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Probabilistic fine-mapping of transcriptome-wide association studies

View ORCID ProfileNicholas Mancuso, View ORCID ProfileGleb Kichaev, Huwenbo Shi, Malika Freund, Alexander Gusev, Bogdan Pasaniuc
doi: https://doi.org/10.1101/236869
Nicholas Mancuso
1Dept of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, 90024
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  • ORCID record for Nicholas Mancuso
Gleb Kichaev
2Bioinformatics Interdepartmental Program, University of California, Los Angeles, 90024
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Huwenbo Shi
2Bioinformatics Interdepartmental Program, University of California, Los Angeles, 90024
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Malika Freund
3Dept of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, 90024
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Alexander Gusev
4Dana-Farber Cancer Institute, Boston, 02215
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Bogdan Pasaniuc
1Dept of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, 90024
2Bioinformatics Interdepartmental Program, University of California, Los Angeles, 90024
3Dept of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, 90024
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Abstract

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.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted June 05, 2018.
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Probabilistic fine-mapping of transcriptome-wide association studies
Nicholas Mancuso, Gleb Kichaev, Huwenbo Shi, Malika Freund, Alexander Gusev, Bogdan Pasaniuc
bioRxiv 236869; doi: https://doi.org/10.1101/236869
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Probabilistic fine-mapping of transcriptome-wide association studies
Nicholas Mancuso, Gleb Kichaev, Huwenbo Shi, Malika Freund, Alexander Gusev, Bogdan Pasaniuc
bioRxiv 236869; doi: https://doi.org/10.1101/236869

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