Genomic prediction models for traits differing in heritability for soybean, rice, and maize.

ABSTRACT:

Background

Genomic selection is a powerful tool in plant breeding. By building a prediction model using a training set with markers and phenotypes, genomic estimated breeding values (GEBVs) can be used as predictions of breeding values in a target set with only genotype data. There is, however, limited information on how prediction accuracy of genomic prediction can be optimized. The objective of this study was to evaluate the performance of 11 genomic prediction models across species in terms of prediction accuracy for two traits with different heritabilities using several subsets of markers and training population proportions. Species studied were maize (Zea mays, L.), soybean (Glycine max, L.), and rice (Oryza sativa, L.), which vary in linkage disequilibrium (LD) decay rates an

SUBMITTER: Kaler AS 

PROVIDER: S-EPMC8881851 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature