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Post-GWAS Prioritization Through Data Integration Provides Novel Insights on Chronic Obstructive Pulmonary Disease

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Abstract

Rich collections of genomic and epigenomic annotations, availabilities of large population cohorts for genome-wide association studies (GWASs), and advancements in data integration techniques provide the unprecedented opportunity to accelerate discoveries in complex disease studies through integrative analyses. In this paper, we apply a variety of approaches to integrate GWAS summary statistics of chronic obstructive pulmonary disease (COPD) with functional annotations to illustrate how data integration could help researchers understand complex human diseases. We show that incorporating functional annotations can better prioritize GWAS signals at both the global and the local levels. Signal prioritization on severe COPD GWAS reveals multiple potential risk loci that are linked with pulmonary functions. Enrichment analysis provides novel insights on the pathogenesis of COPD and hints the existence of genetic contributions to muscle dysfunction and chronic lung inflammation, two symptoms that are often comorbid with COPD. Our results suggest that rich signals for COPD genetics are still buried under the Bonferroni-corrected genome-wide significance threshold. Many more biological findings are expected to emerge as more samples are recruited for COPD studies.

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Acknowledgments

We thank all members of the Statistical and Applied Mathematical Sciences Institute (SAMSI) Data Integration: COPD Working Group as part of the SAMSI Beyond Bioinformatics Program. We also thank Dr. Michael Cho for sharing COPDGene GWAS summary statistics. We are grateful for the support of Drs. Sujit Ghosh and Snehalata Huzurbazar at SAMSI. This work was supported in part by the National Institutes of Health Grant R01 GM59507 and the VA Cooperative Studies Program of the Department of Veterans Affairs, Office of Research and Development. Funding for the COPDGene Study is provided by the National Heart, Lung, and Blood Institute Award Numbers R01HL089897 and R01HL089856. Further support provided by the COPD Foundation through contributions made to an Industry Advisory Board comprising AstraZeneca, Boehringer Ingelheim, Novartis Pfizer, Siemens, and Sunovion.

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Authors and Affiliations

  1. Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT, 06511, USA

    Qiongshi Lu, Jiehuan Sun & Hongyu Zhao

  2. Yale College, New Haven, CT, USA

    Chentian Jin

  3. Department of Medicine, National Jewish Health, Denver, CO, USA

    Russell Bowler

  4. Department of Biostatistics and Informatics, University of Colorado Denver, Denver, CO, USA

    Katerina Kechris

  5. Pulmonary, Critical Care and Sleep Medicine, Yale School of Medicine, New Haven, CT, USA

    Naftali Kaminski

  6. Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA

    Hongyu Zhao

  7. VA Cooperative Studies Program Coordinating Center, West Haven, CT, USA

    Hongyu Zhao

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  1. Qiongshi Lu
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Correspondence to Hongyu Zhao.

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Lu, Q., Jin, C., Sun, J. et al. Post-GWAS Prioritization Through Data Integration Provides Novel Insights on Chronic Obstructive Pulmonary Disease. Stat Biosci 9, 605–621 (2017). https://doi.org/10.1007/s12561-016-9151-2

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