Dataset Information

EGLN2 and RNF150 genetic variants are associated with chronic obstructive pulmonary disease risk in the Chinese population.

ABSTRACT: PURPOSE:Chronic obstructive pulmonary disease (COPD) is a major and an increasingly prevalent health problem worldwide. It has been reported that genetic variation may play a role in the development and severity of COPD. The purpose of this study was to investigate whether single nucleotide polymorphisms in multiple genetic variants were associated with COPD in a Chinese population from Hainan province. METHODS:In this case-control study, including 200 COPD patients and 401 controls, we genotyped 14 tag single nucleotide polymorphisms and evaluated their association with COPD using the ? (2) test and genetic model analysis. RESULTS:The polymorphism, rs10007052, in the RNF150 gene was significantly associated with COPD risk at a 5% level (odds ratio =1.43, 95% confidence interval, 1.06-1.95, P=0.020). In the log-additive model, the minor allele (C) of rs10007052 in the RNF150 gene (P=0.026) and the minor allele (C) of rs3733829 in the EGLN2 gene (P=0.037) were associated with COPD risk after adjustment for age, sex, and smoking status. Further haplotype analysis revealed that the "CT" haplotype composed of the mutant allele (C) of rs7937, rs3733829 in the EGLN2 gene, was associated with increased COPD risk (odds ratio =1.55; 95% confidence interval, 1.05-2.31; P=0.029). CONCLUSION:Our findings indicated that rs10007052 in the RNF150 and rs3733829 in the EGLN2 gene were significantly associated with the risk of COPD in Chinese populations of Hainan province. These data may provide novel insights into the pathogenesis of COPD, although further studies with larger numbers of participants worldwide are needed for validation of our conclusions.

SUBMITTER: Ding Y

PROVIDER: S-EPMC4298296 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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EGLN2 and RNF150 genetic variants are associated with chronic obstructive pulmonary disease risk in the Chinese population.

Ding Yipeng Y Niu Huan H Yang Hua H Sun Pei P Chen Yu Y Duan Mengling M Xu Dongchuan D Xu Junxue J Jin Tianbo T

International journal of chronic obstructive pulmonary disease 20150113

<h4>Purpose</h4>Chronic obstructive pulmonary disease (COPD) is a major and an increasingly prevalent health problem worldwide. It has been reported that genetic variation may play a role in the development and severity of COPD. The purpose of this study was to investigate whether single nucleotide polymorphisms in multiple genetic variants were associated with COPD in a Chinese population from Hainan province.<h4>Methods</h4>In this case-control study, including 200 COPD patients and 401 contro ...[more]

PMID: 25609945

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Project description:BackgroundStudies have identified individual blood biomarkers associated with chronic obstructive pulmonary disease (COPD) and related phenotypes. However, complex diseases such as COPD typically involve changes in multiple molecules with interconnections that may not be captured when considering single molecular features.MethodsLeveraging proteomic data from 3,173 COPDGene Non-Hispanic White (NHW) and African American (AA) participants, we applied sparse multiple canonical correlation network analysis (SmCCNet) to 4,776 proteins assayed on the SomaScan v4.0 platform to derive sparse networks of proteins associated with current vs. former smoking status, airflow obstruction, and emphysema quantitated from high-resolution computed tomography scans. We then used NetSHy, a dimension reduction technique leveraging network topology, to produce summary scores of each proteomic network, referred to as NetSHy scores. We next performed a genome-wide association study (GWAS) to identify variants associated with the NetSHy scores, or network quantitative trait loci (nQTLs). Finally, we evaluated the replicability of the networks in an independent cohort, SPIROMICS.ResultsWe identified networks of 13 to 104 proteins for each phenotype and exposure in NHW and AA, and the derived NetSHy scores significantly associated with the variable of interests. Networks included known (sRAGE, ALPP, MIP1) and novel molecules (CA10, CPB1, HIS3, PXDN) and interactions involved in COPD pathogenesis. We observed 7 nQTL loci associated with NetSHy scores, 4 of which remained after conditional analysis. Networks for smoking status and emphysema, but not airflow obstruction, demonstrated a high degree of replicability across race groups and cohorts.ConclusionsIn this work, we apply state-of-the-art molecular network generation and summarization approaches to proteomic data from COPDGene participants to uncover protein networks associated with COPD phenotypes. We further identify genetic associations with networks. This work discovers protein networks containing known and novel proteins and protein interactions associated with clinically relevant COPD phenotypes across race groups and cohorts.

Project description:BackgroundStudies have identified individual blood biomarkers associated with chronic obstructive pulmonary disease (COPD) and related phenotypes. However, complex diseases such as COPD typically involve changes in multiple molecules with interconnections that may not be captured when considering single molecular features.MethodsLeveraging proteomic data from 3,173 COPDGene Non-Hispanic White (NHW) and African American (AA) participants, we applied sparse multiple canonical correlation network analysis (SmCCNet) to 4,776 proteins assayed on the SomaScan v4.0 platform to derive sparse networks of proteins associated with current vs. former smoking status, airflow obstruction, and emphysema quantitated from high-resolution computed tomography scans. We then used NetSHy, a dimension reduction technique leveraging network topology, to produce summary scores of each proteomic network, referred to as NetSHy scores. We next performed genome-wide association study (GWAS) to identify variants associated with the NetSHy scores, or network quantitative trait loci (nQTLs). Finally, we evaluated the replicability of the networks in an independent cohort, SPIROMICS.ResultsWe identified networks of 13 to 104 proteins for each phenotype and exposure in NHW and AA, and the derived NetSHy scores significantly associated with the variable of interests. Networks included known (sRAGE, ALPP, MIP1) and novel molecules (CA10, CPB1, HIS3, PXDN) and interactions involved in COPD pathogenesis. We observed 7 nQTL loci associated with NetSHy scores, 4 of which remained after conditional analysis. Networks for smoking status and emphysema, but not airflow obstruction, demonstrated a high degree of replicability across race groups and cohorts.ConclusionsIn this work, we apply state-of-the-art molecular network generation and summarization approaches to proteomic data from COPDGene participants to uncover protein networks associated with COPD phenotypes. We further identify genetic associations with networks. This work discovers protein networks containing known and novel proteins and protein interactions associated with clinically relevant COPD phenotypes across race groups and cohorts.

Dataset Information

EGLN2 and RNF150 genetic variants are associated with chronic obstructive pulmonary disease risk in the Chinese population.

Publications

EGLN2 and RNF150 genetic variants are associated with chronic obstructive pulmonary disease risk in the Chinese population.

Similar Datasets

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