Genome-wide quantitative trait loci mapping of the human cerebrospinal fluid proteome
Open accessAbstract
Cerebrospinal fluid (CSF) is virtually the only one accessible source of proteins derived from the central nervous system (CNS) of living humans and possibly reflects the pathophysiology of a variety of neuropsychiatric diseases. However, little is known regarding the genetic basis of variation in protein levels of human CSF. We examined CSF levels of 1,126 proteins in 133 subjects and performed a genome-wide association analysis of 514,227 single nucleotide polymorphisms (SNPs) to detect protein quantitative trait loci (pQTLs). To be conservative, Spearman's correlation was used to identify an association between genotypes of SNPs and protein levels. A total of 421 cis and 25 trans SNP-protein pairs were significantly correlated at a false discovery rate (FDR) of less than 0.01 (nominal P < 7.66 × 10-9). Cis-only analysis revealed additional 580 SNP-protein pairs with FDR < 0.01 (nominal P < 2.13 × 10-5). pQTL SNPs were more likely, compared to non-pQTL SNPs, to be a disease/trait-associated variants identified by previous genome-wide association studies. The present findings suggest that genetic variations play an important role in the regulation of protein expression in the CNS. The obtained database may serve as a valuable resource to understand the genetic bases for CNS protein expression pattern in humans.
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This dataset represents an expansion of previous CSF pQTL work in both sample size (n=3,506) and number of proteins (n=6,361).
“This dataset represents an expansion of previous CSF pQTL work10,16–19 in both sample size (n=3,506) and number of proteins (n=6,361).”
From Proteogenomic analysis of human cerebrospinal fluid identifies neurologically relevant regulation and implicates causal proteins for Alzheimer’s disease (2024) · cites this paper as [16]
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The genetic regulation of protein expression in cerebrospinal fluid
Hansson, O., Kumar, A., Janelidze, S., Stomrud, E., Insel, P. S., Blennow, K., Zetterberg, H., Fauman, E., Hedman, Å. K., Nagle, M. W., et al. (2023) · EMBO Molecular Medicine · doi:10.15252/emmm.202216359 · Cited by 56 · see citing works
- Authors
- Sasayama, D., Hattori, K., Ogawa, S., Yokota, Y., Matsumura, R., Teraishi, T., Hori, H., Ota, M., Yoshida, S., & Kunugi, H.
- Affiliations
- Shinshu University (JP)
- National Center of Neurology and Psychiatry (JP)
- National Center Hospital of Neurology and Psychiatry (JP)
- Corresponding author
- Kunugi, Hiroshi
- ORCID
- Venue
- Human Molecular Genetics
- Publisher
- Oxford University Press (OUP)
- Published
- 2016-12-27
- Volume
- 26
- Issue
- 1
- Pages
- ddw366
- ISSN
- 0964-6906 (ISSN-L)
- 1460-2083
- OpenAlex
- W2566053961
- Cited by
- 43
- References
- 2
- Citations by year
- 2025: 1 · 2024: 5 · 2023: 4 · 2022: 8 · 2021: 8 · 2020: 9 · 2019: 4 · 2018: 2 · 2017: 1
- FWCI
- 1.79
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Human Molecular Genetics (publishedVersion)
- PubMed (publishedVersion)
- Institutional Repositories DataBase (IRDB) (submittedVersion, cc-by-nc-nd)
- Funders
- Japan Agency for Medical Research and Development (15dk0310061h0001, 16dk0307062h0001, 24-11)
- National Center of Neurology and Psychiatry (24-11, 25253075)
- Japan Society for the Promotion of Science (15K19719, 26461731)
- 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
- cerebrospinal fluid proteome, protein quantitative trait loci, single nucleotide polymorphisms, neurodegenerative disease biomarkers, human genetic variation
- MeSH terms
- Mental Disorders, Humans, Phenotype, Biomarkers, Genome, Human, Proteome, Polymorphism, Single Nucleotide, Protein Array Analysis, Quantitative Trait Loci, Proteomics, Genome-Wide Association Study
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