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Hum Mol Genet. 2018 May 15;27(10):1819-1829. doi: 10.1093/hmg/ddy091.

Leveraging lung tissue transcriptome to uncover candidate causal genes in COPD genetic associations.

Author information

1
Institut Universitaire de Cardiologie et de Pneumologie de Québec - Université Laval, Quebec City, QC, Canada.
2
The University of British Columbia Centre for Heart Lung Innovation, St Paul's Hospital, Vancouver, BC, Canada.
3
Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA.
4
Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA.
5
Department of Epidemiology, University Medical Center Groningen, Groningen Research Institute for Asthma and COPD (GRIAC), Groningen, The Netherlands.
6
University of Groningen, University Medical Center Groningen, Groningen Research Institute for Asthma and COPD (GRIAC), Groningen, The Netherlands.
7
Merck Research Laboratories (MRL), Seattle, WA, USA.
8
Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
9
Department of Pathology and Medical Biology, University of Groningen, University Medical Center Groningen, GRIAC Research Institute, Groningen, The Netherlands.
10
Department of Pulmonary Diseases, University of Groningen, University Medical Center Groningen, GRIAC Research Institute, Groningen, The Netherlands.
11
Department of Molecular Biology, Medical Biochemistry and Pathology, Laval University, Quebec City, QC, Canada.
12
Respiratory Division, Department of Medicine, University of British Columbia, Vancouver, BC, Canada.
13
Department of Molecular Medicine, Laval University, Quebec City, QC, Canada.

Abstract

Causal genes of chronic obstructive pulmonary disease (COPD) remain elusive. The current study aims at integrating genome-wide association studies (GWAS) and lung expression quantitative trait loci (eQTL) data to map COPD candidate causal genes and gain biological insights into the recently discovered COPD susceptibility loci. Two complementary genomic datasets on COPD were studied. First, the lung eQTL dataset which included whole-genome gene expression and genotyping data from 1038 individuals. Second, the largest COPD GWAS to date from the International COPD Genetics Consortium (ICGC) with 13 710 cases and 38 062 controls. Methods that integrated GWAS with eQTL signals including transcriptome-wide association study (TWAS), colocalization and Mendelian randomization-based (SMR) approaches were used to map causality genes, i.e. genes with the strongest evidence of being the functional effector at specific loci. These methods were applied at the genome-wide level and at COPD risk loci derived from the GWAS literature. Replication was performed using lung data from GTEx. We collated 129 non-overlapping risk loci for COPD from the GWAS literature. At the genome-wide scale, 12 new COPD candidate genes/loci were revealed and six replicated in GTEx including CAMK2A, DMPK, MYO15A, TNFRSF10A, BTN3A2 and TRBV30. In addition, we mapped candidate causal genes for 60 out of the 129 GWAS-nominated loci and 23 of them were replicated in GTEx. Mapping candidate causal genes in lung tissue represents an important contribution to the genetics of COPD, enriches our biological interpretation of GWAS findings, and brings us closer to clinical translation of genetic associations.

PMID:
29547942
PMCID:
PMC5932553
DOI:
10.1093/hmg/ddy091
[Indexed for MEDLINE]
Free PMC Article

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