Genome-Wide Association Studies (GWAS) have successfully identified numerous genetic variants associated with a variety of complex traits in humans. However, most of these associated variants are not causal, and are simply in Linkage Disequilibrium (LD) with the true causal variants. This problem is addressed by statistical "fine mapping" methods, which attempt to prioritize a small subset of variants for further testing while accounting for LD structure [1]. CAVIAR [2] introduced a widely-adopted Bayesian approach that accounted for uncertainty in association statistics using a multivariate normal (MVN) model and allowed for potentially multiple causal SNPs at a locus. There is growing interest in improving fine-mapping by leveraging information from multiple studies. One example of this is trans-ethnic fine mapping, which can significantly improve fine mapping power and resolution by leveraging the distinct LD structures in each population. However, existing methods either assume a single causal SNP at each locus or do not explicitly model heterogeneity, limiting their power.
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