NG00082 – UAB/HudsonAlpha Families with Neurodegenerative Diseases

The goal of this study is to identify new genes and mutations that cause or increase risk for Alzheimer disease (AD), as well as protective factors. Individuals and families were selected from the Knight-ADRC (Washington University) and the NIA-LOAD study. Only families with at least three first-degree affected individuals were included. Families with pathogenic variants in the known AD or FTD genes, or in which APOE4 segregated with disease were excluded. At least two cases and one control were selected per family. Cases had an age at onset (AAO) after 65 yo and controls had a larger age at last assessment than the latest AAO within the family. Whole exome (WES) and whole genome sequencing (WGS) was generated for 1,235 individuals (285 families) that together with data from our collaborators and the ADSP family-based cohort (3,449 individuals and 757 families) will provide enough statistical power to identify new genes for AD. Dr. Tanzi (Harvard Medical School) will provide WGS from 400 families from the NIMH Alzheimer disease genetics initiative study. We will perform single variant and gene-based analyses to identify genes and variants that increase risk for disease in AD families. Single variant analysis will consist of a combination of association and segregation analyses. We will run family-based gene-based methods to identify genes that show and overall enrichment of variants in AD cases. We will also look for protective and modifier variants. To do this we will identify families loaded with AD cases, that also include individuals with a high burden of known risk variants but that do not develop the disease (escapees). We will use the sequence data and the family structure to identify variants that segregate with the escapee phenotype. The most promising variants and genes will be replicated in independent datasets (ADSP case-control, ADNI, Knight-ADRC, NIA-LOAD ). We will perform single variant and gene-based analyses to replicate the initial findings, and survival analysis to replicate the protective variants. We will select the most promising variants/genes for functional studies

Family-based approaches led to the identification of disease-causing Alzheimer’s Disease (AD) variants in the genes encoding APP, PSEN1 and PSEN2. The identification of these genes led to the A?-cascade hypothesis and to the development of drugs that target this pathway. Recently, we have identified rare coding variants in TREM2, ABCA7, PLD3 and SORL1 with large effect sizes for risk for AD, confirming that rare coding variants play a role in the etiology of AD. In this proposal, we will identify rare risk and protective alleles using sequence data from families densely affected by AD. We hypothesize that these families are enriched for genetic risk factors. We already have sequence data from 695 families (2,462 individuals), that combined with the ADSP and the NIMH dataset will lead to a dataset of more than 1,042 families (4,684 individuals). Our preliminary results support the flexibility of this approach and strongly suggest that protective and risk variants with large effect size will be found, which will lead to a better understanding of the biology of the disease.

Alzheimer's disease (AD) is the most common form of dementia but has no effective prevention or treatment. Developing a comprehensive picture of the genetic architecture of AD including a network level functional assessment of risk/resilience genes is essential to develop novel therapeutic targets. The overarching goals of this study are to use genetic and genomic approaches to: 1) identify genes and variants that are involved in the development of AD and related disorders; 2) identify functional networks enriched for AD or related disorder risk and protective loci; 3) determine how cellular function and physiology is impacted by these genetic factors in disease-relevant cell types and animal models. This study will use publicly available whole genome/exome sequence data generated by the Alzheimer’s Disease Sequencing Project (ADSP) and genome-wide association study (GWAS) data from the International Genomics of Alzheimer’s Project (IGAP) and others. We will apply a suite of case-control and family approaches to investigate genetic association with dichotomous and continuous disease traits. This study will not only further our understanding of the genetic architecture of AD but also provide key information regarding the molecular mechanisms, setting the stage for novel therapeutic development.

Alzheimer’s disease (AD) is the only disease among the top ten killers in the U.S. without a disease modifying therapy. Genetic studies provide a powerful means to identify genes and pathways that are causally linked to disease etiology. We propose to use genomic and functional approaches to identify genes that alter the risk of AD and investigate how these genes disrupt cellular pathways leading to disease.

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.

The objective of the proposed research is to determine the relationship between Alzheimer’s disease (AD) genetic risk and associated molecular phenotypes. Genotype data will be used to compute a polygenic risk score (PRS) for disease-affected and control (non-disease-affected) participants. Statistical regression and mediation analyses will be used to model variation of molecular phenotypes with respect to PRS and, where available, pathology stage or cognitive impairment. Molecular phenotypes to be analyzed include bulk/single-cell/single-nucleus transcriptome, epigenome, proteome, metabolome, lipidome, amyloid, and tau. Molecular phenotypes of participants, including controls, will be matched with molecular phenotypes of in vitro cellular models, informing the design of in vitro perturbation experiments that recapitulate the genetic drivers of AD risk.

Our goal is to determine the relationship between human genetic profiles associated with Alzheimer’s disease (AD) risk and specific measurable characteristics of human cells. Using multiple statistical analysis methods, we will build quantitative models that describe how those characteristics vary as a function of AD genetic risk. The models we build will help us design in vitro cellular systems that reflect different levels of AD risk, enabling experiments that inform new strategies for treating or preventing AD.