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TOPMed imputed genomics enhances genomic atlas of the human proteome in brain, cerebrospinal fluid, and plasma

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Yi, Heng, Yang, Qijun, Repaci, Charlie, Lee, Cheolmin Matthew, Heo, Gyujin, Timsina, Jigyasha, Gorijala, Priyanka, Yang, Chengran +4 more·2024·Scientific Data
DOI 10.1038/s41597-024-03140-3PMC PMC11021418
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Abstract

Comprehensive expression quantitative trait loci studies have been instrumental for understanding tissue-specific gene regulation and pinpointing functional genes for disease-associated loci in a tissue-specific manner. Compared to gene expressions, proteins more directly affect various biological processes, often dysregulated in disease, and are important drug targets. We previously performed and identified tissue-specific protein quantitative trait loci in brain, cerebrospinal fluid, and plasma. We now enhance 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. Our improved genomic atlas uncovers the genetic control of protein regulation across multiple tissues. These resources are accessible through the Online Neurodegenerative Trait Integrative Multi-Omics Explorer for use by the scientific community.

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  • Additional brain pQTL studies exist but generally have smaller sample sizes or analyze fewer proteins.

    “Additional brain pQTL studies exist but generally have smaller sample sizes or analyze fewer proteins (15, 16).”

    From Low overlap of plasma and CSF protein quantitative trait loci affects protein discovery for neurological disease (2026) · cites this paper as [16]

    Table 1 (Methods, Multi-tissue pQTL mapping; page not specified) compares this study’s brain analysis with one previous study: 378 versus 343 subjects and 1,300 versus 1,079 proteins. That supports the existence of an additional, smaller brain pQTL study, but the paper does not establish that other brain pQTL studies generally have smaller samples or analyze fewer proteins. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • We extended and validated these differences in larger plasma pQTL datasets [Fenland and deCODE Genetics] and brain pQTL resources.

    “We extended and validated these differences in larger plasma pQTL datasets [Fenland (9) and deCODE Genetics (10)] and brain pQTL resources (15, 16).”

    From Low overlap of plasma and CSF protein quantitative trait loci affects protein discovery for neurological disease (2026) · cites this paper as [16]

  • Compared with the brain-based SOMAscan analysis, 14 of 621 (2.3%) of the analyzed CSF and 15 of 536 (2.8%) of the plasma index variants showed opposite directionality with P<0.05.

    “Compared with the brain-based SOMAscan analysis (16), 14 of 621 (2.3%) of the analyzed CSF and 15 of 536 (2.8%) of the plasma index variants showed opposite directionality with P < 0.05, even with fewer than 400 samples (table S3).”

    From Low overlap of plasma and CSF protein quantitative trait loci affects protein discovery for neurological disease (2026) · cites this paper as [16]

    The Methods (Proteomic data; page unavailable) confirm that the paper used SOMAscan data from brain, CSF, and plasma. But the passages on multi-tissue pQTL mapping and the additional searches do not report a comparison of index-variant directionality or the claimed 14/621 and 15/536 counts. The attributed result therefore cannot be verified from our copy. (LLM, conf=0.77)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Yi, H., Yang, Q., Repaci, C., Lee, C. M., Heo, G., Timsina, J., Gorijala, P., Yang, C., Budde, J., Wang, L., Cruchaga, C., & Sung, Y. J.
Affiliations
Washington University in St. Louis (US)
Corresponding author
Sung, Yun Ju
ORCID
Venue
Scientific Data
Publisher
Springer Science and Business Media LLC
Published
2024-04-16
Volume
11
Issue
1
Pages
387-387
ISSN
2052-4463 (ISSN-L)
DOI
10.1038/s41597-024-03140-3
PMC
PMC11021418
OpenAlex
W4394856050
Cited by
5
References
0
Citations by year
2026: 3 · 2025: 1 · 2024: 1
Type
data paper
Language
en
Open access
Licence
Locations
  • Scientific Data (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Digital Commons@Becker (Washington University School of Medicine) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Hope Center for Neurological Disorders
  • National Institutes of Health
  • Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University in St. Louis (P01AG003991)
  • National Institute on Aging (RF1AG053303, R01AG044546, P01AG003991, RF1AG074007, U01AG058922, RF1AG058501)
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, expression quantitative trait loci, human proteome, brain, cerebrospinal fluid, plasma, tissue-specific gene regulation
MeSH terms
Brain, Cerebrospinal Fluid, Gene Expression Regulation, Humans, Phenotype, Plasma, Proteome, Genomics, Quantitative Trait Loci, Genome-Wide Association Study

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