Abstract

Genomic Selection: Applying Advances in Genomics to Crop Improvement

Mark Sorrells, Professor, Cornell University

Inexpensive DNA sequencing and new statistical methods are revolutionizing plant breeding.  Genomic selection (GS) is the simultaneous use of genome-wide markers to increase accuracy of performance prediction for both phenotyped and non-phenotyped individuals. In GS, a training population related to the breeding germplasm is genotyped with genome-wide markers and phenotyped in the target set of environments. That data is used in a prediction model to estimate breeding values of non-phenotyped candidates. Design of the training population is crucial to the accuracy of prediction models and can be affected by many factors including population structure and composition. Prediction models can incorporate performance over multiple environments and assess GxE effects to identify a highly predictive subset of environments. We have developed a methodology for unbalanced datasets using genome-wide marker effects to group environments and identify outlier environments. In addition, environmental covariates can be generated using a crop model and used in a GS model to predict GxE in unobserved environments and to predict performance in climate change scenarios. Current research is focused on optimizing the training population to improve efficiency and increase prediction accuracy in terms of genotypes, experimental design and environment sampling.