383: Genetics of the Circulating Proteome: pQTLs, Pathways, and Disease Links

Koprulu M et al., Cell - A 38-cohort proteogenomic meta-analysis of up to 78,664 people maps fine‑mapped protein quantitative trait loci (pQTLs) across 1,116 circulating proteins, uses machine learning to assign trans effector genes, and triangulates genetic and observational evidence to highlight disease mechanisms and therapeutic opportunities. Key terms: proteogenomics, protein QTLs, N-linked glycosylation, Mendelian randomization, drug repurposing.

Study Highlights:
The study meta-analyzed antibody-based proteomic data across 38 cohorts (n up to 78,664) and identified 24,738 fine-mapped pQTL credible sets for 1,116 proteins, including 5,040 cis and 19,698 trans pQTLs. Machine-learning effector-gene assignment for trans-pQTLs revealed enriched pathways and cell types that regulate plasma proteins, with N-linked glycosylation and liver/hepatocyte signals prominent. Systematic causal inference and triangulation with observational biomarker studies identified candidate drug targets and repurposing signals (e.g., TYK2, furin) but also showed limited concordance between cis genetic instruments and measured protein–disease associations.

Conclusion:
Large-scale multi-cohort proteogenomics uncovers widespread distal genetic regulation of the circulating proteome, identifies biological pathways and tissues that shape plasma protein levels (notably N-linked glycosylation and hepatic/immune contributors), and provides genetic evidence to prioritize biomarkers and drug targets while highlighting discordance between genetic and observational signatures that requires careful interpretation.

Music:
Enjoy the music based on this article at the end of the episode.

Article title:
Multi-cohort proteogenomic analyses reveal genetic effects across the proteome and diseasome

First author:
Koprulu M

Journal:
Cell

DOI:
10.1016/j.cell.2026.03.049

Reference:
Koprulu M., Smith-Byrne K., Ferolito B.R., et al., 2026. Multi-cohort proteogenomic analyses reveal genetic effects across the proteome and diseasome. Cell 189, 3339–3357. https://doi.org/10.1016/j.cell.2026.03.049

License:
This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) – https://creativecommons.org/licenses/by/4.0/

Support:
Base by Base – Stripe donations: https://donate.stripe.com/7sY4gz71B2sN3RWac5gEg00

Official website https://basebybase.com

On PaperCast Base by Base you'll discover the latest in genomics, functional genomics, structural genomics, and proteomics.

Episode link: https://basebybase.com/episodes/multi-cohort-proteogenomics-pqtl-diseasome

QC:
This episode was checked against the original article PDF and publication metadata for the episode release published on 2026-06-02.

QC Scope:
- article metadata and core scientific claims from the narration
- excludes analogies, intro/outro, and music
- transcript coverage: Audited the transcript sections describing trans-pQTL dominance, effector-gene mapping, N-linked glycosylation, tissue/cell-type enrichment, MR concordance issues, and clinical exemplars (TYK2 for RA, NT-proBNP for heart failure, extracellular furin).
- transcript topics: Trans-pQTL paradigm and global regulatory architecture; Cis vs trans pQTL landscape and study scale; Olink proximity extension assay methodology; Effector gene assignment and pathway/cell-type enrichment; N-linked glycosylation as a key trans-regulatory pathway; Clinical translation: TYK2 and rheumatoid arthritis

QC Summary:
- factual score: 10/10
- metadata score: 10/10
- supported core claims: 6
- claims flagged for review: 0
- metadata checks passed: 4
- metadata issues found: 0

Metadata Audited:
- article_doi
- article_title
- article_journal
- license

<...