Integrating Crop Growth Models with Whole Genome Prediction through Approximate Bayesian Computation.

ABSTRACT: Genomic selection, enabled by whole genome prediction (WGP) methods, is revolutionizing plant breeding. Existing WGP methods have been shown to deliver accurate predictions in the most common settings, such as prediction of across environment performance for traits with additive gene effects. However, prediction of traits with non-additive gene effects and prediction of genotype by environment interaction (G×E), continues to be challenging. Previous attempts to increase prediction accuracy for these particularly difficult tasks employed prediction methods that are purely statistical in nature. Augmenting the statistical methods with biological knowledge has been largely overlooked thus far. Crop growth models (CGMs) attempt to represent the impact of functional relationships between plant

SUBMITTER: Technow F 

PROVIDER: S-EPMC4488317 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature