Key message
GWAS on multi-environment data identified genomic regions associated with trade-offs for grain weight and grain number.
Grain yield (GY) can be dissected into its components thousand ...grain weight (TGW) and grain number (GN), but little has been achieved in assessing the trade-off between them in spring wheat. In the present study, the Wheat Association Mapping Initiative (WAMI) panel of 287 elite spring bread wheat lines was phenotyped for GY, GN, and TGW in ten environments across different wheat growing regions in Mexico, South Asia, and North Africa. The panel genotyped with the 90 K Illumina Infinitum SNP array resulted in 26,814 SNPs for genome-wide association study (GWAS). Statistical analysis of the multi-environmental data for GY, GN, and TGW observed repeatability estimates of 0.76, 0.62, and 0.95, respectively. GWAS on BLUPs of combined environment analysis identified 38 loci associated with the traits. Among them four loci—6A (85 cM), 5A (98 cM), 3B (99 cM), and 2B (96 cM)—were associated with multiple traits. The study identified two loci that showed positive association between GY and TGW, with allelic substitution effects of 4% (GY) and 1.7% (TGW) for 6A locus and 0.2% (GY) and 7.2% (TGW) for 2B locus. The locus in chromosome 6A (79–85 cM) harbored a gene
TaGW2
-
6A
. We also identified that a combination of markers associated with GY, TGW, and GN together explained higher variation for GY (32%), than the markers associated with GY alone (27%). The marker-trait associations from the present study can be used for marker-assisted selection (MAS) and to discover the underlying genes for these traits in spring wheat.
KEY MESSAGE : Through genome-wide association study, loci for grain yield and yield components were identified in chromosomes 5A and 6A in spring wheat (Triticum aestivum). Genome-wide association ...study (GWAS) was conducted for grain yield (YLD) and yield components on a wheat association mapping initiative (WAMI) population of 287 elite, spring wheat lines grown under temperate irrigated high-yield potential condition in Ciudad Obregón, Mexico, during four crop cycles (from 2009–2010 to 2012–2013). The population was genotyped with high-density Illumina iSelect 90K single nucleotide polymorphisms (SNPs) assay. An analysis of traits across subpopulations indicated that lines with 1B/1R translocation had higher YLD, grain weight, and taller plants than lines without the translocation. GWAS using 18,704 SNPs identified 31 loci that explained 5–14 % of the variation in individual traits. We identified SNPs in chromosome 5A and 6A that were significantly associated with yield and yield components. Four loci were detected for YLD in chromosomes 3B, 5A, 5B, and 6A and the locus in 5A explained 5 % of the variation for grain number/m². The locus for YLD in chromosome 6A also explained 6 % of the variation in grain weight. Loci significantly associated with maturity were identified in chromosomes 2B, 3B, 4B, 4D, and 6A and for plant height in 1A and 6A. Loci were also detected for canopy temperature at grain filling (2D, 4D, 6A), chlorophyll index at grain filling (3B and 6A), biomass (3D and 6A) and harvest index (1D, 1B, and 3B) that explained 5–10 % variation. These markers will be further validated.
Understanding the genetic bases of economically important traits is fundamentally important in enhancing genetic gains in durum wheat. In this study, a durum panel of 208 lines (comprised of elite ...materials and exotics from the International Maize and Wheat Improvement Center gene bank) were subjected to genome wide association study (GWAS) using 6,211 DArTseq single nucleotide polymorphisms (SNPs). The panel was phenotyped under yield potential (YP), drought stress (DT), and heat stress (HT) conditions for 2 years. Mean yield of the panel was reduced by 72% (to 1.64 t/ha) under HT and by 60% (to 2.33 t/ha) under DT, compared to YP (5.79 t/ha). Whereas, the mean yield of the panel under HT was 30% less than under DT. GWAS identified the largest number of significant marker-trait associations on chromosomes 2A and 2B with
-values 10
to 10
and the markers from the whole study explained 7-25% variation in the traits. Common markers were identified for stress tolerance indices: stress susceptibility index, stress tolerance, and stress tolerance index estimated for the traits under DT (82 cM on 2B) and HT (68 and 83 cM on 3B; 25 cM on 7A). GWAS of irrigated (YP and HT combined), stressed (DT and HT combined), combined analysis of three environments (YP + DT + HT), and its comparison with trait
and stress indices identified QTL hotspots on chromosomes 2A (54-70 cM) and 2B (75-82 cM). This study enhances our knowledge about the molecular markers associated with grain yield and its components under different stress conditions. It identifies several marker-trait associations for further exploration and validation for marker-assisted breeding.
Allopolyploidy greatly expands the range of possible regulatory interactions among functionally redundant homoeologous genes. However, connection between the emerging regulatory complexity and ...expression and phenotypic diversity in polyploid crops remains elusive. Here, we use diverse wheat accessions to map expression quantitative trait loci (eQTL) and evaluate their effects on the population-scale variation in homoeolog expression dosage. The relative contribution of cis- and trans-eQTL to homoeolog expression variation is strongly affected by both selection and demographic events. Though trans-acting effects play major role in expression regulation, the expression dosage of homoeologs is largely influenced by cis-acting variants, which appear to be subjected to selection. The frequency and expression of homoeologous gene alleles showing strong expression dosage bias are predictive of variation in yield-related traits, and have likely been impacted by breeding for increased productivity. Our study highlights the importance of genomic variants affecting homoeolog expression dosage in shaping agronomic phenotypes and points at their potential utility for improving yield in polyploid crops.
Abiotic stresses that affect wheat production—heat (H) and drought (D)—often occur concurrently. The genetic dissection of stress tolerance in a population with large range of phenology is difficult ...due to the confounding effects. We developed a recombinant inbred line (RIL) population of 276 entries with a narrow range of phenology, from a cross between a synthetic-derived parent (SYN-D: Croc 1/
Aegilops squarrosa
(224)//Opata) and an elite line (Weebill 1) to (a) understand the individual and combined effects of H and D stresses on yield and related traits, (b) identify the genetic basis of individual and combined stress tolerance, and (c) know the genetics of stress tolerance that can be explored from the line SYN-D. Phenotypic analysis indicated that the detrimental effect of combined stresses was greater than their individual effects. We constructed a genetic map—2771.5 cM—of the population with 569 SNPs (231 DArTseq and 338 Illumina bead chip 90 K array) and identified 71 QTLs, in which eight were common among stresses. We identified five QTL hotspots for yield and related traits under D, H, and H + D in chromosomes 2A (20.5 to 30.5 cM), 3D (92.5 to 108.5 cM), 6D (68.5 to 73.5 cM), 6D (125.5 to 135.5 cM), and 7B (40.5 to 61.5 cM). Among the 71 identified QTLs, SYN-D contributed 37 QTLs (52%) and Weebill 1 contributed 34 QTLs (48%). SYN-D also contributed the common thousand-grain weight QTL detected under H, D, and H + D, which can be used in molecular-assisted breeding.
Core Ideas
Genomic‐enabled prediction accuracy of G×E models was superior to the non‐G×E models.
Forward prediction and sparse testing in durum wheat shows great promise.
Genomic‐enabled prediction ...accuracy of yield and components traits were highly associated with heritability.
Genomic prediction studies incorporating genotype × environment (G×E) interaction effects are limited in durum wheat. We tested the genomic‐enabled prediction accuracy (PA) of Genomic Best Linear Unbiased Predictor (GBLUP) models—six non‐G × E and three G × E models—on three basic cross‐validation (CV) schemes— in predicting incomplete field trials (CV2), new lines (CV1), and lines in untested environments (CV0)— in a durum wheat panel grown under yield potential, drought stress, and heat stress conditions. For CV0, three scenarios were considered: (i) leave‐one environment out (CV0‐Env); (ii) leave one site out (CV0‐Site); and (iii) leave 1 yr out (CV0‐Year). The reaction norm models with G × E effects showed higher PA than the non‐G × E models. Among the CV schemes, CV2 and CV0‐Env had higher PA (0.58 each) than the CV1 scheme (0.35). When the average of all the models and CV schemes were considered, among the eight traits— grain yield, thousand grain weight, grain number, days to anthesis, days to maturity, plant height, and normalized difference vegetation index at vegetative (NDVIvg) and grain filling (NDVIllg)—, plant height had the highest PA (0.68) and moderate values were observed for grain yield (0.34). The results indicated that genomic selection models incorporating G × E interaction show great promise for forward prediction and application in durum wheat breeding to increase genetic gains.
Developing climate-resilient wheat is a priority for South Asia since the effect of climatechange will be pronounced on the major crops that are staple to the region. South Asia mustproduce >400 ...million metric tons (MMT) of wheat by 2050 to meet the demand. However, the currentaverage yield <3 t/ha is not sufficient to meet the requirement. In this review, we are addressinghow pre-breeding methods in wheat can address the gap in grain yield as well as reduce thebottleneck of genetic diversity. Physiological pre-breeding which incorporates screening of diversegermplasm from gene banks for physiological and agronomic traits, the strategic crossing of complementarytraits, high throughput phenotyping, molecular markers-based generation advancement,genomic prediction, and validation of high-value heat and drought tolerant lines to South Asia canhelp to alleviate the drastic effect of climate change on wheat production. There are several genebanks, if utilized well, can play a major role in breeding for climate-resilient wheat. CIMMYTswheat physiological pre-breeding has delivered several hundred lines via the Stress Adapted TraitYield Nursery (SATYN) to the NARS in many South Asian countries; India, Pakistan, Nepal,Bangladesh, Afghanistan, and Iran. Some of these improved germplasms have resulted in varietiesfor farmer's field. We conclude the review by pointing out the importance of collaborative interdisciplinarytranslational research to alleviate the effects of climate change on wheat production inSouth Asia.
