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Modeling Regulatory Network Topology Improves Genome-Wide Analyses of Complex Human Traits

View ORCID ProfileXiang Zhu, Zhana Duren, Wing Hung Wong
doi: https://doi.org/10.1101/2020.03.13.990010
Xiang Zhu
1Pennsylvania State University & Stanford University
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  • For correspondence: xiangzhu@psu.edu whwong@stanford.edu
Zhana Duren
1Pennsylvania State University & Stanford University
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Wing Hung Wong
1Pennsylvania State University & Stanford University
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  • For correspondence: xiangzhu@psu.edu whwong@stanford.edu
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Abstract

Genome-wide association studies (GWAS) have cataloged many significant associations between genetic variants and complex traits. However, most of these findings have unclear biological significance, because they often have small effects and occur in non-coding regions. Integration of GWAS with gene regulatory networks addresses both issues by aggregating weak genetic signals within regulatory programs. Here we develop a Bayesian framework that integrates GWAS summary statistics with regulatory networks to infer genetic enrichments and associations simultaneously. Our method improves upon existing approaches by explicitly modeling network topology to assess enrichments, and by automatically leveraging enrichments to identify associations. Applying this method to 18 human traits and 38 regulatory networks shows that genetic signals of complex traits are often enriched in interconnections specific to trait-relevant cell types or tissues. Prioritizing variants within enriched networks identifies known and new trait-associated genes revealing novel biological and therapeutic insights.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • We summarize our revisions by: (1) new simulation studies to illustrate the robustness of RSS-NET; (2) additional replication and comparative analyses of RSS-NET on real data; (3) expanded discussions of methodology intuition and implementation details. The new results are consistent with conclusions in our original manuscript, and the revised texts improve the readability of this work.

  • https://suwonglab.github.io/rss-net/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted December 08, 2020.
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Modeling Regulatory Network Topology Improves Genome-Wide Analyses of Complex Human Traits
Xiang Zhu, Zhana Duren, Wing Hung Wong
bioRxiv 2020.03.13.990010; doi: https://doi.org/10.1101/2020.03.13.990010
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Modeling Regulatory Network Topology Improves Genome-Wide Analyses of Complex Human Traits
Xiang Zhu, Zhana Duren, Wing Hung Wong
bioRxiv 2020.03.13.990010; doi: https://doi.org/10.1101/2020.03.13.990010

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