Genomic Selection in Plants: Empirical Results and Implications for Wheat Breeding

Abstract

Genotyping-by-sequencing technology is rapidly reducing marker costs and increasing genome coverage allowing the widespread use of molecular markers and methods in plant breeding. Marker assisted selection (MAS) and recurrent selection are based on the selection of statistically significant, marker-trait associations. However, MAS strategies are not well suited for complex traits controlled by many genes. Genomic selection (GS) incorporates genome-wide marker information in a breeding value prediction model, thereby minimizing biased marker effect estimates and capturing more of the variation due to small effect QTL. In GS, a training population related to the breeding germplasm is genotyped with genome-wide markers and phenotyped in a target set of environments. That data is used to train a prediction model that is used to estimate the breeding values of lines in a population using only the marker scores. Prediction models can incorporate performance over multiple environments and assess G x E effects to identify a highly predictive subset of environments. Because of reduced selection cycle time, annual genetic gain for GS is predicted to be two to threefold greater than for a conventional phenotypic selection program. We have developed a new methodology for using genome-wide marker effects to group environments and identify outliers. In addition, environmental covariates can be identified that increase prediction accuracy and facilitate performance prediction in climate change scenarios. This new approach to crop improvement will facilitate a better understanding of the dynamic genome processes that generate and maintain new genetic variation.