The missing link between genetic association and regulatory function

  1. Noah James Connally  Is a corresponding author
  2. Sumaiya Nazeen
  3. Daniel Lee
  4. Huwenbo Shi
  5. John Stamatoyannopoulos
  6. Sung Chun
  7. Chris Cotsapas  Is a corresponding author
  8. Christopher A Cassa  Is a corresponding author
  9. Shamil R Sunyaev  Is a corresponding author
  1. Harvard Medical School, United States
  2. Harvard TH Chan School of Public Health, United States
  3. Altius Institute for Biomedical Sciences, United States
  4. Boston Children's Hospital, United States
  5. Broad Institute, United States
  6. Brigham and Women's Hospital, United States

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

The following data sets were generated
    1. Connally NJ
    (2022) GWAS results conditioned on coding variants
    Dryad Digital Repository, doi:10.5061/dryad.612jm644q.
The following previously published data sets were used
    1. UK Biobank
    (2012) UK Biobank
    http://www.ukbiobank.ac.uk/.
    1. TOPMed Consortium
    (2021) NHLBI TOPMed
    https://www.nhlbiwgs.org/topmed-whole-genome-sequencing-project-freeze-5b-phases-1-and-2.

Article and author information

Author details

  1. Noah James Connally

    Department of Biomedical Informatics, Harvard Medical School, Boston, United States
    For correspondence
    noahconnally@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3818-6739
  2. Sumaiya Nazeen

    Department of Biomedical Informatics, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Daniel Lee

    Department of Biomedical Informatics, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Huwenbo Shi

    Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. John Stamatoyannopoulos

    Altius Institute for Biomedical Sciences, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Sung Chun

    Division of Pulmonary Medicine, Boston Children's Hospital, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Chris Cotsapas

    Program in Medical and Population Genetics, Broad Institute, New Haven, United States
    For correspondence
    cotsapas@broadinstitute.org
    Competing interests
    The authors declare that no competing interests exist.
  8. Christopher A Cassa

    Division of Genetics, Brigham and Women's Hospital, Boston, United States
    For correspondence
    cassa@mit.edu
    Competing interests
    The authors declare that no competing interests exist.
  9. Shamil R Sunyaev

    Division of Genetics, Brigham and Women's Hospital, Boston, United States
    For correspondence
    ssunyaev@rics.bwh.harvard.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5715-5677

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.

Reviewing Editor

  1. Jonathan Flint, University of California, Los Angeles, United States

Version history

  1. Preprint posted: June 11, 2021 (view preprint)
  2. Received: October 25, 2021
  3. Accepted: December 2, 2022
  4. Accepted Manuscript published: December 14, 2022 (version 1)
  5. Version of Record published: January 16, 2023 (version 2)

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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  1. Noah James Connally
  2. Sumaiya Nazeen
  3. Daniel Lee
  4. Huwenbo Shi
  5. John Stamatoyannopoulos
  6. Sung Chun
  7. Chris Cotsapas
  8. Christopher A Cassa
  9. Shamil R Sunyaev
(2022)
The missing link between genetic association and regulatory function
eLife 11:e74970.
https://doi.org/10.7554/eLife.74970

Share this article

https://doi.org/10.7554/eLife.74970

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