{"data":[{"dar_ID":11386,"expiration_date":"2027-03-31 17:02:38","approval_date":"2026-03-31 17:02:38","project_name":"Elucidating sex-specific risk for Alzheimer's disease through state-of-the-art genetics and multi-omics","status_name":"Approved","first_name":"Michael","last_name":"Belloy","institution":"Washington University in St Louis","dataset_accessions":["NG00179"],"publications":["41588232","41437896","41404291","41282793"],"researchSTMT":"•\tObjectives: In this project, we seek to holistically investigate the genetic and molecular drivers of sex dimorphism in Alzheimer’s disease across ancestries. \n•\tStudy design: This study integrates large-scale population genetics with multi-omics and endophenotype analyses. We are integrating all data available from ADGC and ADSP, together with other data from AMP-AD and biobanks such as UKB, FinnGen, and MVP to conduct large-scale multi-ancestry GWAS, rare-variant gene aggregation analyses, QTL studies, PWAS, TWAS, etc. We also particularly focus on X chromosome association studies. The study design also interrogates interactions with ancestry, hormone exposures, and with APOE*4, as well as comparisons to non-stratified GWAS/XWAS of Alzheimer’s disease. Further, we will also employ genetic correlation analyses, mendelian randomization, colocalization, and pleiotropy analyses, to interrogate overlap with other complex traits to better understand the mechanisms underlying sex dimorphism in Alzheimer’s disease. \n•\tAnalysis plan, including the phenotypic characteristics that will be evaluated in association with genetic variants: Our phenotypes will include Alzheimer’s disease risk, conversion risk, various endophenotypes (including amyloid/tau biomarkers, brain imaging metrics, etc.) as well as molecular traits. As noted above, we will conduct large-scale multi-ancestry GWAS, XWAS, rare-variant gene aggregation analyses, QTL studies, PWAS, TWAS, etc. Specific aims include interrogating these question and analyses on (1) the autosomes, (2) the X chromosome, and (3) leveraging sex stratified QTL studies to drive discovery of risk genes.\n","ntResearchSTMT":"Alzheimer’s disease (AD) manifests itself differently across men and women, but the genetic and molecular factors that drive this remain elusive. AD is the most common cause of dementia and till today remains largely untreatable. It is thus crucial to study the genetics of AD in a sex-specific manner, as this will help the field gain important insights into disease pathophysiology, identify novel sex-specific risk factors relevant to personalized genetic medicine, and uncover potential new AD drug targets that may benefit both sexes. This project uses large-scale genomics and multi-omics to elucidate novel sex agnostic and sex-specific AD risk genes. We will interrogate sex dimorphism for AD risk on the autosomes and the sex chromosomes. We similarly interrogate sex dimorphism in the genetic regulation of gene expression and protein levels, which we will integrate with genetic risk for Alzheimer’s disease to further discovery risk genes. Throughout, we will also interrogate how sex-specific risk for AD interactions with hormone exposures, ancestry, and the APOE*4 risk allele."},{"dar_ID":10270,"expiration_date":"2027-03-10 14:45:15","approval_date":"2026-03-10 14:45:15","project_name":"AD Risk Prediction","status_name":"Approved","first_name":"Corinne","last_name":"Engelman","institution":"University of Wisconsin - Madison","dataset_accessions":["NG00179"],"publications":[],"researchSTMT":"Currently, no combined measure of rare (minor allele frequency [MAF] less than 0.5%), low frequency (MAF 0.5% to 5%), and common variant (MAF 5% or higher) risk of Alzheimer’s disease (AD) exists. Common variant polygenic risk scores (PRSs) have been a central approach to predicting genetic risk of AD, however, we know that rare and low frequency variants can account for the missing heritability in AD. One objective of this project is to determine the risk for AD based on variants across the full allele frequency spectrum.  An additional objective of this project is to utilize GWAS summary statistics from multiple ancestries to calculate the PRS and AD risk.\n\nTo accomplish these objectives, we will leverage sequencing data from the ADSP (our study is contributing 1,531 samples to the Follow Up phase) and summary statistics from published GWAS from different ancestries. To generate the common variant PRS, we will use these summary statistics and methods such as PRS-CSx. We will determine the carrier status for rare and low frequency AD risk alleles based on the literature and bioinformatics tools. Prediction of AD case-control status and age-at-onset for quantiles of the common variant PRS and carrier status will be characterized with an empirical receiver operating characteristic (ROC) curve. The statistical software R will be used to perform regression analyses and to evaluate the AUC.","ntResearchSTMT":"Currently, risk prediction for the later-onset form of Alzheimer’s disease (AD) focuses on genetic variants that are more common in the population, but ignores less common variants. Prediction is also largely based on data from populations of European ancestry. The goals of this project are to incorporate genetic variants across the full allele frequency spectrum (more and less common genetic variants) and to include more ancestrally diverse populations. To accomplish these goals, we will leverage genetic data from the ADSP and summary statistics (results) from published studies in diverse populations. We will determine the prediction of AD case-control status and age-at-onset."},{"dar_ID":10008,"expiration_date":"2027-07-08 15:00:01","approval_date":"2026-07-08 15:00:01","project_name":"Study of Alzheimer's disease and other dementias (e.g. frontotemporal dementia) and related phenotypes","status_name":"Approved","first_name":"Alison","last_name":"Goate","institution":"Icahn School of Medicine at Mount Sinai","dataset_accessions":["NG00179"],"publications":[],"researchSTMT":"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.\n","ntResearchSTMT":"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.\n"},{"dar_ID":11542,"expiration_date":"2027-03-31 19:56:47","approval_date":"2026-03-31 19:56:47","project_name":"Genetics of Alzheimer's Disease and Endophenotypes","status_name":"Approved","first_name":"M. Ilyas","last_name":"Kamboh","institution":"University of Pittsburgh","dataset_accessions":["NG00179"],"publications":[],"researchSTMT":"Objectives: \nWe are requesting access to the NIAGADS datasets to augment our ongoing studies on the genetics of Alzheimer’s disease (AD) and AD-related endophenotypes being carried out by Kamboh and his group since 1995.  We are doing GWAS using array genotypes, whole-exome sequencing and whole-genome sequencing on datasets derived from University of Pittsburgh ADRC and ancillary population-based longitudinal studies on dementia and biomarkers. Different available phenotypes include AD and non-AD dementia, age-at-set, disease progression and survival, neuroimaging, cognitive decline, plasma biomarkers for the core ATN and non-ATN pathologies. We also plan to expand on gene-gene interaction and sex-stratified analyses which require the actual genotype data. The NIAGADS datasets will be used for replication and meta-analysis, and for gene-gene interaction and sex-stratified analyses. \nStudy Design: \nA case-control design will incorporate a diverse cohort of individuals with AD and age-matched controls. For quantitative traits (neuroimaging and plasma biomarkers, cognitive performance measures, indicators of disease progression), linear regression analyses will be performed to identify genetic loci. To ensure the findings are robust and inclusive, participants from diverse demographic backgrounds will be included, enabling the exploration of potential genetic variations across populations.\nAnalysis Plan: \nWe will conduct GWAS and targeted analyses on candidate genes on different AD and AD-related phenotypes. Primary phenotypic variables include AD disease status, age-at-onset, last age for controls, APOE genotype, cognitive decline trajectories, sex, and race. Analyses will evaluate the influence of specific genetic variants on disease risk, cognitive performance, and biomarker levels, considering both individual and interactive effects of the APOE genotype. Results will be adjusted for potential confounders, such as demographic factors, to ensure valid associations. Detail analytical methods are described in our published papers for case-control (PMID: 32651314;35694926), quantitative traits (PMID: 30361487;37666928), and cognitive decline (PMID: 37089073; 30954325).\n","ntResearchSTMT":"Our research group at the University of Pittsburgh (Pitt), has been working on the genetics of Alzheimer’s disease (AD) and AD-related endophenotypes for almost three decades, on data derived largely from the University of Pittsburgh  Alzheimer’s Disease Research Center and ancillary dementia studies. We are requesting access to the NIAGADS genotype and phenotype datasets to augment our sample size to increase power to detect novel genetic associations with AD and related endophenotypes."},{"dar_ID":12385,"expiration_date":"2027-07-20 18:24:08","approval_date":"2026-07-20 18:24:08","project_name":"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","status_name":"Approved","first_name":"Jong Hun","last_name":"Kim","institution":"KOREA UNIVERSITY RESEARCH AND BUSINESS FOUNDATION","dataset_accessions":["NG00179"],"publications":[],"researchSTMT":"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.\nStudy 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.\nMulti-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.\nAnalysis 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.\n","ntResearchSTMT":"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.\n\nOur 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.\n\nThe 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.\n"},{"dar_ID":10141,"expiration_date":"2027-07-29 17:05:33","approval_date":"2026-07-29 17:05:33","project_name":"Powerful and novel statistical methods to detect genetic variants associated with or putative causal to Alzheimer’s disease","status_name":"Approved","first_name":"Wei","last_name":"Pan","institution":"University of Minnesota","dataset_accessions":["NG00179"],"publications":["40209152","41537459"],"researchSTMT":"We have been developing more powerful statistical methods to detect common variant (CV)- or rare variant (RV)-complex trait associations and/or putative causal relationships for GWAS and DNA sequencing data. Here we propose applying our new methods, along with other suitable existing methods, to the existing ADSP sequencing data and other AD GWAS data provided by NIA, hence requesting approval for accessing the ADSP sequencing and other related GWAS/genetic data. We have the following two specific Aims:\nAim1. Association testing under genetic heterogeneity: For complex traits, genetic heterogeneity, especially of RVs, is ubiquitous as well acknowledged in the literature, however there is barely any existing methodology to explicitly account for genetic heterogeneity in association analysis of RVs based on a single sample/cohort. We propose using secondary and other omic data, such as transcriptomic or metabolomic data, to stratify the given sample, then apply a weighted test to the resulting strata, explicitly accounting for genetic heterogeneity that causal RVs may be different (with varying effect sizes) across unknown and hidden subpopulations. Some preliminary analyses have confirmed power gains of the proposed approach over the standard analysis. \nAim 2. Meta analysis of RV tests: Although it has been well appreciated that it is necessary to account for varying association effect sizes and directions in meta analysis of RVs for multi-ethnic cohorts, existing tests are not highly adaptive to varying association patterns across the cohorts and across the RVs, leading to power loss. We propose a highly adaptive test based on a family of SPU tests, which cover many existing meta-analysis tests as special cases. Our preliminary results demonstrated possibly substantial power gains.\nAim 3. Inferring (putative) causal genes/proteins/metabolites for AD. We will apply TWA/PWAS/MWAS/xWAS methods to ADSP, AD GWAS and other omic data to identify (putative) causal genes, proteins, metabolites and other molecular traits for AD. These methods may be based on standard linear models or emerging ML/AI methods. \n","ntResearchSTMT":"We propose applying our newly developed statistical and computational methods, along with other suitable existing methods, to the existing ADSP sequencing data, other AD GWAS data and other omic data to detect common or rare genetic variants and other molecular traits, such as genes/proteins/metabolites, associated with and/pr (putative) causal to Alzheimer’s disease (AD). The novelty and power of our new methods are in three aspects: first, we consider and account for possible genetic heterogeneity with several subcategories of AD; second, we apply powerful meta-analysis methods to combine the association analyses across multiple subcategories of AD; third, we will develop and apply standard linear model- and emerging ML/AI-based causal inference methods to infer causal genes, proteins, metabolites and other traits for AD. In addition, our proposed analyses of the existing large amount of ADSP sequencing data and other AD GWAS data with our developed new methods are novel, powerful and cost-effective."}]}