Robust SNP-based prediction of rheumatoid arthritis through machine-learning-optimized polygenic risk score.

ABSTRACT:

Background

The popular statistics-based Genome-wide association studies (GWAS) have provided deep insights into the field of complex disorder genetics. However, its clinical applicability to predict disease/trait outcomes remains unclear as statistical models are not designed to make predictions. This study employs statistics-free machine-learning (ML)-optimized polygenic risk score (PRS) to complement existing GWAS and bring the prediction of disease/trait outcomes closer to clinical application. Rheumatoid Arthritis (RA) was selected as a model disease to demonstrate the robustness of ML in disease prediction as RA is a prevalent chronic inflammatory joint disease with high mortality rates, affecting adults at the economic prime. Early identification of at-risk individuals may fa

SUBMITTER: Lim AJW 

PROVIDER: S-EPMC9903430 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature