Incorporation of Soil-Derived Covariates in Progeny Testing and Line Selection to Enhance Genomic Prediction Accuracy in Soybean Breeding.

ABSTRACT: The availability of high-dimensional molecular markers has allowed plant breeding programs to maximize their efficiency through the genomic prediction of a phenotype of interest. Yield is a complex quantitative trait whose expression is sensitive to environmental stimuli. In this research, we investigated the potential of incorporating soil texture information and its interaction with molecular markers via covariance structures for enhancing predictive ability across breeding scenarios. A total of 797 soybean lines derived from 367 unique bi-parental populations were genotyped using the Illumina BARCSoySNP6K and tested for yield during 5 years in Tiptonville silt loam, Sharkey clay, and Malden fine sand environments. Four statistical models were considered, including the GBLUP model

SUBMITTER: Canella Vieira C 

PROVIDER: S-EPMC9493273 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature