# 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.

## Key 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.