NG00120 – AD-associated cell-type-specific eQTLs (blood and brain) – Patel et al., 2021

The objective of the proposed research is to identify and characterize genetic variants involved in Alzheimer's disease (AD) risk and AD-related phenotypes to ultimately identify potential avenues for therapeutic approaches and prevention of the disease. Our study design will use phenotypic (ex., AD diagnosis, age-at-onset, APOE genotype) and genomic data (ex., WGS, array, imputed genotypes) from NIAGADS studies to investigate genotype-phenotype associations. Strategies include association testing and haplotype- and family-based approaches, including estimates of relatedness and population genetics analyses as needed to perform the association testing (ex. control for population structure). NIAGADS data will not be used to investigate individual identity. Consent type and other Data Use Limitations (DUL) for each study will be respected in all analyses. Data from an individual with disease-specific consent will not be used in analyses outside of that restriction, including indirect uses such as imputation reference panels or variant summary statistics. When an individual’s DUL prohibits investigation of population genetics, population history or related issues, their data will be excluded from studies that address those issues. We intend to publish or otherwise broadly share any findings from this study with the scientific community. As such, genomic summary results from datasets with a “sensitive” designation will only be shared through publications to support study’s conclusions and through NIH-funded data repositories which maintain restricted access (ex. NIAGADS). Data from NIAGADS may be combined with non-NIAGADS data from the same or other studies (obtained from dbGaP or other sources), to improve the power for novel genetic discoveries, while respecting the consent of all participants. We expect that this activity creates no additional risks to participants. Data will be shared only among Internal Collaborators at the University of Washington. We do not plan to collaborate with External Collaborators at other institutions.

We propose to identify and characterize genetic variation involved in Alzheimer's disease (AD), providing insight as to why some individuals develop or avoid AD, and ultimately identifying potential avenues for therapeutic approaches and prevention of the disease. We will combine phenotype and genotype data using association testing and haplotype- and family-based approaches to identify and characterize associations and refine those signals with fine-mapping tools and external data.

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.

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.