Overview
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Description
The majority of metabolite quantitative trait loci (MQTL) were discovered using blood and urine tissues. This data represents the first large-scaled CSF and brain study, which will advance the understanding of genetic regulation in metabolite levels by identifying novel MQTLs. For 440 CSF metabolites, meta-analysis was performed using 2,311 non-related non-Hispanic white individuals from Knight-ADRC, DIAN, ADNI, Fundacio ACE, and Barcelona-1, together with a previous CSF study (N=291). For 962 brain metabolites, meta-analysis in the European ancestry was performed using 405 WashU individuals, 415 ROSMAP individuals, and 196 MAYO individuals. The healthy control individuals accounted for 35% of CSF and 25% of brain individuals in the studies, and the rest individuals either was diagnosed with a type of dementia or showed sign of neurodegeneration. The dataset consists of GWAS summary statistics (GRCh38) calculated for each of 440 CSF metabolites and 962 brain metabolites measured by Metabolon (HD4) platform.
Available Filesets
| Name | Accession | Latest Release | Description |
|---|---|---|---|
| KGAD MQTL: Brain metabolite data P-values only (open access) | fsa000049 | NG00131.v1 | Brain metabolite data P-values only |
| KGAD MQTL: Full brain metabolite data (application needed) | fsa000050 | NG00131.v1 | Full brain metabolite data |
| KGAD MQTL: CSF metabolite data P-values only (open access) | fsa000051 | NG00131.v1 | CSF metabolite data P-values only |
| KGAD MQTL: Full CSF metabolite data (application needed) | fsa000052 | NG00131.v1 | Full CSF metabolite data |
View the File Manifest for a full list of files released in this dataset.
Related Studies
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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 NG00131.
For investigators using Large scale pQTL and mQTL atlas in brain and CSF (sa000031) data:
For use of data in Large scale CSF proteogenomic atlas (ng00130):
We thank all the participants and their families, as well as the many involved institutions and their staff.
This work was supported by grants from the National Institutes of Health (R01AG044546 (CC), P01AG003991 (CC, JCM), RF1AG053303 (CC), RF1AG058501 (CC), U01AG058922 (CC), RF1AG074007 (YJS), R00AG062723 (LI), P30 AG066515 (TWC, MDG), the Chan Zuckerberg Initiative (CZI), the Michael J. Fox Foundation (LI, CC), the Department of Defense (LI- W81XWH2010849), the Alzheimer’s Association Zenith Fellows Award (ZEN-22-848604, awarded to CC), and the Bright Focus Foundation (A2021033S, LI).
GSK provided funding to support the analyses performed in this study.
The recruitment and clinical characterization of research participants at Washington University were supported by NIH P30AG066444 (JCM), P01AG03991 (JCM), and P01AG026276 (JCM).
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/) and the Departments of Neurology and Psychiatry at Washington University School of Medicine.
Data collection and sharing for this project was supported by The Dominantly Inherited Alzheimer Network (DIAN, U19AG032438) funded by the National Institute on Aging (NIA), the Alzheimer’s Association (SG-20- 690363-DIAN), the German Center for Neurodegenerative Diseases (DZNE), Raul Carrea Institute for Neurological Research (FLENI), Partial support by the Research and Development Grants for Dementia from Japan Agency for Medical Research and Development, AMED, the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), Spanish Institute of Health Carlos III (ISCIII), Canadian Institutes of Health Research (CIHR), Canadian Consortium of Neurodegeneration and Aging, Brain Canada Foundation, and Fonds de Recherche du Québec Santé.
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; 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). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
For use of data in Large scale genetics study on CSF and brain metabolite levels (ng00131):
We thank all the participants and their families, as well as the many involved institutions and their staff.
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/)and the Departments of Neurology and Psychiatry at Washington University School of Medicine.
DIAN resources: Data collection and sharing for this project was supported by The Dominantly Inherited Alzheimer Network (DIAN, U19AG032438) funded by the National Institute on Aging (NIA), the Alzheimer’s Association (SG-20-690363-DIAN), the German Center for Neurodegenerative Diseases (DZNE), Raul Carrea Institute for Neurological Research (FLENI), Partial support by the Research and Development Grants for Dementia from Japan Agency for Medical Research and Development, AMED, the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), Spanish Institute of Health Carlos III (ISCIII), Canadian Institutes of Health Research (CIHR), Canadian Consortium of Neurodegeneration and Aging, Brain Canada Foundation, and Fonds de Recherche du Québec Santé. This manuscript has been reviewed by DIAN Study investigators for scientific content and consistency of data interpretation with previous DIAN Study publications. We acknowledge the altruism of the participants and their families and contributions of the DIAN research and support staff at each of the participating sites for their contributions to this study.
ADNI acknowledgement: 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; 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). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
AMP-AD Acknowledgements: The results published here are in whole or in part based on data obtained from the AD Knowledge Portal (https://adknowledgeportal.org). This work was done as part of the National Institute on Aging's Accelerating Medicines Partnership Program for Alzheimer's Disease (AMP-AD) and was supported by NIH grants 1U19AG063744, 1R01AG069901-01A1, U01AG061357, P30AG10161, P30AG72975, R01AG15819, R01AG17917, U01AG46152, U01AG61356, RF1AG058942, RF1AG059093, and U01AG061359. The Religious Orders and the Rush Memory and Aging studies were supported by the National Institute on Aging grants P30AG10161, R01AG15819, R01AG17917, U01AG46152, and U01AG61356. The NIA also supported the Alzheimer Disease Metabolomics Consortium, which is a part of the NIA's national initiatives AMP-AD and M2OVE-AD (R01 AG046171, RF1 AG051550, and 3U01 AG061359-02S1). We thank the participants of ROS and MAP for their essential contributions and the gifts of their brains to these projects. All subjects gave informed consent. The Mayo Clinic samples are part of the RNAseq study data led by Dr. Nilüfer Ertekin-Taner, Mayo Clinic, Jacksonville, FL as part of the multi-PI U01 AG046139 (MPIs Golde, Ertekin-Taner, Younkin, Price). Samples were provided from the following sources: The Mayo Clinic Brain Bank. Data collection was supported through funding by NIA grants P50 AG016574, R01 AG032990, U01 AG046139, R01 AG018023, U01 AG006576, U01 AG006786, R01 AG025711, R01 AG017216, R01 AG003949, NINDS grant R01 NS080820, CurePSP Foundation, and support from Mayo Foundation.
ACE Alzheimer Center acknowledgement: A. Cano received support from the Spanish Ministry of Science, Innovation and Universities under the grant Juan de la Cierva (FJC2018-036012-I) and Instituto de Salud Carlos III (ISCIII) under the grant Sara Borrell (CD22/00125). AC is also funded by the Spanish Ministry of Science and Innovation, Proyectos de Generación de Conocimiento (PID2021-122473OA-I00). P. García-González is supported by CIBERNED employment plan (CNV-304-PRF-866). Authors acknowledge the support of the Acción Estratégica en Salud, integrated in the Spanish National R + D + I Plan and financed by ISCIII Subdirección General de Evaluación and the Fondo Europeo de Desarrollo Regional (FEDER “Una manera de hacer Europa") grants PI17/01474, PI19/00335, PI22/01403 and PI22/00258. A.R. is also supported by ISCIII national grant PMP22/00022, funded by the European Union (NextGenerationEU). The support of CIBERNED (ISCIII) under the grants CB06/05/2004 and CB18/05/00010. The support from PREADAPT project, Joint Program for Neurodegenerative Diseases (JPND) grant Nº AC19/00097, from HARPONE project, Agency for Innovation and Entrepreneurship (VLAIO) grant Nº PR067/21, from ADAPTED project, the EU/EFPIA Innovative Medicines Initiative Joint Undertaking Grant Nº 115975, and from DESCARTES project, German Research Foundation (DFG). The support of Fundación bancaria “La Caixa” and Grífols SA (GR@ACE project).
Wisconsin Acknowledgement: Research reported in this publication was supported by the National Institute on Aging of the National Institutes of Health under Award Number R01AG054047.
Approved Users
- Investigator:Cruchaga, CarlosInstitution:Washington University School of MedicineProject Title:The Familial Alzheimer Sequencing (FASe) ProjectDate of Approval:May 9, 2024Request 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:Greicius, MichaelInstitution:Stanford University School of MedicineProject Title:Examining Genetic Associations in Neurodegenerative DiseasesDate of Approval:December 19, 2024Request status:ApprovedResearch use statements:Show statementsTechnical Research Use Statement:We are studying the effects of rare (minor allele frequency < 5%) genetic variants on the risk of developing late-onset Alzheimer’s Disease (AD). We are interested in variants that have a protective effect in subjects who are at an increased genetic risk, or variants that lead to multiple dementias. Our aim is to identify any genetic variants that are present in the “case” group but not the “AD control” groups for both types of variants. The raw data we receive will be annotated to identify SNP locations and frequencies using existing databases such as 1,000 Genomes. We will filter the data based on genetic models such as compounded heterozygosity, recessive and dominant models to identify different types of variants.Non-Technical Research Use Statement:Current genetic understanding of Alzheimer’s Disease (AD) does not fully explain its heritability. The APOE4 allele is a well-established risk factor for the development of Alzheimer’s Disease (AD). However, some individuals who carry APOE4 remain cognitively healthy until advanced ages. Additionally, the cause of mixed dementia pathology development in individuals remains largely unexplained. We aim to identify genetic factors associated with these “protected” and mixed pathology phenotypes.
- Investigator:Wainberg, MichaelInstitution:Sinai Health SystemProject Title:Uncovering the causal genetic variants, genes and cell types underlying brain disordersDate of Approval:September 5, 2024Request status:ApprovedResearch use statements:Show statementsTechnical Research Use Statement:We propose a multifaceted approach to elucidate and interpret genetic risk factors for Alzheimer's disease. First, we propose to perform a whole-genome sequencing meta-analysis of the Alzheimer's Disease Sequencing Project with the UK Biobank and All of Us to associate rare coding and non-coding variants with Alzheimer's disease and related dementias. We will explore a variety of case definitions in the UK Biobank and All of Us, including those based on ICD codes from electronic medical records (inpatient, primary care and/or death), self-report of Alzheimer's disease or Alzheimer's disease and related dementias, and/or family history of Alzheimer's disease or Alzheimer's disease and related dementias. We will perform single-variant, coding-variant burden, and non-coding variant burden tests using the REGENIE genome-wide association study toolkit.Second, we propose to develop statistical and machine learning models that can effectively infer (“fine-map”) the causal gene(s), variant(s), and cell type(s) underlying each association we find, as well as associations from existing genome-wide association studies and other Alzheimer's- and aging-related cohorts found in NIAGADS. In particular, we propose to improve causal gene identification by incorporating knowledge of gene function as a complement to functional genomics. For instance, we plan to develop improved methods for inferring biological networks, particularly from single-cell data, and integrate these networks with the results of the non-coding associations from our first aim to fine-map causal genes. To fine-map causal variants and cell types, we plan to integrate the associations from our first aim with single-nucleus chromatin accessibility data from postmortem brain cohorts to simultaneously infer which variant(s) are causal for each discovered locus and which cell type(s) they act through.Non-Technical Research Use Statement:We have a comprehensive plan to understand and explain the genetic factors that contribute to Alzheimer's disease. Our approach involves two main steps.First, we'll analyze genetic information from large research databases to identify rare genetic changes associated with Alzheimer's and related memory disorders. We'll look at both specific changes in genes and other parts of the genetic code. We'll use data from different studies and combine them to get a clearer picture.Second, we'll create advanced computer models that can help us figure out which specific genes, genetic changes, and cell types are responsible for these associations. This will help us pinpoint the most important factors contributing to Alzheimer's disease. We'll also analyze data from previous studies to build a more complete understanding of these genetic links.
- Investigator:Zhao, JinyingInstitution:University of FloridaProject Title:Identifying novel biomarkers for human complex diseases using an integrated multi-omics approachDate of Approval:November 21, 2023Request status:ExpiredResearch use statements:Show statementsTechnical Research Use Statement:GWAS, WES and WGS have identified many genes associated with Alzheimer’s Dementia (AD) and its related traits. However, the identified genes thus far collectively explain only a small proportion of disease heritability, suggesting that more genes remained to be identified. Moreover, there is a clear gender and ethnic disparity for AD susceptibility, but little research has been done to identify gender- and ethnic-specific variants associated with AD. Of the many challenges for deciphering AD pathology, lacking of efficient and power statistical methods for genetic association mapping and causal inference represents a major bottleneck. To tackle this challenge, we have developed a set of novel statistical and bioinformatics approaches for genetic association mapping and multi-omics causation inference in large-scale ethnicity-specific epidemiological studies. The goal of this project is to leverage the multi-omics and clinical data archived by the ADSP, ADNI, ADGC as well as other AD-related data repositories to identify novel genes and molecular markers for AD. Specifically, we will (1) validate our novel methods for identifying novel risk and protective genomic variants and multi-omics causal pathways of AD; (2) identify novel ethnicity- and gender-specific genes and molecular causal pathways of AD. We will share our results, statistical methods and computational software with the scientific community.Non-Technical Research Use Statement:Although many genes have been associated with Alzheimer’s Dementia (AD), these genes altogether explain only a small fraction of disease etiology, suggesting more genes remained to be identified. Of the many challenges for deciphering AD pathology, lacking of power statistical methods represents a major bottleneck. To tackle this challenge, we have developed a set of novel statistical and bioinformatics approaches for genetic association mapping and multi-omics causation inference in large-scale ethnicity-specific epidemiological studies. The goal of this project is to leverage the rich genetic and other omic data along with clinical data archived by the ADSP, ADNI, ADGC as well as other AD-related data repositories to identify novel genes and molecular markers for AD. Such results will enhance our understanding of AD pathogenesis and may also serve as biomarkers for early diagnosis and therapeutic targets.