Optimizing Sparse Testing for Genomic Prediction of Plant Breeding Crops.

ABSTRACT: While sparse testing methods have been proposed by researchers to improve the efficiency of genomic selection (GS) in breeding programs, there are several factors that can hinder this. In this research, we evaluated four methods (M1-M4) for sparse testing allocation of lines to environments under multi-environmental trails for genomic prediction of unobserved lines. The sparse testing methods described in this study are applied in a two-stage analysis to build the genomic training and testing sets in a strategy that allows each location or environment to evaluate only a subset of all genotypes rather than all of them. To ensure a valid implementation, the sparse testing methods presented here require BLUEs (or BLUPs) of the lines to be computed at the first stage using an appropriate exper

SUBMITTER: Montesinos-Lopez OA 

PROVIDER: S-EPMC10137724 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature