Genetic-Based Hypertension Subtype Identification Using Informative SNPs

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 underwent GWAS (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.

Unsupervised clustering analysis of SARS-Cov-2 population structure reveals six major subtypes at early stage across the world

“…Our work, as well as previous studies [3,34,35] that use clustering techniques to study the population structure of the SARS-CoV-2 virus, has proved to be a valuable supplemental tool in phylogenetic analyses. For future work, we plan to further apply soft clustering techniques to better account mixtures in clusters, the efficacy of which has been showcased by previous studies in multiple fields [36][37][38][39][40][41][42]. In addition, clustering ideas can be used for further study of variant population structures in specific regions of these fast-growing viruses.…”

Hypertension in African Populations: Review and Computational Insights

“…Many studies have demonstrated that genetic factors are responsible not only for blood pressure elevation but also play a profound role in the interindividual variability in drug response, offering an opportunity for pharmacogenomic investigation and potential individualized drug therapy [ 18 , 19 ]. Therefore, it has been speculated that a patient’s inter-individual genetic variation can be used to understand the pathophysiology of the disease and that this pharmacogenomics approach may have the potential of individualizing HTN drug therapy based on the patient’s genetic background [ 20 , 21 ]. For example, Guo et al, [ 22 ] and Choi et al, [ 23 ] have reported successful examples of genetic polymorphisms on blood pressure response to antihypertensive therapies.…”

“…For example, Guo et al, [ 22 ] and Choi et al, [ 23 ] have reported successful examples of genetic polymorphisms on blood pressure response to antihypertensive therapies. Recently, Ma et al [ 21 ] reported that various informative genetic variants can be utilized for the identification of subtypes of hypertension. These studies have highlighted the importance of understanding the biological basis of the disease and the contribution of genetic variations in the development and progression of the disease.…”

Multi-Trait Genetic Analysis Reveals Clinically Interpretable Hypertension Subtypes

“…To our knowledge, only 2 prior studies have assessed genetic subtyping of hypertension. The first study by Ma et al 6 used a traditional subtyping algorithm, non-negative matrix factorization (NMF), to find genetic hypertension components in 1187 hypertensive participants of the Hypertension Genetic Epidemiology Network. 26 Hypertension Genetic Epidemiology Network is a cross-sectional family study aiming to identify and characterize the genetic basis of familial hypertension.…”

“…In contrast to clinical characteristics, genetic variants are fixed and known to predict the onset of polygenic diseases such as type 2 diabetes. 5 Based on genetic variants, both Ma et al 6 (N=1187) and Luo et al 7 (N=660) subtyped hypertensive individuals and uncovered genetic hypertension components with differing echocardiographic measurements. While these 2 studies have pioneered a classification of hypertension genetics, they are limited in sample size and by phenotype data that were mainly limited to echocardiographic variables.…”