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Cited by 72Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and AnimalsGenetic and phenotypic traits in livestock

Genome-wide pQTL analysis of protein expression regulatory networks in the human liver

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He, Bing, Shi, Jian, Wang, Xinwen, Jiang, Hui, Zhu, Hao-Jie·2020·BMC Biology
DOI 10.1186/s12915-020-00830-3PMC PMC7418398
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Abstract

Background: Previous expression quantitative trait loci (eQTL) studies have identified thousands of genetic variants to be associated with gene expression at the mRNA level in the human liver. However, protein expression often correlates poorly with mRNA levels. Thus, protein quantitative trait loci (pQTL) study is required to identify genetic variants that regulate protein expression in human livers. Results: We conducted a genome-wide pQTL study in 287 normal human liver samples and identified 900 local pQTL variants and 4026 distant pQTL variants. We further discovered 53 genome hotspots of pQTL variants. Transcriptional region mapping analysis showed that 1133 pQTL variants are in transcriptional regulatory regions. Genomic region enrichment analysis of the identified pQTL variants revealed 804 potential regulatory interactions among 595 predicted regulators (e.g., non-coding RNAs) and 394 proteins. Moreover, pQTL variants and trait-variant integration analysis implied several novel mechanisms underlying the relationships between protein expression and liver diseases, such as alcohol dependence. Notably, over 2000 of the identified pQTL variants have not been reported in previous eQTL studies, suggesting extensive involvement of genetic polymorphisms in post-transcriptional regulation of protein expression in human livers. Conclusions: We have partially established protein expression regulation networks in human livers and generated a wealth of pQTL data that could serve as a valuable resource for the scientific community.

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  • Most pQTL studies have been conducted using serum or plasma measurements from blood samples, and only a limited number of studies has involved examination of liver and brain tissue-specific pQTLs.

    “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 [88]

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Cited by

Sorted by citations

Authors
He, B., Shi, J., Wang, X., Jiang, H., & Zhu, H.-J.
Affiliations
University of Michigan (US)
Corresponding author
Zhu, Hao-Jie
ORCID
Venue
BMC Biology
Publisher
Springer Science and Business Media LLC
Published
2020-08-10
Volume
18
Issue
1
Pages
97-97
ISSN
1741-7007 (ISSN-L)
DOI
10.1186/s12915-020-00830-3
PMC
PMC7418398
OpenAlex
W3048578375
Cited by
72
References
7
Citations by year
2026: 11 · 2025: 14 · 2024: 13 · 2023: 15 · 2022: 12 · 2021: 6 · 2020: 1
FWCI
4.91
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Biology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Deep Blue (University of Michigan) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Foundation for the National Institutes of Health (R01HL126969, R01HD093612)
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, protein expression regulation, human liver, post-transcriptional regulation, transcriptional regulatory sequences, non-coding RNA, regulatory networks, alcoholism, liver disease, genetic polymorphism
MeSH terms
Gene Expression Regulation, Humans, Liver, Proteome, Quantitative Trait Loci, Gene Regulatory Networks, Genome-Wide Association Study

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