Enhancing prediction accuracy of coronary artery disease through machine learning-driven genomic variant selection.

ABSTRACT: Machine learning (ML) methods are increasingly becoming crucial in genome-wide association studies for identifying key genetic variants or SNPs that statistical methods might overlook. Statistical methods predominantly identify SNPs with notable effect sizes by conducting association tests on individual genetic variants, one at a time, to determine their relationship with the target phenotype. These genetic variants are then used to create polygenic risk scores (PRSs), estimating an individual's genetic risk for complex diseases like cancer or cardiovascular disorders. Unlike traditional methods, ML algorithms can identify groups of low-risk genetic variants that improve prediction accuracy when combined in a mathematical model. However, the application of ML strategies requires addressing

SUBMITTER: Alireza Z 

PROVIDER: S-EPMC11020205 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature