Assessing Predictive Properties of Genome-Wide Selection in Soybeans.
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 alternative models had minor impacts on prediction accuracy, the most robust prediction model was the combination of reproducing kernel Hilbert space regression and BayesB. Higher genotyping density marginally improved accuracy. Our study finds that breeding programs seeking efficient genomic selection in soybeans would best allocate resources by investing in a representative training set.
Department of Agronomy, Purdue University, West Lafayette, Indiana 47907.
Alencar
A
Xavier
Department of Animal Science, Purdue University, West Lafayette, Indiana 47907.
William M
WM
Muir
Department of Agronomy, Purdue University, West Lafayette, Indiana 47907 krainey@purdue.edu.
Katy Martin
KM
Rainey
D005828:Genetics, Population; D018745:Genome, Plant; D023281:Genomics / Q000379:methods*; D008957:Models, Genetic; D000069600:Plant Breeding / Q000379:methods*; D020641:Polymorphism, Single Nucleotide; D011156:Population Density; D040641:Quantitative Trait Loci; D013025:Glycine max / Q000235:genetics*
SoyNAM; bayesian methods; genomic selection; machine learning