Plasma proteomic associations with genetics and health in the UK Biobank.
Humans
ABO Blood-Group System
/ genetics
Biological Specimen Banks
Blood Proteins
/ analysis
COVID-19
/ genetics
Databases, Factual
Drug Discovery
Epistasis, Genetic
Fucosyltransferases
/ metabolism
Genetic Predisposition to Disease
Genomics
Health
Plasma
/ chemistry
Proprotein Convertase 9
/ metabolism
Proteome
/ analysis
Proteomics
Public-Private Sector Partnerships
Quantitative Trait Loci
United Kingdom
Galactoside 2-alpha-L-fucosyltransferase
Journal
Nature
ISSN: 1476-4687
Titre abrégé: Nature
Pays: England
ID NLM: 0410462
Informations de publication
Date de publication:
Oct 2023
Oct 2023
Historique:
received:
17
06
2022
accepted:
31
08
2023
medline:
23
10
2023
pubmed:
5
10
2023
entrez:
4
10
2023
Statut:
ppublish
Résumé
The Pharma Proteomics Project is a precompetitive biopharmaceutical consortium characterizing the plasma proteomic profiles of 54,219 UK Biobank participants. Here we provide a detailed summary of this initiative, including technical and biological validations, insights into proteomic disease signatures, and prediction modelling for various demographic and health indicators. We present comprehensive protein quantitative trait locus (pQTL) mapping of 2,923 proteins that identifies 14,287 primary genetic associations, of which 81% are previously undescribed, alongside ancestry-specific pQTL mapping in non-European individuals. The study provides an updated characterization of the genetic architecture of the plasma proteome, contextualized with projected pQTL discovery rates as sample sizes and proteomic assay coverages increase over time. We offer extensive insights into trans pQTLs across multiple biological domains, highlight genetic influences on ligand-receptor interactions and pathway perturbations across a diverse collection of cytokines and complement networks, and illustrate long-range epistatic effects of ABO blood group and FUT2 secretor status on proteins with gastrointestinal tissue-enriched expression. We demonstrate the utility of these data for drug discovery by extending the genetic proxied effects of protein targets, such as PCSK9, on additional endpoints, and disentangle specific genes and proteins perturbed at loci associated with COVID-19 susceptibility. This public-private partnership provides the scientific community with an open-access proteomics resource of considerable breadth and depth to help to elucidate the biological mechanisms underlying proteo-genomic discoveries and accelerate the development of biomarkers, predictive models and therapeutics
Identifiants
pubmed: 37794186
doi: 10.1038/s41586-023-06592-6
pii: 10.1038/s41586-023-06592-6
pmc: PMC10567551
doi:
Substances chimiques
ABO Blood-Group System
0
Blood Proteins
0
Fucosyltransferases
EC 2.4.1.-
PCSK9 protein, human
EC 3.4.21.-
Proprotein Convertase 9
EC 3.4.21.-
Proteome
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
329-338Investigateurs
Hyun Ming Kang
(HM)
Informations de copyright
© 2023. The Author(s).
Références
Suhre, K., McCarthy, M. I. & Schwenk, J. M. Genetics meets proteomics: perspectives for large population-based studies. Nat. Rev. Genet. 22, 19–37 (2021).
pubmed: 32860016
doi: 10.1038/s41576-020-0268-2
Finan, C. et al. The druggable genome and support for target identification and validation in drug development. Sci. Transl. Med. https://doi.org/10.1126/scitranslmed.aag1166 (2017).
Schmidt, A. F. et al. Genetic drug target validation using Mendelian randomisation. Nat. Commun. 11, 3255 (2020).
pubmed: 32591531
pmcid: 7320010
doi: 10.1038/s41467-020-16969-0
Nguyen, P. A., Born, D. A., Deaton, A. M., Nioi, P. & Ward, L. D. Phenotypes associated with genes encoding drug targets are predictive of clinical trial side effects. Nat. Commun. 10, 1579 (2019).
pubmed: 30952858
pmcid: 6450952
doi: 10.1038/s41467-019-09407-3
Christiansen, M. K. et al. Polygenic risk score-enhanced risk stratification of coronary artery disease in patients with stable chest pain. Circ. Genom. Precis. Med. 14, e003298 (2021).
pubmed: 34032468
doi: 10.1161/CIRCGEN.120.003298
Reay, W. R. & Cairns, M. J. Advancing the use of genome-wide association studies for drug repurposing. Nat. Rev. Genet. 22, 658–671 (2021).
pubmed: 34302145
doi: 10.1038/s41576-021-00387-z
Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).
pubmed: 30305743
pmcid: 6786975
doi: 10.1038/s41586-018-0579-z
Canela-Xandri, O., Rawlik, K. & Tenesa, A. An atlas of genetic associations in UK Biobank. Nat. Genet. 50, 1593–1599 (2018).
pubmed: 30349118
pmcid: 6707814
doi: 10.1038/s41588-018-0248-z
Littlejohns, T. J. et al. The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions. Nat. Commun. 11, 2624 (2020).
pubmed: 32457287
pmcid: 7250878
doi: 10.1038/s41467-020-15948-9
Szustakowski, J. D. et al. Advancing human genetics research and drug discovery through exome sequencing of the UK Biobank. Nat. Genet. 53, 942–948 (2021).
pubmed: 34183854
doi: 10.1038/s41588-021-00885-0
Julkunen, H., Cichonska, A., Slagboom, P. E., Wurtz, P. & Nightingale Health UK Biobank Initiative. Metabolic biomarker profiling for identification of susceptibility to severe pneumonia and COVID-19 in the general population. eLife https://doi.org/10.7554/eLife.63033 (2021).
Nelson, M. R. et al. The support of human genetic evidence for approved drug indications. Nat. Genet. 47, 856–860 (2015).
pubmed: 26121088
doi: 10.1038/ng.3314
King, E. A., Davis, J. W. & Degner, J. F. Are drug targets with genetic support twice as likely to be approved? Revised estimates of the impact of genetic support for drug mechanisms on the probability of drug approval. PLoS Genet. 15, e1008489 (2019).
pubmed: 31830040
pmcid: 6907751
doi: 10.1371/journal.pgen.1008489
Fauman, E. B. & Hyde, C. An optimal variant to gene distance window derived from an empirical definition of cis and trans protein QTLs. BMC Bioinformatics 23, 169 (2022).
pubmed: 35527238
pmcid: 9082853
doi: 10.1186/s12859-022-04706-x
Anderson, N. L. & Anderson, N. G. The human plasma proteome: history, character, and diagnostic prospects. Mol. Cell. Proteomics 1, 845–867 (2002).
pubmed: 12488461
doi: 10.1074/mcp.R200007-MCP200
Enroth, S., Johansson, A., Enroth, S. B. & Gyllensten, U. Strong effects of genetic and lifestyle factors on biomarker variation and use of personalized cutoffs. Nat. Commun. 5, 4684 (2014).
pubmed: 25147954
doi: 10.1038/ncomms5684
Emilsson, V. et al. Co-regulatory networks of human serum proteins link genetics to disease. Science 361, 769–773 (2018).
pubmed: 30072576
pmcid: 6190714
doi: 10.1126/science.aaq1327
Sun, B. B. et al. Genomic atlas of the human plasma proteome. Nature 558, 73–79 (2018).
pubmed: 29875488
pmcid: 6697541
doi: 10.1038/s41586-018-0175-2
Ferkingstad, E. et al. Large-scale integration of the plasma proteome with genetics and disease. Nat. Genet. 53, 1712–1721 (2021).
pubmed: 34857953
doi: 10.1038/s41588-021-00978-w
Pietzner, M. et al. Mapping the proteo-genomic convergence of human diseases. Science 374, eabj1541 (2021).
pubmed: 34648354
pmcid: 9904207
doi: 10.1126/science.abj1541
Gudjonsson, A. et al. A genome-wide association study of serum proteins reveals shared loci with common diseases. Nat. Commun. 13, 480 (2022).
pubmed: 35078996
pmcid: 8789779
doi: 10.1038/s41467-021-27850-z
Folkersen, L. et al. Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals. Nat. Metab. 2, 1135–1148 (2020).
pubmed: 33067605
pmcid: 7611474
doi: 10.1038/s42255-020-00287-2
Koprulu, M. et al. Proteogenomic links to human metabolic diseases. Nat. Metab. 5, 516–528 (2023).
pubmed: 36823471
pmcid: 7614946
doi: 10.1038/s42255-023-00753-7
Conroy, M. et al. The advantages of UK Biobank’s open-access strategy for health research. J. Intern. Med. 286, 389–397 (2019).
pubmed: 31283063
pmcid: 6790705
doi: 10.1111/joim.12955
Wik, L. et al. Proximity extension assay in combination with next-generation sequencing for high-throughput proteome-wide analysis. Mol. Cell. Proteomics 20, 100168 (2021).
pubmed: 34715355
pmcid: 8633680
doi: 10.1016/j.mcpro.2021.100168
Cao, Z., Jia, Y. & Zhu, B. BNP and NT-proBNP as diagnostic biomarkers for cardiac dysfunction in both clinical and forensic medicine. Int. J. Mol. Sci. https://doi.org/10.3390/ijms20081820 (2019).
Fry, A. et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am. J. Epidemiol. 186, 1026–1034 (2017).
pubmed: 28641372
pmcid: 5860371
doi: 10.1093/aje/kwx246
Karczewski, K. J. et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434–443 (2020).
pubmed: 32461654
pmcid: 7334197
doi: 10.1038/s41586-020-2308-7
Johnson, N. et al. Quantitative proteomics screen identifies a substrate repertoire of rhomboid protease RHBDL2 in human cells and implicates it in epithelial homeostasis. Sci. Rep. 7, 7283 (2017).
pubmed: 28779096
pmcid: 5544772
doi: 10.1038/s41598-017-07556-3
Teshigawara, S. et al. Serum vaspin concentrations are closely related to insulin resistance, and rs77060950 at SERPINA12 genetically defines distinct group with higher serum levels in Japanese population. J. Clin. Endocrinol. Metab. 97, E1202–E1207 (2012).
pubmed: 22539588
doi: 10.1210/jc.2011-3297
The GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).
pmcid: 7737656
doi: 10.1126/science.aaz1776
Macdonald-Dunlop, E. et al. Mapping genetic determinants of 184 circulating proteins in 26,494 individuals to connect proteins and diseases. Preprint at medRxiv https://doi.org/10.1101/2021.08.03.21261494 (2021).
Alanis-Lobato, G., Andrade-Navarro, M. A. & Schaefer, M. H. HIPPIE v2.0: enhancing meaningfulness and reliability of protein-protein interaction networks. Nucleic Acids Res. 45, D408–D414 (2017).
pubmed: 27794551
doi: 10.1093/nar/gkw985
Kirk, J. A., Cheung, J. Y. & Feldman, A. M. Therapeutic targeting of BAG3: considering its complexity in cancer and heart disease. J. Clin. Invest. https://doi.org/10.1172/JCI149415 (2021).
Tadros, R. et al. Shared genetic pathways contribute to risk of hypertrophic and dilated cardiomyopathies with opposite directions of effect. Nat. Genet. 53, 128–134 (2021).
pubmed: 33495596
pmcid: 7611259
doi: 10.1038/s41588-020-00762-2
Villard, E. et al. A genome-wide association study identifies two loci associated with heart failure due to dilated cardiomyopathy. Eur. Heart J. 32, 1065–1076 (2011).
pubmed: 21459883
pmcid: 3086901
doi: 10.1093/eurheartj/ehr105
Fuchs, M. et al. Identification of the key structural motifs involved in HspB8/HspB6-Bag3 interaction. Biochem. J. 425, 245–255 (2009).
pubmed: 19845507
doi: 10.1042/BJ20090907
Perez-Bermejo, J. A. et al. Functional analysis of a common BAG3 allele associated with protection from heart failure. Nat. Cardiovasc. Res. 2, 615–628 (2023).
