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

Mine Koprulu; Karl Smith-Byrne; Brian Richard Ferolito; Erin Macdonald-Dunlop; Jian'an Luan; Åsa K Hedman; Chibuzor Franklin Ogamba; Jurgis Kuliesius; Linda Repetto; Anna Ramisch; Fahim Abbasi; Johan Ärnlöv; Themistocles L Assimes; Hanna M Björck; Sophia Björkander; Morten Böttcher; Adam Stuart Butterworth; Zhengming Chen; Kelly Cho; Robert Joseph Clarke; Simon Riddington Cox; Kamila Czene; John Danesh; George Dedoussis; Sölve Elmståhl; Niclas Eriksson; Per Eriksson; Tõnu Esko; Aida Ferreiro-Iglesias; Paul William Franks; Jingyuan Fu; J Michael Gaziano; Mohsen Ghanbari; Christian Gieger; Arthur Gilly; Harald Grallert; Marc James Gunter; Stefan Gustafsson; Andreas Göteson; Per Frans Leonard Hall; Oskar Hansson; Sarah Elizabeth Harris; Caroline Hayward; Christian Herder; Natalia Hernandez-Pacheco; Ziad Hijazi; Robert F Hillary; Jemma Caroline Hopewell; Shixian Hu; Shih-Jen Hwang; Christina Jern; Asa Johansson; Lina Jonsson; Annette Kalnapenkis; Nicola Dorothy Kerrison; Pik Fang  Kho; Lucija Klaric; Leonhard Kohleick; Julia Kraft; Mikael Landen; Daniel Levy; Liming Li; Lars  Lind; Jirong Long; Niklass Mattsson-Carlgren; Erik Melen; Simon Kebede Merid; Philipp Mertins; Karl Michaelsson; Peter Loof  Moller; Federico Murgia; Mette Nyegaard; Young-Chan Park; Ewan Pearson; James  Peters; John Ross Petrie; Grace  Ping; Ozren Polašek; Bram Peter Prins; Stephan Ripke; Michel Roden; Palle Duun Rohde; Saredo  Said; Xia Shen; Jochen M Schwenk; Agneta Siegbahn; J Gustav Smith; Tara M Stanne; Karsten Suhre; Johan Sundstrom; Barbara Thorand; Elsa Valdes-Marquez; Costanza L Vallerga; Joyce B J  van Meurs; Ana Viñuela; Urmo Vosa; Lars Wallentin; Robin G  Walters; Nicholas John Wareham; Joachim Eduard Weber; Rinse Karel  Weersma; James F Wilson; Simon  Winther; Sumaira Yasmeen; Daniela Zanetti; Eleftheria  Zeggini; Jing Hua Zhao; Alexandra Zhernakova; Daria V Zhernakova; Matthias Ziehm; Benedikt Mathias Kessler; Alexandre C Pereira; Anders Malarstig; Maik Pietzner; Claudia Langenberg

doi:10.1016/j.cell.2026.03.049

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

, Mine Koprulu (Lead / Corresponding author)
, Karl Smith-Byrne
, Brian Richard Ferolito
, Erin Macdonald-Dunlop
, Jian'an Luan
, Åsa K Hedman
, Chibuzor Franklin Ogamba
, Jurgis Kuliesius
, Linda Repetto
, Anna Ramisch
, Fahim Abbasi
, Johan Ärnlöv
, Themistocles L Assimes
, Hanna M Björck
, Sophia Björkander
, Morten Böttcher
, Adam Stuart Butterworth
, Zhengming Chen
, Kelly Cho

Robert Joseph Clarke, Simon Riddington Cox, Kamila Czene, John Danesh, George Dedoussis, Sölve Elmståhl, Niclas Eriksson, Per Eriksson, Tõnu Esko, Aida Ferreiro-Iglesias, Paul William Franks, Jingyuan Fu, J Michael Gaziano, Mohsen Ghanbari, Christian Gieger, Arthur Gilly, Harald Grallert, Marc James Gunter, Stefan Gustafsson, Andreas Göteson, Per Frans Leonard Hall, Oskar Hansson, Sarah Elizabeth Harris, Caroline Hayward, Christian Herder, Natalia Hernandez-Pacheco, Ziad Hijazi, Robert F Hillary, Jemma Caroline Hopewell, Shixian Hu, Shih-Jen Hwang, Christina Jern, Asa Johansson, Lina Jonsson, Annette Kalnapenkis, Nicola Dorothy Kerrison, Pik Fang Kho, Lucija Klaric, Leonhard Kohleick, Julia Kraft, Mikael Landen, Daniel Levy, Liming Li, Lars Lind, Jirong Long, Niklass Mattsson-Carlgren, Erik Melen, Simon Kebede Merid, Philipp Mertins, Karl Michaelsson, Peter Loof Moller, Federico Murgia, Mette Nyegaard, Young-Chan Park, Ewan Pearson, James Peters, John Ross Petrie, Grace Ping, Ozren Polašek, Bram Peter Prins, Stephan Ripke, Michel Roden, Palle Duun Rohde, Saredo Said, Xia Shen, Jochen M Schwenk, Agneta Siegbahn, J Gustav Smith, Tara M Stanne, Karsten Suhre, Johan Sundstrom, Barbara Thorand, Elsa Valdes-Marquez, Costanza L Vallerga, Joyce B J van Meurs, Ana Viñuela, Urmo Vosa, Lars Wallentin, Robin G Walters, Nicholas John Wareham, Joachim Eduard Weber, Rinse Karel Weersma, James F Wilson, Simon Winther, Sumaira Yasmeen, Daniela Zanetti, Eleftheria Zeggini, Jing Hua Zhao, Alexandra Zhernakova, Daria V Zhernakova, Matthias Ziehm, Benedikt Mathias Kessler, Alexandre C Pereira, Anders Malarstig, Maik Pietzner (Lead / Corresponding author), Claudia Langenberg (Lead / Corresponding author)

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Abstract

Understanding the genetic regulation of circulating protein levels can provide new insights into disease mechanisms. Here, we present the largest proteogenomic study to date (n = 78,664 participants across 38 studies), identifying >24,000 protein quantitative trait loci (QTLs) associated with 1,116 proteins, acting near to (n = 5,040) or distant (n = 19,698) from the cognate gene. Using machine learning-guided effector gene assignment, we provide genetic evidence for pathways, cell types, and tissues that modulate circulating protein levels, highlighting N-linked glycosylation as an important regulatory pathway. We demonstrate that genetic instruments of protein production/function ("cis") versus modulation ("trans") reveal distinct phenotypic insights. We identify proteins as candidates for drug targets and engagement (e.g., plasma furin and cardiovascular diseases) by comparing cis-based genetic evidence with protein-disease associations. Systematic triangulation of trans-protein QTLs (pQTLs) with genetic and protein associations across many diseases highlights potential drug repurposing opportunities, e.g., tyrosine kinase 2 (TYK2) inhibitors for rheumatoid arthritis. Our multi-cohort meta-analyses generate proteogenomic insights into disease mechanisms and new treatment opportunities.

Original language	English
Pages (from-to)	3339-3357.e11
Number of pages	31
Journal	Cell
Volume	189
Issue number	11
Early online date	6 May 2026
DOIs	https://doi.org/10.1016/j.cell.2026.03.049
Publication status	Published - 28 May 2026

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

N-linked glycosylation
causal inference
diseasome
drug repurposing
meta-analysis
pleiotropy
proteogenomics
proteomics
trans-pQTLs

ASJC Scopus subject areas

General Biochemistry,Genetics and Molecular Biology

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10.1016/j.cell.2026.03.049Licence: CC BY

Final published version
Koprulu et al., 2026, Cell 189, 1–19 May 28, 2026 © 2026 The Author(s). Published by Elsevier Inc. https://doi.org/10.1016/j.cell.2026.03.049
Final published version, 13.4 MBLicence: CC BY

Cite this

, Koprulu, M., Smith-Byrne, K., Ferolito, B. R., Macdonald-Dunlop, E., Luan, J., Hedman, Å. K., Ogamba, C. F., Kuliesius, J., Repetto, L., Ramisch, A., Abbasi, F., Ärnlöv, J., Assimes, T. L., Björck, H. M., Björkander, S., Böttcher, M., Butterworth, A. S., Chen, Z., ... Langenberg, C. (2026). Multi-cohort proteogenomic analyses reveal genetic effects across the proteome and diseasome. Cell, 189(11), 3339-3357.e11. https://doi.org/10.1016/j.cell.2026.03.049

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title = "Multi-cohort proteogenomic analyses reveal genetic effects across the proteome and diseasome",

abstract = "Understanding the genetic regulation of circulating protein levels can provide new insights into disease mechanisms. Here, we present the largest proteogenomic study to date (n = 78,664 participants across 38 studies), identifying >24,000 protein quantitative trait loci (QTLs) associated with 1,116 proteins, acting near to (n = 5,040) or distant (n = 19,698) from the cognate gene. Using machine learning-guided effector gene assignment, we provide genetic evidence for pathways, cell types, and tissues that modulate circulating protein levels, highlighting N-linked glycosylation as an important regulatory pathway. We demonstrate that genetic instruments of protein production/function ({"}cis{"}) versus modulation ({"}trans{"}) reveal distinct phenotypic insights. We identify proteins as candidates for drug targets and engagement (e.g., plasma furin and cardiovascular diseases) by comparing cis-based genetic evidence with protein-disease associations. Systematic triangulation of trans-protein QTLs (pQTLs) with genetic and protein associations across many diseases highlights potential drug repurposing opportunities, e.g., tyrosine kinase 2 (TYK2) inhibitors for rheumatoid arthritis. Our multi-cohort meta-analyses generate proteogenomic insights into disease mechanisms and new treatment opportunities.",

keywords = "N-linked glycosylation, causal inference, diseasome, drug repurposing, meta-analysis, pleiotropy, proteogenomics, proteomics, trans-pQTLs",

author = "Mine Koprulu and Karl Smith-Byrne and Ferolito, \{Brian Richard\} and Erin Macdonald-Dunlop and Jian'an Luan and Hedman, \{{\AA}sa K\} and Ogamba, \{Chibuzor Franklin\} and Jurgis Kuliesius and Linda Repetto and Anna Ramisch and Fahim Abbasi and Johan {\"A}rnl{\"o}v and Assimes, \{Themistocles L\} and Bj{\"o}rck, \{Hanna M\} and Sophia Bj{\"o}rkander and Morten B{\"o}ttcher and Butterworth, \{Adam Stuart\} and Zhengming Chen and Kelly Cho and Clarke, \{Robert Joseph\} and Cox, \{Simon Riddington\} and Kamila Czene and John Danesh and George Dedoussis and S{\"o}lve Elmst{\aa}hl and Niclas Eriksson and Per Eriksson and T{\~o}nu Esko and Aida Ferreiro-Iglesias and Franks, \{Paul William\} and Jingyuan Fu and Gaziano, \{J Michael\} and Mohsen Ghanbari and Christian Gieger and Arthur Gilly and Harald Grallert and Gunter, \{Marc James\} and Stefan Gustafsson and Andreas G{\"o}teson and Hall, \{Per Frans Leonard\} and Oskar Hansson and Harris, \{Sarah Elizabeth\} and Caroline Hayward and Christian Herder and Natalia Hernandez-Pacheco and Ziad Hijazi and Hillary, \{Robert F\} and Hopewell, \{Jemma Caroline\} and Shixian Hu and Shih-Jen Hwang and Christina Jern and Asa Johansson and Lina Jonsson and Annette Kalnapenkis and Kerrison, \{Nicola Dorothy\} and Kho, \{Pik Fang\} and Lucija Klaric and Leonhard Kohleick and Julia Kraft and Mikael Landen and Daniel Levy and Liming Li and Lars Lind and Jirong Long and Niklass Mattsson-Carlgren and Erik Melen and Merid, \{Simon Kebede\} and Philipp Mertins and Karl Michaelsson and Moller, \{Peter Loof\} and Federico Murgia and Mette Nyegaard and Young-Chan Park and Ewan Pearson and James Peters and Petrie, \{John Ross\} and Grace Ping and Ozren Pola{\v s}ek and \{Peter Prins\}, Bram and Stephan Ripke and Michel Roden and Rohde, \{Palle Duun\} and Saredo Said and Xia Shen and Schwenk, \{Jochen M\} and Agneta Siegbahn and Smith, \{J Gustav\} and Stanne, \{Tara M\} and Karsten Suhre and Johan Sundstrom and Barbara Thorand and Elsa Valdes-Marquez and Vallerga, \{Costanza L\} and \{van Meurs\}, \{Joyce B J\} and Ana Vi{\~n}uela and Urmo Vosa and Lars Wallentin and Walters, \{Robin G\} and Wareham, \{Nicholas John\} and Weber, \{Joachim Eduard\} and Weersma, \{Rinse Karel\} and Wilson, \{James F\} and Simon Winther and Sumaira Yasmeen and Daniela Zanetti and Eleftheria Zeggini and Zhao, \{Jing Hua\} and Alexandra Zhernakova and Zhernakova, \{Daria V\} and Matthias Ziehm and Kessler, \{Benedikt Mathias\} and Pereira, \{Alexandre C\} and Anders Malarstig and Maik Pietzner and Claudia Langenberg",

note = "Publisher Copyright: {\textcopyright} 2026 The Author(s).",

year = "2026",

month = may,

day = "28",

doi = "10.1016/j.cell.2026.03.049",

language = "English",

volume = "189",

pages = "3339--3357.e11",

journal = "Cell",

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Koprulu, M, Smith-Byrne, K, Ferolito, BR, Macdonald-Dunlop, E, Luan, J, Hedman, ÅK, Ogamba, CF, Kuliesius, J, Repetto, L, Ramisch, A, Abbasi, F, Ärnlöv, J, Assimes, TL, Björck, HM, Björkander, S, Böttcher, M, Butterworth, AS, Chen, Z, Cho, K, Clarke, RJ, Cox, SR, Czene, K, Danesh, J, Dedoussis, G, Elmståhl, S, Eriksson, N, Eriksson, P, Esko, T, Ferreiro-Iglesias, A, Franks, PW, Fu, J, Gaziano, JM, Ghanbari, M, Gieger, C, Gilly, A, Grallert, H, Gunter, MJ, Gustafsson, S, Göteson, A, Hall, PFL, Hansson, O, Harris, SE, Hayward, C, Herder, C, Hernandez-Pacheco, N, Hijazi, Z, Hillary, RF, Hopewell, JC, Hu, S, Hwang, S-J, Jern, C, Johansson, A, Jonsson, L, Kalnapenkis, A, Kerrison, ND, Kho, PF, Klaric, L, Kohleick, L, Kraft, J, Landen, M, Levy, D, Li, L, Lind, L, Long, J, Mattsson-Carlgren, N, Melen, E, Merid, SK, Mertins, P, Michaelsson, K, Moller, PL, Murgia, F, Nyegaard, M, Park, Y-C, Pearson, E, Peters, J, Petrie, JR, Ping, G, Polašek, O, Peter Prins, B, Ripke, S, Roden, M, Rohde, PD, Said, S, Shen, X, Schwenk, JM, Siegbahn, A, Smith, JG, Stanne, TM, Suhre, K, Sundstrom, J, Thorand, B, Valdes-Marquez, E, Vallerga, CL, van Meurs, JBJ, Viñuela, A, Vosa, U, Wallentin, L, Walters, RG, Wareham, NJ, Weber, JE, Weersma, RK, Wilson, JF, Winther, S, Yasmeen, S, Zanetti, D, Zeggini, E, Zhao, JH, Zhernakova, A, Zhernakova, DV, Ziehm, M, Kessler, BM, Pereira, AC, Malarstig, A & Pietzner, M & Langenberg, C 2026, 'Multi-cohort proteogenomic analyses reveal genetic effects across the proteome and diseasome', Cell, vol. 189, no. 11, pp. 3339-3357.e11. https://doi.org/10.1016/j.cell.2026.03.049

TY - JOUR

T1 - Multi-cohort proteogenomic analyses reveal genetic effects across the proteome and diseasome

AU - Koprulu, Mine

AU - Smith-Byrne, Karl

AU - Ferolito, Brian Richard

AU - Macdonald-Dunlop, Erin

AU - Luan, Jian'an

AU - Hedman, Åsa K

AU - Ogamba, Chibuzor Franklin

AU - Kuliesius, Jurgis

AU - Repetto, Linda

AU - Ramisch, Anna

AU - Abbasi, Fahim

AU - Ärnlöv, Johan

AU - Assimes, Themistocles L

AU - Björck, Hanna M

AU - Björkander, Sophia

AU - Böttcher, Morten

AU - Butterworth, Adam Stuart

AU - Chen, Zhengming

AU - Cho, Kelly

AU - Clarke, Robert Joseph

AU - Cox, Simon Riddington

AU - Czene, Kamila

AU - Danesh, John

AU - Dedoussis, George

AU - Elmståhl, Sölve

AU - Eriksson, Niclas

AU - Eriksson, Per

AU - Esko, Tõnu

AU - Ferreiro-Iglesias, Aida

AU - Franks, Paul William

AU - Fu, Jingyuan

AU - Gaziano, J Michael

AU - Ghanbari, Mohsen

AU - Gieger, Christian

AU - Gilly, Arthur

AU - Grallert, Harald

AU - Gunter, Marc James

AU - Gustafsson, Stefan

AU - Göteson, Andreas

AU - Hall, Per Frans Leonard

AU - Hansson, Oskar

AU - Harris, Sarah Elizabeth

AU - Hayward, Caroline

AU - Herder, Christian

AU - Hernandez-Pacheco, Natalia

AU - Hijazi, Ziad

AU - Hillary, Robert F

AU - Hopewell, Jemma Caroline

AU - Hu, Shixian

AU - Hwang, Shih-Jen

AU - Jern, Christina

AU - Johansson, Asa

AU - Jonsson, Lina

AU - Kalnapenkis, Annette

AU - Kerrison, Nicola Dorothy

AU - Kho, Pik Fang

AU - Klaric, Lucija

AU - Kohleick, Leonhard

AU - Kraft, Julia

AU - Landen, Mikael

AU - Levy, Daniel

AU - Li, Liming

AU - Lind, Lars

AU - Long, Jirong

AU - Mattsson-Carlgren, Niklass

AU - Melen, Erik

AU - Merid, Simon Kebede

AU - Mertins, Philipp

AU - Michaelsson, Karl

AU - Moller, Peter Loof

AU - Murgia, Federico

AU - Nyegaard, Mette

AU - Park, Young-Chan

AU - Pearson, Ewan

AU - Peters, James

AU - Petrie, John Ross

AU - Ping, Grace

AU - Polašek, Ozren

AU - Peter Prins, Bram

AU - Ripke, Stephan

AU - Roden, Michel

AU - Rohde, Palle Duun

AU - Said, Saredo

AU - Shen, Xia

AU - Schwenk, Jochen M

AU - Siegbahn, Agneta

AU - Smith, J Gustav

AU - Stanne, Tara M

AU - Suhre, Karsten

AU - Sundstrom, Johan

AU - Thorand, Barbara

AU - Valdes-Marquez, Elsa

AU - Vallerga, Costanza L

AU - van Meurs, Joyce B J

AU - Viñuela, Ana

AU - Vosa, Urmo

AU - Wallentin, Lars

AU - Walters, Robin G

AU - Wareham, Nicholas John

AU - Weber, Joachim Eduard

AU - Weersma, Rinse Karel

AU - Wilson, James F

AU - Winther, Simon

AU - Yasmeen, Sumaira

AU - Zanetti, Daniela

AU - Zeggini, Eleftheria

AU - Zhao, Jing Hua

AU - Zhernakova, Alexandra

AU - Zhernakova, Daria V

AU - Ziehm, Matthias

AU - Kessler, Benedikt Mathias

AU - Pereira, Alexandre C

AU - Malarstig, Anders

AU - Pietzner, Maik

AU - Langenberg, Claudia

PY - 2026/5/28

Y1 - 2026/5/28

N2 - Understanding the genetic regulation of circulating protein levels can provide new insights into disease mechanisms. Here, we present the largest proteogenomic study to date (n = 78,664 participants across 38 studies), identifying >24,000 protein quantitative trait loci (QTLs) associated with 1,116 proteins, acting near to (n = 5,040) or distant (n = 19,698) from the cognate gene. Using machine learning-guided effector gene assignment, we provide genetic evidence for pathways, cell types, and tissues that modulate circulating protein levels, highlighting N-linked glycosylation as an important regulatory pathway. We demonstrate that genetic instruments of protein production/function ("cis") versus modulation ("trans") reveal distinct phenotypic insights. We identify proteins as candidates for drug targets and engagement (e.g., plasma furin and cardiovascular diseases) by comparing cis-based genetic evidence with protein-disease associations. Systematic triangulation of trans-protein QTLs (pQTLs) with genetic and protein associations across many diseases highlights potential drug repurposing opportunities, e.g., tyrosine kinase 2 (TYK2) inhibitors for rheumatoid arthritis. Our multi-cohort meta-analyses generate proteogenomic insights into disease mechanisms and new treatment opportunities.

AB - Understanding the genetic regulation of circulating protein levels can provide new insights into disease mechanisms. Here, we present the largest proteogenomic study to date (n = 78,664 participants across 38 studies), identifying >24,000 protein quantitative trait loci (QTLs) associated with 1,116 proteins, acting near to (n = 5,040) or distant (n = 19,698) from the cognate gene. Using machine learning-guided effector gene assignment, we provide genetic evidence for pathways, cell types, and tissues that modulate circulating protein levels, highlighting N-linked glycosylation as an important regulatory pathway. We demonstrate that genetic instruments of protein production/function ("cis") versus modulation ("trans") reveal distinct phenotypic insights. We identify proteins as candidates for drug targets and engagement (e.g., plasma furin and cardiovascular diseases) by comparing cis-based genetic evidence with protein-disease associations. Systematic triangulation of trans-protein QTLs (pQTLs) with genetic and protein associations across many diseases highlights potential drug repurposing opportunities, e.g., tyrosine kinase 2 (TYK2) inhibitors for rheumatoid arthritis. Our multi-cohort meta-analyses generate proteogenomic insights into disease mechanisms and new treatment opportunities.

KW - N-linked glycosylation

KW - causal inference

KW - diseasome

KW - drug repurposing

KW - meta-analysis

KW - pleiotropy

KW - proteogenomics

KW - proteomics

KW - trans-pQTLs

U2 - 10.1016/j.cell.2026.03.049

DO - 10.1016/j.cell.2026.03.049

M3 - Article

C2 - 42097137

SN - 0092-8674

VL - 189

SP - 3339-3357.e11

JO - Cell

JF - Cell

IS - 11

ER -

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

Abstract

UN SDGs

Keywords

ASJC Scopus subject areas

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