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      Genetic variants in FAM13A and IREB2 are associated with the susceptibility to COPD in a Chinese rural population: a case-control study

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          Abstract

          Background

          Genome-wide association studies identified several genomic regions associated with the risk of chronic obstructive pulmonary disease (COPD), including the 4q22 and 15q25 regions. These regions contain the FAM13A and IREB2 genes, which have been associated with COPD but data are lacking for Chinese patients. The objective of the study was to identify new genetic variants in the FAM13A and IREB2 associated with COPD in Northwestern China.

          Methods

          This was a case-control study performed in the Ningxia Hui Autonomous Region between January 2014 and December 2016. Patients were grouped as COPD and controls based on FEV 1/FVC<70%. Seven tag single-nucleotide polymorphisms (SNPs) in the FAM13A and IREB2 genes were genotyped using the Agena MassARRAY platform. Logistic regression was used to determine the association between SNPs and COPD risk.

          Results

          rs17014601 in FAM13A was significantly associated with COPD in the additive (odds ratio [OR]=1.36, 95% confidence interval [CI]: 1.11–1.67, P=0.003), heterozygote (OR=1.76, 95% CI: 1.33–2.32, P=0.0001), and dominant (OR=1.67, 95% CI: 1.28–2.18, P=0.0001) models. Stratified analyses indicated that the risk was higher in never smokers. rs16969858 in IREB2 was significantly associated with COPD but in the univariate analysis only, and the multivariate analysis did not show any association.

          Conclusion

          The results suggest that the new variant rs17014601 in the FAM13A gene was significantly associated with COPD risk in a Chinese rural population. Additional studies are required to confirm the role of this variant in COPD development and progression.

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          Most cited references 26

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          COPD in Never Smokers

          Background: Never smokers comprise a substantial proportion of patients with COPD. Their characteristics and possible risk factors in this population are not yet well defined. Methods: We analyzed data from 14 countries that participated in the international, population-based Burden of Obstructive Lung Disease (BOLD) study. Participants were aged ≥ 40 years and completed postbronchodilator spirometry testing plus questionnaires about respiratory symptoms, health status, and exposure to COPD risk factors. A diagnosis of COPD was based on the postbronchodilator FEV1/FVC ratio, according to current GOLD (Global Initiative for Obstructive Lung Disease) guidelines. In addition to this, the lower limit of normal (LLN) was evaluated as an alternative threshold for the FEV1/FVC ratio. Results: Among 4,291 never smokers, 6.6% met criteria for mild (GOLD stage I) COPD, and 5.6% met criteria for moderate to very severe (GOLD stage II+) COPD. Although never smokers were less likely to have COPD and had less severe COPD than ever smokers, never smokers nonetheless comprised 23.3% (240/1,031) of those classified with GOLD stage II+ COPD. This proportion was similar, 20.5% (171/832), even when the LLN was used as a threshold for the FEV1/FVC ratio. Predictors of COPD in never smokers include age, education, occupational exposure, childhood respiratory diseases, and BMI alterations. Conclusion: This multicenter international study confirms previous evidence that never smokers comprise a substantial proportion of individuals with COPD. Our data suggest that, in addition to increased age, a prior diagnosis of asthma and, among women, lower education levels are associated with an increased risk for COPD among never smokers.
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            Genomic dissection of population substructure of Han Chinese and its implication in association studies.

            To date, most genome-wide association studies (GWAS) and studies of fine-scale population structure have been conducted primarily on Europeans. Han Chinese, the largest ethnic group in the world, composing 20% of the entire global human population, is largely underrepresented in such studies. A well-recognized challenge is the fact that population structure can cause spurious associations in GWAS. In this study, we examined population substructures in a diverse set of over 1700 Han Chinese samples collected from 26 regions across China, each genotyped at approximately 160K single-nucleotide polymorphisms (SNPs). Our results showed that the Han Chinese population is intricately substructured, with the main observed clusters corresponding roughly to northern Han, central Han, and southern Han. However, simulated case-control studies showed that genetic differentiation among these clusters, although very small (F(ST) = 0.0002 approximately 0.0009), is sufficient to lead to an inflated rate of false-positive results even when the sample size is moderate. The top two SNPs with the greatest frequency differences between the northern Han and southern Han clusters (F(ST) > 0.06) were found in the FADS2 gene, which associates with the fatty acid composition in phospholipids, and in the HLA complex P5 gene (HCP5), which associates with HIV infection, psoriasis, and psoriatic arthritis. Ingenuity Pathway Analysis (IPA) showed that most differentiated genes among clusters are involved in cardiac arteriopathy (p < 10(-101)). These signals indicating significant differences among Han Chinese subpopulations should be carefully explained in case they are also detected in association studies, especially when sample sources are diverse.
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              Haploview: Visualization and analysis of SNP genotype data.

               J. Barrett (2009)
              Association studies involve accessing, parsing, generating, and analyzing large volumes of data, often carried out in many steps over many months. Large-scale surveys of genetic variation, such as the International HapMap Project, and rapidly increasing volumes of single-nucleotide polymorphism (SNP) genotyping data have created exciting opportunities for association studies. However, they have further exacerbated the difficulty of curating and analyzing such data. Haploview is a program developed in Mark Daly's lab at the Broad Institute of MIT and Harvard, which is designed to bundle many everyday analysis tasks into one easy-to-use package. Haploview has several features that are useful throughout different phases of association studies. Several of these features are illustrated in this article by following a hypothetical association study from design to execution. Haploview is used to (1) analyze HapMap data and choose tag-SNPs, (2) evaluate the quality of disease genotype data, (3) test for association, and (4) evaluate a region for follow-up of a positive association.
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                Author and article information

                Journal
                Int J Chron Obstruct Pulmon Dis
                Int J Chron Obstruct Pulmon Dis
                International Journal of COPD
                International Journal of Chronic Obstructive Pulmonary Disease
                Dove Medical Press
                1176-9106
                1178-2005
                2018
                25 May 2018
                : 13
                : 1735-1745
                Affiliations
                [1 ]Department of Respiratory and Critical Care Medicine, General Hospital of Ningxia Medical University, Ningxia Medical University, Yinchuan, People’s Republic of China
                [2 ]National Engineering Research Center for Beijing Biochip Technology, Sub-center in Ningxia, General Hospital of Ningxia Medical University, Yinchuan, People’s Republic of China
                [3 ]Department of Medical Genetic and Cell Biology, Ningxia Medical University, Yinchuan, People’s Republic of China
                Author notes
                Correspondence: Jin Zhang, Department of Respiratory and Critical Care Medicine, General Hospital of Ningxia Medical University, Ningxia Medical University, Yinchuan 750004, People’s Republic of China, Tel +1 399 508 2493, Fax +86 0951 408 0632, Email jinzhangnew@ 123456hotmail.com
                Article
                copd-13-1735
                10.2147/COPD.S162241
                5973397
                © 2018 Zhang et al. This work is published and licensed by Dove Medical Press Limited

                The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License ( http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed.

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