Unconditional and conditional QTL analysis of kernel weight related traits in wheat (Triticum aestivum L.) in multiple genetic backgrounds

Wheat thousand kernel weight (TKW) is a complex trait, and is largely controlled by several kernel traits, including kernel length (KL) and kernel width (KW). In order to reveal the genetic relationship between TKW and these kernel traits (KW and KL) as accurate as possible, we applied both unconditional and conditional mapping analyses to three distinct genetic populations, one DH population and two RIL populations. This report describes the identifications of 36 unconditional and conditional additive QTLs and 30 pairs of unconditional and conditional epistatic QTLs, all of which are closely associated with TKW. While the conditional additive locus Qtkw1B, detected in the RIL2 population, exhibited the largest contribution, explaining 14.12 % of TKW variance, the unconditional epistatic QTLs Qtkw3A-2/Qtkw5B.1, detected in the DH population, accounted for 11.95 % of phenotypic variance. This study also showed that, compared with unconditional mapping, conditional mapping resulted in very different numbers and different extent of effects of additive and epistatic QTLs that were associated with TKW when TKW was conditioned on kernel traits (KW and KL). These data strongly suggest that KW and KL indeed play a significant role in determining TKW. Furthermore, we demonstrated that the effects of the 25 additive QTLs for TKW were either entirely or largely determined by KW, while the effects of the other 25 additive QTLs for TKW were either entirely or largely affected by KL. We conclude that the conditional mapping can be useful for a better understanding of the interrelationship between the yield contributing traits at the QTL level.     

Wheat kernel dimensions: how do they contribute to kernel weight at an individual QTL level?

Kernel dimensions (KD) contribute greatly to thousand-kernel weight (TKW) in wheat. In the present study, quantitative trait loci (QTL) for TKW, kernel length (KL), kernel width (KW) and kernel diameter ratio (KDR) were detected by both conditional and unconditional QTL mapping methods. Two related F(8:9) recombinant inbred line (RIL) populations, comprising 485 and 229 lines, respectively, were used in this study, and the trait phenotypes were evaluated in four environments. Unconditional QTL mapping analysis detected 77 additive QTL for four traits in two populations. Of these, 24 QTL were verified in at least three trials, and five of them were major QTL, thus being of great value for marker assisted selection in breeding programmes. Conditional QTL mapping analysis, compared with unconditional QTL mapping analysis, resulted in reduction in the number of QTL for TKW due to the elimination of TKW variations caused by its conditional traits; based on which we first dissected genetic control system involved in the synthetic process between TKW and KD at an individual QTL level. Results indicated that, at the QTL level, KW had the strongest influence on TKW, followed by KL, and KDR had the lowest level contribution to TKW. In addition, the present study proved that it is not all-inclusive to determine genetic relationships of a pairwise QTL for two related/causal traits based on whether they were co-located. Thus, conditional QTL mapping method should be used to evaluate possible genetic relationships of two related/causal traits.     

Genome-wide linkage mapping of yield-related traits in three Chinese bread wheat populations using high-density SNP markers

Key message

We identified 21 new and stable QTL, and 11 QTL clusters for yield-related traits in three bread wheat populations using the wheat 90 K SNP assay.

Abstract

Identification of quantitative trait loci (QTL) for yield-related traits and closely linked molecular markers is important in order to identify gene/QTL for marker-assisted selection (MAS) in wheat breeding. The objectives of the present study were to identify QTL for yield-related traits and dissect the relationships among different traits in three wheat recombinant inbred line (RIL) populations derived from crosses Doumai?×?Shi 4185 (D?×?S), Gaocheng 8901?×?Zhoumai 16 (G?×?Z) and Linmai 2?×?Zhong 892 (L?×?Z). Using the available high-density linkage maps previously constructed with the wheat 90 K iSelect single nucleotide polymorphism (SNP) array, 65, 46 and 53 QTL for 12 traits were identified in the three RIL populations, respectively. Among them, 34, 23 and 27 were likely to be new QTL. Eighteen common QTL were detected across two or three populations. Eleven QTL clusters harboring multiple QTL were detected in different populations, and the interval 15.5–32.3 cM around the Rht-B1 locus on chromosome 4BS harboring 20 QTL is an important region determining grain yield (GY). Thousand-kernel weight (TKW) is significantly affected by kernel width and plant height (PH), whereas flag leaf width can be used to select lines with large kernel number per spike. Eleven candidate genes were identified, including eight cloned genes for kernel, heading date (HD) and PH-related traits as well as predicted genes for TKW, spike length and HD. The closest SNP markers of stable QTL or QTL clusters can be used for MAS in wheat breeding using kompetitive allele-specific PCR or semi-thermal asymmetric reverse PCR assays for improvement of GY.

     

春小麦千粒重相关性状的QTL定位及其耐热性分析

春小麦灌浆中后期正逢高温天气,适于发掘与耐高温相关的QTL。本研究利用春小麦Avocet/Sujata重组自交系群体,自2016-2018年在石家庄藁城区和天津两地4个环境下种植,进行千粒重(TKW)、粒长(KL)和粒宽(KW)等性状QTL定位,探讨这些QTL与灌浆期高温和品种适应性的关系。结果显示:在4个环境下共检测到20个QTL。其中,5个与粒长相关,4个与粒宽相关,11个与千粒重相关。在千粒重相关QTL中,有1个兼控粒长(QTkw-5A.1/QKl-5A),3个兼控粒宽(QTkw-2A.1/QKw-2A.2、QTkw-3B.2/QKw-3B和QTkw-6A/QKw-6A);3个QTL(QTkw-2A.1、QTkw-4B和QTkw-5A.1)可在不同环境下重复检测到。在2017年(持续高温环境)和2018年(高温+超高温环境)石家庄试点共检测到7个千粒重QTL,可能与耐高温有关。其中,有2个主效QTL(QTkw-2A.1和QTkw-5A.1),分别解释13.8%和17.3%的表型变异,5个微效QTL(QTkw-2A.2、QTkw-3B.1、QTkw-3B.2,QTkw-4A.2和QTkw-6A),解释7.4%~9.9%的表型变异。这些QTL可能在今后的抗干热风育种中发挥重要作用。在石家庄试点共检测9个千粒重QTL,其中6个加性效应来自Sujata(5个可在2017年和2018年高温环境下被检测到),3个来自Avocet(2个可在高温环境下被检测到)。可见,聚合了多个优异位点可能是Sujata具有高千粒重和广泛适应性的遗传基础。     

QTL mapping for some grain traits in bread wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.)

Grain traits are important agronomic attributes with the market value as well as milling yield of bread wheat. In the present study, quantitative trait loci (QTL) regulating grain traits in wheat were identified. Data for grain area size (GAS), grain width (GWid), factor form density (FFD), grain length-width ratio (GLWR), thousand grain weight (TGW), grain perimeter length (GPL) and grain length (GL) were recorded on a recombinant inbred line derived from the cross of NW1014?×?HUW468 at Meerut and Varanasi locations. A linkage map of 55 simple sequence repeat markers for 8 wheat chromosomes was used for QTL analysis by Composite interval mapping. Eighteen QTLs distributed on 8 chromosomes were identified for seven grain traits. Of these, five QTLs for GLWR were found on chromosomes 1A, 6A, 2B, and 7B, three QTLs for GPL were located on chromosomes 4A, 5A and 7B and three QTLs for GAS were mapped on 5D and 7D. Two QTLs were identified on chromosomes 4A and 5A for GL and two QTLs for GWid were identified on chromosomes 7D and 6A. Similarly, two QTLs for FFD were found on chromosomes 1A and 5D. A solitary QTL for TGW was identified on chromosome 2B. For several traits, QTLs were also co-localized on chromosomes 2B, 4A, 5A, 6A, 5D, 7B and 7D. The QTLs detected in the present study may be validated for specific crosses and then used for marker-assisted selection to improve grain quality in bread wheat.     

Genetic analysis of grain protein-content,grain yield and thousand-kernel weight in bread wheat

Grain yield and grain protein content are two very important traits in bread wheat. They are controlled by genetic factors, but environmental conditions considerably affect their expression. The aim of this study was to determine the genetic basis of these two traits by analysis of a segregating population of 194 F(7) recombinant inbred lines derived from a cross between two wheat varieties, grown at six locations in France in 1999. A genetic map of 254 loci was constructed, covering about 75% of the bread wheat genome. QTLs were detected for grain protein-content (GPC), yield and thousand-kernel weight (TKW). 'Stable' QTLs (i.e. detected in at least four of the six locations) were identified for grain protein-content on chromosomes 2A, 3A, 4D and 7D, each explaining about 10% of the phenotypic variation of GPC. For yield, only one important QTL was found on chromosome 7D, explaining up to 15.7% of the phenotypic variation. For TKW, three QTLs were detected on chromosomes 2B, 5B and 7A for all environments. No negative relationships between QTLs for yield and GPC were observed. Factorial Regression on GxE interaction allowed determination of some genetic regions involved in the differential reaction of genotypes to specific climatic factors, such as mean temperature and the number of days with a maximum temperature above 25 degrees C during grain filling.     

Mapping quantitative trait loci for quality factors in an inter-class cross of US and Chinese wheat

Wheat quality factors are critical in determining the suitability of wheat (Triticum aestivum L.) for end-use product and economic value, and they are prime targets for marker-assisted selection. Objectives of this study were to identify quantitative trait loci (QTLs) that ultimately influence wheat market class and milling quality. A population of 132 F₁₂ recombinant inbred lines (RILs) was derived by single-seed descent from a cross between the Chinese hard wheat line Ning7840 and the soft wheat cultivar Clark and grown at three Oklahoma locations from 2001 to 2003. Milling factors such as test weight (volumetric grain weight, TW), kernel weight (KW), and kernel diameter (KD) and market class factors such as wheat grain protein content (GPC) and kernel hardness index (HI) were characterized on the basis of a genetic map constructed from 367 SSR and 241 AFLP markers covering all 21 chromosomes. Composite interval mapping identified eight QTLs for TW, seven for KW, six for KD, two each for GPC and HI measured by near-infrared reflectance (NIR) spectroscopy, and four for HI measured by single kernel characterization system. Positive phenotypic correlations were found among milling factors. Consistent co-localized QTLs were identified for TW, KW, and KD on the short arms of chromosomes 5A and 6A. A common QTL was identified for TW and KD on the long arm of chromosome 5A. A consistent major QTL for HI peaked at the Pinb-D1 locus on the short arm of chromosome 5D and explained up to 85% of the phenotypic variation for hardness. We identified QTLs for GPC on 4B and the short arm of 3A chromosomes. The consistency of quality factor QTLs across environments reveals their potential for marker-assisted selection.     

Identification of QTL for flag leaf length in common wheat and their pleiotropic effects

Leaf size is an important factor contributing to the photosynthetic capability of wheat plants. It also significantly affects various agronomic traits. In particular, the flag leaves contribute significantly to grain yield in wheat. A recombinant inbred line (RIL) population developed between varieties with significant differences in flag leaf traits was used to map quantitative trait loci (QTL) of flag leaf length (FLL) and to evaluate its pleiotropic effects on five yield-related traits, including spike length (SL), spikelet number per spike (SPN), kernel number per spike (KN), kernel length (KL), and thousand-kernel weight (TKW). Two additional RIL populations were used to validate the detected QTL and reveal the relationships in different genetic backgrounds. Using the diversity arrays technology (DArT) genetic linkage map, three major QTL for FLL were detected, with single QTL in different environments explaining 8.6–23.3% of the phenotypic variation. All the QTL were detected in at least four environments, and validated in two related populations based on the designed primers. These QTL and the newly developed primers are expected to be valuable for fine mapping and marker-assisted selection in wheat breeding programs.     

Genetic analysis of grain and malt quality in an elite barley population

Quantitative trait loci (QTLs) associated with grain weight, grain width, kernel hardness and malting quality were mapped in a doubled haploid population derived from two elite Australian malting barley varieties, Navigator and Admiral. A total of 30 QTLs for grain weight, grain width and kernel hardness were identified in three environments, and 63 QTLs were identified for ten malting quality traits in two environments. Three malting quality traits, namely β-amylase, diastatic power and apparent attenuation limit, were mainly controlled by a QTL linked to the Bmy1 gene at the distal end of chromosome 4H encoding a β-amylase enzyme. Six other malting quality traits, namely α-amylase, soluble protein, Kolbach index, free amino-acid nitrogen, wort β-glucan and viscosity, had coincident QTL clustered on chromosomes 1HS, 4HS, 7HS and 7HL, which demonstrated the interdependence of these traits. There was a strong association between these malt quality QTL clusters on chromosomes 1HS and 7HL and the major QTL for kernel hardness, suggesting that the use of this trait to enable early selection for malting quality in breeding programs would be feasible. In contrast, the majority of QTLs for hot-water extract were not coincident with those identified for other malt quality traits, which suggested differences in the mechanism controlling this trait. Novel QTLs have been identified for kernel hardness on chromosomes 2HL and 7HL, hot-water extract on 7HL and wort β-glucan on 6HL, and the resulting markers may be useful for marker-assisted selection in breeding programs.     

Mixed-model QTL mapping for kernel hardness and dough strength in bread wheat

Plant breeding data comprise unbalanced phenotypic data for inbreds with complex pedigrees. As traditional methods to map quantitative trait loci (QTL) cannot exploit plant breeding data, an alternative approach is QTL mapping via a mixed-model procedure. Our objective was to validate mixed-model QTL mapping for self-pollinated crops by detecting QTL for kernel hardness and dough strength from data in a bread wheat (Triticum aestivum L.) breeding program. We studied 80 parental and 373 experimental inbreds genotyped for 65 simple sequence repeat (SSR) markers and three candidate loci. The methodology involved three steps: variance component estimation, single-marker analyses, and a final multiple-marker analysis with marker effects treated as fixed effects. Two QTLs for kernel hardness were detected on chromosomes 1A (close to candidate locus GluA3) and 5D (close to candidate locus Ha). Four QTLs were detected for dough strength on chromosomes 1A, 1B, 1D, and 5B. Candidate gene GluA1, which was associated with dough strength, was the only candidate locus found significant. Results were consistent with previously reported markers and QTLs associated with kernel hardness and dough strength. Unlike previous studies that have assumed QTL effects as random, the assumption of fixed marker effects identified the favorable marker alleles to select for. We conclude that the detection of previously mapped QTL validates the usefulness of mixed-model QTL mapping in the context of a plant-breeding program.     

Mapping QTLs controlling kernel dimensions in a wheat inter-varietal RIL mapping population

Key message

Seven kernel dimension QTLs were identified in wheat, and kernel thickness was found to be the most important dimension for grain weight improvement.

Abstract

Kernel morphology and weight of wheat (Triticum aestivum L.) affect both yield and quality; however, the genetic basis of these traits and their interactions has not been fully understood. In this study, to investigate the genetic factors affecting kernel morphology and the association of kernel morphology traits with kernel weight, kernel length (KL), width (KW) and thickness (KT) were evaluated, together with hundred-grain weight (HGW), in a recombinant inbred line population derived from Nanda2419?×?Wangshuibai, with data from five trials (two different locations over 3 years). The results showed that HGW was more closely correlated with KT and KW than with KL. A whole genome scan revealed four QTLs for KL, one for KW and two for KT, distributed on five different chromosomes. Of them, QKl.nau-2D for KL, and QKt.nau-4B and QKt.nau-5A for KT were newly identified major QTLs for the respective traits, explaining up to 32.6 and 41.5% of the phenotypic variations, respectively. Increase of KW and KT and reduction of KL/KT and KW/KT ratios always resulted in significant higher grain weight. Lines combining the Nanda 2419 alleles of the 4B and 5A intervals had wider, thicker, rounder kernels and a 14% higher grain weight in the genotype-based analysis. A strong, negative linear relationship of the KW/KT ratio with grain weight was observed. It thus appears that kernel thickness is the most important kernel dimension factor in wheat improvement for higher yield. Mapping and marker identification of the kernel dimension-related QTLs definitely help realize the breeding goals.

     

不同发育时期小麦粒重性状QTL的动态分析

利用6044×01-35构建的重组自交系(RIL)群体为试验材料,对小麦粒重性状进行发育动态QTL分析。结果表明,在小麦花后子粒灌浆的7个不同时期,两个试验点共检测到16个与粒重性状相关的QTL。其中开花后20d检测到的单穗粒重QTL位于2A染色体上,解释率达12%,遗传效应超过10;两环境下控制千粒重QTL在7个时期均被检测到。花后的各个时期均能在Xgwm448-Xgpw7399标记区间定位到千粒重QTL。其中花后10d检测到1个千粒重QTL,位于2A染色体的Xgwm448-Xgpw7399标记区间,解释较大的表型变异,达到18%。Qtl8、Qtl13和Qtl14均定位在Xgwm448-Xgpw7399标记区间的同一位置,共同解释11%的表型变异。花后20d和花后25d均检测到1个QTL,位于2A染色体的Xgwm372-Xgwm95标记区间的不同位点,均能解释4%的表型变异。花后40d检测到1个QTL,位于1D染色体的Xwmc93-Xgpw2224标记区间,解释1%的表型变异。从连锁群的位置上看,控制千粒重的QTL主要集中在2A染色体的Xgwm448-Xgpw7399标记区间,这是一个控制千粒重QTL的富集区域,以期进行精细定位和图位克隆。     

Genomic distribution of quantitative trait loci for yield and yield-related traits in common wheat

A major objective of quantitative trait locus(QTL)studies is to find genes/markers that can be used in breeding programs via marker assisted selection(MAS).We surveyed the QTLs for yield and yield related traits and their genomic distributions in common wheat(Triticum aestivum L.)in the available published reports.We then carried out a meta-QTL(MQTL)analysis to identify the major and consistent QTLs for these traits.In total,55 MQTLs were identified,of which 12 significant MQTLs were located on wheat chromosomes 1A,1B,2A,2D,3B,4A,4B,4D and 5A.Our study showed that the genetic control of yield and its components in common wheat involved the important genes such as Rht and Vrn.Furthermore,several significant MQTLs were found in the chromosomal regions corresponding to several rice genomic locations containing important QTLs for yield related traits.Our results demonstrate that meta-QTL analysis is a powerful tool for confirming the major and stable QTLs and refining their chromosomal positions in common wheat,which may be useful for improving the MAS efficiency of yield related traits.     

QTL mapping of 1000-kernel weight, kernel length, and kernel width in bread wheat (Triticum aestivum L.)

Kernel size and morphology influence the market value and milling yield of bread wheat (Triticum aestivum L.). The objective of this study was to identify quantitative trait loci (QTLs) controlling kernel traits in hexaploid wheat. We recorded 1000-kernel weight, kernel length, and kernel width for 185 recombinant inbred lines from the cross Rye Selection 111 × Chinese Spring grown in 2 agro-climatic regions in India for many years. Composite interval mapping (CIM) was employed for QTL detection using a linkage map with 169 simple sequence repeat (SSR) markers. For 1000-kernel weight, 10 QTLs were identified on wheat chromosomes 1A, 1D, 2B, 2D, 4B, 5B, and 6B, whereas 6 QTLs for kernel length were detected on 1A, 2B, 2D, 5A, 5B and 5D. Chromosomes 1D, 2B, 2D, 4B, 5B and 5D had 9 QTLs for kernel width. Chromosomal regions with QTLs detected consistently for multiple year-location combinations were identified for each trait. Pleiotropic QTLs were found on chromosomes 2B, 2D, 4B, and 5B. The identified genomic regions controlling wheat kernel size and shape can be targeted during further studies for their genetic dissection.     

A intervarietal genetic map and QTL analysis for yield traits in wheat

A new genetic linkage map was constructed based on recombinant inbred lines (RILs) derived from the cross between the Chinese winter wheat (Triticum aestivum L.) varieties, Chuang 35050 and Shannong 483 (ChSh). The map included 381 loci on all the wheat chromosomes, which were composed of 167 SSR, 94 EST-SSR, 76 ISSR, 26 SRAP, 15 TRAP, and 3 Glu loci. This map covered 3636.7 cM with 1327.7 cM (36.5%), 1485.5 cM (40.9%), and 823.5 cM (22.6%) for A, B, and D genome, respectively, and contained 13 linkage gaps. Using the RILs and the map, we detected 46 putative QTLs on 12 chromosomes for grain yield (GY) per m², thousand-kernel weight (TKW), spike number (SN) per m², kernel number per spike (KNS), sterile spikelet number per spike (SSS), fertile spikelet number per spike (FSS), and total spikelet number per spike (TSS) in four environments. Each QTL explained 4.42–70.25% phenotypic variation. Four QTL cluster regions were detected on chromosomes 1D, 2A, 6B, and 7D. The most important QTL cluster was located on chromosome 7D near the markers of Xwmc31, Xgdm67, and Xgwm428, in which 8 QTLs for TKW, SN, SSS and FSS were observed with very high contributions (27.53–67.63%).     

Kernel weight per spike: what contributes to it at the individual QTL level?

Spike length (SL), spikelet number (SPN) per spike, kernel number per spike (KNPS), and thousand-kernel weight (TKW) have strong genetic associations with kernel weight per spike (KWPS) in wheat. To investigate their genetic relationships at the individual quantitative trait locus (QTL) level, both unconditional and conditional QTL mapping for KWPS with respect to SL, SPN, KNPS, and TKW were conducted. Two related F_8:9 recombinant inbred line populations, comprising 485 and 229 lines, respectively, were used. The trait phenotypic performances of each population were evaluated in four different environments. Unconditional QTL mapping analysis identified 22 putative additive QTL for KWPS, five of which were stable QTL, and only QKwps-WJ-1B.2 showed significant additive-by-environment interaction effects. In comparison with unconditional QTL mapping analysis, conditional QTL mapping analysis indicated that, at the QTL level, KNPS and TKW contributed more to KWPS than did SL and SPN. Any unconditional QTL for KWPS detected in this study was associated with at least one of its four related traits. The present study will provide assistance in the understanding of the genetic relationships between KWPS and its related traits.     

QTL for yield and associated traits in the Seri/Babax population grown across several environments in Mexico, in the West Asia, North Africa, and South Asia regions

Heat and drought adaptive quantitative trait loci (QTL) in a spring bread wheat population resulting from the Seri/Babax cross designed to minimize confounding agronomic traits have been identified previously in trials conducted in Mexico. The same population was grown across a wide range of environments where heat and drought stress are naturally experienced including environments in Mexico, West Asia, North Africa (WANA), and South Asia regions. A molecular genetic linkage map including 475 marker loci associated to 29 linkage groups was used for QTL analysis of yield, days to heading (DH) and to maturity (DM), grain number (GM2), thousand kernel weight (TKW), plant height (PH), canopy temperature at the vegetative and grain filling stages (CTvg and CTgf), and early ground cover. A QTL for yield on chromosome 4A was confirmed across several environments, in subsets of lines with uniform allelic expression of a major phenology QTL, but not independently from PH. With terminal stress, TKW QTL was linked or pleiotropic to DH and DM. The link between phenology and TKW suggested that early maturity would favor the post—anthesis grain growth periods resulting in increased grain size and yields under terminal stress. GM2 and TKW were partially associated with markers at different positions suggesting different genetic regulation and room for improvement of both traits. Prediction accuracy of yield was improved by 5 % when using marker scores of component traits (GM2 and DH) together with yield in multiple regression. This procedure may provide accumulation of more favorable alleles during selection.     

