Genetic-based hypertension subtype identification using informative SNPs

Yuanjing Ma, Hongmei Jiang, Sanjiv J. Shah, Donna Arnett, Marguerite R. Irvin, Yuan Luo*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

In this work, we proposed a process to select informative genetic variants for identifying clinically meaningful subtypes of hypertensive patients. We studied 575 African American (AA) and 612 Caucasian hypertensive participants enrolled in the Hypertension Genetic Epidemiology Network (HyperGEN) study and analyzed each race-based group separately. All study participants underwentGWAS (Genome-Wide Association Studies) and echocardiography. We applied a variety of statistical methods and filtering criteria, including generalized linear models, F statistics, burden tests, deleterious variant filtering, and others to select the most informative hypertension-related genetic variants. We performed an unsupervised learning algorithm non-negative matrix factorization (NMF) to identify hypertension subtypes with similar genetic characteristics. Kruskal–Wallis tests were used to demonstrate the clinical meaningfulness of genetic-based hypertension subtypes. Two subgroups were identified for both African American and Caucasian HyperGEN participants. In both AAs and Caucasians, indices of cardiac mechanics differed significantly by hypertension subtypes. African Americans tend to have more genetic variants compared to Caucasians; therefore, using genetic information to distinguish the disease subtypes for this group of people is relatively challenging, but we were able to identify two subtypes whose cardiac mechanics have statistically different distributions using the proposed process. The research gives a promising direction in using statistical methods to select genetic information and identify subgroups of diseases, which may inform the development and trial of novel targeted therapies.

Original languageEnglish (US)
Article number1265
Pages (from-to)1-13
Number of pages13
JournalGenes
Volume11
Issue number11
DOIs
StatePublished - Nov 2020

Keywords

  • Clustering algorithm
  • Hypertension
  • NMF
  • Subtype identification
  • Variable selection

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)

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