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Multi-context genetic modeling of transcriptional regulation resolves novel disease loci

Thompson, Mike and Gordon, Mary Grace and Lu, Andrew and Tandon, Anchit and Halperin, Eran and Gusev, Alexander and Ye, Chun Jimmie and Balliu, Brunilda and Zaitlen, Noah (2022) Multi-context genetic modeling of transcriptional regulation resolves novel disease loci. Nature Communications, 13 . Art. No. 5704. ISSN 2041-1723. PMCID PMC9519579. doi:10.1038/s41467-022-33212-0.

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AbstractA majority of the variants identified in genome-wide association studies fall in non-coding regions of the genome, indicating their mechanism of impact is mediated via gene expression. Leveraging this hypothesis, transcriptome-wide association studies (TWAS) have assisted in both the interpretation and discovery of additional genes associated with complex traits. However, existing methods for conducting TWAS do not take full advantage of the intra-individual correlation inherently present in multi-context expression studies and do not properly adjust for multiple testing across contexts. We introduce CONTENT—a computationally efficient method with proper cross-context false discovery correction that leverages correlation structure across contexts to improve power and generate context-specific and context-shared components of expression. We apply CONTENT to bulk multi-tissue and single-cell RNA-seq data sets and show that CONTENT leads to a 42% (bulk) and 110% (single cell) increase in the number of genetically predicted genes relative to previous approaches. We find the context-specific component of expression comprises 30% of heritability in tissue-level bulk data and 75% in single-cell data, consistent with cell-type heterogeneity in bulk tissue. In the context of TWAS, CONTENT increases the number of locus-phenotype associations discovered by over 51% relative to previous methods across 22 complex traits.

Item Type:Article
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URLURL TypeDescription ItemDiscussion Paper CentralArticle
Thompson, Mike0000-0003-1546-0512
Lu, Andrew0000-0002-7594-6445
Halperin, Eran0000-0002-3379-2935
Ye, Chun Jimmie0000-0001-6560-3783
Balliu, Brunilda0000-0001-8953-804X
Zaitlen, Noah0000-0002-3553-3670
Additional Information:M.T. is supported in part by NIH Training Grant in Genomic Analysis and Interpretation T32HG002536. N.Z. was funded by NIH, CZI, and V.A. grants U01HG012079, U01MH126798, R01MH125252, 1R01HG011345, U01HG009080, CZF2019-002449, R01ES029929, R01HL155024, 1I01CX002011. B.B. received support from U01HG012079. This work was also funded by the National Science Foundation (Grant No. 1705197), and by NIH/NHGRI HG010505-02. C.J.Y. received funding from NIH grants P30AR070155, R01AR071522, U01HG012192, R21AI133337, and CZI P0535277. M.G.G. was supported by NIH grant 1F31HG011007. Human and organ icons in Fig. 1 were created by and further edited individually; the entire illustration was made using Microsoft Powerpoint.
Funding AgencyGrant Number
NIH Predoctoral FellowshipT32HG002536
Chan-Zuckerberg InitiativeCZF2019-002449
Department of Veterans Affairs1I01CX002011
Chan-Zuckerberg InitiativeCZI P0535277
NIH Postdoctoral Fellowship1F31HG011007
PubMed Central ID:PMC9519579
Record Number:CaltechAUTHORS:20221013-48885100.14
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:117403
Deposited By: Research Services Depository
Deposited On:18 Oct 2022 22:33
Last Modified:18 Oct 2022 22:33

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