A Bayesian genome-wide linkage analysis of quantitative traits for rheumatoid arthritis via perfect sampling

Cheongeun Oh

doi:10.1186/1753-6561-1-s1-s110

Abstract

Rheumatoid arthritis is a complex disease caused by a combination of genetic, environmental, and hormonal factors, and their additive and/or non-additive effects. We performed a linkage analysis to provide evidence of rheumatoid factor IgM on linkage, based on Bayesian variable selection coupled with the new Haseman-Elston method. For statistical inferences to estimate unknown parameters, we utilized the perfect sampling algorithm, an emerging simulation technique that alleviates concerns over convergence and sampling mixing. Our methods provide powerful and conceptually simple approaches to simultaneous genome scans of main effects and all possible pairwise interactions. We apply them to the Genetic Analysis Workshop 15 data (Problem 2) provided by the North American Rheumatoid Arthritis Consortium (NARAC).

Highlights

Rheumatoid arthritis (RA) is a clinically heterogeneous disorder with variability in severity, disease course, and response to therapy
The exact cause of rheumatoid arthritis is still unknown, RA is known to be a complex disease caused by a combination of genetic, environmental, and hormonal factors, and their additive and/or non-additive effects
The use of Bayesian model selection has been the popular method of remedying the pitfalls of conventional methods in recent years, whereby identifying loci with significant effects is viewed as a model selection problem

Summary

Introduction

Rheumatoid arthritis (RA) is a clinically heterogeneous disorder with variability in severity, disease course, and response to therapy. Rheumatoid factor IgM is a significant and common measure for diagnosis of RA. While recent interest has been focused on genome scans using a large number of marker loci, the common approaches of existing statistical methods produce often inconsistent results. This is due in part to the fact that they test markers one after another and fail to capture the substantial information of epistases among disease loci. The use of Bayesian model selection has been the popular method of remedying the pitfalls of conventional methods in recent years, whereby identifying loci with significant effects is viewed as a model selection problem. One of the powerful Bayesian model selection approaches is the use of stochastic search variable selection (SSVS) [1,2,3,4], in which Markov chain Monte (page number not for citation purposes)

Methods

Results

Conclusion