physiological breeding: strategies & genetic gains matthew reynolds (cimmyt) contributions from:...
Post on 17-Jan-2016
221 Views
Preview:
TRANSCRIPT
Physiological Breeding: strategies & genetic gains
Matthew Reynolds (CIMMYT)
Contributions from:Gemma Molero, Maria Tattaris
Carolina Saint Pierre, Mariano Cossani
Siva Sukumaran, Alistair Pask, Ravi Valluru
Marc Ellis, Yann Manes
Richard Trethowan
International Wheat Improvement Network (IWIN) Coordinated by CIMMYT since 1960s
Latin America
Africa Middle East
South & East Asia
CIMMYT distributes 1,000 new wheat genotypes annually targeted to a range of environments
Average genetic gains at 556 international sites: ~1% per year from 1996-2010
Manes et al. 2012 .Genetic yield gains of CIMMYT international semi-arid wheat yield trials from 1994 to 2010.
Crop Science 52:1543-1552.
Complementary strategies to increase genetic gains
1) Identify crop characteristics conferring adaptation
2) Precision and high throughput phenotyping
3) Exploration of genetic resources for adaptive traits
4) Inter-specific hybridization to broaden the crop genepool
5) Genomics to increase breeding efficiency
6) Strategic crossing to achieve cumulative gene action
Complementary strategies to increase genetic gains
1) Identify crop characteristics conferring adaptation
2) Precision and high throughput phenotyping
3) Exploration of genetic resources for adaptive traits
4) Inter-specific hybridization to broaden the crop genepool
5) Genomics to increase breeding efficiency
6) Strategic crossing to achieve cumulative gene action
Water Use (RUE)
•Roots match evaporative demand•Regulation of transpiration (VPD; ABA)
Partitioning (HI)
•Spike fertility (meiosis, pollen, etc)•Stress signaling (e.g. ethylene) regulating
• senescence rate• floret abortion
•Grain filling (starch synthase)•Stem carbohydrate storage & remobilization
Photo-Protection (RUE)
•Leaf morphology (display, wax)•Down regulation•Pigment composition
• Chl a:b• Carotenoids
•Antioxidants
Conceptual Model of Heat-Adaptive TraitsYIELD = LI x RUE x HI
Efficient metabolism (RUE)
•CO2 fixation•CO2 conductance•Rubsico (>>)
•Canopy photosynthesis•spike photosynthesis
•Respiration
Light interception (LI)
•Rapid ground cover•Functional stay-green
G x E?G x G?
Cossanni & Reynolds, 2012. Plant Physiology 160 1710-18
Complementary Strategies
1) Identify crop characteristics conferring adaptation
2) Precision and high throughput phenotyping
3) Exploration of genetic resources for adaptive traits
4) Inter-specific hybridization to broaden the crop genepool
5) Genomics to increase breeding efficiency
6) Strategic crossing to achieve cumulative gene action
Phenotyping is not just about tools!
►Design experimental populations to avoid confounding agronomic traits
Seri/Babax population
Representative phenotyping platforms (e.g. IWYP-PLAT)
Located at heart of high yield wheat agro-ecosystem (Yaqui Valley NW Mexico)
• Production >1 m tons
• Farm yields avg 6.5 t/ha• Maximum yields ~10 t/ha
• Research and breeding conducted side by side, encouraging maximum accountability of both.
