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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 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.

journal_name

Nat Genet

journal_title

Nature genetics

authors

Mancuso N,Freund MK,Johnson R,Shi H,Kichaev G,Gusev A,Pasaniuc B

doi

10.1038/s41588-019-0367-1

subject

Has Abstract

pub_date

2019-04-01 00:00:00

pages

675-682

issue

4

eissn

1061-4036

issn

1546-1718

pii

10.1038/s41588-019-0367-1

journal_volume

51

pub_type

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