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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
1Department Of Statistics, Stanford University, 390 Jane Stanford Way, Stanford, CA 94305, USA
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  • For correspondence: xiangzhu@stanford.edu whwong@stanford.edu
Zhana Duren
1Department Of Statistics, Stanford University, 390 Jane Stanford Way, Stanford, CA 94305, USA
2Department Of Biomedical Data Science, Stanford University, 1265 Welch Road, Stanford, CA 94305, USA
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Wing Hung Wong
1Department Of Statistics, Stanford University, 390 Jane Stanford Way, Stanford, CA 94305, USA
2Department Of Biomedical Data Science, Stanford University, 1265 Welch Road, Stanford, CA 94305, USA
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  • For correspondence: xiangzhu@stanford.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 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 networks specific to trait-relevant tissue or cell types. Prioritizing variants within enriched networks identifies known and new trait-associated genes revealing novel biological and therapeutic insights.

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Posted March 14, 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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