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      Integration of SNP Disease Association, eQTL, and Enrichment Analyses to Identify Risk SNPs and Susceptibility Genes in Chronic Obstructive Pulmonary Disease

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          Abstract

          Chronic obstructive pulmonary disease (COPD) is a complex disease caused by the disturbance of genetic and environmental factors. Single-nucleotide polymorphisms (SNPs) play a vital role in the genetic dissection of complex diseases. In-depth analysis of SNP-related information could recognize disease-associated biomarkers and further uncover the genetic mechanism of complex diseases. Risk-related variants might act on the disease by affecting gene expression and gene function. Through integrating SNP disease association study and expression quantitative trait loci (eQTL) analysis, as well as functional enrichment of containing known causal genes, four risk SNPs and four corresponding susceptibility genes were identified utilizing next-generation sequencing (NGS) data of COPD. Of the four risk SNPs, one could be found in the SNPedia database that stored disease-related SNPs and has been linked to a disease in the literature. Four genes showed significant differences from the perspective of normal/disease or variant/nonvariant samples, as well as the high performance of sample classification. It is speculated that the four susceptibility genes could be used as biomarkers of COPD. Furthermore, three of our susceptibility genes have been confirmed in the literature to be associated with COPD. Among them, two genes had an impact on the significance of expression correlation of known causal genes they interact with, respectively. Overall, this research may present novel insights into the diagnosis and pathogenesis of COPD and susceptibility gene identification of other complex diseases.

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          Most cited references36

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          clusterProfiler: an R package for comparing biological themes among gene clusters.

          Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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            PLINK: a tool set for whole-genome association and population-based linkage analyses.

            Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
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              Five years of GWAS discovery.

              The past five years have seen many scientific and biological discoveries made through the experimental design of genome-wide association studies (GWASs). These studies were aimed at detecting variants at genomic loci that are associated with complex traits in the population and, in particular, at detecting associations between common single-nucleotide polymorphisms (SNPs) and common diseases such as heart disease, diabetes, auto-immune diseases, and psychiatric disorders. We start by giving a number of quotes from scientists and journalists about perceived problems with GWASs. We will then briefly give the history of GWASs and focus on the discoveries made through this experimental design, what those discoveries tell us and do not tell us about the genetics and biology of complex traits, and what immediate utility has come out of these studies. Rather than giving an exhaustive review of all reported findings for all diseases and other complex traits, we focus on the results for auto-immune diseases and metabolic diseases. We return to the perceived failure or disappointment about GWASs in the concluding section. Copyright © 2012 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                Journal
                Biomed Res Int
                Biomed Res Int
                BMRI
                BioMed Research International
                Hindawi
                2314-6133
                2314-6141
                2020
                29 December 2020
                : 2020
                : 3854196
                Affiliations
                1College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
                2Department of Respiratory, The Second Affiliated Hospital, Harbin Medical University, Harbin, China
                Author notes

                Academic Editor: Ravesh Singh

                Author information
                https://orcid.org/0000-0003-1680-0398
                https://orcid.org/0000-0001-7427-9647
                https://orcid.org/0000-0001-6645-8485
                https://orcid.org/0000-0002-1798-7513
                https://orcid.org/0000-0003-0846-1277
                https://orcid.org/0000-0003-2061-3333
                https://orcid.org/0000-0003-0611-9056
                https://orcid.org/0000-0002-2775-4625
                https://orcid.org/0000-0002-4576-6814
                https://orcid.org/0000-0002-9797-0315
                Article
                10.1155/2020/3854196
                7785362
                f2a38fba-94ac-42a7-9328-692aa897ce1d
                Copyright © 2020 Yang Liu et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 27 July 2020
                : 9 December 2020
                : 15 December 2020
                Funding
                Funded by: Harbin Medical University
                Award ID: 201910226382
                Award ID: 201910226139
                Funded by: University Student Innovation and Entrepreneurship Training Program in Heilongjiang Province
                Award ID: 201910226380
                Award ID: 201910226036
                Award ID: 201910226001
                Funded by: Fundamental Research Funds for Provincial of Universities
                Award ID: 2019-KYYWF-0395
                Funded by: National Natural Science Foundation of China
                Award ID: 61702141
                Award ID: 61272388
                Award ID: 81627901
                Categories
                Research Article

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