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

Post-GWAS Prioritization Through Data Integration Provides Novel Insights on Chronic Obstructive... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Statistics in Biosciences Springer Journals

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

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Publisher
Springer US
Copyright
Copyright © 2016 by International Chinese Statistical Association
Subject
Statistics; Statistics for Life Sciences, Medicine, Health Sciences; Biostatistics; Theoretical Ecology/Statistics
ISSN
1867-1764
eISSN
1867-1772
D.O.I.
10.1007/s12561-016-9151-2
Publisher site
See Article on Publisher Site

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