The missing link between genetic association and regulatory function
Abstract
The genetic basis of most traits is highly polygenic and dominated by non-coding alleles. It is widely assumed that such alleles exert small regulatory effects on the expression of cis-linked genes. However, despite the availability of gene expression and epigenomic data sets, few variant-to-gene links have emerged. It is unclear whether these sparse results are due to limitations in available data and methods, or to deficiencies in the underlying assumed model. To better distinguish between these possibilities, we identified 220 gene-trait pairs in which protein-coding variants influence a complex trait or its Mendelian cognate. Despite the presence of expression quantitative trait loci near most GWAS associations, by applying a gene-based approach we found limited evidence that the baseline expression of trait-related genes explains GWAS associations, whether using colocalization methods (8% of genes implicated), transcription-wide association (2% of genes implicated), or a combination of regulatory annotations and distance (4% of genes implicated). These results contradict the hypothesis that most complex trait-associated variants coincide with homeostatic eQTLs, suggesting that better models are needed. The field must confront this deficit, and pursue this 'missing regulation'.
Data availability
Numerical data for results is included in Source Data 1.The dataset generated (GWAS summary statistics conditioned on coding variants) can be found at doi:10.5061/dryad.612jm644q
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GWAS results conditioned on coding variantsDryad Digital Repository, doi:10.5061/dryad.612jm644q.
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NHLBI TOPMedhttps://www.nhlbiwgs.org/topmed-whole-genome-sequencing-project-freeze-5b-phases-1-and-2.
Article and author information
Author details
Funding
National Institutes of Health (R35GM127131)
- Shamil R Sunyaev
National Institutes of Health (R01HG010372)
- Shamil R Sunyaev
National Institutes of Health (R01MH101244)
- Shamil R Sunyaev
National Institutes of Health (U01HG012009)
- Chris Cotsapas
National Institutes of Health (T32GM74897)
- Shamil R Sunyaev
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2022, Connally et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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