The DOI matches its Crossref record; 89 of the 89 references it cites were found in a registry or an index.
Genetic control of the human brain proteome
Open accessAbstract
Alteration of protein abundance and conformation are widely believed to be the hallmark of neurodegenerative diseases. Yet relatively little is known about the genetic variation that controls protein abundance in the healthy human brain. The genetic control of protein abundance is generally thought to parallel that of RNA expression, but there is little direct evidence to support this view. Here, we performed a large-scale protein quantitative trait locus (pQTL) analysis using single nucleotide variants (SNVs) from whole-genome sequencing and tandem mass spectrometry-based proteomic quantification of 12,691 unique proteins (7,901 after quality control) from the dorsolateral prefrontal cortex (dPFC) in 144 cognitively normal individuals. We identified 28,211 pQTLs that were significantly associated with the abundance of 864 proteins. These pQTLs were compared to dPFC expression quantitative trait loci (eQTL) in cognitive normal individuals (n=169; 81 had protein data) and a meta-analysis of dPFC eQTLs (n=1,433). We found that strong pQTLs are generally only weak eQTLs, and that the majority of strong eQTLs are not detectable pQTLs. These results suggest that the genetic control of mRNA and protein abundance may be substantially distinct and suggests inference concerning protein abundance made from mRNA in human brain should be treated with caution.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“No statistical methods were used to predetermine sample sizes within each tissue type, but our sample sizes are much larger or comparable to those in previous publications13,14,16.”
From Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders (2021) · cites this paper as [16]
The cited source says what the claim attributes to it.
In the citing paper
“Second, most pQTL studies have been conducted using serum or plasma measurements from blood samples4,6,8,51 and only a limited number of studies has involved the examination of liver and brain tissue–specific pQTLs88,89.”
From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as [89]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“CSF Siglec-9, plasma sICAM-1, and brain PPAC have been replicated in other studies16,29,31,32 using a different proteomic approach, which indicates that these findings may not be driven by platform.”
From Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders (2021) · cites this paper as [16]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“For example, individuals carrying the APOE ε4 allele display distinct proteo-metabolomic signatures in lipid transport and immune pathways206,207.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [207]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
PLINK: a tool set for whole-genome association and population-based linkage analyses.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/519795 · Cited by 34,089
McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E. M. (1984) · Neurology · doi:10.1212/wnl.34.7.939 · Cited by 27,915
The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data
McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., et al. (2010) · Genome Research · doi:10.1101/gr.107524.110 · Cited by 27,895
Robust enumeration of cell subsets from tissue expression profiles
Newman, A. M., Liu, C. L., Green, M. R., Gentles, A. J., Feng, W., Xu, Y., Hoang, C. D., Diehn, M., & Alizadeh, A. A. (2015) · Nature Methods · doi:10.1038/nmeth.3337 · Cited by 13,224
A framework for variation discovery and genotyping using next-generation DNA sequencing data
DePristo, M. A., Banks, E., Poplin, R., Garimella, K. V., Maguire, J. R., Hartl, C., Philippakis, A. A., del Angel, G., Rivas, M. A., Hanna, M., et al. (2011) · Nature Genetics · doi:10.1038/ng.806 · Cited by 11,456
The Clinical Dementia Rating (CDR): Current version and scoring rules
Morris, J. C. (1993) · Neurology · doi:10.1212/wnl.43.11.2412-a · Cited by 9,882
Statistical significance for genomewide studies
Storey, J. D., & Tibshirani, R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1530509100 · Cited by 9,692
Global quantification of mammalian gene expression control
Schwanhäusser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., Chen, W., & Selbach, M. (2011) · Nature · doi:10.1038/nature10098 · Cited by 6,566
Cited by
Johnson, E. C. B., Carter, E. K., Dammer, E. B., Duong, D. M., Gerasimov, E. S., Liu, Y., Liu, J., Betarbet, R., Ping, L., Yin, L., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-021-00999-y · Cited by 561
