Dataset Information

Genome-Wide Association Study Reveals Novel Genomic Regions for Grain Yield and Yield-Related Traits in Drought-Stressed Synthetic Hexaploid Wheat.

ABSTRACT: Synthetic hexaploid wheat (SHW; 2n = 6x = 42, AABBDD, Triticum aestivum L.) is produced from an interspecific cross between durum wheat (2n = 4x = 28, AABB, T. turgidum L.) and goat grass (2n = 2x = 14, DD, Aegilops tauschii Coss.) and is reported to have significant novel alleles-controlling biotic and abiotic stresses resistance. A genome-wide association study (GWAS) was conducted to unravel these loci [marker⁻trait associations (MTAs)] using 35,648 genotyping-by-sequencing-derived single nucleotide polymorphisms in 123 SHWs. We identified 90 novel MTAs (45, 11, and 34 on the A, B, and D genomes, respectively) and haplotype blocks associated with grain yield and yield-related traits including root traits under drought stress. The phenotypic variance explained by the MTAs ranged from 1.1% to 32.3%. Most of the MTAs (120 out of 194) identified were found in genes, and of these 45 MTAs were in genes annotated as having a potential role in drought stress. This result provides further evidence for the reliability of MTAs identified. The large number of MTAs (53) identified especially on the D-genome demonstrate the potential of SHWs for elucidating the genetic architecture of complex traits and provide an opportunity for further improvement of wheat under rapidly changing climatic conditions.

SUBMITTER: Bhatta M

PROVIDER: S-EPMC6212811 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Genome-Wide Association Study Reveals Novel Genomic Regions for Grain Yield and Yield-Related Traits in Drought-Stressed Synthetic Hexaploid Wheat.

Bhatta Madhav M Morgounov Alexey A Belamkar Vikas V Baenziger P Stephen PS

International journal of molecular sciences 20181002 10

Synthetic hexaploid wheat (SHW; 2n = 6x = 42, AABBDD, Triticum aestivum L.) is produced from an interspecific cross between durum wheat (2n = 4x = 28, AABB, T. turgidum L.) and goat grass (2n = 2x = 14, DD, Aegilops tauschii Coss.) and is reported to have significant novel alleles-controlling biotic and abiotic stresses resistance. A genome-wide association study (GWAS) was conducted to unravel these loci [marker⁻trait associations ( ...[more]

PMID: 30279375

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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.

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.

Dataset Information

Genome-Wide Association Study Reveals Novel Genomic Regions for Grain Yield and Yield-Related Traits in Drought-Stressed Synthetic Hexaploid Wheat.

Publications

Genome-Wide Association Study Reveals Novel Genomic Regions for Grain Yield and Yield-Related Traits in Drought-Stressed Synthetic Hexaploid Wheat.

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