SNPs Filtered by Allele Frequency Improve the Prediction of Hypertension Subtypes

Yiming Li; Sanjiv J. Shah; Donna Arnett; Ryan Irvin; Yuan Luo

doi:10.1109/BIBM52615.2021.9669758

SNPs Filtered by Allele Frequency Improve the Prediction of Hypertension Subtypes

Yiming Li
, Sanjiv J. Shah
, Donna Arnett
, Ryan Irvin
, Yuan Luo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Hypertension is the leading global cause of cardiovascular disease and premature death. Distinct hypertension subtypes may vary in their prognoses and require different treatments. An individual's risk for hypertension is determined by genetic and environmental factors as well as their interactions. In this work, we studied 911 African Americans and 1,171 European Americans in the Hypertension Genetic Epidemiology Network (HyperGEN) cohort. We built hypertension subtype classification models using both environmental variables and sets of genetic features selected based on different criteria. The fitted prediction models provided insights into the genetic landscape of hypertension subtypes, which may aid personalized diagnosis and treatment of hypertension in the future.

Original language	English
Title of host publication	Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Editors	Yufei Huang, Lukasz Kurgan, Feng Luo, Xiaohua Tony Hu, Yidong Chen, Edward Dougherty, Andrzej Kloczkowski, Yaohang Li
Pages	2796-2802
Number of pages	7
ISBN (Electronic)	9781665401265
DOIs	https://doi.org/10.1109/BIBM52615.2021.9669758
State	Published - 2021
Event	2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 - Virtual, Online, United States Duration: Dec 9 2021 → Dec 12 2021

Publication series

Name	Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

Conference

Conference	2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Country/Territory	United States
City	Virtual, Online
Period	12/9/21 → 12/12/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Funding

This project was partially supported by grants from the National Institutes of Health [U01TR003528, R01LM013337, R01HL107577, andR01HL55673].

Funders	Funder number
National Institutes of Health (NIH)	R01LM013337, U01TR003528, R01HL107577

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

genetic risk prediction
hypertension
machine learning

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Biomedical Engineering
Health Informatics
Information Systems and Management

Access to Document

10.1109/BIBM52615.2021.9669758

Cite this

Li, Y., Shah, S. J., Arnett, D., Irvin, R., & Luo, Y. (2021). SNPs Filtered by Allele Frequency Improve the Prediction of Hypertension Subtypes. In Y. Huang, L. Kurgan, F. Luo, X. T. Hu, Y. Chen, E. Dougherty, A. Kloczkowski, & Y. Li (Eds.), Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 (pp. 2796-2802). (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021). https://doi.org/10.1109/BIBM52615.2021.9669758

Li, Yiming ; Shah, Sanjiv J. ; Arnett, Donna et al. / SNPs Filtered by Allele Frequency Improve the Prediction of Hypertension Subtypes. Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. editor / Yufei Huang ; Lukasz Kurgan ; Feng Luo ; Xiaohua Tony Hu ; Yidong Chen ; Edward Dougherty ; Andrzej Kloczkowski ; Yaohang Li. 2021. pp. 2796-2802 (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021).

@inproceedings{f5837f6340324f89af5e69c4b6904cba,

title = "SNPs Filtered by Allele Frequency Improve the Prediction of Hypertension Subtypes",

abstract = "Hypertension is the leading global cause of cardiovascular disease and premature death. Distinct hypertension subtypes may vary in their prognoses and require different treatments. An individual's risk for hypertension is determined by genetic and environmental factors as well as their interactions. In this work, we studied 911 African Americans and 1,171 European Americans in the Hypertension Genetic Epidemiology Network (HyperGEN) cohort. We built hypertension subtype classification models using both environmental variables and sets of genetic features selected based on different criteria. The fitted prediction models provided insights into the genetic landscape of hypertension subtypes, which may aid personalized diagnosis and treatment of hypertension in the future.",

keywords = "genetic risk prediction, hypertension, machine learning",

author = "Yiming Li and Shah, \{Sanjiv J.\} and Donna Arnett and Ryan Irvin and Yuan Luo",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 ; Conference date: 09-12-2021 Through 12-12-2021",

year = "2021",

doi = "10.1109/BIBM52615.2021.9669758",

language = "English",

series = "Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021",

pages = "2796--2802",

editor = "Yufei Huang and Lukasz Kurgan and Feng Luo and Hu, \{Xiaohua Tony\} and Yidong Chen and Edward Dougherty and Andrzej Kloczkowski and Yaohang Li",

booktitle = "Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021",

}

Li, Y, Shah, SJ, Arnett, D, Irvin, R & Luo, Y 2021, SNPs Filtered by Allele Frequency Improve the Prediction of Hypertension Subtypes. in Y Huang, L Kurgan, F Luo, XT Hu, Y Chen, E Dougherty, A Kloczkowski & Y Li (eds), Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021, pp. 2796-2802, 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021, Virtual, Online, United States, 12/9/21. https://doi.org/10.1109/BIBM52615.2021.9669758

SNPs Filtered by Allele Frequency Improve the Prediction of Hypertension Subtypes. / Li, Yiming; Shah, Sanjiv J.; Arnett, Donna et al.
Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. ed. / Yufei Huang; Lukasz Kurgan; Feng Luo; Xiaohua Tony Hu; Yidong Chen; Edward Dougherty; Andrzej Kloczkowski; Yaohang Li. 2021. p. 2796-2802 (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Arnett, Donna

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PY - 2021

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AB - Hypertension is the leading global cause of cardiovascular disease and premature death. Distinct hypertension subtypes may vary in their prognoses and require different treatments. An individual's risk for hypertension is determined by genetic and environmental factors as well as their interactions. In this work, we studied 911 African Americans and 1,171 European Americans in the Hypertension Genetic Epidemiology Network (HyperGEN) cohort. We built hypertension subtype classification models using both environmental variables and sets of genetic features selected based on different criteria. The fitted prediction models provided insights into the genetic landscape of hypertension subtypes, which may aid personalized diagnosis and treatment of hypertension in the future.

KW - genetic risk prediction

KW - hypertension

KW - machine learning

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M3 - Conference contribution

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BT - Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

A2 - Huang, Yufei

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A2 - Hu, Xiaohua Tony

A2 - Chen, Yidong

A2 - Dougherty, Edward

A2 - Kloczkowski, Andrzej

A2 - Li, Yaohang

T2 - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

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ER -

Li Y, Shah SJ, Arnett D, Irvin R, Luo Y. SNPs Filtered by Allele Frequency Improve the Prediction of Hypertension Subtypes. In Huang Y, Kurgan L, Luo F, Hu XT, Chen Y, Dougherty E, Kloczkowski A, Li Y, editors, Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. 2021. p. 2796-2802. (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021). doi: 10.1109/BIBM52615.2021.9669758

SNPs Filtered by Allele Frequency Improve the Prediction of Hypertension Subtypes

Abstract

Publication series

Conference

Bibliographical note

Funding

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this