Integrating GWAS and machine learning for disease risk prediction in the Taiwanese Hakka population.

ABSTRACT:

Introduction

Genome-wide association studies (GWAS) have identified numerous loci associated with complex diseases, yet their predictive power in small or genetically homogeneous populations remains limited. Integrating machine learning with GWAS offers a path to improve risk prediction and uncover functional variants relevant to precision medicine.

Methods

DNA samples from Taiwanese Hakka individuals with type 2 diabetes, hypertension, and eye diseases were analyzed. After standard quality control, 295,589 SNPs were retained. Fourteen machine-learning algorithms were evaluated using SNPs selected through traditional GWAS filtering and refined via wrapper-based feature selection with a best-first search algorithm. Model performance was assessed by internal cross-validation a

SUBMITTER: Xiao JH 

PROVIDER: S-EPMC12713315 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature