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Genomic approaches to identify and investigate genes associated with atrial fibrillation and heart failure susceptibility
Atrial fibrillation (AF) and heart failure (HF) contribute to about 45% of all cardiovascular disease (CVD) deaths in the USA and around the globe. Due to the complex nature, progression, inherent genetic makeup, and heterogeneity of CVDs, personalized treatments are believed to be critical. To impr...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10239148/ https://www.ncbi.nlm.nih.gov/pubmed/37270590 http://dx.doi.org/10.1186/s40246-023-00498-0 |
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author | Patel, Kush Ketan Venkatesan, Cynthia Abdelhalim, Habiba Zeeshan, Saman Arima, Yuichiro Linna-Kuosmanen, Suvi Ahmed, Zeeshan |
author_facet | Patel, Kush Ketan Venkatesan, Cynthia Abdelhalim, Habiba Zeeshan, Saman Arima, Yuichiro Linna-Kuosmanen, Suvi Ahmed, Zeeshan |
author_sort | Patel, Kush Ketan |
collection | PubMed |
description | Atrial fibrillation (AF) and heart failure (HF) contribute to about 45% of all cardiovascular disease (CVD) deaths in the USA and around the globe. Due to the complex nature, progression, inherent genetic makeup, and heterogeneity of CVDs, personalized treatments are believed to be critical. To improve the deciphering of CVD mechanisms, we need to deeply investigate well-known and identify novel genes that are responsible for CVD development. With the advancements in sequencing technologies, genomic data have been generated at an unprecedented pace to foster translational research. Correct application of bioinformatics using genomic data holds the potential to reveal the genetic underpinnings of various health conditions. It can help in the identification of causal variants for AF, HF, and other CVDs by moving beyond the one-gene one-disease model through the integration of common and rare variant association, the expressed genome, and characterization of comorbidities and phenotypic traits derived from the clinical information. In this study, we examined and discussed variable genomic approaches investigating genes associated with AF, HF, and other CVDs. We collected, reviewed, and compared high-quality scientific literature published between 2009 and 2022 and accessible through PubMed/NCBI. While selecting relevant literature, we mainly focused on identifying genomic approaches involving the integration of genomic data; analysis of common and rare genetic variants; metadata and phenotypic details; and multi-ethnic studies including individuals from ethnic minorities, and European, Asian, and American ancestries. We found 190 genes associated with AF and 26 genes linked to HF. Seven genes had implications in both AF and HF, which are SYNPO2L, TTN, MTSS1, SCN5A, PITX2, KLHL3, and AGAP5. We listed our conclusion, which include detailed information about genes and SNPs associated with AF and HF. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40246-023-00498-0. |
format | Online Article Text |
id | pubmed-10239148 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-102391482023-06-04 Genomic approaches to identify and investigate genes associated with atrial fibrillation and heart failure susceptibility Patel, Kush Ketan Venkatesan, Cynthia Abdelhalim, Habiba Zeeshan, Saman Arima, Yuichiro Linna-Kuosmanen, Suvi Ahmed, Zeeshan Hum Genomics Review Atrial fibrillation (AF) and heart failure (HF) contribute to about 45% of all cardiovascular disease (CVD) deaths in the USA and around the globe. Due to the complex nature, progression, inherent genetic makeup, and heterogeneity of CVDs, personalized treatments are believed to be critical. To improve the deciphering of CVD mechanisms, we need to deeply investigate well-known and identify novel genes that are responsible for CVD development. With the advancements in sequencing technologies, genomic data have been generated at an unprecedented pace to foster translational research. Correct application of bioinformatics using genomic data holds the potential to reveal the genetic underpinnings of various health conditions. It can help in the identification of causal variants for AF, HF, and other CVDs by moving beyond the one-gene one-disease model through the integration of common and rare variant association, the expressed genome, and characterization of comorbidities and phenotypic traits derived from the clinical information. In this study, we examined and discussed variable genomic approaches investigating genes associated with AF, HF, and other CVDs. We collected, reviewed, and compared high-quality scientific literature published between 2009 and 2022 and accessible through PubMed/NCBI. While selecting relevant literature, we mainly focused on identifying genomic approaches involving the integration of genomic data; analysis of common and rare genetic variants; metadata and phenotypic details; and multi-ethnic studies including individuals from ethnic minorities, and European, Asian, and American ancestries. We found 190 genes associated with AF and 26 genes linked to HF. Seven genes had implications in both AF and HF, which are SYNPO2L, TTN, MTSS1, SCN5A, PITX2, KLHL3, and AGAP5. We listed our conclusion, which include detailed information about genes and SNPs associated with AF and HF. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40246-023-00498-0. BioMed Central 2023-06-03 /pmc/articles/PMC10239148/ /pubmed/37270590 http://dx.doi.org/10.1186/s40246-023-00498-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/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/ (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 | Review Patel, Kush Ketan Venkatesan, Cynthia Abdelhalim, Habiba Zeeshan, Saman Arima, Yuichiro Linna-Kuosmanen, Suvi Ahmed, Zeeshan Genomic approaches to identify and investigate genes associated with atrial fibrillation and heart failure susceptibility |
title | Genomic approaches to identify and investigate genes associated with atrial fibrillation and heart failure susceptibility |
title_full | Genomic approaches to identify and investigate genes associated with atrial fibrillation and heart failure susceptibility |
title_fullStr | Genomic approaches to identify and investigate genes associated with atrial fibrillation and heart failure susceptibility |
title_full_unstemmed | Genomic approaches to identify and investigate genes associated with atrial fibrillation and heart failure susceptibility |
title_short | Genomic approaches to identify and investigate genes associated with atrial fibrillation and heart failure susceptibility |
title_sort | genomic approaches to identify and investigate genes associated with atrial fibrillation and heart failure susceptibility |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10239148/ https://www.ncbi.nlm.nih.gov/pubmed/37270590 http://dx.doi.org/10.1186/s40246-023-00498-0 |
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