Genetic Burden and APOE Methylation in a Korean Multi-Generational Alzheimer’s Disease Family: An Exploratory Multi-Omics Case Study

TL;DR

This study explores genetic and methylation patterns in a Korean family with Alzheimer's disease to better understand individual risk factors.

Contribution

The study presents an exploratory multi-omics analysis of a multi-generational Alzheimer's family, linking genetic burden and APOE methylation.

Findings

An affected family member had the lowest genetic burden score but lower APOE methylation compared to healthy relatives.

CR1 contributed the most risk alleles in the family, followed by PICALM.

The findings suggest that methylation patterns may influence Alzheimer's risk but cannot establish causality from this single case.

Abstract

Background/Objectives: Alzheimer’s disease (AD) exhibits high heritability (60–80%), yet individual-level genetic risk prediction remains challenging. While APOE ε4 is the strongest genetic risk factor, incomplete penetrance complicates risk assessment. Methods: We analyzed seven blood-related members across three generations using the Korean Chip v2.0 genotyping (~1.2 M SNPs) and Illumina EPICv2 DNA methylation profiling. Genetic burden score (GBS) was calculated by summing risk alleles across 320 variants in six AD-associated genes (APOE, PICALM, CLU, CR1, BIN1, and ABCA7). Results: An unexpected pattern was observed in this family: the affected individual (J-003) had the lowest GBS (39 alleles), while individuals with higher genetic burden (51–61 alleles) remained cognitively healthy. J-003 also exhibited lower APOE methylation (β = 0.495) compared to the family mean (β = 0.523). CR1 contributed the most risk alleles across the family, followed by PICALM. Conclusions: This single-case observation cannot establish causality, generalizability, or biological significance. The affected individual’s lower APOE methylation may represent a causal factor, disease consequence, or coincidental variation—scenarios that cannot be distinguished from this dataset. Validation in larger cohorts with multiple affected individuals is required to determine whether integrated multi-omics approaches can inform personalized risk assessment in familial contexts.

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