Machine Learning Models of Polygenic Risk for Enhanced Prediction of Alzheimer Disease Endophenotypes.

ABSTRACT:

Background and objectives

Alzheimer disease (AD) has a polygenic architecture, for which genome-wide association studies (GWAS) have helped elucidate sequence variants (SVs) influencing susceptibility. Polygenic risk score (PRS) approaches show promise for generating summary measures of inherited risk for clinical AD based on the effects of APOE and other GWAS hits. However, existing PRS approaches, based on traditional regression models, explain only modest variation in AD dementia risk and AD-related endophenotypes. We hypothesized that machine learning (ML) models of polygenic risk (ML-PRS) could outperform standard regression-based PRS methods and therefore have the potential for greater clinical utility.

Methods

We analyzed combined data from the Mayo Clinic Stud

SUBMITTER: Gunter NB 

PROVIDER: S-EPMC10798228 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature