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Disease patterns of coronary heart disease and type 2 diabetes harbored distinct and shared genetic architecture

BACKGROUND: Coronary heart disease (CHD) and type 2 diabetes (T2D) are two complex diseases with complex interrelationships. However, the genetic architecture of the two diseases is often studied independently by the individual single-nucleotide polymorphism (SNP) approach. Here, we presented a geno...

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Autores principales: Xiao, Han, Ma, Yujia, Zhou, Zechen, Li, Xiaoyi, Ding, Kexin, Wu, Yiqun, Wu, Tao, Chen, Dafang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9738029/
https://www.ncbi.nlm.nih.gov/pubmed/36494812
http://dx.doi.org/10.1186/s12933-022-01715-1
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author Xiao, Han
Ma, Yujia
Zhou, Zechen
Li, Xiaoyi
Ding, Kexin
Wu, Yiqun
Wu, Tao
Chen, Dafang
author_facet Xiao, Han
Ma, Yujia
Zhou, Zechen
Li, Xiaoyi
Ding, Kexin
Wu, Yiqun
Wu, Tao
Chen, Dafang
author_sort Xiao, Han
collection PubMed
description BACKGROUND: Coronary heart disease (CHD) and type 2 diabetes (T2D) are two complex diseases with complex interrelationships. However, the genetic architecture of the two diseases is often studied independently by the individual single-nucleotide polymorphism (SNP) approach. Here, we presented a genotypic-phenotypic framework for deciphering the genetic architecture underlying the disease patterns of CHD and T2D. METHOD: A data-driven SNP-set approach was performed in a genome-wide association study consisting of subpopulations with different disease patterns of CHD and T2D (comorbidity, CHD without T2D, T2D without CHD and all none). We applied nonsmooth nonnegative matrix factorization (nsNMF) clustering to generate SNP sets interacting the information of SNP and subject. Relationships between SNP sets and phenotype sets harboring different disease patterns were then assessed, and we further co-clustered the SNP sets into a genetic network to topologically elucidate the genetic architecture composed of SNP sets. RESULTS: We identified 23 non-identical SNP sets with significant association with CHD or T2D (SNP-set based association test, P < 3.70 × [Formula: see text] ). Among them, disease patterns involving CHD and T2D were related to distinct SNP sets (Hypergeometric test, P < 2.17 × [Formula: see text] ). Accordingly, numerous genes (e.g., KLKs, GRM8, SHANK2) and pathways (e.g., fatty acid metabolism) were diversely implicated in different subtypes and related pathophysiological processes. Finally, we showed that the genetic architecture for disease patterns of CHD and T2D was composed of disjoint genetic networks (heterogeneity), with common genes contributing to it (pleiotropy). CONCLUSION: The SNP-set approach deciphered the complexity of both genotype and phenotype as well as their complex relationships. Different disease patterns of CHD and T2D share distinct genetic architectures, for which lipid metabolism related to fibrosis may be an atherogenic pathway that is specifically activated by diabetes. Our findings provide new insights for exploring new biological pathways. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12933-022-01715-1.
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spelling pubmed-97380292022-12-11 Disease patterns of coronary heart disease and type 2 diabetes harbored distinct and shared genetic architecture Xiao, Han Ma, Yujia Zhou, Zechen Li, Xiaoyi Ding, Kexin Wu, Yiqun Wu, Tao Chen, Dafang Cardiovasc Diabetol Research BACKGROUND: Coronary heart disease (CHD) and type 2 diabetes (T2D) are two complex diseases with complex interrelationships. However, the genetic architecture of the two diseases is often studied independently by the individual single-nucleotide polymorphism (SNP) approach. Here, we presented a genotypic-phenotypic framework for deciphering the genetic architecture underlying the disease patterns of CHD and T2D. METHOD: A data-driven SNP-set approach was performed in a genome-wide association study consisting of subpopulations with different disease patterns of CHD and T2D (comorbidity, CHD without T2D, T2D without CHD and all none). We applied nonsmooth nonnegative matrix factorization (nsNMF) clustering to generate SNP sets interacting the information of SNP and subject. Relationships between SNP sets and phenotype sets harboring different disease patterns were then assessed, and we further co-clustered the SNP sets into a genetic network to topologically elucidate the genetic architecture composed of SNP sets. RESULTS: We identified 23 non-identical SNP sets with significant association with CHD or T2D (SNP-set based association test, P < 3.70 × [Formula: see text] ). Among them, disease patterns involving CHD and T2D were related to distinct SNP sets (Hypergeometric test, P < 2.17 × [Formula: see text] ). Accordingly, numerous genes (e.g., KLKs, GRM8, SHANK2) and pathways (e.g., fatty acid metabolism) were diversely implicated in different subtypes and related pathophysiological processes. Finally, we showed that the genetic architecture for disease patterns of CHD and T2D was composed of disjoint genetic networks (heterogeneity), with common genes contributing to it (pleiotropy). CONCLUSION: The SNP-set approach deciphered the complexity of both genotype and phenotype as well as their complex relationships. Different disease patterns of CHD and T2D share distinct genetic architectures, for which lipid metabolism related to fibrosis may be an atherogenic pathway that is specifically activated by diabetes. Our findings provide new insights for exploring new biological pathways. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12933-022-01715-1. BioMed Central 2022-12-09 /pmc/articles/PMC9738029/ /pubmed/36494812 http://dx.doi.org/10.1186/s12933-022-01715-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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.
spellingShingle Research
Xiao, Han
Ma, Yujia
Zhou, Zechen
Li, Xiaoyi
Ding, Kexin
Wu, Yiqun
Wu, Tao
Chen, Dafang
Disease patterns of coronary heart disease and type 2 diabetes harbored distinct and shared genetic architecture
title Disease patterns of coronary heart disease and type 2 diabetes harbored distinct and shared genetic architecture
title_full Disease patterns of coronary heart disease and type 2 diabetes harbored distinct and shared genetic architecture
title_fullStr Disease patterns of coronary heart disease and type 2 diabetes harbored distinct and shared genetic architecture
title_full_unstemmed Disease patterns of coronary heart disease and type 2 diabetes harbored distinct and shared genetic architecture
title_short Disease patterns of coronary heart disease and type 2 diabetes harbored distinct and shared genetic architecture
title_sort disease patterns of coronary heart disease and type 2 diabetes harbored distinct and shared genetic architecture
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9738029/
https://www.ncbi.nlm.nih.gov/pubmed/36494812
http://dx.doi.org/10.1186/s12933-022-01715-1
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