Self-citationYang, C., Farias, F. H. G., Ibanez, L., Suhy, A., Sadler, B., Fernandez, M. V., Wang, F., Bradley, J. L., Eiffert, B., Bahena, J. A., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00886-6 · Cited by 290
Ren, F., Wei, J., Chen, Q., Hu, M., Yu, L., Mi, J., Zhou, X., Qin, D., Wu, J., & Wu, A. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.07.030 · Cited by 97
Integrated analysis of the aging brain transcriptome and proteome in tauopathy
Mangleburg, C. G., Wu, T., Yalamanchili, H. K., Guo, C., Hsieh, Y.-C., Duong, D. M., Dammer, E. B., De Jager, P. L., Seyfried, N. T., Liu, Z., et al. (2020) · Molecular Neurodegeneration · doi:10.1186/s13024-020-00405-4 · Cited by 75
Self-citationLarge‐scale deep proteomic analysis in Alzheimer's disease brain regions across race and ethnicity
Seifar, F., Fox, E. J., Shantaraman, A., Liu, Y., Dammer, E. B., Modeste, E., Duong, D. M., Yin, L., Trautwig, A. N., Guo, Q., et al. (2024) · Alzheimer's & Dementia · doi:10.1002/alz.14360 · Cited by 52
Self-citationCary, G. A., Wiley, J. C., Gockley, J., Keegan, S., Amirtha Ganesh, S. S., Heath, L., Butler, R. R., Mangravite, L. M., Logsdon, B. A., Longo, F. M., et al. (2024) · Alzheimer's & Dementia: Translational Research & Clinical Interventions · doi:10.1002/trc2.12461 · Cited by 49
Self-citationINPP5D modulates TREM2 loss-of-function phenotypes in a β-amyloidosis mouse model
Iguchi, A., Takatori, S., Kimura, S., Muneto, H., Wang, K., Etani, H., Ito, G., Sato, H., Hori, Y., Sasaki, J., et al. (2023) · iScience · doi:10.1016/j.isci.2023.106375 · Cited by 37
Proprotein convertase subtilisin/kexin type 9 (PCSK9) in the central nervous system
Bell, A. S., Wagner, J., Rosoff, D. B., & Lohoff, F. W. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105155 · Cited by 36
Genetic determinants of plasma protein levels in the Estonian population
Kalnapenkis, A., Jõeloo, M., Lepik, K., Kukuškina, V., Kals, M., Alasoo, K., Estonian Biobank Research Team, Metspalu, A., Milani, L., Nelis, M., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-57966-3 · Cited by 11
Metadata
- Authors
- Robins, C., Wingo, A. P., Fan, W., Duong, D. M., Meigs, J., Gerasimov, E. S., Dammer, E. B., Cutler, D. J., Jager, P. L. D., Bennett, D. A., Lah, J. J., Levey, A. I., Seyfried, N. T., & Wingo, T. S.
- Affiliations
- Emory University (US)
- Columbia University Irving Medical Center (US)
- Rush University Medical Center (US)
- Corresponding author
- Seyfried, Nicholas T.
- Wingo, Thomas S.
- ORCID
- Venue
- The American Journal of Human Genetics
- Publisher
- openRxiv
- Published
- 2019-10-24
- Volume
- 108
- Issue
- 3
- Pages
- 400-410
- ISSN
- 0002-9297 (ISSN-L)
- PMC
- PMC8008492
- OpenAlex
- W3128936277
- Cited by
- 104
- References
- 89
- Citations by year
- 2026: 14 · 2025: 19 · 2024: 29 · 2023: 14 · 2022: 18 · 2021: 7 · 2020: 3
- FWCI
- 10.03
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The American Journal of Human Genetics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- U.S. Department of Veterans Affairs (BX003853, I01BX003853)
- Michael J. Fox Foundation for Parkinson's Research
- Illinois Department of Public Health (R01AG017917, P30AG10161, R01AG15819, R01AG17917, R01AG30146, R01AG015819)
- Arizona Department of Health Services
- Translational Genomics Research Institute
- Arizona Biomedical Research Commission
- National Institutes of Health (T32 NS00748018, NS007480, AG057195, AG025688)
- National Institute on Aging (R01AG017917, P30-AG 10161, R01AG056533, U01 AG32984, R01AG030146, AG057195, R01 AG30146, R01‐AG17917, R01 AG15819, R01-AG36836, AG 025688, U01AG057195, U01‐AG61356, R01AG036836, U01AG046152, R01AG036042, R01AG015819, R56AG060757, P30AG010161, U01AG032984, U01AG061357, U01AG061356, P30AG019610, R56AG062256, RF1AG051633, AG056533, R56AG062633, P50AG025688, U01‐AG46152, AG051633, P30AG072980, P30AG066511)
- National Institute of Mental Health (U01MH115484)
- National Institute of Neurological Disorders and Stroke (T32NS007480, U24NS072026)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Keywords
- protein quantitative trait loci, brain proteome, dorsolateral prefrontal cortex, postmortem brain, expression quantitative trait loci, non-synonymous variants, protein abundance, evolutionary constraints, protein-protein interaction
- MeSH terms
- Autopsy, Brain, Female, Gene Expression Regulation, Genotype, Humans, Male, RNA, Messenger, Genetic Variation, Proteome, Quantitative Trait Loci, Proteomics, Genome-Wide Association Study, Transcriptome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.