Machine learning-based quantification for disease uncertainty increases the statistical power of genetic association studies.

ABSTRACT:

Motivation

Allowance for increasingly large samples is a key to identify the association of genetic variants with Alzheimer's disease (AD) in genome-wide association studies (GWAS). Accordingly, we aimed to develop a method that incorporates patients with mild cognitive impairment and unknown cognitive status in GWAS using a machine learning-based AD prediction model.

Results

Simulation analyses showed that weighting imputed phenotypes method increased the statistical power compared to ordinary logistic regression using only AD cases and controls. Applied to real-world data, the penalized logistic method had the highest AUC (0.96) for AD prediction and weighting imputed phenotypes method performed well in terms of power. We identified an association (P<5.0×10-8) of AD with s

SUBMITTER: Park JY 

PROVIDER: S-EPMC10539075 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature