Integrating crop growth models with whole genome prediction through approximate Bayesian

摘要: 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 of across environment performance for traits with additive gene effects. However, non-additive effects and genotype interaction (GE), continues be challenging. Previous attempts increase accuracy these particularly difficult tasks employed that are purely statistical nature. Augmenting biological knowledge has largely overlooked thus far. Crop growth models (CGMs) attempt represent functional relationships between physiology formation yield similar output interest. Thus, they can explain impact GE certain types on expressed phenotype. Approximate Bayesian computation (ABC), a novel powerful computational procedure, allows incorporation CGMs directly into estimation marker WGP. Here we provide proof concept study this approach demonstrate its use simulated data set. We show considerably more than benchmark method GBLUP predicting environments represented set well previously unobserved determined conclude demonstrates using ABC incorporating form very promising improving some challenging scenarios interest applied geneticists.