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Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics

Author

Listed:
  • Alvaro N. Barbeira

    (The University of Chicago)

  • Scott P. Dickinson

    (The University of Chicago)

  • Rodrigo Bonazzola

    (The University of Chicago)

  • Jiamao Zheng

    (The University of Chicago)

  • Heather E. Wheeler

    (Loyola University Chicago
    Loyola University Chicago)

  • Jason M. Torres

    (The University of Chicago)

  • Eric S. Torstenson

    (Vanderbilt University Medical Center)

  • Kaanan P. Shah

    (The University of Chicago)

  • Tzintzuni Garcia

    (The University of Chicago)

  • Todd L. Edwards

    (Vanderbilt University Medical Center)

  • Eli A. Stahl

    (Icahn School of Medicine at Mount Sinai
    Icahn School of Medicine at Mount Sinai)

  • Laura M. Huckins

    (Icahn School of Medicine at Mount Sinai
    Icahn School of Medicine at Mount Sinai)

  • Dan L. Nicolae

    (The University of Chicago)

  • Nancy J. Cox

    (Vanderbilt University Medical Center)

  • Hae Kyung Im

    (The University of Chicago)

Abstract

Scalable, integrative methods to understand mechanisms that link genetic variants with phenotypes are needed. Here we derive a mathematical expression to compute PrediXcan (a gene mapping approach) results using summary data (S-PrediXcan) and show its accuracy and general robustness to misspecified reference sets. We apply this framework to 44 GTEx tissues and 100+ phenotypes from GWAS and meta-analysis studies, creating a growing public catalog of associations that seeks to capture the effects of gene expression variation on human phenotypes. Replication in an independent cohort is shown. Most of the associations are tissue specific, suggesting context specificity of the trait etiology. Colocalized significant associations in unexpected tissues underscore the need for an agnostic scanning of multiple contexts to improve our ability to detect causal regulatory mechanisms. Monogenic disease genes are enriched among significant associations for related traits, suggesting that smaller alterations of these genes may cause a spectrum of milder phenotypes.

Suggested Citation

  • Alvaro N. Barbeira & Scott P. Dickinson & Rodrigo Bonazzola & Jiamao Zheng & Heather E. Wheeler & Jason M. Torres & Eric S. Torstenson & Kaanan P. Shah & Tzintzuni Garcia & Todd L. Edwards & Eli A. St, 2018. "Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics," Nature Communications, Nature, vol. 9(1), pages 1-20, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-03621-1
    DOI: 10.1038/s41467-018-03621-1
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    1. Kevin L Keys & Angel C Y Mak & Marquitta J White & Walter L Eckalbar & Andrew W Dahl & Joel Mefford & Anna V Mikhaylova & María G Contreras & Jennifer R Elhawary & Celeste Eng & Donglei Hu & Scott Hun, 2020. "On the cross-population generalizability of gene expression prediction models," PLOS Genetics, Public Library of Science, vol. 16(8), pages 1-28, August.
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    4. William J. Young & Jeffrey Haessler & Jan-Walter Benjamins & Linda Repetto & Jie Yao & Aaron Isaacs & Andrew R. Harper & Julia Ramirez & Sophie Garnier & Stefan Duijvenboden & Antoine R. Baldassari & , 2023. "Genetic architecture of spatial electrical biomarkers for cardiac arrhythmia and relationship with cardiovascular disease," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    5. Han Zhang & Lu Deng & William Wheeler & Jing Qin & Kai Yu, 2022. "Integrative analysis of multiple case‐control studies," Biometrics, The International Biometric Society, vol. 78(3), pages 1080-1091, September.
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    7. Xinyuan Dong & Yu-Ru Su & Richard Barfield & Stephanie A Bien & Qianchuan He & Tabitha A Harrison & Jeroen R Huyghe & Temitope O Keku & Noralane M Lindor & Clemens Schafmayer & Andrew T Chan & Stephen, 2020. "A general framework for functionally informed set-based analysis: Application to a large-scale colorectal cancer study," PLOS Genetics, Public Library of Science, vol. 16(8), pages 1-21, August.
    8. Benjamin J. Schmiedel & Job Rocha & Cristian Gonzalez-Colin & Sourya Bhattacharyya & Ariel Madrigal & Christian H. Ottensmeier & Ferhat Ay & Vivek Chandra & Pandurangan Vijayanand, 2021. "COVID-19 genetic risk variants are associated with expression of multiple genes in diverse immune cell types," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    9. Bingxin Zhao & Fei Zou & Hongtu Zhu, 2023. "Cross‐trait prediction accuracy of summary statistics in genome‐wide association studies," Biometrics, The International Biometric Society, vol. 79(2), pages 841-853, June.
    10. Angela Andaleon & Lauren S Mogil & Heather E Wheeler, 2019. "Genetically regulated gene expression underlies lipid traits in Hispanic cohorts," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-21, August.
    11. Corbin Quick & Xiaoquan Wen & Gonçalo Abecasis & Michael Boehnke & Hyun Min Kang, 2020. "Integrating comprehensive functional annotations to boost power and accuracy in gene-based association analysis," PLOS Genetics, Public Library of Science, vol. 16(12), pages 1-23, December.
    12. Michael G. Levin & Noah L. Tsao & Pankhuri Singhal & Chang Liu & Ha My T. Vy & Ishan Paranjpe & Joshua D. Backman & Tiffany R. Bellomo & William P. Bone & Kiran J. Biddinger & Qin Hui & Ozan Dikilitas, 2022. "Genome-wide association and multi-trait analyses characterize the common genetic architecture of heart failure," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    13. Xena Marie Mapel & Naveen Kumar Kadri & Alexander S. Leonard & Qiongyu He & Audald Lloret-Villas & Meenu Bhati & Maya Hiltpold & Hubert Pausch, 2024. "Molecular quantitative trait loci in reproductive tissues impact male fertility in cattle," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    14. Sébastien Thériault & Zhonglin Li & Erik Abner & Jian’an Luan & Hasanga D. Manikpurage & Ursula Houessou & Pardis Zamani & Mewen Briend & Dominique K. Boudreau & Nathalie Gaudreault & Lily Frenette & , 2024. "Integrative genomic analyses identify candidate causal genes for calcific aortic valve stenosis involving tissue-specific regulation," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    15. Xiaoyu Song & Jiayi Ji & Joseph H. Rothstein & Stacey E. Alexeeff & Lori C. Sakoda & Adriana Sistig & Ninah Achacoso & Eric Jorgenson & Alice S. Whittemore & Robert J. Klein & Laurel A. Habel & Pei Wa, 2023. "MiXcan: a framework for cell-type-aware transcriptome-wide association studies with an application to breast cancer," Nature Communications, Nature, vol. 14(1), pages 1-15, December.

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