Assessing Predictive Properties of Genome-Wide Selection in Soybeans.

ABSTRACT: Many economically important traits in plant breeding have low heritability or are difficult to measure. For these traits, genomic selection has attractive features and may boost genetic gains. Our goal was to evaluate alternative scenarios to implement genomic selection for yield components in soybean (Glycine max L. merr). We used a nested association panel with cross validation to evaluate the impacts of training population size, genotyping density, and prediction model on the accuracy of genomic prediction. Our results indicate that training population size was the factor most relevant to improvement in genome-wide prediction, with greatest improvement observed in training sets up to 2000 individuals. We discuss assumptions that influence the choice of the prediction model. Although alt

SUBMITTER: Xavier A 

PROVIDER: S-EPMC4978914 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature