QTL mapping is an important tool for identifying regions in chromosomes which are relevant to explain a response of interest. It is a special case of the regression model where an unknown number of missing (non-observable) covariates is involved leading to a complex variable selection procedure. Although several methods have been proposed to identify QTLs and to estimate parameters in the associated model, minimum attention has been devoted to the estimated model adequacy. In this paper, we present an overview of a few methods for residual and diagnostic analysis in the context of Bayesian regression modeling and adapt them to work with QTL mapping. The motivation of this study is to identify QTLs associated with the blood pressure of F2 rats and check the fitted model adequacy.
Keywords: Posterior predictive checks; QTL mapping; blood pressure; data-driven reversible jump; local and global influence; residual analysis.