Dataset Information

Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimes.

ABSTRACT: The performance of genomic prediction (GP) on genetically correlated traits can be improved through an interdependence multi-trait model under a multi-environment context. In this study, a panel of 237 soft facultative wheat (Triticum aestivum L.) lines was evaluated to compare single- and multi-trait models for predicting grain yield (GY), harvest index (HI), spike fertility (SF), and thousand grain weight (TGW). The panel was phenotyped in two locations and two years in Florida under drought and moderately drought stress conditions, while the genotyping was performed using 27,957 genotyping-by-sequencing (GBS) single nucleotide polymorphism (SNP) makers. Five predictive models including Multi-environment Genomic Best Linear Unbiased Predictor (MGBLUP), Bayesian Multi-trait Multi-environment (BMTME), Bayesian Multi-output Regressor Stacking (BMORS), Single-trait Multi-environment Deep Learning (SMDL), and Multi-trait Multi-environment Deep Learning (MMDL) were compared. Across environments, the multi-trait statistical model (BMTME) was superior to the multi-trait DL model for prediction accuracy in most scenarios, but the DL models were comparable to the statistical models for response to selection. The multi-trait model also showed 5 to 22% more genetic gain compared to the single-trait model across environment reflected by the response to selection. Overall, these results suggest that multi-trait genomic prediction can be an efficient strategy for economically important yield component related traits in soft wheat.

SUBMITTER: Guo J

PROVIDER: S-EPMC7716228 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimes.

Guo Jia J Khan Jahangir J Pradhan Sumit S Shahi Dipendra D Khan Naeem N Avci Muhsin M Mcbreen Jordan J Harrison Stephen S Brown-Guedira Gina G Murphy J Paul JP Johnson Jerry J Mergoum Mohamed M Esten Mason Richanrd R Ibrahim Amir M H AMH Sutton Russel R Griffey Carl C Babar M A MA

Genes 20201028 11

The performance of genomic prediction (GP) on genetically correlated traits can be improved through an interdependence multi-trait model under a multi-environment context. In this study, a panel of 237 soft facultative wheat (<i>Triticum</i><i>aestivum</i> L.) lines was evaluated to compare single- and multi-trait models for predicting grain yield (GY), harvest index (HI), spike fertility (SF), and thousand grain weight (TGW). The panel was phenotyped in two locations and two years in Florida un ...[more]

PMID: 33126620

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Project description:A genome-wide association study (GWAS) for 10 yield and yield component traits was conducted using an association panel comprising 225 diverse spring wheat genotypes. The panel was genotyped using 10,904 SNPs and evaluated for three years (2016-2019), which constituted three environments (E1, E2 and E3). Heritability for different traits ranged from 29.21 to 97.69%. Marker-trait associations (MTAs) were identified for each trait using data from each environment separately and also using BLUP values. Four different models were used, which included three single trait models (CMLM, FarmCPU, SUPER) and one multi-trait model (mvLMM). Hundreds of MTAs were obtained using each model, but after Bonferroni correction, only 6 MTAs for 3 traits were available using CMLM, and 21 MTAs for 4 traits were available using FarmCPU; none of the 525 MTAs obtained using SUPER could qualify after Bonferroni correction. Using BLUP, 20 MTAs were available, five of which also figured among MTAs identified for individual environments. Using mvLMM model, after Bonferroni correction, 38 multi-trait MTAs, for 15 different trait combinations were available. Epistatic interactions involving 28 pairs of MTAs were also available for seven of the 10 traits; no epistatic interactions were available for GNPS, PH, and BYPP. As many as 164 putative candidate genes (CGs) were identified using all the 50 MTAs (CMLM, 3; FarmCPU, 9; mvLMM, 6, epistasis, 21 and BLUP, 11 MTAs), which ranged from 20 (CMLM) to 66 (epistasis) CGs. In-silico expression analysis of CGs was also conducted in different tissues at different developmental stages. The information generated through the present study proved useful for developing a better understanding of the genetics of each of the 10 traits; the study also provided novel markers for marker-assisted selection (MAS) to be utilized for the development of wheat cultivars with improved agronomic traits.Supplementary informationThe online version contains supplementary material available at 10.1007/s11032-021-01240-1.

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Dataset Information

Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimes.

Publications

Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimes.

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