Developing genomic selection (GS) models is an important step in applying GS to accelerate the rate of genetic gain in grain yield in plant breeding. In this study, seven genomic prediction models ...under two cross-validation (CV) scenarios were tested on 287 advanced elite spring wheat lines phenotyped for grain yield (GY), thousand-grain weight (GW), grain number (GN), and thermal time for flowering (TTF) in 18 international environments (year-location combinations) in major wheat-producing countries in 2010 and 2011. Prediction models with genomic and pedigree information included main effects and interaction with environments. Two random CV schemes were applied to predict a subset of lines that were not observed in any of the 18 environments (CV1), and a subset of lines that were not observed in a set of the environments, but were observed in other environments (CV2). Genomic prediction models, including genotype × environment (G×E) interaction, had the highest average prediction ability under the CV1 scenario for GY (0.31), GN (0.32), GW (0.45), and TTF (0.27). For CV2, the average prediction ability of the model including the interaction terms was generally high for GY (0.38), GN (0.43), GW (0.63), and TTF (0.53). Wheat lines in site-year combinations in Mexico and India had relatively high prediction ability for GY and GW. Results indicated that prediction ability of lines not observed in certain environments could be relatively high for genomic selection when predicting G×E interaction in multi-environment trials.
Grain yield (YLD) is a function of the total biomass (BM) and of partitioning the biomass by grains, i.e., the harvest index (HI). The most critical developmental stage for their determination is the ...flowering time, which mainly depends on the vernalization requirement (Vrn) and photoperiod sensitivity genes (Ppd) loci. Allelic variants at the Vrn, Ppd, and earliness per se (Eps) genes of elite spring wheat genotypes included in High Biomass Association Panel (HiBAP) I and II were used to estimate their effects on the phenological stages BM, HI, and YLD. Each panel was grown for two consecutive years in Northwest Mexico. Spring alleles at Vrn-1 had the largest effect on shortening the time to anthesis, and the Ppd-insensitive allele Ppd-D1a had the most significant positive effect on YLD in both panels. In addition, alleles at TaTOE-B1 and TaFT3-B1 promoted between 3.8% and 7.6% higher YLD and 4.2% and 10.2% higher HI in HiBAP I and II, respectively. When the possible effects of the TaTOE-B1 and TaFT3-B1 alleles on the sink and source traits were explored, the favorable allele at TaTOE-B1 showed positive effects on several sink traits mainly related to grain number. The favorable alleles at TaFT3-B1 followed a different pattern, with positive effects on the traits related to grain weight. The results of this study expanded the wheat breeders’ toolbox in the quest to breed better-adapted and higher-yielding wheat cultivars.
The application of spectral reflectance indices (SRIs) as proxies to screen for yield potential (YP) and heat stress (HS) is emerging in crop breeding programs. Thus, a comparison of SRIs and their ...associations with grain yield (GY) under YP and HS conditions is important. In this study, we assessed the usefulness of 27 SRIs for indirect selection for agronomic traits by evaluating an elite spring wheat association mapping initiative (WAMI) population comprising 287 elite lines under YP and HS conditions. Genetic and phenotypic analysis identified 11 and 9 SRIs in different developmental stages as efficient indirect selection indices for yield in YP and HS conditions, respectively. We identified enhanced vegetation index (EVI) as the common SRI associated with GY under YP at booting, heading and late heading stages, whereas photochemical reflectance index (PRI) and normalized difference vegetation index (NDVI) were the common SRIs under booting and heading stages in HS. Genome-wide association study (GWAS) using 18704 single nucleotide polymorphisms (SNPs) from Illumina iSelect 90K identified 280 and 43 marker-trait associations for efficient SRIs at different developmental stages under YP and HS, respectively. Common genomic regions for multiple SRIs were identified in 14 regions in 9 chromosomes: 1B (60-62 cM), 3A (15, 85-90, 101- 105 cM), 3B (132-134 cM), 4A (47-51 cM), 4B (71- 75 cM), 5A (43-49, 56-60, 89-93 cM), 5B (124-125 cM), 6A (80-85 cM), and 6B (57-59, 71 cM). Among them, SNPs in chromosome 5A (89-93 cM) and 6A (80-85 cM) were co-located for yield and yield related traits. Overall, this study highlights the utility of SRIs as proxies for GY under YP and HS. High heritability estimates and identification of marker-trait associations indicate that SRIs are useful tools for understanding the genetic basis of agronomic and physiological traits.
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