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crop science, vol. 56, july–august 2016 www.crops.org 1429

RESEARCH

Sorghum (Sorghum bicolor L. Moench) is a dietary staple for 500 million people in ~30 countries in the tropical regions of the

world. It is ranked fifth in importance in terms of production and acreage, grown on 42 million hectares in ~105 countries in Asia, Africa, Oceania, and the Americas. It is a recommended cereal for those who suffer from celiac disease (gluten intolerance) and serves as a rich source of antioxidants (Anglani, 1998). In regions where rainfall is limited and irrigation is not affordable, sorghum is a good substitute for other cereals (Nedumaran et al., 2013; Reddy et al., 2012). Genetic gain in grain yield is the primary objective of many grain breeding programs. Sorghum grain prices were predicted to increase because of its use in ethanol production (Scheinost et al., 2001). In Kansas, 50% of the grain sorghum was used for ethanol production in 2010 (Yan et al., 2012), indicating a growing demand for increased production of grain sorghum not just for feed and food, but also for fuel production.

Grain yield, however, is a complex trait controlled by multiple QTLs, and progress in improving grain yield through traditional breeding has been slow (Bartels and Sunkar, 2005), but molecu-lar marker based breeding can achieve rapid genetic gains in yield (Bernardo, 2013; Collins et al., 2008). Most studies have reported

QTL Mapping for Grain Yield, Flowering Time, and Stay-Green Traits in Sorghum

with Genotyping-by-Sequencing Markers

Sivakumar Sukumaran, Xin Li, Xianran Li, Chengsong Zhu, Guihua Bai, Ramasamy Perumal, Mitchell R. Tuinstra, P.V. Vara Prasad, Sharon E. Mitchell, Tesfaye T. Tesso, and Jianming Yu*

ABSTRACTMolecular breeding can complement traditional breeding approaches to achieve genetic gains in a more efficient way. In the present study, genetic mapping was conducted in a sorghum recombinant inbred line (RIL) population developed from Tx436 (a non-stay-green high food quality inbred) × 00MN7645 (a stay-green high yield inbred) and evaluated in eight environments (location and year combination) in a hybrid background of Tx3042 (a non-stay-green A-line). Phenotyping was conducted for agronomic traits (grain yield and flowering time), physiological traits of stay-green (chlorophyll content [SPAD] and chlorophyll fluorescence [Fv/Fm] measured on the leaves), and green leaf area visual score (GLAVS). This population was genotyped with genotyping-by-sequencing (GBS) technology. Data processing resulted in 7144 high quality single nucleotide polymorphisms (SNPs) that were used in a genome-wide single marker scan with physical distance. A selected subset of 1414 SNPs was used for composite interval mapping (CIM) with genetic distance. These complementary methods revealed fifteen QTLs for the traits studied. In addition, QTL mapping for individual environments and year-wise combinations revealed 42 QTLs. A consistent QTL for grain yield under normal and stressed conditions was identified in chromosome 1 that explained 8 to 16% of the phenotypic variation. QTLs for flowering time were identified in chromosomes 2, 6, and 9 that explained 6 to 11% of the phenotypic variation. Stay-green QTLs in chromosomes 3 and 4 explained 8 to 24% of the phenotypic variation. These identified QTLs with flanking SNPs of known genomic positions could be used to improve grain yield, flowering time, and stay-green in sorghum molecular breeding programs.

S. Sukumaran, X. Li, X. Li, C. Zhu, R. Perumal, P.V.V. Prasad, T.T. Tesso, and J. Yu, Dep. of Agronomy, Kansas State Univ., Manhattan, KS 66506; G. Bai, Dep. of Agronomy, Kansas State Univ., Manhattan, KS 66506 and USDA-ARS Hard Winter Wheat Genetics Research Unit, Manhattan, KS 66506; M.R. Tuinstra, Dep. of Agronomy, 915 West State Street, West Lafayette, IN 47907; S.E. Mitchell, Institute of Genomic Diversity, Cornell Univ., Ithaca, NY 14853. Received 13 Feb. 2015. Accepted 9 Jan. 2016. *Corresponding author ([email protected]).

Abbreviations: CIM, composite interval mapping; GBS, genotyping-by-sequencing; GLAVS, green leaf area visual score; LOD, logarithm of odds; PVE, phenotypic variation explained; QTLs, quantitative trait loci; RIL, recombinant inbred line; SMA, single marker analysis; SNP, single nucleotide polymorphism.

Published in Crop Sci. 56:1429–1442 (2016). doi: 10.2135/cropsci2015.02.0097 © Crop Science Society of America | 5585 Guilford Rd., Madison, WI 53711 USA All rights reserved.

Published June 15, 2016

1430 www.crops.org crop science, vol. 56, july–august 2016

QTLs for grain yield in sorghum in combination with drought tolerance or cold tolerance or yield components (Kapanigowda et al., 2014; Nagaraja Reddy et al., 2013; Phuong et al., 2013; Rama Reddy et al., 2014; Sabadin et al., 2012). In addition, physiological parameters for leaf greenness (chlorophyll content and chlorophyll fluores-cence) are reported to be positively correlated with grain yield under normal and drought conditions in wheat (Lopes and Reynolds, 2012; Pinto et al., 2010; Thomas and Smart, 1993) and sorghum (Harris et al., 2007; Xu et al., 2000a).

Stay-green is a physiological mechanism that delays or renders inoperative foliar senescence (Thomas and How-arth, 2000; Thomas and Ougham, 2014). Stay-green has been widely studied in several crops: sorghum (Jordan et al., 2012; Kebede et al., 2001; Mace et al., 2012; Crasta et al., 1999; Sanchez et al., 2002; Subudhi et al., 2000; Tao et al., 1998), wheat (Christopher et al., 2008; Joshi et al., 2007; Kumar et al., 2010; Lopes and Reynolds, 2012), maize (Wang et al., 2012a; Zheng et al., 2009), and rice (Gustafson et al., 2011; Jiang et al., 2004; Lim and Paek, 2015; Rong et al., 2013). The expression of stay-green in sorghum under normal and stress conditions is a complex physiological process. Stay-green genotypes maintain tran-spiration under stress conditions, making it beneficial if sufficient water reserves are available at the end of the crop cycle; if not it will cause severe stress (Hammer et al., 2006).

A recent study evaluated the relationship between stay-green and grain yield on F1 hybrids in 23 environ-ments, both stressed and non-stressed, in Australia ( Jordan et al., 2012). The study concluded that stay-green mostly correlated positively with grain yield in environments where yield was lower than 6 t ha–1 (stressed environ-ments), but in environments where yield was higher than 6 t ha–1 (non-stressed environment), an increased negative trend or no significant associations were observed. This suggests that the stay-green trait is more discriminating under drought stress, but under normal conditions, the expression of the trait may be low or not associated with yield (Van Oosterom et al., 1996). In sorghum, stay-green ratings were correlated with SPAD readings, estimated by the broken stick function as the rate of decline in chloro-phyll content (r = −0.91) (Xu et al., 2000a). Also, some reported QTLs for SPAD readings overlapped with QTLs for stay-green under drought conditions (Borrell et al., 2014b; Harris et al., 2007; Xu et al., 2000b).

QTL mapping studies for stay-green in sorghum have so far identified several key QTLs (Stg1-Stg4) (Harris et al., 2007; Kebede et al., 2001; Crasta et al., 1999; Sanchez et al., 2002; Subudhi et al., 2000). A study of QTLs for nodal root angle (important in determining root architecture through vertical and horizontal distribution of roots) and stay-green traits indicated an association between stay-green, grain yield, and nodal root angle QTLs (Mace et al., 2012). Stay-green QTLs (Stg1-Stg4) affected grain yield positively

under drought conditions through its effect on leaf area dynamics and temporal and spatial water use patterns. The stay-green lines showed reduced tillering by increasing the size of lower leaves, reducing the size of upper leaves, and by decreasing the number of leaves per culm thereby more effectively harvesting light energy. Stay-green QTLs reduce the canopy development during pre-anthesis stage and reduce water demand, resulting in higher post-anthesis water use (Borrell et al., 2014a). Efforts to introgress stay-green QTLs into different genetic backgrounds indicated that the effect of stay-green varies depending on the genetic background. A recent study also proved that stay-green genotypes had higher harvest index and grain yield com-pared with non-stay-green lines (Vadez et al., 2011).

Flowering time is an adaptive trait that determines the extent of the distribution of a crop in different climatic conditions, its reproductive success, and breeding meth-odology to be used (Bhosale et al., 2012; Craufurd et al., 1999; El Mannai et al., 2011; Quinby, 1967; Yang et al., 2014). Grain sorghum is originally a short day plant and mostly photoperiod sensitive (Childs et al., 1997). Several loci (Ma1 to Ma6) related to flowering time and matu-rity has been identified in sorghum (Childs et al., 1997; El Mannai et al., 2011; Higgins et al., 2014; Murphy et al., 2011, 2014; Quinby, 1974; Rooney and Aydin, 1999; Thurber et al., 2013). Ma1 and Ma6 acts as repressor of flowering under long day conditions (Murphy et al., 2014; Quinby, 1974). Ma1 encodes PRR37, a pseudo-response regulator protein (Murphy et al., 2011). Ma2, Ma4, and Ma5 determine photoperiod sensitivity of the plant (Quinby, 1967, 1974). Ma3 produces Phytochrome B, which is a photoperiod sensing light receptor controlling matu-rity (Childs et al., 1997). Ma5 and Ma6 are maturity loci with large effects (Rooney and Aydin, 1999).

Advances in sequencing technologies have made it possible to exploit genotyping-by-sequencing (GBS) for genetic studies in different crops (Elshire et al., 2011; Mir et al., 2013). Repetitive DNA in plant genomes is highly methylated and use of methylation sensitive restriction enzymes reduces the need to process repetitive sequences and the size of the genome to be sequenced, thus reducing the cost (Davey et al., 2011; Elshire et al., 2011; Poland et al., 2012; Sonah et al., 2013). In a comparison with array-based methods, where markers are typically developed from populations studied earlier, GBS uses sequence data directly from the population under study (Deschamps et al., 2012).

In the present study, a RIL population was developed from a cross between Tx436 and 00MN7645 (R45) with the aim of mapping QTLs for grain yield, flowering time, and stay-green traits in a hybrid background. The RILs were crossed to ATx3042, and the testcross hybrids were evaluated under field conditions. The population was gen-otyped with GBS and genetic mapping for agronomic and physiological traits were conducted.


environments in 2011 (Supplemental Fig. S1). In 2011, the exper-imental locations had more days with maximum temperatures exceeding 35°C (40 d at Manhattan and 46 d at Hays, whereas there were fewer than 12 d in each environment in 2008 and 2009) and lower cumulative precipitation than 2008 and 2009, making them likely drought environments. This is also sup-ported by the low average yield of the RIL testcrosses, 1.0 t ha–1 at Manhattan in 2011 and 1.8 t ha–1 at Hays in 2011 compared with >2 t ha–1 in all other environments (Table 1; Supplemental Fig. S1). Standard cultural practices were applied throughout the experiments in each environment.

Phenotyping was conducted for agronomic traits of grain yield and flowering time and for physiological traits relative chlo-rophyll content (SPAD) and chlorophyll fluorescence (Fv/Fm) in 2008 and 2009. In 2011, green leaf area visual score (GLAVS) was also estimated. A Minolta SPAD-502 meter was used to measure relative chlorophyll content from five tagged plants in a plot. These measurements were taken 10, 20, 30, and 40 d after flowering (P.V.V. Prasad, personal communication, 2008) on the flag leaf of the plants (Talukder et al., 2015). In 2011, these measurements were taken on the second and fourth leaves of the plant from the top (Xu et al., 2000a). Measurements were taken at the middle of the flag leaf 1 cm from the edge of the leaf lamina. Averages of these measurements in each plot were used for analysis.

Chlorophyll fluorescence was measured with an OS 30p-chlorophyll fluorometer (Opti-Sciences, Hudson, NH) on five tagged plants in a plot as above. The plants were dark adapted for 20 min before measurement. Dark adaptation is a technique used to fix a non-stressed reference point in chloro-phyll measurements. Fv/Fm indicates maximum quantum yield of photosystem II that indicates tolerance to drought (Azam et al., 2015; Li et al., 2006; Murchie and Lawson, 2013). In addi-tion, green leaf area was visually scored 45 d after flowering only in two environments in 2011 on a scale of 1 to 5, where 1 = completely dead plant and 5 = completely green plant. This was done to make comparison of physiological measurements with GLAVS. The score was based on the number of green leaves on the plant, the size of the leaves, and dry areas on the leaf at maturity. These plots were harvested with a combine harvester and grain moisture content was recorded to report grain yield at the 13.5% moisture content. Yield data from Ottawa in 2008 were not used for further analysis because the plots were dam-aged by heavy wind and precipitation after flowering.

MATERIALS AND METHODSGenetic MaterialsWe used two overlapping sets of RILs (i.e., containing many common lines) developed through single seed descent method from a cross of Tx436 (elite food grain quality line) with 00MN7645 (stay-green line) (Table 1). Tx436 is a 3-dwarf (dw1Dw2dw3dw4), non-stay-green, tan color (ppQQ), food qual-ity sorghum pollinator line used in sorghum breeding programs. The panicle of this line is semi-open with erect branches at maturity. Tx436 is also resistant to anthracnose (Colletotrichum graminicola), fusarium head blight (Fusarium spp.), leaf blight (Exserohilium turticium), and downy mildew (Peronosclerospora sorghi), and tolerant to head smut (Sporisorium holci-sorghi). The pedigree of Tx436 is (SC120-6-sel/2*Tx7000)-10-4-6-l-l-l-bk, where SC120-6-sel is a BC1F3–partially converted line IS2816, zera-zera sorghum, from southern Rhodesia (Miller et al., 1992). The stay-green line 00MN7645 is pollinator line with red caryopsis and outstanding yield potential, released in 2003 by Kansas State University. The pedigree of 00MN7645 (SC35//BTx642/80060) involves not only the historically important lines SC35 and BTx642 (B35), but also 80060, a parental line from Department of Primary Industry, Queensland, Australia. SC35 is a stay-green sorghum line converted from IS12555 (Ste-phens et al., 1967). The pedigree of Tx642, [BTx406*IS12555 (SC35)F3]*IS12555, also involves IS12555 (Harris et al., 2007; Xin et al., 2008). The population we developed had the stay-green source from IS12555 but was evaluated in a hybrid background. This RIL population was crossed to ATx3042, a non-stay-green line used in sorghum hybrid breeding program in Kansas (Tesso et al., 2011), to produce RIL testcrosses, which were subsequently evaluated under field conditions.

Phenotypic EvaluationTwo sets of RIL testcrosses were evaluated for grain yield, flow-ering time, and physiological traits in randomized complete block design with two replications (Table 1). The first set of 188 RIL testcrosses and the two parents, crossed to ATx3042, were evaluated under six environments at Manhattan, Hesston, and Ottawa in Kansas in 2008 and 2009, and the second set of 248 RIL testcrosses and the two parents, crossed to ATx3042, were evaluated under two stressed environments at Manhattan and Hays in Kansas in 2011. The cumulative precipitation in each environment in the years 2008 and 2009 was higher than the

Table 1. Details of the trial environments and weather data in Kansas from March to October.

Year Location RIL testcrosses† Planting date Precipitation Tmin‡ Tmax‡ Tmax >35°C§

mm -------------- °C --------------- d

2008 Manhattan 188 10 June 2008 1012.4 12.0 24.9 9.0

Hesston 188 5 June 2008 874.7 13.4 24.4 11.0

Ottawa 188 23 June 2008 972.3 12.6 23.7 9.0

2009 Manhattan 188 29 May 2009 884.9 11.8 24.1 7.0

Hesston 188 3 June 2009 857.5 13.4 23.8 10.0

Ottawa 188 16 June 2009 1034.2 13.0 23.2 3.0

2011 Manhattan 248 10 June 2011 621.5 13.3 27.5 40.0

Hays 248 15 June 2011 340.1 12.3 27.5 46.0

† Number of recombinant inbred line testcrosses.

‡ Tmin, average minimum temperature during the crop growth period; Tmax, average maximum temperature during the crop growth period.

§ Number of days the maximum temperature was over 35°C.


Phenotypic Data AnalysisAnalysis of variance was conducted with PROC GLM in SAS version 9.1 (SAS Institute, 2014). Genetic variances (s2

g) and genotype × environment interaction variances (s2

ge) were esti-mated from the expected mean square equations. In addition, variance components were also obtained from PROC MIXED, and entry-mean based heritability (h2) was estimated with the following formula:

2g2

2 2ge2 eg

h

l rl

s=

s ss + +

where s2g is variance component for genotype, s2

ge for genotype-by-environment, and s2

e for error; r is the number of replications; and l is the number of environments. Least square means were estimated for each environment and year. Correlation coefficient between pairs of traits was calculated from the least square means.

Genotyping and Linkage Map ConstructionDNA extraction of the RILs and their parents was conducted with a modified cetyltrimethylammonium bromide method (Murray and Thompson, 1980). Genotyping was done through genotyping-by-sequencing (Davey et al., 2011; Elshire et al., 2011) by a contracted service at the Institute for Genomic Diver-sity at Cornell University. In brief, DNA samples were digested with ApeKI and then ligated to adapters with barcodes using T4 ligase (New England Biolabs, Ipswitch, MA). All ligated DNA samples were pooled and cleaned up. Primers complementary to the adaptor sequences were used for PCR to enrich the frag-ment pool. After cleaning up and checking fragment sizes, the PCR product (a library) was sequenced on a Genome Analyzer II (Illumina, San Diego, CA). The resulting 86 bp reads were filtered by sequencing quality and then aligned to the sorghum reference genome (Sorghum bicolor v1.4) for SNP calling. TASSEL GBS pipeline was used for SNP calling following the previously described methods by Institute for Genomic Diversity at Cornell University (Elshire et al., 2011; Glaubitz et al., 2014).

A linkage map was constructed using minimum spanning tree map (MSTmap) (Wu et al., 2008) and JoinMap (Van Ooijen, 2006). Linkage groups were assigned using genomic position of SNPs determined in the GBS process. Within each linkage group, genetic distance was determined using the Kosambi mapping function (Kosambi, 1943). Due to high density, several markers in the same genomic region were clustered and redundant SNPs that did not carry additional recombination information were removed before constructing the linkage map. The linkage map was graphically visualized with MapChart 2.2 (Voorrips, 2002).

QTL Analysis with Genetic and Physical DistanceEstimated least square means for each trait in each environment (eight environments), year-wise data (2008, 2009, and 2011), and combined least square means of traits of six environments from 2008 and 2009, and least square means for two environments from 2011 were used for QTL mapping. Interval mapping and composite interval mapping (CIM) was done in QTL Cartographer (Basten et al., 2005) and inclusive composite interval mapping in QTL

IciMapping v.3.2 (Wang et al., 2012b) to detect QTLs. Flowering time was used as a covariate in the QTL analysis, when it was correlated with other traits. The standard stepwise regression model 6 was used to select cofactors for CIM with default parameters in QTL cartographer. The LOD significance thresholds (P < 0.05) were determined by running 1000 permutations (Churchill and Doerge, 1994). The phenotypic variation explained (PVE) by the QTLs were also obtained from the Windows QTL Cartographer v.2.5 software (Wang et al., 2005).

A specific QTL (q) was named based on the trait abbreviation followed by the chromosome number and a number denoting the order of QTL from the start position of the chromosome for the same trait. The boundaries of the QTLs were estimated with the positions where the LOD value drop-off was equal to 1 (Lander and Botstein, 1989). Genome-wide scans (i.e., single marker anal-ysis, or SMA) using 7144 SNPs were conducted as t tests in R software (R Foundation, 2015) and a general linear model (GLM) in TASSEL (Bradbury et al., 2007). QTLs detected through CIM with genetic distance were compared with QTLs from SMA and reported. The same permutation thresholds obtained from the CIM analysis was adopted for the SMA with physical distance. These thresholds expressed in LOD were converted into likelihood ratios (LR) and then to p-values based on a Chi-square distribution with one degree of freedom. The thresholds of −log10(p) of 3.7 and 3.9 were used to compare the CIM and SMA results.

RESULTSEnvironments, Means, Heritability, and CorrelationsA combined analysis of phenotypic data from all eight envi-ronments indicated very significant genotype × environment interaction, agreed with the known weather conditions and field observations. Final groupings of six normal environments and separation of two drought environments were based on analysis of variance and heritability estimation, in addition to weather conditions and number of entries tested at different environments (Supplemental Table S1). Combined analysis of the two stressed locations in 2011 produced low heritability estimates for agronomic traits (0.07 for yield) and very low estimates for SPAD, Fv/Fm, and GLAVS due to high geno-type × environment interaction (Supplemental Table S1).

Environmental conditions varied across the eight envi-ronments during the crop cycle for these 3 yr (Table 1). Two environments in 2011 were considered stressed environments while the other environments were considered as normal (Table 1; Supplemental Fig. S1). Heritability estimates were highest for flowering time (0.79) followed by yield (0.44) under normal conditions. The lowest heritability estimate was for Fv/Fm (0.15) in 2008 and 2009. In 2011, we observed very different patterns of drought stress in two locations. These two locations were also analyzed individually for each trait.

ATx3042/00MN7645 (P2) had higher yield than ATx3042/Tx436 (P1) in all environments except at Hays in 2011 where drought stress was severe. Transgressive segre-gation was observed for all traits under normal and stressed environments (Tables 2 and 3; Supplemental Fig. S2). The


yield of the RIL testcrosses in each stressed environment in 2011 was (<2 t ha–1) lower than mean yields of RIL test-crosses in each environment in 2008 and 2009 (>2 t ha–1).

Flowering time, SPAD, and Fv/Fm were positively correlated with grain yield under normal conditions (Table 4). In 2011, we observed no significant associations between traits in each environment except SPAD and GLAVS. At the Hays location, the correlation coefficient (r) between SPAD and GLAVS was 0.65, and at Manhat-tan in 2011, it was 0.49.

parent P2 was earlier than P1 in all environments, and the flowering time range for the RIL testcrosses was 6 d (54–60) under normal conditions and 12 d (54–66) under stressed conditions. P2 had higher SPAD values than P1 in individ-ual environments and in the combined analysis; however, the differences between values under normal conditions was smaller than the differences under stressed conditions; Fv/Fm showed similar patterns. GLAVS was only measured under stress conditions, and P2 retained more green leaf area than P1 at Hays in 2011 and at Manhattan in 2011. Mean

Table 2. Trait means for parents ATx3042/Tx436 (P1) and ATx3042/00MN7645 (P2) and mean and range of 188 RIL testcrosses grown under six normal environments in 2008 and 2009.

2008 2009

P1 P2 RIL testcrosses P1 P2 RIL testcrosses

Location Trait Mean Mean Mean Range Mean Mean Mean Range

Manhattan Grain yield, t ha-1 2.5 3.5 2.3 1.4–2.4 2.3 5.4 3.1 1.4–4.6

Flowering time, d 60.0 57.5 55.8 53.0–61.0 68.0 67.5 67.7 64.5–71.0

SPAD† 60.3 59.2 57.4 46.6–63.7 56.5 63.9 57.7 51.4–63.7

Fv/Fm‡ 0.74 0.74 0.75 0.70–0.78 0.71 0.71 0.71 0.71–0.78

Hesston Grain yield, t ha-1 2.6 4.5 2.4 1.3–3.8 3.4 3.8 2.7 1.9–3.7

Flowering time, d 59.4 54.5 54.8 50.5–59.5 65.0 62.5 61.1 57.0–64.0

SPAD 51.5 58.6 55.6 45.3–63.2 51.7 51.0 49.1 42.5–56.4

Fv/Fm 0.74 0.76 0.75 0.71–0.78 0.67 0.76 0.73 0.61–0.82

Ottawa Grain yield, t ha-1 – – 1.5 0.5–2.5 3.2 3.8 1.9 0.7–3.7

Flowering time, d 60.0 58.0 56.8 51.0–62.5 59.9 56.5 56.2 57–64.9

SPAD 47.3 47.9 48.5 40.4–59.7 52.0 46.9 50.6 41.8–60.7

Fv/Fm 0.75 0.70 0.70 0.50–0.78 0.70 0.69 0.67 0.61–0.82

† SPAD, chlorophyll content using SPAD meter.

‡ Fv/Fm, chlorophyll fluorescence.

Table 3. Trait means for parents ATx3042/Tx436 (P1) and ATx3042/00MN7645 (P2) and mean and range of 248 RIL testcrosses grown under two stressed environments in 2011.

P1 P2 RIL testcrosses

Location Trait Mean Mean Mean Range

Manhattan Grain yield, t ha-1 0.9 1.1 1.0 0.6–1.9

Flowering time, d 54.0 51.0 52.5 47.5–63.7

SPAD† 27.6 46.4 39.8 12.7–60.6

Fv/Fm‡ 0.66 0.73 0.68 0.51–0.77

GLAVS§ 2.9 2.8 3.0 1.2–4.6

Hays Grain yield, t ha-1 2.0 1.4 1.8 0.8–3.9

Flowering time, d 68.0 64.5 65.7 58.5–77.5

SPAD 33.5 51.9 37.6 11.5–66.3

Fv/Fm 0.51 0.66 0.62 0.45–0.74

GLAVS 1.8 4.2 2.9 1.3–4.6

† SPAD, chlorophyll content using SPAD meter.

‡ Fv/Fm, chlorophyll fluorescence.

§ GLAVS, green leaf area visual score.

Table 4. Correlations of least square means for traits of 188 RIL testcrosses under six normal environments in 2008 and 2009.

Grain yield Flowering time Chlorophyll content Chlorophyll fluorescence

Grain yield – 0.38** 0.16 0.12

Flowering time – 0.14 0.27*

Chlorophyll content – 0.18*

Chlorophyll fluorescence –

* Significant at the 0.05 probability level.

** Significant at the 0.01 probability level.


GBS Markers and Linkage MapOut of 57,858 SNPs identified for a large number of sorghum accessions processed as a pool, 7144 SNPs were detected for the RIL population by GBS. After filtering for missing rate (<10%), chi-square test for segregation distortion, similarity of markers, double crossovers, and marker distribution in the genome, 1414 SNPs were selected for constructing a linkage map. This map of 2225.3 cM had an average distance of 1.5 cM between two adjacent SNPs. A plot of genetic distance

versus physical distance suggests GBS SNPs had good coverage of the genome (Supplemental Fig. S3). Clearly, the centromere region has lower recombination rates than the chromosome arms, as indicated by the much slower increase of genetic distance over this part of the chromosome.

QTL MappingAmong 15 QTLs detected (Fig. 1 and 2), 11 were for the traits measured under normal conditions using 188 RIL

Fig. 1. QTLs detected from a combined analysis of data collected on 188 RIL testcrosses grown under six normal environments in 2008 and 2009: (a) grain yield, (b) flowering time, (c) chlorophyll content, and (d) chlorophyll fluorescence.

Fig. 2. QTLs detected from a combined analysis of data collected on 248 RIL testcrosses grown under two stressed environments in 2011: (a) grain yield and (b) flowering time.


testcrosses and 4 QTLs were under stress conditions using 248 RIL testcrosses. QTL analyses for individual environments resulted in 42 QTLs, and year-wise QTL analysis resulted in 9 QTLs. The LOD score of the detected QTLs ranged from 2.5 to 6.9 and the phenotypic variation explained (PVE) values ranged from 5.0 to 26.4%. Consistent QTLs from combined as well as individual environment analyses were reported (Supplemental Fig. S4). Although the higher marker number used in the single maker scan is not expected to increase mapping power or resolution, this analysis did provide supporting evidence to the CIM mapping findings as the later approach can be sensitive to cofactor selection. Overall, the mapping results using physical positions and genetic positions were consistent (Supplemental Fig. S5).

Grain YieldFor grain yield, seven QTLs were detected in chromo-somes 1, 2, 3, 4, and 6 with LOD scores >3.1 and PVE values from 7.0 to 16.0% (Tables 5 and 6). A grain yield QTL qYLD1.1 under normal conditions and qYLD1.3 under drought conditions were mapped to the same region of the chromosome 1 (Fig. 1a, 2a). This QTL was also detected in the combined analysis of the data in 2008, 2009, and 2011 (Fig. 3; Supplemental Tables S2; Supplemental Fig. S4). This is a constitutive QTL that

explained up to 16% of the phenotypic variation. Indi-vidual environment QTL analysis detected several yield QTLs in chromosomes 1, 2, 3, and 4 with PVE values 4.0 to 17.0%. Grain yield QTLs in chromosomes 2, 3, 4, 6, and 7 overlapped with Fv/Fm QTLs (Supplemental Fig. S4).

Flowering TimeThree QTLs were detected for flowering time in chro-mosomes 2, 6, and 9 with LOD scores ranging from 2.5 to 3.3 and PVE values from 6 to 11% (Tables 5 and 6; Fig. 1). The QTL in chromosome 2 was detected under normal and stressed conditions with a peak position around 100 cM (Supplemental Fig. S4). Combined analy-sis of the 2008 data detected the QTL in chromosome 2 with PVE 10% and data from 2009 detected the QTL in chromosome 6 with PVE of 11%. Flowering time QTLs were detected in chromosome 6 through differ-ent combinations of analyses with the environments, that is, year-wise, grouped analysis of six environments, and individual environment analysis. The QTL in chromo-some 9 was detected in combined analysis of the data from six normal environments, three environments in 2009, and in the environment of Ottawa in 2008.

Table 6. QTLs detected for yield and flowering time of 248 RIL testcrosses grown under two stressed environments in 2011. Least square means from two environments were used, LOD is logarithm of odds, and PVE is phenotypic variation explained by QTLs.

Trait QTL ChrPeak

positionConfidence

intervalQTL start QTL end LOD PVE

Additive effect†

-------------------- cM -------------------- -------------------- bp -------------------- %

Grain yield qYLD1.3 1 78.2 72.5–86.1 S1_11203256 S1_21602500 3.1 8.0 -0.33

qYLD2.1 2 119.9 111.2–126.3 S2_63084956 S2_63712593 3.9 7.0 -0.18

qYLD4.1 4 86.7 85.6–95.1 S4_45937548 S4_62339532 3.6 8.0 -0.36

Flowering time qFT2.1 2 101.7 100.6–105.8 S2_62371241 S2_63084300 4.2 9.0 0.92

† Additive effect: positive values indicate contribution by Tx436 and negative values by 00MN7645.

Table 5. QTLs detected for traits of 188 RIL testcrosses grown under six normal environments in 2008 and 2009. Least square means from six environments were used, LOD is the logarithm of odds, and PVE is the phenotypic variation explained by QTLs.

Trait QTL ChrPeak

positionConfidence

intervalQTL start QTL end LOD PVE

Additive effect†

-------------------- cM -------------------- -------------------- bp -------------------- %Grain yield (CovFT)‡ qYLD1.1§ 1 67.3 53.6–83.5 S1_10629136 S1_10704841 6.4 16.0 -0.24

qYLD1.2 1 171.3 169.9–176.5 S1_11145830 S1_12704841 6.9 14.7 0.45

qYLD3.1 3 69.3 64.5–79.4 S3_69614572 S3_69859889 4.0 7.8 -0.22

qYLD6.1 6 130.3 129.7–134.7 S6_50360463 S6_52945337 4.1 7.1 0.23

Flowering time qFT2.1 2 96.8 91.3–103.9 S2_63084956 S2_62976360 3.1 6.0 -0.51

qFT6.1 6 49.6 43.7–76.3 S6_1402697 S6_40763291 2.5 11.0 -0.27

qFT9.1 9 94.8 91.5–104.5 S9_7580762 S9_4719436 3.1 6.0 -0.81

Chlorophyll content qSPAD4.1 4 213.0 209.6–217.6 S4_10119054 S4_6803028 3.1 8.7 0.74

Chlorophyll fluorescence qFv/Fm3.1 3 199.0 196.2–200.6 S3_56382011 S3_57860807 5.2 13.1 -0.0079

qFv/Fm4.1 4 57.3 41.0–73.6 S4_65560043 S4_64497114 4.1 26.4 0.0078

qFv/Fm4.2 4 236.5 229.4–239.5 S4_6803028 S4_6984568 3.4 10.4 0.0062


‡ CovFT, flowering time added as a covariate in analysis.

§ QTL detected consistently in 2008, 2009, and 2011.


Chlorophyll ContentFor SPAD under normal conditions, a QTL qSPAD4.1 was detected in chromosome 4 with a peak position 213.0 cM with an LOD score of 3.1 and PVE value of 8.7 (Table 5). This QTL was detected using the combined analysis of six environments and this QTL overlapped with Fv/Fm QTLs (Supplemental Fig. S4). The favorable allele is contributed by the parent Tx436. QTL mapping for individual environments detected presence of QTLs in chromosomes 2, 3, 4, 5, and 9 with LOD range from 2.8 to 4.2 and PVE values from 6 to 11% (Table 7).

Chlorophyll FluorescenceFor Fv/Fm, QTLs were detected in chromosome 3 with peak position at 99.0 cM and in chromosome 4 at 57.3 and 236.5 cM under normal conditions. The QTLs in chro-mosome 4 overlapped with QTLs for SPAD. QTLs for Fv/Fm were detected in chromosomes 2, 4, 5, and 7 in individual location analysis. The QTLs for Fv/Fm in 2011 at Manhattan and Hays were different. This might be due to the high genotype × environment interaction, and in fact, the mechanism for drought and drought + heat toler-ance is different (Supplemental Fig. S4).

Green Leaf Area Visual ScoreMeasurements of GLAVS were taken only in 2011 in two environments. At Hays in 2011, a QTL detected in chro-mosome 2 at 148.7 cM explained 7% of the phenotypic variation (Table 7). This QTL overlapped with the Fv/Fm QTL detected in the same environment (Supplemental

Fig. S4). The expression of stay-green traits differs under different levels of drought stress. In our study, we had two stressed environments that differed drastically and we detected no common stay-green QTL under these conditions.

QTLs Detected for Individual EnvironmentsComposite interval mapping was also conducted on the data from individual environments for each trait (Table 7). The results indicated consistent QTL for yield at Manhattan in 2008 (126.5–139.5 cM) and at Hesston in 2009 (126.3–132.5 cM) in chromosome 1. The flower-ing time QTL in chromosome 2 was consistent under three environments Manhattan in 2008 (90.6–100.7 cM), Hesston in 2009 (91.1–101.1 cM), and Manhattan in 2011 (100.5–105.4 cM). Fv/Fm and GLAVS QTLs overlapped in chromosomes 2, 3, 4, 6, and 7 (Supplemental Fig. S4). Physical positions of the QTLs detected are also shown in respective tables for each analysis.

DISCUSSIONGrain yield is a complex trait governed by polygenes and molecular markers associated with the trait can help to introgress the QTLs into a single variety (Collard et al., 2005; Gupta et al., 2009; Tanksley, 1993). This study reports QTLs for grain yield under different moisture conditions, among them, a grain yield QTL qYLD1.1 in chromosome 1 was consistent under drought and normal conditions. This grain yield per se QTL was consistently detected independent of flowering time and other traits. QTLs were also detected in chromosomes 2, 3, 4, and 6 with PVE values ranging from 7.1 to 14.7%. Earlier studies have reported yield QTLs in all chromosomes (Kapanigowda et al., 2014; Nagaraja Reddy et al., 2013; Rajkumar et al., 2013; Sabadin et al., 2012; Shehzad and Okuno, 2015) but in sorghum, 20% of the QTLs are reported in chromo-some 1 (Mace and Jordan, 2011). Chromosomes 1, 6, and 7 contain gene rich regions for plant height, grain yield, and tiller number (Mace and Jordan, 2011).

Grain weight is one of the major yield components in addition to grains per spike and tillers per spike that deter-mine grain yield (Maranville and Clegg, 1977). These components are correlated with grain yield and might share common markers. A QTL for 100-grain weight (qGW1) was reported in chromosome 1 on an F2 popula-tion in a recent study (Han et al., 2015). Also plant height and tillers per plant are associated with grain yield in sor-ghum (Rama Reddy et al., 2014). They also reported a very strong correlation between plant height and grain yield, co-localization of three major plant height QTLs with grain yield QTLs. Among the major genes Dw1, Dw2, Dw3, and Dw4 affecting plant height (Brown et al., 2006; Brown et al., 2008; Klein et al., 2008; Mace and Jordan, 2011; Morris et al., 2013) only Dw3 has been

Fig. 3. A robust QTL detected in chromosome 1 for grain yield under both normal and stressed conditions. YLD08 indicates the mapping result with the least square means of the 2008 data, YLD09 of 2009 data, and YLD11 of 2011 data.YLD2Yrs is the LS mean of the 5-environment data in 2008 and 2009.


Table 7. QTLs detected for grain yield (YLD), flowering time (FT), chlorophyll content using SPAD meter (SPAD), chlorophyll fluorescence (Fv/Fm), and green leaf area visual score (GLAVS) through composite interval mapping of the data from individual environments. LOD is the logarithm of odds, and PVE is the phenotypic variation explained by QTLs.

Location Trait Chr Peak position

Confidence interval QTL start QTL end LOD PVE Additive

effect†

-------------------- cM -------------------- -------------------- bp -------------------- %Manhattan 2008 YLD 1 101.2 97.4–104.8 S1_55698234 S1_53540535 3.8 17.0 0.27

1 133.9 126.5–139.5 S1_4454605 S1_1965883 6.9 8.0 0.543 71.6 66.5–79.7 S3_69614572 S1_69859889 3.4 8.0 -0.32

FT 2 94.9 90.6–100.7 S2_63084956 S2_62371215 3.8 8.0 -0.54

SPAD 5 56.0 45.1–68.4 S5_46575996 S5_1734124 3.0 10.0 -0.95

Fv/Fm 7 152.5 144.1–152.4 S7_57834662 S7_57080678 3.5 9.0 0.005

Hesston 2008 YLD 1 168.1 165.0–177.8 S1_10629136 S1_12704841 3.3 9.0 0.47FT 6 24.9 16.2–29.1 S6_1893765 S6_1980781 2.6 7.0 -0.56

SPAD 9 93.4 77.4–99.6 S9_13772251 S9_4882907 2.8 8.0 0.94Fv/Fm 4 214.8 202.9–226.0 S4_9731798 S4_6803028 4.1 11.0 0.008

6 124.5 108.4–129.9 S6_48270182 S6_50437639 2.8 7.0 0.0057 32.7 19.5–40.1 S7_3831383 S7_668774 3.4 13.0 0.009

Ottawa 2008 YLD na‡ na na na na na na naFT 4 0.4 0.0–14.0 S4_66498596 S4_65569720 3.7 9.0 -0.79

9 99.6 90.4–104.3 S9_9083255 S9_4719436 2.5 6.0 -0.65

SPAD 4 105.8 100.6–110.8 S4_64497114 S4_63944556 3.6 10.0 -1.30

Fv/Fm na na na na na na na na

Manhattan 2009 YLD na na na na na na na naFT 1 208.6 200.7–216.3 S1_21637169 S1_52056855 3.0 7.0 -0.37

6 70.0 51.7–71.5 S6_2577808 S6_40763291 3.2 11.0 -0.46

SPAD 3 109.0 96.3–113.9 S3_70217184 S3_74170649 3.6 11.0 0.77Fv/Fm na na na na na na na na

Hesston 2009 YLD 1 130.7 126.3–132.5 S1_4454605 S1_4054570 3.3 10.0 0.427 34.2 25.9–40.3 S7_3831383 S7_668774 3.2 9.0 0.38

FT 2 98.1 91.1–101.1 S2_63434503 S2_62371215 3.3 9.0 -0.56

SPAD 2 57.6 51.8–65.5 S2_69013996 S2_66169596 4.2 12.0 1.105 165.0 159.6–173.9 S5_55583917 S5_47611479 4.1 12.0 -1.25

Fv/Fm na na na na na na na na

Ottawa 2009 YLD 3 90.3 82.3–95.8 S3_69859889 S3_72378997 2.9 7.0 -0.40

6 155.6 142.0–163.3 S6_52945337 S6_56395217 3.0 7.0 0.447 88.7 77.8–95.5 S7_59949379 S7_63515732 5.7 17.0 0.71

FT 6 51.4 35.7–64.6 S6_2126887 S6_3539360 4.4 16.0 -0.36

SPAD na na na na na na na naFv/Fm 4 185.2 174.9–192.5 S4_11064879 S4_11347993 3.0 8.0 0.011

6 183.7 181.8–190.8 S6_59498146 S6_59465589 2.7 7.0 0.01

Manhattan 2011 YLD 4 110.0 95.5–124.5 S4_44430345 S4_62339532 5.0 17.0 -0.31

FT 2 102.9 100.5–105.4 S2_62371241 S2_63434503 2.1 9.0 0.92SPAD 2 52.3 40.1–51.0 S2_5295260 S2_11937449 2.8 6.0 2.26

6 236.9 221.0–248.9 S6_59465589 S6_60559033 3.8 5.0 1.67Fv/Fm 5 6.5 5.0–14.4 S5_1123271 S5_2005775 2.8 5.0 -0.01

Hays 2011 YLD 1 82.9 64.3–92.8 S1_11143739 S1_21602500 2.1 5.0 -0.31

2 120.9 109.5–128.3 S2_63084300 S2_63712593 4.0 7.0 -0.30

6 117.6 104.4–124.2 S6_40413905 S6_49294422 2.7 4.0 -0.24

FT 6 93.1 74.7–102.3 S6_3518216 S6_48182871 2.9 6.0 0.756 269.6 261.7–269.0 S6_61461650 S6_61279427 2.6 6.0 0.65

SPAD 5 129.1 123.3–129.8 S5_51077646 S5_51679634 3.6 7.0 3.42Fv/Fm 2 134.0 128.1–142.3 S2_63712593 S2_67313481 3.4 7.0 0.02GLAVS 2 148.7 138.4–158.1 S2_65997416 S2_69609259 3.9 7.0 0.20


‡ na, not available.


cloned in chromosome 9 (Multani et al., 2003). Dw1 is located in chromosome 9, but also linked to flowering time QTLs (Higgins et al., 2014). Dw2 is linked to Ma1 in chromosome 6 (Lin et al., 1995).

Flowering time is the most important trait for adapt-ability of a crop to different climatic regions and for yield stability (Craufurd et al., 1999). QTLs for flowering time were detected in chromosomes 2, 6, and 9 on the RIL testcrosses in the present study. The flowering time QTL qFT6.1 in chromosome 6 was detected from 43.7 to 76.3 cM (Chr6: 1,402,697 to 40,763,291) and individual envi-ronment analysis detected flowering time QTLs around the same region. Earlier studies have detected flowering time locus Ma1 in chromosome 6 (Feltus et al., 2006; Hig-gins et al., 2014; Klein et al., 2008; Lin et al., 1995; Zou et al., 2012). Ma1 is a flowering time repressor gene under long day conditions (Murphy et al., 2011). BLAST search of the sequence of PRR37 (Ma1) gene from NCBI on phy-tozome sorghum browser indicated the physical position Chr6: 40,265,943 to 40,266,766. In addition, the Ma6 maturity locus is located in chromosome 6 in sorghum (Brady, 2006; Mullet et al., 2012). Another flowering time locus was detected in chromosome 2 from 91.3 to 103.9 cM (Chr6: 63,084,956 to 62,976,360) under normal and drought conditions. Earlier studies have detected flower-ing time locus Ma5 in chromosome 2 (Aydin et al., 1996; Chantereau et al., 2001; Kim, 2004; Rooney and Aydin, 1999). Another QTL for flowering time was detected in chromosome 9 between 91.5 to 103.9 cM (Chr9: 7,580,762 to 4,719,436). Several others have reported flowering time QTLs on chromosome 9 (Hart et al., 2001; Higgins et al., 2014; Lin et al., 1995; Murray et al., 2008). Earlier studies also have reported QTLs for plant height in chromosome 9 and normally plant height and flowering time are cor-related traits (Brown et al., 2008; Lin et al., 1995).

Stay-green QTLs were reported in all chromosomes in various previous studies (Haussmann et al., 2002; Kebede et al., 2001; Crasta et al., 1999; Srinivas et al., 2008; Subudhi et al., 2000; Tuinstra et al., 1997; Xu et al., 2000b) and this information was projected into a consensus map (Mace and Jordan, 2011). Our comparisons with these studies indicated a QTL for nodal root angle was co-localized with QTLs for SPAD at Hesston in 2009 with a peak position 169 cM in chromosome 5 (Chr5: 47,611,479 to 55,583,917) that explained 12% of the phenotypic variation. This QTL overlapped with the QTL qRA2_5 for nodal root angle (Chr5: 55,169,818 to 55,690,833), qRDW1_5 for root dry weight (Chr5: 50,729,356 to 51,538,433), qSDW1_5 for shoot dry weight (Chr5: 50,729,356 to 51,538,433) from a recent study (Mace et al., 2012). Nodal root angle is closely related to drought adaptation in sorghum; it influences the vertical and horizontal distribution of root systems and thereby root architecture. Also a QTL was detected in chromosome 5 at Hays in 2011 under drought conditions with a peak position

129.1 cM (Chr5: 51,077,646 to 51,679,634) that overlapped with qRDW1_5 (Chr5: 50,729,356 to 51,538,433) and qSDW1_5 (Chr5: 50,729,356 to 51,538,433). The marker for nodal root angle was significantly associated with grain yield, and the nodal root angle QTL has been co-localized with stay-green QTLs based on projected maps (Mace and Jordan, 2011).

The source of stay-green in the present study is durra IS12555, but in a hybrid background. In our study, pheno-type of RILs was scored in an elite, testcross background (RIL testcrosses) thus reducing the confounding effect of flowering time in QTL detection. Testcross populations facilitates the genetic mapping in potential hybrids, but one disadvantage of this method is that the population itself is not immortal compared with RILs (You et al., 2006) and making the testcrosses requires extra efforts. Testcross progenies show reduced variation for a trait because of the contribution of only one allele from the corresponding RIL population. The QTLs detected in the RIL testcrosses are different from the RILs due to domi-nance effect of the heterozygous loci as well as epistasis between locus and genetic background ( Jines et al., 2007; Kerns et al., 1999; Mayfield et al., 2011). Several QTLs reported in the study are unique to the study and the QTLs that coincided with earlier studies were described.

The genetic map we used differs from earlier mapping studies in sorghum. Here we note the physical position of the markers and the genetic positions. Thus, assigning the markers to linkage groups was done according to the physi-cal chromosome positions. Different approaches were used to construct the linkage map, especially when many mark-ers are available to make the linkage map. Another feature of high density GBS SNPs on RILs is the option to choose different sets of markers to make linkage maps. The current number of RILs cannot take advantage of the high density markers to achieve higher resolution and several SNPs were clustered to chromosome regions, indicating that 1000 to 2,000 SNPs are adequate to construct a linkage map. This was demonstrated by comparing the SMA with the interval mapping and composite interval mapping methodologies. Further methodology research is needed to discover how to best exploit the large number of SNPs generated from GBS for linkage mapping, particularly if the GBS SNPs cover the potential candidate genes.

The RIL testcrosses were phenotyped in eight environ-ments in Kansas. According to the historical precipitation data western Kansas is drier, receiving far less rainfall than eastern Kansas. Each environment differed significantly in soil moisture levels, especially in 2011 where drought stress was observed. Also, unpredictable rainfall events can hinder progress in drought research in field based studies. Flower-ing time was correlated with most of the traits, and similar observations were recorded in earlier publications, prompt-ing our use of flowering time as a covariate in analysis. An


alternative to this is using mapping populations controlled for phenology (Pinto et al., 2010; Sukumaran et al., 2015).

Our results agree with earlier studies that conclude stay-green can be detected under drought stress conditions, but under normal conditions, the stay-green may be more difficult to detect (Van Oosterom et al., 1996). Stay-green is a beneficial trait under stressed conditions, but consistency in detecting the constitutive QTLs for stress related traits is still problematic (Collins et al., 2008). High-throughput genotyping technology integrated with high-throughput phenotyping crop breeding strategies might help to solve this problem (Mir et al., 2012). Apart from stay-green, other traits that allow crops to produce grain yield under adverse conditions, like floral fertility, root growth, water use efficiency, radiation use efficiency, early vigor, root architecture, carbon isotope discrimination, stomatal con-ductance, canopy temperature depression, abscissic acid concentration, and remobilization of water soluble carbo-hydrates, might also need to be integrated with stay-green traits (Passioura, 2006; Tuberosa, 2012).

Supplemental Materials AvailableSupplemental material is available with the online version of this article.

AcknowledgmentsThis work was supported by the Agriculture and Food Research Initiative Competitive Grant (2011-03587) from USDA National Institute of Food and Agriculture, Kansas Grain Sor-ghum Commission, and Kansas State University Center for Sorghum Improvement.

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