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Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits

Author

Listed:
  • Marion Patxot

    (University of Lausanne)

  • Daniel Trejo Banos

    (University of Lausanne)

  • Athanasios Kousathanas

    (University of Lausanne)

  • Etienne J. Orliac

    (University of Lausanne)

  • Sven E. Ojavee

    (University of Lausanne)

  • Gerhard Moser

    (Australian Agricultural Company Limited)

  • Alexander Holloway

    (University of Lausanne)

  • Julia Sidorenko

    (University of Queensland)

  • Zoltan Kutalik

    (University of Lausanne
    University Center for Primary Care and Public Health
    Swiss Institute of Bioinformatics)

  • Reedik Mägi

    (University of Tartu)

  • Peter M. Visscher

    (University of Queensland)

  • Lars Rönnegård

    (Dalarna University
    Swedish University of Agricultural Sciences)

  • Matthew R. Robinson

    (Institute of Science and Technology Austria)

Abstract

We develop a Bayesian model (BayesRR-RC) that provides robust SNP-heritability estimation, an alternative to marker discovery, and accurate genomic prediction, taking 22 seconds per iteration to estimate 8.4 million SNP-effects and 78 SNP-heritability parameters in the UK Biobank. We find that only ≤10% of the genetic variation captured for height, body mass index, cardiovascular disease, and type 2 diabetes is attributable to proximal regulatory regions within 10kb upstream of genes, while 12-25% is attributed to coding regions, 32–44% to introns, and 22-28% to distal 10-500kb upstream regions. Up to 24% of all cis and coding regions of each chromosome are associated with each trait, with over 3,100 independent exonic and intronic regions and over 5,400 independent regulatory regions having ≥95% probability of contributing ≥0.001% to the genetic variance of these four traits. Our open-source software (GMRM) provides a scalable alternative to current approaches for biobank data.

Suggested Citation

  • Marion Patxot & Daniel Trejo Banos & Athanasios Kousathanas & Etienne J. Orliac & Sven E. Ojavee & Gerhard Moser & Alexander Holloway & Julia Sidorenko & Zoltan Kutalik & Reedik Mägi & Peter M. Vissch, 2021. "Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits," Nature Communications, Nature, vol. 12(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-27258-9
    DOI: 10.1038/s41467-021-27258-9
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    References listed on IDEAS

    as
    1. Daniel Trejo Banos & Daniel L. McCartney & Marion Patxot & Lucas Anchieri & Thomas Battram & Colette Christiansen & Ricardo Costeira & Rosie M. Walker & Stewart W. Morris & Archie Campbell & Qian Zhan, 2020. "Bayesian reassessment of the epigenetic architecture of complex traits," Nature Communications, Nature, vol. 11(1), pages 1-14, December.
    2. Qianqian Zhang & Florian Privé & Bjarni Vilhjálmsson & Doug Speed, 2021. "Improved genetic prediction of complex traits from individual-level data or summary statistics," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    3. Gerhard Moser & Sang Hong Lee & Ben J Hayes & Michael E Goddard & Naomi R Wray & Peter M Visscher, 2015. "Simultaneous Discovery, Estimation and Prediction Analysis of Complex Traits Using a Bayesian Mixture Model," PLOS Genetics, Public Library of Science, vol. 11(4), pages 1-22, April.
    4. Gao Wang & Abhishek Sarkar & Peter Carbonetto & Matthew Stephens, 2020. "A simple new approach to variable selection in regression, with application to genetic fine mapping," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(5), pages 1273-1300, December.
    5. Rohan Fernando & Ali Toosi & Anna Wolc & Dorian Garrick & Jack Dekkers, 2017. "Application of Whole-Genome Prediction Methods for Genome-Wide Association Studies: A Bayesian Approach," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(2), pages 172-193, June.
    6. Luke R. Lloyd-Jones & Jian Zeng & Julia Sidorenko & Loïc Yengo & Gerhard Moser & Kathryn E. Kemper & Huanwei Wang & Zhili Zheng & Reedik Magi & Tõnu Esko & Andres Metspalu & Naomi R. Wray & Michael E., 2019. "Improved polygenic prediction by Bayesian multiple regression on summary statistics," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
    7. Daniel Trejo Banos & Daniel L. McCartney & Marion Patxot & Lucas Anchieri & Thomas Battram & Colette Christiansen & Ricardo Costeira & Rosie M. Walker & Stewart W. Morris & Archie Campbell & Qian Zhan, 2020. "Author Correction: Bayesian reassessment of the epigenetic architecture of complex traits," Nature Communications, Nature, vol. 11(1), pages 1-1, December.
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