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Discovering genetic interactions bridging pathways in genome-wide association studies

Author

Listed:
  • Gang Fang

    (Icahn School of Medicine at Mount Sinai)

  • Wen Wang

    (University of Minnesota)

  • Vanja Paunic

    (University of Minnesota)

  • Hamed Heydari

    (University of Toronto)

  • Michael Costanzo

    (University of Toronto)

  • Xiaoye Liu

    (University of Minnesota)

  • Xiaotong Liu

    (University of Minnesota)

  • Benjamin VanderSluis

    (University of Minnesota)

  • Benjamin Oately

    (University of Minnesota)

  • Michael Steinbach

    (University of Minnesota)

  • Brian Van Ness

    (University of Minnesota)

  • Eric E. Schadt

    (Icahn School of Medicine at Mount Sinai)

  • Nathan D. Pankratz

    (University of Minnesota)

  • Charles Boone

    (University of Toronto)

  • Vipin Kumar

    (University of Minnesota)

  • Chad L. Myers

    (University of Minnesota)

Abstract

Genetic interactions have been reported to underlie phenotypes in a variety of systems, but the extent to which they contribute to complex disease in humans remains unclear. In principle, genome-wide association studies (GWAS) provide a platform for detecting genetic interactions, but existing methods for identifying them from GWAS data tend to focus on testing individual locus pairs, which undermines statistical power. Importantly, a global genetic network mapped for a model eukaryotic organism revealed that genetic interactions often connect genes between compensatory functional modules in a highly coherent manner. Taking advantage of this expected structure, we developed a computational approach called BridGE that identifies pathways connected by genetic interactions from GWAS data. Applying BridGE broadly, we discover significant interactions in Parkinson’s disease, schizophrenia, hypertension, prostate cancer, breast cancer, and type 2 diabetes. Our novel approach provides a general framework for mapping complex genetic networks underlying human disease from genome-wide genotype data.

Suggested Citation

  • Gang Fang & Wen Wang & Vanja Paunic & Hamed Heydari & Michael Costanzo & Xiaoye Liu & Xiaotong Liu & Benjamin VanderSluis & Benjamin Oately & Michael Steinbach & Brian Van Ness & Eric E. Schadt & Nath, 2019. "Discovering genetic interactions bridging pathways in genome-wide association studies," Nature Communications, Nature, vol. 10(1), pages 1-18, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-12131-7
    DOI: 10.1038/s41467-019-12131-7
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    Cited by:

    1. Mengyun Wu & Fan Wang & Yeheng Ge & Shuangge Ma & Yang Li, 2023. "Bi‐level structured functional analysis for genome‐wide association studies," Biometrics, The International Biometric Society, vol. 79(4), pages 3359-3373, December.

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