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

Mike Thompson (), Mary Grace Gordon, Andrew Lu, Anchit Tandon, Eran Halperin, Alexander Gusev, Chun Jimmie Ye, Brunilda Balliu and Noah Zaitlen ()
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Mike Thompson: University of California Los Angeles
Mary Grace Gordon: University of California, San Francisco
Andrew Lu: University of California Los Angeles
Anchit Tandon: Indian Institute of Technology Delhi
Eran Halperin: University of California Los Angeles
Alexander Gusev: Dana-Farber Cancer Institute and Harvard Medical School
Chun Jimmie Ye: University of California, San Francisco
Brunilda Balliu: University of California Los Angeles
Noah Zaitlen: University of California Los Angeles

Nature Communications, 2022, vol. 13, issue 1, 1-15

Abstract: Abstract A 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.

Date: 2022
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DOI: 10.1038/s41467-022-33212-0

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