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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

NameAccessionLatest ReleaseDescription
Sex Differences Resilience: Full Summary Statistics (application needed)fsa000095NG00161.v1Full Summary Statistics
Sex Differences Resilience: P-values only (open access)fsa000096NG00161.v1P-values only

View the File Manifest for a full list of files released in this dataset.

Consent LevelNumber of Subjects

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Total number of approved DARs: 1
  • Investigator:
    Cruchaga, Carlos
    Washington University School of Medicine
    Project Title:
    The Familial Alzheimer Sequencing (FASe) Project
    Date of Approval:
    May 9, 2024
    Request status:
    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.

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.

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