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Published on: August 25, 2017
Identification of FGF7 as a novel susceptibility locus for chronic obstructive pulmonary disease
John M Brehm1, Koichi Hagiwara, Yohannes Tesfaigzi
1Division of Pediatric Pulmonary Medicine, Allergy and Immunology, Children's Hospital of Pittsburgh of UPMC, 4401 Penn Avenue, Pittsburgh, PA 15224, USA.
Rationale:
Traditional genome-wide association studies (GWASs) of large cohorts of subjects with chronic obstructive pulmonary disease (COPD) have successfully identified novel candidate genes, but several other plausible loci do not meet strict criteria for genome-wide significance after correction for multiple testing.
Objectives:
The authors hypothesise that by applying unbiased weights derived from unique populations we can identify additional COPD susceptibility loci. Methods The authors performed a homozygosity haplotype analysis on a group of subjects with and without COPD to identify regions of conserved homozygosity haplotype (RCHHs). Weights were constructed based on the frequency of these RCHHs in case versus controls, and used to adjust the p values from a large collaborative GWAS of COPD.
Results:
The authors identified 2318 RCHHs, of which 576 were significantly (p<0.05) over-represented in cases. After applying the weights constructed from these regions to a collaborative GWAS of COPD, the authors identified two single nucleotide polymorphisms (SNPs) in a novel gene (fibroblast growth factor-7 (FGF7)) that gained genome-wide significance by the false discovery rate method. In a follow-up analysis, both SNPs (rs12591300 and rs4480740) were significantly associated with COPD in an independent population (combined p values of 7.9E-7 and 2.8E-6, respectively). In another independent population, increased lung tissue FGF7 expression was associated with worse measures of lung function.
Conclusion:
Weights constructed from a homozygosity haplotype analysis of an isolated population successfully identify novel genetic associations from a GWAS on a separate population. This method can be used to identify promising candidate genes that fail to meet strict correction for multiple testing.
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