Journal
NATURE GENETICS
Volume 51, Issue 4, Pages 675-+Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41588-019-0367-1
Keywords
-
Categories
Funding
- NIH [T32NS048004, T32LM012424, R01HG009120, R01MH115676, R01HG006399, U01CA194393]
- NSF [DGE-1829071]
- Claudia Adams Barr Award
Ask authors/readers for more resources
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 yields credible sets of genes containing the causal gene at a nominal confidence level (for example, 90%) that can be used to prioritize genes for functional assays. We illustrate our approach by using an integrative analysis of lipid traits, where our approach prioritizes genes with strong evidence for causality.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available