Wang, Y. & Colonna, M. Interkeukin-34, a cytokine crucial for the differentiation and maintenance of tissue resident macrophages and Langerhans cells. Eur. J. Immunol. 44, 1575–1581 (2014).
pubmed: 24737461
pmcid: 4137395
doi: 10.1002/eji.201344365
Chen, M. H. et al. Trans-ethnic and ancestry-specific blood-cell genetics in 746,667 individuals from 5 global populations. Cell 182, 1198–1213 (2020).
pubmed: 32888493
pmcid: 7480402
doi: 10.1016/j.cell.2020.06.045
Steri, M. et al. Overexpression of the cytokine BAFF and autoimmunity risk. N. Engl. J. Med. 376, 1615–1626 (2017).
pubmed: 28445677
pmcid: 5605835
doi: 10.1056/NEJMoa1610528
Astle, W. J. et al. The allelic landscape of human blood cell trait variation and links to common complex disease. Cell 167, 1415–1429 (2016).
pubmed: 27863252
pmcid: 5300907
doi: 10.1016/j.cell.2016.10.042
Dubey, A. K. et al. Belimumab: first targeted biological treatment for systemic lupus erythematosus. J. Pharmacol. Pharmacother. 2, 317–319 (2011).
pubmed: 22025872
pmcid: 3198539
doi: 10.4103/0976-500X.85930
Michalski, M. et al. Primary ficolin-3 deficiency—is it associated with increased susceptibility to infections? Immunobiology 220, 711–713 (2015).
pubmed: 25662573
doi: 10.1016/j.imbio.2015.01.003
Michalski, M. et al. H-ficolin (ficolin-3) concentrations and FCN3 gene polymorphism in neonates. Immunobiology 217, 730–737 (2012).
pubmed: 22226667
doi: 10.1016/j.imbio.2011.12.004
Schlapbach, L. J. et al. Congenital H-ficolin deficiency in premature infants with severe necrotising enterocolitis. Gut 60, 1438–1439 (2011).
pubmed: 20971976
doi: 10.1136/gut.2010.226027
Sokolowska, A. et al. Mannan-binding lectin-associated serine protease-2 (MASP-2) deficiency in two patients with pulmonary tuberculosis and one healthy control. Cell. Mol. Immunol. 12, 119–121 (2015).
pubmed: 24658431
doi: 10.1038/cmi.2014.19
St Swierzko, A. et al. Mannan-binding lectin-associated serine protease-2 (MASP-2) in a large cohort of neonates and its clinical associations. Mol. Immunol. 46, 1696–1701 (2009).
doi: 10.1016/j.molimm.2009.02.022
Stengaard-Pedersen, K. et al. Inherited deficiency of mannan-binding lectin-associated serine protease 2. N. Engl. J. Med. 349, 554–560 (2003).
pubmed: 12904520
doi: 10.1056/NEJMoa022836
Katz, D. H. et al. Proteomic profiling platforms head to head: leveraging genetics and clinical traits to compare aptamer- and antibody-based methods. Sci. Adv. 8, eabm5164 (2022).
pubmed: 35984888
pmcid: 9390994
doi: 10.1126/sciadv.abm5164
Pietzner, M. et al. Synergistic insights into human health from aptamer- and antibody-based proteomic profiling. Nat. Commun. 12, 6822 (2021).
pubmed: 34819519
pmcid: 8613205
doi: 10.1038/s41467-021-27164-0
Haslam, D. E. et al. Stability and reproducibility of proteomic profiles in epidemiological studies: comparing the Olink and SOMAscan platforms. Proteomics 22, e2100170 (2022).
pubmed: 35598103
pmcid: 9923770
doi: 10.1002/pmic.202100170
Raffield, L. M. et al. Comparison of proteomic assessment methods in multiple cohort studies. Proteomics 20, e1900278 (2020).
pubmed: 32386347
pmcid: 7425176
doi: 10.1002/pmic.201900278
Sirugo, G., Williams, S. M. & Tishkoff, S. A. The missing diversity in human genetic studies. Cell 177, 26–31 (2019).