GW3-1和IND109标记对普通小麦粒重的QTL定位分析

应用一个由115个系组成的W7984/Opata85的重组自交系(RIL)群体,建立了一个由394个(292个RFLP、94个SSR和8个特殊的基因杂交探针)DNA分子标记组成的遗传连锁图,对小麦千粒重进行了单个标记的回归分析和复合区间作图的QTL定位,在单个标记的回归分析中检测到11个千粒重的QTLs(P<0.01);复合区间作图分析结果表明,其中4个标记bcd348a、GW3-1、IND109和Rz2的遗传效应较大,其贡献率分别为9.1%、19.0%、8.07%和8.14%,分别位于小麦的2BS、4AL、5BL和7DS上,特别是在水稻第3条染色体上控制籽粒大小的GW3-1和IND109分别位于小麦4A和5B染色体的长臂端.研究结果对小麦应用分子标记辅助选择或分子克隆基因有重要的参考价值.     

Mapping QTLs for chlorophyll content and chlorophyll fluorescence in wheat under heat stress

Heat stress, one of the major abiotic stresses in wheat, affects chlorophyll fluorescence and chlorophyll content and thereby photosynthesis. To identify quantitative trait loci (QTLs) associated with these traits under terminal heat stress, 251 recombinant inbred lines (RILs) derived from a cross HD 2808/HUW510 were phenotyped. Using composite interval mapping, 40 QTLs were identified; 17 were related to conditions after timely sowing and 23 to heat stress after late sowing. The various parameters of chlorophyll fluorescence were associated with 23 QTLs, which were located on chromosomes 1A, 2A, 3A, and 2D and explained 3.67 to 18.04 % of phenotypic variation, whereas chlorophyll content was associated with 17 QTLs on chromosomes 2A, 2B, 2D, 5B, and 7A explaining 3.49 to 31.36 % of phenotypic variation. Most of the identified QTLs were clustered on chromosome 2D followed by 2A and 1A. The QTL Qchc.iiwbr-2A for chlorophyll content linked with marker gwm372 was stable over conditions and explained 3.81 to 18.05 % of phenotypic variation. In addition, 7 epistatic QTL pairs were also detected which explained 1.67 to 11.0 % of phenotypic variance. These identified genomic regions can be used in marker assisted breeding after validation for heat tolerance in wheat.     

Quantitative trait loci mapping reveals pleiotropic effect for grain iron and zinc concentrations in wheat

Malnutrition because of the deficiency of minerals such as iron (Fe) and zinc (Zn) afflicts over 2 billion people worldwide. Wheat is a major staple crop, providing 20% of dietary energy and protein consumption worldwide. Breeding wheat with elevated levels of grain Zn and Fe concentrations (GZn and GFe) represents a significant opportunity to increase the intake of these micronutrients for the resource poor people who depend on it as a major source of dietary energy. Synthetic hexaploid wheats (SHWs) have large genetic variation for GZn and GFe, which can be exploited for developing wheat varieties with higher concentrations of these minerals. The objective of this study was to localise genomic regions associated with GZn and GFe, thousand kernel weight (TKW) and test weight (TW) in a mapping population derived from the cross of Seri M82 and the SHW CWI76364. Major quantitative trait loci (QTL) on chromosome 4BS were detected for GZn and GFe; the QTL explained up to 19.6% of the total phenotypic variation for GZn and showed pleiotropic effects on GFe. This indicates that simultaneous improvement of GZn and GFe is feasible. Three and five QTL for TW and TKW were detected, respectively. One of the QTL for TKW was also located on chromosome 4BS. Positive correlations between plant height and GZn/GFe were observed. The 4BS QTL is of great interest for breeding biofortified wheat by means of marker‐assisted selection.