An individualized Bayesian method for estimating genomic variants of hypertension.

ABSTRACT:

Background

Genomic variants of the disease are often discovered nowadays through population-based genome-wide association studies (GWAS). Identifying genomic variations potentially underlying a phenotype, such as hypertension, in an individual is important for designing personalized treatment; however, population-level models, such as GWAS, may not capture all the important, individualized factors well. In addition, GWAS typically requires a large sample size to detect the association of low-frequency genomic variants with sufficient power. Here, we report an individualized Bayesian inference (IBI) algorithm for estimating the genomic variants that influence complex traits, such as hypertension, at the level of an individual (e.g., a patient). By modeling at the level of the indivi

SUBMITTER: Rahman MA 

PROVIDER: S-EPMC10631115 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature