Genome-wide association study for grain yield and related traits in elite wheat varieties and advanced lines using SNP markers.
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ABSTRACT:
SUBMITTER: Wang SX
PROVIDER: S-EPMC5703539 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Grain yield, which is mainly contributed by tillering capacity as well as kernel number and weight, is the most important trait to plant breeders and agronomists. Label-free quantitative proteomics was used to analyse yield-contributing organs in wheat. These were leaf sample, tiller initiation, spike initiation, ovary and three successive kernel development stages at 5, 10 and 15 days after anthesis (DAA). We identified 3182 proteins across all samples. The largest number was obtained for spike initiation (1673), while the smallest was kernel sample at 15 DAA (709). Of the 3182 proteins, 296 of them were common to all seven organs. Organ-specific proteins ranged from 148 in ovary to 561 in spike initiation. When relative protein abundances were compared to that of leaf sample, 347 and 519 proteins were identified as differentially abundant in tiller initiation and spike initiation, respectively. When compared with ovary, 81, 35 and 96 proteins were identified as differentially abundant in kernels sampled at 5, 10 and 15 DAA, respectively. Our study indicated that two Argonaute proteins were solely expressed in spike initiation. Of the four expansin proteins detected, three of them were mainly expressed during the first 10 days of kernel development after anthesis. We also detected cell wall invertases and sucrose and starch synthases mainly during the kernel development period. The manipulation of these proteins could lead to increases in tillers, kernels per spike or final grain weight, and is worth exploring in future studies.
| S-EPMC7586745 | biostudies-literature
Project description:Genomic selection (GS) is a strategy to predict the genetic merits of individuals using genome-wide markers. However, GS prediction accuracy is affected by many factors, including missing rate and minor allele frequency (MAF) of genotypic data, GS models, trait features, etc. In this study, we used one wheat population to investigate prediction accuracies of various GS models on yield and yield-related traits from various quality control (QC) scenarios, missing genotype imputation, and genome-wide association studies (GWAS)-derived markers. Missing rate and MAF of single nucleotide polymorphism (SNP) markers were two major factors in QC. Five missing rate levels (0%, 20%, 40%, 60%, and 80%) and three MAF levels (0%, 5%, and 10%) were considered and the five-fold cross validation was used to estimate the prediction accuracy. The results indicated that a moderate missing rate level (20% to 40%) and MAF (5%) threshold provided better prediction accuracy. Under this QC scenario, prediction accuracies were further calculated for imputed and GWAS-derived markers. It was observed that the accuracies of the six traits were related to their heritability and genetic architecture, as well as the GS prediction model. Moore-Penrose generalized inverse (GenInv), ridge regression (RidgeReg), and random forest (RForest) resulted in higher prediction accuracies than other GS models across traits. Imputation of missing genotypic data had marginal effect on prediction accuracy, while GWAS-derived markers improved the prediction accuracy in most cases. These results demonstrate that QC on missing rate and MAF had positive impact on the predictability of GS models. We failed to identify one single combination of QC scenarios that could outperform the others for all traits and GS models. However, the balance between marker number and marker quality is important for the deployment of GS in wheat breeding. GWAS is able to select markers which are mostly related to traits, and therefore can be used to improve the prediction accuracy of GS.
| S-EPMC7073225 | biostudies-literature
Project description:The genetic gain in yield and quality are two major targets of wheat breeding programs around the world. In this study, a high density genetic map consisting of 10,172 SNP markers identified a total of 43 genomic regions associated with three quality traits, three yield traits and two agronomic traits in hard red spring wheat (HRSW). When compared with six grain shape and size traits, the quality traits showed mostly independent genetic control (~18% common loci), while the yield traits showed moderate association (~53% common loci). Association of genomic regions for grain area (GA) and thousand-grain weight (TGW), with yield suggests that targeting an increase in GA may help enhancing wheat yield through an increase in TGW. Flour extraction (FE), although has a weak positive phenotypic association with grain shape and size, they do not share any common genetic loci. A major contributor to plant height was the Rht8 locus and the reduced height allele was associated with significant increase in grains per spike (GPS) and FE, and decrease in number of spikes per square meter and test weight. Stable loci were identified for almost all the traits. However, we could not find any QTL in the region of major known genes like GPC-B1, Ha, Rht-1, and Ppd-1. Epistasis also played an important role in the genetics of majority of the traits. In addition to enhancing our knowledge about the association of wheat quality and yield with grain shape and size, this study provides novel loci, genetic information and pre-breeding material (combining positive alleles from both parents) to enhance the cultivated gene pool in wheat germplasm. These resources are valuable in facilitating molecular breeding for wheat quality and yield improvement.
| S-EPMC6750600 | biostudies-literature
Project description:Grain yield, which is mainly contributed by tillering capacity as well as kernel number and weight, is the most important traits to plant breeders and agronomists. Label-free quantitative proteomics was used to analyze yield-contributing organs in wheat. These were leaf sample, tiller initiation, spike initiation, ovary, and three successive kernel development stages at five, ten, and fifteen days after anthesis. We identified 3,182 proteins across all samples. The largest number was obtained for spike initiation (1673), while the smallest was kernel sample at fifteen days after anthesis (709). Of the 3,182 proteins, 296 of them were common to all seven organs. Organ-specific proteins ranged from 148 in ovary to 561 in spike initiation. When relative protein abundances were compared to that of leaf sample, 347 and 519 proteins were identified as differentially abundant in tiller initiation and spike initiation, respectively. When compared with ovary, 81, 35, and 96 proteins were identified as differentially abundant in five, ten, and fifteen days after anthesis, respectively. Our study indicated that two argonaute proteins were solely expressed in spike initiation. Of the four expansin proteins detected, three of them were mainly expressed during the first 10 days of kernel development after anthesis. We also detected cell wall invertses and sucrose and starch synthases mainly during the kernel development period. The manipulation of these proteins could lead to increases in tillers, kernels per spike, or final grain weight, and is worth exploring in future studies.
2019-05-21 | MSV000083833 | MassIVE
Project description:BackgroundEstablish a molecular breeding program involved assembling a diverse germplasm collection and generating accurate genotypes to characterize their genetic potential and associate them with agronomic traits. In this study, we acquired over eight hundred wheat accessions from international gene banks and assessed their genetic relatedness using high-quality SNP genotypes. Understanding the scope of genomic variation in this collection allows the breeders to utilize the genetic resources efficiently while improving wheat yield and quality.ResultsA wheat diversity panel comprising 39 durum wheat, 60 spelt wheat, and 765 bread wheat accessions was genotyped on iSelect 90 K wheat SNP arrays. A total of 57,398 SNP markers were mapped to IWGSC RefSeq v2.1 assembly, over 30,000 polymorphic SNPs in the A, B, D genomes were used to analyze population structure and diversity, the results revealed the separation of the three species and the differentiation of CIMMYT improved breeding lines and landraces or widely grown cultivars. In addition, several chromosomal regions under selection were detected. A subset of 280 bread wheat accessions was evaluated for grain traits, including grain length, width, surface area, and color. Genome-wide association studies (GWAS) revealed that several chromosomal regions were significantly linked to known quantitative trait loci (QTL) controlling grain-related traits. One of the SNP peaks at the end of chromosome 7A was in strong linkage disequilibrium (LD) with WAPO-A1, a gene that governs yield components.ConclusionsHere, the most updated and accurate physical positions of SNPs on 90 K genotyping array are provided for the first time. The diverse germplasm collection and associated genotypes are available for the wheat researchers to use in their molecular breeding program. We expect these resources to broaden the genetic basis of original breeding and pre-breeding materials and ultimately identify molecular markers associated with important agronomic traits which are evaluated in diverse environmental conditions.
