Bayesian statistical methods for genetic association studies - Stephens, Matthew; Balding, David J. (Nature reviews. Genetics, 2009)
Key Points p -values are commonly used as summaries of evidence for association between a genetic variant and phenotype, but they have an important limitation in that they are unable to quantify how confident one should be that a given SNP is truly associated with a phenotype. Bayesian methods provide an alternative approach to assessing associations. We show that Bayesian analyses are not too difficult and can be rewarding — for example, unlike p -values, a Bayesian probability of association is comparable across SNPs and across studies. For a Bayesian analysis of single-SNP association in a case–control study, we discuss genetic models that can form an alternative to the null hypothesis of no association, in addition to effect-size distributions for the parameters of these models. An alternative Bayesian analysis derives a posterior distribution for effect size, without reference to a null hypothesis. We give an example of a multi-SNP Bayesian analysis for fine-scale mapping and discuss Bayesian approaches to multiple testing and meta-analysis. Broad guidelines are suggested for editors and reviewers of Bayesian analyses. Bayesian analyses are increasingly being used in genetics, particularly in the context of genome-wide association studies. This article provides a guide to using Bayesian analyses for assessing single-SNP associations and highlights the advantages of these methods compared with standard frequentist analyses. Bayesian statistical methods have recently made great inroads into many areas of science, and this advance is now extending to the assessment of association between genetic variants and disease or other phenotypes. We review these methods, focusing on single-SNP tests in genome-wide association studies. We discuss the advantages of the Bayesian approach over classical (frequentist) approaches in this setting and provide a tutorial on basic analysis steps, including practical guidelines for appropriate prior specification. We demonstrate the use of Bayesian methods for fine mapping in candidate regions, discuss meta-analyses and provide guidance for refereeing manuscripts that contain Bayesian analyses.