RT Journal Article
SR Electronic
T1 Probabilistic fine-mapping of transcriptome-wide association studies
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 236869
DO 10.1101/236869
A1 Mancuso, Nicholas
A1 Kichaev, Gleb
A1 Shi, Huwenbo
A1 Freund, Malika
A1 Gusev, Alexander
A1 Pasaniuc, Bogdan
YR 2018
UL http://biorxiv.org/content/early/2018/06/05/236869.abstract
AB Transcriptome-wide association studies (TWAS) using predicted expression have identified thousands of genes whose locally-regulated expression is associated to complex traits and diseases. In this work, we show that linkage disequilibrium (LD) among SNPs induce significant gene-trait associations at non-causal genes as a function of the overlap between eQTL weights used in expression prediction. We introduce a probabilistic framework that models the induced correlation among TWAS signals to assign a probability for every gene in the risk region to explain the observed association signal while controlling for pleiotropic SNP effects and unmeasured causal expression. 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 yields credible-sets of genes containing the causal gene at a nominal confidence level (e.g., 90%) that can be used to prioritize and select genes for functional assays. We illustrate our approach using an integrative analysis of lipids traits where our approach prioritizes genes with strong evidence for causality.