NG00157 - Sex-specific genetic predictors of Alzheimer's disease biomarkers

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Description

Cerebrospinal fluid (CSF) levels of amyloid-β 42 (Aβ42) and tau have been evaluated as endophenotypes in Alzheimer’s disease (AD) genetic studies. Although there are sex differences in AD risk, sex differences have not been evaluated in genetic studies of AD endophenotypes.

We performed sex-stratified and sex interaction genetic analyses of CSF biomarkers to identify sex-specific associations. This study leveraged data from seven studies of cognitive aging (Knight ADRC, ADNI1, ADNI2, BIOCARD, Mayo, Sweden, UPENN, UW) with 1,527 males and 1,509 females. CSF Ab42 and t-tau biomarker data were harmonized across cohorts and genotypes underwent standard quality control and imputation (genome build 37). A SNP*sex interaction GWAS evaluated differences in variant associations with pathology by sex and stratified GWAS assessed for associations with neuropathology within males and females separately.

Available Filesets

Name	Accession	Latest Release	Description
Sex Differences CSF: Full Summary Statistics (application needed)	fsa000091	NG00157.v1	Full Summary Statistics
Sex Differences CSF: P-values only (open access)	fsa000092	NG00157.v1	P-values only

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

Consent Level	Number of Subjects
DS-ADRD-IRB-PUB-NPU	NA

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 NG00157.

For investigators using Sex-specific genetic predictors of Alzheimer's disease biomarkers – Deming, et al. 2018 (sa000043) data:

This research was supported in part by K01 AG049164, K12 HD043483, K24 AG046373, HHSN311201600276P, S10 OD023680, R01 AG034962, R01 HL111516, R01 NS100980, R01 AG056534, P30 AG10161, RF1 AG15819, R01 AG17917, R01 AG30146, R01 AG019085, R01 AG15819, R01 AG30146, R01 AG027161, R01 AG021155, R01 AG037639, U01 AG46152, U01 AG006781, U01 AG032984, U01 HG004610, U01 HG006375, U24 AG021886, U24 AG041689, R01 AG044546, P01 AG003991, RF1 AG053303, R01 AG035083, R01 NS085419, and the Alzheimer’s Association (NIRG-11–200110), further supported in part by the Intramural Research Program, NIA, NIH and the Vanderbilt Memory & Alzheimer’s Center. YD is supported by an NIMH training grant (T32MH014877). Support for PDJ was provided by R01 AG048015. SK received support from NIA R03 AG050856, Alzheimer’s Association, Michael J Fox Foundation, and ARUK Biomarkers Across Neurodegenerative Diseases (BAND). MR received support from the German Federal Ministry of Education and Research (BMBF) National Genome Research Network (NGFN) Grant No. 01GS08125 and through the Helmholtz Alliance for Mental Health in an Aging Society (HELMA) Grant No. Ha-15. HZ is a Wallenberg Academy Fellow and is further supported but the Swedish and European Research Councils and the UK Dementia Research Institute. KB holds the Torsten Söderberg professorship at the Royal Swedish Academy of Sciences.

The NACC database is funded by NIA/NIH Grant U01 AG016976. NACC data are contributed by the NIA-funded ADCs: P30 AG019610 (PI Eric Reiman, MD), P30 AG013846 (PI Neil Kowall, MD), P50 AG008702 (PI Scott Small, MD), P50 AG025688 (PI Allan Levey, MD, PhD), P30 AG010133 (PI Andrew Saykin, PsyD), P50 AG005146 (PI Marilyn Albert, PhD), P50 AG005134 (PI Bradley Hyman, MD, PhD), P50 AG016574 (PI Ronald Petersen, MD, PhD), P50 AG005138 (PI Mary Sano, PhD), P30 AG008051 (PI Steven Ferris, PhD), P30 AG013854 (PI M. Marsel Mesulam, MD), P30 AG008017 (PI Jeffrey Kaye, MD), P30 AG010161 (PI David Bennett, MD), P30 AG010129 (PI Charles DeCarli, MD), P50 AG016573 (PI Frank LaFerla, PhD), P50 AG016570 (PI David Teplow, PhD), P50 AG005131 (PI Douglas Galasko, MD), P50 AG023501 (PI Bruce Miller, MD), P30 AG035982 (PI Russell Swerdlow, MD), P30 AG028383 (PI Linda Van Eldik, PhD), P30 AG010124 (PI John Trojanowski, MD, PhD), P50 AG005133 (PI Oscar Lopez, MD), P50 AG005142 (PI Helena Chui, MD), P30 AG012300 (PI Roger Rosenberg, MD), P50 AG005136 (PI Thomas Grabowski, MD, PhD), P50 AG033514 (PI Sanjay Asthana, MD, FRCP), and P50 AG005681 (PI John Morris, MD).

Samples from the National Cell Repository for Alzheimer’s Disease (NCRAD), which receives government support under a cooperative agreement grant (U24 AG21886) awarded by the National Institute on Aging (NIA), were used in this study. We thank contributors who collected samples used in this study, as well as patients and their families, whose help and participation made this work possible; 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-AG041689–01).

Total number of approved DARs: 1

Investigator:
Cruchaga, Carlos
Institution:
Washington University School of Medicine
Project Title:
The Familial Alzheimer Sequencing (FASe) Project
Date of Approval:
May 9, 2024
Request status:
Approved
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.

NG00157 – Sex-specific genetic predictors of Alzheimer’s disease biomarkers – Deming et al., 2018

Overview

Description

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Acknowledgement

Acknowledgment statement for any data distributed by NIAGADS:

For investigators using any data from this dataset:

For investigators using Sex-specific genetic predictors of Alzheimer's disease biomarkers – Deming, et al. 2018 (sa000043) data:

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Approved Users

Total number of samples: 0