Bayesian Modeling for Epistasis Analysis Using Data-Driven Reversible Jump.

Abstract

We propose a procedure for modeling a phenotype using QTLs which estimate the additive and dominance effects of genotypes and epistasis. The estimation of the model is implemented through a Bayesian approach which uses the data-driven reversible jump (DDRJ) for multiple QTL mapping and model selection. We compare the DDRJ's performance with the usual reversible jump (RJ), QTLBim, multiple interval mapping (MIM) and LASSO using real and simulated data sets. The DDRJ outperforms the available methods to estimate the number of QTLs in epistatic models and it identifies their locations in the genome, without increasing the number of false-positive QTLs in the considered data. Since QTL mapping is a regression model involving complex non-observable variables and their interactions, the model selection procedure proposed here is also useful in other areas of research. The application for identifying main and epistatic relevant QTLs to systolic blood pressure after salt intervention is our main motivation.