Discovery of APOE-Interacting Genes Through Trans-Ancestry and Sex-Stratified Analysis to Elucidate Alzheimer's Disease Risk Mechanisms and Stratify ARIA Risk Using Proxy Outcomes
- PI
- Kim, Jong Hun
- DAR ID
- 12385
- Initial Approval Date
- July 20, 2026
- Current DAR Status
- Active
- PI Institution
- KOREA UNIVERSITY RESEARCH AND BUSINESS FOUNDATION
- PI Institution Country
- South Korea
- PI Institution Type
- Non-Profit
Requested Datasets (64)
NG00067Alzheimer's Disease Sequencing Project Umbrella StudyNG00108Profiling microglia expression profiles in AD using single-nuclei RNA-seqNG00105MiGA – Microglia Genomic AtlasNG00106LASI-DAD Genotyping Chip Data (Illumina Infinium GSA-24 v2.0), Imputed Data (1000 Genomes Phase 3 v5 and TOPMed r2)NG00113Metabolomic and lipidomic signatures in Alzheimer disease brainsNG00114DNA Methylation in Alzheimer disease brainsNG00104Dataset from Clinical and Pathological Studies in the Oldest Old (90+ Study)NG00119Health and Retirement Study Genotype Data 2006-2012NG00117NCRAD Families GWASNG00127A longitudinal study of Alzheimer's Disease and other dementing illnesses – KnightADRC GWASNG00129NCRAD Families Whole Exome SequencingNG00126A fast and robust strategy to remove variant level artifacts in Alzheimer’s Disease Sequencing Project data (Belloy et al. 2022)
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NG00132Health and Retirement Study (HRS) APOE and Serotonin Transporter AllelesNG00118AMP-AD SV WGS and SV-xQTLNG00134National Health and Aging Trends Study (NHATS) GWASNG00130Large scale CSF proteogenomic atlasNG00144The Prediction of Alzheimer’s Disease through Multi-Trait Genetic ModelingNG00131Large scale genetics study on CSF and brain metabolite levelsNG00135UAB ADRC - University of Alabama at Birmingham Alzheimer’s Disease Research CenterNG00146Singapore Longitudinal study of Vascular Cognitive Impairment and DementiaNG00102Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disordersNG00153Health and Retirement Study (HRS) DNA MethylationNG00160Sex-specific genetic architecture of late-life memory performanceNG00022ADC1 - Alzheimer's Disease Center Dataset 1NG00023ADC2 - Alzheimer's Disease Center Dataset 2NG00024ADC3 - Alzheimer's Disease Center Dataset 3NG00068ADC4 - Alzheimer's Disease Center Dataset 4NG00069ADC5 - Alzheimer's Disease Center Dataset 5NG00070ADC6 - Alzheimer's Disease Center Dataset 6NG00071ADC7 - Alzheimer's Disease Center Dataset 7NG00136ADC8 - Alzheimer's Disease Center Dataset 8NG00137ADC9 - Alzheimer's Disease Center Dataset 9NG00138ADC10 - Alzheimer's Disease Center Dataset 10NG00139ADC11 - Alzheimer's Disease Center Dataset 11NG00140ADC12 - Alzheimer's Disease Center Dataset 12NG00149ADC13 - Alzheimer's Disease Center Dataset 13NG00150ADC14 - Alzheimer's Disease Center Dataset 14NG00151ADC15 - Alzheimer's Disease Center Dataset 15NG00159Longitudinal Change in Memory Performance as a Strong Endophenotype for Alzheimer’s Disease – Archer, et al, 2023NG00116Resolving mutations in challenging genomic regions to test association with disease phenotypesNG00157Sex-specific genetic predictors of Alzheimer's disease biomarkers – Deming, et al. 2018NG00158Sex differences in the genetic predictors of Alzheimer’s pathology – Dumitrescu, et al. 2019NG00161Sex differences in the genetic architecture of cognitive resilience to Alzheimer's disease – Eissman, et al. 2022NG00145Colorado Adoption/Twin Study of Lifespan behavioral development & cognitive aging (CATSLife) APOENG00103Association Between Common Variants in RBFOX1, an RNA-Binding Protein, and Brain Amyloidosis in Early and Preclinical Alzheimer DiseaseNG00156Genetic variants and functional pathways associated with resilience to Alzheimer's diseaseNG00147Amyloid Neuroimaging and Genetics Initiative (ANGI) GWASNG00165CHARGE Association results from the ADSP R1 WGS data setNG00162Oligodendrocytes single-cell whole genome and RNA sequencingNG00166Association results from the ADSP R3 17k WGS data set - Lee et al., 2023NG00141The Sacramento Area Latino Study on AgingNG00174Whole Genome Sequencing (WGS) and SNP Array data for the SEA-AD Consortium CohortNG00100Novel Alzheimer Disease Risk Loci and Pathways in African American Individuals Using the African Genome Resources Panel: A Meta-analysis. Kunkle et al. (2021)NG00035GWAS of CSF tau levels identifies risk variants for Alzheimer’s DiseaseNG00082UAB/HudsonAlpha Families with Neurodegenerative DiseasesNG00171Population structure in LASI-DADNG00075IGAP Rare Variant Summary Statistics- Kunkle et al. (2019)NG00177ADGC Multi-Ancestry GWAS Meta-Analysis – Rajabli et al., 2025NG00179AD GWAS in participants of African ancestry – Ray et al., 2024NG00175Genome-wide association study of neuropathology endophenotypesNG00189GWAS summary statistics of neuropsychiatric symptoms in MCI and dementia – Vattathil et al., 2025NG00186Genetic Component of the Mapping the Human Connectome During Typical Aging (HCP-A) & Vulnerability and Resiliency in the Aging Adult Brain Connectome (AABC)NG00176CNVs from ADSP WES data using CANOES softwareNG00120AD-associated cell-type-specific eQTLs (blood and brain) – Patel et al., 2021
Research Use Statement
Objectives: This project identifies ancestry- and sex-specific APOE ε4 modifier genes—variants that amplify or attenuate APOE ε4’s effect on AD risk and ARIA susceptibility from anti-amyloid immunotherapy. Aim 1: Trans-ancestry sex-stratified GWIS to construct an APOE-Wide Epistasis Map. Aim 2: Mechanistic validation via eQTL/pQTL colocalization and epistasis network. Aim 3: Explainable AI (XAI) integrating modifier SNPs, multi-omics subtypes, and ARIA proxy outcomes to stratify pre-treatment ARIA risk.
Study Design: Multi-cohort secondary analysis using NIAGADS-controlled ADSP data exclusively. Individual-level data from all 15 ADC cohorts (NG00022–NG00151) and multi-ancestry ADSP WGS (NG00067, NG00166) span European, African American, Hispanic/Latino, and South/East Asian ancestries. Functional datasets (eQTL/pQTL: NG00102, NG00118, NG00120, NG00130) support Aim 2; imaging and neuropathology datasets (NG00103, NG00147, NG00175) enable Aim 3 ARIA proxy development. No prospective recruitment.
Multi-dataset rationale: GWIS requires 4–8× more samples than standard GWAS (Gauderman 2002); no single cohort is independently powered—all 15 ADC cohorts must be pooled. Trans-ancestry GWIS requires ancestry-matched datasets (NG00100/African, NG00106/South Asian, NG00141/Hispanic) because population-specific LD cannot be imputed from summary statistics. Functional datasets (eQTL, pQTL, methylation) are non-redundant—each covers a distinct regulatory layer for Aim 2. All datasets are AD-specific; non-AD neurodegeneration data are excluded.
Analysis Plan: Phenotypes: AD case/control (primary); APOE ε4 × SNP interaction; lobar microbleed count (ARIA-H proxy); SVD score (WMH, lacunar infarcts, perivascular spaces); longitudinal cognitive decline. Covariates: age, sex, top 20 ancestry PCs, stratum. Methods: logistic GWIS; trans-ancestry meta-analysis (METAL/MR-MEGA); sex-stratified/X-chromosome analyses; eQTL/pQTL colocalization (COLOC2/SMR); XGBoost XAI with 5-fold CV and SHAP.
Study Design: Multi-cohort secondary analysis using NIAGADS-controlled ADSP data exclusively. Individual-level data from all 15 ADC cohorts (NG00022–NG00151) and multi-ancestry ADSP WGS (NG00067, NG00166) span European, African American, Hispanic/Latino, and South/East Asian ancestries. Functional datasets (eQTL/pQTL: NG00102, NG00118, NG00120, NG00130) support Aim 2; imaging and neuropathology datasets (NG00103, NG00147, NG00175) enable Aim 3 ARIA proxy development. No prospective recruitment.
Multi-dataset rationale: GWIS requires 4–8× more samples than standard GWAS (Gauderman 2002); no single cohort is independently powered—all 15 ADC cohorts must be pooled. Trans-ancestry GWIS requires ancestry-matched datasets (NG00100/African, NG00106/South Asian, NG00141/Hispanic) because population-specific LD cannot be imputed from summary statistics. Functional datasets (eQTL, pQTL, methylation) are non-redundant—each covers a distinct regulatory layer for Aim 2. All datasets are AD-specific; non-AD neurodegeneration data are excluded.
Analysis Plan: Phenotypes: AD case/control (primary); APOE ε4 × SNP interaction; lobar microbleed count (ARIA-H proxy); SVD score (WMH, lacunar infarcts, perivascular spaces); longitudinal cognitive decline. Covariates: age, sex, top 20 ancestry PCs, stratum. Methods: logistic GWIS; trans-ancestry meta-analysis (METAL/MR-MEGA); sex-stratified/X-chromosome analyses; eQTL/pQTL colocalization (COLOC2/SMR); XGBoost XAI with 5-fold CV and SHAP.
Non-Technical Summary
Alzheimer’s disease affects tens of millions worldwide. Lecanemab, approved in 2024, slows Alzheimer’s progression by removing amyloid plaques—but causes dangerous brain side effects (ARIA: Amyloid-Related Imaging Abnormalities) especially in APOE ε4 carriers, who also most need treatment. Currently, doctors cannot predict which APOE ε4 carriers will benefit versus be harmed.
Our research identifies modifier genes controlling how dangerous APOE ε4 is. We leverage the ADSP’s diverse dataset spanning 15+ cohorts across European, African American, Hispanic/Latino, and Asian ancestries—a scale statistically necessary because detecting gene–gene interactions requires 4–8× more samples than standard genetic studies. Population-specific patterns allow high-confidence modifier identification. MRI-based brain bleeds and vascular markers serve as validated ARIA surrogates available at scale.
The result is an explainable AI tool that predicts—before treatment begins—which APOE ε4 patients face high ARIA risk and which will benefit from lecanemab, enabling precision Alzheimer’s therapy.
Our research identifies modifier genes controlling how dangerous APOE ε4 is. We leverage the ADSP’s diverse dataset spanning 15+ cohorts across European, African American, Hispanic/Latino, and Asian ancestries—a scale statistically necessary because detecting gene–gene interactions requires 4–8× more samples than standard genetic studies. Population-specific patterns allow high-confidence modifier identification. MRI-based brain bleeds and vascular markers serve as validated ARIA surrogates available at scale.
The result is an explainable AI tool that predicts—before treatment begins—which APOE ε4 patients face high ARIA risk and which will benefit from lecanemab, enabling precision Alzheimer’s therapy.
Reported Publications
Publications shown are those reported by investigators through NIAGADS DAR reporting and renewal processes and may not represent a complete list of publications associated with the project.
No publications have been reported for this DAR.