Abstract
The successful implementation of Bayesian shrinkage analysis of high-dimensional regression models, as often encountered in quantitative trait locus (QTL) mapping, is contingent upon the choice of suitable sparsity-inducing priors. In practice, the shape (that is, the rate of tail decay) of such priors is typically preset, with no regard for the range of plausible alternatives and the fact that the most appropriate shape may depend on the data at hand. This study is presumably the first attempt to tackle this oversight through the shape-adaptive shrinkage prior (SASP) approach, with a focus on the mapping of QTLs in experimental crosses. Simulation results showed that the separation between genuine QTL effects and spurious ones can be made clearer using the SASP-based approach as compared with existing competitors. This feature makes our new method a promising approach to QTL mapping, where good separation is the ultimate goal. We also discuss a re-estimation procedure intended to improve the accuracy of the estimated genetic effects of detected QTLs with regard to shrinkage-induced bias, which may be particularly important in large-scale models with collinear predictors. The re-estimation procedure is relevant to any shrinkage method, and is potentially valuable for many scientific disciplines such as bioinformatics and quantitative genetics, where oversaturated models are booming.