CARMA is a new Bayesian model for fine-mapping in genome-wide association meta-analyses

Fine-mapping is commonly used to identify putative causal variants at genome-wide significant loci. Here we propose a Bayesian model for fine-mapping that has several advantages over existing methods, including flexible specification of the prior distribution of effect sizes, joint modeling of summary statistics and functional annotations and accounting for discrepancies between summary statistics and external linkage disequilibrium in meta-analyses. Using simulations, we compare performance with commonly used fine-mapping methods and show that the proposed model has higher power and lower false discovery rate (FDR) when including functional annotations, and higher power, lower FDR and higher coverage for credible sets in meta-analyses. We further illustrate our approach by applying it to a meta-analysis of Alzheimer's disease genome-wide association studies where we prioritize putatively causal variants and genes. Causal robust mapping method in meta-analysis (CARMA) studies incorporates flexible prior distributions, joint modeling of summary statistics and functional annotations and outlier detection for improved causal variant fine-mapping in genome-wide association meta-analyses.

Automated summary

Fine-mapping is improved with a Bayesian model that allows for flexible effect size priors, joint modeling of summary statistics and functional annotations, and accounting for discrepancies in meta-analyses. Simulation studies show that this model outperforms existing methods in terms of power, false discovery rate, and coverage of credible sets. The proposed approach is also applied to an Alzheimer's disease meta-analysis, resulting in the prioritization of putative causal variants and genes. Causal robust mapping method in meta-analysis (CARMA) combines flexible priors, joint modeling, and outlier detection for improved fine-mapping in genome-wide association studies.