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Mapping the proteo-genomic convergence of human diseases

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Pietzner, Maik, Wheeler, Eleanor, Carrasco-Zanini, Julia, Cortes, Adrian, Koprulu, Mine, Wörheide, Maria A., Oerton, Erin, Cook, James +13 more·2021·Science
DOI 10.1126/science.abj1541PMC PMC9904207
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Abstract

Detangling gene-disease connections: Many diseases are at least partially due to genetic causes that are not always understood or targetable with specific treatments. To provide insight into the biology of various human diseases as well as potential leads for therapeutic development, Pietzner et al . undertook detailed, genome-wide proteogenomic mapping. The authors analyzed thousands of connections between potential disease-associated mutations, specific proteins, and medical conditions, thereby providing a detailed map for use by future researchers. They also supplied some examples in which they applied their approach to medical contexts as varied as connective tissue disorders, gallstones, and COVID-19 infections, sometimes even identifying single genes that play roles in multiple clinical scenarios. —YN

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Crossref · 10.1126/science.abj1541

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  • Integrating human genomics and proteomics can help elucidate disease mechanisms, identify clinical biomarkers and discover drug targets.

    “Integrating human genomics and proteomics can help elucidate disease mechanisms, identify clinical biomarkers and discover drug targets1–4.”

    From Rare variant associations with plasma protein levels in the UK Biobank (2023) · cites this paper as [3]

    The paper’s Discussion describes the integration of proteomic and genomic data as providing mechanistic understanding and identifying potential causal disease genes. Its introductory passage also links the approach to drug-target discovery. The passages found do not say that this integration identifies clinical biomarkers; the Results mention biomarkers measured in biobanks, but not their discovery through this approach. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Most pQTLs to date have been discovered via genome-wide association studies, which predominantly focus on common variants.

    “To date, most pQTLs have been discovered via genome-wide association studies (GWASs), which predominantly focus on common variants3,5–8.”

    From Rare variant associations with plasma protein levels in the UK Biobank (2023) · cites this paper as [3]

    The paper describes its own genome–proteome-wide association study as identifying pQTLs (Abstract) and says it concentrated on common genetic variation (Discussion; page unavailable). It does not establish the broader claim that most pQTLs discovered to date came from GWASs. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • PAVs may result in pQTL associations that reflect differential probe binding to alternate isoforms rather than differential protein abundances.

    “Nevertheless, interpreting cis- and trans-pQTL remains complicated, as PAVs may result in pQTL associations that reflect differential probe binding to alternate isoforms12,17–19 rather than differential protein abundances.”

    From Cross-ancestry comparison of aptamer and antibody protein measures (2026) · cites this paper as [19]

    The Results section on cis and trans genetic architecture says PAVs can affect a protein’s binding epitope and that conformation, rather than plasma abundance, may explain some genetic effects. A separate Results section on gene expression and splicing suggests that alternative protein isoforms can have different affinities for the assay reagent. The paper does not link PAVs specifically to differential binding to alternate isoforms. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Platform-concordant cis-pQTL signals were most likely to contain a GTEx v8 eQTL lead variant for the protein-encoding gene in the credible set, compared to platform-specific or platform-discordant signals.

    “Consistent with prior reports17,19, we found that platform-concordant cis-pQTL signals were most likely to contain a GTEx v8 eQTL lead variant for the protein-encoding gene in the credible set, compared to platform-specific or platform-discordant signals.”

    From Cross-ancestry comparison of aptamer and antibody protein measures (2026) · cites this paper as [19]

    The Abstract reports that 64% of pQTLs were consistent across the SomaScan and Olink platforms, and the Results section ‘Shared genetic architecture with gene expression and splicing’ reports GTEx v8 eQTL colocalization for cis-pQTLs (page numbers unavailable). Neither passage compares platform-concordant, platform-specific, and platform-discordant signals for the likelihood that a credible set contains the protein-encoding gene’s eQTL lead variant. The cited paper supports the broader context, but not the specific comparison attributed to it. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • The pleiotropic loci included well-established, previously identified pleiotropic loci such as MHC, ABO, ZFPM2, ARHGEF3, GCKR, SERPINA1, SH2B3 and ASGR1.

    “These included well-established, previously identified [17][18][19][20] pleiotropic loci such as MHC, ABO, ZFPM2, ARHGEF3, GCKR, SERPINA1, SH2B3 and ASGR1”

    From Plasma proteomic associations with genetics and health in the UK Biobank (2023) · cites this paper as [20]

    The Results section (‘Genetic associations for protein targets’; page unavailable) identifies ARF4-ARHGEF3, ABO and GCKR as substantially pleiotropic regions. The ‘A map of proteo-genomic connections across the phenome’ section also calls GCKR a known pleiotropic locus. Our copy does not establish the remaining named loci as previously identified pleiotropic loci, so it does not support the full list. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

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Authors
Pietzner, M., Wheeler, E., Carrasco-Zanini, J., Cortes, A., Koprulu, M., Wörheide, M. A., Oerton, E., Cook, J., Stewart, I. D., Kerrison, N. D., Luan, J., Raffler, J., Arnold, M., Arlt, W., O’Rahilly, S., Kastenmüller, G., Gamazon, E. R., Hingorani, A. D., Scott, R. A., Wareham, N. J., & Langenberg, C.
Affiliations
  • University of Cambridge (GB)
  • GlaxoSmithKline, Stevenage SG1 2NY, UK.
  • Helmholtz Munich (DE)
  • University Hospital Augsburg (DE)
  • Duke University (US)
  • University of Birmingham (GB)
  • British Heart Foundation (GB)
  • GlaxoSmithKline (United Kingdom) (GB)
Corresponding author
  • Pietzner, Maik
  • Wheeler, Eleanor
  • Langenberg, Claudia
ORCID
Venue
Science
Publisher
American Association for the Advancement of Science (AAAS)
Published
2021-11-12
Volume
374
Issue
6569
Pages
eabj1541-eabj1541
ISSN
  • 0036-8075 (ISSN-L)
  • 1095-9203
DOI
10.1126/science.abj1541
PMC
PMC9904207
OpenAlex
W3201934315
Cited by
657
References
10
Citations by year
2026: 162 · 2025: 192 · 2024: 156 · 2023: 97 · 2022: 48 · 2021: 2
FWCI
43.73
Type
journal article
Language
en
Open access
Locations
  • Science (publishedVersion)
  • PubMed (publishedVersion)
  • Queen Mary Research Online (Queen Mary University of London) (publishedVersion)
  • PubMed Central (acceptedVersion)
  • UCL Discovery (University College London) (submittedVersion)
  • University of Birmingham Research Portal (University of Birmingham) (submittedVersion, other-oa)
  • PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion)
  • OPUS (Augsburg University) (submittedVersion)
  • Apollo (University of Cambridge) (submittedVersion)
  • Apollo
Funders
  • Wellcome Trust (214274/Z/18/Z)
  • National Institute for Health and Care Research (NIHR202383)
  • Medical Research Council (MC_UU_12015/1, MC_PC_13046)
  • National Institute on Aging (R56AG068026, RF1AG059093, RF1AG057452, U01AG061359)
  • National Human Genome Research Institute (R35HG010718, R01HG011138)
  • National Institute of General Medical Sciences (R01GM140287)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
Keywords
plasma proteins, genetic association, etiology, genome-wide association study loci
MeSH terms
Drug Development, COVID-19, Aging, Blood Proteins, Connective Tissue Diseases, Disease, Female, Humans, Male, Phenotype, Proteins, Sex Characteristics, Genetic Variation, Genome, Human, Alternative Splicing, Internet, Proteome, Genomics, Quantitative Trait Loci, Gallstones, Genome-Wide Association Study, Genetic Association Studies

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