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Incorporation of soil-derived covariates in progeny testing and line selection to enhance genomic prediction accuracy in soybean breeding

Citation

Canella Vieira, Caio; Persa, Reyna; Chen, Pengyin; Jarquin, Diego (2022), Incorporation of soil-derived covariates in progeny testing and line selection to enhance genomic prediction accuracy in soybean breeding, Dryad, Dataset, https://doi.org/10.5061/dryad.z8w9ghxf9

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 highly complex and quantitative trait whose expression is sensitive to environmental stimuli. In this research, we investigated the potential of incorporating soil texture and its interaction with molecular markers through covariance structures to enhance predictive ability. A total of 797 advanced soybean breeding lines derived from 367 unique bi-parental populations were genotyped using the Illumina Infinium BARCSoySNP6K BeadChip and tested for yield for five years in Tiptonville silt loam, Sharkey clay, and Malden fine sand environments. Four statistical models were considered, including a default GBLUP model (M1), a reaction norm model (M2) accounting for the interaction between molecular markers and the environment (GE), an expansion of M2 including soil type (S), and the interaction between soil type and molecular markers (GS) (M3), and an alternative version of M3 without the GE term. Four cross-validation scenarios simulating progeny testing and line selection were implemented (CV2, CV1, CV0, and CV00). Across environments, the addition of GS in M3 decreased the amount of variability captured by both the environment (-30.4%) and residual (-39.2%) terms as compared to M1. Within environments, the GS term in M3 reduced the variability captured by the residual term by roughly 60% and 30% when compared to M1 and M2, respectively. M3 outperformed all models in CV2 (0.577), CV1 (0.480), and CV0 (0.488). The addition of soil texture seems to structure the environment term revealing its components that could enhance or hinder the predictability of a model. The availability of soil texture before the growing season may maximize the functionality of covariance structures, particularly in scenarios with untested genotypes in untested environments. Genomic selection can optimize the efficiency of a soybean breeding program by allowing the reconsideration of field experimental design, allocation of resources, reduction of preliminary trials, and shortening of the breeding cycle.