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Hum Mol Genet. 2015 Dec 1;24(23):6836-48. doi: 10.1093/hmg/ddv378. Epub 2015 Sep 22.

Integrative pathway genomics of lung function and airflow obstruction.

Author information

1
Computational Medicine Core, Center for Lung Biology, Department of Medicine, sagharib@u.washington.edu.
2
Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands.
3
Genetic Epidemiology Group, Department of Health Sciences, University of Leicester, Leicester, UK, National Institute for Health Research (NIHR) Leicester Respiratory Biomedical Research Unit, Glenfield Hospital, Leicester, UK.
4
Computational Medicine Core, Center for Lung Biology.
5
Human Genetics and Computational Biomedicine, Pfizer Global Research and Development, Cambridge, MA, USA.
6
Cardiovascular Health Research Unit.
7
University of British Columbia, Centre for Heart Lung Innovation, Vancouver, BC, Canada.
8
Behavioral and Urban Health Program, Behavioral Health and Criminal Justice Division, Research Triangle Institute (RTI) International, Research Triangle Park, NC, USA.
9
Division of Nutritional Sciences, Cornell University, Ithaca, NY, USA.
10
Institute of Genetic Epidemiology.
11
Department of Epidemiology, GRIAC Research Institute, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
12
Swiss Tropical and Public Health Institute, Basel, Switzerland, University of Basel, Basel, Switzerland.
13
MRC Human Genetics Unit, MRC IGMM.
14
Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands, Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium.
15
School of Public Health.
16
Center for Public Health Genomics, Division of Biostatistics and Epidemiology, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA.
17
PathWest Laboratory Medicine WA, Nedlands, Australia, School of Pathology and Laboratory Medicine, School of Population Health, The University of Western Australia, Nedlands, Australia.
18
Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA.
19
Department of Medical and Molecular Genetics.
20
Iceland Heart Association, Kopavogur, Iceland, University of Iceland, Reykjavik, Iceland.
21
Public Health Genomics Unit, Department of Chronic Disease Prevention, National Institute for Health and Welfare (THL), Helsinki, Finland.
22
University of Queensland Diamantina Institute, Translational Research Institute, 37 Kent St Woolloongabba, QLD 4102, Australia, MRC Integrative Epidemiology Unit, Oakfield Road, Oakfield Grove BS82BN, Bristol, UK.
23
Population Health Research Institute, St George's, University of London, London, UK.
24
Centre for Cognitive Ageing and Cognitive Epidemiology.
25
Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands, Netherlands Genomics Initiative (NGI)-Sponsored Netherlands Consortium for Healthy Aging (NCHA), Rotterdam, The Netherlands.
26
Department of Internal Medicine B, Pneumology, Cardiology, Intensive Care and Infectious Diseases, University Hospital Greifswald, Greifswald, Germany.
27
Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Teviot Place, Edinburgh, Scotland, UK.
28
Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
29
MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, UK.
30
Cardiovascular Health Research Unit, Department of Epidemiology, Group Health Research Institute, Group Health Cooperative, Seattle, WA, USA.
31
Respiratory Epidemiology and Public Health Group, National Heart and Lung Institute.
32
Institute of Epidemiology I, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
33
Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA.
34
Department of Epidemiology and Biostatistics, MRC Health Protection Agency (HPA) Centre for Environment and Health, School of Public Health, Imperial College London, London, UK, Institute of Health Sciences, Biocenter Oulu, University of Oulu, Oulu, Finland, Unit of Primary Care, Oulu University Hospital, Oulu, Finland, Department of Children and Young People and Families, National Institute for Health and Welfare, Oulu, Finland.
35
Pulmonary Center, Boston University School of Medicine, Boston, MA, USA, The NHLBI's Framingham Heart Study, Framingham, MA, USA.
36
Department of Clinical Physiology, University of Tampere and Tampere University Hospital, Tampere, Finland.
37
Division of Nutritional Sciences, Cornell University, Ithaca, NY, USA, Department of Healthcare Policy and Research, Division of Biostatistics and Epidemiology, Weill Cornell Medical College, New York, NY, USA.
38
Department of Twins Research and Genetic Epidemiology, King's College, London, UK.
39
The NHLBI's Framingham Heart Study, Framingham, MA, USA, Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
40
Department of Medicine, Cardiovascular Health Research Unit, Department of Epidemiology, Department of Health Services, University of Washington, Seattle, WA, USA, Group Health Research Institute, Group Health Cooperative, Seattle, WA, USA.
41
Division of Respiratory Medicine, University Hospital of Nottingham, Nottingham, UK.
42
Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA and.
43
Epidemiology Branch, National Institute of Environmental Health Sciences, National Institute of Health, Department of Health and Human Services, Research Triangle Park, NC, USA.

Abstract

Chronic respiratory disorders are important contributors to the global burden of disease. Genome-wide association studies (GWASs) of lung function measures have identified several trait-associated loci, but explain only a modest portion of the phenotypic variability. We postulated that integrating pathway-based methods with GWASs of pulmonary function and airflow obstruction would identify a broader repertoire of genes and processes influencing these traits. We performed two independent GWASs of lung function and applied gene set enrichment analysis to one of the studies and validated the results using the second GWAS. We identified 131 significantly enriched gene sets associated with lung function and clustered them into larger biological modules involved in diverse processes including development, immunity, cell signaling, proliferation and arachidonic acid. We found that enrichment of gene sets was not driven by GWAS-significant variants or loci, but instead by those with less stringent association P-values. Next, we applied pathway enrichment analysis to a meta-analyzed GWAS of airflow obstruction. We identified several biologic modules that functionally overlapped with those associated with pulmonary function. However, differences were also noted, including enrichment of extracellular matrix (ECM) processes specifically in the airflow obstruction study. Network analysis of the ECM module implicated a candidate gene, matrix metalloproteinase 10 (MMP10), as a putative disease target. We used a knockout mouse model to functionally validate MMP10's role in influencing lung's susceptibility to cigarette smoke-induced emphysema. By integrating pathway analysis with population-based genomics, we unraveled biologic processes underlying pulmonary function traits and identified a candidate gene for obstructive lung disease.

PMID:
26395457
PMCID:
PMC4643644
DOI:
10.1093/hmg/ddv378
[Indexed for MEDLINE]
Free PMC Article

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