Probabilistic fine-mapping of transcriptome-wide association studies.

ABSTRACT: Transcriptome-wide association studies using predicted expression have identified thousands of genes whose locally regulated expression is associated with complex traits and diseases. In this work, we show that linkage disequilibrium induces significant gene-trait associations at non-causal genes as a function of the expression quantitative trait loci weights used in expression prediction. We introduce a probabilistic framework that models correlation among transcriptome-wide association study signals to assign a probability for every gene in the risk region to explain the observed association signal. Importantly, our approach remains accurate when expression data for causal genes are not available in the causal tissue by leveraging expression prediction from other tissues. Our approach yi

SUBMITTER: Mancuso N 

PROVIDER: S-EPMC6619422 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature