DH)mapping population will be usedto associatemarkers with the trait of our interest However inperennials the germplasm lines and the cultivated accessionsare often highly heterozygous consequently F

1population

developed from heterozygous parents was used in this studyand showed considerable phenotypic and genetic variations

One of the objectives of studying themapping populationwas to score the variability in roots and other physiologicaltraits under drought and thus mapping population was gownup in 70 FC of water stress to study the heritability ofroot traits Indeed root traits are highly complex geneticmechanism controlled by multi genes and highly herita-ble in water non limiting environment [27] The resultsdemonstrated significant variability was observed in root

dry weight (RDW) total dry matter (TDM) root to shootratio (RSR) cumulativewater transpired (CWT)with consid-erable transgressive segregation (Figure 1) and quantitativeinheritance of drought tolerance [28] However significantpositive correlation between RDWTDM RDWCW andTDMCWT indicates that under drought increased rootgrowth maintains the shoot hydraulic conductance as adop-tive strategy [29 30] The decreased mean transpiration(MTR) and increased water use efficiency (WUE) could bedue to decreased stomatal aperture through chemical signalssuch as ABA [31] However some of the clones showedthat increased MTR with increased CWT would favor thestomatal conductance and net photosynthesis Such clonesare better for breeding drought tolerance Inverse relationshipbetween Δ13C and WUE in robusta coffee accessions wasobserved (Figure 2) thus measuring Δ13C which can bea surrogate method to identify best WUE types clones inbreeding [19 32] Breeding for such quantitative traits ishighly complex cumbersome and thus by determining theallele of aDNAmarker plants that possess particular genes orquantitative trait loci (QTLs) are better than their phenotypeWith this background effort wasmade to identify themarkerslinked to root and associated physiological traits in Coffeacanephora

Despite the efforts no successful linkage map could beobtained partly due to the inadequate number of polymor-phic markers generated from this study The output of theassociation analysis indicates that out of 29 traits only 24traits were linked to 85 markers Even markers linked totraits explaining the phenotypic variationwere notmore than10 In the present study phenotypic variation explanationfor all the traits ranges from 261 to 986 This suggeststhat the successful application of molecular markers in traitmapping greatly requires more polymorphic markers whilesuccessful mapping attempts could still be carried out in theabsence of a linkage map as done in chickpea [33] Althoughconstruction of linkage map was done in C arabica [34]and C canephora [35] predominantly using AFLP and RAPDmarkers association with the traits was not attempted Simi-larly the first molecular linkage map generated using pseudotestcross strategy for Coffea canephora (CxR times Kagnalla) hasthe largest number of mapped SSRs (71 genomic includingnine EST-SSRs) [36] but trait association was not correlated

Most of the markers explained phenotypic variancebetween 3 and 4 however only two markers that isOPZ11

1500andOPV14

500 were significantly (119875 lt 001) linked

to photosynthetic rate (Pn) accounting the variation of about923 and 930 respectively (Table 7) The root traits suchas root length number of secondary roots root dry weightand root shoot ratio revealed significant association (119875 lt005) with markers and the variability ranges from 295 to686 (Table 6)This suggests the trait variability explanationis not sufficient to answer the probabilities of occurrence ofthese markers due to limited polymorphic markers

In the present study codominant SSR markers werealso used for the marker-trait association study The SSRmarkers identified herein are significantly associated withmorphological and physiological traits but these markers

10 Molecular Biology International

Table 7 List of RAPD and SSR markers overlapped with more than one trait

Markers Traits 1198772 ()

(119875 lt 005) Markers Traits 1198772

(119875 lt 005)

OPK111000

Internodal length 474OPR4600OPR4750

CWT 508Root to shoot ratio 326 RSR 330

Number of secondary roots 551 Number of leaves 565Stem dry weight 344 gs 293

OPK181200Specific leaf area 470 OPV14550

Pn 930Plant height 451 Number of secondary Roots 582

OPK192000Photosynthetic rate (Pn) 555 OPY201200

WUE 310WUE 443 Plant height 423

K20350NAR 482

OPZ111500Number of leaves 399

WUE 445 Root length 295Ci 432 Pn 923

OPL11400

Plant height 577

OPZ101350OPZ101030

MTR 821Internodal length 381 13

ΔC 412NAR 427 Plant height 303

Shoot dry weight 400 Root length 356Root dry weight 476 WUE 302

Pn 261 Cigs 509

OPL122000Plant height 368 Pn 756

CWT 474 MTR 438

OPL12165013ΔC 361 OPZ10720

gs 296pn 307 gs 326

OPL181000Number of nodes 778 Pn 314

LAD 382 Internodal length 358

OPL19900SLW 333 CPCM13450

Root length 512RSR 360 Cigs 483

OPM11600Ags 385

CM171300CM166260

Stem girth 454RSR 411 Ags 345MTR 294 No of nodes 393

OPO101400Ci 313 SLA 719E 318 119864 418

OPP51800LAD 382

CM211300SLW 283

Internodal length 582 RSR 496Shoot dry weight 271 119864 423

OPP91030Cigs 612SLW 333RSR 350

were explaining the trait in the range between 283 and719 This study has provided more detailed information ofroot and associated physiological traits relationships underdrought and identification of markers linked to such traitswith the limited polymorphic markers by employing SingleMarker Analysis (SMA) QTL maps could be used to forlong-term drought breeding Further studies are needed toconfirm the estimation of QTL positions and effects and tovalidatemarkers prior to routinemarker assisted selection fordrought tolerance in coffee

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

References

[1] F M DaMatta ldquoExploring drought tolerance in coffee aphysiological approach with some insights for plant breedingrdquoBrazilian Journal of Plant Physiology vol 16 no 1 pp 1ndash6 2004

Molecular Biology International 11

[2] M Nesbitt The Cultural History of Plants Taylor amp Francis2005

[3] F M Damatta ldquoDrought as a multidimensional stress affectingphotosynthesis in tropical tree cropsrdquo in Advances in PlantPhysiology A Hemantaranjan Ed vol 5 pp 227ndash265 2003

[4] K Harovon Mogel ldquoGenotype times environment times managementinteractions key to beating future droughtsrdquo CSA News vol 58pp 4ndash9 2013

[5] F M DaMatta A R M Chaves H A Pinheiro C Ducatti andM E Loureiro ldquoDrought tolerance of two field-grown clones ofCoffea canephorardquo Plant Science vol 164 no 1 pp 111ndash117 2003

[6] H A Pinheiro F M DaMatta A R M Chaves M E Loureiroand C Ducatti ldquoDrought tolerance is associated with rootingdepth and stomatal control of water use in clones of Coffeacanephorardquo Annals of Botany vol 96 no 1 pp 101ndash108 2005

[7] J B Passioura ldquoPhenotyping for drought tolerance in graincrops when is it useful to breedersrdquo Functional Plant Biologyvol 39 no 11 pp 851ndash859 2012

[8] J S Sperry ldquoHydraulics of vascular water transportrdquo inMechan-ical Integration of Plant Cells and Plants P Wojtaszek Edvol 9 of Signaling and Communication in Plants pp 303ndash327Springer Berlin Germany 2011

[9] A P Wasson R A Richards R Chatrath et al ldquoTraits andselection strategies to improve root systems and water uptakein water-limited wheat cropsrdquo Journal of Experimental Botanyvol 63 no 9 pp 3485ndash3498 2012

[10] G D Herder G van Isterdael T Beeckman and I de SmetldquoThe roots of a new green revolutionrdquo Trends in Plant Sciencevol 15 no 11 pp 600ndash607 2010

[11] H T Nguyen R C Babu and A Blum ldquoBreeding for droughtresistance in rice physiology and molecular genetics consider-ationsrdquo Crop Science vol 37 no 5 pp 1426ndash1434 1997

[12] A Blum Plant Breeding for Water-Limited EnvironmentsSpringer Science+Business Media LLC New York NY USA2011

[13] L B Motta T C B Soares M A G Ferrao E T Caixeta R MLorenzoni and J D de SouzaNeto ldquoMolecular characterizationof arabica and Conilon coffee plants genotypes by SSR and ISSRmarkersrdquo Brazilian Archives of Biology and Technology vol 57no 5 pp 728ndash735 2014

[14] PMarraccini L P Freire G S CAlves et al ldquoRBCS1 expressionin coffee Coffea orthologs Coffea arabica homeologs andexpression variability between genotypes and under droughtstressrdquo BMC Plant Biology vol 11 article 85 2011

[15] P Marraccini F Vinecky G S C Alves et al ldquoDifferentiallyexpressed genes and proteins upon drought acclimation intolerant and sensitive genotypes of Coffea canephorardquo Journalof Experimental Botany vol 63 no 11 pp 4191ndash4212 2012

[16] F Denoeud L Carretero-Paulet A Dereeper et al ldquoThecoffee genome provides insight into the convergent evolution ofcaffeine biosynthesisrdquo Science vol 345 no 6201 pp 1181ndash11842014

[17] A Dereeper S Bocs M Rouard et al ldquoThe coffee genome huba resource for coffee genomesrdquo Nucleic Acids Research vol 43no 1 pp D1028ndashD1035 2015

[18] M Udayakumar M S Sheshshayee K N Nataraj et al ldquoWhyhas breeding for water use efficiency not been successful Ananalysis and alternate approach to exploit this trait for cropimprovementrdquo Current Science vol 74 no 11 pp 1000ndash10031998

[19] M G Awati Genetic diversity and molecular markers for rootand associated physiological traits of Robusta (coffea canephoraPierre ex F) accessions [PhD thesis] University of AgriculturalSciences Bangalore India 2005

[20] C E Williams and P C Ronald ldquoPCR template-DNA isolatedquickly from monocot and dicot leaves without tissue homog-enizationrdquo Nucleic Acids Research vol 22 no 10 pp 1917ndash19181994

[21] S D Tanksley ldquoMapping polygenesrdquoAnnual Review of Geneticsvol 27 no 1 pp 205ndash233 1993

[22] T Halward T Stalker E LaRue and G Kochert ldquoUse ofsingle-primer DNA amplifications in genetic studies of peanut(Arachis hypogaea L)rdquo Plant Molecular Biology vol 18 no 2pp 315ndash325 1992

[23] SAS Institute SASSTAT Userrsquos Guide Version 6 SAS InstituteCary NC USA 4th edition 1989

[24] M J Kearsey and A G L Farquhar ldquoQTL analysis in plantswhere are we nowrdquo Heredity vol 80 no 2 pp 137ndash142 1998

[25] M J Kearsey and V Hyne ldquoQTL analysis a simple lsquomarker-regressionrsquo approachrdquoTheoretical and Applied Genetics vol 89no 6 pp 698ndash702 1994

[26] C S Haley and S A Knott ldquoA simple regression method formapping quantitative trait loci in line crosses using flankingmarkersrdquo Heredity vol 69 no 4 pp 315ndash324 1992

[27] B M Atta T Mahmood and R M Trethowan ldquoRelationshipbetween root morphology and grain yield of wheat in North-Western NSW Australiardquo Australian Journal of Crop Sciencevol 7 no 13 pp 2108ndash2115 2013

[28] N T Lang C T Nha P T T Ha and B C Buu ldquoQuantitativetrait loci (QTLs) associated with drought tolerance in rice(Oryza sativa L)rdquo Sabrao Journal of Breeding and Genetics vol45 no 3 pp 409ndash421 2013

[29] H J Schenk andR B Jackson ldquoMapping the global distributionof deep roots in relation to climate and soil characteristicsrdquoGeoderma vol 126 no 1-2 pp 129ndash140 2005

[30] A Srividya L R Vemireddy P V Ramanarao et al ldquoMolecularmapping of QTLs for drought related traits at seedling stageunder PEG induced stress conditions in ricerdquoAmerican Journalof Plant Sciences vol 2 no 2 pp 190ndash201 2011

[31] N-N Shukor H A Hamid A Abdu andM-K Ismail ldquoEffectsof soil compaction on growth and physiological characteristicsofAzadirachta excelsa seedlingsrdquoTheAmerican Journal of PlantPhysiology vol 10 no 1 pp 25ndash42 2015

[32] P R Bhat Phenotyping and molecular analysis of Coffea arabicaL accessions and mapping population to identify RAPD markersassociated with root characteristics and associated physiologicaltraits [PhD thesis] University of Agricultural Sciences Banga-lore India 2002

[33] S Chandra H K Buhariwalla J Kashiwagi et al ldquoIdentifyingQTL linked markers in marker-29 deficient cropsrdquo in Proceed-ings of the 4th International Crop Science Congress BrisbaneAustralia 2004

[34] H M Pearl C Nagai P H Moore D L Steiger R V Osgoodand RMing ldquoConstruction of a genetic map for arabica coffeerdquoTheoretical and Applied Genetics vol 108 no 5 pp 829ndash8352004

[35] P Lashermes M C Combes N S Prakash P Trouslot MLorieux and A Charrier ldquoGenetic linkage map of Coffeacanephora effect of segregation distortion and analysis ofrecombination rate in male and female meiosesrdquo Genome vol44 no 4 pp 589ndash596 2001

[36] P Hendre S Lalji Singh and R K Aggarwal ldquoConstructionof framework molecular linkage map of Robusta coffee Coffeacanephora with RAPD AFLP g-SSRs And EST-SSRs usingpseudo-testcross strategyrdquo in Proceedings of the Plant amp AnimalGenomes 15th Conference January 2007

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

PeptidesInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

International Journal of

Volume 2014

Zoology

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Molecular Biology International

GenomicsInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioinformaticsAdvances in

Marine BiologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Signal TransductionJournal of

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BioMed Research International

Evolutionary BiologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Genetics Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Virolog y

Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Enzyme Research

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Microbiology

2 Molecular Biology International

research has focused on aboveground traits while the ldquohiddenhalf rdquo of the plant is much less represented in recent research[10] With the conventional breeding methods introgressionof complexquantitative traits is unfeasibleMolecularmarkertechnology and genomics serve as a tool for selecting suchcomplex traits and allow breeders to track genetic locicontrolling drought resistance traits without measuring thephenotype thus reducing the need for extensive field testingover space and time [11ndash13] With the functional genomicsit was shown that the increase in the amount of RBCS1(Rubisco small subunit) protein could contribute to theantioxidative function of photorespiration in water-stressedCoffea canephora plants [14] Furthermore it was also shownthat drought acclimation in Coffee canephora clones probablyinvolving the abscisic signalling pathway and nitric oxide aremajor molecular determinants that might explain the betterefficiency in controlling stomata closure and transpirationdisplayed by drought-tolerant clones of C canephora [15]

With an advent of Next Generation Sequencing (NGS)technologies Coffea canephora is the first coffee species fullysequenced [16] The developed crop-specific hub the CoffeeGenome Hub (CGH) (httpcoffee-genomeorg) can beexploited to identify the genesSNPmarkers conditioning fordrought and root associated secondary traits by resequencingintra- and interspecific genetic resources of robust coffeespecies [17] Therefore it is very important to identify thegenetic loci conditioning root associated traits for breedingbetter drought tolerance clones With this background ourstudy aims at (1) developing a mapping population withcontrasting parental lines for root traits and phenotyping(2) genotyping the mapping population with RAPD and SSRmarkers and (3) detecting the association ofmolecularmark-ers with complex physiological and morphological traits

2 Materials and Methods

21 Plant Materials To identify the markers linked to rootand associated physiological traits F1 mapping populationwas developed by crossing low root type L1 valley as femaleparent with high root type S3334 as male parent Aftersuccessful pollination matured 300 coffee fruits were sownin nursery and after 48 days and 135 healthy seedlings weretransplanted to carbonized rubber containers (35 kg capacity)for better establishment This experiment was conductedat Central Coffee Research Institute Chikmagalur DistrictKarnataka State India Institute is situated at 13∘2210158401015840 northLatitude and 75∘2810158401015840 east Longitude at an elevation of 824 to884 meters above mean sea level

22 Phenotypic Analysis The moisture regime of 70 fieldcapacity (FC) was imposed for 180 days after six months ofcoffee seedlings established in carbonized rubber containersGravimetric approach was followed to maintain 70 FCwhere potted plants were daily weighed to add water whichwas evapotranspired [18]

23 Observations Recorded during Treatment Period Dur-ing the treatment period (180 days) cumulative wateradded evaporation evapotranspiration were recorded After

the treatment period root traits such as root length (cm)number of secondary roots root dry weight (gplant) shootdry weight (gplant) and root to shoot ratio were recordedBesidesmorphological gravimetric and gas exchange param-eters were also recoded [19]

24 Genomic DNA Extraction Coffee leaves were frozen inliquid nitrogen and stored at minus80∘C DNA was extractedfrom frozen leaves using cetyltrimethyl ammonium bromide(CTAB) method [20] For the CTAB technique 900 120583L ofCTAB extraction buffer was added to lyophilized leaf tissuein 2mL Eppendorf tubes and then lightly vortexed Thetubes were placed in hot water bath (65∘C) for 45min andmixed with 400120583L of chloroform isoamylalcohol (24 1) andcentrifuged for 15min The aqueous layer was collected and800 120583L of isopropanol was added to precipitate the nucleicacids Nucleic acid pellets were washed with 400120583L of 70ethanol dried and resuspended in 100 120583L of Tris-EDTAbuffer (10mM Tris with pH 75 and 05mM EDTA)

25 Polymerase Chain Reaction (PCR) RAPD (RandomAmplified Polymorphic DNA Operon Technologies)primers were used to genotype mapping populationPolymerase chain reaction was carried out in 15120583L reactioncontaining 1x buffer 2mM dNTPs 25mM MgCl

2 5 120583M

primer and 1U Taq DNA polymerase (NEB) Amplificationwas performed with the following thermal cycle profile94∘C4min hot start denaturation followed by 35 cycles of94∘C for 1min primer annealing at 38∘C for 1min extensionat 72∘C for 2min and a final extension at 72∘C for 8minThe PCR was performed using Eppendorf thermocycler(Eppendorf Hamburg Germany) The PCR products wererun on 15 agarose gel at 90 volts for 1 h 30min andamplified fragments were documented using Hero Lab GelDocumentation system (Inkarp)

SSR (simple sequence repeats) primers for polymerasechain reaction were synthesized based on the informationavailable in coffee genome database and also from in-housedeveloped from S3334 coffee accession

PCR amplification was carried out with 15 120583L reactionmixture having 50 ng DNA 1x PCR buffer 100 120583M dNTPs250 120583M primers and 1 unit Taq polymerase enzyme (NEB)Amplification was performed with the following thermalcycle profile 95∘C for 5min followed by 35 cycles ofpolymerization reaction each consisting of denaturation at94∘C for 15 s annealing at 60∘C for 45 s and an extensionstep at 72∘C for 1min A final extension step was run for5min at 72∘C The PCR was performed using Eppendorfthermocycler (Eppendorf Hamburg Germany) The PCRproducts were run on 6 polyacrylamide denaturing gelsAmplified fragments were detected using a silver-stainingprocedure (Promega Madison WI USA)

26 Study of Parental Polymorphism The contrasting parents(L1 valley times S3334) for root traits were screened with 320RAPD (series from OPK1 to OPZ20) and 55 SSR markersThe polymorphic RAPD bands were visually scored for thepresence (1) or absence (0) and in SSR analysis the segregatingband from the female parent was scored as 3 male parent as 1

Molecular Biology International 3

Table 1Genotypic variation of parental accessions S3334 (high roottype) and L1 valley (low root type)

S3334 L1 valley1 Root length 45 302 Secondary roots 40 153 Root dry weight 3181 4954 Root to shoot ratio 041 0365 Total dry matter 10677 15496 Cumulative water transpired 2886 31157 water use efficiency 372 3628 Net assimilation rate 1375 9099 Mean transpiration rate 372 22310 Pngs 7839 437811 Pn119864 299 18412 Cigs 38635 33994

and heterozygous bands were scored as 2 in all individuals ofF1 mapping populationThe binary data was used for furtherstatistical analysis Based on the segregation of RAPD mark-ers in the mapping population the putative genotypic inter-pretation of the parents for the marker locus was made andtheChi-square testwas performed (httpwwwphysicscsbsjuedustatschi-square formhtml)

27 Association of Identified Polymorphic Markers with Physi-ological Traits Single point analysis [21 22] for detecting theassociation of molecular markers with complex physiologicaland morphological traits was done using SAS software Tofind the amount of variability explained by these markersregression (1198772) values were worked out by one-way analysisof variance (ANOVA) by general linear model (GLM)procedure In this analysis different traits were treated asdependent variable and the various molecular markers asindependent variables A total of 30 different physiologicaland morphological traits were used to associate with the 85polymorphic molecular markers [23]

3 Results

31 Phenotyping Root Length Secondary Roots Root DryWeight and Physiological Traits At the end of experimentperiod about 180 days water stress pots were completelysaturated with water and then in next day all plants werecarefully detached with water force to avoid root loss Thedata on root length secondary roots and root biomass dataof parental lines (Table 1) and F1 mapping population wererecorded (Table 2) There was significant genetic variabilityobserved in root length ranging from 415 cm to 830 cmwith a mean of 5708 cm Similar trend was followed indry weight ranging from 257 g to 3469 g with a mean of1381 gplant and root to shoot ratio varies from 257 to 441with mean ratio of 377 It was observed that F1s root traitswere distributed between the values of the respective parentallines without much considerable transgressive segregation(Figure 1) The quantitative nature of these traits showedcontinuous variation confirming the metric nature of thetraits

Morphological traits such as plant height number ofnodes and leaves stem girth internodal length leaf areashoot dry weight and total dry matter were recordedNonetheless all these traits showed significant and contin-uous variation (Table 2) Besides genetic variability in themapping population in net photosynthesis (Pn) stomatalconductance (gs) transpiration rate (119864) intrinsic WUE(Pngs) and mesophyll efficiency (Cigs) was observed atsingle leaf level

Significant variations were observed in water use effi-ciency (WUE) and associated physiological traits like cumu-lative water transpired (CWT) net assimilation rate (NAR)mean transpiration rate (MTR) leaf area duration (LAD)and Δ13C in the population The WUE ranges from 386to 684 gkg with an average of 540 gkg and considerabletransgressive segregation was observed The variation inCWT was observed between 250 and 2350 kgplant Theaverage transpiration rate was 1081 Lplant in the popula-tion The NAR and MTR also varied with mean values of1307mgdm2 and 244mLdm2 of leaf area respectivelySimilarly the functional leaf area (LAD) varied between106589 and 799277 dm2day with mean leaf area duration of437657 dm2dayΔ13C also varied between 1975 and 2653permilwith a mean of 2180permil (Table 2) The variation in WUECWT Δ13C and so forth showed that traits are metricnature and mapping population could be utilized for theidentification of marker linked to quantitative traits

32 Association of Root with Other Physiological Traits Theplants with the higher root biomass showed higher totalbiomass and correlation between these two traits is highlysignificant (119903 = 0968) (Figure 2) It signifies the importanceof root system in determining the total dry matter by absorb-ing the water from sub-soil layers from its better root systemcompared to parental lines Subsequently strong positivesignificant correlations were noticed between root weightand total transpiration (119903 = 0940) CWT and MTR (119903 =0524) and total dry matter and CWT (119903 = 0970) (Figure 2)suggesting that maintaining canopy hydraulic conductancewith high carbon assimilation is one of the drought tolerancemechanisms

33 Relationship between WUE with Other PhysiologicalTraits Significant inverse relationship between WUE andΔ13C (119903 = minus0413) was observed (Figure 2) It suggests

that Δ13C could be a strong surrogate measure for WUEeven in mapping populationThe relationship between CWTand WUE was poor suggesting stomatal control of WUE inmapping population and similarly between root weight andWUE However significant negative relationship betweenMTR and WUE was observed (119903 = minus0552) in mapping pop-ulation (data not shown) This signifies the genetic nature ofthe coffee plants for its heritable conductance type It suggeststhat stomatal factors regulate the WUE in these plants ratherthan photosynthetic efficiency (mesophyll factors)

34 Identification of Polymorphic RAPD and MicrosatellitePrimers in Parental Lines The contrasting parental lines L1

4 Molecular Biology International

S3334 L1valley

05

10152025303540

Freq

uenc

y

RDWt

05

10152025

S3334 L1valley

0

5

10

15

20

25

30

35

Freq

uenc

y

WUE

44485256

S3334 L1valley

2ndash6

6ndash10

10

ndash14

14

ndash18

18

ndash22

22

ndash26

26

ndash30

30

ndash34

34

ndash38

385

ndash420

420

ndash455

455

ndash490

490

ndash525

525

ndash560

560

ndash595

595

ndash630

630

ndash665

665

ndash700

Figure 1 Continued

Molecular Biology International 5

0

5

10

15

20

25

30Fr

eque

ncy

TDM

020406080

100

S3334 L1valley

05

1015202530354045

Freq

uenc

y

RSR

0250025402580262

S3334 L1valley

0

5

10

15

20

25

30

35

Freq

uenc

y

CWT

05

101520

S3334 L1valley

0

5

10

15

20

25

30

Freq

uenc

y

LAD

0200040006000

S3334 L1valley

05

10152025303540

Freq

uenc

y

MTR

262728293031

S3334 L1valley

05

101520253035404550

Freq

uenc

y

NAR

05

101520

S3334 L1valley

10

ndash22

22

ndash34

34

ndash46

46

ndash58

58

ndash70

70

ndash82

82

ndash94

94

ndash106

106

ndash118

118

ndash130

20

ndash45

45

ndash70

70

ndash95

95

ndash120

120

ndash145

145

ndash170

170

ndash195

195

ndash220

220

ndash245

022

ndash024

024

ndash026

026

ndash028

028

ndash030

030

ndash032

032

ndash034

034

ndash036

036

ndash038

038

ndash040

1000

ndash1800

1800

ndash2600

2600

ndash3400

3400

ndash4200

4200

ndash5000

5000

ndash5800

5800

ndash6600

6600

ndash7400

7400

ndash8200

15

ndash175

20

ndash225

250

ndash275

30

ndash325

350

ndash375

85

ndash99

99

ndash113

113

ndash127

127

ndash141

141

ndash155

155

ndash169

169

ndash183

183

ndash197

Figure 1 Genetic variability of root system and quantitative traits of mapping population of L1 valley times S334

valley (low root type) and S3334 (high root type) weregenotyped with 320 RAPD primers of which 41 polymor-phic primers generated 70 polymorphic loci (Table 3) SinceRAPD is dominant marker the PCR amplified DNA frag-ments were scored as dominant (AAAa) whereas absenceof bands was scored as recessive (aa) We have analyzed ourdata within the framework of these assumptions

Among them more than 85 of the primers producedsingle polymorphic locus and 15 of the primers yieldedmore than three polymorphic loci

All the RAPD markers in the F1 generation of Ccanephora segregated in ratios that were not consistent withMendelian inheritanceThe Chi-square tests were performed

for eachmarker to determine segregation distortion from theexpected allele frequency ratio of 1 1 At a significance levelof 119875 = 005 about 62 of the marker loci were in agreementwith the expected ratios and 38 of the marker loci were notfollowing the Mendelian inheritance (Table 4) It is expectedthat RAPD bands of maternal DNA origin would show non-Mendelian inheritance

Besides RAPD marker system 55 SSR markers were alsoused for genotyping however only nine primers showedpolymorphism (Table 5) but did not follow the Mendeliansegregation The low level of DNA polymorphism betweenthe two parental accessions coupled with the large number ofcommon bands implies parental lines could be less diverse

6 Molecular Biology International

Table 2 Range and mean values of the morphophysiological parameters in 134 F1 mapping population of Coffea canephora (L1 valley timesS3334)

Serial number Parameters Range Mean STDEV

Root traits

1 Root length (cm2) 415ndash830 5708 7292 Number of secondary roots 2ndash60 269 12613 Root dry weight (gplant) 257ndash3469 1377 664 Root shoot ratio 023ndash039 027 003

Morphological traits

5 Plant height (cm2) 135ndash1053 726 15696 Number of nodes 110ndash420 2408 6887 Number of leaves 2ndash70 3402 13428 Stem girth (mm) 588ndash1365 1028 1799 Internodal length (cm) 19ndash125 854 210 Specific leaf dry weight (mgdm2) 141ndash264 18 02211 Final leaf area (cm2) 113692ndash819061 449119 17101712 Specific leaf area (cm) 37899ndash71097 56244 653413 Total leaf dry weight (gplant) 578ndash4859 2602 104314 Stem dry weight (gplant) 481ndash5262 2433 10415 Shoot dry weight (gplant) 1059ndash9664 5035 206316 Total dry matter (gplant) 116ndash12143 5793 2477

Gravimetric traits

17 Leaf area duration (dm2 days) 106589ndash799277 437252 16580418 Net assimilation rate 89ndash1938 1307 17119 Cumulative water transpiration (ltplant) 25ndash235 1081 46220 Water use efficiency (gkg) 386ndash684 54 05821 Mean transpiration rate (mLdm2 days) 15ndash352 244 03822 Delta 13c (mill) 1975ndash2653 218 111

Gas exchange parameters

23 Photosynthesis (Pn) (mmolm2S) 545ndash2727 169 39124 Transpiration rate (119864) (mgm2s) 071ndash747 338 13425 Conductance (gs) (mmolm2s) 005ndash1 035 01726 Cigs 28876ndash348425 8593 405127 Ci 1865ndash29667 24798 248828 Ags 2406ndash9326 5442 155229 A119864 263ndash1407 552 176

Table 3 Polymorphic RAPD primers and their sequences used to study the segregation of markers in the mapping population

Polymorphicbands

Primername Sequence Primer

name Sequence Primername Sequence Primer

name Sequence

1

OPK18 CCTAGTCGAG OPN6 GAGACGCACA OPQ5 CCGCGTCTTG OPV5 TCCGAGAGGGOPK19 CACAGGCGGA OPO1 GGCACGTAAG OPS1 CTACTGCGCT OPW9 GTGACCGAGTOPK20 GTGTCGCGAG OPO18 CTCGCTATCC OPV11 CTCGACAGAG OPY20 AGCCGTGGAAOPM5 GGGAACGTGT OPO4 AAGTCCGCTC OPV14 AGATCCCGCC OPZ11 CTCAGTCGCAOPN19 GTCCGTACTG OPP2 TCGGCACGCA OPV2 AGTCACTCCC

2

OPK10 GTGCSSCGTG OPL18 ACCACCCACC OPO16 TCGGCGGTTC OPT7 GTCCATGCCAOPK11 AATGCCCCAG OPM11 GTCCACTGTG OPP5 CCCCGGTAAC OPU1 ACGGACGTCAOPL1 GGCATGACCT OPM17 TCAGTCCGGG OPQ20 TCGCCCAGTC OPW17 GTCCTGGGTTOPL12 GGGCGGTACT OPO10 TCAGAGCGCC OPR4 CCCGTAGCAC OPZ3 CAGCACCGCA

3 OPL19 GAGTGGTGAC OPM2 ACAACGCCTC OPP11 AACGCGTCGG OPP9 GTGGTCCGCAOPZ10 CCGACAAACC

4 OPK17 CCCAGCTGTG

Molecular Biology International 7

0 5 10 15 20 25 30 350

20

40

60

80

100

120

140

1

2

3 4

5

6

7

8

9

10

1113

14

15

161718

19

20

21

22

23

2425

26 2728

29

3032

33

34

35

36

37

38 3940

41

43

44

45

46

47

48

49

50

5152

5354

55

5657

58

59

60

61

62

63

64

65

66

67

68

69

70

72

73

75

7677

78

79

80

81828384

85

86

87

88

89

90

9192

939495

96

97

98

99100

101102

103104

105106

107

108

109110

111

113

114

115

116

117

118

119

120

121

122123

124

125

126

127128129

130

131

132

133

134

135

136

137

138

139140

141

Tota

l dry

mat

ter (

gpl

ant)

Root dry weight (gplant)

r = 0968

P lt 0001 n = 135

(a)

0 5 10 15 20 25 30 350

5

10

15

20

25

1

2

3

4

5

6

7

8

910

1113

14

15

161718

19

20

21

22

23

2425

262728 29

3032

33

34

35

36

37

38 3940

41

43

44

45

4647

48

49

50

5152

5354

55

5657

58

59

6061

62

63

64

65

66

67

68

69

70

72

73

75

76

77

78

7980

81

8283

84

85

86

87

88

89

90

9192 93

9495

96

97

98 99

100101

102

103104

105

106

107

108

109110

111

113

114

115

116

117

118119

120

121

122123

124

125

126

127128

129

130

131

132

133

134

135

136

137138

139140

141

CWT

(L)

Root dry weight (gplant)

r = 0940

P lt 0001 n = 135

(b)

0 20 40 60 80 100 120 1400

5

10

15

20

25

1

2

3

4

5

6

7

8

910

1113

1415

1617

18

19

20

21

22

23

2425

2627 28 29

3032

33

34

35

36

37

383940

41

43

44

45

4647

48

49

50

5152

5354

55

5657

58

59

6061

62

63

64

65

6667

68

69

70

72

73

75

76

77

78

7980

81

8283

84

85

86

8788

89

90

9192 93

9495

96

97

98 99

100101

102

103104

105

106

107

108

109110

111

113

114

115

116

117

118 119

120

121

122123

124

125

126

127128

129

130

131

132

133

134

135

136

137138

139140

141

CWT

(L)

Total dry matter (gplant)

r = 0970

P lt 0001 n = 135

(c)

0 5 10 15 20 25

15

20

25

30

35

12

3

45

67

8

9

10

11 1314

1516

17

18

1920

2122

23

24

25

26

27

28

29 30

3233

34

35 36

37 38

39

40

4143

44

45

4647

4849

50

5152 53

5455

56

57

5859

60

61

62 6364 65

66

67

68

69

70

7273

75

76

77

78

79

80

81

8283

84

85 86

8788

89

9091

92

93

94

9596

9798

99

100

101

102

103

104

105106

107

108

109110

111

113

114115

116

117

118119

120

121122

123124

125

126127

128

129130

131

132133

134

135136

137

138

139

140141

CWT (L)

r = 0524

P lt 0001 n = 135

MTR

(mL

dm2)

(d)

0 5 10 15 20 25

15

20

25

30

35

12

3

45

67

8

9

10

11 1314

1516

17

18

1920

2122

23

24

25

26

27

28

29 30

3233

34

35 36

37 38

39

40

4143

44

45

4647

4849

50

5152 53

5455

56

57

5859

60

61

62 6364 65

66

67

68

69

70

7273

75

76

77

78

79

80

81

8283

84

85 86

8788

89

9091

92

93

94

9596

9798

99

100

101

102

103

104

105106

107

108

109110

111

113

114115

116

117

118119

120

121122

123124

125

126127

128

129130

131

132133

134

135136

137

138

139

140141

CWT (L)

r = 0524

P lt 0001 n = 135

MTR

(mL

dm2)

(e)

35 40 45 50 55 60 65 70

20

22

24

26

28

12

345 6 7

89

10

11

131415

16

17

18

1920

2122

23

24

25

26

27

28

2930

32 3334

35

36 37383940

4143

44

45

4647

4849

50

5152

535455

5657 58

59

60

61

62 63 646566

67

68

69

707273 7576

77

78

7980

8182

8384

85

86878889

9091

92

9394

95

96

97

98

99

100101

102103104

105

106107 108109110111

113114115

116

117

118 119120

121122

123124

125

126127

128

129

130131

132133

134

135136 137

138139

140

141

WUE (gkg)

r = minus0413

P lt 0001 n = 135

Δ13

C

(f)

Figure 2 Relationship between the RDWTDM RDWCWT TDMCWT CWTMTR CWTMTR and WUEΔ13C in the mappingpopulation of C canephora (L1 valley times S334)

8 Molecular Biology International

Table 4 Chi-square tests (1205942) for RAPD and SSR markers in the Coffea canephoramapping population

Parents F1progenies

Markersystem

Total polymor-phic markers

Marker loci notfollowing expected ratio

Marker loci followingexpected ratio

Number of significant markers119875 = 095 119875 = 005 119875 = 0001

L1 valley timesS3334 134 RAPD 70 28 42 (1 1) 32 3 7

SSR 9 9 0 (3 1) mdash mdash mdash

Table 5 Polymorphic microsatellite primers and their sequences used to study the segregation of markers in the mapping population

Serial number Primer Sequence Tm (∘C) Size range

1 CM11 F GCTGCCAGAAAAATGTTGCAGTG 58 270ndash338R CTGCCTCGTAAAAGCTTGCGTTG

2 CM13 F GCTATGCAGCTTGTTCGCAATCC 59 350ndash411R CCAGCTATATCAGGAGCAGAACC

3 CM180 F CATGTGTAATACATTCAACAGTGA 60 300ndash350R GCAATAGTGGTTGTCATCCTT

4 CM32 F AACTCTCCATTCCCGCATTC 60 180ndash200R CTGGGTTTTCTGTGTTCTCG

5 CM46 F CAGCTAGTGTGAAGGGAAAC 55 300ndash350R GTTATCATGGTCTTACACG

6 CM20 F CTTGTTTGAGTCTGTCGCTG 60 250ndash300R TTTCCCTCCCAATGTCTGTA

7 CM171 F TTCCCCCATCTTTTTCTTTC 60 250ndash300R TTGTATACGGCTCGTCAGGT

8 CM166 F AAGAGGTGCCTATCACCGTC 60 230ndash260R CGAGGTATCAAAAAGCACCT

9 CM211 F TCATGCCAAATATGAGTGGA 60 300ndash350R GAGATGGCAAAGGCTGTTC

35 Single Marker Analysis for Root and Associated Physiolog-ical Traits Complex physiological traits have been describedby a small number of major QTL [24] but it is intricateto find useful QTL for a particular trait as their individualcontribution is smaller Therefore under such conditionSingle Marker Analysis (SMA) is generally a good choicewhen the goal is simply a detection of marker locus linkedto a trait However estimation of its position and its effectsrequires further complex analysis such as marker regression[25] or interval analysis [26]

In our study Single Marker ANOVA is used to identifymarkers showing significant association with 29 morpho-physiological traits (SAS software with 119875 le 005)

In the single marker analysis simple regression modelwas examined to study the association between marker loci(independent) and trait score (dependent variable) and alsocomputed the percent phenotypic variance explained by eachmarker Seventy polymorphic marker loci were employed forsimple regression analysis however it was discovered that 37RAPD and 5 SSR markers explained their association withmorphophysiological traits

36Markers Linked to Root Traits The singlemarker analysisrevealed fourmarkers such as OPS1

850 OPK11

780 OPY20

1200

OPZ101350

and CM13400

were significantly associated withroot length (119875 = 005) and explained phenotypic vari-ance of 441 295 356 512 and 512 respectively

A number of secondary roots linked to three markers suchas OPO16

450(440) OPK11

1000(551) and OPV14

550

(582) were significantly (119875 = 005) associated Similarlysix markers such as OPK11

1000(326) OPP9

1030(35)

OPM11600

(375) OPL19900

(561) OPL19600

(411)OPR4

600(330) and CM211

300(1198772 = 496) revealed

significant (119875 = 005) association with the root to shootratio (Table 6) This implies the root associated traits arepolygenic controlling many genes However root dry weightis associated with only one maker OPL1

1400(486)

37 Markers Linked to Morphophysiological Traits Besidesroot traits polygenic inheritance traits such as morpho-logical gravimetric and gas exchange at single leaf leveltraits were also deployed for marker trait association Singlemarker analysis revealed more than three markers linked toeach trait and 25 markers are colocalized with more thanone trait and 17 markers are associated with single trait(Table 7)ThemarkerOPP5

1800is coassociatedwith shoot dry

weight and leaf area duration (LAD) and these two traits arepositively correlated (1198772 = 093) Four markers (OPL1

1450

OPL191650

OPM171400

and OPK20350

) linked to NAR andtwo markers OPR4

600and OPL12

2000are significantly (119875 =

001) associated with cumulative water transpired (CWT)The 13ΔC is the surrogate method to quantify the water

use efficiency and it was demonstrated that there is inverse

Molecular Biology International 9

Table 6 RAPD markers linked to root traits in the mappingpopulation of C canephora (L1 valley times S3334)

Trait Marker 1198772 () 119875 lt 005

Root length

OPY201200 295 00449OPZ101350 356 00286OPK11780 441 00178OPS1850 686 00001

CPCM13400 512 00105

Number of secondary rootsOPO16450 44 00179OPK111000 551 00059OPV14550 582 00042

Root dry weight OPL11450 476 00073

Root to shoot ratio

OPK111000 326 0038OPR4600 33 00367OPP91030 35 00288OPM11600 375 00267OPL19600 411 00223OPL19900 561 00062CM211300 496 00105

relationship between these two traits Of the six associatedmarkers OPZ10

1350was found to be associatedwith 13ΔCand

WUE with phenotypic variance of 412 and 302 respec-tively This marker locus could be negative additive effectsince traits are negatively related The mesophyll efficiency(Cigs) intrinsic WUE (Ags) and photosynthetic rate (Pn)are respectively associated with CM13

400(483) CM171

210

(345) and CM171190

(372) (Table 7) Whereas positivelyrelated traits are associated with the same marker such asOPP5

1800(LAD and shoot dry weight) OPZ10

1030(MTR and

gs) and OPK20350

(Ci and NAR) alleles contribution fromS3334 The markers linked to other gas exchange parameterswere given in Table 7 which are contributed from S3334

4 Discussion

We developed a F1 mapping population by crossing contrast-ing root traits S3334 high root type as male and L1 valley lowroot type as female to identify makers linked to root traitsand water use efficient types However in annual crops F

2

recombinant inbred lines (RILs) near isogenic lines (NILs)and doubled haploids (DH)mapping population will be usedto associatemarkers with the trait of our interest However inperennials the germplasm lines and the cultivated accessionsare often highly heterozygous consequently F

1population

developed from heterozygous parents was used in this studyand showed considerable phenotypic and genetic variations

One of the objectives of studying themapping populationwas to score the variability in roots and other physiologicaltraits under drought and thus mapping population was gownup in 70 FC of water stress to study the heritability ofroot traits Indeed root traits are highly complex geneticmechanism controlled by multi genes and highly herita-ble in water non limiting environment [27] The resultsdemonstrated significant variability was observed in root

dry weight (RDW) total dry matter (TDM) root to shootratio (RSR) cumulativewater transpired (CWT)with consid-erable transgressive segregation (Figure 1) and quantitativeinheritance of drought tolerance [28] However significantpositive correlation between RDWTDM RDWCW andTDMCWT indicates that under drought increased rootgrowth maintains the shoot hydraulic conductance as adop-tive strategy [29 30] The decreased mean transpiration(MTR) and increased water use efficiency (WUE) could bedue to decreased stomatal aperture through chemical signalssuch as ABA [31] However some of the clones showedthat increased MTR with increased CWT would favor thestomatal conductance and net photosynthesis Such clonesare better for breeding drought tolerance Inverse relationshipbetween Δ13C and WUE in robusta coffee accessions wasobserved (Figure 2) thus measuring Δ13C which can bea surrogate method to identify best WUE types clones inbreeding [19 32] Breeding for such quantitative traits ishighly complex cumbersome and thus by determining theallele of aDNAmarker plants that possess particular genes orquantitative trait loci (QTLs) are better than their phenotypeWith this background effort wasmade to identify themarkerslinked to root and associated physiological traits in Coffeacanephora

Despite the efforts no successful linkage map could beobtained partly due to the inadequate number of polymor-phic markers generated from this study The output of theassociation analysis indicates that out of 29 traits only 24traits were linked to 85 markers Even markers linked totraits explaining the phenotypic variationwere notmore than10 In the present study phenotypic variation explanationfor all the traits ranges from 261 to 986 This suggeststhat the successful application of molecular markers in traitmapping greatly requires more polymorphic markers whilesuccessful mapping attempts could still be carried out in theabsence of a linkage map as done in chickpea [33] Althoughconstruction of linkage map was done in C arabica [34]and C canephora [35] predominantly using AFLP and RAPDmarkers association with the traits was not attempted Simi-larly the first molecular linkage map generated using pseudotestcross strategy for Coffea canephora (CxR times Kagnalla) hasthe largest number of mapped SSRs (71 genomic includingnine EST-SSRs) [36] but trait association was not correlated

Most of the markers explained phenotypic variancebetween 3 and 4 however only two markers that isOPZ11

1500andOPV14

500 were significantly (119875 lt 001) linked

to photosynthetic rate (Pn) accounting the variation of about923 and 930 respectively (Table 7) The root traits suchas root length number of secondary roots root dry weightand root shoot ratio revealed significant association (119875 lt005) with markers and the variability ranges from 295 to686 (Table 6)This suggests the trait variability explanationis not sufficient to answer the probabilities of occurrence ofthese markers due to limited polymorphic markers

In the present study codominant SSR markers werealso used for the marker-trait association study The SSRmarkers identified herein are significantly associated withmorphological and physiological traits but these markers

10 Molecular Biology International

Table 7 List of RAPD and SSR markers overlapped with more than one trait

Markers Traits 1198772 ()

(119875 lt 005) Markers Traits 1198772

(119875 lt 005)

OPK111000

Internodal length 474OPR4600OPR4750

CWT 508Root to shoot ratio 326 RSR 330

Number of secondary roots 551 Number of leaves 565Stem dry weight 344 gs 293

OPK181200Specific leaf area 470 OPV14550

Pn 930Plant height 451 Number of secondary Roots 582

OPK192000Photosynthetic rate (Pn) 555 OPY201200

WUE 310WUE 443 Plant height 423

K20350NAR 482

OPZ111500Number of leaves 399

WUE 445 Root length 295Ci 432 Pn 923

OPL11400

Plant height 577

OPZ101350OPZ101030

MTR 821Internodal length 381 13

ΔC 412NAR 427 Plant height 303

Shoot dry weight 400 Root length 356Root dry weight 476 WUE 302

Pn 261 Cigs 509

OPL122000Plant height 368 Pn 756

CWT 474 MTR 438

OPL12165013ΔC 361 OPZ10720

gs 296pn 307 gs 326

OPL181000Number of nodes 778 Pn 314

LAD 382 Internodal length 358

OPL19900SLW 333 CPCM13450

Root length 512RSR 360 Cigs 483

OPM11600Ags 385

CM171300CM166260

Stem girth 454RSR 411 Ags 345MTR 294 No of nodes 393

OPO101400Ci 313 SLA 719E 318 119864 418

OPP51800LAD 382

CM211300SLW 283

Internodal length 582 RSR 496Shoot dry weight 271 119864 423

OPP91030Cigs 612SLW 333RSR 350

were explaining the trait in the range between 283 and719 This study has provided more detailed information ofroot and associated physiological traits relationships underdrought and identification of markers linked to such traitswith the limited polymorphic markers by employing SingleMarker Analysis (SMA) QTL maps could be used to forlong-term drought breeding Further studies are needed toconfirm the estimation of QTL positions and effects and tovalidatemarkers prior to routinemarker assisted selection fordrought tolerance in coffee

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

References

[1] F M DaMatta ldquoExploring drought tolerance in coffee aphysiological approach with some insights for plant breedingrdquoBrazilian Journal of Plant Physiology vol 16 no 1 pp 1ndash6 2004

Molecular Biology International 11

[2] M Nesbitt The Cultural History of Plants Taylor amp Francis2005

[3] F M Damatta ldquoDrought as a multidimensional stress affectingphotosynthesis in tropical tree cropsrdquo in Advances in PlantPhysiology A Hemantaranjan Ed vol 5 pp 227ndash265 2003

[4] K Harovon Mogel ldquoGenotype times environment times managementinteractions key to beating future droughtsrdquo CSA News vol 58pp 4ndash9 2013

[5] F M DaMatta A R M Chaves H A Pinheiro C Ducatti andM E Loureiro ldquoDrought tolerance of two field-grown clones ofCoffea canephorardquo Plant Science vol 164 no 1 pp 111ndash117 2003

[6] H A Pinheiro F M DaMatta A R M Chaves M E Loureiroand C Ducatti ldquoDrought tolerance is associated with rootingdepth and stomatal control of water use in clones of Coffeacanephorardquo Annals of Botany vol 96 no 1 pp 101ndash108 2005

[7] J B Passioura ldquoPhenotyping for drought tolerance in graincrops when is it useful to breedersrdquo Functional Plant Biologyvol 39 no 11 pp 851ndash859 2012

[8] J S Sperry ldquoHydraulics of vascular water transportrdquo inMechan-ical Integration of Plant Cells and Plants P Wojtaszek Edvol 9 of Signaling and Communication in Plants pp 303ndash327Springer Berlin Germany 2011

[9] A P Wasson R A Richards R Chatrath et al ldquoTraits andselection strategies to improve root systems and water uptakein water-limited wheat cropsrdquo Journal of Experimental Botanyvol 63 no 9 pp 3485ndash3498 2012

[10] G D Herder G van Isterdael T Beeckman and I de SmetldquoThe roots of a new green revolutionrdquo Trends in Plant Sciencevol 15 no 11 pp 600ndash607 2010

[11] H T Nguyen R C Babu and A Blum ldquoBreeding for droughtresistance in rice physiology and molecular genetics consider-ationsrdquo Crop Science vol 37 no 5 pp 1426ndash1434 1997

[12] A Blum Plant Breeding for Water-Limited EnvironmentsSpringer Science+Business Media LLC New York NY USA2011

[13] L B Motta T C B Soares M A G Ferrao E T Caixeta R MLorenzoni and J D de SouzaNeto ldquoMolecular characterizationof arabica and Conilon coffee plants genotypes by SSR and ISSRmarkersrdquo Brazilian Archives of Biology and Technology vol 57no 5 pp 728ndash735 2014

[14] PMarraccini L P Freire G S CAlves et al ldquoRBCS1 expressionin coffee Coffea orthologs Coffea arabica homeologs andexpression variability between genotypes and under droughtstressrdquo BMC Plant Biology vol 11 article 85 2011

[15] P Marraccini F Vinecky G S C Alves et al ldquoDifferentiallyexpressed genes and proteins upon drought acclimation intolerant and sensitive genotypes of Coffea canephorardquo Journalof Experimental Botany vol 63 no 11 pp 4191ndash4212 2012

[16] F Denoeud L Carretero-Paulet A Dereeper et al ldquoThecoffee genome provides insight into the convergent evolution ofcaffeine biosynthesisrdquo Science vol 345 no 6201 pp 1181ndash11842014

[17] A Dereeper S Bocs M Rouard et al ldquoThe coffee genome huba resource for coffee genomesrdquo Nucleic Acids Research vol 43no 1 pp D1028ndashD1035 2015

[18] M Udayakumar M S Sheshshayee K N Nataraj et al ldquoWhyhas breeding for water use efficiency not been successful Ananalysis and alternate approach to exploit this trait for cropimprovementrdquo Current Science vol 74 no 11 pp 1000ndash10031998

[19] M G Awati Genetic diversity and molecular markers for rootand associated physiological traits of Robusta (coffea canephoraPierre ex F) accessions [PhD thesis] University of AgriculturalSciences Bangalore India 2005

[20] C E Williams and P C Ronald ldquoPCR template-DNA isolatedquickly from monocot and dicot leaves without tissue homog-enizationrdquo Nucleic Acids Research vol 22 no 10 pp 1917ndash19181994

[21] S D Tanksley ldquoMapping polygenesrdquoAnnual Review of Geneticsvol 27 no 1 pp 205ndash233 1993

[22] T Halward T Stalker E LaRue and G Kochert ldquoUse ofsingle-primer DNA amplifications in genetic studies of peanut(Arachis hypogaea L)rdquo Plant Molecular Biology vol 18 no 2pp 315ndash325 1992

[23] SAS Institute SASSTAT Userrsquos Guide Version 6 SAS InstituteCary NC USA 4th edition 1989

[24] M J Kearsey and A G L Farquhar ldquoQTL analysis in plantswhere are we nowrdquo Heredity vol 80 no 2 pp 137ndash142 1998

[25] M J Kearsey and V Hyne ldquoQTL analysis a simple lsquomarker-regressionrsquo approachrdquoTheoretical and Applied Genetics vol 89no 6 pp 698ndash702 1994

[26] C S Haley and S A Knott ldquoA simple regression method formapping quantitative trait loci in line crosses using flankingmarkersrdquo Heredity vol 69 no 4 pp 315ndash324 1992

[27] B M Atta T Mahmood and R M Trethowan ldquoRelationshipbetween root morphology and grain yield of wheat in North-Western NSW Australiardquo Australian Journal of Crop Sciencevol 7 no 13 pp 2108ndash2115 2013

[28] N T Lang C T Nha P T T Ha and B C Buu ldquoQuantitativetrait loci (QTLs) associated with drought tolerance in rice(Oryza sativa L)rdquo Sabrao Journal of Breeding and Genetics vol45 no 3 pp 409ndash421 2013

[29] H J Schenk andR B Jackson ldquoMapping the global distributionof deep roots in relation to climate and soil characteristicsrdquoGeoderma vol 126 no 1-2 pp 129ndash140 2005

[30] A Srividya L R Vemireddy P V Ramanarao et al ldquoMolecularmapping of QTLs for drought related traits at seedling stageunder PEG induced stress conditions in ricerdquoAmerican Journalof Plant Sciences vol 2 no 2 pp 190ndash201 2011

[31] N-N Shukor H A Hamid A Abdu andM-K Ismail ldquoEffectsof soil compaction on growth and physiological characteristicsofAzadirachta excelsa seedlingsrdquoTheAmerican Journal of PlantPhysiology vol 10 no 1 pp 25ndash42 2015

[32] P R Bhat Phenotyping and molecular analysis of Coffea arabicaL accessions and mapping population to identify RAPD markersassociated with root characteristics and associated physiologicaltraits [PhD thesis] University of Agricultural Sciences Banga-lore India 2002

[33] S Chandra H K Buhariwalla J Kashiwagi et al ldquoIdentifyingQTL linked markers in marker-29 deficient cropsrdquo in Proceed-ings of the 4th International Crop Science Congress BrisbaneAustralia 2004

[34] H M Pearl C Nagai P H Moore D L Steiger R V Osgoodand RMing ldquoConstruction of a genetic map for arabica coffeerdquoTheoretical and Applied Genetics vol 108 no 5 pp 829ndash8352004

[35] P Lashermes M C Combes N S Prakash P Trouslot MLorieux and A Charrier ldquoGenetic linkage map of Coffeacanephora effect of segregation distortion and analysis ofrecombination rate in male and female meiosesrdquo Genome vol44 no 4 pp 589ndash596 2001

[36] P Hendre S Lalji Singh and R K Aggarwal ldquoConstructionof framework molecular linkage map of Robusta coffee Coffeacanephora with RAPD AFLP g-SSRs And EST-SSRs usingpseudo-testcross strategyrdquo in Proceedings of the Plant amp AnimalGenomes 15th Conference January 2007

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

PeptidesInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

International Journal of

Volume 2014

Zoology

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Molecular Biology International

GenomicsInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioinformaticsAdvances in

Marine BiologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Signal TransductionJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

Evolutionary BiologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Genetics Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Virolog y

Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Enzyme Research

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Microbiology

Molecular Biology International 3

Table 1Genotypic variation of parental accessions S3334 (high roottype) and L1 valley (low root type)

S3334 L1 valley1 Root length 45 302 Secondary roots 40 153 Root dry weight 3181 4954 Root to shoot ratio 041 0365 Total dry matter 10677 15496 Cumulative water transpired 2886 31157 water use efficiency 372 3628 Net assimilation rate 1375 9099 Mean transpiration rate 372 22310 Pngs 7839 437811 Pn119864 299 18412 Cigs 38635 33994

and heterozygous bands were scored as 2 in all individuals ofF1 mapping populationThe binary data was used for furtherstatistical analysis Based on the segregation of RAPD mark-ers in the mapping population the putative genotypic inter-pretation of the parents for the marker locus was made andtheChi-square testwas performed (httpwwwphysicscsbsjuedustatschi-square formhtml)

27 Association of Identified Polymorphic Markers with Physi-ological Traits Single point analysis [21 22] for detecting theassociation of molecular markers with complex physiologicaland morphological traits was done using SAS software Tofind the amount of variability explained by these markersregression (1198772) values were worked out by one-way analysisof variance (ANOVA) by general linear model (GLM)procedure In this analysis different traits were treated asdependent variable and the various molecular markers asindependent variables A total of 30 different physiologicaland morphological traits were used to associate with the 85polymorphic molecular markers [23]

3 Results

31 Phenotyping Root Length Secondary Roots Root DryWeight and Physiological Traits At the end of experimentperiod about 180 days water stress pots were completelysaturated with water and then in next day all plants werecarefully detached with water force to avoid root loss Thedata on root length secondary roots and root biomass dataof parental lines (Table 1) and F1 mapping population wererecorded (Table 2) There was significant genetic variabilityobserved in root length ranging from 415 cm to 830 cmwith a mean of 5708 cm Similar trend was followed indry weight ranging from 257 g to 3469 g with a mean of1381 gplant and root to shoot ratio varies from 257 to 441with mean ratio of 377 It was observed that F1s root traitswere distributed between the values of the respective parentallines without much considerable transgressive segregation(Figure 1) The quantitative nature of these traits showedcontinuous variation confirming the metric nature of thetraits

Morphological traits such as plant height number ofnodes and leaves stem girth internodal length leaf areashoot dry weight and total dry matter were recordedNonetheless all these traits showed significant and contin-uous variation (Table 2) Besides genetic variability in themapping population in net photosynthesis (Pn) stomatalconductance (gs) transpiration rate (119864) intrinsic WUE(Pngs) and mesophyll efficiency (Cigs) was observed atsingle leaf level

Significant variations were observed in water use effi-ciency (WUE) and associated physiological traits like cumu-lative water transpired (CWT) net assimilation rate (NAR)mean transpiration rate (MTR) leaf area duration (LAD)and Δ13C in the population The WUE ranges from 386to 684 gkg with an average of 540 gkg and considerabletransgressive segregation was observed The variation inCWT was observed between 250 and 2350 kgplant Theaverage transpiration rate was 1081 Lplant in the popula-tion The NAR and MTR also varied with mean values of1307mgdm2 and 244mLdm2 of leaf area respectivelySimilarly the functional leaf area (LAD) varied between106589 and 799277 dm2day with mean leaf area duration of437657 dm2dayΔ13C also varied between 1975 and 2653permilwith a mean of 2180permil (Table 2) The variation in WUECWT Δ13C and so forth showed that traits are metricnature and mapping population could be utilized for theidentification of marker linked to quantitative traits

32 Association of Root with Other Physiological Traits Theplants with the higher root biomass showed higher totalbiomass and correlation between these two traits is highlysignificant (119903 = 0968) (Figure 2) It signifies the importanceof root system in determining the total dry matter by absorb-ing the water from sub-soil layers from its better root systemcompared to parental lines Subsequently strong positivesignificant correlations were noticed between root weightand total transpiration (119903 = 0940) CWT and MTR (119903 =0524) and total dry matter and CWT (119903 = 0970) (Figure 2)suggesting that maintaining canopy hydraulic conductancewith high carbon assimilation is one of the drought tolerancemechanisms

33 Relationship between WUE with Other PhysiologicalTraits Significant inverse relationship between WUE andΔ13C (119903 = minus0413) was observed (Figure 2) It suggests

that Δ13C could be a strong surrogate measure for WUEeven in mapping populationThe relationship between CWTand WUE was poor suggesting stomatal control of WUE inmapping population and similarly between root weight andWUE However significant negative relationship betweenMTR and WUE was observed (119903 = minus0552) in mapping pop-ulation (data not shown) This signifies the genetic nature ofthe coffee plants for its heritable conductance type It suggeststhat stomatal factors regulate the WUE in these plants ratherthan photosynthetic efficiency (mesophyll factors)

34 Identification of Polymorphic RAPD and MicrosatellitePrimers in Parental Lines The contrasting parental lines L1

4 Molecular Biology International

S3334 L1valley

05

10152025303540

Freq

uenc

y

RDWt

05

10152025

S3334 L1valley

0

5

10

15

20

25

30

35

Freq

uenc

y

WUE

44485256

S3334 L1valley

2ndash6

6ndash10

10

ndash14

14

ndash18

18

ndash22

22

ndash26

26

ndash30

30

ndash34

34

ndash38

385

ndash420

420

ndash455

455

ndash490

490

ndash525

525

ndash560

560

ndash595

595

ndash630

630

ndash665

665

ndash700

Figure 1 Continued

Molecular Biology International 5

0

5

10

15

20

25

30Fr

eque

ncy

TDM

020406080

100

S3334 L1valley

05

1015202530354045

Freq

uenc

y

RSR

0250025402580262

S3334 L1valley

0

5

10

15

20

25

30

35

Freq

uenc

y

CWT

05

101520

S3334 L1valley

0

5

10

15

20

25

30

Freq

uenc

y

LAD

0200040006000

S3334 L1valley

05

10152025303540

Freq

uenc

y

MTR

262728293031

S3334 L1valley

05

101520253035404550

Freq

uenc

y

NAR

05

101520

S3334 L1valley

10

ndash22

22

ndash34

34

ndash46

46

ndash58

58

ndash70

70

ndash82

82

ndash94

94

ndash106

106

ndash118

118

ndash130

20

ndash45

45

ndash70

70

ndash95

95

ndash120

120

ndash145

145

ndash170

170

ndash195

195

ndash220

220

ndash245

022

ndash024

024

ndash026

026

ndash028

028

ndash030

030

ndash032

032

ndash034

034

ndash036

036

ndash038

038

ndash040

1000

ndash1800

1800

ndash2600

2600

ndash3400

3400

ndash4200

4200

ndash5000

5000

ndash5800

5800

ndash6600

6600

ndash7400

7400

ndash8200

15

ndash175

20

ndash225

250

ndash275

30

ndash325

350

ndash375

85

ndash99

99

ndash113

113

ndash127

127

ndash141

141

ndash155

155

ndash169

169

ndash183

183

ndash197

Figure 1 Genetic variability of root system and quantitative traits of mapping population of L1 valley times S334

valley (low root type) and S3334 (high root type) weregenotyped with 320 RAPD primers of which 41 polymor-phic primers generated 70 polymorphic loci (Table 3) SinceRAPD is dominant marker the PCR amplified DNA frag-ments were scored as dominant (AAAa) whereas absenceof bands was scored as recessive (aa) We have analyzed ourdata within the framework of these assumptions

Among them more than 85 of the primers producedsingle polymorphic locus and 15 of the primers yieldedmore than three polymorphic loci

All the RAPD markers in the F1 generation of Ccanephora segregated in ratios that were not consistent withMendelian inheritanceThe Chi-square tests were performed

for eachmarker to determine segregation distortion from theexpected allele frequency ratio of 1 1 At a significance levelof 119875 = 005 about 62 of the marker loci were in agreementwith the expected ratios and 38 of the marker loci were notfollowing the Mendelian inheritance (Table 4) It is expectedthat RAPD bands of maternal DNA origin would show non-Mendelian inheritance

Besides RAPD marker system 55 SSR markers were alsoused for genotyping however only nine primers showedpolymorphism (Table 5) but did not follow the Mendeliansegregation The low level of DNA polymorphism betweenthe two parental accessions coupled with the large number ofcommon bands implies parental lines could be less diverse

6 Molecular Biology International

Table 2 Range and mean values of the morphophysiological parameters in 134 F1 mapping population of Coffea canephora (L1 valley timesS3334)

Serial number Parameters Range Mean STDEV

Root traits

1 Root length (cm2) 415ndash830 5708 7292 Number of secondary roots 2ndash60 269 12613 Root dry weight (gplant) 257ndash3469 1377 664 Root shoot ratio 023ndash039 027 003

Morphological traits

5 Plant height (cm2) 135ndash1053 726 15696 Number of nodes 110ndash420 2408 6887 Number of leaves 2ndash70 3402 13428 Stem girth (mm) 588ndash1365 1028 1799 Internodal length (cm) 19ndash125 854 210 Specific leaf dry weight (mgdm2) 141ndash264 18 02211 Final leaf area (cm2) 113692ndash819061 449119 17101712 Specific leaf area (cm) 37899ndash71097 56244 653413 Total leaf dry weight (gplant) 578ndash4859 2602 104314 Stem dry weight (gplant) 481ndash5262 2433 10415 Shoot dry weight (gplant) 1059ndash9664 5035 206316 Total dry matter (gplant) 116ndash12143 5793 2477

Gravimetric traits

17 Leaf area duration (dm2 days) 106589ndash799277 437252 16580418 Net assimilation rate 89ndash1938 1307 17119 Cumulative water transpiration (ltplant) 25ndash235 1081 46220 Water use efficiency (gkg) 386ndash684 54 05821 Mean transpiration rate (mLdm2 days) 15ndash352 244 03822 Delta 13c (mill) 1975ndash2653 218 111

Gas exchange parameters

23 Photosynthesis (Pn) (mmolm2S) 545ndash2727 169 39124 Transpiration rate (119864) (mgm2s) 071ndash747 338 13425 Conductance (gs) (mmolm2s) 005ndash1 035 01726 Cigs 28876ndash348425 8593 405127 Ci 1865ndash29667 24798 248828 Ags 2406ndash9326 5442 155229 A119864 263ndash1407 552 176

Table 3 Polymorphic RAPD primers and their sequences used to study the segregation of markers in the mapping population

Polymorphicbands

Primername Sequence Primer

name Sequence Primername Sequence Primer

name Sequence

1

OPK18 CCTAGTCGAG OPN6 GAGACGCACA OPQ5 CCGCGTCTTG OPV5 TCCGAGAGGGOPK19 CACAGGCGGA OPO1 GGCACGTAAG OPS1 CTACTGCGCT OPW9 GTGACCGAGTOPK20 GTGTCGCGAG OPO18 CTCGCTATCC OPV11 CTCGACAGAG OPY20 AGCCGTGGAAOPM5 GGGAACGTGT OPO4 AAGTCCGCTC OPV14 AGATCCCGCC OPZ11 CTCAGTCGCAOPN19 GTCCGTACTG OPP2 TCGGCACGCA OPV2 AGTCACTCCC

2

OPK10 GTGCSSCGTG OPL18 ACCACCCACC OPO16 TCGGCGGTTC OPT7 GTCCATGCCAOPK11 AATGCCCCAG OPM11 GTCCACTGTG OPP5 CCCCGGTAAC OPU1 ACGGACGTCAOPL1 GGCATGACCT OPM17 TCAGTCCGGG OPQ20 TCGCCCAGTC OPW17 GTCCTGGGTTOPL12 GGGCGGTACT OPO10 TCAGAGCGCC OPR4 CCCGTAGCAC OPZ3 CAGCACCGCA

3 OPL19 GAGTGGTGAC OPM2 ACAACGCCTC OPP11 AACGCGTCGG OPP9 GTGGTCCGCAOPZ10 CCGACAAACC

4 OPK17 CCCAGCTGTG

Molecular Biology International 7

0 5 10 15 20 25 30 350

20

40

60

80

100

120

140

1

2

3 4

5

6

7

8

9

10

1113

14

15

161718

19

20

21

22

23

2425

26 2728

29

3032

33

34

35

36

37

38 3940

41

43

44

45

46

47

48

49

50

5152

5354

55

5657

58

59

60

61

62

63

64

65

66

67

68

69

70

72

73

75

7677

78

79

80

81828384

85

86

87

88

89

90

9192

939495

96

97

98

99100

101102

103104

105106

107

108

109110

111

113

114

115

116

117

118

119

120

121

122123

124

125

126

127128129

130

131

132

133

134

135

136

137

138

139140

141

Tota

l dry

mat

ter (

gpl

ant)

Root dry weight (gplant)

r = 0968

P lt 0001 n = 135

(a)

0 5 10 15 20 25 30 350

5

10

15

20

25

1

2

3

4

5

6

7

8

910

1113

14

15

161718

19

20

21

22

23

2425

262728 29

3032

33

34

35

36

37

38 3940

41

43

44

45

4647

48

49

50

5152

5354

55

5657

58

59

6061

62

63

64

65

66

67

68

69

70

72

73

75

76

77

78

7980

81

8283

84

85

86

87

88

89

90

9192 93

9495

96

97

98 99

100101

102

103104

105

106

107

108

109110

111

113

114

115

116

117

118119

120

121

122123

124

125

126

127128

129

130

131

132

133

134

135

136

137138

139140

141

CWT

(L)

Root dry weight (gplant)

r = 0940

P lt 0001 n = 135

(b)

0 20 40 60 80 100 120 1400

5

10

15

20

25

1

2

3

4

5

6

7

8

910

1113

1415

1617

18

19

20

21

22

23

2425

2627 28 29

3032

33

34

35

36

37

383940

41

43

44

45

4647

48

49

50

5152

5354

55

5657

58

59

6061

62

63

64

65

6667

68

69

70

72

73

75

76

77

78

7980

81

8283

84

85

86

8788

89

90

9192 93

9495

96

97

98 99

100101

102

103104

105

106

107

108

109110

111

113

114

115

116

117

118 119

120

121

122123

124

125

126

127128

129

130

131

132

133

134

135

136

137138

139140

141

CWT

(L)

Total dry matter (gplant)

r = 0970

P lt 0001 n = 135

(c)

0 5 10 15 20 25

15

20

25

30

35

12

3

45

67

8

9

10

11 1314

1516

17

18

1920

2122

23

24

25

26

27

28

29 30

3233

34

35 36

37 38

39

40

4143

44

45

4647

4849

50

5152 53

5455

56

57

5859

60

61

62 6364 65

66

67

68

69

70

7273

75

76

77

78

79

80

81

8283

84

85 86

8788

89

9091

92

93

94

9596

9798

99

100

101

102

103

104

105106

107

108

109110

111

113

114115

116

117

118119

120

121122

123124

125

126127

128

129130

131

132133

134

135136

137

138

139

140141

CWT (L)

r = 0524

P lt 0001 n = 135

MTR

(mL

dm2)

(d)

0 5 10 15 20 25

15

20

25

30

35

12

3

45

67

8

9

10

11 1314

1516

17

18

1920

2122

23

24

25

26

27

28

29 30

3233

34

35 36

37 38

39

40

4143

44

45

4647

4849

50

5152 53

5455

56

57

5859

60

61

62 6364 65

66

67

68

69

70

7273

75

76

77

78

79

80

81

8283

84

85 86

8788

89

9091

92

93

94

9596

9798

99

100

101

102

103

104

105106

107

108

109110

111

113

114115

116

117

118119

120

121122

123124

125

126127

128

129130

131

132133

134

135136

137

138

139

140141

CWT (L)

r = 0524

P lt 0001 n = 135

MTR

(mL

dm2)

(e)

35 40 45 50 55 60 65 70

20

22

24

26

28

12

345 6 7

89

10

11

131415

16

17

18

1920

2122

23

24

25

26

27

28

2930

32 3334

35

36 37383940

4143

44

45

4647

4849

50

5152

535455

5657 58

59

60

61

62 63 646566

67

68

69

707273 7576

77

78

7980

8182

8384

85

86878889

9091

92

9394

95

96

97

98

99

100101

102103104

105

106107 108109110111

113114115

116

117

118 119120

121122

123124

125

126127

128

129

130131

132133

134

135136 137

138139

140

141

WUE (gkg)

r = minus0413

P lt 0001 n = 135

Δ13

C

(f)

Figure 2 Relationship between the RDWTDM RDWCWT TDMCWT CWTMTR CWTMTR and WUEΔ13C in the mappingpopulation of C canephora (L1 valley times S334)

8 Molecular Biology International

Table 4 Chi-square tests (1205942) for RAPD and SSR markers in the Coffea canephoramapping population

Parents F1progenies

Markersystem

Total polymor-phic markers

Marker loci notfollowing expected ratio

Marker loci followingexpected ratio

Number of significant markers119875 = 095 119875 = 005 119875 = 0001

L1 valley timesS3334 134 RAPD 70 28 42 (1 1) 32 3 7

SSR 9 9 0 (3 1) mdash mdash mdash

Table 5 Polymorphic microsatellite primers and their sequences used to study the segregation of markers in the mapping population

Serial number Primer Sequence Tm (∘C) Size range

1 CM11 F GCTGCCAGAAAAATGTTGCAGTG 58 270ndash338R CTGCCTCGTAAAAGCTTGCGTTG

2 CM13 F GCTATGCAGCTTGTTCGCAATCC 59 350ndash411R CCAGCTATATCAGGAGCAGAACC

3 CM180 F CATGTGTAATACATTCAACAGTGA 60 300ndash350R GCAATAGTGGTTGTCATCCTT

4 CM32 F AACTCTCCATTCCCGCATTC 60 180ndash200R CTGGGTTTTCTGTGTTCTCG

5 CM46 F CAGCTAGTGTGAAGGGAAAC 55 300ndash350R GTTATCATGGTCTTACACG

6 CM20 F CTTGTTTGAGTCTGTCGCTG 60 250ndash300R TTTCCCTCCCAATGTCTGTA

7 CM171 F TTCCCCCATCTTTTTCTTTC 60 250ndash300R TTGTATACGGCTCGTCAGGT

8 CM166 F AAGAGGTGCCTATCACCGTC 60 230ndash260R CGAGGTATCAAAAAGCACCT

9 CM211 F TCATGCCAAATATGAGTGGA 60 300ndash350R GAGATGGCAAAGGCTGTTC

35 Single Marker Analysis for Root and Associated Physiolog-ical Traits Complex physiological traits have been describedby a small number of major QTL [24] but it is intricateto find useful QTL for a particular trait as their individualcontribution is smaller Therefore under such conditionSingle Marker Analysis (SMA) is generally a good choicewhen the goal is simply a detection of marker locus linkedto a trait However estimation of its position and its effectsrequires further complex analysis such as marker regression[25] or interval analysis [26]

In our study Single Marker ANOVA is used to identifymarkers showing significant association with 29 morpho-physiological traits (SAS software with 119875 le 005)

In the single marker analysis simple regression modelwas examined to study the association between marker loci(independent) and trait score (dependent variable) and alsocomputed the percent phenotypic variance explained by eachmarker Seventy polymorphic marker loci were employed forsimple regression analysis however it was discovered that 37RAPD and 5 SSR markers explained their association withmorphophysiological traits

36Markers Linked to Root Traits The singlemarker analysisrevealed fourmarkers such as OPS1

850 OPK11

780 OPY20

1200

OPZ101350

and CM13400

were significantly associated withroot length (119875 = 005) and explained phenotypic vari-ance of 441 295 356 512 and 512 respectively

A number of secondary roots linked to three markers suchas OPO16

450(440) OPK11

1000(551) and OPV14

550

(582) were significantly (119875 = 005) associated Similarlysix markers such as OPK11

1000(326) OPP9

1030(35)

OPM11600

(375) OPL19900

(561) OPL19600

(411)OPR4

600(330) and CM211

300(1198772 = 496) revealed

significant (119875 = 005) association with the root to shootratio (Table 6) This implies the root associated traits arepolygenic controlling many genes However root dry weightis associated with only one maker OPL1

1400(486)

37 Markers Linked to Morphophysiological Traits Besidesroot traits polygenic inheritance traits such as morpho-logical gravimetric and gas exchange at single leaf leveltraits were also deployed for marker trait association Singlemarker analysis revealed more than three markers linked toeach trait and 25 markers are colocalized with more thanone trait and 17 markers are associated with single trait(Table 7)ThemarkerOPP5

1800is coassociatedwith shoot dry

weight and leaf area duration (LAD) and these two traits arepositively correlated (1198772 = 093) Four markers (OPL1

1450

OPL191650

OPM171400

and OPK20350

) linked to NAR andtwo markers OPR4

600and OPL12

2000are significantly (119875 =

001) associated with cumulative water transpired (CWT)The 13ΔC is the surrogate method to quantify the water

use efficiency and it was demonstrated that there is inverse

Molecular Biology International 9

Table 6 RAPD markers linked to root traits in the mappingpopulation of C canephora (L1 valley times S3334)

Trait Marker 1198772 () 119875 lt 005

Root length

OPY201200 295 00449OPZ101350 356 00286OPK11780 441 00178OPS1850 686 00001

CPCM13400 512 00105

Number of secondary rootsOPO16450 44 00179OPK111000 551 00059OPV14550 582 00042

Root dry weight OPL11450 476 00073

Root to shoot ratio

OPK111000 326 0038OPR4600 33 00367OPP91030 35 00288OPM11600 375 00267OPL19600 411 00223OPL19900 561 00062CM211300 496 00105

relationship between these two traits Of the six associatedmarkers OPZ10

1350was found to be associatedwith 13ΔCand

WUE with phenotypic variance of 412 and 302 respec-tively This marker locus could be negative additive effectsince traits are negatively related The mesophyll efficiency(Cigs) intrinsic WUE (Ags) and photosynthetic rate (Pn)are respectively associated with CM13

400(483) CM171

210

(345) and CM171190

(372) (Table 7) Whereas positivelyrelated traits are associated with the same marker such asOPP5

1800(LAD and shoot dry weight) OPZ10

1030(MTR and

gs) and OPK20350

(Ci and NAR) alleles contribution fromS3334 The markers linked to other gas exchange parameterswere given in Table 7 which are contributed from S3334

4 Discussion

We developed a F1 mapping population by crossing contrast-ing root traits S3334 high root type as male and L1 valley lowroot type as female to identify makers linked to root traitsand water use efficient types However in annual crops F

2

recombinant inbred lines (RILs) near isogenic lines (NILs)and doubled haploids (DH)mapping population will be usedto associatemarkers with the trait of our interest However inperennials the germplasm lines and the cultivated accessionsare often highly heterozygous consequently F

1population

developed from heterozygous parents was used in this studyand showed considerable phenotypic and genetic variations

One of the objectives of studying themapping populationwas to score the variability in roots and other physiologicaltraits under drought and thus mapping population was gownup in 70 FC of water stress to study the heritability ofroot traits Indeed root traits are highly complex geneticmechanism controlled by multi genes and highly herita-ble in water non limiting environment [27] The resultsdemonstrated significant variability was observed in root

dry weight (RDW) total dry matter (TDM) root to shootratio (RSR) cumulativewater transpired (CWT)with consid-erable transgressive segregation (Figure 1) and quantitativeinheritance of drought tolerance [28] However significantpositive correlation between RDWTDM RDWCW andTDMCWT indicates that under drought increased rootgrowth maintains the shoot hydraulic conductance as adop-tive strategy [29 30] The decreased mean transpiration(MTR) and increased water use efficiency (WUE) could bedue to decreased stomatal aperture through chemical signalssuch as ABA [31] However some of the clones showedthat increased MTR with increased CWT would favor thestomatal conductance and net photosynthesis Such clonesare better for breeding drought tolerance Inverse relationshipbetween Δ13C and WUE in robusta coffee accessions wasobserved (Figure 2) thus measuring Δ13C which can bea surrogate method to identify best WUE types clones inbreeding [19 32] Breeding for such quantitative traits ishighly complex cumbersome and thus by determining theallele of aDNAmarker plants that possess particular genes orquantitative trait loci (QTLs) are better than their phenotypeWith this background effort wasmade to identify themarkerslinked to root and associated physiological traits in Coffeacanephora

Despite the efforts no successful linkage map could beobtained partly due to the inadequate number of polymor-phic markers generated from this study The output of theassociation analysis indicates that out of 29 traits only 24traits were linked to 85 markers Even markers linked totraits explaining the phenotypic variationwere notmore than10 In the present study phenotypic variation explanationfor all the traits ranges from 261 to 986 This suggeststhat the successful application of molecular markers in traitmapping greatly requires more polymorphic markers whilesuccessful mapping attempts could still be carried out in theabsence of a linkage map as done in chickpea [33] Althoughconstruction of linkage map was done in C arabica [34]and C canephora [35] predominantly using AFLP and RAPDmarkers association with the traits was not attempted Simi-larly the first molecular linkage map generated using pseudotestcross strategy for Coffea canephora (CxR times Kagnalla) hasthe largest number of mapped SSRs (71 genomic includingnine EST-SSRs) [36] but trait association was not correlated

Most of the markers explained phenotypic variancebetween 3 and 4 however only two markers that isOPZ11

1500andOPV14

500 were significantly (119875 lt 001) linked

to photosynthetic rate (Pn) accounting the variation of about923 and 930 respectively (Table 7) The root traits suchas root length number of secondary roots root dry weightand root shoot ratio revealed significant association (119875 lt005) with markers and the variability ranges from 295 to686 (Table 6)This suggests the trait variability explanationis not sufficient to answer the probabilities of occurrence ofthese markers due to limited polymorphic markers

In the present study codominant SSR markers werealso used for the marker-trait association study The SSRmarkers identified herein are significantly associated withmorphological and physiological traits but these markers

10 Molecular Biology International

Table 7 List of RAPD and SSR markers overlapped with more than one trait

Markers Traits 1198772 ()

(119875 lt 005) Markers Traits 1198772

(119875 lt 005)

OPK111000

Internodal length 474OPR4600OPR4750

CWT 508Root to shoot ratio 326 RSR 330

Number of secondary roots 551 Number of leaves 565Stem dry weight 344 gs 293

OPK181200Specific leaf area 470 OPV14550

Pn 930Plant height 451 Number of secondary Roots 582

OPK192000Photosynthetic rate (Pn) 555 OPY201200

WUE 310WUE 443 Plant height 423

K20350NAR 482

OPZ111500Number of leaves 399

WUE 445 Root length 295Ci 432 Pn 923

OPL11400

Plant height 577

OPZ101350OPZ101030

MTR 821Internodal length 381 13

ΔC 412NAR 427 Plant height 303

Shoot dry weight 400 Root length 356Root dry weight 476 WUE 302

Pn 261 Cigs 509

OPL122000Plant height 368 Pn 756

CWT 474 MTR 438

OPL12165013ΔC 361 OPZ10720

gs 296pn 307 gs 326

OPL181000Number of nodes 778 Pn 314

LAD 382 Internodal length 358

OPL19900SLW 333 CPCM13450

Root length 512RSR 360 Cigs 483

OPM11600Ags 385

CM171300CM166260

Stem girth 454RSR 411 Ags 345MTR 294 No of nodes 393

OPO101400Ci 313 SLA 719E 318 119864 418

OPP51800LAD 382

CM211300SLW 283

Internodal length 582 RSR 496Shoot dry weight 271 119864 423

OPP91030Cigs 612SLW 333RSR 350

were explaining the trait in the range between 283 and719 This study has provided more detailed information ofroot and associated physiological traits relationships underdrought and identification of markers linked to such traitswith the limited polymorphic markers by employing SingleMarker Analysis (SMA) QTL maps could be used to forlong-term drought breeding Further studies are needed toconfirm the estimation of QTL positions and effects and tovalidatemarkers prior to routinemarker assisted selection fordrought tolerance in coffee

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

References

[1] F M DaMatta ldquoExploring drought tolerance in coffee aphysiological approach with some insights for plant breedingrdquoBrazilian Journal of Plant Physiology vol 16 no 1 pp 1ndash6 2004

Molecular Biology International 11

[2] M Nesbitt The Cultural History of Plants Taylor amp Francis2005

[3] F M Damatta ldquoDrought as a multidimensional stress affectingphotosynthesis in tropical tree cropsrdquo in Advances in PlantPhysiology A Hemantaranjan Ed vol 5 pp 227ndash265 2003

[4] K Harovon Mogel ldquoGenotype times environment times managementinteractions key to beating future droughtsrdquo CSA News vol 58pp 4ndash9 2013

[5] F M DaMatta A R M Chaves H A Pinheiro C Ducatti andM E Loureiro ldquoDrought tolerance of two field-grown clones ofCoffea canephorardquo Plant Science vol 164 no 1 pp 111ndash117 2003

[6] H A Pinheiro F M DaMatta A R M Chaves M E Loureiroand C Ducatti ldquoDrought tolerance is associated with rootingdepth and stomatal control of water use in clones of Coffeacanephorardquo Annals of Botany vol 96 no 1 pp 101ndash108 2005

[7] J B Passioura ldquoPhenotyping for drought tolerance in graincrops when is it useful to breedersrdquo Functional Plant Biologyvol 39 no 11 pp 851ndash859 2012

[8] J S Sperry ldquoHydraulics of vascular water transportrdquo inMechan-ical Integration of Plant Cells and Plants P Wojtaszek Edvol 9 of Signaling and Communication in Plants pp 303ndash327Springer Berlin Germany 2011

[9] A P Wasson R A Richards R Chatrath et al ldquoTraits andselection strategies to improve root systems and water uptakein water-limited wheat cropsrdquo Journal of Experimental Botanyvol 63 no 9 pp 3485ndash3498 2012

[10] G D Herder G van Isterdael T Beeckman and I de SmetldquoThe roots of a new green revolutionrdquo Trends in Plant Sciencevol 15 no 11 pp 600ndash607 2010

[11] H T Nguyen R C Babu and A Blum ldquoBreeding for droughtresistance in rice physiology and molecular genetics consider-ationsrdquo Crop Science vol 37 no 5 pp 1426ndash1434 1997

[12] A Blum Plant Breeding for Water-Limited EnvironmentsSpringer Science+Business Media LLC New York NY USA2011

[13] L B Motta T C B Soares M A G Ferrao E T Caixeta R MLorenzoni and J D de SouzaNeto ldquoMolecular characterizationof arabica and Conilon coffee plants genotypes by SSR and ISSRmarkersrdquo Brazilian Archives of Biology and Technology vol 57no 5 pp 728ndash735 2014

[14] PMarraccini L P Freire G S CAlves et al ldquoRBCS1 expressionin coffee Coffea orthologs Coffea arabica homeologs andexpression variability between genotypes and under droughtstressrdquo BMC Plant Biology vol 11 article 85 2011

[15] P Marraccini F Vinecky G S C Alves et al ldquoDifferentiallyexpressed genes and proteins upon drought acclimation intolerant and sensitive genotypes of Coffea canephorardquo Journalof Experimental Botany vol 63 no 11 pp 4191ndash4212 2012

[16] F Denoeud L Carretero-Paulet A Dereeper et al ldquoThecoffee genome provides insight into the convergent evolution ofcaffeine biosynthesisrdquo Science vol 345 no 6201 pp 1181ndash11842014

[17] A Dereeper S Bocs M Rouard et al ldquoThe coffee genome huba resource for coffee genomesrdquo Nucleic Acids Research vol 43no 1 pp D1028ndashD1035 2015

[18] M Udayakumar M S Sheshshayee K N Nataraj et al ldquoWhyhas breeding for water use efficiency not been successful Ananalysis and alternate approach to exploit this trait for cropimprovementrdquo Current Science vol 74 no 11 pp 1000ndash10031998

[19] M G Awati Genetic diversity and molecular markers for rootand associated physiological traits of Robusta (coffea canephoraPierre ex F) accessions [PhD thesis] University of AgriculturalSciences Bangalore India 2005

[20] C E Williams and P C Ronald ldquoPCR template-DNA isolatedquickly from monocot and dicot leaves without tissue homog-enizationrdquo Nucleic Acids Research vol 22 no 10 pp 1917ndash19181994

[21] S D Tanksley ldquoMapping polygenesrdquoAnnual Review of Geneticsvol 27 no 1 pp 205ndash233 1993

[22] T Halward T Stalker E LaRue and G Kochert ldquoUse ofsingle-primer DNA amplifications in genetic studies of peanut(Arachis hypogaea L)rdquo Plant Molecular Biology vol 18 no 2pp 315ndash325 1992

[23] SAS Institute SASSTAT Userrsquos Guide Version 6 SAS InstituteCary NC USA 4th edition 1989

[24] M J Kearsey and A G L Farquhar ldquoQTL analysis in plantswhere are we nowrdquo Heredity vol 80 no 2 pp 137ndash142 1998

[25] M J Kearsey and V Hyne ldquoQTL analysis a simple lsquomarker-regressionrsquo approachrdquoTheoretical and Applied Genetics vol 89no 6 pp 698ndash702 1994

[26] C S Haley and S A Knott ldquoA simple regression method formapping quantitative trait loci in line crosses using flankingmarkersrdquo Heredity vol 69 no 4 pp 315ndash324 1992

[27] B M Atta T Mahmood and R M Trethowan ldquoRelationshipbetween root morphology and grain yield of wheat in North-Western NSW Australiardquo Australian Journal of Crop Sciencevol 7 no 13 pp 2108ndash2115 2013

[28] N T Lang C T Nha P T T Ha and B C Buu ldquoQuantitativetrait loci (QTLs) associated with drought tolerance in rice(Oryza sativa L)rdquo Sabrao Journal of Breeding and Genetics vol45 no 3 pp 409ndash421 2013

[29] H J Schenk andR B Jackson ldquoMapping the global distributionof deep roots in relation to climate and soil characteristicsrdquoGeoderma vol 126 no 1-2 pp 129ndash140 2005

[30] A Srividya L R Vemireddy P V Ramanarao et al ldquoMolecularmapping of QTLs for drought related traits at seedling stageunder PEG induced stress conditions in ricerdquoAmerican Journalof Plant Sciences vol 2 no 2 pp 190ndash201 2011

[31] N-N Shukor H A Hamid A Abdu andM-K Ismail ldquoEffectsof soil compaction on growth and physiological characteristicsofAzadirachta excelsa seedlingsrdquoTheAmerican Journal of PlantPhysiology vol 10 no 1 pp 25ndash42 2015

[32] P R Bhat Phenotyping and molecular analysis of Coffea arabicaL accessions and mapping population to identify RAPD markersassociated with root characteristics and associated physiologicaltraits [PhD thesis] University of Agricultural Sciences Banga-lore India 2002

[33] S Chandra H K Buhariwalla J Kashiwagi et al ldquoIdentifyingQTL linked markers in marker-29 deficient cropsrdquo in Proceed-ings of the 4th International Crop Science Congress BrisbaneAustralia 2004

[34] H M Pearl C Nagai P H Moore D L Steiger R V Osgoodand RMing ldquoConstruction of a genetic map for arabica coffeerdquoTheoretical and Applied Genetics vol 108 no 5 pp 829ndash8352004

[35] P Lashermes M C Combes N S Prakash P Trouslot MLorieux and A Charrier ldquoGenetic linkage map of Coffeacanephora effect of segregation distortion and analysis ofrecombination rate in male and female meiosesrdquo Genome vol44 no 4 pp 589ndash596 2001

[36] P Hendre S Lalji Singh and R K Aggarwal ldquoConstructionof framework molecular linkage map of Robusta coffee Coffeacanephora with RAPD AFLP g-SSRs And EST-SSRs usingpseudo-testcross strategyrdquo in Proceedings of the Plant amp AnimalGenomes 15th Conference January 2007

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

PeptidesInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

International Journal of

Volume 2014

Zoology

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Molecular Biology International

GenomicsInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioinformaticsAdvances in

Marine BiologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Signal TransductionJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

Evolutionary BiologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Genetics Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Virolog y

Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Enzyme Research

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Microbiology

4 Molecular Biology International

S3334 L1valley

05

10152025303540

Freq

uenc

y

RDWt

05

10152025

S3334 L1valley

0

5

10

15

20

25

30

35

Freq

uenc

y

WUE

44485256

S3334 L1valley

2ndash6

6ndash10

10

ndash14

14

ndash18

18

ndash22

22

ndash26

26

ndash30

30

ndash34

34

ndash38

385

ndash420

420

ndash455

455

ndash490

490

ndash525

525

ndash560

560

ndash595

595

ndash630

630

ndash665

665

ndash700

Figure 1 Continued

Molecular Biology International 5

0

5

10

15

20

25

30Fr

eque

ncy

TDM

020406080

100

S3334 L1valley

05

1015202530354045

Freq

uenc

y

RSR

0250025402580262

S3334 L1valley

0

5

10

15

20

25

30

35

Freq

uenc

y

CWT

05

101520

S3334 L1valley

0

5

10

15

20

25

30

Freq

uenc

y

LAD

0200040006000

S3334 L1valley

05

10152025303540

Freq

uenc

y

MTR

262728293031

S3334 L1valley

05

101520253035404550

Freq

uenc

y

NAR

05

101520

S3334 L1valley

10

ndash22

22

ndash34

34

ndash46

46

ndash58

58

ndash70

70

ndash82

82

ndash94

94

ndash106

106

ndash118

118

ndash130

20

ndash45

45

ndash70

70

ndash95

95

ndash120

120

ndash145

145

ndash170

170

ndash195

195

ndash220

220

ndash245

022

ndash024

024

ndash026

026

ndash028

028

ndash030

030

ndash032

032

ndash034

034

ndash036

036

ndash038

038

ndash040

1000

ndash1800

1800

ndash2600

2600

ndash3400

3400

ndash4200

4200

ndash5000

5000

ndash5800

5800

ndash6600

6600

ndash7400

7400

ndash8200

15

ndash175

20

ndash225

250

ndash275

30

ndash325

350

ndash375

85

ndash99

99

ndash113

113

ndash127

127

ndash141

141

ndash155

155

ndash169

169

ndash183

183

ndash197

Figure 1 Genetic variability of root system and quantitative traits of mapping population of L1 valley times S334

valley (low root type) and S3334 (high root type) weregenotyped with 320 RAPD primers of which 41 polymor-phic primers generated 70 polymorphic loci (Table 3) SinceRAPD is dominant marker the PCR amplified DNA frag-ments were scored as dominant (AAAa) whereas absenceof bands was scored as recessive (aa) We have analyzed ourdata within the framework of these assumptions

Among them more than 85 of the primers producedsingle polymorphic locus and 15 of the primers yieldedmore than three polymorphic loci

All the RAPD markers in the F1 generation of Ccanephora segregated in ratios that were not consistent withMendelian inheritanceThe Chi-square tests were performed

for eachmarker to determine segregation distortion from theexpected allele frequency ratio of 1 1 At a significance levelof 119875 = 005 about 62 of the marker loci were in agreementwith the expected ratios and 38 of the marker loci were notfollowing the Mendelian inheritance (Table 4) It is expectedthat RAPD bands of maternal DNA origin would show non-Mendelian inheritance

Besides RAPD marker system 55 SSR markers were alsoused for genotyping however only nine primers showedpolymorphism (Table 5) but did not follow the Mendeliansegregation The low level of DNA polymorphism betweenthe two parental accessions coupled with the large number ofcommon bands implies parental lines could be less diverse

6 Molecular Biology International

Table 2 Range and mean values of the morphophysiological parameters in 134 F1 mapping population of Coffea canephora (L1 valley timesS3334)

Serial number Parameters Range Mean STDEV

Root traits

1 Root length (cm2) 415ndash830 5708 7292 Number of secondary roots 2ndash60 269 12613 Root dry weight (gplant) 257ndash3469 1377 664 Root shoot ratio 023ndash039 027 003

Morphological traits

5 Plant height (cm2) 135ndash1053 726 15696 Number of nodes 110ndash420 2408 6887 Number of leaves 2ndash70 3402 13428 Stem girth (mm) 588ndash1365 1028 1799 Internodal length (cm) 19ndash125 854 210 Specific leaf dry weight (mgdm2) 141ndash264 18 02211 Final leaf area (cm2) 113692ndash819061 449119 17101712 Specific leaf area (cm) 37899ndash71097 56244 653413 Total leaf dry weight (gplant) 578ndash4859 2602 104314 Stem dry weight (gplant) 481ndash5262 2433 10415 Shoot dry weight (gplant) 1059ndash9664 5035 206316 Total dry matter (gplant) 116ndash12143 5793 2477

Gravimetric traits

17 Leaf area duration (dm2 days) 106589ndash799277 437252 16580418 Net assimilation rate 89ndash1938 1307 17119 Cumulative water transpiration (ltplant) 25ndash235 1081 46220 Water use efficiency (gkg) 386ndash684 54 05821 Mean transpiration rate (mLdm2 days) 15ndash352 244 03822 Delta 13c (mill) 1975ndash2653 218 111

Gas exchange parameters

23 Photosynthesis (Pn) (mmolm2S) 545ndash2727 169 39124 Transpiration rate (119864) (mgm2s) 071ndash747 338 13425 Conductance (gs) (mmolm2s) 005ndash1 035 01726 Cigs 28876ndash348425 8593 405127 Ci 1865ndash29667 24798 248828 Ags 2406ndash9326 5442 155229 A119864 263ndash1407 552 176

Table 3 Polymorphic RAPD primers and their sequences used to study the segregation of markers in the mapping population

Polymorphicbands

Primername Sequence Primer

name Sequence Primername Sequence Primer

name Sequence

1

OPK18 CCTAGTCGAG OPN6 GAGACGCACA OPQ5 CCGCGTCTTG OPV5 TCCGAGAGGGOPK19 CACAGGCGGA OPO1 GGCACGTAAG OPS1 CTACTGCGCT OPW9 GTGACCGAGTOPK20 GTGTCGCGAG OPO18 CTCGCTATCC OPV11 CTCGACAGAG OPY20 AGCCGTGGAAOPM5 GGGAACGTGT OPO4 AAGTCCGCTC OPV14 AGATCCCGCC OPZ11 CTCAGTCGCAOPN19 GTCCGTACTG OPP2 TCGGCACGCA OPV2 AGTCACTCCC

2

OPK10 GTGCSSCGTG OPL18 ACCACCCACC OPO16 TCGGCGGTTC OPT7 GTCCATGCCAOPK11 AATGCCCCAG OPM11 GTCCACTGTG OPP5 CCCCGGTAAC OPU1 ACGGACGTCAOPL1 GGCATGACCT OPM17 TCAGTCCGGG OPQ20 TCGCCCAGTC OPW17 GTCCTGGGTTOPL12 GGGCGGTACT OPO10 TCAGAGCGCC OPR4 CCCGTAGCAC OPZ3 CAGCACCGCA

3 OPL19 GAGTGGTGAC OPM2 ACAACGCCTC OPP11 AACGCGTCGG OPP9 GTGGTCCGCAOPZ10 CCGACAAACC

4 OPK17 CCCAGCTGTG

Molecular Biology International 7

0 5 10 15 20 25 30 350

20

40

60

80

100

120

140

1

2

3 4

5

6

7

8

9

10

1113

14

15

161718

19

20

21

22

23

2425

26 2728

29

3032

33

34

35

36

37

38 3940

41

43

44

45

46

47

48

49

50

5152

5354

55

5657

58

59

60

61

62

63

64

65

66

67

68

69

70

72

73

75

7677

78

79

80

81828384

85

86

87

88

89

90

9192

939495

96

97

98

99100

101102

103104

105106

107

108

109110

111

113

114

115

116

117

118

119

120

121

122123

124

125

126

127128129

130

131

132

133

134

135

136

137

138

139140

141

Tota

l dry

mat

ter (

gpl

ant)

Root dry weight (gplant)

r = 0968

P lt 0001 n = 135

(a)

0 5 10 15 20 25 30 350

5

10

15

20

25

1

2

3

4

5

6

7

8

910

1113

14

15

161718

19

20

21

22

23

2425

262728 29

3032

33

34

35

36

37

38 3940

41

43

44

45

4647

48

49

50

5152

5354

55

5657

58

59

6061

62

63

64

65

66

67

68

69

70

72

73

75

76

77

78

7980

81

8283

84

85

86

87

88

89

90

9192 93

9495

96

97

98 99

100101

102

103104

105

106

107

108

109110

111

113

114

115

116

117

118119

120

121

122123

124

125

126

127128

129

130

131

132

133

134

135

136

137138

139140

141

CWT

(L)

Root dry weight (gplant)

r = 0940

P lt 0001 n = 135

(b)

0 20 40 60 80 100 120 1400

5

10

15

20

25

1

2

3

4

5

6

7

8

910

1113

1415

1617

18

19

20

21

22

23

2425

2627 28 29

3032

33

34

35

36

37

383940

41

43

44

45

4647

48

49

50

5152

5354

55

5657

58

59

6061

62

63

64

65

6667

68

69

70

72

73

75

76

77

78

7980

81

8283

84

85

86

8788

89

90

9192 93

9495

96

97

98 99

100101

102

103104

105

106

107

108

109110

111

113

114

115

116

117

118 119

120

121

122123

124

125

126

127128

129

130

131

132

133

134

135

136

137138

139140

141

CWT

(L)

Total dry matter (gplant)

r = 0970

P lt 0001 n = 135

(c)

0 5 10 15 20 25

15

20

25

30

35

12

3

45

67

8

9

10

11 1314

1516

17

18

1920

2122

23

24

25

26

27

28

29 30

3233

34

35 36

37 38

39

40

4143

44

45

4647

4849

50

5152 53

5455

56

57

5859

60

61

62 6364 65

66

67

68

69

70

7273

75

76

77

78

79

80

81

8283

84

85 86

8788

89

9091

92

93

94

9596

9798

99

100

101

102

103

104

105106

107

108

109110

111

113

114115

116

117

118119

120

121122

123124

125

126127

128

129130

131

132133

134

135136

137

138

139

140141

CWT (L)

r = 0524

P lt 0001 n = 135

MTR

(mL

dm2)

(d)

0 5 10 15 20 25

15

20

25

30

35

12

3

45

67

8

9

10

11 1314

1516

17

18

1920

2122

23

24

25

26

27

28

29 30

3233

34

35 36

37 38

39

40

4143

44

45

4647

4849

50

5152 53

5455

56

57

5859

60

61

62 6364 65

66

67

68

69

70

7273

75

76

77

78

79

80

81

8283

84

85 86

8788

89

9091

92

93

94

9596

9798

99

100

101

102

103

104

105106

107

108

109110

111

113

114115

116

117

118119

120

121122

123124

125

126127

128

129130

131

132133

134

135136

137

138

139

140141

CWT (L)

r = 0524

P lt 0001 n = 135

MTR

(mL

dm2)

(e)

35 40 45 50 55 60 65 70

20

22

24

26

28

12

345 6 7

89

10

11

131415

16

17

18

1920

2122

23

24

25

26

27

28

2930

32 3334

35

36 37383940

4143

44

45

4647

4849

50

5152

535455

5657 58

59

60

61

62 63 646566

67

68

69

707273 7576

77

78

7980

8182

8384

85

86878889

9091

92

9394

95

96

97

98

99

100101

102103104

105

106107 108109110111

113114115

116

117

118 119120

121122

123124

125

126127

128

129

130131

132133

134

135136 137

138139

140

141

WUE (gkg)

r = minus0413

P lt 0001 n = 135

Δ13

C

(f)

Figure 2 Relationship between the RDWTDM RDWCWT TDMCWT CWTMTR CWTMTR and WUEΔ13C in the mappingpopulation of C canephora (L1 valley times S334)

8 Molecular Biology International

Table 4 Chi-square tests (1205942) for RAPD and SSR markers in the Coffea canephoramapping population

Parents F1progenies

Markersystem

Total polymor-phic markers

Marker loci notfollowing expected ratio

Marker loci followingexpected ratio

Number of significant markers119875 = 095 119875 = 005 119875 = 0001

L1 valley timesS3334 134 RAPD 70 28 42 (1 1) 32 3 7

SSR 9 9 0 (3 1) mdash mdash mdash

Table 5 Polymorphic microsatellite primers and their sequences used to study the segregation of markers in the mapping population

Serial number Primer Sequence Tm (∘C) Size range

1 CM11 F GCTGCCAGAAAAATGTTGCAGTG 58 270ndash338R CTGCCTCGTAAAAGCTTGCGTTG

2 CM13 F GCTATGCAGCTTGTTCGCAATCC 59 350ndash411R CCAGCTATATCAGGAGCAGAACC

3 CM180 F CATGTGTAATACATTCAACAGTGA 60 300ndash350R GCAATAGTGGTTGTCATCCTT

4 CM32 F AACTCTCCATTCCCGCATTC 60 180ndash200R CTGGGTTTTCTGTGTTCTCG

5 CM46 F CAGCTAGTGTGAAGGGAAAC 55 300ndash350R GTTATCATGGTCTTACACG

6 CM20 F CTTGTTTGAGTCTGTCGCTG 60 250ndash300R TTTCCCTCCCAATGTCTGTA

7 CM171 F TTCCCCCATCTTTTTCTTTC 60 250ndash300R TTGTATACGGCTCGTCAGGT

8 CM166 F AAGAGGTGCCTATCACCGTC 60 230ndash260R CGAGGTATCAAAAAGCACCT

9 CM211 F TCATGCCAAATATGAGTGGA 60 300ndash350R GAGATGGCAAAGGCTGTTC

35 Single Marker Analysis for Root and Associated Physiolog-ical Traits Complex physiological traits have been describedby a small number of major QTL [24] but it is intricateto find useful QTL for a particular trait as their individualcontribution is smaller Therefore under such conditionSingle Marker Analysis (SMA) is generally a good choicewhen the goal is simply a detection of marker locus linkedto a trait However estimation of its position and its effectsrequires further complex analysis such as marker regression[25] or interval analysis [26]

In our study Single Marker ANOVA is used to identifymarkers showing significant association with 29 morpho-physiological traits (SAS software with 119875 le 005)

In the single marker analysis simple regression modelwas examined to study the association between marker loci(independent) and trait score (dependent variable) and alsocomputed the percent phenotypic variance explained by eachmarker Seventy polymorphic marker loci were employed forsimple regression analysis however it was discovered that 37RAPD and 5 SSR markers explained their association withmorphophysiological traits

36Markers Linked to Root Traits The singlemarker analysisrevealed fourmarkers such as OPS1

850 OPK11

780 OPY20

1200

OPZ101350

and CM13400

were significantly associated withroot length (119875 = 005) and explained phenotypic vari-ance of 441 295 356 512 and 512 respectively

A number of secondary roots linked to three markers suchas OPO16

450(440) OPK11

1000(551) and OPV14

550

(582) were significantly (119875 = 005) associated Similarlysix markers such as OPK11

1000(326) OPP9

1030(35)

OPM11600

(375) OPL19900

(561) OPL19600

(411)OPR4

600(330) and CM211

300(1198772 = 496) revealed

significant (119875 = 005) association with the root to shootratio (Table 6) This implies the root associated traits arepolygenic controlling many genes However root dry weightis associated with only one maker OPL1

1400(486)

37 Markers Linked to Morphophysiological Traits Besidesroot traits polygenic inheritance traits such as morpho-logical gravimetric and gas exchange at single leaf leveltraits were also deployed for marker trait association Singlemarker analysis revealed more than three markers linked toeach trait and 25 markers are colocalized with more thanone trait and 17 markers are associated with single trait(Table 7)ThemarkerOPP5

1800is coassociatedwith shoot dry

weight and leaf area duration (LAD) and these two traits arepositively correlated (1198772 = 093) Four markers (OPL1

1450

OPL191650

OPM171400

and OPK20350

) linked to NAR andtwo markers OPR4

600and OPL12

2000are significantly (119875 =

001) associated with cumulative water transpired (CWT)The 13ΔC is the surrogate method to quantify the water

use efficiency and it was demonstrated that there is inverse

Molecular Biology International 9

Table 6 RAPD markers linked to root traits in the mappingpopulation of C canephora (L1 valley times S3334)

Trait Marker 1198772 () 119875 lt 005

Root length

OPY201200 295 00449OPZ101350 356 00286OPK11780 441 00178OPS1850 686 00001

CPCM13400 512 00105

Number of secondary rootsOPO16450 44 00179OPK111000 551 00059OPV14550 582 00042

Root dry weight OPL11450 476 00073

Root to shoot ratio

OPK111000 326 0038OPR4600 33 00367OPP91030 35 00288OPM11600 375 00267OPL19600 411 00223OPL19900 561 00062CM211300 496 00105

relationship between these two traits Of the six associatedmarkers OPZ10

1350was found to be associatedwith 13ΔCand

WUE with phenotypic variance of 412 and 302 respec-tively This marker locus could be negative additive effectsince traits are negatively related The mesophyll efficiency(Cigs) intrinsic WUE (Ags) and photosynthetic rate (Pn)are respectively associated with CM13

400(483) CM171

210

(345) and CM171190

(372) (Table 7) Whereas positivelyrelated traits are associated with the same marker such asOPP5

1800(LAD and shoot dry weight) OPZ10

1030(MTR and

gs) and OPK20350

(Ci and NAR) alleles contribution fromS3334 The markers linked to other gas exchange parameterswere given in Table 7 which are contributed from S3334

4 Discussion

We developed a F1 mapping population by crossing contrast-ing root traits S3334 high root type as male and L1 valley lowroot type as female to identify makers linked to root traitsand water use efficient types However in annual crops F

2

recombinant inbred lines (RILs) near isogenic lines (NILs)and doubled haploids (DH)mapping population will be usedto associatemarkers with the trait of our interest However inperennials the germplasm lines and the cultivated accessionsare often highly heterozygous consequently F

1population

developed from heterozygous parents was used in this studyand showed considerable phenotypic and genetic variations

One of the objectives of studying themapping populationwas to score the variability in roots and other physiologicaltraits under drought and thus mapping population was gownup in 70 FC of water stress to study the heritability ofroot traits Indeed root traits are highly complex geneticmechanism controlled by multi genes and highly herita-ble in water non limiting environment [27] The resultsdemonstrated significant variability was observed in root

dry weight (RDW) total dry matter (TDM) root to shootratio (RSR) cumulativewater transpired (CWT)with consid-erable transgressive segregation (Figure 1) and quantitativeinheritance of drought tolerance [28] However significantpositive correlation between RDWTDM RDWCW andTDMCWT indicates that under drought increased rootgrowth maintains the shoot hydraulic conductance as adop-tive strategy [29 30] The decreased mean transpiration(MTR) and increased water use efficiency (WUE) could bedue to decreased stomatal aperture through chemical signalssuch as ABA [31] However some of the clones showedthat increased MTR with increased CWT would favor thestomatal conductance and net photosynthesis Such clonesare better for breeding drought tolerance Inverse relationshipbetween Δ13C and WUE in robusta coffee accessions wasobserved (Figure 2) thus measuring Δ13C which can bea surrogate method to identify best WUE types clones inbreeding [19 32] Breeding for such quantitative traits ishighly complex cumbersome and thus by determining theallele of aDNAmarker plants that possess particular genes orquantitative trait loci (QTLs) are better than their phenotypeWith this background effort wasmade to identify themarkerslinked to root and associated physiological traits in Coffeacanephora

Despite the efforts no successful linkage map could beobtained partly due to the inadequate number of polymor-phic markers generated from this study The output of theassociation analysis indicates that out of 29 traits only 24traits were linked to 85 markers Even markers linked totraits explaining the phenotypic variationwere notmore than10 In the present study phenotypic variation explanationfor all the traits ranges from 261 to 986 This suggeststhat the successful application of molecular markers in traitmapping greatly requires more polymorphic markers whilesuccessful mapping attempts could still be carried out in theabsence of a linkage map as done in chickpea [33] Althoughconstruction of linkage map was done in C arabica [34]and C canephora [35] predominantly using AFLP and RAPDmarkers association with the traits was not attempted Simi-larly the first molecular linkage map generated using pseudotestcross strategy for Coffea canephora (CxR times Kagnalla) hasthe largest number of mapped SSRs (71 genomic includingnine EST-SSRs) [36] but trait association was not correlated

Most of the markers explained phenotypic variancebetween 3 and 4 however only two markers that isOPZ11

1500andOPV14

500 were significantly (119875 lt 001) linked

to photosynthetic rate (Pn) accounting the variation of about923 and 930 respectively (Table 7) The root traits suchas root length number of secondary roots root dry weightand root shoot ratio revealed significant association (119875 lt005) with markers and the variability ranges from 295 to686 (Table 6)This suggests the trait variability explanationis not sufficient to answer the probabilities of occurrence ofthese markers due to limited polymorphic markers

In the present study codominant SSR markers werealso used for the marker-trait association study The SSRmarkers identified herein are significantly associated withmorphological and physiological traits but these markers

10 Molecular Biology International

Table 7 List of RAPD and SSR markers overlapped with more than one trait

Markers Traits 1198772 ()

(119875 lt 005) Markers Traits 1198772

(119875 lt 005)

OPK111000

Internodal length 474OPR4600OPR4750

CWT 508Root to shoot ratio 326 RSR 330

Number of secondary roots 551 Number of leaves 565Stem dry weight 344 gs 293

OPK181200Specific leaf area 470 OPV14550

Pn 930Plant height 451 Number of secondary Roots 582

OPK192000Photosynthetic rate (Pn) 555 OPY201200

WUE 310WUE 443 Plant height 423

K20350NAR 482

OPZ111500Number of leaves 399

WUE 445 Root length 295Ci 432 Pn 923

OPL11400

Plant height 577

OPZ101350OPZ101030

MTR 821Internodal length 381 13

ΔC 412NAR 427 Plant height 303

Shoot dry weight 400 Root length 356Root dry weight 476 WUE 302

Pn 261 Cigs 509

OPL122000Plant height 368 Pn 756

CWT 474 MTR 438

OPL12165013ΔC 361 OPZ10720

gs 296pn 307 gs 326

OPL181000Number of nodes 778 Pn 314

LAD 382 Internodal length 358

OPL19900SLW 333 CPCM13450

Root length 512RSR 360 Cigs 483

OPM11600Ags 385

CM171300CM166260

Stem girth 454RSR 411 Ags 345MTR 294 No of nodes 393

OPO101400Ci 313 SLA 719E 318 119864 418

OPP51800LAD 382

CM211300SLW 283

Internodal length 582 RSR 496Shoot dry weight 271 119864 423

OPP91030Cigs 612SLW 333RSR 350

were explaining the trait in the range between 283 and719 This study has provided more detailed information ofroot and associated physiological traits relationships underdrought and identification of markers linked to such traitswith the limited polymorphic markers by employing SingleMarker Analysis (SMA) QTL maps could be used to forlong-term drought breeding Further studies are needed toconfirm the estimation of QTL positions and effects and tovalidatemarkers prior to routinemarker assisted selection fordrought tolerance in coffee

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

References

[1] F M DaMatta ldquoExploring drought tolerance in coffee aphysiological approach with some insights for plant breedingrdquoBrazilian Journal of Plant Physiology vol 16 no 1 pp 1ndash6 2004

Molecular Biology International 11

[2] M Nesbitt The Cultural History of Plants Taylor amp Francis2005

[3] F M Damatta ldquoDrought as a multidimensional stress affectingphotosynthesis in tropical tree cropsrdquo in Advances in PlantPhysiology A Hemantaranjan Ed vol 5 pp 227ndash265 2003

[4] K Harovon Mogel ldquoGenotype times environment times managementinteractions key to beating future droughtsrdquo CSA News vol 58pp 4ndash9 2013

[5] F M DaMatta A R M Chaves H A Pinheiro C Ducatti andM E Loureiro ldquoDrought tolerance of two field-grown clones ofCoffea canephorardquo Plant Science vol 164 no 1 pp 111ndash117 2003

[6] H A Pinheiro F M DaMatta A R M Chaves M E Loureiroand C Ducatti ldquoDrought tolerance is associated with rootingdepth and stomatal control of water use in clones of Coffeacanephorardquo Annals of Botany vol 96 no 1 pp 101ndash108 2005

[7] J B Passioura ldquoPhenotyping for drought tolerance in graincrops when is it useful to breedersrdquo Functional Plant Biologyvol 39 no 11 pp 851ndash859 2012

[8] J S Sperry ldquoHydraulics of vascular water transportrdquo inMechan-ical Integration of Plant Cells and Plants P Wojtaszek Edvol 9 of Signaling and Communication in Plants pp 303ndash327Springer Berlin Germany 2011

[9] A P Wasson R A Richards R Chatrath et al ldquoTraits andselection strategies to improve root systems and water uptakein water-limited wheat cropsrdquo Journal of Experimental Botanyvol 63 no 9 pp 3485ndash3498 2012

[10] G D Herder G van Isterdael T Beeckman and I de SmetldquoThe roots of a new green revolutionrdquo Trends in Plant Sciencevol 15 no 11 pp 600ndash607 2010

[11] H T Nguyen R C Babu and A Blum ldquoBreeding for droughtresistance in rice physiology and molecular genetics consider-ationsrdquo Crop Science vol 37 no 5 pp 1426ndash1434 1997

[12] A Blum Plant Breeding for Water-Limited EnvironmentsSpringer Science+Business Media LLC New York NY USA2011

[13] L B Motta T C B Soares M A G Ferrao E T Caixeta R MLorenzoni and J D de SouzaNeto ldquoMolecular characterizationof arabica and Conilon coffee plants genotypes by SSR and ISSRmarkersrdquo Brazilian Archives of Biology and Technology vol 57no 5 pp 728ndash735 2014

[14] PMarraccini L P Freire G S CAlves et al ldquoRBCS1 expressionin coffee Coffea orthologs Coffea arabica homeologs andexpression variability between genotypes and under droughtstressrdquo BMC Plant Biology vol 11 article 85 2011

[15] P Marraccini F Vinecky G S C Alves et al ldquoDifferentiallyexpressed genes and proteins upon drought acclimation intolerant and sensitive genotypes of Coffea canephorardquo Journalof Experimental Botany vol 63 no 11 pp 4191ndash4212 2012

[16] F Denoeud L Carretero-Paulet A Dereeper et al ldquoThecoffee genome provides insight into the convergent evolution ofcaffeine biosynthesisrdquo Science vol 345 no 6201 pp 1181ndash11842014

[17] A Dereeper S Bocs M Rouard et al ldquoThe coffee genome huba resource for coffee genomesrdquo Nucleic Acids Research vol 43no 1 pp D1028ndashD1035 2015

[18] M Udayakumar M S Sheshshayee K N Nataraj et al ldquoWhyhas breeding for water use efficiency not been successful Ananalysis and alternate approach to exploit this trait for cropimprovementrdquo Current Science vol 74 no 11 pp 1000ndash10031998

[19] M G Awati Genetic diversity and molecular markers for rootand associated physiological traits of Robusta (coffea canephoraPierre ex F) accessions [PhD thesis] University of AgriculturalSciences Bangalore India 2005

[20] C E Williams and P C Ronald ldquoPCR template-DNA isolatedquickly from monocot and dicot leaves without tissue homog-enizationrdquo Nucleic Acids Research vol 22 no 10 pp 1917ndash19181994

[21] S D Tanksley ldquoMapping polygenesrdquoAnnual Review of Geneticsvol 27 no 1 pp 205ndash233 1993

[22] T Halward T Stalker E LaRue and G Kochert ldquoUse ofsingle-primer DNA amplifications in genetic studies of peanut(Arachis hypogaea L)rdquo Plant Molecular Biology vol 18 no 2pp 315ndash325 1992

[23] SAS Institute SASSTAT Userrsquos Guide Version 6 SAS InstituteCary NC USA 4th edition 1989

[24] M J Kearsey and A G L Farquhar ldquoQTL analysis in plantswhere are we nowrdquo Heredity vol 80 no 2 pp 137ndash142 1998

[25] M J Kearsey and V Hyne ldquoQTL analysis a simple lsquomarker-regressionrsquo approachrdquoTheoretical and Applied Genetics vol 89no 6 pp 698ndash702 1994

[26] C S Haley and S A Knott ldquoA simple regression method formapping quantitative trait loci in line crosses using flankingmarkersrdquo Heredity vol 69 no 4 pp 315ndash324 1992

[27] B M Atta T Mahmood and R M Trethowan ldquoRelationshipbetween root morphology and grain yield of wheat in North-Western NSW Australiardquo Australian Journal of Crop Sciencevol 7 no 13 pp 2108ndash2115 2013

[28] N T Lang C T Nha P T T Ha and B C Buu ldquoQuantitativetrait loci (QTLs) associated with drought tolerance in rice(Oryza sativa L)rdquo Sabrao Journal of Breeding and Genetics vol45 no 3 pp 409ndash421 2013

[29] H J Schenk andR B Jackson ldquoMapping the global distributionof deep roots in relation to climate and soil characteristicsrdquoGeoderma vol 126 no 1-2 pp 129ndash140 2005

[30] A Srividya L R Vemireddy P V Ramanarao et al ldquoMolecularmapping of QTLs for drought related traits at seedling stageunder PEG induced stress conditions in ricerdquoAmerican Journalof Plant Sciences vol 2 no 2 pp 190ndash201 2011

[31] N-N Shukor H A Hamid A Abdu andM-K Ismail ldquoEffectsof soil compaction on growth and physiological characteristicsofAzadirachta excelsa seedlingsrdquoTheAmerican Journal of PlantPhysiology vol 10 no 1 pp 25ndash42 2015

[32] P R Bhat Phenotyping and molecular analysis of Coffea arabicaL accessions and mapping population to identify RAPD markersassociated with root characteristics and associated physiologicaltraits [PhD thesis] University of Agricultural Sciences Banga-lore India 2002

[33] S Chandra H K Buhariwalla J Kashiwagi et al ldquoIdentifyingQTL linked markers in marker-29 deficient cropsrdquo in Proceed-ings of the 4th International Crop Science Congress BrisbaneAustralia 2004

[34] H M Pearl C Nagai P H Moore D L Steiger R V Osgoodand RMing ldquoConstruction of a genetic map for arabica coffeerdquoTheoretical and Applied Genetics vol 108 no 5 pp 829ndash8352004

[35] P Lashermes M C Combes N S Prakash P Trouslot MLorieux and A Charrier ldquoGenetic linkage map of Coffeacanephora effect of segregation distortion and analysis ofrecombination rate in male and female meiosesrdquo Genome vol44 no 4 pp 589ndash596 2001

[36] P Hendre S Lalji Singh and R K Aggarwal ldquoConstructionof framework molecular linkage map of Robusta coffee Coffeacanephora with RAPD AFLP g-SSRs And EST-SSRs usingpseudo-testcross strategyrdquo in Proceedings of the Plant amp AnimalGenomes 15th Conference January 2007

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

PeptidesInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

International Journal of

Volume 2014

Zoology

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Molecular Biology International

GenomicsInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioinformaticsAdvances in

Marine BiologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Signal TransductionJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

Evolutionary BiologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Genetics Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Virolog y

Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Enzyme Research

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Microbiology

Molecular Biology International 5

0

5

10

15

20

25

30Fr

eque

ncy

TDM

020406080

100

S3334 L1valley

05

1015202530354045

Freq

uenc

y

RSR

0250025402580262

S3334 L1valley

0

5

10

15

20

25

30

35

Freq

uenc

y

CWT

05

101520

S3334 L1valley

0

5

10

15

20

25

30

Freq

uenc

y

LAD

0200040006000

S3334 L1valley

05

10152025303540

Freq

uenc

y

MTR

262728293031

S3334 L1valley

05

101520253035404550

Freq

uenc

y

NAR

05

101520

S3334 L1valley

10

ndash22

22

ndash34

34

ndash46

46

ndash58

58

ndash70

70

ndash82

82

ndash94

94

ndash106

106

ndash118

118

ndash130

20

ndash45

45

ndash70

70

ndash95

95

ndash120

120

ndash145

145

ndash170

170

ndash195

195

ndash220

220

ndash245

022

ndash024

024

ndash026

026

ndash028

028

ndash030

030

ndash032

032

ndash034

034

ndash036

036

ndash038

038

ndash040

1000

ndash1800

1800

ndash2600

2600

ndash3400

3400

ndash4200

4200

ndash5000

5000

ndash5800

5800

ndash6600

6600

ndash7400

7400

ndash8200

15

ndash175

20

ndash225

250

ndash275

30

ndash325

350

ndash375

85

ndash99

99

ndash113

113

ndash127

127

ndash141

141

ndash155

155

ndash169

169

ndash183

183

ndash197

Figure 1 Genetic variability of root system and quantitative traits of mapping population of L1 valley times S334

valley (low root type) and S3334 (high root type) weregenotyped with 320 RAPD primers of which 41 polymor-phic primers generated 70 polymorphic loci (Table 3) SinceRAPD is dominant marker the PCR amplified DNA frag-ments were scored as dominant (AAAa) whereas absenceof bands was scored as recessive (aa) We have analyzed ourdata within the framework of these assumptions

Among them more than 85 of the primers producedsingle polymorphic locus and 15 of the primers yieldedmore than three polymorphic loci

All the RAPD markers in the F1 generation of Ccanephora segregated in ratios that were not consistent withMendelian inheritanceThe Chi-square tests were performed

for eachmarker to determine segregation distortion from theexpected allele frequency ratio of 1 1 At a significance levelof 119875 = 005 about 62 of the marker loci were in agreementwith the expected ratios and 38 of the marker loci were notfollowing the Mendelian inheritance (Table 4) It is expectedthat RAPD bands of maternal DNA origin would show non-Mendelian inheritance

Besides RAPD marker system 55 SSR markers were alsoused for genotyping however only nine primers showedpolymorphism (Table 5) but did not follow the Mendeliansegregation The low level of DNA polymorphism betweenthe two parental accessions coupled with the large number ofcommon bands implies parental lines could be less diverse

6 Molecular Biology International

Table 2 Range and mean values of the morphophysiological parameters in 134 F1 mapping population of Coffea canephora (L1 valley timesS3334)

Serial number Parameters Range Mean STDEV

Root traits

1 Root length (cm2) 415ndash830 5708 7292 Number of secondary roots 2ndash60 269 12613 Root dry weight (gplant) 257ndash3469 1377 664 Root shoot ratio 023ndash039 027 003

Morphological traits

5 Plant height (cm2) 135ndash1053 726 15696 Number of nodes 110ndash420 2408 6887 Number of leaves 2ndash70 3402 13428 Stem girth (mm) 588ndash1365 1028 1799 Internodal length (cm) 19ndash125 854 210 Specific leaf dry weight (mgdm2) 141ndash264 18 02211 Final leaf area (cm2) 113692ndash819061 449119 17101712 Specific leaf area (cm) 37899ndash71097 56244 653413 Total leaf dry weight (gplant) 578ndash4859 2602 104314 Stem dry weight (gplant) 481ndash5262 2433 10415 Shoot dry weight (gplant) 1059ndash9664 5035 206316 Total dry matter (gplant) 116ndash12143 5793 2477

Gravimetric traits

17 Leaf area duration (dm2 days) 106589ndash799277 437252 16580418 Net assimilation rate 89ndash1938 1307 17119 Cumulative water transpiration (ltplant) 25ndash235 1081 46220 Water use efficiency (gkg) 386ndash684 54 05821 Mean transpiration rate (mLdm2 days) 15ndash352 244 03822 Delta 13c (mill) 1975ndash2653 218 111

Gas exchange parameters

23 Photosynthesis (Pn) (mmolm2S) 545ndash2727 169 39124 Transpiration rate (119864) (mgm2s) 071ndash747 338 13425 Conductance (gs) (mmolm2s) 005ndash1 035 01726 Cigs 28876ndash348425 8593 405127 Ci 1865ndash29667 24798 248828 Ags 2406ndash9326 5442 155229 A119864 263ndash1407 552 176

Table 3 Polymorphic RAPD primers and their sequences used to study the segregation of markers in the mapping population

Polymorphicbands

Primername Sequence Primer

name Sequence Primername Sequence Primer

name Sequence

1

OPK18 CCTAGTCGAG OPN6 GAGACGCACA OPQ5 CCGCGTCTTG OPV5 TCCGAGAGGGOPK19 CACAGGCGGA OPO1 GGCACGTAAG OPS1 CTACTGCGCT OPW9 GTGACCGAGTOPK20 GTGTCGCGAG OPO18 CTCGCTATCC OPV11 CTCGACAGAG OPY20 AGCCGTGGAAOPM5 GGGAACGTGT OPO4 AAGTCCGCTC OPV14 AGATCCCGCC OPZ11 CTCAGTCGCAOPN19 GTCCGTACTG OPP2 TCGGCACGCA OPV2 AGTCACTCCC

2

OPK10 GTGCSSCGTG OPL18 ACCACCCACC OPO16 TCGGCGGTTC OPT7 GTCCATGCCAOPK11 AATGCCCCAG OPM11 GTCCACTGTG OPP5 CCCCGGTAAC OPU1 ACGGACGTCAOPL1 GGCATGACCT OPM17 TCAGTCCGGG OPQ20 TCGCCCAGTC OPW17 GTCCTGGGTTOPL12 GGGCGGTACT OPO10 TCAGAGCGCC OPR4 CCCGTAGCAC OPZ3 CAGCACCGCA

3 OPL19 GAGTGGTGAC OPM2 ACAACGCCTC OPP11 AACGCGTCGG OPP9 GTGGTCCGCAOPZ10 CCGACAAACC

4 OPK17 CCCAGCTGTG

Molecular Biology International 7

0 5 10 15 20 25 30 350

20

40

60

80

100

120

140

1

2

3 4

5

6

7

8

9

10

1113

14

15

161718

19

20

21

22

23

2425

26 2728

29

3032

33

34

35

36

37

38 3940

41

43

44

45

46

47

48

49

50

5152

5354

55

5657

58

59

60

61

62

63

64

65

66

67

68

69

70

72

73

75

7677

78

79

80

81828384

85

86

87

88

89

90

9192

939495

96

97

98

99100

101102

103104

105106

107

108

109110

111

113

114

115

116

117

118

119

120

121

122123

124

125

126

127128129

130

131

132

133

134

135

136

137

138

139140

141

Tota

l dry

mat

ter (

gpl

ant)

Root dry weight (gplant)

r = 0968

P lt 0001 n = 135

(a)

0 5 10 15 20 25 30 350

5

10

15

20

25

1

2

3

4

5

6

7

8

910

1113

14

15

161718

19

20

21

22

23

2425

262728 29

3032

33

34

35

36

37

38 3940

41

43

44

45

4647

48

49

50

5152

5354

55

5657

58

59

6061

62

63

64

65

66

67

68

69

70

72

73

75

76

77

78

7980

81

8283

84

85

86

87

88

89

90

9192 93

9495

96

97

98 99

100101

102

103104

105

106

107

108

109110

111

113

114

115

116

117

118119

120

121

122123

124

125

126

127128

129

130

131

132

133

134

135

136

137138

139140

141

CWT

(L)

Root dry weight (gplant)

r = 0940

P lt 0001 n = 135

(b)

0 20 40 60 80 100 120 1400

5

10

15

20

25

1

2

3

4

5

6

7

8

910

1113

1415

1617

18

19

20

21

22

23

2425

2627 28 29

3032

33

34

35

36

37

383940

41

43

44

45

4647

48

49

50

5152

5354

55

5657

58

59

6061

62

63

64

65

6667

68

69

70

72

73

75

76

77

78

7980

81

8283

84

85

86

8788

89

90

9192 93

9495

96

97

98 99

100101

102

103104

105

106

107

108

109110

111

113

114

115

116

117

118 119

120

121

122123

124

125

126

127128

129

130

131

132

133

134

135

136

137138

139140

141

CWT

(L)

Total dry matter (gplant)

r = 0970

P lt 0001 n = 135

(c)

0 5 10 15 20 25

15

20

25

30

35

12

3

45

67

8

9

10

11 1314

1516

17

18

1920

2122

23

24

25

26

27

28

29 30

3233

34

35 36

37 38

39

40

4143

44

45

4647

4849

50

5152 53

5455

56

57

5859

60

61

62 6364 65

66

67

68

69

70

7273

75

76

77

78

79

80

81

8283

84

85 86

8788

89

9091

92

93

94

9596

9798

99

100

101

102

103

104

105106

107

108

109110

111

113

114115

116

117

118119

120

121122

123124

125

126127

128

129130

131

132133

134

135136

137

138

139

140141

CWT (L)

r = 0524

P lt 0001 n = 135

MTR

(mL

dm2)

(d)

0 5 10 15 20 25

15

20

25

30

35

12

3

45

67

8

9

10

11 1314

1516

17

18

1920

2122

23

24

25

26

27

28

29 30

3233

34

35 36

37 38

39

40

4143

44

45

4647

4849

50

5152 53

5455

56

57

5859

60

61

62 6364 65

66

67

68

69

70

7273

75

76

77

78

79

80

81

8283

84

85 86

8788

89

9091

92

93

94

9596

9798

99

100

101

102

103

104

105106

107

108

109110

111

113

114115

116

117

118119

120

121122

123124

125

126127

128

129130

131

132133

134

135136

137

138

139

140141

CWT (L)

r = 0524

P lt 0001 n = 135

MTR

(mL

dm2)

(e)

35 40 45 50 55 60 65 70

20

22

24

26

28

12

345 6 7

89

10

11

131415

16

17

18

1920

2122

23

24

25

26

27

28

2930

32 3334

35

36 37383940

4143

44

45

4647

4849

50

5152

535455

5657 58

59

60

61

62 63 646566

67

68

69

707273 7576

77

78

7980

8182

8384

85

86878889

9091

92

9394

95

96

97

98

99

100101

102103104

105

106107 108109110111

113114115

116

117

118 119120

121122

123124

125

126127

128

129

130131

132133

134

135136 137

138139

140

141

WUE (gkg)

r = minus0413

P lt 0001 n = 135

Δ13

C

(f)

Figure 2 Relationship between the RDWTDM RDWCWT TDMCWT CWTMTR CWTMTR and WUEΔ13C in the mappingpopulation of C canephora (L1 valley times S334)

8 Molecular Biology International

Table 4 Chi-square tests (1205942) for RAPD and SSR markers in the Coffea canephoramapping population

Parents F1progenies

Markersystem

Total polymor-phic markers

Marker loci notfollowing expected ratio

Marker loci followingexpected ratio

Number of significant markers119875 = 095 119875 = 005 119875 = 0001

L1 valley timesS3334 134 RAPD 70 28 42 (1 1) 32 3 7

SSR 9 9 0 (3 1) mdash mdash mdash

Table 5 Polymorphic microsatellite primers and their sequences used to study the segregation of markers in the mapping population

Serial number Primer Sequence Tm (∘C) Size range

1 CM11 F GCTGCCAGAAAAATGTTGCAGTG 58 270ndash338R CTGCCTCGTAAAAGCTTGCGTTG

2 CM13 F GCTATGCAGCTTGTTCGCAATCC 59 350ndash411R CCAGCTATATCAGGAGCAGAACC

3 CM180 F CATGTGTAATACATTCAACAGTGA 60 300ndash350R GCAATAGTGGTTGTCATCCTT

4 CM32 F AACTCTCCATTCCCGCATTC 60 180ndash200R CTGGGTTTTCTGTGTTCTCG

5 CM46 F CAGCTAGTGTGAAGGGAAAC 55 300ndash350R GTTATCATGGTCTTACACG

6 CM20 F CTTGTTTGAGTCTGTCGCTG 60 250ndash300R TTTCCCTCCCAATGTCTGTA

7 CM171 F TTCCCCCATCTTTTTCTTTC 60 250ndash300R TTGTATACGGCTCGTCAGGT

8 CM166 F AAGAGGTGCCTATCACCGTC 60 230ndash260R CGAGGTATCAAAAAGCACCT

9 CM211 F TCATGCCAAATATGAGTGGA 60 300ndash350R GAGATGGCAAAGGCTGTTC

35 Single Marker Analysis for Root and Associated Physiolog-ical Traits Complex physiological traits have been describedby a small number of major QTL [24] but it is intricateto find useful QTL for a particular trait as their individualcontribution is smaller Therefore under such conditionSingle Marker Analysis (SMA) is generally a good choicewhen the goal is simply a detection of marker locus linkedto a trait However estimation of its position and its effectsrequires further complex analysis such as marker regression[25] or interval analysis [26]

In our study Single Marker ANOVA is used to identifymarkers showing significant association with 29 morpho-physiological traits (SAS software with 119875 le 005)

In the single marker analysis simple regression modelwas examined to study the association between marker loci(independent) and trait score (dependent variable) and alsocomputed the percent phenotypic variance explained by eachmarker Seventy polymorphic marker loci were employed forsimple regression analysis however it was discovered that 37RAPD and 5 SSR markers explained their association withmorphophysiological traits

36Markers Linked to Root Traits The singlemarker analysisrevealed fourmarkers such as OPS1

850 OPK11

780 OPY20

1200

OPZ101350

and CM13400

were significantly associated withroot length (119875 = 005) and explained phenotypic vari-ance of 441 295 356 512 and 512 respectively

A number of secondary roots linked to three markers suchas OPO16

450(440) OPK11

1000(551) and OPV14

550

(582) were significantly (119875 = 005) associated Similarlysix markers such as OPK11

1000(326) OPP9

1030(35)

OPM11600

(375) OPL19900

(561) OPL19600

(411)OPR4

600(330) and CM211

300(1198772 = 496) revealed

significant (119875 = 005) association with the root to shootratio (Table 6) This implies the root associated traits arepolygenic controlling many genes However root dry weightis associated with only one maker OPL1

1400(486)

37 Markers Linked to Morphophysiological Traits Besidesroot traits polygenic inheritance traits such as morpho-logical gravimetric and gas exchange at single leaf leveltraits were also deployed for marker trait association Singlemarker analysis revealed more than three markers linked toeach trait and 25 markers are colocalized with more thanone trait and 17 markers are associated with single trait(Table 7)ThemarkerOPP5

1800is coassociatedwith shoot dry

weight and leaf area duration (LAD) and these two traits arepositively correlated (1198772 = 093) Four markers (OPL1

1450

OPL191650

OPM171400

and OPK20350

) linked to NAR andtwo markers OPR4

600and OPL12

2000are significantly (119875 =

001) associated with cumulative water transpired (CWT)The 13ΔC is the surrogate method to quantify the water

use efficiency and it was demonstrated that there is inverse

Molecular Biology International 9

Table 6 RAPD markers linked to root traits in the mappingpopulation of C canephora (L1 valley times S3334)

Trait Marker 1198772 () 119875 lt 005

Root length

OPY201200 295 00449OPZ101350 356 00286OPK11780 441 00178OPS1850 686 00001

CPCM13400 512 00105

Number of secondary rootsOPO16450 44 00179OPK111000 551 00059OPV14550 582 00042

Root dry weight OPL11450 476 00073

Root to shoot ratio

OPK111000 326 0038OPR4600 33 00367OPP91030 35 00288OPM11600 375 00267OPL19600 411 00223OPL19900 561 00062CM211300 496 00105

relationship between these two traits Of the six associatedmarkers OPZ10

1350was found to be associatedwith 13ΔCand

WUE with phenotypic variance of 412 and 302 respec-tively This marker locus could be negative additive effectsince traits are negatively related The mesophyll efficiency(Cigs) intrinsic WUE (Ags) and photosynthetic rate (Pn)are respectively associated with CM13

400(483) CM171

210

(345) and CM171190

(372) (Table 7) Whereas positivelyrelated traits are associated with the same marker such asOPP5

1800(LAD and shoot dry weight) OPZ10

1030(MTR and

gs) and OPK20350

(Ci and NAR) alleles contribution fromS3334 The markers linked to other gas exchange parameterswere given in Table 7 which are contributed from S3334

4 Discussion

We developed a F1 mapping population by crossing contrast-ing root traits S3334 high root type as male and L1 valley lowroot type as female to identify makers linked to root traitsand water use efficient types However in annual crops F

2

recombinant inbred lines (RILs) near isogenic lines (NILs)and doubled haploids (DH)mapping population will be usedto associatemarkers with the trait of our interest However inperennials the germplasm lines and the cultivated accessionsare often highly heterozygous consequently F

1population

developed from heterozygous parents was used in this studyand showed considerable phenotypic and genetic variations

One of the objectives of studying themapping populationwas to score the variability in roots and other physiologicaltraits under drought and thus mapping population was gownup in 70 FC of water stress to study the heritability ofroot traits Indeed root traits are highly complex geneticmechanism controlled by multi genes and highly herita-ble in water non limiting environment [27] The resultsdemonstrated significant variability was observed in root

dry weight (RDW) total dry matter (TDM) root to shootratio (RSR) cumulativewater transpired (CWT)with consid-erable transgressive segregation (Figure 1) and quantitativeinheritance of drought tolerance [28] However significantpositive correlation between RDWTDM RDWCW andTDMCWT indicates that under drought increased rootgrowth maintains the shoot hydraulic conductance as adop-tive strategy [29 30] The decreased mean transpiration(MTR) and increased water use efficiency (WUE) could bedue to decreased stomatal aperture through chemical signalssuch as ABA [31] However some of the clones showedthat increased MTR with increased CWT would favor thestomatal conductance and net photosynthesis Such clonesare better for breeding drought tolerance Inverse relationshipbetween Δ13C and WUE in robusta coffee accessions wasobserved (Figure 2) thus measuring Δ13C which can bea surrogate method to identify best WUE types clones inbreeding [19 32] Breeding for such quantitative traits ishighly complex cumbersome and thus by determining theallele of aDNAmarker plants that possess particular genes orquantitative trait loci (QTLs) are better than their phenotypeWith this background effort wasmade to identify themarkerslinked to root and associated physiological traits in Coffeacanephora

Despite the efforts no successful linkage map could beobtained partly due to the inadequate number of polymor-phic markers generated from this study The output of theassociation analysis indicates that out of 29 traits only 24traits were linked to 85 markers Even markers linked totraits explaining the phenotypic variationwere notmore than10 In the present study phenotypic variation explanationfor all the traits ranges from 261 to 986 This suggeststhat the successful application of molecular markers in traitmapping greatly requires more polymorphic markers whilesuccessful mapping attempts could still be carried out in theabsence of a linkage map as done in chickpea [33] Althoughconstruction of linkage map was done in C arabica [34]and C canephora [35] predominantly using AFLP and RAPDmarkers association with the traits was not attempted Simi-larly the first molecular linkage map generated using pseudotestcross strategy for Coffea canephora (CxR times Kagnalla) hasthe largest number of mapped SSRs (71 genomic includingnine EST-SSRs) [36] but trait association was not correlated

Most of the markers explained phenotypic variancebetween 3 and 4 however only two markers that isOPZ11

1500andOPV14

500 were significantly (119875 lt 001) linked

to photosynthetic rate (Pn) accounting the variation of about923 and 930 respectively (Table 7) The root traits suchas root length number of secondary roots root dry weightand root shoot ratio revealed significant association (119875 lt005) with markers and the variability ranges from 295 to686 (Table 6)This suggests the trait variability explanationis not sufficient to answer the probabilities of occurrence ofthese markers due to limited polymorphic markers

In the present study codominant SSR markers werealso used for the marker-trait association study The SSRmarkers identified herein are significantly associated withmorphological and physiological traits but these markers

10 Molecular Biology International

Table 7 List of RAPD and SSR markers overlapped with more than one trait

Markers Traits 1198772 ()

(119875 lt 005) Markers Traits 1198772

(119875 lt 005)

OPK111000

Internodal length 474OPR4600OPR4750

CWT 508Root to shoot ratio 326 RSR 330

Number of secondary roots 551 Number of leaves 565Stem dry weight 344 gs 293

OPK181200Specific leaf area 470 OPV14550

Pn 930Plant height 451 Number of secondary Roots 582

OPK192000Photosynthetic rate (Pn) 555 OPY201200

WUE 310WUE 443 Plant height 423

K20350NAR 482

OPZ111500Number of leaves 399

WUE 445 Root length 295Ci 432 Pn 923

OPL11400

Plant height 577

OPZ101350OPZ101030

MTR 821Internodal length 381 13

ΔC 412NAR 427 Plant height 303

Shoot dry weight 400 Root length 356Root dry weight 476 WUE 302

Pn 261 Cigs 509

OPL122000Plant height 368 Pn 756

CWT 474 MTR 438

OPL12165013ΔC 361 OPZ10720

gs 296pn 307 gs 326

OPL181000Number of nodes 778 Pn 314

LAD 382 Internodal length 358

OPL19900SLW 333 CPCM13450

Root length 512RSR 360 Cigs 483

OPM11600Ags 385

CM171300CM166260

Stem girth 454RSR 411 Ags 345MTR 294 No of nodes 393

OPO101400Ci 313 SLA 719E 318 119864 418

OPP51800LAD 382

CM211300SLW 283

Internodal length 582 RSR 496Shoot dry weight 271 119864 423

OPP91030Cigs 612SLW 333RSR 350

were explaining the trait in the range between 283 and719 This study has provided more detailed information ofroot and associated physiological traits relationships underdrought and identification of markers linked to such traitswith the limited polymorphic markers by employing SingleMarker Analysis (SMA) QTL maps could be used to forlong-term drought breeding Further studies are needed toconfirm the estimation of QTL positions and effects and tovalidatemarkers prior to routinemarker assisted selection fordrought tolerance in coffee

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

References

[1] F M DaMatta ldquoExploring drought tolerance in coffee aphysiological approach with some insights for plant breedingrdquoBrazilian Journal of Plant Physiology vol 16 no 1 pp 1ndash6 2004

Molecular Biology International 11

[2] M Nesbitt The Cultural History of Plants Taylor amp Francis2005

[3] F M Damatta ldquoDrought as a multidimensional stress affectingphotosynthesis in tropical tree cropsrdquo in Advances in PlantPhysiology A Hemantaranjan Ed vol 5 pp 227ndash265 2003

[4] K Harovon Mogel ldquoGenotype times environment times managementinteractions key to beating future droughtsrdquo CSA News vol 58pp 4ndash9 2013

[5] F M DaMatta A R M Chaves H A Pinheiro C Ducatti andM E Loureiro ldquoDrought tolerance of two field-grown clones ofCoffea canephorardquo Plant Science vol 164 no 1 pp 111ndash117 2003

[6] H A Pinheiro F M DaMatta A R M Chaves M E Loureiroand C Ducatti ldquoDrought tolerance is associated with rootingdepth and stomatal control of water use in clones of Coffeacanephorardquo Annals of Botany vol 96 no 1 pp 101ndash108 2005

[7] J B Passioura ldquoPhenotyping for drought tolerance in graincrops when is it useful to breedersrdquo Functional Plant Biologyvol 39 no 11 pp 851ndash859 2012

[8] J S Sperry ldquoHydraulics of vascular water transportrdquo inMechan-ical Integration of Plant Cells and Plants P Wojtaszek Edvol 9 of Signaling and Communication in Plants pp 303ndash327Springer Berlin Germany 2011

[9] A P Wasson R A Richards R Chatrath et al ldquoTraits andselection strategies to improve root systems and water uptakein water-limited wheat cropsrdquo Journal of Experimental Botanyvol 63 no 9 pp 3485ndash3498 2012

[10] G D Herder G van Isterdael T Beeckman and I de SmetldquoThe roots of a new green revolutionrdquo Trends in Plant Sciencevol 15 no 11 pp 600ndash607 2010

[11] H T Nguyen R C Babu and A Blum ldquoBreeding for droughtresistance in rice physiology and molecular genetics consider-ationsrdquo Crop Science vol 37 no 5 pp 1426ndash1434 1997

[12] A Blum Plant Breeding for Water-Limited EnvironmentsSpringer Science+Business Media LLC New York NY USA2011

[13] L B Motta T C B Soares M A G Ferrao E T Caixeta R MLorenzoni and J D de SouzaNeto ldquoMolecular characterizationof arabica and Conilon coffee plants genotypes by SSR and ISSRmarkersrdquo Brazilian Archives of Biology and Technology vol 57no 5 pp 728ndash735 2014

[14] PMarraccini L P Freire G S CAlves et al ldquoRBCS1 expressionin coffee Coffea orthologs Coffea arabica homeologs andexpression variability between genotypes and under droughtstressrdquo BMC Plant Biology vol 11 article 85 2011

[15] P Marraccini F Vinecky G S C Alves et al ldquoDifferentiallyexpressed genes and proteins upon drought acclimation intolerant and sensitive genotypes of Coffea canephorardquo Journalof Experimental Botany vol 63 no 11 pp 4191ndash4212 2012

[16] F Denoeud L Carretero-Paulet A Dereeper et al ldquoThecoffee genome provides insight into the convergent evolution ofcaffeine biosynthesisrdquo Science vol 345 no 6201 pp 1181ndash11842014

[17] A Dereeper S Bocs M Rouard et al ldquoThe coffee genome huba resource for coffee genomesrdquo Nucleic Acids Research vol 43no 1 pp D1028ndashD1035 2015

[18] M Udayakumar M S Sheshshayee K N Nataraj et al ldquoWhyhas breeding for water use efficiency not been successful Ananalysis and alternate approach to exploit this trait for cropimprovementrdquo Current Science vol 74 no 11 pp 1000ndash10031998

[19] M G Awati Genetic diversity and molecular markers for rootand associated physiological traits of Robusta (coffea canephoraPierre ex F) accessions [PhD thesis] University of AgriculturalSciences Bangalore India 2005

[20] C E Williams and P C Ronald ldquoPCR template-DNA isolatedquickly from monocot and dicot leaves without tissue homog-enizationrdquo Nucleic Acids Research vol 22 no 10 pp 1917ndash19181994

[21] S D Tanksley ldquoMapping polygenesrdquoAnnual Review of Geneticsvol 27 no 1 pp 205ndash233 1993

[22] T Halward T Stalker E LaRue and G Kochert ldquoUse ofsingle-primer DNA amplifications in genetic studies of peanut(Arachis hypogaea L)rdquo Plant Molecular Biology vol 18 no 2pp 315ndash325 1992

[23] SAS Institute SASSTAT Userrsquos Guide Version 6 SAS InstituteCary NC USA 4th edition 1989

[24] M J Kearsey and A G L Farquhar ldquoQTL analysis in plantswhere are we nowrdquo Heredity vol 80 no 2 pp 137ndash142 1998

[25] M J Kearsey and V Hyne ldquoQTL analysis a simple lsquomarker-regressionrsquo approachrdquoTheoretical and Applied Genetics vol 89no 6 pp 698ndash702 1994

[26] C S Haley and S A Knott ldquoA simple regression method formapping quantitative trait loci in line crosses using flankingmarkersrdquo Heredity vol 69 no 4 pp 315ndash324 1992

[27] B M Atta T Mahmood and R M Trethowan ldquoRelationshipbetween root morphology and grain yield of wheat in North-Western NSW Australiardquo Australian Journal of Crop Sciencevol 7 no 13 pp 2108ndash2115 2013

[28] N T Lang C T Nha P T T Ha and B C Buu ldquoQuantitativetrait loci (QTLs) associated with drought tolerance in rice(Oryza sativa L)rdquo Sabrao Journal of Breeding and Genetics vol45 no 3 pp 409ndash421 2013

[29] H J Schenk andR B Jackson ldquoMapping the global distributionof deep roots in relation to climate and soil characteristicsrdquoGeoderma vol 126 no 1-2 pp 129ndash140 2005

[30] A Srividya L R Vemireddy P V Ramanarao et al ldquoMolecularmapping of QTLs for drought related traits at seedling stageunder PEG induced stress conditions in ricerdquoAmerican Journalof Plant Sciences vol 2 no 2 pp 190ndash201 2011

[31] N-N Shukor H A Hamid A Abdu andM-K Ismail ldquoEffectsof soil compaction on growth and physiological characteristicsofAzadirachta excelsa seedlingsrdquoTheAmerican Journal of PlantPhysiology vol 10 no 1 pp 25ndash42 2015

[32] P R Bhat Phenotyping and molecular analysis of Coffea arabicaL accessions and mapping population to identify RAPD markersassociated with root characteristics and associated physiologicaltraits [PhD thesis] University of Agricultural Sciences Banga-lore India 2002

[33] S Chandra H K Buhariwalla J Kashiwagi et al ldquoIdentifyingQTL linked markers in marker-29 deficient cropsrdquo in Proceed-ings of the 4th International Crop Science Congress BrisbaneAustralia 2004

[34] H M Pearl C Nagai P H Moore D L Steiger R V Osgoodand RMing ldquoConstruction of a genetic map for arabica coffeerdquoTheoretical and Applied Genetics vol 108 no 5 pp 829ndash8352004

[35] P Lashermes M C Combes N S Prakash P Trouslot MLorieux and A Charrier ldquoGenetic linkage map of Coffeacanephora effect of segregation distortion and analysis ofrecombination rate in male and female meiosesrdquo Genome vol44 no 4 pp 589ndash596 2001

[36] P Hendre S Lalji Singh and R K Aggarwal ldquoConstructionof framework molecular linkage map of Robusta coffee Coffeacanephora with RAPD AFLP g-SSRs And EST-SSRs usingpseudo-testcross strategyrdquo in Proceedings of the Plant amp AnimalGenomes 15th Conference January 2007

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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PeptidesInternational Journal of

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International Journal of

Volume 2014

Zoology

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Molecular Biology International

GenomicsInternational Journal of

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The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

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BioinformaticsAdvances in

Marine BiologyJournal of

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Signal TransductionJournal of

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ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Nucleic AcidsJournal of

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Stem CellsInternational

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Enzyme Research

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International Journal of

Microbiology

6 Molecular Biology International

Table 2 Range and mean values of the morphophysiological parameters in 134 F1 mapping population of Coffea canephora (L1 valley timesS3334)

Serial number Parameters Range Mean STDEV

Root traits

1 Root length (cm2) 415ndash830 5708 7292 Number of secondary roots 2ndash60 269 12613 Root dry weight (gplant) 257ndash3469 1377 664 Root shoot ratio 023ndash039 027 003

Morphological traits

5 Plant height (cm2) 135ndash1053 726 15696 Number of nodes 110ndash420 2408 6887 Number of leaves 2ndash70 3402 13428 Stem girth (mm) 588ndash1365 1028 1799 Internodal length (cm) 19ndash125 854 210 Specific leaf dry weight (mgdm2) 141ndash264 18 02211 Final leaf area (cm2) 113692ndash819061 449119 17101712 Specific leaf area (cm) 37899ndash71097 56244 653413 Total leaf dry weight (gplant) 578ndash4859 2602 104314 Stem dry weight (gplant) 481ndash5262 2433 10415 Shoot dry weight (gplant) 1059ndash9664 5035 206316 Total dry matter (gplant) 116ndash12143 5793 2477

Gravimetric traits

17 Leaf area duration (dm2 days) 106589ndash799277 437252 16580418 Net assimilation rate 89ndash1938 1307 17119 Cumulative water transpiration (ltplant) 25ndash235 1081 46220 Water use efficiency (gkg) 386ndash684 54 05821 Mean transpiration rate (mLdm2 days) 15ndash352 244 03822 Delta 13c (mill) 1975ndash2653 218 111

Gas exchange parameters

23 Photosynthesis (Pn) (mmolm2S) 545ndash2727 169 39124 Transpiration rate (119864) (mgm2s) 071ndash747 338 13425 Conductance (gs) (mmolm2s) 005ndash1 035 01726 Cigs 28876ndash348425 8593 405127 Ci 1865ndash29667 24798 248828 Ags 2406ndash9326 5442 155229 A119864 263ndash1407 552 176

Table 3 Polymorphic RAPD primers and their sequences used to study the segregation of markers in the mapping population

Polymorphicbands

Primername Sequence Primer

name Sequence Primername Sequence Primer

name Sequence

1

OPK18 CCTAGTCGAG OPN6 GAGACGCACA OPQ5 CCGCGTCTTG OPV5 TCCGAGAGGGOPK19 CACAGGCGGA OPO1 GGCACGTAAG OPS1 CTACTGCGCT OPW9 GTGACCGAGTOPK20 GTGTCGCGAG OPO18 CTCGCTATCC OPV11 CTCGACAGAG OPY20 AGCCGTGGAAOPM5 GGGAACGTGT OPO4 AAGTCCGCTC OPV14 AGATCCCGCC OPZ11 CTCAGTCGCAOPN19 GTCCGTACTG OPP2 TCGGCACGCA OPV2 AGTCACTCCC

2

OPK10 GTGCSSCGTG OPL18 ACCACCCACC OPO16 TCGGCGGTTC OPT7 GTCCATGCCAOPK11 AATGCCCCAG OPM11 GTCCACTGTG OPP5 CCCCGGTAAC OPU1 ACGGACGTCAOPL1 GGCATGACCT OPM17 TCAGTCCGGG OPQ20 TCGCCCAGTC OPW17 GTCCTGGGTTOPL12 GGGCGGTACT OPO10 TCAGAGCGCC OPR4 CCCGTAGCAC OPZ3 CAGCACCGCA

3 OPL19 GAGTGGTGAC OPM2 ACAACGCCTC OPP11 AACGCGTCGG OPP9 GTGGTCCGCAOPZ10 CCGACAAACC

4 OPK17 CCCAGCTGTG

Molecular Biology International 7

0 5 10 15 20 25 30 350

20

40

60

80

100

120

140

1

2

3 4

5

6

7

8

9

10

1113

14

15

161718

19

20

21

22

23

2425

26 2728

29

3032

33

34

35

36

37

38 3940

41

43

44

45

46

47

48

49

50

5152

5354

55

5657

58

59

60

61

62

63

64

65

66

67

68

69

70

72

73

75

7677

78

79

80

81828384

85

86

87

88

89

90

9192

939495

96

97

98

99100

101102

103104

105106

107

108

109110

111

113

114

115

116

117

118

119

120

121

122123

124

125

126

127128129

130

131

132

133

134

135

136

137

138

139140

141

Tota

l dry

mat

ter (

gpl

ant)

Root dry weight (gplant)

r = 0968

P lt 0001 n = 135

(a)

0 5 10 15 20 25 30 350

5

10

15

20

25

1

2

3

4

5

6

7

8

910

1113

14

15

161718

19

20

21

22

23

2425

262728 29

3032

33

34

35

36

37

38 3940

41

43

44

45

4647

48

49

50

5152

5354

55

5657

58

59

6061

62

63

64

65

66

67

68

69

70

72

73

75

76

77

78

7980

81

8283

84

85

86

87

88

89

90

9192 93

9495

96

97

98 99

100101

102

103104

105

106

107

108

109110

111

113

114

115

116

117

118119

120

121

122123

124

125

126

127128

129

130

131

132

133

134

135

136

137138

139140

141

CWT

(L)

Root dry weight (gplant)

r = 0940

P lt 0001 n = 135

(b)

0 20 40 60 80 100 120 1400

5

10

15

20

25

1

2

3

4

5

6

7

8

910

1113

1415

1617

18

19

20

21

22

23

2425

2627 28 29

3032

33

34

35

36

37

383940

41

43

44

45

4647

48

49

50

5152

5354

55

5657

58

59

6061

62

63

64

65

6667

68

69

70

72

73

75

76

77

78

7980

81

8283

84

85

86

8788

89

90

9192 93

9495

96

97

98 99

100101

102

103104

105

106

107

108

109110

111

113

114

115

116

117

118 119

120

121

122123

124

125

126

127128

129

130

131

132

133

134

135

136

137138

139140

141

CWT

(L)

Total dry matter (gplant)

r = 0970

P lt 0001 n = 135

(c)

0 5 10 15 20 25

15

20

25

30

35

12

3

45

67

8

9

10

11 1314

1516

17

18

1920

2122

23

24

25

26

27

28

29 30

3233

34

35 36

37 38

39

40

4143

44

45

4647

4849

50

5152 53

5455

56

57

5859

60

61

62 6364 65

66

67

68

69

70

7273

75

76

77

78

79

80

81

8283

84

85 86

8788

89

9091

92

93

94

9596

9798

99

100

101

102

103

104

105106

107

108

109110

111

113

114115

116

117

118119

120

121122

123124

125

126127

128

129130

131

132133

134

135136

137

138

139

140141

CWT (L)

r = 0524

P lt 0001 n = 135

MTR

(mL

dm2)

(d)

0 5 10 15 20 25

15

20

25

30

35

12

3

45

67

8

9

10

11 1314

1516

17

18

1920

2122

23

24

25

26

27

28

29 30

3233

34

35 36

37 38

39

40

4143

44

45

4647

4849

50

5152 53

5455

56

57

5859

60

61

62 6364 65

66

67

68

69

70

7273

75

76

77

78

79

80

81

8283

84

85 86

8788

89

9091

92

93

94

9596

9798

99

100

101

102

103

104

105106

107

108

109110

111

113

114115

116

117

118119

120

121122

123124

125

126127

128

129130

131

132133

134

135136

137

138

139

140141

CWT (L)

r = 0524

P lt 0001 n = 135

MTR

(mL

dm2)

(e)

35 40 45 50 55 60 65 70

20

22

24

26

28

12

345 6 7

89

10

11

131415

16

17

18

1920

2122

23

24

25

26

27

28

2930

32 3334

35

36 37383940

4143

44

45

4647

4849

50

5152

535455

5657 58

59

60

61

62 63 646566

67

68

69

707273 7576

77

78

7980

8182

8384

85

86878889

9091

92

9394

95

96

97

98

99

100101

102103104

105

106107 108109110111

113114115

116

117

118 119120

121122

123124

125

126127

128

129

130131

132133

134

135136 137

138139

140

141

WUE (gkg)

r = minus0413

P lt 0001 n = 135

Δ13

C

(f)

Figure 2 Relationship between the RDWTDM RDWCWT TDMCWT CWTMTR CWTMTR and WUEΔ13C in the mappingpopulation of C canephora (L1 valley times S334)

8 Molecular Biology International

Table 4 Chi-square tests (1205942) for RAPD and SSR markers in the Coffea canephoramapping population

Parents F1progenies

Markersystem

Total polymor-phic markers

Marker loci notfollowing expected ratio

Marker loci followingexpected ratio

Number of significant markers119875 = 095 119875 = 005 119875 = 0001

L1 valley timesS3334 134 RAPD 70 28 42 (1 1) 32 3 7

SSR 9 9 0 (3 1) mdash mdash mdash

Table 5 Polymorphic microsatellite primers and their sequences used to study the segregation of markers in the mapping population

Serial number Primer Sequence Tm (∘C) Size range

1 CM11 F GCTGCCAGAAAAATGTTGCAGTG 58 270ndash338R CTGCCTCGTAAAAGCTTGCGTTG

2 CM13 F GCTATGCAGCTTGTTCGCAATCC 59 350ndash411R CCAGCTATATCAGGAGCAGAACC

3 CM180 F CATGTGTAATACATTCAACAGTGA 60 300ndash350R GCAATAGTGGTTGTCATCCTT

4 CM32 F AACTCTCCATTCCCGCATTC 60 180ndash200R CTGGGTTTTCTGTGTTCTCG

5 CM46 F CAGCTAGTGTGAAGGGAAAC 55 300ndash350R GTTATCATGGTCTTACACG

6 CM20 F CTTGTTTGAGTCTGTCGCTG 60 250ndash300R TTTCCCTCCCAATGTCTGTA

7 CM171 F TTCCCCCATCTTTTTCTTTC 60 250ndash300R TTGTATACGGCTCGTCAGGT

8 CM166 F AAGAGGTGCCTATCACCGTC 60 230ndash260R CGAGGTATCAAAAAGCACCT

9 CM211 F TCATGCCAAATATGAGTGGA 60 300ndash350R GAGATGGCAAAGGCTGTTC

35 Single Marker Analysis for Root and Associated Physiolog-ical Traits Complex physiological traits have been describedby a small number of major QTL [24] but it is intricateto find useful QTL for a particular trait as their individualcontribution is smaller Therefore under such conditionSingle Marker Analysis (SMA) is generally a good choicewhen the goal is simply a detection of marker locus linkedto a trait However estimation of its position and its effectsrequires further complex analysis such as marker regression[25] or interval analysis [26]

In our study Single Marker ANOVA is used to identifymarkers showing significant association with 29 morpho-physiological traits (SAS software with 119875 le 005)

In the single marker analysis simple regression modelwas examined to study the association between marker loci(independent) and trait score (dependent variable) and alsocomputed the percent phenotypic variance explained by eachmarker Seventy polymorphic marker loci were employed forsimple regression analysis however it was discovered that 37RAPD and 5 SSR markers explained their association withmorphophysiological traits

36Markers Linked to Root Traits The singlemarker analysisrevealed fourmarkers such as OPS1

850 OPK11

780 OPY20

1200

OPZ101350

and CM13400

were significantly associated withroot length (119875 = 005) and explained phenotypic vari-ance of 441 295 356 512 and 512 respectively

A number of secondary roots linked to three markers suchas OPO16

450(440) OPK11

1000(551) and OPV14

550

(582) were significantly (119875 = 005) associated Similarlysix markers such as OPK11

1000(326) OPP9

1030(35)

OPM11600

(375) OPL19900

(561) OPL19600

(411)OPR4

600(330) and CM211

300(1198772 = 496) revealed

significant (119875 = 005) association with the root to shootratio (Table 6) This implies the root associated traits arepolygenic controlling many genes However root dry weightis associated with only one maker OPL1

1400(486)

37 Markers Linked to Morphophysiological Traits Besidesroot traits polygenic inheritance traits such as morpho-logical gravimetric and gas exchange at single leaf leveltraits were also deployed for marker trait association Singlemarker analysis revealed more than three markers linked toeach trait and 25 markers are colocalized with more thanone trait and 17 markers are associated with single trait(Table 7)ThemarkerOPP5

1800is coassociatedwith shoot dry

weight and leaf area duration (LAD) and these two traits arepositively correlated (1198772 = 093) Four markers (OPL1

1450

OPL191650

OPM171400

and OPK20350

) linked to NAR andtwo markers OPR4

600and OPL12

2000are significantly (119875 =

001) associated with cumulative water transpired (CWT)The 13ΔC is the surrogate method to quantify the water

use efficiency and it was demonstrated that there is inverse

Molecular Biology International 9

Table 6 RAPD markers linked to root traits in the mappingpopulation of C canephora (L1 valley times S3334)

Trait Marker 1198772 () 119875 lt 005

Root length

OPY201200 295 00449OPZ101350 356 00286OPK11780 441 00178OPS1850 686 00001

CPCM13400 512 00105

Number of secondary rootsOPO16450 44 00179OPK111000 551 00059OPV14550 582 00042

Root dry weight OPL11450 476 00073

Root to shoot ratio

OPK111000 326 0038OPR4600 33 00367OPP91030 35 00288OPM11600 375 00267OPL19600 411 00223OPL19900 561 00062CM211300 496 00105

relationship between these two traits Of the six associatedmarkers OPZ10

1350was found to be associatedwith 13ΔCand

WUE with phenotypic variance of 412 and 302 respec-tively This marker locus could be negative additive effectsince traits are negatively related The mesophyll efficiency(Cigs) intrinsic WUE (Ags) and photosynthetic rate (Pn)are respectively associated with CM13

400(483) CM171

210

(345) and CM171190

(372) (Table 7) Whereas positivelyrelated traits are associated with the same marker such asOPP5

1800(LAD and shoot dry weight) OPZ10

1030(MTR and

gs) and OPK20350

(Ci and NAR) alleles contribution fromS3334 The markers linked to other gas exchange parameterswere given in Table 7 which are contributed from S3334

4 Discussion

We developed a F1 mapping population by crossing contrast-ing root traits S3334 high root type as male and L1 valley lowroot type as female to identify makers linked to root traitsand water use efficient types However in annual crops F

2

recombinant inbred lines (RILs) near isogenic lines (NILs)and doubled haploids (DH)mapping population will be usedto associatemarkers with the trait of our interest However inperennials the germplasm lines and the cultivated accessionsare often highly heterozygous consequently F

1population

developed from heterozygous parents was used in this studyand showed considerable phenotypic and genetic variations

One of the objectives of studying themapping populationwas to score the variability in roots and other physiologicaltraits under drought and thus mapping population was gownup in 70 FC of water stress to study the heritability ofroot traits Indeed root traits are highly complex geneticmechanism controlled by multi genes and highly herita-ble in water non limiting environment [27] The resultsdemonstrated significant variability was observed in root

dry weight (RDW) total dry matter (TDM) root to shootratio (RSR) cumulativewater transpired (CWT)with consid-erable transgressive segregation (Figure 1) and quantitativeinheritance of drought tolerance [28] However significantpositive correlation between RDWTDM RDWCW andTDMCWT indicates that under drought increased rootgrowth maintains the shoot hydraulic conductance as adop-tive strategy [29 30] The decreased mean transpiration(MTR) and increased water use efficiency (WUE) could bedue to decreased stomatal aperture through chemical signalssuch as ABA [31] However some of the clones showedthat increased MTR with increased CWT would favor thestomatal conductance and net photosynthesis Such clonesare better for breeding drought tolerance Inverse relationshipbetween Δ13C and WUE in robusta coffee accessions wasobserved (Figure 2) thus measuring Δ13C which can bea surrogate method to identify best WUE types clones inbreeding [19 32] Breeding for such quantitative traits ishighly complex cumbersome and thus by determining theallele of aDNAmarker plants that possess particular genes orquantitative trait loci (QTLs) are better than their phenotypeWith this background effort wasmade to identify themarkerslinked to root and associated physiological traits in Coffeacanephora

Despite the efforts no successful linkage map could beobtained partly due to the inadequate number of polymor-phic markers generated from this study The output of theassociation analysis indicates that out of 29 traits only 24traits were linked to 85 markers Even markers linked totraits explaining the phenotypic variationwere notmore than10 In the present study phenotypic variation explanationfor all the traits ranges from 261 to 986 This suggeststhat the successful application of molecular markers in traitmapping greatly requires more polymorphic markers whilesuccessful mapping attempts could still be carried out in theabsence of a linkage map as done in chickpea [33] Althoughconstruction of linkage map was done in C arabica [34]and C canephora [35] predominantly using AFLP and RAPDmarkers association with the traits was not attempted Simi-larly the first molecular linkage map generated using pseudotestcross strategy for Coffea canephora (CxR times Kagnalla) hasthe largest number of mapped SSRs (71 genomic includingnine EST-SSRs) [36] but trait association was not correlated

Most of the markers explained phenotypic variancebetween 3 and 4 however only two markers that isOPZ11

1500andOPV14

500 were significantly (119875 lt 001) linked

to photosynthetic rate (Pn) accounting the variation of about923 and 930 respectively (Table 7) The root traits suchas root length number of secondary roots root dry weightand root shoot ratio revealed significant association (119875 lt005) with markers and the variability ranges from 295 to686 (Table 6)This suggests the trait variability explanationis not sufficient to answer the probabilities of occurrence ofthese markers due to limited polymorphic markers

In the present study codominant SSR markers werealso used for the marker-trait association study The SSRmarkers identified herein are significantly associated withmorphological and physiological traits but these markers

10 Molecular Biology International

Table 7 List of RAPD and SSR markers overlapped with more than one trait

Markers Traits 1198772 ()

(119875 lt 005) Markers Traits 1198772

(119875 lt 005)

OPK111000

Internodal length 474OPR4600OPR4750

CWT 508Root to shoot ratio 326 RSR 330

Number of secondary roots 551 Number of leaves 565Stem dry weight 344 gs 293

OPK181200Specific leaf area 470 OPV14550

Pn 930Plant height 451 Number of secondary Roots 582

OPK192000Photosynthetic rate (Pn) 555 OPY201200

WUE 310WUE 443 Plant height 423

K20350NAR 482

OPZ111500Number of leaves 399

WUE 445 Root length 295Ci 432 Pn 923

OPL11400

Plant height 577

OPZ101350OPZ101030

MTR 821Internodal length 381 13

ΔC 412NAR 427 Plant height 303

Shoot dry weight 400 Root length 356Root dry weight 476 WUE 302

Pn 261 Cigs 509

OPL122000Plant height 368 Pn 756

CWT 474 MTR 438

OPL12165013ΔC 361 OPZ10720

gs 296pn 307 gs 326

OPL181000Number of nodes 778 Pn 314

LAD 382 Internodal length 358

OPL19900SLW 333 CPCM13450

Root length 512RSR 360 Cigs 483

OPM11600Ags 385

CM171300CM166260

Stem girth 454RSR 411 Ags 345MTR 294 No of nodes 393

OPO101400Ci 313 SLA 719E 318 119864 418

OPP51800LAD 382

CM211300SLW 283

Internodal length 582 RSR 496Shoot dry weight 271 119864 423

OPP91030Cigs 612SLW 333RSR 350

were explaining the trait in the range between 283 and719 This study has provided more detailed information ofroot and associated physiological traits relationships underdrought and identification of markers linked to such traitswith the limited polymorphic markers by employing SingleMarker Analysis (SMA) QTL maps could be used to forlong-term drought breeding Further studies are needed toconfirm the estimation of QTL positions and effects and tovalidatemarkers prior to routinemarker assisted selection fordrought tolerance in coffee

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

References

[1] F M DaMatta ldquoExploring drought tolerance in coffee aphysiological approach with some insights for plant breedingrdquoBrazilian Journal of Plant Physiology vol 16 no 1 pp 1ndash6 2004

Molecular Biology International 11

[2] M Nesbitt The Cultural History of Plants Taylor amp Francis2005

[3] F M Damatta ldquoDrought as a multidimensional stress affectingphotosynthesis in tropical tree cropsrdquo in Advances in PlantPhysiology A Hemantaranjan Ed vol 5 pp 227ndash265 2003

[4] K Harovon Mogel ldquoGenotype times environment times managementinteractions key to beating future droughtsrdquo CSA News vol 58pp 4ndash9 2013

[5] F M DaMatta A R M Chaves H A Pinheiro C Ducatti andM E Loureiro ldquoDrought tolerance of two field-grown clones ofCoffea canephorardquo Plant Science vol 164 no 1 pp 111ndash117 2003

[6] H A Pinheiro F M DaMatta A R M Chaves M E Loureiroand C Ducatti ldquoDrought tolerance is associated with rootingdepth and stomatal control of water use in clones of Coffeacanephorardquo Annals of Botany vol 96 no 1 pp 101ndash108 2005

[7] J B Passioura ldquoPhenotyping for drought tolerance in graincrops when is it useful to breedersrdquo Functional Plant Biologyvol 39 no 11 pp 851ndash859 2012

[8] J S Sperry ldquoHydraulics of vascular water transportrdquo inMechan-ical Integration of Plant Cells and Plants P Wojtaszek Edvol 9 of Signaling and Communication in Plants pp 303ndash327Springer Berlin Germany 2011

[9] A P Wasson R A Richards R Chatrath et al ldquoTraits andselection strategies to improve root systems and water uptakein water-limited wheat cropsrdquo Journal of Experimental Botanyvol 63 no 9 pp 3485ndash3498 2012

[10] G D Herder G van Isterdael T Beeckman and I de SmetldquoThe roots of a new green revolutionrdquo Trends in Plant Sciencevol 15 no 11 pp 600ndash607 2010

[11] H T Nguyen R C Babu and A Blum ldquoBreeding for droughtresistance in rice physiology and molecular genetics consider-ationsrdquo Crop Science vol 37 no 5 pp 1426ndash1434 1997

[12] A Blum Plant Breeding for Water-Limited EnvironmentsSpringer Science+Business Media LLC New York NY USA2011

[13] L B Motta T C B Soares M A G Ferrao E T Caixeta R MLorenzoni and J D de SouzaNeto ldquoMolecular characterizationof arabica and Conilon coffee plants genotypes by SSR and ISSRmarkersrdquo Brazilian Archives of Biology and Technology vol 57no 5 pp 728ndash735 2014

[14] PMarraccini L P Freire G S CAlves et al ldquoRBCS1 expressionin coffee Coffea orthologs Coffea arabica homeologs andexpression variability between genotypes and under droughtstressrdquo BMC Plant Biology vol 11 article 85 2011

[15] P Marraccini F Vinecky G S C Alves et al ldquoDifferentiallyexpressed genes and proteins upon drought acclimation intolerant and sensitive genotypes of Coffea canephorardquo Journalof Experimental Botany vol 63 no 11 pp 4191ndash4212 2012

[16] F Denoeud L Carretero-Paulet A Dereeper et al ldquoThecoffee genome provides insight into the convergent evolution ofcaffeine biosynthesisrdquo Science vol 345 no 6201 pp 1181ndash11842014

[17] A Dereeper S Bocs M Rouard et al ldquoThe coffee genome huba resource for coffee genomesrdquo Nucleic Acids Research vol 43no 1 pp D1028ndashD1035 2015

[18] M Udayakumar M S Sheshshayee K N Nataraj et al ldquoWhyhas breeding for water use efficiency not been successful Ananalysis and alternate approach to exploit this trait for cropimprovementrdquo Current Science vol 74 no 11 pp 1000ndash10031998

[19] M G Awati Genetic diversity and molecular markers for rootand associated physiological traits of Robusta (coffea canephoraPierre ex F) accessions [PhD thesis] University of AgriculturalSciences Bangalore India 2005

[20] C E Williams and P C Ronald ldquoPCR template-DNA isolatedquickly from monocot and dicot leaves without tissue homog-enizationrdquo Nucleic Acids Research vol 22 no 10 pp 1917ndash19181994

[21] S D Tanksley ldquoMapping polygenesrdquoAnnual Review of Geneticsvol 27 no 1 pp 205ndash233 1993

[22] T Halward T Stalker E LaRue and G Kochert ldquoUse ofsingle-primer DNA amplifications in genetic studies of peanut(Arachis hypogaea L)rdquo Plant Molecular Biology vol 18 no 2pp 315ndash325 1992

[23] SAS Institute SASSTAT Userrsquos Guide Version 6 SAS InstituteCary NC USA 4th edition 1989

[24] M J Kearsey and A G L Farquhar ldquoQTL analysis in plantswhere are we nowrdquo Heredity vol 80 no 2 pp 137ndash142 1998

[25] M J Kearsey and V Hyne ldquoQTL analysis a simple lsquomarker-regressionrsquo approachrdquoTheoretical and Applied Genetics vol 89no 6 pp 698ndash702 1994

[26] C S Haley and S A Knott ldquoA simple regression method formapping quantitative trait loci in line crosses using flankingmarkersrdquo Heredity vol 69 no 4 pp 315ndash324 1992

[27] B M Atta T Mahmood and R M Trethowan ldquoRelationshipbetween root morphology and grain yield of wheat in North-Western NSW Australiardquo Australian Journal of Crop Sciencevol 7 no 13 pp 2108ndash2115 2013

[28] N T Lang C T Nha P T T Ha and B C Buu ldquoQuantitativetrait loci (QTLs) associated with drought tolerance in rice(Oryza sativa L)rdquo Sabrao Journal of Breeding and Genetics vol45 no 3 pp 409ndash421 2013

[29] H J Schenk andR B Jackson ldquoMapping the global distributionof deep roots in relation to climate and soil characteristicsrdquoGeoderma vol 126 no 1-2 pp 129ndash140 2005

[30] A Srividya L R Vemireddy P V Ramanarao et al ldquoMolecularmapping of QTLs for drought related traits at seedling stageunder PEG induced stress conditions in ricerdquoAmerican Journalof Plant Sciences vol 2 no 2 pp 190ndash201 2011

[31] N-N Shukor H A Hamid A Abdu andM-K Ismail ldquoEffectsof soil compaction on growth and physiological characteristicsofAzadirachta excelsa seedlingsrdquoTheAmerican Journal of PlantPhysiology vol 10 no 1 pp 25ndash42 2015

[32] P R Bhat Phenotyping and molecular analysis of Coffea arabicaL accessions and mapping population to identify RAPD markersassociated with root characteristics and associated physiologicaltraits [PhD thesis] University of Agricultural Sciences Banga-lore India 2002

[33] S Chandra H K Buhariwalla J Kashiwagi et al ldquoIdentifyingQTL linked markers in marker-29 deficient cropsrdquo in Proceed-ings of the 4th International Crop Science Congress BrisbaneAustralia 2004

[34] H M Pearl C Nagai P H Moore D L Steiger R V Osgoodand RMing ldquoConstruction of a genetic map for arabica coffeerdquoTheoretical and Applied Genetics vol 108 no 5 pp 829ndash8352004

[35] P Lashermes M C Combes N S Prakash P Trouslot MLorieux and A Charrier ldquoGenetic linkage map of Coffeacanephora effect of segregation distortion and analysis ofrecombination rate in male and female meiosesrdquo Genome vol44 no 4 pp 589ndash596 2001

[36] P Hendre S Lalji Singh and R K Aggarwal ldquoConstructionof framework molecular linkage map of Robusta coffee Coffeacanephora with RAPD AFLP g-SSRs And EST-SSRs usingpseudo-testcross strategyrdquo in Proceedings of the Plant amp AnimalGenomes 15th Conference January 2007

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

PeptidesInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

International Journal of

Volume 2014

Zoology

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Molecular Biology International

GenomicsInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioinformaticsAdvances in

Marine BiologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Signal TransductionJournal of

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BioMed Research International

Evolutionary BiologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Genetics Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Virolog y

Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational

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Enzyme Research

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Microbiology

Molecular Biology International 7

0 5 10 15 20 25 30 350

20

40

60

80

100

120

140

1

2

3 4

5

6

7

8

9

10

1113

14

15

161718

19

20

21

22

23

2425

26 2728

29

3032

33

34

35

36

37

38 3940

41

43

44

45

46

47

48

49

50

5152

5354

55

5657

58

59

60

61

62

63

64

65

66

67

68

69

70

72

73

75

7677

78

79

80

81828384

85

86

87

88

89

90

9192

939495

96

97

98

99100

101102

103104

105106

107

108

109110

111

113

114

115

116

117

118

119

120

121

122123

124

125

126

127128129

130

131

132

133

134

135

136

137

138

139140

141

Tota

l dry

mat

ter (

gpl

ant)

Root dry weight (gplant)

r = 0968

P lt 0001 n = 135

(a)

0 5 10 15 20 25 30 350

5

10

15

20

25

1

2

3

4

5

6

7

8

910

1113

14

15

161718

19

20

21

22

23

2425

262728 29

3032

33

34

35

36

37

38 3940

41

43

44

45

4647

48

49

50

5152

5354

55

5657

58

59

6061

62

63

64

65

66

67

68

69

70

72

73

75

76

77

78

7980

81

8283

84

85

86

87

88

89

90

9192 93

9495

96

97

98 99

100101

102

103104

105

106

107

108

109110

111

113

114

115

116

117

118119

120

121

122123

124

125

126

127128

129

130

131

132

133

134

135

136

137138

139140

141

CWT

(L)

Root dry weight (gplant)

r = 0940

P lt 0001 n = 135

(b)

0 20 40 60 80 100 120 1400

5

10

15

20

25

1

2

3

4

5

6

7

8

910

1113

1415

1617

18

19

20

21

22

23

2425

2627 28 29

3032

33

34

35

36

37

383940

41

43

44

45

4647

48

49

50

5152

5354

55

5657

58

59

6061

62

63

64

65

6667

68

69

70

72

73

75

76

77

78

7980

81

8283

84

85

86

8788

89

90

9192 93

9495

96

97

98 99

100101

102

103104

105

106

107

108

109110

111

113

114

115

116

117

118 119

120

121

122123

124

125

126

127128

129

130

131

132

133

134

135

136

137138

139140

141

CWT

(L)

Total dry matter (gplant)

r = 0970

P lt 0001 n = 135

(c)

0 5 10 15 20 25

15

20

25

30

35

12

3

45

67

8

9

10

11 1314

1516

17

18

1920

2122

23

24

25

26

27

28

29 30

3233

34

35 36

37 38

39

40

4143

44

45

4647

4849

50

5152 53

5455

56

57

5859

60

61

62 6364 65

66

67

68

69

70

7273

75

76

77

78

79

80

81

8283

84

85 86

8788

89

9091

92

93

94

9596

9798

99

100

101

102

103

104

105106

107

108

109110

111

113

114115

116

117

118119

120

121122

123124

125

126127

128

129130

131

132133

134

135136

137

138

139

140141

CWT (L)

r = 0524

P lt 0001 n = 135

MTR

(mL

dm2)

(d)

0 5 10 15 20 25

15

20

25

30

35

12

3

45

67

8

9

10

11 1314

1516

17

18

1920

2122

23

24

25

26

27

28

29 30

3233

34

35 36

37 38

39

40

4143

44

45

4647

4849

50

5152 53

5455

56

57

5859

60

61

62 6364 65

66

67

68

69

70

7273

75

76

77

78

79

80

81

8283

84

85 86

8788

89

9091

92

93

94

9596

9798

99

100

101

102

103

104

105106

107

108

109110

111

113

114115

116

117

118119

120

121122

123124

125

126127

128

129130

131

132133

134

135136

137

138

139

140141

CWT (L)

r = 0524

P lt 0001 n = 135

MTR

(mL

dm2)

(e)

35 40 45 50 55 60 65 70

20

22

24

26

28

12

345 6 7

89

10

11

131415

16

17

18

1920

2122

23

24

25

26

27

28

2930

32 3334

35

36 37383940

4143

44

45

4647

4849

50

5152

535455

5657 58

59

60

61

62 63 646566

67

68

69

707273 7576

77

78

7980

8182

8384

85

86878889

9091

92

9394

95

96

97

98

99

100101

102103104

105

106107 108109110111

113114115

116

117

118 119120

121122

123124

125

126127

128

129

130131

132133

134

135136 137

138139

140

141

WUE (gkg)

r = minus0413

P lt 0001 n = 135

Δ13

C

(f)

Figure 2 Relationship between the RDWTDM RDWCWT TDMCWT CWTMTR CWTMTR and WUEΔ13C in the mappingpopulation of C canephora (L1 valley times S334)

8 Molecular Biology International

Table 4 Chi-square tests (1205942) for RAPD and SSR markers in the Coffea canephoramapping population

Parents F1progenies

Markersystem

Total polymor-phic markers

Marker loci notfollowing expected ratio

Marker loci followingexpected ratio

Number of significant markers119875 = 095 119875 = 005 119875 = 0001

L1 valley timesS3334 134 RAPD 70 28 42 (1 1) 32 3 7

SSR 9 9 0 (3 1) mdash mdash mdash

Table 5 Polymorphic microsatellite primers and their sequences used to study the segregation of markers in the mapping population

Serial number Primer Sequence Tm (∘C) Size range

1 CM11 F GCTGCCAGAAAAATGTTGCAGTG 58 270ndash338R CTGCCTCGTAAAAGCTTGCGTTG

2 CM13 F GCTATGCAGCTTGTTCGCAATCC 59 350ndash411R CCAGCTATATCAGGAGCAGAACC

3 CM180 F CATGTGTAATACATTCAACAGTGA 60 300ndash350R GCAATAGTGGTTGTCATCCTT

4 CM32 F AACTCTCCATTCCCGCATTC 60 180ndash200R CTGGGTTTTCTGTGTTCTCG

5 CM46 F CAGCTAGTGTGAAGGGAAAC 55 300ndash350R GTTATCATGGTCTTACACG

6 CM20 F CTTGTTTGAGTCTGTCGCTG 60 250ndash300R TTTCCCTCCCAATGTCTGTA

7 CM171 F TTCCCCCATCTTTTTCTTTC 60 250ndash300R TTGTATACGGCTCGTCAGGT

8 CM166 F AAGAGGTGCCTATCACCGTC 60 230ndash260R CGAGGTATCAAAAAGCACCT

9 CM211 F TCATGCCAAATATGAGTGGA 60 300ndash350R GAGATGGCAAAGGCTGTTC

35 Single Marker Analysis for Root and Associated Physiolog-ical Traits Complex physiological traits have been describedby a small number of major QTL [24] but it is intricateto find useful QTL for a particular trait as their individualcontribution is smaller Therefore under such conditionSingle Marker Analysis (SMA) is generally a good choicewhen the goal is simply a detection of marker locus linkedto a trait However estimation of its position and its effectsrequires further complex analysis such as marker regression[25] or interval analysis [26]

In our study Single Marker ANOVA is used to identifymarkers showing significant association with 29 morpho-physiological traits (SAS software with 119875 le 005)

In the single marker analysis simple regression modelwas examined to study the association between marker loci(independent) and trait score (dependent variable) and alsocomputed the percent phenotypic variance explained by eachmarker Seventy polymorphic marker loci were employed forsimple regression analysis however it was discovered that 37RAPD and 5 SSR markers explained their association withmorphophysiological traits

36Markers Linked to Root Traits The singlemarker analysisrevealed fourmarkers such as OPS1

850 OPK11

780 OPY20

1200

OPZ101350

and CM13400

were significantly associated withroot length (119875 = 005) and explained phenotypic vari-ance of 441 295 356 512 and 512 respectively

A number of secondary roots linked to three markers suchas OPO16

450(440) OPK11

1000(551) and OPV14

550

(582) were significantly (119875 = 005) associated Similarlysix markers such as OPK11

1000(326) OPP9

1030(35)

OPM11600

(375) OPL19900

(561) OPL19600

(411)OPR4

600(330) and CM211

300(1198772 = 496) revealed

significant (119875 = 005) association with the root to shootratio (Table 6) This implies the root associated traits arepolygenic controlling many genes However root dry weightis associated with only one maker OPL1

1400(486)

37 Markers Linked to Morphophysiological Traits Besidesroot traits polygenic inheritance traits such as morpho-logical gravimetric and gas exchange at single leaf leveltraits were also deployed for marker trait association Singlemarker analysis revealed more than three markers linked toeach trait and 25 markers are colocalized with more thanone trait and 17 markers are associated with single trait(Table 7)ThemarkerOPP5

1800is coassociatedwith shoot dry

weight and leaf area duration (LAD) and these two traits arepositively correlated (1198772 = 093) Four markers (OPL1

1450

OPL191650

OPM171400

and OPK20350

) linked to NAR andtwo markers OPR4

600and OPL12

2000are significantly (119875 =

001) associated with cumulative water transpired (CWT)The 13ΔC is the surrogate method to quantify the water

use efficiency and it was demonstrated that there is inverse

Molecular Biology International 9

Table 6 RAPD markers linked to root traits in the mappingpopulation of C canephora (L1 valley times S3334)

Trait Marker 1198772 () 119875 lt 005

Root length

OPY201200 295 00449OPZ101350 356 00286OPK11780 441 00178OPS1850 686 00001

CPCM13400 512 00105

Number of secondary rootsOPO16450 44 00179OPK111000 551 00059OPV14550 582 00042

Root dry weight OPL11450 476 00073

Root to shoot ratio

OPK111000 326 0038OPR4600 33 00367OPP91030 35 00288OPM11600 375 00267OPL19600 411 00223OPL19900 561 00062CM211300 496 00105

relationship between these two traits Of the six associatedmarkers OPZ10

1350was found to be associatedwith 13ΔCand

WUE with phenotypic variance of 412 and 302 respec-tively This marker locus could be negative additive effectsince traits are negatively related The mesophyll efficiency(Cigs) intrinsic WUE (Ags) and photosynthetic rate (Pn)are respectively associated with CM13

400(483) CM171

210

(345) and CM171190

(372) (Table 7) Whereas positivelyrelated traits are associated with the same marker such asOPP5

1800(LAD and shoot dry weight) OPZ10

1030(MTR and

gs) and OPK20350

(Ci and NAR) alleles contribution fromS3334 The markers linked to other gas exchange parameterswere given in Table 7 which are contributed from S3334

4 Discussion

We developed a F1 mapping population by crossing contrast-ing root traits S3334 high root type as male and L1 valley lowroot type as female to identify makers linked to root traitsand water use efficient types However in annual crops F

2

recombinant inbred lines (RILs) near isogenic lines (NILs)and doubled haploids (DH)mapping population will be usedto associatemarkers with the trait of our interest However inperennials the germplasm lines and the cultivated accessionsare often highly heterozygous consequently F

1population

developed from heterozygous parents was used in this studyand showed considerable phenotypic and genetic variations

One of the objectives of studying themapping populationwas to score the variability in roots and other physiologicaltraits under drought and thus mapping population was gownup in 70 FC of water stress to study the heritability ofroot traits Indeed root traits are highly complex geneticmechanism controlled by multi genes and highly herita-ble in water non limiting environment [27] The resultsdemonstrated significant variability was observed in root

dry weight (RDW) total dry matter (TDM) root to shootratio (RSR) cumulativewater transpired (CWT)with consid-erable transgressive segregation (Figure 1) and quantitativeinheritance of drought tolerance [28] However significantpositive correlation between RDWTDM RDWCW andTDMCWT indicates that under drought increased rootgrowth maintains the shoot hydraulic conductance as adop-tive strategy [29 30] The decreased mean transpiration(MTR) and increased water use efficiency (WUE) could bedue to decreased stomatal aperture through chemical signalssuch as ABA [31] However some of the clones showedthat increased MTR with increased CWT would favor thestomatal conductance and net photosynthesis Such clonesare better for breeding drought tolerance Inverse relationshipbetween Δ13C and WUE in robusta coffee accessions wasobserved (Figure 2) thus measuring Δ13C which can bea surrogate method to identify best WUE types clones inbreeding [19 32] Breeding for such quantitative traits ishighly complex cumbersome and thus by determining theallele of aDNAmarker plants that possess particular genes orquantitative trait loci (QTLs) are better than their phenotypeWith this background effort wasmade to identify themarkerslinked to root and associated physiological traits in Coffeacanephora

Despite the efforts no successful linkage map could beobtained partly due to the inadequate number of polymor-phic markers generated from this study The output of theassociation analysis indicates that out of 29 traits only 24traits were linked to 85 markers Even markers linked totraits explaining the phenotypic variationwere notmore than10 In the present study phenotypic variation explanationfor all the traits ranges from 261 to 986 This suggeststhat the successful application of molecular markers in traitmapping greatly requires more polymorphic markers whilesuccessful mapping attempts could still be carried out in theabsence of a linkage map as done in chickpea [33] Althoughconstruction of linkage map was done in C arabica [34]and C canephora [35] predominantly using AFLP and RAPDmarkers association with the traits was not attempted Simi-larly the first molecular linkage map generated using pseudotestcross strategy for Coffea canephora (CxR times Kagnalla) hasthe largest number of mapped SSRs (71 genomic includingnine EST-SSRs) [36] but trait association was not correlated

Most of the markers explained phenotypic variancebetween 3 and 4 however only two markers that isOPZ11

1500andOPV14

500 were significantly (119875 lt 001) linked

to photosynthetic rate (Pn) accounting the variation of about923 and 930 respectively (Table 7) The root traits suchas root length number of secondary roots root dry weightand root shoot ratio revealed significant association (119875 lt005) with markers and the variability ranges from 295 to686 (Table 6)This suggests the trait variability explanationis not sufficient to answer the probabilities of occurrence ofthese markers due to limited polymorphic markers

In the present study codominant SSR markers werealso used for the marker-trait association study The SSRmarkers identified herein are significantly associated withmorphological and physiological traits but these markers

10 Molecular Biology International

Table 7 List of RAPD and SSR markers overlapped with more than one trait

Markers Traits 1198772 ()

(119875 lt 005) Markers Traits 1198772

(119875 lt 005)

OPK111000

Internodal length 474OPR4600OPR4750

CWT 508Root to shoot ratio 326 RSR 330

Number of secondary roots 551 Number of leaves 565Stem dry weight 344 gs 293

OPK181200Specific leaf area 470 OPV14550

Pn 930Plant height 451 Number of secondary Roots 582

OPK192000Photosynthetic rate (Pn) 555 OPY201200

WUE 310WUE 443 Plant height 423

K20350NAR 482

OPZ111500Number of leaves 399

WUE 445 Root length 295Ci 432 Pn 923

OPL11400

Plant height 577

OPZ101350OPZ101030

MTR 821Internodal length 381 13

ΔC 412NAR 427 Plant height 303

Shoot dry weight 400 Root length 356Root dry weight 476 WUE 302

Pn 261 Cigs 509

OPL122000Plant height 368 Pn 756

CWT 474 MTR 438

OPL12165013ΔC 361 OPZ10720

gs 296pn 307 gs 326

OPL181000Number of nodes 778 Pn 314

LAD 382 Internodal length 358

OPL19900SLW 333 CPCM13450

Root length 512RSR 360 Cigs 483

OPM11600Ags 385

CM171300CM166260

Stem girth 454RSR 411 Ags 345MTR 294 No of nodes 393

OPO101400Ci 313 SLA 719E 318 119864 418

OPP51800LAD 382

CM211300SLW 283

Internodal length 582 RSR 496Shoot dry weight 271 119864 423

OPP91030Cigs 612SLW 333RSR 350

were explaining the trait in the range between 283 and719 This study has provided more detailed information ofroot and associated physiological traits relationships underdrought and identification of markers linked to such traitswith the limited polymorphic markers by employing SingleMarker Analysis (SMA) QTL maps could be used to forlong-term drought breeding Further studies are needed toconfirm the estimation of QTL positions and effects and tovalidatemarkers prior to routinemarker assisted selection fordrought tolerance in coffee

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

References

[1] F M DaMatta ldquoExploring drought tolerance in coffee aphysiological approach with some insights for plant breedingrdquoBrazilian Journal of Plant Physiology vol 16 no 1 pp 1ndash6 2004

Molecular Biology International 11

[2] M Nesbitt The Cultural History of Plants Taylor amp Francis2005

[3] F M Damatta ldquoDrought as a multidimensional stress affectingphotosynthesis in tropical tree cropsrdquo in Advances in PlantPhysiology A Hemantaranjan Ed vol 5 pp 227ndash265 2003

[4] K Harovon Mogel ldquoGenotype times environment times managementinteractions key to beating future droughtsrdquo CSA News vol 58pp 4ndash9 2013

[5] F M DaMatta A R M Chaves H A Pinheiro C Ducatti andM E Loureiro ldquoDrought tolerance of two field-grown clones ofCoffea canephorardquo Plant Science vol 164 no 1 pp 111ndash117 2003

[6] H A Pinheiro F M DaMatta A R M Chaves M E Loureiroand C Ducatti ldquoDrought tolerance is associated with rootingdepth and stomatal control of water use in clones of Coffeacanephorardquo Annals of Botany vol 96 no 1 pp 101ndash108 2005

[7] J B Passioura ldquoPhenotyping for drought tolerance in graincrops when is it useful to breedersrdquo Functional Plant Biologyvol 39 no 11 pp 851ndash859 2012

[8] J S Sperry ldquoHydraulics of vascular water transportrdquo inMechan-ical Integration of Plant Cells and Plants P Wojtaszek Edvol 9 of Signaling and Communication in Plants pp 303ndash327Springer Berlin Germany 2011

[9] A P Wasson R A Richards R Chatrath et al ldquoTraits andselection strategies to improve root systems and water uptakein water-limited wheat cropsrdquo Journal of Experimental Botanyvol 63 no 9 pp 3485ndash3498 2012

[10] G D Herder G van Isterdael T Beeckman and I de SmetldquoThe roots of a new green revolutionrdquo Trends in Plant Sciencevol 15 no 11 pp 600ndash607 2010

[11] H T Nguyen R C Babu and A Blum ldquoBreeding for droughtresistance in rice physiology and molecular genetics consider-ationsrdquo Crop Science vol 37 no 5 pp 1426ndash1434 1997

[12] A Blum Plant Breeding for Water-Limited EnvironmentsSpringer Science+Business Media LLC New York NY USA2011

[13] L B Motta T C B Soares M A G Ferrao E T Caixeta R MLorenzoni and J D de SouzaNeto ldquoMolecular characterizationof arabica and Conilon coffee plants genotypes by SSR and ISSRmarkersrdquo Brazilian Archives of Biology and Technology vol 57no 5 pp 728ndash735 2014

[14] PMarraccini L P Freire G S CAlves et al ldquoRBCS1 expressionin coffee Coffea orthologs Coffea arabica homeologs andexpression variability between genotypes and under droughtstressrdquo BMC Plant Biology vol 11 article 85 2011

[15] P Marraccini F Vinecky G S C Alves et al ldquoDifferentiallyexpressed genes and proteins upon drought acclimation intolerant and sensitive genotypes of Coffea canephorardquo Journalof Experimental Botany vol 63 no 11 pp 4191ndash4212 2012

[16] F Denoeud L Carretero-Paulet A Dereeper et al ldquoThecoffee genome provides insight into the convergent evolution ofcaffeine biosynthesisrdquo Science vol 345 no 6201 pp 1181ndash11842014

[17] A Dereeper S Bocs M Rouard et al ldquoThe coffee genome huba resource for coffee genomesrdquo Nucleic Acids Research vol 43no 1 pp D1028ndashD1035 2015

[18] M Udayakumar M S Sheshshayee K N Nataraj et al ldquoWhyhas breeding for water use efficiency not been successful Ananalysis and alternate approach to exploit this trait for cropimprovementrdquo Current Science vol 74 no 11 pp 1000ndash10031998

[19] M G Awati Genetic diversity and molecular markers for rootand associated physiological traits of Robusta (coffea canephoraPierre ex F) accessions [PhD thesis] University of AgriculturalSciences Bangalore India 2005

[20] C E Williams and P C Ronald ldquoPCR template-DNA isolatedquickly from monocot and dicot leaves without tissue homog-enizationrdquo Nucleic Acids Research vol 22 no 10 pp 1917ndash19181994

[21] S D Tanksley ldquoMapping polygenesrdquoAnnual Review of Geneticsvol 27 no 1 pp 205ndash233 1993

[22] T Halward T Stalker E LaRue and G Kochert ldquoUse ofsingle-primer DNA amplifications in genetic studies of peanut(Arachis hypogaea L)rdquo Plant Molecular Biology vol 18 no 2pp 315ndash325 1992

[23] SAS Institute SASSTAT Userrsquos Guide Version 6 SAS InstituteCary NC USA 4th edition 1989

[24] M J Kearsey and A G L Farquhar ldquoQTL analysis in plantswhere are we nowrdquo Heredity vol 80 no 2 pp 137ndash142 1998

[25] M J Kearsey and V Hyne ldquoQTL analysis a simple lsquomarker-regressionrsquo approachrdquoTheoretical and Applied Genetics vol 89no 6 pp 698ndash702 1994

[26] C S Haley and S A Knott ldquoA simple regression method formapping quantitative trait loci in line crosses using flankingmarkersrdquo Heredity vol 69 no 4 pp 315ndash324 1992

[27] B M Atta T Mahmood and R M Trethowan ldquoRelationshipbetween root morphology and grain yield of wheat in North-Western NSW Australiardquo Australian Journal of Crop Sciencevol 7 no 13 pp 2108ndash2115 2013

[28] N T Lang C T Nha P T T Ha and B C Buu ldquoQuantitativetrait loci (QTLs) associated with drought tolerance in rice(Oryza sativa L)rdquo Sabrao Journal of Breeding and Genetics vol45 no 3 pp 409ndash421 2013

[29] H J Schenk andR B Jackson ldquoMapping the global distributionof deep roots in relation to climate and soil characteristicsrdquoGeoderma vol 126 no 1-2 pp 129ndash140 2005

[30] A Srividya L R Vemireddy P V Ramanarao et al ldquoMolecularmapping of QTLs for drought related traits at seedling stageunder PEG induced stress conditions in ricerdquoAmerican Journalof Plant Sciences vol 2 no 2 pp 190ndash201 2011

[31] N-N Shukor H A Hamid A Abdu andM-K Ismail ldquoEffectsof soil compaction on growth and physiological characteristicsofAzadirachta excelsa seedlingsrdquoTheAmerican Journal of PlantPhysiology vol 10 no 1 pp 25ndash42 2015

[32] P R Bhat Phenotyping and molecular analysis of Coffea arabicaL accessions and mapping population to identify RAPD markersassociated with root characteristics and associated physiologicaltraits [PhD thesis] University of Agricultural Sciences Banga-lore India 2002

[33] S Chandra H K Buhariwalla J Kashiwagi et al ldquoIdentifyingQTL linked markers in marker-29 deficient cropsrdquo in Proceed-ings of the 4th International Crop Science Congress BrisbaneAustralia 2004

[34] H M Pearl C Nagai P H Moore D L Steiger R V Osgoodand RMing ldquoConstruction of a genetic map for arabica coffeerdquoTheoretical and Applied Genetics vol 108 no 5 pp 829ndash8352004

[35] P Lashermes M C Combes N S Prakash P Trouslot MLorieux and A Charrier ldquoGenetic linkage map of Coffeacanephora effect of segregation distortion and analysis ofrecombination rate in male and female meiosesrdquo Genome vol44 no 4 pp 589ndash596 2001

[36] P Hendre S Lalji Singh and R K Aggarwal ldquoConstructionof framework molecular linkage map of Robusta coffee Coffeacanephora with RAPD AFLP g-SSRs And EST-SSRs usingpseudo-testcross strategyrdquo in Proceedings of the Plant amp AnimalGenomes 15th Conference January 2007

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

PeptidesInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

International Journal of

Volume 2014

Zoology

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Molecular Biology International

GenomicsInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioinformaticsAdvances in

Marine BiologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Signal TransductionJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

Evolutionary BiologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Genetics Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Virolog y

Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Enzyme Research

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Microbiology

8 Molecular Biology International

Table 4 Chi-square tests (1205942) for RAPD and SSR markers in the Coffea canephoramapping population

Parents F1progenies

Markersystem

Total polymor-phic markers

Marker loci notfollowing expected ratio

Marker loci followingexpected ratio

Number of significant markers119875 = 095 119875 = 005 119875 = 0001

L1 valley timesS3334 134 RAPD 70 28 42 (1 1) 32 3 7

SSR 9 9 0 (3 1) mdash mdash mdash

Table 5 Polymorphic microsatellite primers and their sequences used to study the segregation of markers in the mapping population

Serial number Primer Sequence Tm (∘C) Size range

1 CM11 F GCTGCCAGAAAAATGTTGCAGTG 58 270ndash338R CTGCCTCGTAAAAGCTTGCGTTG

2 CM13 F GCTATGCAGCTTGTTCGCAATCC 59 350ndash411R CCAGCTATATCAGGAGCAGAACC

3 CM180 F CATGTGTAATACATTCAACAGTGA 60 300ndash350R GCAATAGTGGTTGTCATCCTT

4 CM32 F AACTCTCCATTCCCGCATTC 60 180ndash200R CTGGGTTTTCTGTGTTCTCG

5 CM46 F CAGCTAGTGTGAAGGGAAAC 55 300ndash350R GTTATCATGGTCTTACACG

6 CM20 F CTTGTTTGAGTCTGTCGCTG 60 250ndash300R TTTCCCTCCCAATGTCTGTA

7 CM171 F TTCCCCCATCTTTTTCTTTC 60 250ndash300R TTGTATACGGCTCGTCAGGT

8 CM166 F AAGAGGTGCCTATCACCGTC 60 230ndash260R CGAGGTATCAAAAAGCACCT

9 CM211 F TCATGCCAAATATGAGTGGA 60 300ndash350R GAGATGGCAAAGGCTGTTC

35 Single Marker Analysis for Root and Associated Physiolog-ical Traits Complex physiological traits have been describedby a small number of major QTL [24] but it is intricateto find useful QTL for a particular trait as their individualcontribution is smaller Therefore under such conditionSingle Marker Analysis (SMA) is generally a good choicewhen the goal is simply a detection of marker locus linkedto a trait However estimation of its position and its effectsrequires further complex analysis such as marker regression[25] or interval analysis [26]

In our study Single Marker ANOVA is used to identifymarkers showing significant association with 29 morpho-physiological traits (SAS software with 119875 le 005)

In the single marker analysis simple regression modelwas examined to study the association between marker loci(independent) and trait score (dependent variable) and alsocomputed the percent phenotypic variance explained by eachmarker Seventy polymorphic marker loci were employed forsimple regression analysis however it was discovered that 37RAPD and 5 SSR markers explained their association withmorphophysiological traits

36Markers Linked to Root Traits The singlemarker analysisrevealed fourmarkers such as OPS1

850 OPK11

780 OPY20

1200

OPZ101350

and CM13400

were significantly associated withroot length (119875 = 005) and explained phenotypic vari-ance of 441 295 356 512 and 512 respectively

A number of secondary roots linked to three markers suchas OPO16

450(440) OPK11

1000(551) and OPV14

550

(582) were significantly (119875 = 005) associated Similarlysix markers such as OPK11

1000(326) OPP9

1030(35)

OPM11600

(375) OPL19900

(561) OPL19600

(411)OPR4

600(330) and CM211

300(1198772 = 496) revealed

significant (119875 = 005) association with the root to shootratio (Table 6) This implies the root associated traits arepolygenic controlling many genes However root dry weightis associated with only one maker OPL1

1400(486)

37 Markers Linked to Morphophysiological Traits Besidesroot traits polygenic inheritance traits such as morpho-logical gravimetric and gas exchange at single leaf leveltraits were also deployed for marker trait association Singlemarker analysis revealed more than three markers linked toeach trait and 25 markers are colocalized with more thanone trait and 17 markers are associated with single trait(Table 7)ThemarkerOPP5

1800is coassociatedwith shoot dry

weight and leaf area duration (LAD) and these two traits arepositively correlated (1198772 = 093) Four markers (OPL1

1450

OPL191650

OPM171400

and OPK20350

) linked to NAR andtwo markers OPR4

600and OPL12

2000are significantly (119875 =

001) associated with cumulative water transpired (CWT)The 13ΔC is the surrogate method to quantify the water

use efficiency and it was demonstrated that there is inverse

Molecular Biology International 9

Table 6 RAPD markers linked to root traits in the mappingpopulation of C canephora (L1 valley times S3334)

Trait Marker 1198772 () 119875 lt 005

Root length

OPY201200 295 00449OPZ101350 356 00286OPK11780 441 00178OPS1850 686 00001

CPCM13400 512 00105

Number of secondary rootsOPO16450 44 00179OPK111000 551 00059OPV14550 582 00042

Root dry weight OPL11450 476 00073

Root to shoot ratio

OPK111000 326 0038OPR4600 33 00367OPP91030 35 00288OPM11600 375 00267OPL19600 411 00223OPL19900 561 00062CM211300 496 00105

relationship between these two traits Of the six associatedmarkers OPZ10

1350was found to be associatedwith 13ΔCand

WUE with phenotypic variance of 412 and 302 respec-tively This marker locus could be negative additive effectsince traits are negatively related The mesophyll efficiency(Cigs) intrinsic WUE (Ags) and photosynthetic rate (Pn)are respectively associated with CM13

400(483) CM171

210

(345) and CM171190

(372) (Table 7) Whereas positivelyrelated traits are associated with the same marker such asOPP5

1800(LAD and shoot dry weight) OPZ10

1030(MTR and

gs) and OPK20350

(Ci and NAR) alleles contribution fromS3334 The markers linked to other gas exchange parameterswere given in Table 7 which are contributed from S3334

4 Discussion

We developed a F1 mapping population by crossing contrast-ing root traits S3334 high root type as male and L1 valley lowroot type as female to identify makers linked to root traitsand water use efficient types However in annual crops F

2

recombinant inbred lines (RILs) near isogenic lines (NILs)and doubled haploids (DH)mapping population will be usedto associatemarkers with the trait of our interest However inperennials the germplasm lines and the cultivated accessionsare often highly heterozygous consequently F

1population

developed from heterozygous parents was used in this studyand showed considerable phenotypic and genetic variations

One of the objectives of studying themapping populationwas to score the variability in roots and other physiologicaltraits under drought and thus mapping population was gownup in 70 FC of water stress to study the heritability ofroot traits Indeed root traits are highly complex geneticmechanism controlled by multi genes and highly herita-ble in water non limiting environment [27] The resultsdemonstrated significant variability was observed in root

dry weight (RDW) total dry matter (TDM) root to shootratio (RSR) cumulativewater transpired (CWT)with consid-erable transgressive segregation (Figure 1) and quantitativeinheritance of drought tolerance [28] However significantpositive correlation between RDWTDM RDWCW andTDMCWT indicates that under drought increased rootgrowth maintains the shoot hydraulic conductance as adop-tive strategy [29 30] The decreased mean transpiration(MTR) and increased water use efficiency (WUE) could bedue to decreased stomatal aperture through chemical signalssuch as ABA [31] However some of the clones showedthat increased MTR with increased CWT would favor thestomatal conductance and net photosynthesis Such clonesare better for breeding drought tolerance Inverse relationshipbetween Δ13C and WUE in robusta coffee accessions wasobserved (Figure 2) thus measuring Δ13C which can bea surrogate method to identify best WUE types clones inbreeding [19 32] Breeding for such quantitative traits ishighly complex cumbersome and thus by determining theallele of aDNAmarker plants that possess particular genes orquantitative trait loci (QTLs) are better than their phenotypeWith this background effort wasmade to identify themarkerslinked to root and associated physiological traits in Coffeacanephora

Despite the efforts no successful linkage map could beobtained partly due to the inadequate number of polymor-phic markers generated from this study The output of theassociation analysis indicates that out of 29 traits only 24traits were linked to 85 markers Even markers linked totraits explaining the phenotypic variationwere notmore than10 In the present study phenotypic variation explanationfor all the traits ranges from 261 to 986 This suggeststhat the successful application of molecular markers in traitmapping greatly requires more polymorphic markers whilesuccessful mapping attempts could still be carried out in theabsence of a linkage map as done in chickpea [33] Althoughconstruction of linkage map was done in C arabica [34]and C canephora [35] predominantly using AFLP and RAPDmarkers association with the traits was not attempted Simi-larly the first molecular linkage map generated using pseudotestcross strategy for Coffea canephora (CxR times Kagnalla) hasthe largest number of mapped SSRs (71 genomic includingnine EST-SSRs) [36] but trait association was not correlated

Most of the markers explained phenotypic variancebetween 3 and 4 however only two markers that isOPZ11

1500andOPV14

500 were significantly (119875 lt 001) linked

to photosynthetic rate (Pn) accounting the variation of about923 and 930 respectively (Table 7) The root traits suchas root length number of secondary roots root dry weightand root shoot ratio revealed significant association (119875 lt005) with markers and the variability ranges from 295 to686 (Table 6)This suggests the trait variability explanationis not sufficient to answer the probabilities of occurrence ofthese markers due to limited polymorphic markers

In the present study codominant SSR markers werealso used for the marker-trait association study The SSRmarkers identified herein are significantly associated withmorphological and physiological traits but these markers

10 Molecular Biology International

Table 7 List of RAPD and SSR markers overlapped with more than one trait

Markers Traits 1198772 ()

(119875 lt 005) Markers Traits 1198772

(119875 lt 005)

OPK111000

Internodal length 474OPR4600OPR4750

CWT 508Root to shoot ratio 326 RSR 330

Number of secondary roots 551 Number of leaves 565Stem dry weight 344 gs 293

OPK181200Specific leaf area 470 OPV14550

Pn 930Plant height 451 Number of secondary Roots 582

OPK192000Photosynthetic rate (Pn) 555 OPY201200

WUE 310WUE 443 Plant height 423

K20350NAR 482

OPZ111500Number of leaves 399

WUE 445 Root length 295Ci 432 Pn 923

OPL11400

Plant height 577

OPZ101350OPZ101030

MTR 821Internodal length 381 13

ΔC 412NAR 427 Plant height 303

Shoot dry weight 400 Root length 356Root dry weight 476 WUE 302

Pn 261 Cigs 509

OPL122000Plant height 368 Pn 756

CWT 474 MTR 438

OPL12165013ΔC 361 OPZ10720

gs 296pn 307 gs 326

OPL181000Number of nodes 778 Pn 314

LAD 382 Internodal length 358

OPL19900SLW 333 CPCM13450

Root length 512RSR 360 Cigs 483

OPM11600Ags 385

CM171300CM166260

Stem girth 454RSR 411 Ags 345MTR 294 No of nodes 393

OPO101400Ci 313 SLA 719E 318 119864 418

OPP51800LAD 382

CM211300SLW 283

Internodal length 582 RSR 496Shoot dry weight 271 119864 423

OPP91030Cigs 612SLW 333RSR 350

were explaining the trait in the range between 283 and719 This study has provided more detailed information ofroot and associated physiological traits relationships underdrought and identification of markers linked to such traitswith the limited polymorphic markers by employing SingleMarker Analysis (SMA) QTL maps could be used to forlong-term drought breeding Further studies are needed toconfirm the estimation of QTL positions and effects and tovalidatemarkers prior to routinemarker assisted selection fordrought tolerance in coffee

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

References

[1] F M DaMatta ldquoExploring drought tolerance in coffee aphysiological approach with some insights for plant breedingrdquoBrazilian Journal of Plant Physiology vol 16 no 1 pp 1ndash6 2004

Molecular Biology International 11

[2] M Nesbitt The Cultural History of Plants Taylor amp Francis2005

[3] F M Damatta ldquoDrought as a multidimensional stress affectingphotosynthesis in tropical tree cropsrdquo in Advances in PlantPhysiology A Hemantaranjan Ed vol 5 pp 227ndash265 2003

[4] K Harovon Mogel ldquoGenotype times environment times managementinteractions key to beating future droughtsrdquo CSA News vol 58pp 4ndash9 2013

[5] F M DaMatta A R M Chaves H A Pinheiro C Ducatti andM E Loureiro ldquoDrought tolerance of two field-grown clones ofCoffea canephorardquo Plant Science vol 164 no 1 pp 111ndash117 2003

[6] H A Pinheiro F M DaMatta A R M Chaves M E Loureiroand C Ducatti ldquoDrought tolerance is associated with rootingdepth and stomatal control of water use in clones of Coffeacanephorardquo Annals of Botany vol 96 no 1 pp 101ndash108 2005

[7] J B Passioura ldquoPhenotyping for drought tolerance in graincrops when is it useful to breedersrdquo Functional Plant Biologyvol 39 no 11 pp 851ndash859 2012

[8] J S Sperry ldquoHydraulics of vascular water transportrdquo inMechan-ical Integration of Plant Cells and Plants P Wojtaszek Edvol 9 of Signaling and Communication in Plants pp 303ndash327Springer Berlin Germany 2011

[9] A P Wasson R A Richards R Chatrath et al ldquoTraits andselection strategies to improve root systems and water uptakein water-limited wheat cropsrdquo Journal of Experimental Botanyvol 63 no 9 pp 3485ndash3498 2012

[10] G D Herder G van Isterdael T Beeckman and I de SmetldquoThe roots of a new green revolutionrdquo Trends in Plant Sciencevol 15 no 11 pp 600ndash607 2010

[11] H T Nguyen R C Babu and A Blum ldquoBreeding for droughtresistance in rice physiology and molecular genetics consider-ationsrdquo Crop Science vol 37 no 5 pp 1426ndash1434 1997

[12] A Blum Plant Breeding for Water-Limited EnvironmentsSpringer Science+Business Media LLC New York NY USA2011

[13] L B Motta T C B Soares M A G Ferrao E T Caixeta R MLorenzoni and J D de SouzaNeto ldquoMolecular characterizationof arabica and Conilon coffee plants genotypes by SSR and ISSRmarkersrdquo Brazilian Archives of Biology and Technology vol 57no 5 pp 728ndash735 2014

[14] PMarraccini L P Freire G S CAlves et al ldquoRBCS1 expressionin coffee Coffea orthologs Coffea arabica homeologs andexpression variability between genotypes and under droughtstressrdquo BMC Plant Biology vol 11 article 85 2011

[15] P Marraccini F Vinecky G S C Alves et al ldquoDifferentiallyexpressed genes and proteins upon drought acclimation intolerant and sensitive genotypes of Coffea canephorardquo Journalof Experimental Botany vol 63 no 11 pp 4191ndash4212 2012

[16] F Denoeud L Carretero-Paulet A Dereeper et al ldquoThecoffee genome provides insight into the convergent evolution ofcaffeine biosynthesisrdquo Science vol 345 no 6201 pp 1181ndash11842014

[17] A Dereeper S Bocs M Rouard et al ldquoThe coffee genome huba resource for coffee genomesrdquo Nucleic Acids Research vol 43no 1 pp D1028ndashD1035 2015

[18] M Udayakumar M S Sheshshayee K N Nataraj et al ldquoWhyhas breeding for water use efficiency not been successful Ananalysis and alternate approach to exploit this trait for cropimprovementrdquo Current Science vol 74 no 11 pp 1000ndash10031998

[19] M G Awati Genetic diversity and molecular markers for rootand associated physiological traits of Robusta (coffea canephoraPierre ex F) accessions [PhD thesis] University of AgriculturalSciences Bangalore India 2005

[20] C E Williams and P C Ronald ldquoPCR template-DNA isolatedquickly from monocot and dicot leaves without tissue homog-enizationrdquo Nucleic Acids Research vol 22 no 10 pp 1917ndash19181994

[21] S D Tanksley ldquoMapping polygenesrdquoAnnual Review of Geneticsvol 27 no 1 pp 205ndash233 1993

[22] T Halward T Stalker E LaRue and G Kochert ldquoUse ofsingle-primer DNA amplifications in genetic studies of peanut(Arachis hypogaea L)rdquo Plant Molecular Biology vol 18 no 2pp 315ndash325 1992

[23] SAS Institute SASSTAT Userrsquos Guide Version 6 SAS InstituteCary NC USA 4th edition 1989

[24] M J Kearsey and A G L Farquhar ldquoQTL analysis in plantswhere are we nowrdquo Heredity vol 80 no 2 pp 137ndash142 1998

[25] M J Kearsey and V Hyne ldquoQTL analysis a simple lsquomarker-regressionrsquo approachrdquoTheoretical and Applied Genetics vol 89no 6 pp 698ndash702 1994

[26] C S Haley and S A Knott ldquoA simple regression method formapping quantitative trait loci in line crosses using flankingmarkersrdquo Heredity vol 69 no 4 pp 315ndash324 1992

[27] B M Atta T Mahmood and R M Trethowan ldquoRelationshipbetween root morphology and grain yield of wheat in North-Western NSW Australiardquo Australian Journal of Crop Sciencevol 7 no 13 pp 2108ndash2115 2013

[28] N T Lang C T Nha P T T Ha and B C Buu ldquoQuantitativetrait loci (QTLs) associated with drought tolerance in rice(Oryza sativa L)rdquo Sabrao Journal of Breeding and Genetics vol45 no 3 pp 409ndash421 2013

[29] H J Schenk andR B Jackson ldquoMapping the global distributionof deep roots in relation to climate and soil characteristicsrdquoGeoderma vol 126 no 1-2 pp 129ndash140 2005

[30] A Srividya L R Vemireddy P V Ramanarao et al ldquoMolecularmapping of QTLs for drought related traits at seedling stageunder PEG induced stress conditions in ricerdquoAmerican Journalof Plant Sciences vol 2 no 2 pp 190ndash201 2011

[31] N-N Shukor H A Hamid A Abdu andM-K Ismail ldquoEffectsof soil compaction on growth and physiological characteristicsofAzadirachta excelsa seedlingsrdquoTheAmerican Journal of PlantPhysiology vol 10 no 1 pp 25ndash42 2015

[32] P R Bhat Phenotyping and molecular analysis of Coffea arabicaL accessions and mapping population to identify RAPD markersassociated with root characteristics and associated physiologicaltraits [PhD thesis] University of Agricultural Sciences Banga-lore India 2002

[33] S Chandra H K Buhariwalla J Kashiwagi et al ldquoIdentifyingQTL linked markers in marker-29 deficient cropsrdquo in Proceed-ings of the 4th International Crop Science Congress BrisbaneAustralia 2004

[34] H M Pearl C Nagai P H Moore D L Steiger R V Osgoodand RMing ldquoConstruction of a genetic map for arabica coffeerdquoTheoretical and Applied Genetics vol 108 no 5 pp 829ndash8352004

[35] P Lashermes M C Combes N S Prakash P Trouslot MLorieux and A Charrier ldquoGenetic linkage map of Coffeacanephora effect of segregation distortion and analysis ofrecombination rate in male and female meiosesrdquo Genome vol44 no 4 pp 589ndash596 2001

[36] P Hendre S Lalji Singh and R K Aggarwal ldquoConstructionof framework molecular linkage map of Robusta coffee Coffeacanephora with RAPD AFLP g-SSRs And EST-SSRs usingpseudo-testcross strategyrdquo in Proceedings of the Plant amp AnimalGenomes 15th Conference January 2007

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

PeptidesInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

International Journal of

Volume 2014

Zoology

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Molecular Biology International

GenomicsInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioinformaticsAdvances in

Marine BiologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Signal TransductionJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

Evolutionary BiologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Genetics Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Virolog y

Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Enzyme Research

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Microbiology

Molecular Biology International 9

Table 6 RAPD markers linked to root traits in the mappingpopulation of C canephora (L1 valley times S3334)

Trait Marker 1198772 () 119875 lt 005

Root length

OPY201200 295 00449OPZ101350 356 00286OPK11780 441 00178OPS1850 686 00001

CPCM13400 512 00105

Number of secondary rootsOPO16450 44 00179OPK111000 551 00059OPV14550 582 00042

Root dry weight OPL11450 476 00073

Root to shoot ratio

OPK111000 326 0038OPR4600 33 00367OPP91030 35 00288OPM11600 375 00267OPL19600 411 00223OPL19900 561 00062CM211300 496 00105

relationship between these two traits Of the six associatedmarkers OPZ10

1350was found to be associatedwith 13ΔCand

WUE with phenotypic variance of 412 and 302 respec-tively This marker locus could be negative additive effectsince traits are negatively related The mesophyll efficiency(Cigs) intrinsic WUE (Ags) and photosynthetic rate (Pn)are respectively associated with CM13

400(483) CM171

210

(345) and CM171190

(372) (Table 7) Whereas positivelyrelated traits are associated with the same marker such asOPP5

1800(LAD and shoot dry weight) OPZ10

1030(MTR and

gs) and OPK20350

(Ci and NAR) alleles contribution fromS3334 The markers linked to other gas exchange parameterswere given in Table 7 which are contributed from S3334

4 Discussion

We developed a F1 mapping population by crossing contrast-ing root traits S3334 high root type as male and L1 valley lowroot type as female to identify makers linked to root traitsand water use efficient types However in annual crops F