Plant selection tools
Visual selection ++
(Molecular markers)Spectral reflectance
Canopy temperature
Canopy temperature shows consistent association with yield under drought and heat
Deeper roots under drought confer stress adaptation
140
190
240
290
340
390
440
490
0 10 20 30 40 50 60 70
Root DW 60-120 cm (gm-2)
Yie
ld (
gm
-2)
0
5
10
15
20
25
30
35
40
CT
gf (
o C)
CT=-0.20x+34.3, R2=0.88
Yield=2.07x+254.9, R2=0.35
Lopes MS and Reynolds MP, 2010. Partitioning of assimilates to deeper roots is associated with cooler canopies and increased yield under drought in wheat. Functional Plant Biology 37:147-156
Pinto & Reynolds, 2015. Common genetic basis for canopy temperature depression under heat and drought stress associated with optimized root distribution. TAG: 128
GIDDINGS SOIL CORER
TO SAMPLE ROOTS & MEASURE SOIL MOISTURE
Aerial remote sensing
Recently featured on BBC Horizons
Thermal Imagery: data processing
Removal of outlying pixels
Ground v Airborne: UAV & Blimp:
125 150 175 20025
26
27
28
29
30
f(x) = 0.0340484014144242 x + 21.764091264697R² = 0.591929180109435
Thermal Index UAV VS CT GroundHeat_1
Thermal Index UAV
CT
GR
OU
ND
2.5 2.9 3.3 3.70.38
0.43
0.48
f(x) = 0.0593427427166828 x + 0.248722335188861R² = 0.568074593278231
Thermal Index UAV VS CT GroundHeat_2
Thermal Index UAV*
CT
Gro
un
d*
0.50 0.60 0.70 0.800.50
0.60
0.70
0.80
f(x) = 0.771202441869446 x + 0.131097474414508R² = 0.712717074376989
MSAVI BLIMP VS NDVI Ground Drought_1
MSAVI BLIMP
ND
VI G
RO
UN
D
0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.450.50
0.60
0.70
0.80
f(x) = − 0.741831729424865 x + 0.863742285002526R² = 0.73620187199727
NCPI BLIMP VS NDVI GroundDrought_1
NCPI BLIMP
ND
VI G
RO
UN
D
in most cases data from airborne platforms explains genetic variation in yield etc. better than with ground based readings
Complementary Strategies
1) Identify crop characteristics conferring adaptation
2) Precision and high throughput phenotyping
3) Exploration of genetic resources for adaptive traits
4) Inter-specific hybridization to broaden the crop genepool
5) Genomics to increase breeding efficiency
6) Strategic crossing to achieve cumulative gene action
Genetic resources:
~ 0.5 million accessions of wheat genetic resources in collections worldwide
The World Wheat Collection at CIMMYT has ~170,000
Wheat ‘landraces’ in Oaxaca
70,000 wheat genetic resources screened under drought and heat, Sonora,
Mexico, 2011-2013
FIGS drought set, Sonora, 2013Focused Identification of Germplasm Strategy (http://www.figs.icarda.net/)
A
FIGS drought set, Sonora, 2013Focused Identification of Germplasm Strategy (http://www.figs.icarda.net/)
A B
Complementary Strategies
1) Identify crop characteristics conferring adaptation
2) Precision and high throughput phenotyping
3) Exploration of genetic resources for adaptive traits
4) Inter-specific hybridization to broaden the genepool
5) Genomics to increase breeding efficiency
6) Strategic crossing to achieve cumulative gene action
T. durumAABB
T. tauschiiDD
Hexaploid syntheticAABBDD
Wide crossing with close relatives
e.g. “Synthetics”
► Sources of disease resistance
► Redistribution of roots to deeper soil profiles under water stress
X =
80 120
160
200
240
280
320
360
400
440
480
520
560
600
640
680
720
760
800
840
880
920
960
1000
1040
1080
1120
1160
1200
0
5
10
15
20
25
30
35
Check
1,000 new primary synthetics screened for biomass –heat environment-
# lines
Dry weight (g)
Complementary Genetic Strategies
1) Identify crop characteristics conferring adaptation
2) Precision and high throughput phenotyping
3) Exploration of genetic resources for adaptive traits
4) Inter-specific hybridization to broaden the crop genepool
5) Genomics to increase breeding efficiency
6) Strategic crossing to achieve cumulative gene action
Canopy temp as a surrogate for root function
.
CTAMVEGCTPMVEGCTAMGFCTPMGF
0
50
100
150
200
250
300
350
400
450
500
18 20 22 24 26 28 30
y = -0.003x + 21.54, r2 = 0.61y = -0.004x + 25.904, r2 = 0.68y = -0.005x + 24.545, r2 = 0.64y = -0.006x + 27.98, r2 = 0.62
YIE
LD
(g/m
2 )
CANOPY TEMPERATURE (oC)
Figure1. Association of yield performance (g/m2) and canopy temperature (oC)of Seri-Babax population under drought (cycle Y01/02).
CT is robustly associated with performance under heat and drought stress
CANOPY TEMPERATURE (0C)
R2 = 0.47
200
250
300
350
400
450
27.0 28.0 29.0 30.0 31.0 32.0
CT-boot
Yield
Drought stress
Heat stress
Consistent QTL identified in the Seri/Babax Population
1B-a.aac/caa-41B-a.wPt-14031B-a.wPt-52811B-a.aca/cac-51B-a.gwm2731B-a.wPt-01701B-a.aac/ctg-41B-a.wPt-75291B-a.agg/cat-41B-a.acc/cat-41B-a.act/ctc-71B-a.agg/cat-111B-a.barc0651B-a.gwm4131B-a.agg/ctg-51B-a.wPt-34651B-a.aac/cta-51B-a.agg/cat-181B-a.gwm1311B-a.agg/cac-31B-a.agc/cta-91B-a.agc/cta-21B-a.agc/cta-61B-a.agc/cag-51B-a.aag/ctg-141B-a.wPt-89301B-a.act/ctc-91B-a.aca/cta-91B-a.gwm5821B-a.gwm301b1B-a.wPt-17811B-a.aag/ctc-61B-a.wPt-20521B-a.aca/cac-21B-a.wPt-78331B-a.acc/ctc-41B-a.acg/cta-21B-a.act/ctc-51B-a.wPt-86161B-a.aca/cag-51B-a.aca/caa-31B-a.agg/ctg-31B-a.aac/ctc-6
Yie
ld
GM
2
ND
VIv
CT
v
CT
g
CH
Lg
1B-a
2B-a.wPt-96682B-a.aac/cta-12B-a.wPt-73202B-a.wPt-06152B-a.aag/ctc-32B-a.wPt-64772B-a.acc/ctc-22B-a.acc/ctg-42B-a.acc/ctc-102B-a.wPt-77502B-a.aag/ctg-52B-a.agg/cat-72B-a.agg/cac-102B-a.agc/cag-42B-a.aag/ctg-152B-a.agg/cac-52B-a.gwm3882B-a.acg/cta-12B-a.gwm191a2B-a.aca/ctg-12B-a.aag/ctg-122B-a.act/ctc-112B-a.wPt-56802B-a.wPt-97362B-a.aca/caa-42B-a.agg/cta-32B-a.agg/cac-132B-a.agg/ctg-22B-a.act/ctc-1
ND
VIg
CT
v
CT
g
2B-a
3B-b.wPt-82383B-b.aag/ctc-93B-b.gwm644
3B-b.aca/ctg-53B-b.gdm0083B-b.wPt-60473B-b.aac/cac-53B-b.wPt-19403B-b.aag/ctc-1
3B-b.agg/cta-63B-b.wPt-53583B-b.wPt-71863B-b.acc/ctg-53B-b.wPt-03843B-b.wPt-44283B-b.aca/cag-93B-b.wPt-1804
3B-b.wPt-00213B-b.gwm301e3B-b.aca/caa-93B-b.acc/ctg-113B-b.wPt-80213B-b.acc/ctc-8
3B-b.wPt-44123B-b.wPt-4370
Yie
ld
GM
2
CT
v
CT
g
3B-b
4A-a.gwm3974A-a.act/cag-54A-a.act/cag-34A-a.wmc048d4A-a.agg/cta-124A-a.aac/ctg-3
4A-a.wmc048c
Yie
ld
GM
2
ND
VIg
CT
v
4A-a
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
Common QTL identified for heat and drought adaptation
Empty bars: Drought specific QTLLined bars: Stress QTL specific for DRT & HOT environmentsSolid bars: Robust QTL identified under stress and irrigated environments
Pinto et al , 2010 . Heat and drought adaptive QTL in a wheat
population designed to minimize confounding agronomic effects.
TAG 121:1001–21
Root distribution in Seri/Babax ‘iso-QTL’ lines
0
40
80
120
160
200
COOL-Drt HOT-Drt COOL-Heat HOT-Heat
0-30 cm 30-60 cm 60-90 cm 90-120 cm
Ro
ots
(g
/m2 )
46%
35%
16%
56%
33%
8%
79%
16%
5%
82%
13%
4%
T-tests for COOL v HOT genotypes: DROUGHT 30-120 cm (p=0.002) ; HEAT 30-90 cm (p=0.0025)Pinto & Reynolds, 2015. TAG
Adaptation to plant density
Differences between inner and outer rows:
Sukumaran et al. Crop Sci. (2015)
Candidate gene: SET domain protein PI94960 for pollen abortion
GWAS candidate genes: A) ADi Yield, C) ADi KNO
QTL for spike photosynthesis
Complementary Genetic Strategies
1) Identify crop characteristics conferring adaptation
2) Precision and high throughput phenotyping
3) Exploration of genetic resources for adaptive traits
4) Inter-specific hybridization to broaden the crop genepool
5) Genomics to increase breeding efficiency
6) Strategic crossing to achieve cumulative gene action
Strategic crossing for cumulative gene action
WUE: Transpiration
Efficiency•Efficient leaf photosynthesis
(CID)
Strategic Crossing to Combine Adaptive TraitsDROUGHT YIELD = WU x WUE x HI
Partitioning (HI)• Stem carbohydrate
storage
WUE: Photo-Protection• Leaf wax• Pigments
Water Uptake •Ground cover•Access to water by roots
First new generation of lines based on physiological crosses & selection, (2007)
Yield distribution of 3 years mean drought trials (Cd Obregon, Mexico)
0
5
10
15
20
25
30
35
40
45
% of check
%
Conventionalcrosses
Physiologicaltrait crosses
83-89 90-94 95-99 100-104 105-109 110-114 115-119 120-129 130-1330
5
10
15
20
25
30
35
40
45
12
16
24
30
22
41
20 20
5
Yield as % of drought adapted check Vorobey
Nu
mb
er o
f lin
es
70% of new lines out-yielded the check, 2012
Check 3.5 t/ha (Vorobey)
New lines based on physiological trait (PT) criteria
Yield traits considered in strategic crosses:YIELD = LI x RUE x HI
SINKS pre-grainfill:• Spike fertility
• grain number• kernel weight potential• avoid floret abortion
• Development pattern• long juvenile spike phase
SINK (grain-filling)•Harvest Index
•tiller survival•grain growth rate
SOURCE (pre-grainfill):
• Light interception (LI)• Growth rate
• Canopy temperature
SOURCE (grain-filling):
•Canopy photosynthesis (RUE/LI)•Leaf conductance•Carbohydrate storage in stem•stay green
26 international sites of the 2nd WYCYT
35 new (PT) lines7 elite checks
Abbreviation Site Country
BGLD J BARI Joydebpur Bangladesh
BGLD D BARI Dinjpur Bangladesh
BGLD R BARI Rajshahi Bangladesh
China L LAOMANCHENG China
Egypt A Assiut Egypt
India D Delhi India
India L Ludhiana India
India V Varanasi India
India K Karnal India
India H Dharwad India
India I Indore India
India U Ugar India
Iran D DARAB-HASSAN-ABAD Iran
Iran Z ZARGAN Iran
Iran SP SPII - KARAJ Iran
Iran S SAFIABAD AGRIC. RES. CENTER Iran
MEX Bajio INIFAP-Bajio Mexico
MEX CM CIMMYT CENEB Mexico
MEX BC INIFAP-Mexicali Baja California Mexico
MEX JAL INIFAP-Tepatitlan Jalisco Mexico
MEX SIN INIFAP-Valle del Fuerte, Sinaloa Mexico
MEX SON INIFAP_Valle del Yaqui Mexico
Nepal B Bhairahawa Nepal
PAK I Islamabad Pakistan
PAK F Faisalabad Pakistan
PAK P Pirsabak Pakistan
Mean yield of 7 elite checks: 2nd WYCYT, 2014
Yie
ld g
/m2
Mean yields of 35 new PT lines v 7 elite checks:(average 7% advantage of new lines)
Yie
ld g
/m2
CROPDESIGN
GENETICRESOURCES
PHENO-TYPING
GENETICANALYSIS
BREEDING DELIVERY through
IWIN
Physiological Breeding Pipeline
INFORMATICS
Determine traits/genes needed to adapt crops to target environments
•Landraces•Wild relatives •Advanced lines•Transgenics
•High thru-put remote sensing
•Precision phenotyping
Strategic crossing
Select best progeny using state-of-the-art
phenotyping /molecular tools
QTL identified and MAS systems developed
Standard Phenotyping Protocols
http://libcatalog.cimmyt.org/download/cim/96140.pdf
http://libcatalog.cimmyt.org/download/cim/96144.pdf
Conclusions● Investment in understanding the ‘phenome’ and trade-offs between traits facilitate
breeding decisions
● Genetic resources represent a vast and largely untapped opportunity for crop improvement, if evaluated using appropriate screens:
Aerial high throughput approaches on large numbers Precision phenotyping approaches on selected material Molecular markers (especially for hard to phenotype traits)
● Strategic trait-based crossing increases genetic gains compared with crossing the best x best yielding lines
● Phenomic and genomic technologies can deliver genetic gains in farmers’ fields; sooner when integrated with proven techniques
PT Heat + Parents (late sown Mexico)
PT CAL +PADs Y13-14 (march)Y13-14 YLD Gain BIOM NDVI Vg NDVI LLg
Cross Name g/m2 % BP g/m2
PASTOR//HXL7573/2*BAU/3/WBLL1 190 45% 395 0.675 0.472
PASTOR//HXL7573/2*BAU/3/WBLL1 179 37% 381 0.671 0.439SOKOLL/WBLL1 157 18% 457 0.690 0.550SOKOLL/WBLL1 142 7% 348 0.657 0.498SOKOLL/WBLL1 141 7% 330 0.658 0.488SOKOLL 132 315 0.659 0.469WEEBIL (CHECK) 131 294 0.671 0.448PASTOR//HXL7573/2*BAU 93 312 0.638 0.455MEAN 127.7 313 0.648 0.452r- yld 0.67 0.73 0.12LSD (5%) 33.5 96.2 0.07 0.05
PT CAL +PADs Y14-15 (march)Y14-15 YLD Gain BIOM NDVI Vg NDVI LLg
Cross Name g/m2 % BP g/m2
PASTOR//HXL7573/2*BAU/3/WBLL1 93 56% 196 0.262 0.335
PASTOR//HXL7573/2*BAU/3/WBLL1 78 30% 171 0.275 0.284SOKOLL/WBLL1 103 40% 253 0.323 0.418SOKOLL/WBLL1 118 61% 282 0.307 0.348SOKOLL/WBLL1 113 54% 265 0.363 0.356SOKOLL 73 176 0.255 0.317WEEBIL (CHECK) 60 140 0.307 0.279PASTOR//HXL7573/2*BAU 48 137 0.252 0.272MEAN 91.1 212 0.299 0.334r- yld 0.97 0.65 0.80LSD (5%) 37.1 84.2 0.12 0.09
top related