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

Qiongshi Lu, Chentian Jin, Jiehuan Sun, Russell Bowler, Katerina Kechris, Naftali Kaminski and Hongyu Zhao ()
Additional contact information
Qiongshi Lu: Yale School of Public Health
Chentian Jin: Yale College
Jiehuan Sun: Yale School of Public Health
Russell Bowler: National Jewish Health
Katerina Kechris: University of Colorado Denver
Naftali Kaminski: Yale School of Medicine
Hongyu Zhao: Yale School of Public Health

Statistics in Biosciences, 2017, vol. 9, issue 2, No 16, 605-621

Abstract: 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.

Keywords: Chronic obstructive pulmonary disease; Data integration; GWAS; Functional annotation (search for similar items in EconPapers)
Date: 2017
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s12561-016-9151-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:stabio:v:9:y:2017:i:2:d:10.1007_s12561-016-9151-2

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/12561

DOI: 10.1007/s12561-016-9151-2

Access Statistics for this article

Statistics in Biosciences is currently edited by Hongyu Zhao and Xihong Lin

More articles in Statistics in Biosciences from Springer, International Chinese Statistical Association
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().