Integrating crop growth models with whole genome prediction through approximate Bayesian computation

Integrating crop growth models with whole genome prediction through approximate Bayesian computation

Frank Technow, Carlos D. Messina, L. Radu Totir, Mark Cooper
doi: http://dx.doi.org/10.1101/014100

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 (GxE), 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 functional relationships between plant physiology and the environment in the formation of yield and similar output traits of interest. Thus, they can explain the impact of GxE and certain types of non-additive gene effects on the expressed phenotype. Approximate Bayesian computation (ABC), a novel and powerful computational procedure, allows the incorporation of CGMs directly into the estimation of whole genome marker effects in WGP. Here we provide a proof of concept study for this novel approach and demonstrate its use with a simulated data set. We show that this novel approach can be considerably more accurate than the benchmark WGP method GBLUP in predicting performance in environments represented in the estimation set as well as in previously unobserved environments for traits determined by non-additive gene effects. We conclude that this proof of concept demonstrates that using ABC for incorporating biological knowledge in the form of CGMs into WGP is a very promising novel approach to improving prediction accuracy for some of the most challenging scenarios of interest to applied geneticists.

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