Overview
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Description
This dataset contains the sex stratified and interaction summary statistics memory and memory slopes published in Eissman, et al, 2022 (Brain, PMID: 35552371). Cognitive and pathology data were harmonized across two datasets with autopsy measures of amyloid (ROS/MAP and ACT) and two datasets with amyloid PET (A4 and ADNI). Resilience phenotypes were calculated using a latent variable modeling approach, modeling better than expected cognition for a given level of amyloid pathology. Cognitive resilience took into account amyloid levels, age, and sex. Global cognitive resilience further incorporated education into the residual cognitive resilience. Genotype data in each study underwent standard quality control and imputation onto the TOPMed reference panel (genome build 38). GWAS on both resilience phenotypes were performed including all samples as well as subsetted to only cognitively normal samples to evaluate resilience at the preclinical stage of disease.
All GWAS described were performed in males, in females, and with a sex-interaction and were run in the combined autopsy dataset and in the combined PET dataset for all resilience phenotypes. Sex-stratified GWAS covaried for age and the first three genetic principal components. The sex-interaction GWAS also covaried for sex and included a single nucleotide polymorphism (SNP) × sex interaction term. GWAS results were then meta-analysed across cohorts using a fixed-effects model with beta and standard error input (GWAMA v2.2.2). The above models were also run identically in the sample restricted to cognitively normal individuals, with the fixed effects meta-analyses implementing the minor allele frequencies calculated based on these individuals only. Additionally, an identical GWAS and meta-analysis pipeline as described above was implemented with the X-chromosome genetic data for the sex-stratified models. All meta-analysis results were restricted to SNPs present in both the autopsy and the PET dataset.
Available Filesets
| Name | Accession | Latest Release | Description |
|---|---|---|---|
| Sex Differences Resilience: Full Summary Statistics (application needed) | fsa000095 | NG00161.v1 | Full Summary Statistics |
| Sex Differences Resilience: P-values only (open access) | fsa000096 | NG00161.v1 | P-values only |
View the File Manifest for a full list of files released in this dataset.
Related Studies
- This dataset contains the sex stratified and interaction summary statistics memory and memory slopes published in Eissman, et al, 2022 (Brain, PMID: 35552371). Cognitive and pathology data were harmonized across…
Consent Levels
| Consent Level | Number of Subjects |
|---|---|
| DS-ADRD-IRB-PUB-NPU | NA |
Visit the Data Use Limitations page 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 NG00161.
For investigators using Sex differences in the genetic architecture of cognitive resilience to Alzheimer's disease – Eissman, et al. 2022 (sa000045) data:
This research was supported in part by K01-AG049164, R01-AG059716, R21-AG05994, K12-HD043483, K24-AG046373, HHSN311201600276P, S10-OD023680, R01-AG034962, R01-NS100980, R01-AG056534, P30-AG010161, P30-AG072975, R01-AG057914, R01-AG015819, R01-AG017917, R13-AG030995, U01-AG061356, U01-AG006781, U19-AG066567, K99/R00-AG061238, U01-AG046152, U01-AG068057, UL1-TR000445, T32-GM080178, R01-AG073439, U24-AG074855, P20-AG068082 (Vanderbilt Alzheimer’s Disease Research Center), and the Vanderbilt Memory & Alzheimer’s Center. Data collection was supported through funding by NIA grants P50-AG016574, P50-AG005136, R01-AG032990, U01-AG046139, R01-AG018023, U01-AG006576, U01-AG006781, U01-AG006786, R01-AG025711, R01-AG017216, R01-AG003949, P30-AG019610, U01-AG024904, U01-AG032984, U24-AG041689, R01-AG046171, RF1-AG051550, 3U01-AG024904-09S4, NINDS grant R01-NS080820, CurePSP Foundation, and support from Mayo Foundation.
The Brain and Body Donation Program is supported by the National Institute of Neurological Disorders and Stroke (U24-NS072026 National Brain and Tissue Resource for Parkinson’s Disease and Related Disorders), the National Institute on Aging (P30-AG019610 Arizona Alzheimer’s Disease Core Center), the Arizona Department of Health Services (contract 211002, Arizona Alzheimer’s Research Center), the Arizona Biomedical Research Commission (contracts 4001, 0011, 05-901 and 1001 to the Arizona Parkinson's Disease Consortium) and the Michael J. Fox Foundation for Parkinson’s Research. Data were generated as part of the CommonMind Consortium supported by funding from Takeda Pharmaceuticals Company Limited, F. Hoffman-La Roche Ltd and NIH grants R01-MH085542, R01-MH093725, R01-AG074012, P50-MH066392, P50-MH080405, R01-MH097276, R01-MH075916, P50-M096891, P50-MH084053S1, R37-MH057881, AG02219, AG05138, MH06692, R01-MH110921, R01-MH109677, R01-MH109897, U01-MH103392, and contract HHSN271201300031C through IRP NIMH. 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: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc; Biogen Inc Cambridge, MA 02139, provided support for genotyping of the A4 Study cohort; Bristol-Myers Squibb Company; CereSpir, Inc; Cogstate; Eisai Inc; 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; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of 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). Additional data collection and sharing for this project was funded by the Alzheimer's Disease Metabolomics Consortium (National Institute on Aging R01-AG046171, RF1-AG051550 and 3U01-AG024904-09S4).
Related Publications
Eissman, J.M., et al. Sex differences in the genetic architecture of cognitive resilience to Alzheimer’s disease. Brain. 2022 Jul. doi: 10.1093/brain/awac177 PubMed link
Approved Users
- Investigator:Belloy, MichaelInstitution:Washington University in St LouisProject Title:Elucidating sex-specific risk for Alzheimer's disease through state-of-the-art genetics and multi-omicsDate of Approval:January 6, 2025Request status:ApprovedResearch use statements:Show statementsTechnical 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:Cruchaga, CarlosInstitution:Washington University School of MedicineProject Title:The Familial Alzheimer Sequencing (FASe) ProjectDate of Approval:March 18, 2025Request status:ApprovedResearch use statements:Show statementsTechnical 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 studiesNon-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:Kamboh, M. IlyasInstitution:University of PittsburghProject Title:Genetics of Alzheimer's Disease and EndophenotypesDate of Approval:January 7, 2025Request status:ApprovedResearch use statements:Show statementsTechnical 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:Pan, WeiInstitution:University of MinnesotaProject Title:Powerful and novel statistical methods to detect genetic variants associated with or putative causal to Alzheimer’s diseaseDate of Approval:March 25, 2025Request status:ApprovedResearch use statements:Show statementsTechnical Research Use Statement:We have been developing more powerful statistical methods to detect common variant (CV)- or rare variant (RV)-complex trait associations and/or putative causal relationships for GWAS and DNA sequencing data. Here we propose applying our new methods, along with other suitable existing methods, to the existing ADSP sequencing data and other AD GWAS data provided by NIA, hence requesting approval for accessing the ADSP sequencing and other related GWAS/genetic data. We have the following specific Aims: Aim 1. Association testing using the ADSP data. We'd like to detect CV- and RV-AD associations based on the ADSP data. Aim 2. Association testing under genetic heterogeneity: For complex traits, genetic heterogeneity, especially of RVs, is ubiquitous as well acknowledged in the literature, however there is barely any existing methodology to explicitly account for genetic heterogeneity in association analysis of RVs based on a single sample/cohort. We propose using secondary and other omic data, such as transcriptomic or metabolomic data, to stratify the given sample, then apply a weighted test to the resulting strata, explicitly accounting for genetic heterogeneity that causal RVs may be different (with varying effect sizes) across unknown and hidden subpopulations. Some preliminary analyses have confirmed power gains of the proposed approach over the standard analysis. Aim 3. Meta analysis of RV tests: Although it has been well appreciated that it is necessary to account for varying association effect sizes and directions in meta analysis of RVs for multi-ethnic cohorts, existing tests are not highly adaptive to varying association patterns across the cohorts and across the RVs, leading to power loss. We propose a highly adaptive test based on a family of SPU tests, which cover many existing meta-analysis tests as special cases. Our preliminary results demonstrated possibly substantial power gains. Aim 4. Multi-ancestry association analysis. We'd like to use both individual-level GWAS/WGS data and GWAS summary test for genetic associations with AD.Non-Technical Research Use Statement:We propose applying our newly developed statistical analysis methods, along with other suitable existing methods, to the existing ADSP sequencing data and other AD GWAS data to detect common or rare genetic variants associated with Alzheimer’s disease (AD). The novelty and power of our new methods are in two aspects: first, we consider and account for possible genetic heterogeneity with several subcategories of AD; second, we apply powerful meta-analysis methods to combine the association analyses across multiple subcategories of AD. The proposed research is feasible, promising and potentially significant to AD research. In addition, our proposed analyses of the existing large amount of ADSP sequencing data and other AD GWAS data with our developed new methods are novel, powerful and cost-effective.
- Investigator:Zhao, ZhongmingInstitution:University of Texas Health Science Center at HoustonProject Title:AIM-AI: an Actionable, Integrated and Multiscale genetic map of Alzheimer's disease via deep learningDate of Approval:March 27, 2025Request status:ApprovedResearch use statements:Show statementsTechnical Research Use Statement:Objectives: The objective of our study is to advance our understanding of the genetic basis of Alzheimer’s Disease (AD) through the analysis of comprehensive genomic datasets such as Whole Exome Sequencing (WES), Whole Genome Sequencing (WGS), single-nuclei RNA sequencing, and Genome-Wide Association Studies (GWAS), as well as the related phenotype. We aim to identify genetic variants that are integral to the development and progression of AD.Study Design: Our approach involves a detailed multi-omics analysis focusing on both coding and non-coding regions within these datasets. We will develop new analytical variables from existing data, ensuring that our research adheres to the established data use limitations and contributes meaningfully to the field of genetic research in AD.Analysis Plan: The plan centers on investigating the correlation between genetic variants and AD, exploring how these variants influence the disease at a genetic level. We will employ cutting-edge computational methods to analyze interactions between these genetic markers and their potential role in AD pathogenesis. The integration of data from multiple sources will be carefully executed to maintain compliance with data use agreements, emphasizing the scientific exploration of AD.Non-Technical Research Use Statement:Our research is dedicated to unraveling the genetic components of Alzheimer’s Disease. By analyzing genetic sequences and variations through various genomic datasets, we seek to deepen the scientific understanding of how these genetic elements contribute to AD. The outcomes of this study will be shared with the public, enhancing general knowledge of Alzheimer’s Disease and supporting the global research community in its ongoing efforts to decode this complex condition.