SNPs Filtered by Allele Frequency Improve the Prediction of Hypertension Subtypes

TL;DR

This study demonstrates that filtering SNPs by allele frequency enhances the accuracy of predicting hypertension subtypes, aiding personalized diagnosis and treatment.

Contribution

The paper introduces a novel approach of filtering genetic variants by allele frequency to improve hypertension subtype prediction models.

Findings

Filtering SNPs by allele frequency improves prediction accuracy.

Genetic features provide valuable insights into hypertension subtypes.

Models incorporating genetic and environmental data outperform baseline models.

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.