A Bayesian approach to mediation analysis predicts 206 causal target genes in Alzheimer’s disease

Characterizing the intermediate phenotypes, such as gene expression, that mediate genetic effects on complex diseases is a fundamental problem in human genetics. Existing methods utilize genotypic data and summary statistics to identify putative disease genes, but cannot distinguish pleiotropy from causal mediation and are limited by overly strong assumptions about the data. To overcome these limitations, we develop Causal Multivariate Mediation within Extended Linkage disequilibrium (CaMMEL), a novel Bayesian inference framework to jointly model multiple mediated and unmediated effects relying only on summary statistics. We show in simulation that CaMMEL accurately distinguishes between mediating and pleiotropic genes unlike existing methods. We applied CaMMEL to Alzheimer’s disease (AD) and found 206 causal genes in sub-threshold loci (p < 10 −4 ). We prioritized 21 genes which mediate at least 5% of local genetic variance, disrupting innate immune pathways in AD.