Polygenic prediction of treatment efficacy with causal transfer learning.

ABSTRACT: Therapeutic interventions often exhibit heterogeneous treatment effects (HTE) across individuals. A central goal of precision medicine is to enable personalized treatment recommendations based on patients' measurable characteristics. Identifying factors that explain HTE is therefore essential. However, detecting HTE remains challenging due to limited sample size in randomized controlled trials (RCTs), often-missing base-line information, and suboptimal statistical methods with limited power. Here, we introduce a principled statistical framework named M-Learner to identify genetically-driven HTE. This approach leverages genetic variation involved in diverse biological pathways influencing drug response, integrates insights from two decades of com-plex trait genetics, and employs causal tran

SUBMITTER: Miao J 

PROVIDER: S-EPMC12633083 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature