---
title: "NG00035 – GWAS of CSF tau levels identifies risk variants for Alzheimer’s Disease"
id: "7242"
type: "dataset"
slug: "ng00035"
published_at: "2025-02-06T19:22:47+00:00"
modified_at: "2026-09-01T19:50:19+00:00"
url: "https://dss.niagads.org/datasets/ng00035/"
markdown_url: "https://dss.niagads.org/datasets/ng00035.md"
excerpt: "To access the full dataset, please log into DSS and submit an application.Within the application, add this dataset (accession NG00035) in the “Choose a Dataset” section.Once approved, you will be able to log in and access the data within the..."
taxonomy_dataset_categories:
  - "AD"
  - "Controlled Access"
  - "Data Type"
  - "Disease"
  - "Genotyping SNP Array"
  - "HapMap"
  - "Illumina Human610-Quad"
  - "Illumina OmniExpress"
  - "Imputation"
---

## Overview

To access the full dataset, please log into DSS and submit an application.  
Within the application, add this dataset (accession NG00035) in the “Choose a Dataset” section.  
Once approved, you will be able to log in and access the data within the DARM portal.

#### Description

Cerebrospinal fluid (CSF) tau, tau phosphorylated at threonine 181 (ptau), and Aβ₄₂ are established biomarkers for Alzheimer’s disease (AD) and have been used as quantitative traits for genetic analyses. This is the largest genome-wide association study for cerebrospinal fluid (CSF) tau/ptau levels published to date (n = 1,269). Imputed data consists of 5,815,690 SNPS using HapMap release 22 CEU (build 36) as a reference panel.

This dataset is comprised of 769 individuals (506 Washington University, 208 University of Washington, 55 University of Pennsylvania). An additional 395 ADNI samples were used in this study but are not part of this dataset. Contact ADNI to apply for access to their GWAS data at [adni.loni.usc.edu](http://adni.loni.usc.edu/)
.

This dataset is part of the Knight ADRC Collection. Other datasets in this collection can be found at: [https://www.niagads.org/knight-adrc-collection](https://www.niagads.org/knight-adrc-collection)
.

This dataset was originally published on the NIAGADS archive site on 03/17/2014 and was moved to DSS on 02/20/2025.

#### Sample Summary per Data Type

| Sample Set | Accession | Data Type | Number of Samples |
| --- | --- | --- | --- |
| Knight ADRC GWAS of CSF | snd10111 | GWAS-Imputation | 769 |

#### Available Filesets

| Name | Accession | Latest Release | Description |
| --- | --- | --- | --- |
| Knight GWAS of CSF: HapMap imputation and phenotypes | fsa000121 | NG00035.v1 | HapMap GWAS, imputation and phenotpyes |

View the [File Manifest](https://st1.niagads.org/portal/download-public/NG00035.v1/fm)
 for a full list of files released in this dataset.

#### Data Dictionary Files

[Phenotype Data Dictionary](https://dss.niagads.org/wp-content/uploads/2025/02/csf_dataset_neuron_cruchaga_phenotype_Data_Dictionary.txt)
​

## Participant Information

Cerebrospinal fluid (CSF) tau, tau phosphorylated at threonine 181 (ptau), and Aβ₄₂ are established biomarkers for Alzheimer's disease (AD) and have been used as quantitative traits for genetic analyses. We performed the largest genome-wide association study for cerebrospinal fluid (CSF) tau/ptau levels published to date (n = 1,269), identifying three genome-wide significant loci for CSF tau and ptau: rs9877502 (p = 4.89 × 10⁻⁹ for tau) located at 3q28 between GEMC1 and OSTN, rs514716 (p = 1.07 × 10⁻⁸ and p = 3.22 × 10⁻⁹ for tau and ptau, respectively), located at 9p24.2 within GLIS3 and rs6922617 (p = 3.58 × 10⁻⁸ for CSF ptau) at 6p21.1 within the TREM gene cluster, a region recently reported to harbor rare variants that increase AD risk. In independent data sets, rs9877502 showed a strong association with risk for AD, tangle pathology, and global cognitive decline (p = 2.67 × 10⁻⁴, 0.039, 4.86 × 10⁻⁵, respectively) illustrating how this endophenotype-based approach can be used to identify new AD risk loci.

| Sample Set | Accession Number | Number of Participants | Number of Samples |
| --- | --- | --- | --- |
| Knight ADRC GWAS of CSF | snd10111 | 769 | 769 |

## Related Studies

- [sa000008 - Charles F. and Joanne Knight Alzheimer's Disease Research Center (Knight ADRC)](https://dss.niagads.org/studies/sa000008/) The search for novel risk factors for Alzheimer disease relies on access to accurate and deeply phenotyped datasets. The Memory and Aging Project at the Knight-ADRC (Knight ADRC-MAP) collects plasma,… [Learn more](https://dss.niagads.org/studies/sa000008/)

## Cohorts

| Cohort | Number of Participants | Number of Samples |
| --- | --- | --- |
| Knight Alzheimer’s Disease Research Center (KGAD) | 497 | 497 |
| National Institute of Aging Alzheimer’s Disease Family Based Study (NIA AD-FBS) | 9 | 9 |
| NIA Alzheimer's Disease Research Centers (ADRC) | 176 | 176 |
| University of Washington Families (RAS) | 87 | 87 |

## Consent Levels

| Consent Level | Number of Participants | Number of Samples |
| --- | --- | --- |
| DS-ADRD-IRB-PUB | 497 | 497 |
| DS-NEURO-IRB-PUB | 87 | 87 |
| GRU-IRB-PUB | 130 | 130 |
| HMB-IRB-PUB | 55 | 55 |

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 [NG00035](https://archive.niagads.org/datasets/NG00035)
.

### For investigators using Charles F. and Joanne Knight Alzheimer’s Disease Research Center (Knight ADRC) (sa000008) data:

This work was supported by grants from the National Institutes of Health (R01AG044546, P01AG003991, RF1AG053303, R01AG058501, U01AG058922, RF1AG058501 and R01AG057777). The recruitment and clinical characterization of research participants at Washington University were supported by NIH P50 AG05681, P01 AG03991, and P01 AG026276. This work was supported by access to equipment made possible by the Hope Center for Neurological Disorders, and the Departments of Neurology and Psychiatry at Washington University School of Medicine.

We thank the contributors who collected samples used in this study, as well as patients and their families, whose help and participation made this work possible. This work was supported by access to equipment made possible by the Hope Center for Neurological Disorders, and the Departments of Neurology and Psychiatry at Washington University School of Medicine.

**See below for additional dataset specific acknowledgments:**

*For use of the ADSP-PHC harmonized phenotypes deposited within dataset, ng00067, use the following statement:*

The Memory and Aging Project at the Knight-ADRC (Knight-ADRC), supported by NIH grants R01AG064614, R01AG044546, RF1AG053303, RF1AG058501, U01AG058922 and R01AG064877 to Carlos Cruchaga. The recruitment and clinical characterization of research participants at Washington University was supported by NIH grants P30AG066444, P01AG03991, and P01AG026276. Data collection and sharing for this project was supported by NIH grants RF1AG054080, P30AG066462, R01AG064614 and U01AG052410. This work was supported by access to equipment made possible by the Hope Center for Neurological Disorders, the Neurogenomics and Informatics Center (NGI: [https://neurogenomics.wustl.edu/](https://neurogenomics.wustl.edu/)
) and the Departments of Neurology and Psychiatry at Washington University School of Medicine.

*For use of ng00050 and ng00052, use the following statement:*  
This work was supported by Pfizer and grants from the National Institutes of Health (R01-AG044546, P01-AG003991), and the Alzheimer's Association (NIRG-11–200110). This research was conducted while Carlos Cruchaga was a recipient of a New Investigator Award in Alzheimer's disease from the American Federation for Aging Research. Carlos Cruchaga is a recipient of a BrightFocus Foundation Alzheimer's Disease Research Grant (A2013359S). The recruitment and clinical characterization of research participants at Washington University were supported by NIHP50 AG05681, P01 AG03991, and P01 AG026276. Some of the samples used in this study were genotyped by the ADGC and GERAD. ADGC is supported by grants from the NIH (#U01AG032984) and GERAD from the Wellcome Trust (GR082604MA) and the Medical Research Council (G0300429). Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen Idec; Bristol-Myers Squibb Company; Eisai; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd. and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC; Johnson & Johnson Pharmaceutical Research & Development LLC; Medpace; Merck; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Rev December 5, 2013 Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ([www.fnih.org](http://www.fnih.org/)
). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

## Publications

- Cruchaga C. **GWAS of cerebrospinal fluid tau levels identifies risk variants for Alzheimer's disease.***Neuron. 2013 Apr 24.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/23562540/)

## Approved Users

**Total number of approved DARs:** 11

[JSON Export](https://st1.niagads.org/portal/v1/adars/NG00035)
|[CSV Export](https://dss.niagads.org/wp-admin/admin-post.php?action=niagads_export_dars_csv&accession=NG00035)

- 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: Chen, Jingchun Institution: University of Nevada, Las Vegas Project Title: Classification of Alzheimer’s disease with Genetic Data and Artificial Intelligence Date of Initial Approval: March 28, 2023 Current Status: Approved Publications: None Reported Research Use Statements: Show statements Technical Research Use Statement: Alzheimer's disease(AD) is the most common cause of dementia, accounting for 60% to 80% of cases that affect over six million people in the United States. The disease gradually progresses from mild cognitive impairment(MCI) to dementia, which takes more than a decade. Identifying individuals who have a high risk of AD earlier is essential for AD prevention and intervention. As the heritability of AD is high(up to 79%), genetic data should be powerful to identify individuals at high risk. Indeed, polygenic risk score (PRS), designed to estimate individual genetic liability by integrating large GWAS summary statistics and individual genotype data, has been shown to be promising for AD risk prediction(AUCs up to 84%). However, the prediction accuracy using a single PRS is still not sufficient for MCI and AD classification in clinical practice. We hypothesize that convolution neural network(CNN) models can improve the classification of AD and MCI by multiple integrating PRSs from multiple traits, multi-omics data (genotyping data, scRNA-seq), clinical data, and imaging data. The objective is to develop advanced AI algorithms and build data-driven models for disease risk assessment, earlier identifying individuals with high risk for MCI and AD. Our long-term goal is to develop and validate a prediction model that can be translated into clinical practice. Our CNN model has recently shown an improved performance for AD with PRSs from multiple traits(AUC 92.4%). We want to extend our approach to predicting AD and MCI in different ethnic groups and validate the results with independent datasets. To this end, we would like to apply for multi-omics data in NG00067.v9 from https://dss.niagads.org/datasets/ng00067/. With an extensive experience in genetic studies on complex disorders and disease modeling, we are confident that we will achieve the specified goals and promote the integration of genetic data with AI algorithms, facilitating data-driven, personalized care of AD. We expect to finish this study within 2 years with publication and grant application. We have IRB approval and will follow the rules for data sharing and acknowledgment. Non-Technical Research Use Statement: Alzheimer’s disease (AD), the most common form of dementia, that usually develops from mild cognitive impairment to dementia. There is currently no treatment to slow the progression of this disorder. But earlier identification of the individuals with higher risk maybe critical to prevent the disease. We propose a new approach to create models for classification of AD and MCI with artificial intelligence and genetic data. This study will have a significant value in personalized medicine for AD risk assessment, classification, and earlier intervention. We don’t have the planned collaboration with researchers outside Cleveland Clinic in the current analytic plans.
- 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: Goate, Alison Institution: Icahn School of Medicine at Mount Sinai Project Title: Study of Alzheimer's disease and other dementias (e.g. frontotemporal dementia) and related phenotypes Date of Initial Approval: September 17, 2018 Current Status: Approved Publications: Show publications (26) - Chia R. **Genome sequencing analysis identifies new loci associated with Lewy body dementia and provides insights into its genetic architecture.***Nature genetics. 2021 Mar.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/33589841/) - Novikova G. **Integration of Alzheimer's disease genetics and myeloid genomics identifies disease risk regulatory elements and genes.***Nature communications. 2021 Mar 12.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/33712570/) - Riaz M. **Effect of APOE and a polygenic risk score on incident dementia and cognitive decline in a healthy older population.***Aging cell. 2021 Jun.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/34041846/) - Wu HM. **Heterogeneous effects of genetic risk for Alzheimer's disease on the phenome.***Translational psychiatry. 2021 Jul 23.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/34301914/) - Huq AJ. **Polygenic score modifies risk for Alzheimer's disease in APOE ε4 homozygotes at phenotypic extremes.***Alzheimer's & dementia (Amsterdam, Netherlands). 2021.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/34386572/) - Kapoor M. **Multi-omics integration analysis identifies novel genes for alcoholism with potential overlap with neurodegenerative diseases.***Nature communications. 2021 Aug 20.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/34417470/) - McInerney TW. **A globally diverse reference alignment and panel for imputation of mitochondrial DNA variants.***BMC bioinformatics. 2021 Sep 1.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/34470617/) - Farrell K. **Genome-wide association study and functional validation implicates JADE1 in tauopathy.***Acta neuropathologica. 2022 Jan.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/34719765/) - Horgusluoglu E. **Integrative metabolomics-genomics approach reveals key metabolic pathways and regulators of Alzheimer's disease.***Alzheimer's & dementia : the journal of the Alzheimer's Association. 2022 Jun.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/34757660/) - Bowles KR. **Dysregulated coordination of MAPT exon 2 and exon 10 splicing underlies different tau pathologies in PSP and AD.***Acta neuropathologica. 2022 Feb.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/34874463/) - Reyes-Dumeyer D. **The National Institute on Aging Late-Onset Alzheimer's Disease Family Based Study: A resource for genetic discovery.***Alzheimer's & dementia : the journal of the Alzheimer's Association. 2022 Oct.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/34978149/) - Xue D. **Large-scale sequencing studies expand the known genetic architecture of Alzheimer's disease.***Alzheimer's & dementia (Amsterdam, Netherlands). 2021.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/35005195/) - Bellenguez C. **New insights into the genetic etiology of Alzheimer's disease and related dementias.***Nature genetics. 2022 Apr.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/35379992/) - Tcw J. **Cholesterol and matrisome pathways dysregulated in astrocytes and microglia.***Cell. 2022 Jun 23.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/35750033/) - Podlesny-Drabiniok A. **BHLHE40/41 regulate macrophage/microglia responses associated with Alzheimer's disease and other disorders of lipid-rich tissues.***bioRxiv : the preprint server for biology. 2023 Feb 13.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/36824752/) - Andrews SJ. **The complex genetic architecture of Alzheimer's disease: novel insights and future directions.***EBioMedicine. 2023 Apr.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/36907103/) - Podleśny-Drabiniok A. **BHLHE40/41 regulate microglia and peripheral macrophage responses associated with Alzheimer's disease and other disorders of lipid-rich tissues.***Nature communications. 2024 Mar 6.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/38448474/) - Farrell K. **Genetic, transcriptomic, histological, and biochemical analysis of progressive supranuclear palsy implicates glial activation and novel risk genes.***Nature communications. 2024 Sep 9.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/39251599/) - Nguyen L. **CASP8 intronic expansion identified by poly-glycine-arginine pathology increases Alzheimer's disease risk.***Proceedings of the National Academy of Sciences of the United States of America. 2025 Feb 18.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/39937857/) - Humphrey J. **Long-read RNA sequencing atlas of human microglia isoforms elucidates disease-associated genetic regulation of splicing.***Nature genetics. 2025 Mar.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/40033057/) - 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/) - Phillips JM. **Novel modelling approaches to elucidate the genetic architecture of resilience to Alzheimer's disease.***Brain : a journal of neurology. 2025 Aug 1.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/40111762/) - Cruchaga C. **GWAS meta-analysis of CSF Alzheimer's disease biomarkers 18,948 individuals reveal novel loci and genes regulating lipid metabolism, brain volume and autophagy.***Research square. 2025 May 21.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/40470240/) - Nicolas A. **Transferability of European-derived Alzheimer's disease polygenic risk scores across multiancestry populations.***Nature genetics. 2025 Jul.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/40533518/) - Rajabli F. **Multi-ancestry genome-wide meta-analysis of 56,241 individuals identifies known and novel cross-population and ancestry-specific associations as novel risk loci for Alzheimer's disease.***Genome biology. 2025 Jul 17.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/40676597/) - Chatterjee A. **Evaluating the causal effect of mitochondrial dysfunction on Alzheimer's and Parkinson's disease using Polygenic Risk Scores and Mendelian Randomization.***medRxiv : the preprint server for health sciences. 2025 Sep 27.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/41040731/) Research Use Statements: Show statements Technical Research Use Statement: 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. Non-Technical Research Use Statement: 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.
- Investigator: Kamboh, M. Ilyas Institution: University of Pittsburgh Project Title: Genetics of Alzheimer's Disease and Endophenotypes Date of Initial Approval: January 7, 2025 Current Status: Approved Publications: None Reported Research Use Statements: Show statements Technical Research Use Statement: Objectives: We 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. Study Design: A 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. Analysis Plan: We 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). Non-Technical Research Use Statement: 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.
- 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: 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: Shelton, Janie Institution: Bristol Myers Squibb Project Title: A longitudinal study of Alzheimer’s Disease and other dementing illnesses – KnightADRC GWAS Date of Initial Approval: January 30, 2026 Current Status: Approved Publications: None Reported Research Use Statements: Show statements Technical Research Use Statement: Recently approved Alzheimer’s disease (AD) therapies, such as lecanemab (Leqembi) and donanemab (Kisunla), represent a significant advancement toward disease-modifying treatment. However, their impact on cognitive decline remains modest, and both are associated with potentially serious adverse events, including amyloid-related imaging abnormalities (ARIA). These limitations underscore the urgent need for additional therapeutic strategies to reduce disease burden. Genetic approaches offer a powerful avenue for drug target discovery, with evidence suggesting that genetically supported targets are at least twice as likely to progress successfully through clinical development to FDA approval (Nelson et al., 2015, Nat Genet; King et al., 2019, PLoS Genet; Minikel et al., 2024, Nature). To date, most genetic studies in AD have focused on identifying loci associated with disease risk. Large-scale genome-wide association studies (GWAS) have uncovered approximately 75 risk loci (Bellenguez et al., 2022, Nat Genet), providing valuable insights into disease etiology. However, therapeutic interventions are typically aimed at individuals already diagnosed with AD, making the genetics of disease progression a critical—yet underexplored—complementary approach for target discovery. Progression-focused genetic studies face challenges due to limited availability of longitudinal phenotypic data. To address this, meta-analysis of multiple GWAS datasets offers a practical strategy to increase statistical power and detect robust associations. We propose to incorporate summary statistics from the Knight Alzheimer Disease Research Center (Knight-ADRC) AD progression GWAS into a meta-analysis alongside several publicly available and proprietary datasets. Our objective is to identify novel genetic drivers of AD progression, prioritize new therapeutic targets, and assess the impact of existing pipeline candidates on disease trajectory. Non-Technical Research Use Statement: New Alzheimer’s treatments like lecanemab (Leqembi) and donanemab (Kisunla) are an important step forward in the search for ways to help patients, but these drugs have only moderate benefits and can come with serious side effects. Better therapies are still needed to reduce the impact of the disease. Genetics offers a powerful way to discover new drugs—studies show that treatments based on genetic findings are more likely to succeed. So far most genetic research has focused on the genes which increase the risk of developing Alzheimer’s, but understanding genes that drive how the disease progresses in Alzheimer’s patients may be even more beneficial. However this type of data, which involves following participants over time, is limited, combining results from multiple smaller studies (a meta-analysis) can help uncover important patterns. We plan to add data from the Knight Alzheimer Disease Research Center to a larger analysis to find new genetic clues, identify better treatment targets, and evaluate how current and future drugs may slow disease progression.
- Investigator: Yang, Jingjing Institution: Emory University Project Title: Novel statistical methods for integrating transcriptomic and proteomic data in GWAS Date of Initial Approval: November 8, 2019 Current Status: Approved Publications: Show publications (2) - Dai Q. **OTTERS: a powerful TWAS framework leveraging summary-level reference data.***Nature communications. 2023 Mar 7.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/36882394/) - Parrish RL. **SR-TWAS: Leveraging Multiple Reference Panels to Improve TWAS Power by Ensemble Machine Learning.***medRxiv : the preprint server for health sciences. 2024 May 13.*[PubMed link](https://pubmed.ncbi.nlm.nih.gov/37425698/) Research Use Statements: Show statements Technical Research Use Statement: The objective of the proposed project is to derive novel statistical methods to integrate multi-omics data and pathology data in genome-wide association studies (GWAS) for studying complex phenotypes, with the goal of prioritizing genetic variants and identifying causal genes. First, we will develop novel statistical methods to integrate summary-level omics data and pathology data of diverse populations with GWAS data to prioritize risk genes. Second, we will apply our tools to publicly available xQTL data and the ADSP GWAS data. Third, we will also use the ADSP GWAS summary data to conduct causal analysis of other aging-related phenotypes and AD dementia. We will first develop novel statistical methods to integrate summary-level xQTL data of multiple populations with GWAS data to test gene associations with complex human diseases. We are interested in studying all complex phenotypes that were profiled for the ADSP samples, especially Alzheimer’s disease (AD) and AD-related complex phenotypes. Especially, our lab has access to the ROS/MAP multi-omics data shared by the Rush Alzheimer’s disease center (http://www.radc.rush.edu/), and GTEx data. All samples in the ROS/MAP study are well-characterized with extensive complex phenotypes profiled, including clinical diagnosis of AD, AD-related complex phenotypes, and psychological phenotypes. GTEx provides transcriptomic data of multiple human tissues. We will leverage multiple omics data profiled from the ROS/MAP study and transcriptomics data profiled from GTEx to learn SNP-omics relations, and then integrate such learned relationships with ADSP data to identify risk genes of complex diseases. We will also validate our findings by using omics and pathology data in the requested data sets. The purpose of using ADSP data is to increase sample size for testing our derived methods for functional genetic association studies of complex phenotypes, studying the genetic etiology of AD and AD-related phenotypes, and validating our finding by using the omics data from Rush Alzheimer's Disease Center. We are not limited to studying AD only. We are flexible to study any complex phenotypes that are profiled for ADSP samples. Non-Technical Research Use Statement: This proposed project is to develop novel statistical methods to integrate summary-level multi-omics data such as transcriptomic, proteomics, and epigenetics, and pathology data, in genome-wide association studies (GWAS) of complex phenotypes, with the goal of identifying causal genes. i) We will develop novel statistical method for integrating summary-level omics data and pathology data with GWAS data. ii) We will apply our tools to publicly available summary-level omics data, omics data from the ROS/MAP study, and ADSP GWAS data for studying AD and AD-related phenotypes. iii) We will conduct causal inference to test the causal relationship between AD and other aging-related phenotypes. We propose to test our proposed methods on the applied genomic analysis data to study complex phenotypes that are profiled for ADSP, including AD, AD-related pathology traits, and related psychological disorders.

### Total number of participants: 769

SexRaceEthnicityDiagnosisAPOE

Female41954.5 %

Male34344.6 %

Sex not reported: 7 (0.9%)

| NA | 769 |
| --- | --- |

- 224 (0.5%)
- 2376 (9.9%)
- 2422 (2.9%)
- 33381 (49.5%)
- 34221 (28.7%)
- 4445 (5.9%)
- NA20 (2.6%)

| AD |  |  |
| --- | --- | --- |
| Control | 506 | 65.8% |
| Case | 256 | 33.3% |
| Unknown | 7 | 0.9% |

Not Applicable/Not Available

769

100.0%