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      Genome-wide association study identifies novel susceptible loci and evaluation of polygenic risk score for chronic obstructive pulmonary disease in a Taiwanese population

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          Abstract

          Background

          Chronic Obstructive Pulmonary Disease (COPD) describes a group of progressive lung diseases causing breathing difficulties. While COPD development typically involves a complex interplay between genetic and environmental factors, genetics play a role in disease susceptibility. This study used genome-wide association studies (GWAS) and polygenic risk score (PRS) to elucidate the genetic basis for COPD in Taiwanese patients.

          Results

          GWAS was performed on a Taiwanese COPD case–control cohort with a sample size of 5,442 cases and 17,681 controls. Additionally, the PRS was calculated and assessed in our target groups. GWAS results indicate that although there were no single nucleotide polymorphisms (SNPs) of genome-wide significance, prominent COPD susceptibility loci on or nearby genes such as WWTR1, EXT1, INTU, MAP3K7CL, MAMDC2, BZW1/CLK1, LINC01197, LINC01894, and CFAP95 ( C9orf135) were identified, which had not been reported in previous studies. Thirteen susceptibility loci, such as CHRNA4, AFAP1, and DTWD1, previously reported in other populations were replicated and confirmed to be associated with COPD in Taiwanese populations. The PRS was determined in the target groups using the summary statistics from our base group, yielding an effective association with COPD (odds ratio [OR] 1.09, 95% confidence interval [CI] 1.02–1.17, p = 0.011). Furthermore, replication a previous lung function trait PRS model in our target group, showed a significant association of COPD susceptibility with PRS of Forced Expiratory Volume in one second (FEV 1)/Forced Vital Capacity (FCV) (OR 0.89, 95% CI 0.83–0.95, p = 0.001).

          Conclusions

          Novel COPD-related genes were identified in the studied Taiwanese population. The PRS model, based on COPD or lung function traits, enables disease risk estimation and enhances prediction before suffering. These results offer new perspectives on the genetics of COPD and serve as a basis for future research.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12864-024-10526-5.

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          Most cited references79

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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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            Ferroptosis: an iron-dependent form of nonapoptotic cell death.

            Nonapoptotic forms of cell death may facilitate the selective elimination of some tumor cells or be activated in specific pathological states. The oncogenic RAS-selective lethal small molecule erastin triggers a unique iron-dependent form of nonapoptotic cell death that we term ferroptosis. Ferroptosis is dependent upon intracellular iron, but not other metals, and is morphologically, biochemically, and genetically distinct from apoptosis, necrosis, and autophagy. We identify the small molecule ferrostatin-1 as a potent inhibitor of ferroptosis in cancer cells and glutamate-induced cell death in organotypic rat brain slices, suggesting similarities between these two processes. Indeed, erastin, like glutamate, inhibits cystine uptake by the cystine/glutamate antiporter (system x(c)(-)), creating a void in the antioxidant defenses of the cell and ultimately leading to iron-dependent, oxidative death. Thus, activation of ferroptosis results in the nonapoptotic destruction of certain cancer cells, whereas inhibition of this process may protect organisms from neurodegeneration. Copyright © 2012 Elsevier Inc. All rights reserved.
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              Second-generation PLINK: rising to the challenge of larger and richer datasets

              PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for even faster and more scalable implementations of key functions. In addition, GWAS and population-genetic data now frequently contain probabilistic calls, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1's primary data format. To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, O(sqrt(n))-time/constant-space Hardy-Weinberg equilibrium and Fisher's exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. This will be followed by PLINK 2.0, which will introduce (a) a new data format capable of efficiently representing probabilities, phase, and multiallelic variants, and (b) extensions of many functions to account for the new types of information. The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.
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                Author and article information

                Contributors
                000704@tool.caaumed.org.tw
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                17 June 2024
                17 June 2024
                2024
                : 25
                : 607
                Affiliations
                [1 ]Department of Medical Research, China Medical University Hospital, ( https://ror.org/0368s4g32) Taichung, 404327 Taiwan
                [2 ]School of Post Baccalaureate Chinese Medicine, China Medical University, ( https://ror.org/00v408z34) Taichung, 404333 Taiwan
                [3 ]Graduate Institute of Integrated Medicine, College of Chinese Medicine, China Medical University, ( https://ror.org/00v408z34) Taichung, 404333 Taiwan
                [4 ]Center for Personalized Medicine, China Medical University Hospital, ( https://ror.org/0368s4g32) Taichung, 404327 Taiwan
                [5 ]Department of Internal Medicine, Pulmonary and Critical Care Medicine, China Medical University Hospital, ( https://ror.org/0368s4g32) Taichung, 404333 Taiwan
                [6 ]Graduate Institute of Biomedical Sciences, China Medical University, ( https://ror.org/00v408z34) Taichung, 404327 Taiwan
                [7 ]Department of Medical Research, Million-Person Precision Medicine Initiative, China Medical University Hospital, ( https://ror.org/0368s4g32) Taichung, 404327 Taiwan
                [8 ]School of Chinese Medicine, China Medical University, ( https://ror.org/00v408z34) Taichung, 404333 Taiwan
                [9 ]GRID grid.254145.3, ISNI 0000 0001 0083 6092, Division of Genetics and Metabolism, , China Medical University Children’s Hospital, ; Taichung, 404327 Taiwan
                [10 ]Department of Medical Genetics, China Medical University Hospital, ( https://ror.org/0368s4g32) Taichung, 404327 Taiwan
                [11 ]Department of Medical Laboratory Science and Biotechnology, Asia University, ( https://ror.org/038a1tp19) Taichung, 413305 Taiwan
                [12 ]Department of Medical Research, China Medical University Hospital, ( https://ror.org/0368s4g32) No. 2, Yude Road, North District, Taichung, 404327 Taiwan
                Article
                10526
                10.1186/s12864-024-10526-5
                11184693
                38886662
                49ef5200-1622-4dc0-90ea-4fcb9b058f1f
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 9 December 2023
                : 14 June 2024
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2024

                Genetics
                chronic obstructive pulmonary disease,genome-wide association study,polygenic risk score,taiwanese population,genetic association,genetic biobank of china medical university hospital

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