Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses.

ABSTRACT: Horizontal integration of summary statistics from different GWAS traits can be used to evaluate evidence for their shared genetic causality. One popular method to do this is a Bayesian method, coloc, which is attractive in requiring only GWAS summary statistics and no linkage disequilibrium estimates and is now being used routinely to perform thousands of comparisons between traits. Here we show that while most users do not adjust default software values, misspecification of prior parameters can substantially alter posterior inference. We suggest data driven methods to derive sensible prior values, and demonstrate how sensitivity analysis can be used to assess robustness of posterior inference. The flexibility of coloc comes at the expense of an unrealistic assumption of a single causal va

SUBMITTER: Wallace C 

PROVIDER: S-EPMC7192519 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature