Harnessing Genetic Diversity in the USDA Pea Germplasm Collection Through Genomic Prediction - Bari, Md. Abdullah Al; Zheng, Ping; Viera, Indalecio; Worral, Hannah; Szwiec, Stephen; Ma, Yu; Main, Dorrie; Coyne, Clarice J.; McGee, Rebecca J.; Bandillo, Nonoy (Frontiers in Genetics, 2021)
Phenotypic evaluation and efficient utilization of germplasm collections can be time-intensive, laborious, and expensive. However, with the plummeting costs of next-generation sequencing and the addition of genomic selection to the plant breeder's toolbox, we now can more efficiently tap the genetic diversity within large germplasm collections. In this study, we applied and evaluated genomic selection's potential to a set of 482 pea accessions – genotyped with 30,600 SNP markers and phenotyped for seed yield and yield-related components – for enhancing selection of accessions from the USDA Pea Germplasm Collection. Genomic prediction models and several factors affecting predictive ability were evaluated in a series of cross-validation schemes across complex traits. Different genomic prediction models gave similar results, with predictive ability across traits ranging from 0.23 to 0.60, with no model working best across all traits. Increasing the training population size improved the predictive ability of most traits, including seed yield. An increasing trend in predictive ability was also observed with an increasing number of SNPs. Accounting for population structure effects did not significantly boost predictive ability, but we observed a slight improvement in seed yield. By applying the genomic prediction model from this study, we then examined the distribution of nonphenotyped accessions, and the reliability of genomic estimated breeding values (GEBV) of the USDA Pea accessions genotyped but not phenotyped. The distribution of GEBV suggested that none of the nonphenotyped accessions were expected to perform outside the range of the phenotyped accessions. Desirable breeding values with higher reliability can be used to identify and screen favorable germplasm accessions. Expanding the training set and incorporating additional orthogonal information into the genomic prediction framework could enhance prediction accuracy.