SNPs Filtered by Allele Frequency Improve the Prediction of Hypertension Subtypes
This work addresses the need for improved prediction of hypertension subtypes to enhance personalized treatment for patients at risk of cardiovascular disease, but it appears incremental as it builds on existing genetic and environmental modeling approaches.
The study tackled the problem of predicting hypertension subtypes by building classification models using environmental variables and genetic features selected based on different criteria, including SNPs filtered by allele frequency, on a cohort of 911 African Americans and 1,171 European Americans, resulting in insights into the genetic landscape that may aid personalized diagnosis and treatment.
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.