Exploring Random Forest in Genetic Risk Score Construction.

ABSTRACT: Genetic risk scores (GRS) are crucial tools for estimating an individual's genetic liability to various traits and diseases, computed as a weighted sum of trait-associated allele counts. Traditionally, GRS models assume additive, linear effects of risk variants. However, complex traits often involve nonadditive interactions, such as epistasis, which are not captured by these conventional methods. In this study, we investigate the use of random forest (RF) models as a model-free approach for constructing GRS, leveraging RF's capacity to capture complex, nonlinear interactions among genetic variants. Specifically, we introduce two new RF-based GRS strategies to boost RF performance and to incorporate base data information if available, including (1) ctRF, which optimizes linkage disequilibri

SUBMITTER: Venkat V 

PROVIDER: S-EPMC12553327 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature