The pQTL summary statistics are available in the “Open Access Dataset” tab.
To access the proteomic data, please log into DSS and submit an application.
Within the application, add this dataset (accession NG00102) in the “Choose a Dataset” section.
Once approved, you will be able to log in and access the data within the DARM portal.
Understanding the tissue-specific genetic controls of protein levels is essential to uncover mechanisms of post-transcriptional gene regulation. We previously generated a genomic atlas of protein levels in three tissues relevant to neurological disorders (brain, cerebrospinal fluid and plasma) by profiling thousands of proteins from participants with and without Alzheimer’s disease. We now enhanced this work by analyzing more proteins (1,300 versus 1,079) and an almost twofold increase in high-quality imputed genetic variants (8.4 million versus 4.4 million) by using TOPMed reference panel. We identified 38 genomic regions associated with 43 proteins in brain, 150 regions associated with 247 proteins in cerebrospinal fluid, and 95 regions associated with 145 proteins in plasma. Compared to our previous study, this study newly identified 12 loci in brain, 30 loci in cerebrospinal fluid, and 22 loci in plasma. cis-pQTLs were more likely to be tissue shared, but trans-pQTLs tended to be tissue specific. Between 48.0% and 76.6% of pQTLs did not co-localize with expression, splicing, DNA methylation or histone acetylation QTLs. Using Mendelian randomization, we nominated proteins implicated in neurological diseases, including Alzheimer’s disease, Parkinson’s disease and stroke. This first multi-tissue study will be instrumental to map signals from genome-wide association studies onto functional genes, to discover pathways and to identify drug targets for neurological diseases.
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
Sample Summary per Data Type
|Sample Set||Accession||Data Type||Number of Samples|
|Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders||snd10048||Proteomic||1157|
|KGAD Proteomics: pQTL summary statistics and protein annotations (open access)||fsa000065||NG00102.v1||pQTL summary statistics and protein annotations|
|KGAD Proteomics: Proteomics, protein annotations, and QC documents (application needed)||fsa000066||NG00102.v1||Proteomics, protein annotations, and QC documents|
View the File Manifest for a full list of files released in this dataset.
Provided in this dataset is a set of multi-tissue proteomic data that underwent a process of quality control measures by the Cruchaga Lab at Washington University in St. Louis, as well as pQTL summary statistics. From 1157 subjects, 1300 protein analytes were measured for 328 brain samples, 869 protein analytes were measured for 770 CSF samples, and 953 protein analytes were measured for 500 plasma samples on the SomaLogic SomaScan 1.3K platform at the Washington University Neurogenomics and Informatics Center.
|Sample Set||Accession Number||Number of Subjects|
|Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders||snd10048||1157|
|Consent Level||Number of Subjects|
Visit the Data Use Limitations page for definitions of the consent levels above.
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 NG00102.
For investigators using Charles F. and Joanne Knight Alzheimer’s Disease Research Center (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.
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/) and the Departments of Neurology and Psychiatry at Washington University School of Medicine.
Yang C, et al. Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders. Nat Neurosci. 2021 Sep;24(9):1302-1312. doi: 10.1038/s41593-021-00886-6. PMID: 34239129; PMCID: PMC8521603. PubMed link