| S-EPMC9494784 | biostudies-literature
Project description:The main goal of this study was to investigate the genetic basis of yield and grain quality traits in winter wheat genotypes using association mapping approach, and identify linked molecular markers for marker assisted selection. A total of 120 elite facultative/winter wheat genotypes were evaluated for yield, quality and other agronomic traits under rain-fed and irrigated conditions for two years (2011-2012) at the Tel Hadya station of ICARDA, Syria. The same genotypes were genotyped using 3,051 Diversity Array Technologies (DArT) markers, of which 1,586 were of known chromosome positions. The grain yield performance of the genotypes was highly significant both in rain-fed and irrigated sites. Average yield of the genotypes ranged from 2295 to 4038 kg/ha and 4268 to 7102 kg/ha under rain-fed and irrigated conditions, respectively. Protein content and alveograph strength (W) ranged from 13.6-16.1% and 217.6-375 Jx10-4, respectively. DArT markers wPt731910 (3B), wPt4680 (4A), wPt3509 (5A), wPt8183 (6B), and wPt0298 (2D) were significantly associated with yield under rain-fed conditions. Under irrigated condition, tPt4125 on chromosome 2B was significantly associated with yield explaining about 13% of the variation. Markers wPt2607 and wPt1482 on 5B were highly associated with protein content and alveograph strength explaining 16 and 14% of the variations, respectively. The elite genotypes have been distributed to many countries using ICARDA's International system for potential direct release and/or use as parents after local adaptation trials by the NARSs of respective countries. The QTLs identified in this study are recommended to be used for marker assisted selection after through validation using bi-parental populations.
| S-EPMC4619745 | biostudies-literature
Project description:Wheat (Triticum aestivum L.) is one of the major staples in Nepal providing the bulk of food calories and at least 30% of Fe and Zn intake and 20% of dietary energy and protein consumption; thus, it is essential to improve its nutritional quality. To select high-yielding genotypes with elevated grain zinc and iron concentration, the sixth, seventh, eighth, and ninth HarvestPlus Yield Trials (HPYTs) were conducted across diverse locations in Nepal for four consecutive years: 2015-16, 2016-17, 2017-18, and 2018-19, using 47 biofortified and 3 non-biofortified CIMMYT-bred, bread wheat genotypes: Baj#1, Kachu#1, and WK1204 (local check). Genotypic and spatial variations were found in agro-morphological traits; grain yield and its components; and the grain zinc and iron concentration of tested genotypes. Grain zinc concentration was highest in Khumaltar and lowest in Kabre. Likewise, grain iron concentration was highest in Doti and lowest in Surkhet. Most of the biofortified genotypes were superior for grain yield and for grain zinc and iron concentration to the non-biofortified checks. Combined analyses across environments showed moderate to high heritability for both Zn (0.48-0.81) and Fe (0.46-0.79) except a low heritability for Fe observed for 7th HPYT (0.15). Grain yield was positively correlated with the number of tillers per m2, while negatively correlated with days to heading and maturity, grain iron, grain weight per spike, and thousand grain weight. The grain zinc and iron concentration were positively correlated, suggesting that the simultaneous improvement of both micronutrients is possible through wheat breeding. Extensive testing of CIMMYT derived high Zn wheat lines in Nepal led to the release of five biofortified wheat varieties in 2020 with superior yield, better disease resistance, and 30-40% increased grain Zn and adaptable to a range of wheat growing regions in the country - from the hotter lowland, or Terai, regions to the dry mid- and high-elevation areas.
| S-EPMC9249123 | biostudies-literature
Project description:BackgroundGenomic selection has the potential to increase genetic gains by using molecular markers as predictors of breeding values of individuals. This study evaluated the accuracy of predictions for grain yield, heading date, plant height, and yield components in soft red winter wheat under different prediction scenarios. Response to selection for grain yield was also compared across different selection strategies- phenotypic, marker-based, genomic, combination of phenotypic and genomic, and random selections.ResultsGenomic selection was implemented through a ridge regression best linear unbiased prediction model in two scenarios- cross-validations and independent predictions. Accuracy for cross-validations was assessed using a diverse panel under different marker number, training population size, relatedness between training and validation populations, and inclusion of fixed effect in the model. The population in the first scenario was then trained and used to predict grain yield of biparental populations for independent validations. Using subsets of significant markers from association mapping increased accuracy by 64-70% for grain yield but resulted in lower accuracy for traits with high heritability such as plant height. Increasing size of training population resulted in an increase in accuracy, with maximum values reached when ~ 60% of the lines were used as a training panel. Predictions using related subpopulations also resulted in higher accuracies. Inclusion of major growth habit genes as fixed effect in the model caused increase in grain yield accuracy under a cross-validation procedure. Independent predictions resulted in accuracy ranging between - 0.14 and 0.43, dependent on the grouping of site-year data for the training and validation populations. Genomic selection was "superior" to marker-based selection in terms of response to selection for yield. Supplementing phenotypic with genomic selection resulted in approximately 10% gain in response compared to using phenotypic selection alone.ConclusionsOur results showed the effects of different factors on accuracy for yield and agronomic traits. Among the factors studied, training population size and relatedness between training and validation population had the greatest impact on accuracy. Ultimately, combining phenotypic with genomic selection would be relevant for accelerating genetic gains for yield in winter wheat.
