---
title: "NG00132 – Health and Retirement Study (HRS) APOE and Serotonin Transporter Alleles"
id: "4556"
type: "dataset"
slug: "ng00132"
published_at: "2023-04-10T13:35:36+00:00"
modified_at: "2024-10-04T20:35:27+00:00"
url: "https://dss.niagads.org/datasets/ng00132/"
markdown_url: "https://dss.niagads.org/datasets/ng00132.md"
excerpt: "To access this data, please log into DSS and submit an application.Within the application, add this dataset (accession NG00132) in the “Choose a Dataset” section.Once approved, you will be able to log in and access the data within the DARM..."
taxonomy_dataset_categories:
  - "Controlled Access"
  - "Data Type"
  - "Platform"
  - "TaqMan"
  - "Targeted Genotyping"
  - "Thermo Hybaid MBS 0.2S"
---

## Overview

To access this data, please log into DSS and submit an application.  
Within the application, add this dataset (accession NG00132) in the “Choose a Dataset” section.  
Once approved, you will be able to log in and access the data within the DARM portal.

#### Description

The APOE and Serotonin Transporter Alleles data product includes data for the APOE isoform, directly genotyped using a TaqMan allelic discrimination SNP assay, where available, or imputed from preexisting genotype array data otherwise. This file also includes human serotonin transporter (5HTTLPR) short and long alleles measured using polymerase chain reaction (PCR). In total, there are 19,193 HRS participants in the data file: 17,237 with directly genotyped data for APOE and 1,956 additional participants with imputed data. There are 17,364 participants with valid values for 5HTTLPR.

Genotype data for HRS subjects is available at [NG00119 – Health and Retirement Study Genotype Data 2006-2012](https://dss.niagads.org/datasets/ng00119/)
, and DNA methylation data for HRS subjects is available at [NG00153 – Health and Retirement Study (HRS) DNA Methylation](https://dss.niagads.org/datasets/ng00153/)
. To obtain subject ID mapping between HRS datasets, please submit a [Genetic Data Cross-Reference Request Form](https://hrsdata.isr.umich.edu/data-products/genetic-cross-reference/request-form)
 on the HRS website.

#### Sample Summary per Data Type

| Sample Set | Accession | Data Type | Number of Samples |
| --- | --- | --- | --- |
| HRS APOE | snd10040 | Targeted genotyping | 19,193 |

#### Available Filesets

| Name | Accession | Latest Release | Description |
| --- | --- | --- | --- |
| HRS APOE: Targeted genotype data | fsa000040 | NG00132.v1 | Targeted genotype data |

View the [File Manifest](https://st1.niagads.org/portal/download-public/NG00132.v1/fm)
 for a full list of files released in this dataset.

## Participant Information

Provided in this dataset is a csv file containing APOE genotypes and HTTLPR calls for 19,193 total subjects. 17,237 subjects’ APOE genotypes were collected directly using a TaqMan allelic discrimination SNP assay at the Center for Inherited Disease Research (CIDR) Genetic Resources Core Facility (GRCF) and Fragment Analysis Facility (FAF) at Johns Hopkins University. 1,956 subjects’ APOE genotypes were imputed from a preexisting genotype array to the 1000G (Phase3 v5) reference panel. 17,364 subjects’ HTTLPR short and long alleles were measured using PCR.

| Sample Set | Accession Number | Number of Participants | Number of Samples |
| --- | --- | --- | --- |
| HRS - APOE and Serotonin Transporter Alleles | snd10040 | 19,193 | 19,193 |

## Related Studies

- [sa000021 - Health and Retirement Study (HRS)](https://dss.niagads.org/studies/sa000021/) Since 1992, the Health and Retirement Study (HRS, a cooperative agreement between the National Institute on Aging (NIA) and the University of Michigan, NIA U01AG009740) has been the largest, representative… [Learn more](https://dss.niagads.org/studies/sa000021/)

## Cohorts

| Cohort | Number of Participants | Number of Samples |
| --- | --- | --- |
| Health and Retirement Study (HRS) | 19,193 | 19,193 |

## Consent Levels

| Consent Level | Number of Participants | Number of Samples |
| --- | --- | --- |
| GRU-IRB-PUB-NPU | 19,193 | 19,193 |

Visit the [Data Use Limitations page](/documentation/policies-and-guidelines/data-use-limitations/)
 for definitions of the consent levels above.

## Acknowledgement

### Acknowledgment statement for any data distributed by NIAGADS:

Data for this study were prepared, archived, and distributed by the National Institute on Aging Alzheimer's Disease Data Storage Site (NIAGADS) at the University of Pennsylvania (U24-AG041689), funded by the National Institute on Aging.

Use the study-specific acknowledgement statements below (as applicable):

### For investigators using any data from this dataset:

Please cite/reference the use of NIAGADS data by including the accession [NG00132](https://archive.niagads.org/datasets/NG00132)
.

### For investigators using Health and Retirement Study (HRS) (sa000021) data:

HRS is supported by the National Institute on Aging (NIA U01AG009740). The genotyping was partially funded by separate awards from NIA (RC2 AG036495 and RC4 AG039029). Our genotyping was conducted by the NIH Center for Inherited Disease Research (CIDR) at Johns Hopkins University. Genotyping quality control and final preparation were performed by the Genetics Coordinating Center at University of Washington (Phases 1-3) and the University of Michigan (Phase 4).

## Approved Users

**Total number of approved DARs:** 15

[JSON Export](https://st1.niagads.org/portal/v1/adars/NG00132)
|[CSV Export](https://dss.niagads.org/wp-admin/admin-post.php?action=niagads_export_dars_csv&accession=NG00132)

- Investigator: Belloy, Michael Institution: Washington University in St Louis Project Title: Elucidating sex-specific risk for Alzheimer's disease through state-of-the-art genetics and multi-omics Date of Initial Approval: January 6, 2025 Current Status: Approved Publications: Show publications (4) - Cook N. **Integrative Genetic, Proteogenomic, and Multi-omics Analyses Reveal Sex-Biased Causal Genes and Drug Targets in Alzheimer's Disease.***medRxiv : the preprint server for health sciences. 2025 Nov 2.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/41282793/) - Zeng Y. **APOE*4 Risk-Modifying Genes and Drug Targets in Alzheimer's Disease through Cell-Type Specific Genomic Analyses.***medRxiv : the preprint server for health sciences. 2025 Dec 4.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/41404291/) - Lona-Durazo F. **Sex-aware causal inference assessment of the immune system in complex neurodegenerative diseases.***Brain : a journal of neurology. 2026 Aug 3.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/41437896/) - Belloy ME. **A quantitative trait locus for reduced microglial APOE expression associates with reduced cerebral amyloid angiopathy.***Nature genetics. 2026 Feb.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/41588232/) Research Use Statements: Show statements Technical Research Use Statement: • Objectives: In this project, we seek to holistically investigate the genetic and molecular drivers of sex dimorphism in Alzheimer’s disease across ancestries. • Study 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. • Analysis 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. Non-Technical Research Use Statement: 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.
- Investigator: Blue, Elizabeth Institution: University of Washington Project Title: Genetic modifiers of Alzheimer's disease Date of Initial Approval: July 15, 2025 Current Status: Approved Publications: None Reported Research Use Statements: Show statements Technical Research Use Statement: 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. Non-Technical Research Use Statement: 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.
- Investigator: Cruchaga, Carlos Institution: Washington University School of Medicine Project Title: The Familial Alzheimer Sequencing (FASe) Project Date of Initial Approval: January 23, 2019 Current Status: Approved Publications: Show publications (10) - Cruchaga C. **Polygenic risk score of sporadic late-onset Alzheimer's disease reveals a shared architecture with the familial and early-onset forms.***Alzheimer's & dementia : the journal of the Alzheimer's Association. 2018 Feb.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/28943286/) - Fernández MV. **Analysis of neurodegenerative Mendelian genes in clinically diagnosed Alzheimer Disease.***PLoS genetics. 2017 Nov.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/29091718/) - Ridge PG. **Linkage, whole genome sequence, and biological data implicate variants in RAB10 in Alzheimer's disease resilience.***Genome medicine. 2017 Nov 29.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/29183403/) - Fernández MV. **Evaluation of Gene-Based Family-Based Methods to Detect Novel Genes Associated With Familial Late Onset Alzheimer Disease.***Frontiers in neuroscience. 2018.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/29670507/) - Olive C. **Examination of the Effect of Rare Variants in TREM2, ABI3, and PLCG2 in LOAD Through Multiple Phenotypes.***Journal of Alzheimer's disease : JAD. 2020.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/32894242/) - Kirola L. **Lack of evidence supporting a role for DPP6 sequence variants in Alzheimer's disease in the European American population.***Acta neuropathologica. 2021 Apr.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/33591372/) - Moreno-Grau S. **Long runs of homozygosity are associated with Alzheimer's disease.***Translational psychiatry. 2021 Feb 24.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/33627629/) - Cruchaga C. **Proteogenomic analysis of human cerebrospinal fluid identifies neurologically relevant regulation and informs causal proteins for Alzheimer's disease.***Research square. 2023 Jun 9.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/37333337/) - Wang L. **Proteo-genomics of soluble TREM2 in cerebrospinal fluid provides novel insights and identifies novel modulators for Alzheimer's disease.***Molecular neurodegeneration. 2024 Jan 3.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/38172904/) - Patel M. **Whole-genome sequencing reveals the impact of lipid pathway and APOE genotype on brain amyloidosis.***Human molecular genetics. 2025 Apr 6.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/39927718/) Research Use Statements: Show statements Technical Research Use Statement: 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 Non-Technical Research Use Statement: 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.
- Investigator: Greicius, Michael Institution: Stanford University School of Medicine Project Title: Examining Genetic Associations in Neurodegenerative Diseases Date of Initial Approval: January 10, 2019 Current Status: Approved Publications: None Reported Research Use Statements: Show statements Technical Research Use Statement: We are studying the effects of rare (minor allele frequency < 5%) genetic variants on the risk of developing late-onset Alzheimer’s Disease (AD). We are interested in variants that have a protective effect in subjects who are at an increased genetic risk, or variants that lead to multiple dementias. Our aim is to identify any genetic variants that are present in the “case” group but not the “AD control” groups for both types of variants. The raw data we receive will be annotated to identify SNP locations and frequencies using existing databases such as 1,000 Genomes. We will filter the data based on genetic models such as compounded heterozygosity, recessive and dominant models to identify different types of variants. Non-Technical Research Use Statement: Current genetic understanding of Alzheimer’s Disease (AD) does not fully explain its heritability. The APOE4 allele is a well-established risk factor for the development of Alzheimer’s Disease (AD). However, some individuals who carry APOE4 remain cognitively healthy until advanced ages. Additionally, the cause of mixed dementia pathology development in individuals remains largely unexplained. We aim to identify genetic factors associated with these “protected” and mixed pathology phenotypes.
- Investigator: Kim, Jong Hun Institution: KOREA UNIVERSITY RESEARCH AND BUSINESS FOUNDATION Project Title: 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 Date of Initial Approval: July 20, 2026 Current Status: Approved Publications: None Reported Research Use Statements: Show statements Technical 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. Non-Technical Research Use Statement: 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.
- Investigator: Konermann, Silvana Institution: Arc institute Project Title: Modeling Alzheimer’s disease risk and associated molecular phenotypes Date of Initial Approval: August 8, 2025 Current Status: Expired Publications: None Reported Research Use Statements: Show statements Technical Research Use Statement: 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. Non-Technical Research Use Statement: 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.
- Investigator: Maier, Andrea Institution: National University of Singapore Project Title: Integrating DNA Methylation Surrogates of Clinical, Functional, and Metabolomic Biomarkers into a Systems or Disease-specific Framework for Biological Ageing Date of Initial Approval: July 30, 2026 Current Status: Approved Publications: None Reported Research Use Statements: Show statements Technical Research Use Statement: This project will develop DNA methylation (DNAm)-based surrogates of clinical, functional, metabolomic, proteomic and other molecular biomarkers, and integrate them into interpretable physiological system- and disease-specific predictors of biological ageing. The objectives are to improve prediction of mortality, frailty, disability, cognitive decline, ADRD/dementia and other age-related diseases, and to identify pathways underlying multisystem ageing. The study will use an observational cohort design. HRS will serve as the primary training dataset because it links DNAm profiles with clinical, functional, genetic and longitudinal health data. Other DNAm datasets will be used as external validation cohorts to assess generalisability across populations and study settings. Analyses will include epigenome-wide association and differential methylation analyses to identify CpGs associated with target biomarkers and ageing-related phenotypes. Penalised regression, primarily elastic net with nested cross-validation, will be used to train DNAm surrogate models. Other machine-learning methods may be explored where appropriate. DNAm surrogates will be grouped into physiological systems or disease domains using biological annotation, clustering and model-based approaches. The resulting DNAm proxies and integrated scores will be evaluated in association with mortality, multimorbidity, frailty, disability, functional performance, cognitive performance, cognitive decline, ADRD/dementia and other age-related diseases. Where genotype data are available, genome-wide variants and polygenic risk scores will be assessed in relation to DNAm surrogates and ageing-related phenotypes to evaluate genetic contributions to biological ageing and disease susceptibility. Restricted data will be analysed only within approved secure environments. Exported results will be aggregate only, such as regression coefficients, hazard ratios, odds ratios, summary statistics and model performance metrics. No individual-level data, direct identifiers or small-cell outputs will be exported. Planned collaborators include Prof Andrea B. Maier and researchers at the Academy for Healthy Longevity, NUS. Non-Technical Research Use Statement: Ageing affects many parts of the body, but current biological ageing tests do not fully capture this complexity. This project aims to develop new DNA methylation-based measures that reflect different aspects of health, including clinical markers, physical function, metabolism and age-related diseases. DNA methylation is a chemical mark on DNA that can provide information about biological ageing. The Health and Retirement Study will be used as the main dataset to develop these measures because it includes DNA methylation data linked with long-term health information. Other DNA methylation datasets will be used to test whether the measures work well in different populations. The goal is to create more accurate and interpretable tools for studying ageing, predicting risks such as frailty, disability, dementia and mortality, and understanding how different body systems age together. Findings will be reported only as summary results, with no release of individual-level participant data.
- Investigator: Monti, Stefano Institution: Boston University Project Title: Longevity Consortium Date of Initial Approval: January 30, 2026 Current Status: Approved Publications: None Reported Research Use Statements: Show statements Technical Research Use Statement: We study human exceptional longevity and healthy aging and we have generated many results that connect genetic variants to these traits, to intermediate molecular profiles, including gene expression, serum proteomics and metabolomics, and to nutrition. We would like to use the genetic, methylation and phenotypic data generated in the HRS for replication of our results linking genetics, multiomics profiles of cognitive aging, and nutrition. We will use these data to investigate the associations between genetic variants, multi-omics profiles, longevity and healthy cognitive aging, and nutrition using mixed effect models adjusted by genome-wide principal components and including random effects with variance covariance matrix that depends on the genetic relation matrix. We will investigate the relations between genetic variants and multi-omics profile using our pipeline for yQTL that uses mixed effect models as above. We will integrate the various results using mediation analysis. We will ask for access to deidentified data and not biological specimens. Non-Technical Research Use Statement: Our research focuses on understanding why some people live longer and age more healthily, especially when it comes to brain function and nutrition. The study has found many links between genes and these healthy aging traits by looking at different biological factors like gene activity and blood proteins. We aim to leverage the HRS data to confirm our findings. We will analyze this information to explore how genes, biological markers, and nutrition work together to influence long life and healthy brain aging. The data used will be anonymous, ensuring privacy, and no physical samples will be needed.
- Investigator: Pathak, Gita Institution: Institute for Genomic Health, Genetics and Genomic Sciences at Mount Sinai Project Title: Multi-modal analysis of psychiatric and dementia outcomes Date of Initial Approval: August 12, 2025 Current Status: Approved Publications: None Reported Research Use Statements: Show statements Technical Research Use Statement: a. Objectives of the Proposed Research This study aims to investigate the relationship between psychiatric traits and age-related cognitive decline, addressing a critical knowledge gap in understanding how mental health influences aging outcomes. b. Study Design The study employs a multi-level investigative approach combining epidemiological, genetic, and molecular methodologies. The design incorporates three complementary components: first, identification of phenotypic associations between psychiatric traits and MCI/AD through comprehensive clinical assessment; second, investigation of genetic architecture through analysis of coding and non-coding variants, genetic correlation assessments, polygenic scoring, and Mendelian randomization for causal inference; and third, examination of molecular mechanisms through genetically regulated epigenetic and proteomic processes. The study design enables stratified analyses by sex and ethnicity while controlling for demographic and lifestyle confounders, providing a comprehensive framework for understanding the psychiatric-cognitive decline relationship across multiple biological levels. c. Analytical Plan The analytical approach will proceed in sequential phases, beginning with statistical modeling to identify psychiatric traits significantly associated with MCI and AD outcomes while adjusting for demographic and lifestyle factors. Genetic analyses will employ polygenic risk scores and Mendelian randomization techniques to establish causal relationships between psychiatric conditions (particularly depression and alcohol use disorder) and cognitive outcomes. Molecular analyses will focus on identifying shared genetic loci between psychiatric and cognitive phenotypes, followed by investigation of genetically regulated methylation and proteomic markers as potential mediators. The analysis plan includes development of molecular weights to aid causal inference analyses and determination of effect directionality, with stratified results reported by sex and ethnicity to identify population-specific risk patterns and potential intervention targets. Non-Technical Research Use Statement: This research examines how mental health conditions like depression and anxiety may increase the risk of memory problems and Alzheimer's disease as people age. Using genetic data and biological markers, we'll study whether psychiatric conditions directly cause cognitive decline or if they share common underlying causes. The study will identify which mental health factors pose the greatest risk for dementia, particularly looking at differences between men and women and various ethnic groups. Results could help better predict and prevent cognitive decline by addressing mental health early in life, potentially improving outcomes for millions facing both psychiatric and age-related brain conditions.
- Investigator: Seshadri, Sudha Institution: Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX Project Title: Therapeutic target discovery in ADSP data via comprehensive whole-genome analysis incorporating ethnic diversity and systems approaches Date of Initial Approval: October 14, 2020 Current Status: Expired Publications: Show publications (6) - Bouzid H. **Clonal hematopoiesis is associated with protection from Alzheimer's disease.***Nature medicine. 2023 Jul.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/37322115/) - Horimoto ARVR. **Admixture mapping implicates 13q33.3 as ancestry-of-origin locus for Alzheimer disease in Hispanic and Latino populations.***HGG advances. 2023 Jul 13.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/37333771/) - Tin A. **Identification of circulating proteins associated with general cognitive function among middle-aged and older adults.***Communications biology. 2023 Nov 3.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/37923804/) - Wang Y. **Key variants via the Alzheimer's Disease Sequencing Project whole genome sequence data.***Alzheimer's & dementia : the journal of the Alzheimer's Association. 2024 May.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/38511601/) - Lee WP. **Association of common and rare variants with Alzheimer's disease in more than 13,000 diverse individuals with whole-genome sequencing from the Alzheimer's Disease Sequencing Project.***Alzheimer's & dementia : the journal of the Alzheimer's Association. 2024 Dec.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/39428839/) - Wang D. **Frequency of variants in Mendelian Alzheimer's disease genes within the Alzheimer's Disease Sequencing Project.***Journal of Alzheimer's disease : JAD. 2025 Apr.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/40084664/) Research Use Statements: Show statements Technical Research Use Statement: Objective: Utilize ADSP data sets to identify genes & specific genetic variants that confer risk for or protection from Alzheimer disease. Aim 1: Using combined WGS/WES across the ADSP Discovery, Disc-Ext, and FUS Phases, including single nucleotide variants, small insertion/deletions, and structural variants. We will: Aim 1a. Perform whole genome single variant and rare variant case/control association analyses of AD using ADSP and other available data; Aim 1b. Target protective variant identification via association analysis using selected controls within the ADSP data and performing meta analysis across association results based on selected controls from non-ADSP data sets. Aim 1c. Perform endophenotype analyses including cognitive function measures, hippocampal volume and circulation beta-amyloid ADSP data in subjects for which these measures are available. Meta analysis will be conducted across ADSP and non-ADSP analysis results. Aim 2: To leverage ethnically-diverse and admixed populations to identify AD variants we will: Aim 2a. Estimate and account for global and local ancestry in all analyses; Aim 2b. Perform admixture mapping in samples of admixed ancestry; and Aim 2c. Perform ethnicity-specific and trans-ethnic meta-analyses. Aim 3: To identify putative therapeutic targets through functional characterization of genes and networks via bioinformatics, integrative ‘omics analyses. We will: Aim 3a. Annotate variants with their functional consequences using bioinformatic tools and publicly available “omics” data. Aim 3b. Prioritize results, group variants with shared function, and identify key genes functionally related to AD via weighted association analyses and network approaches. Analyses will be performed in coordination with the following PIs. Coordination will involve sharing expertise, analysis plans or analysis results. No individual level data will be shared across institutions. Philip De Jager, Columbia University; Eric Boerwinkle & Myriam Fornage, U of Texas Health Science Center, Houston; Sudha Seshadri, U of Texas, San Antonio; Ellen Wijsman, U of Washington. William Salerno, Baylor College of Medicine Non-Technical Research Use Statement: This proposal seeks to analyze existing genetic sequencing data generated as part of the Alzheimer’s Disease Sequencing Project (ADSP) including the ADSP Follow-up Study (FUS) with the goal of identifying genes and specific changes within those genes that either confer risk for Alzheimer’s Disease or provide protection from Alzheimer’s Disease. Analytic challenges include analysis of whole genome sequencing data, appropriately accounting for population structure across European ancestry, Hispanic, and African American participants, and interpreting results in the context of other genomic data available.
- Investigator: Verghese, Joe Institution: Stony Brook University Project Title: Super Mover Gentics using HRS data Date of Initial Approval: October 6, 2026 Current Status: Approved Publications: None Reported Research Use Statements: Show statements Technical Research Use Statement: The objective of this research is to investigate the biological mechanisms underlying resilience in aging by focusing on “Super movers” older adults who maintain exceptional cognitive and physical function well into late life. While much prior work has emphasized risk factors for decline, this study seeks to identify biomarkers and genetic signatures that distinguish individuals with preserved function, thereby illuminating pathways that support healthy longevity. Specifically, we aim to evaluate the predictive value of biomarkers and genetic variants for future cognitive and physical outcomes.The study design is a longitudinal observational analysis utilizing secondary, de-identified data from the Health and Retirement Study (HRS). The HRS provides nationally representative, multi-wave data on adults over age 50, including extensive biomarker, cognitive, physical, and genetic measures. Our analysis will focus on coded biomarker and performance data from waves spanning 2006–2016, linked with available genetic data, to examine associations between biological measures and aging-related outcomes without direct participant contact. The analysis plan involves cross-sectional and longitudinal approaches. First, biomarker and genetic profiles of Super movers will be compared to those of individuals with average function to identify distinct biological signatures. Second, longitudinal models will assess whether specific biomarkers and genetic variants predict trajectories of cognitive performance (e.g., memory, executive function) and physical outcomes (e.g., gait speed, grip strength, mobility) independent of age, sex, and conventional risk factors. Third, mechanistic analyses will evaluate how biomarker patterns and genetic variation jointly reflect processes such as immune regulation, metabolic resilience, and cellular aging. Phenotypic characteristics to be evaluated include cognitive test scores, physical performance measures, and biomarker indices of inflammation, metabolic function, and cellular aging, analyzed in association with genetic variants available through NIAGADS ADRD datasets. Non-Technical Research Use Statement: As people live longer, understanding why some older adults stay sharp and physically active is critical. This study focuses on “Super movers” individuals who maintain exceptional memory, thinking skills, and mobility well into late life. Using data from the nationally representative Health and Retirement Study, we will examine blood-based biomarkers and genetic information to identify biological patterns that support resilience in aging. By comparing Super movers to peers with average function, and tracking outcomes over time, we aim to uncover protective mechanisms such as reduced inflammation, efficient metabolism, and vascular health. These insights can guide strategies to detect risk earlier, promote healthy aging, and improve quality of life for older adults.
- Investigator: Wang, Lily Institution: University of Miami Project Title: DNA methylation associated with Alzheimer’s disease and cognitive outcomes in the Health and Retirement Study Date of Initial Approval: November 21, 2024 Current Status: Approved Publications: None Reported Research Use Statements: Show statements Technical Research Use Statement: Objectives The goal of this study is to perform statistical and bioinformatics analyses and identify DNA methylation changes associated with cognitive data and dementia outcomes collected by the Health and Retirement Study. Our specific aims are: (1) Identify blood DNAm associated with aging, AD diagnosis and longitudinal cognitive outcomes (2) Validate DNAm-based prediction models for identifying subjects with high risk for dementia. Study design. The Health and Retirement Study (HRS) is a longitudinal panel study Analysis plan To identify blood DNAm associated with LOAD diagnosis and longitudinal cognitive outcomes, we will fit a mixed effects model with LOAD diagnosis (or longitudinal outcome) as the dependent variable, CpG methylation as the main independent variable, along with age, sex, batch type, estimated blood cell type proportions as covariates. In addition, we will apply our DNAm-based prediction models for LOAD to the Health and Retirement Study data and evaluate the prediction model by computing AUC (area under ROC curve). Phenotypic characteristics. We will evaluate the association of DNA methylation variants with dementia diagnosis and chronological age, adjusting for covariate variables race/ethnicity, APOE genotype, baseline MMSE at time of blood draw, age, sex, education, smoking history. Non-Technical Research Use Statement: This study aims to explore how DNA methylation changes are associated dementia outcomes in older adults using data from the Health and Retirement Study. We will identify specific DNA methylation patterns associated with aging and Alzheimer's dementia, while also validating prediction models to identify individuals at high risk for dementia.
- Investigator: Ware, Erin Institution: University of Michigan Project Title: Alzheimer's disease polygenic scores and cognition Date of Initial Approval: April 5, 2023 Current Status: Approved Publications: Show publications (4) - Tejera CH. **The Mediating Role of Systemic Inflammation and Moderating Role of Race/Ethnicity in Racialized Disparities in Incident Dementia: A Decomposition Analysis.***medRxiv : the preprint server for health sciences. 2023 Mar 28.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/37034792/) - Higgins Tejera C. **Decomposing interaction and mediating effects of race/ethnicity and circulating blood levels of cystatin C on cognitive status in the United States health and retirement study.***Frontiers in human neuroscience. 2023.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/37323925/) - Campbell KA. **Relationship between alcohol consumption and dementia with Mendelian randomization approaches among older adults in the United States.***medRxiv : the preprint server for health sciences. 2023 Dec 22.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/38196582/) - Ware EB. **Associations of perceived neighborhood factors and Alzheimer's disease polygenic score with cognition: Evidence from the Health and Retirement Study.***PloS one. 2025.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/41264616/) Research Use Statements: Show statements Technical Research Use Statement: Our goal is to investigate the roles of cumulative genetic variation, disparity-related factors, and their interactions on late-onset Alzheimer’s disease (LOAD) and dementia phenotypes, extending precision public health to environmental susceptibility across ancestries. LOAD is the leading terminal form of dementia affecting a growing number of aging U.S. adults. As LOAD risk is disproportionately high among minorities, women, rural inhabitants, and people with lower education, disparities in LOAD risk represent a critical knowledge gap. Novel approaches characterizing the multifaceted etiology of LOAD disparities are needed to identify the genetic underpinnings, biological pathways, and potentially modifiable environmental factors that lead to sustained LOAD disparities. We propose whole genome estimations of polygenic risk of cognition, LOAD, and LOAD risk factors to be examined for their effect on dementia phenotypes among individuals >70, independently and in concert; potential interactions between PGS and factors with disparities in LOAD; and application of our methods in European and African ancestry groups (Fig. 1). AIM 1. Determine the cumulative genetic risk of LOAD by estimating the effect of cognitive polygenic scores on dementia phenotypes in individuals of European and African ancestry. AIM 2. Determine the association between polygenic scores for a) behavioral, b) physiological, and c) social/psychosocial domains and dementia phenotypes in individuals of European and African ancestry. We will consider Mendelian Randomization approaches for this aim. AIM 3. For the relationships between polygenic scores and dementia phenotypes (AIMS 1 and 2), test for effect modification by LOAD disparity-related factors (sex, educational attainment, urban/rural), in individuals of European and African ancestry. We area also exploring stratification by the APOE risk variants. Non-Technical Research Use Statement: The overall purpose of this proposal is to establish the relevance of polygenic risk in susceptibility to dementia, particularly among groups at increased risk of disease, including women, minorities, rural inhabitants, and those with low educational attainment. Because an individual’s susceptibility to dementia is likely a combination of genetics and environmental risk factors, we will jointly test the effects of cumulative genetic risk and dementia risk factors in our analysis. The proposal provides an opportunity to identify a genetic etiologic component in vulnerable groups that could lead to mechanistic understanding or targeted interventions to substantially benefit public health in the US.
- Investigator: Ware, Erin Institution: University of Michigan Project Title: DNA Methylation, Genetics, and Modifiable Risk Factors of Dementia in a Nationally Representative, Multi-Ethnic Cohort Date of Initial Approval: July 2, 2024 Current Status: Approved Publications: Show publications (1) - Dou J. **DNA methylation-wide association study of prevalent and incident dementia in the US Health and Retirement Study.***medRxiv : the preprint server for health sciences. 2025 Nov 13.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/41292628/) Research Use Statements: Show statements Technical Research Use Statement: Our goal is to determine the joint epigenetic and environmental contributions to ADRD risk that underlie these health disparities. Using existing epigenetic and genetic data, well-characterized dementia phenotypes, and diverse risk factor data, we will analyze a population representative, multi-ethnic aging sample from the Health and Retirement Study (HRS). We aim to (1) test the associations between DNA methylation and dementia phenotypes (prevalent, 8-year incident), stratified by race/ethnicity and test for effect modification by ADRD disparity-related factors (educational attainment, sex, urban/rural); (2) identify associations between longitudinal measures of modifiable risk factors for ADRD and DNA methylation, stratified by race/ethnicity and test for effect modification or mediation by ADRD disparity-related factors; and finally, (3) identify genetic polymorphisms controlling DNA methylation and whether these are enriched in dementia outcomes to evaluate the role of DNA methylation in disease development. This study will likely impact the field of Alzheimer’s research and contribute to public health because it will a) establish the relevance of DNA methylation on ADRD in multiple race/ethnicities; b) elucidate important biological epigenetic mechanisms; c) determine the combined and individual epigenetic-environment interplay contributions to ADRD; and d) consider the effects of sex, educational attainment, race/ethnicity, younger age groups, and urban/rural status in the same study where comparisons of relative contribution to risk can be made. Here, we have the opportunity to simultaneously and substantially improve our understanding of the genetic and environmental etiologic contributions to health disparities in ADRD. We are extending this work to include delirium with and without cognitive impairment. Non-Technical Research Use Statement: The overall purpose of this proposal is to identify modifiable risk factors for Alzheimer’s disease and related dementias (including delirium) that influence DNA methylation and dementia status among groups at increased risk for dementia including women, minorities, rural inhabitants, and those with low educational attainment. Results from this proposal may provide an opportunity to identify epigenetic components that contribute to the prevalence and risk of dementia that could lead to a mechanistic understanding or targeted interventions that may substantially decrease the burden of Alzheimer’s disease and related dementias in the US population
- Investigator: Zhao, Jinying Institution: University of Florida Project Title: Identifying novel biomarkers for human complex diseases using an integrated multi-omics approach Date of Initial Approval: March 19, 2019 Current Status: Closed Publications: Show publications (5) - Huo Z. **DNA methylation variability in Alzheimer's disease.***Neurobiology of aging. 2019 Apr.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/30660039/) - Qin H. **Identifying Multi-Omics Causers and Causal Pathways for Complex Traits.***Frontiers in genetics. 2019.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/30847004/) - Qin H. **Identifying Rare Variant Associations in Admixed Populations.***Scientific reports. 2019 Apr 1.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/30931973/) - Huo Z. **Brain and blood metabolome for Alzheimer's dementia: findings from a targeted metabolomics analysis.***Neurobiology of aging. 2020 Feb.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/31785839/) - Palma-Gudiel H. **Fine-mapping and replication of EWAS loci harboring putative epigenetic alterations associated with AD neuropathology in a large collection of human brain tissue samples.***Alzheimer's & dementia : the journal of the Alzheimer's Association. 2023 Apr.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/35959851/) Research Use Statements: Show statements Technical Research Use Statement: GWAS, WES and WGS have identified many genes associated with Alzheimer’s Dementia (AD) and its related traits. However, the identified genes thus far collectively explain only a small proportion of disease heritability, suggesting that more genes remained to be identified. Moreover, there is a clear gender and ethnic disparity for AD susceptibility, but little research has been done to identify gender- and ethnic-specific variants associated with AD. Of the many challenges for deciphering AD pathology, lacking of efficient and power statistical methods for genetic association mapping and causal inference represents a major bottleneck. To tackle this challenge, we have developed a set of novel statistical and bioinformatics approaches for genetic association mapping and multi-omics causation inference in large-scale ethnicity-specific epidemiological studies. The goal of this project is to leverage the multi-omics and clinical data archived by the ADSP, ADNI, ADGC as well as other AD-related data repositories to identify novel genes and molecular markers for AD. Specifically, we will (1) validate our novel methods for identifying novel risk and protective genomic variants and multi-omics causal pathways of AD; (2) identify novel ethnicity- and gender-specific genes and molecular causal pathways of AD. We will share our results, statistical methods and computational software with the scientific community. Non-Technical Research Use Statement: Although many genes have been associated with Alzheimer’s Dementia (AD), these genes altogether explain only a small fraction of disease etiology, suggesting more genes remained to be identified. Of the many challenges for deciphering AD pathology, lacking of power statistical methods represents a major bottleneck. To tackle this challenge, we have developed a set of novel statistical and bioinformatics approaches for genetic association mapping and multi-omics causation inference in large-scale ethnicity-specific epidemiological studies. The goal of this project is to leverage the rich genetic and other omic data along with clinical data archived by the ADSP, ADNI, ADGC as well as other AD-related data repositories to identify novel genes and molecular markers for AD. Such results will enhance our understanding of AD pathogenesis and may also serve as biomarkers for early diagnosis and therapeutic targets.

### Total number of participants: 19,193

APOE

  - 22127 (0.7%)
- 232,352 (12.3%)
- 24468 (2.4%)
- 3311,451 (59.7%)
- 344,356 (22.7%)
- 44439 (2.3%)