Assessing a Spatial Boost Model for Quantitative Trait GWAS

Abstract

Bayesian variable selection provides a principled framework for incorporating prior information to regularize parameters in high-dimensional large-p-small-n regression models such as genomewide association studies (GWAS). Although these models produce more informative results, researchers often disregard them in favor of simpler models because of their high computational cost. We explore our recently proposed spatial boost model for GWAS on quantitative traits to assess the computational efficiency of a more representative model. The spatial boost model is a Bayesian hierarchical model that exploits spatial information on the genome to uniquely define prior probabilities of association of genetic markers based on their proximities to relevant genes. We propose analyzing large data sets by first applying an expectation–maximization filter to reduce the dimensionality of the space and then applying an efficient Gibbs sampler on the remaining markers. Finally we conduct a thorough simulation study based on real genotypes provided by the Wellcome Trust Case Control Consortium and compare our model to single association tests.