Genomic approaches to identify and investigate genes associated with atrial fibrillation and heart failure susceptibility
Abstract 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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BioMed Central
2023
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| Online Access: | https://hdl.handle.net/1721.1/150862 |
| _version_ | 1826213112539250688 |
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| author | Patel, Kush K. Venkatesan, Cynthia Abdelhalim, Habiba Zeeshan, Saman Arima, Yuichiro Linna-Kuosmanen, Suvi Ahmed, Zeeshan |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Patel, Kush K. Venkatesan, Cynthia Abdelhalim, Habiba Zeeshan, Saman Arima, Yuichiro Linna-Kuosmanen, Suvi Ahmed, Zeeshan |
| author_sort | Patel, Kush K. |
| collection | MIT |
| description | Abstract
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. |
| first_indexed | 2024-09-23T15:43:32Z |
| format | Article |
| id | mit-1721.1/150862 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T15:43:32Z |
| publishDate | 2023 |
| publisher | BioMed Central |
| record_format | dspace |
| spelling | mit-1721.1/1508622024-02-05T19:59:25Z Genomic approaches to identify and investigate genes associated with atrial fibrillation and heart failure susceptibility Patel, Kush K. Venkatesan, Cynthia Abdelhalim, Habiba Zeeshan, Saman Arima, Yuichiro Linna-Kuosmanen, Suvi Ahmed, Zeeshan Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Abstract 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. 2023-06-06T19:06:25Z 2023-06-06T19:06:25Z 2023-06-03 2023-06-04T03:11:04Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/150862 Human Genomics. 2023 Jun 03;17(1):47 PUBLISHER_CC en https://doi.org/10.1186/s40246-023-00498-0 Creative Commons Attribution http://creativecommons.org/licenses/by/4.0/ The Author(s) application/pdf BioMed Central BioMed Central |
| spellingShingle | Patel, Kush K. 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 |
| url | https://hdl.handle.net/1721.1/150862 |
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