| S-EPMC6823964 | biostudies-literature
Project description:In wheat (Triticum spp.), modifying inflorescence (spike) morphology can increase grain number and size and thus improve yield. Here, we demonstrated the potential for manipulating and predicting spike morphology, based on 44 traits. In 12 wheat cultivars, we observed that detillering (removal of branches), which alters photosynthate distribution, changed spike morphology. Our genome-wide association study detected close associations between carbon partitioning (e.g. tiller number, main shoot dry weight) and spike morphology (e.g. spike length, spikelet density) traits in 210 cultivars. Most carbon-partitioning traits (e.g. tiller dry weight, harvest index) demonstrated high prediction abilities (>0.5). For spike morphology, some traits (e.g. total and fertile spikelet number, spike length) displayed high prediction abilities (0.3-0.5), but others (e.g. spikelet fertility, spikelet density) exhibited low prediction abilities (<0.2). Grain size traits were closely correlated in field and greenhouse experiments. Stepwise regression analysis suggests that significantly associated traits in the greenhouse explain 35.35% of the variation in grain yield and 67.63% of the variation in thousand-kernel weight in the field. Therefore, the traits identified in this study affect spike morphology; these traits can be used to predict and improve plant architecture and thus increase yield.
| S-EPMC6158183 | biostudies-literature
Project description:Hybrid rice breeding is an important strategy for enhancing grain yield. Breeding high-performance parental lines and identifying combining abilities is a top priority for hybrid breeding. Yuenongsimiao (YNSM) and its derivative variety Yuehesimiao (YHSM) are elite restorer lines with a high ability of fertility restoration, from which 67 derived hybrid combinations have been authorized to different degrees in more than 110 instances in China. In this study, we found that YNSM and YHSM contained three candidate restorer-of-fertility (Rf) genes, Rf3, Rf4, and Rf5/Rf1a, that might confer their restoration ability. Subsequently, we investigated heterosis and combining ability of YNSM and YHSM using 50 F1 hybrids from a 5 × 10 incomplete diallelic mating design. Our results indicated that hybrid combinations exhibited significant genetic differences, and the additive effects of the parental genes played a preponderant role in the inheritance of observed traits. The metrics of plant height (PH), 1000-grain weight (TGW), panicle length (PL), and the number of spikelets per panicle (NSP) were mainly affected by genetic inheritance with higher heritability. Notably, the general combining ability (GCA) of YHSM exhibited the largest positive effect on the number of grains per panicle (NGP), NSP, PL, and TGW. Thus, YHSM had the largest GCA effect on yield per plant (YPP). In addition, the GCA of YNSM exhibited a positive impact on YPP, mainly due to the critical contribution of seed setting percentage (SSP). Moreover, YNSM and YHSM exhibited negative GCA effects on PH, implying that YNSM and YHSM could effectively enhance plant lodging resistance by reducing the plant height of the derived hybrids. Remarkably, among the hybrids, Yuanxiang A/YNSM (YXA/YNSM), Shen 08S/Yuemeizhan (S08S/YMZ), and Quan 9311A/YHSM (Q9311A/YHSM) represent promising new combinations with a higher specific combining ability (SCA) effect value on YPP with a value more than 3.50. Our research thus highlights the promising application for the rational utilization of YNSM and YHSM in hybrid rice breeding.
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