pubmed: 30901543
pmcid: 7380073
doi: 10.1016/j.cell.2019.02.048
Zhang, J. et al. Plasma proteome analyses in individuals of European and African ancestry identify cis-pQTLs and models for proteome-wide association studies. Nat. Genet. 54, 593–602 (2022).
pubmed: 35501419
pmcid: 9236177
doi: 10.1038/s41588-022-01051-w
Inker, L. A. et al. Estimating glomerular filtration rate from serum creatinine and cystatin C. N. Engl. J. Med. 367, 20–29 (2012).
pubmed: 22762315
pmcid: 4398023
doi: 10.1056/NEJMoa1114248
Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).
pubmed: 20808728
pmcid: 2929880
doi: 10.18637/jss.v033.i01
Kuhn, R. M., Haussler, D. & Kent, W. J. The UCSC genome browser and associated tools. Brief. Bioinform. 14, 144–161 (2013).
pubmed: 22908213
doi: 10.1093/bib/bbs038
Mbatchou, J. et al. Computationally efficient whole-genome regression for quantitative and binary traits. Nat. Genet. 53, 1097–1103 (2021).
pubmed: 34017140
doi: 10.1038/s41588-021-00870-7
Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).
pubmed: 25722852
pmcid: 4342193
doi: 10.1186/s13742-015-0047-8
Wang, G., Sarkar, A., Carbonetto, P. & Stephens, M. A simple new approach to variable selection in regression, with application to genetic fine mapping. J. R. Stat. Soc. B 82, 1273–1300 (2020).
doi: 10.1111/rssb.12388
Bulik-Sullivan, B. K. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).
pubmed: 25642630
pmcid: 4495769
doi: 10.1038/ng.3211
Vuckovic, D. et al. The polygenic and monogenic basis of blood traits and diseases. Cell 182, 1214–1231 (2020).
pubmed: 32888494
pmcid: 7482360
doi: 10.1016/j.cell.2020.08.008
Foley, C. N. et al. A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits. Nat. Commun. 12, 764 (2021).
pubmed: 33536417
pmcid: 7858636
doi: 10.1038/s41467-020-20885-8
Groot, H. E. et al. Genetically determined ABO blood group and its associations with health and disease. Arterioscler. Thromb. Vasc. Biol. 40, 830–838 (2020).
pubmed: 31969017
doi: 10.1161/ATVBAHA.119.313658
Wolpin, B. M. et al. Pancreatic cancer risk and ABO blood group alleles: results from the pancreatic cancer cohort consortium. Cancer Res. 70, 1015–1023 (2010).
pubmed: 20103627
pmcid: 2943735
doi: 10.1158/0008-5472.CAN-09-2993
Pare, G. et al. Novel association of ABO histo-blood group antigen with soluble ICAM-1: results of a genome-wide association study of 6,578 women. PLoS Genet. 4, e1000118 (2008).
pubmed: 18604267
pmcid: 2432033
doi: 10.1371/journal.pgen.1000118
Melzer, D. et al. A genome-wide association study identifies protein quantitative trait loci (pQTLs). PLoS Genet. 4, e1000072 (2008).
pubmed: 18464913
pmcid: 2362067
doi: 10.1371/journal.pgen.1000072
Jain, A. & Tuteja, G. TissueEnrich: tissue-specific gene enrichment analysis. Bioinformatics 35, 1966–1967 (2019).
pubmed: 30346488
doi: 10.1093/bioinformatics/bty890
Uhlen, M. et al. Proteomics. tissue-based map of the human proteome. Science 347, 1260419 (2015).
pubmed: 25613900
doi: 10.1126/science.1260419
Shen, Y. et al. A map of the cis-regulatory sequences in the mouse genome. Nature 488, 116–120 (2012).
pubmed: 22763441
pmcid: 4041622
doi: 10.1038/nature11243
Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife https://doi.org/10.7554/eLife.34408 (2018).
Elsworth, B. et al. The MRC IEU OpenGWAS data infrastructure. Preprint at bioRxiv https://doi.org/10.1101/2020.08.10.244293 (2020).