2

recombinant inbred lines (RILs) near isogenic lines (NILs)and doubled haploids (DH)mapping population will be usedto associatemarkers with the trait of our interest However inperennials the germplasm lines and the cultivated accessionsare often highly heterozygous consequently F

1population

developed from heterozygous parents was used in this studyand showed considerable phenotypic and genetic variations

One of the objectives of studying themapping populationwas to score the variability in roots and other physiologicaltraits under drought and thus mapping population was gownup in 70 FC of water stress to study the heritability ofroot traits Indeed root traits are highly complex geneticmechanism controlled by multi genes and highly herita-ble in water non limiting environment [27] The resultsdemonstrated significant variability was observed in root

dry weight (RDW) total dry matter (TDM) root to shootratio (RSR) cumulativewater transpired (CWT)with consid-erable transgressive segregation (Figure 1) and quantitativeinheritance of drought tolerance [28] However significantpositive correlation between RDWTDM RDWCW andTDMCWT indicates that under drought increased rootgrowth maintains the shoot hydraulic conductance as adop-tive strategy [29 30] The decreased mean transpiration(MTR) and increased water use efficiency (WUE) could bedue to decreased stomatal aperture through chemical signalssuch as ABA [31] However some of the clones showedthat increased MTR with increased CWT would favor thestomatal conductance and net photosynthesis Such clonesare better for breeding drought tolerance Inverse relationshipbetween Δ13C and WUE in robusta coffee accessions wasobserved (Figure 2) thus measuring Δ13C which can bea surrogate method to identify best WUE types clones inbreeding [19 32] Breeding for such quantitative traits ishighly complex cumbersome and thus by determining theallele of aDNAmarker plants that possess particular genes orquantitative trait loci (QTLs) are better than their phenotypeWith this background effort wasmade to identify themarkerslinked to root and associated physiological traits in Coffeacanephora

Despite the efforts no successful linkage map could beobtained partly due to the inadequate number of polymor-phic markers generated from this study The output of theassociation analysis indicates that out of 29 traits only 24traits were linked to 85 markers Even markers linked totraits explaining the phenotypic variationwere notmore than10 In the present study phenotypic variation explanationfor all the traits ranges from 261 to 986 This suggeststhat the successful application of molecular markers in traitmapping greatly requires more polymorphic markers whilesuccessful mapping attempts could still be carried out in theabsence of a linkage map as done in chickpea [33] Althoughconstruction of linkage map was done in C arabica [34]and C canephora [35] predominantly using AFLP and RAPDmarkers association with the traits was not attempted Simi-larly the first molecular linkage map generated using pseudotestcross strategy for Coffea canephora (CxR times Kagnalla) hasthe largest number of mapped SSRs (71 genomic includingnine EST-SSRs) [36] but trait association was not correlated

Most of the markers explained phenotypic variancebetween 3 and 4 however only two markers that isOPZ11

1500andOPV14

500 were significantly (119875 lt 001) linked

to photosynthetic rate (Pn) accounting the variation of about923 and 930 respectively (Table 7) The root traits suchas root length number of secondary roots root dry weightand root shoot ratio revealed significant association (119875 lt005) with markers and the variability ranges from 295 to686 (Table 6)This suggests the trait variability explanationis not sufficient to answer the probabilities of occurrence ofthese markers due to limited polymorphic markers

In the present study codominant SSR markers werealso used for the marker-trait association study The SSRmarkers identified herein are significantly associated withmorphological and physiological traits but these markers

10 Molecular Biology International

Table 7 List of RAPD and SSR markers overlapped with more than one trait

Markers Traits 1198772 ()

(119875 lt 005) Markers Traits 1198772

(119875 lt 005)

OPK111000

Internodal length 474OPR4600OPR4750

CWT 508Root to shoot ratio 326 RSR 330

Number of secondary roots 551 Number of leaves 565Stem dry weight 344 gs 293

OPK181200Specific leaf area 470 OPV14550

Pn 930Plant height 451 Number of secondary Roots 582

OPK192000Photosynthetic rate (Pn) 555 OPY201200

WUE 310WUE 443 Plant height 423

K20350NAR 482

OPZ111500Number of leaves 399

WUE 445 Root length 295Ci 432 Pn 923

OPL11400

Plant height 577

OPZ101350OPZ101030

MTR 821Internodal length 381 13

ΔC 412NAR 427 Plant height 303

Shoot dry weight 400 Root length 356Root dry weight 476 WUE 302

Pn 261 Cigs 509

OPL122000Plant height 368 Pn 756

CWT 474 MTR 438

OPL12165013ΔC 361 OPZ10720

gs 296pn 307 gs 326

OPL181000Number of nodes 778 Pn 314

LAD 382 Internodal length 358

OPL19900SLW 333 CPCM13450

Root length 512RSR 360 Cigs 483

OPM11600Ags 385

CM171300CM166260

Stem girth 454RSR 411 Ags 345MTR 294 No of nodes 393

OPO101400Ci 313 SLA 719E 318 119864 418

OPP51800LAD 382

CM211300SLW 283

Internodal length 582 RSR 496Shoot dry weight 271 119864 423

OPP91030Cigs 612SLW 333RSR 350

were explaining the trait in the range between 283 and719 This study has provided more detailed information ofroot and associated physiological traits relationships underdrought and identification of markers linked to such traitswith the limited polymorphic markers by employing SingleMarker Analysis (SMA) QTL maps could be used to forlong-term drought breeding Further studies are needed toconfirm the estimation of QTL positions and effects and tovalidatemarkers prior to routinemarker assisted selection fordrought tolerance in coffee

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

References

[1] F M DaMatta ldquoExploring drought tolerance in coffee aphysiological approach with some insights for plant breedingrdquoBrazilian Journal of Plant Physiology vol 16 no 1 pp 1ndash6 2004

Molecular Biology International 11

[2] M Nesbitt The Cultural History of Plants Taylor amp Francis2005

[3] F M Damatta ldquoDrought as a multidimensional stress affectingphotosynthesis in tropical tree cropsrdquo in Advances in PlantPhysiology A Hemantaranjan Ed vol 5 pp 227ndash265 2003

[4] K Harovon Mogel ldquoGenotype times environment times managementinteractions key to beating future droughtsrdquo CSA News vol 58pp 4ndash9 2013

[5] F M DaMatta A R M Chaves H A Pinheiro C Ducatti andM E Loureiro ldquoDrought tolerance of two field-grown clones ofCoffea canephorardquo Plant Science vol 164 no 1 pp 111ndash117 2003

[6] H A Pinheiro F M DaMatta A R M Chaves M E Loureiroand C Ducatti ldquoDrought tolerance is associated with rootingdepth and stomatal control of water use in clones of Coffeacanephorardquo Annals of Botany vol 96 no 1 pp 101ndash108 2005

[7] J B Passioura ldquoPhenotyping for drought tolerance in graincrops when is it useful to breedersrdquo Functional Plant Biologyvol 39 no 11 pp 851ndash859 2012

[8] J S Sperry ldquoHydraulics of vascular water transportrdquo inMechan-ical Integration of Plant Cells and Plants P Wojtaszek Edvol 9 of Signaling and Communication in Plants pp 303ndash327Springer Berlin Germany 2011

[9] A P Wasson R A Richards R Chatrath et al ldquoTraits andselection strategies to improve root systems and water uptakein water-limited wheat cropsrdquo Journal of Experimental Botanyvol 63 no 9 pp 3485ndash3498 2012

[10] G D Herder G van Isterdael T Beeckman and I de SmetldquoThe roots of a new green revolutionrdquo Trends in Plant Sciencevol 15 no 11 pp 600ndash607 2010

[11] H T Nguyen R C Babu and A Blum ldquoBreeding for droughtresistance in rice physiology and molecular genetics consider-ationsrdquo Crop Science vol 37 no 5 pp 1426ndash1434 1997

[12] A Blum Plant Breeding for Water-Limited EnvironmentsSpringer Science+Business Media LLC New York NY USA2011

[13] L B Motta T C B Soares M A G Ferrao E T Caixeta R MLorenzoni and J D de SouzaNeto ldquoMolecular characterizationof arabica and Conilon coffee plants genotypes by SSR and ISSRmarkersrdquo Brazilian Archives of Biology and Technology vol 57no 5 pp 728ndash735 2014

[14] PMarraccini L P Freire G S CAlves et al ldquoRBCS1 expressionin coffee Coffea orthologs Coffea arabica homeologs andexpression variability between genotypes and under droughtstressrdquo BMC Plant Biology vol 11 article 85 2011

[15] P Marraccini F Vinecky G S C Alves et al ldquoDifferentiallyexpressed genes and proteins upon drought acclimation intolerant and sensitive genotypes of Coffea canephorardquo Journalof Experimental Botany vol 63 no 11 pp 4191ndash4212 2012

[16] F Denoeud L Carretero-Paulet A Dereeper et al ldquoThecoffee genome provides insight into the convergent evolution ofcaffeine biosynthesisrdquo Science vol 345 no 6201 pp 1181ndash11842014

[17] A Dereeper S Bocs M Rouard et al ldquoThe coffee genome huba resource for coffee genomesrdquo Nucleic Acids Research vol 43no 1 pp D1028ndashD1035 2015

[18] M Udayakumar M S Sheshshayee K N Nataraj et al ldquoWhyhas breeding for water use efficiency not been successful Ananalysis and alternate approach to exploit this trait for cropimprovementrdquo Current Science vol 74 no 11 pp 1000ndash10031998

[19] M G Awati Genetic diversity and molecular markers for rootand associated physiological traits of Robusta (coffea canephoraPierre ex F) accessions [PhD thesis] University of AgriculturalSciences Bangalore India 2005

[20] C E Williams and P C Ronald ldquoPCR template-DNA isolatedquickly from monocot and dicot leaves without tissue homog-enizationrdquo Nucleic Acids Research vol 22 no 10 pp 1917ndash19181994

[21] S D Tanksley ldquoMapping polygenesrdquoAnnual Review of Geneticsvol 27 no 1 pp 205ndash233 1993

[22] T Halward T Stalker E LaRue and G Kochert ldquoUse ofsingle-primer DNA amplifications in genetic studies of peanut(Arachis hypogaea L)rdquo Plant Molecular Biology vol 18 no 2pp 315ndash325 1992

[23] SAS Institute SASSTAT Userrsquos Guide Version 6 SAS InstituteCary NC USA 4th edition 1989

[24] M J Kearsey and A G L Farquhar ldquoQTL analysis in plantswhere are we nowrdquo Heredity vol 80 no 2 pp 137ndash142 1998

[25] M J Kearsey and V Hyne ldquoQTL analysis a simple lsquomarker-regressionrsquo approachrdquoTheoretical and Applied Genetics vol 89no 6 pp 698ndash702 1994

[26] C S Haley and S A Knott ldquoA simple regression method formapping quantitative trait loci in line crosses using flankingmarkersrdquo Heredity vol 69 no 4 pp 315ndash324 1992

[27] B M Atta T Mahmood and R M Trethowan ldquoRelationshipbetween root morphology and grain yield of wheat in North-Western NSW Australiardquo Australian Journal of Crop Sciencevol 7 no 13 pp 2108ndash2115 2013

[28] N T Lang C T Nha P T T Ha and B C Buu ldquoQuantitativetrait loci (QTLs) associated with drought tolerance in rice(Oryza sativa L)rdquo Sabrao Journal of Breeding and Genetics vol45 no 3 pp 409ndash421 2013

[29] H J Schenk andR B Jackson ldquoMapping the global distributionof deep roots in relation to climate and soil characteristicsrdquoGeoderma vol 126 no 1-2 pp 129ndash140 2005

[30] A Srividya L R Vemireddy P V Ramanarao et al ldquoMolecularmapping of QTLs for drought related traits at seedling stageunder PEG induced stress conditions in ricerdquoAmerican Journalof Plant Sciences vol 2 no 2 pp 190ndash201 2011

[31] N-N Shukor H A Hamid A Abdu andM-K Ismail ldquoEffectsof soil compaction on growth and physiological characteristicsofAzadirachta excelsa seedlingsrdquoTheAmerican Journal of PlantPhysiology vol 10 no 1 pp 25ndash42 2015

[32] P R Bhat Phenotyping and molecular analysis of Coffea arabicaL accessions and mapping population to identify RAPD markersassociated with root characteristics and associated physiologicaltraits [PhD thesis] University of Agricultural Sciences Banga-lore India 2002

[33] S Chandra H K Buhariwalla J Kashiwagi et al ldquoIdentifyingQTL linked markers in marker-29 deficient cropsrdquo in Proceed-ings of the 4th International Crop Science Congress BrisbaneAustralia 2004

[34] H M Pearl C Nagai P H Moore D L Steiger R V Osgoodand RMing ldquoConstruction of a genetic map for arabica coffeerdquoTheoretical and Applied Genetics vol 108 no 5 pp 829ndash8352004

[35] P Lashermes M C Combes N S Prakash P Trouslot MLorieux and A Charrier ldquoGenetic linkage map of Coffeacanephora effect of segregation distortion and analysis ofrecombination rate in male and female meiosesrdquo Genome vol44 no 4 pp 589ndash596 2001

[36] P Hendre S Lalji Singh and R K Aggarwal ldquoConstructionof framework molecular linkage map of Robusta coffee Coffeacanephora with RAPD AFLP g-SSRs And EST-SSRs usingpseudo-testcross strategyrdquo in Proceedings of the Plant amp AnimalGenomes 15th Conference January 2007

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

PeptidesInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

International Journal of

Volume 2014

Zoology

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Molecular Biology International

GenomicsInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioinformaticsAdvances in

Marine BiologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Signal TransductionJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

Evolutionary BiologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Genetics Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Virolog y

Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Enzyme Research

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Microbiology

10 Molecular Biology International

Table 7 List of RAPD and SSR markers overlapped with more than one trait

Markers Traits 1198772 ()

(119875 lt 005) Markers Traits 1198772

(119875 lt 005)

OPK111000

Internodal length 474OPR4600OPR4750

CWT 508Root to shoot ratio 326 RSR 330

Number of secondary roots 551 Number of leaves 565Stem dry weight 344 gs 293

OPK181200Specific leaf area 470 OPV14550

Pn 930Plant height 451 Number of secondary Roots 582

OPK192000Photosynthetic rate (Pn) 555 OPY201200

WUE 310WUE 443 Plant height 423

K20350NAR 482

OPZ111500Number of leaves 399

WUE 445 Root length 295Ci 432 Pn 923

OPL11400

Plant height 577

OPZ101350OPZ101030

MTR 821Internodal length 381 13

ΔC 412NAR 427 Plant height 303

Shoot dry weight 400 Root length 356Root dry weight 476 WUE 302

Pn 261 Cigs 509

OPL122000Plant height 368 Pn 756

CWT 474 MTR 438

OPL12165013ΔC 361 OPZ10720

gs 296pn 307 gs 326

OPL181000Number of nodes 778 Pn 314

LAD 382 Internodal length 358

OPL19900SLW 333 CPCM13450

Root length 512RSR 360 Cigs 483

OPM11600Ags 385

CM171300CM166260

Stem girth 454RSR 411 Ags 345MTR 294 No of nodes 393

OPO101400Ci 313 SLA 719E 318 119864 418

OPP51800LAD 382

CM211300SLW 283

Internodal length 582 RSR 496Shoot dry weight 271 119864 423

OPP91030Cigs 612SLW 333RSR 350

were explaining the trait in the range between 283 and719 This study has provided more detailed information ofroot and associated physiological traits relationships underdrought and identification of markers linked to such traitswith the limited polymorphic markers by employing SingleMarker Analysis (SMA) QTL maps could be used to forlong-term drought breeding Further studies are needed toconfirm the estimation of QTL positions and effects and tovalidatemarkers prior to routinemarker assisted selection fordrought tolerance in coffee

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

References

[1] F M DaMatta ldquoExploring drought tolerance in coffee aphysiological approach with some insights for plant breedingrdquoBrazilian Journal of Plant Physiology vol 16 no 1 pp 1ndash6 2004

Molecular Biology International 11

[2] M Nesbitt The Cultural History of Plants Taylor amp Francis2005

[3] F M Damatta ldquoDrought as a multidimensional stress affectingphotosynthesis in tropical tree cropsrdquo in Advances in PlantPhysiology A Hemantaranjan Ed vol 5 pp 227ndash265 2003

[4] K Harovon Mogel ldquoGenotype times environment times managementinteractions key to beating future droughtsrdquo CSA News vol 58pp 4ndash9 2013

[5] F M DaMatta A R M Chaves H A Pinheiro C Ducatti andM E Loureiro ldquoDrought tolerance of two field-grown clones ofCoffea canephorardquo Plant Science vol 164 no 1 pp 111ndash117 2003

[6] H A Pinheiro F M DaMatta A R M Chaves M E Loureiroand C Ducatti ldquoDrought tolerance is associated with rootingdepth and stomatal control of water use in clones of Coffeacanephorardquo Annals of Botany vol 96 no 1 pp 101ndash108 2005

[7] J B Passioura ldquoPhenotyping for drought tolerance in graincrops when is it useful to breedersrdquo Functional Plant Biologyvol 39 no 11 pp 851ndash859 2012

[8] J S Sperry ldquoHydraulics of vascular water transportrdquo inMechan-ical Integration of Plant Cells and Plants P Wojtaszek Edvol 9 of Signaling and Communication in Plants pp 303ndash327Springer Berlin Germany 2011

[9] A P Wasson R A Richards R Chatrath et al ldquoTraits andselection strategies to improve root systems and water uptakein water-limited wheat cropsrdquo Journal of Experimental Botanyvol 63 no 9 pp 3485ndash3498 2012

[10] G D Herder G van Isterdael T Beeckman and I de SmetldquoThe roots of a new green revolutionrdquo Trends in Plant Sciencevol 15 no 11 pp 600ndash607 2010

[11] H T Nguyen R C Babu and A Blum ldquoBreeding for droughtresistance in rice physiology and molecular genetics consider-ationsrdquo Crop Science vol 37 no 5 pp 1426ndash1434 1997

[12] A Blum Plant Breeding for Water-Limited EnvironmentsSpringer Science+Business Media LLC New York NY USA2011

[13] L B Motta T C B Soares M A G Ferrao E T Caixeta R MLorenzoni and J D de SouzaNeto ldquoMolecular characterizationof arabica and Conilon coffee plants genotypes by SSR and ISSRmarkersrdquo Brazilian Archives of Biology and Technology vol 57no 5 pp 728ndash735 2014

[14] PMarraccini L P Freire G S CAlves et al ldquoRBCS1 expressionin coffee Coffea orthologs Coffea arabica homeologs andexpression variability between genotypes and under droughtstressrdquo BMC Plant Biology vol 11 article 85 2011

[15] P Marraccini F Vinecky G S C Alves et al ldquoDifferentiallyexpressed genes and proteins upon drought acclimation intolerant and sensitive genotypes of Coffea canephorardquo Journalof Experimental Botany vol 63 no 11 pp 4191ndash4212 2012

[16] F Denoeud L Carretero-Paulet A Dereeper et al ldquoThecoffee genome provides insight into the convergent evolution ofcaffeine biosynthesisrdquo Science vol 345 no 6201 pp 1181ndash11842014

[17] A Dereeper S Bocs M Rouard et al ldquoThe coffee genome huba resource for coffee genomesrdquo Nucleic Acids Research vol 43no 1 pp D1028ndashD1035 2015

[18] M Udayakumar M S Sheshshayee K N Nataraj et al ldquoWhyhas breeding for water use efficiency not been successful Ananalysis and alternate approach to exploit this trait for cropimprovementrdquo Current Science vol 74 no 11 pp 1000ndash10031998

[19] M G Awati Genetic diversity and molecular markers for rootand associated physiological traits of Robusta (coffea canephoraPierre ex F) accessions [PhD thesis] University of AgriculturalSciences Bangalore India 2005

[20] C E Williams and P C Ronald ldquoPCR template-DNA isolatedquickly from monocot and dicot leaves without tissue homog-enizationrdquo Nucleic Acids Research vol 22 no 10 pp 1917ndash19181994

[21] S D Tanksley ldquoMapping polygenesrdquoAnnual Review of Geneticsvol 27 no 1 pp 205ndash233 1993

[22] T Halward T Stalker E LaRue and G Kochert ldquoUse ofsingle-primer DNA amplifications in genetic studies of peanut(Arachis hypogaea L)rdquo Plant Molecular Biology vol 18 no 2pp 315ndash325 1992

[23] SAS Institute SASSTAT Userrsquos Guide Version 6 SAS InstituteCary NC USA 4th edition 1989

[24] M J Kearsey and A G L Farquhar ldquoQTL analysis in plantswhere are we nowrdquo Heredity vol 80 no 2 pp 137ndash142 1998

[25] M J Kearsey and V Hyne ldquoQTL analysis a simple lsquomarker-regressionrsquo approachrdquoTheoretical and Applied Genetics vol 89no 6 pp 698ndash702 1994

[26] C S Haley and S A Knott ldquoA simple regression method formapping quantitative trait loci in line crosses using flankingmarkersrdquo Heredity vol 69 no 4 pp 315ndash324 1992

[27] B M Atta T Mahmood and R M Trethowan ldquoRelationshipbetween root morphology and grain yield of wheat in North-Western NSW Australiardquo Australian Journal of Crop Sciencevol 7 no 13 pp 2108ndash2115 2013

[28] N T Lang C T Nha P T T Ha and B C Buu ldquoQuantitativetrait loci (QTLs) associated with drought tolerance in rice(Oryza sativa L)rdquo Sabrao Journal of Breeding and Genetics vol45 no 3 pp 409ndash421 2013

[29] H J Schenk andR B Jackson ldquoMapping the global distributionof deep roots in relation to climate and soil characteristicsrdquoGeoderma vol 126 no 1-2 pp 129ndash140 2005

[30] A Srividya L R Vemireddy P V Ramanarao et al ldquoMolecularmapping of QTLs for drought related traits at seedling stageunder PEG induced stress conditions in ricerdquoAmerican Journalof Plant Sciences vol 2 no 2 pp 190ndash201 2011

[31] N-N Shukor H A Hamid A Abdu andM-K Ismail ldquoEffectsof soil compaction on growth and physiological characteristicsofAzadirachta excelsa seedlingsrdquoTheAmerican Journal of PlantPhysiology vol 10 no 1 pp 25ndash42 2015

[32] P R Bhat Phenotyping and molecular analysis of Coffea arabicaL accessions and mapping population to identify RAPD markersassociated with root characteristics and associated physiologicaltraits [PhD thesis] University of Agricultural Sciences Banga-lore India 2002

[33] S Chandra H K Buhariwalla J Kashiwagi et al ldquoIdentifyingQTL linked markers in marker-29 deficient cropsrdquo in Proceed-ings of the 4th International Crop Science Congress BrisbaneAustralia 2004

[34] H M Pearl C Nagai P H Moore D L Steiger R V Osgoodand RMing ldquoConstruction of a genetic map for arabica coffeerdquoTheoretical and Applied Genetics vol 108 no 5 pp 829ndash8352004

[35] P Lashermes M C Combes N S Prakash P Trouslot MLorieux and A Charrier ldquoGenetic linkage map of Coffeacanephora effect of segregation distortion and analysis ofrecombination rate in male and female meiosesrdquo Genome vol44 no 4 pp 589ndash596 2001

[36] P Hendre S Lalji Singh and R K Aggarwal ldquoConstructionof framework molecular linkage map of Robusta coffee Coffeacanephora with RAPD AFLP g-SSRs And EST-SSRs usingpseudo-testcross strategyrdquo in Proceedings of the Plant amp AnimalGenomes 15th Conference January 2007

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

PeptidesInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

International Journal of

Volume 2014

Zoology

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Molecular Biology International

GenomicsInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioinformaticsAdvances in

Marine BiologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Signal TransductionJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

Evolutionary BiologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Genetics Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Virolog y

Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Enzyme Research

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Microbiology

Molecular Biology International 11

[2] M Nesbitt The Cultural History of Plants Taylor amp Francis2005

[3] F M Damatta ldquoDrought as a multidimensional stress affectingphotosynthesis in tropical tree cropsrdquo in Advances in PlantPhysiology A Hemantaranjan Ed vol 5 pp 227ndash265 2003

[4] K Harovon Mogel ldquoGenotype times environment times managementinteractions key to beating future droughtsrdquo CSA News vol 58pp 4ndash9 2013

[5] F M DaMatta A R M Chaves H A Pinheiro C Ducatti andM E Loureiro ldquoDrought tolerance of two field-grown clones ofCoffea canephorardquo Plant Science vol 164 no 1 pp 111ndash117 2003

[6] H A Pinheiro F M DaMatta A R M Chaves M E Loureiroand C Ducatti ldquoDrought tolerance is associated with rootingdepth and stomatal control of water use in clones of Coffeacanephorardquo Annals of Botany vol 96 no 1 pp 101ndash108 2005

[7] J B Passioura ldquoPhenotyping for drought tolerance in graincrops when is it useful to breedersrdquo Functional Plant Biologyvol 39 no 11 pp 851ndash859 2012

[8] J S Sperry ldquoHydraulics of vascular water transportrdquo inMechan-ical Integration of Plant Cells and Plants P Wojtaszek Edvol 9 of Signaling and Communication in Plants pp 303ndash327Springer Berlin Germany 2011

[9] A P Wasson R A Richards R Chatrath et al ldquoTraits andselection strategies to improve root systems and water uptakein water-limited wheat cropsrdquo Journal of Experimental Botanyvol 63 no 9 pp 3485ndash3498 2012

[10] G D Herder G van Isterdael T Beeckman and I de SmetldquoThe roots of a new green revolutionrdquo Trends in Plant Sciencevol 15 no 11 pp 600ndash607 2010

[11] H T Nguyen R C Babu and A Blum ldquoBreeding for droughtresistance in rice physiology and molecular genetics consider-ationsrdquo Crop Science vol 37 no 5 pp 1426ndash1434 1997

[12] A Blum Plant Breeding for Water-Limited EnvironmentsSpringer Science+Business Media LLC New York NY USA2011

[13] L B Motta T C B Soares M A G Ferrao E T Caixeta R MLorenzoni and J D de SouzaNeto ldquoMolecular characterizationof arabica and Conilon coffee plants genotypes by SSR and ISSRmarkersrdquo Brazilian Archives of Biology and Technology vol 57no 5 pp 728ndash735 2014

[14] PMarraccini L P Freire G S CAlves et al ldquoRBCS1 expressionin coffee Coffea orthologs Coffea arabica homeologs andexpression variability between genotypes and under droughtstressrdquo BMC Plant Biology vol 11 article 85 2011

[15] P Marraccini F Vinecky G S C Alves et al ldquoDifferentiallyexpressed genes and proteins upon drought acclimation intolerant and sensitive genotypes of Coffea canephorardquo Journalof Experimental Botany vol 63 no 11 pp 4191ndash4212 2012

[16] F Denoeud L Carretero-Paulet A Dereeper et al ldquoThecoffee genome provides insight into the convergent evolution ofcaffeine biosynthesisrdquo Science vol 345 no 6201 pp 1181ndash11842014

[17] A Dereeper S Bocs M Rouard et al ldquoThe coffee genome huba resource for coffee genomesrdquo Nucleic Acids Research vol 43no 1 pp D1028ndashD1035 2015

[18] M Udayakumar M S Sheshshayee K N Nataraj et al ldquoWhyhas breeding for water use efficiency not been successful Ananalysis and alternate approach to exploit this trait for cropimprovementrdquo Current Science vol 74 no 11 pp 1000ndash10031998

[19] M G Awati Genetic diversity and molecular markers for rootand associated physiological traits of Robusta (coffea canephoraPierre ex F) accessions [PhD thesis] University of AgriculturalSciences Bangalore India 2005

[20] C E Williams and P C Ronald ldquoPCR template-DNA isolatedquickly from monocot and dicot leaves without tissue homog-enizationrdquo Nucleic Acids Research vol 22 no 10 pp 1917ndash19181994

[21] S D Tanksley ldquoMapping polygenesrdquoAnnual Review of Geneticsvol 27 no 1 pp 205ndash233 1993

[22] T Halward T Stalker E LaRue and G Kochert ldquoUse ofsingle-primer DNA amplifications in genetic studies of peanut(Arachis hypogaea L)rdquo Plant Molecular Biology vol 18 no 2pp 315ndash325 1992

[23] SAS Institute SASSTAT Userrsquos Guide Version 6 SAS InstituteCary NC USA 4th edition 1989

[24] M J Kearsey and A G L Farquhar ldquoQTL analysis in plantswhere are we nowrdquo Heredity vol 80 no 2 pp 137ndash142 1998

[25] M J Kearsey and V Hyne ldquoQTL analysis a simple lsquomarker-regressionrsquo approachrdquoTheoretical and Applied Genetics vol 89no 6 pp 698ndash702 1994

[26] C S Haley and S A Knott ldquoA simple regression method formapping quantitative trait loci in line crosses using flankingmarkersrdquo Heredity vol 69 no 4 pp 315ndash324 1992

[27] B M Atta T Mahmood and R M Trethowan ldquoRelationshipbetween root morphology and grain yield of wheat in North-Western NSW Australiardquo Australian Journal of Crop Sciencevol 7 no 13 pp 2108ndash2115 2013

[28] N T Lang C T Nha P T T Ha and B C Buu ldquoQuantitativetrait loci (QTLs) associated with drought tolerance in rice(Oryza sativa L)rdquo Sabrao Journal of Breeding and Genetics vol45 no 3 pp 409ndash421 2013

[29] H J Schenk andR B Jackson ldquoMapping the global distributionof deep roots in relation to climate and soil characteristicsrdquoGeoderma vol 126 no 1-2 pp 129ndash140 2005

[30] A Srividya L R Vemireddy P V Ramanarao et al ldquoMolecularmapping of QTLs for drought related traits at seedling stageunder PEG induced stress conditions in ricerdquoAmerican Journalof Plant Sciences vol 2 no 2 pp 190ndash201 2011

[31] N-N Shukor H A Hamid A Abdu andM-K Ismail ldquoEffectsof soil compaction on growth and physiological characteristicsofAzadirachta excelsa seedlingsrdquoTheAmerican Journal of PlantPhysiology vol 10 no 1 pp 25ndash42 2015

[32] P R Bhat Phenotyping and molecular analysis of Coffea arabicaL accessions and mapping population to identify RAPD markersassociated with root characteristics and associated physiologicaltraits [PhD thesis] University of Agricultural Sciences Banga-lore India 2002

[33] S Chandra H K Buhariwalla J Kashiwagi et al ldquoIdentifyingQTL linked markers in marker-29 deficient cropsrdquo in Proceed-ings of the 4th International Crop Science Congress BrisbaneAustralia 2004

[34] H M Pearl C Nagai P H Moore D L Steiger R V Osgoodand RMing ldquoConstruction of a genetic map for arabica coffeerdquoTheoretical and Applied Genetics vol 108 no 5 pp 829ndash8352004

[35] P Lashermes M C Combes N S Prakash P Trouslot MLorieux and A Charrier ldquoGenetic linkage map of Coffeacanephora effect of segregation distortion and analysis ofrecombination rate in male and female meiosesrdquo Genome vol44 no 4 pp 589ndash596 2001

[36] P Hendre S Lalji Singh and R K Aggarwal ldquoConstructionof framework molecular linkage map of Robusta coffee Coffeacanephora with RAPD AFLP g-SSRs And EST-SSRs usingpseudo-testcross strategyrdquo in Proceedings of the Plant amp AnimalGenomes 15th Conference January 2007

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

PeptidesInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

International Journal of

Volume 2014

Zoology

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Molecular Biology International

GenomicsInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioinformaticsAdvances in

Marine BiologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Signal TransductionJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

Evolutionary BiologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Genetics Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Virolog y

Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Enzyme Research

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Microbiology

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

PeptidesInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

International Journal of

Volume 2014

Zoology

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Molecular Biology International

GenomicsInternational Journal of

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The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

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BioinformaticsAdvances in

Marine BiologyJournal of

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Signal TransductionJournal of

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BioMed Research International

Evolutionary BiologyInternational Journal of

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Genetics Research International

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Advances in

Virolog y

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Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Enzyme Research

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International Journal of

Microbiology