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Updated: Jul 17, 2025
Why Quantification Matters: Characterization of Phenotypes at the Drosophila Larval Neuromuscular Junction
Published on: May 12, 2016
Machine learning-based quantification for disease uncertainty increases the statistical power of genetic association
Jun Young Park1,2,3, Jang Jae Lee3, Younghwa Lee1
1Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul 08826, Korea.
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 several variants in the APOE region and rs143625563 in LMX1A. Our method, which allows the inclusion of individuals with mild cognitive impairment, improves the statistical power of GWAS for AD. We discovered a novel association with LMX1A.
Availability And Implementation:
Simulation codes can be accessed at https://github.com/Junkkkk/wGEE_GWAS.
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