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Cited by 57Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and AnimalsBioinformatics and Genomic Networks

Proteogenomic links to human metabolic diseases

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Koprulu, Mine, Carrasco-Zanini, Julia, Wheeler, Eleanor, Lockhart, Sam, Kerrison, Nicola D., Wareham, Nicholas J., Pietzner, Maik, Langenberg, Claudia·2023·Nature Metabolism
DOI 10.1038/s42255-023-00753-7PMC PMC7614946
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Abstract

Abstract Studying the plasma proteome as the intermediate layer between the genome and the phenome has the potential to identify disease causing genes and proteins and to improve our understanding of the underlying mechanisms. Here, we conducted a cis -focused proteogenomic analysis of 2,923 plasma proteins measured in 1,180 individuals using novel antibody-based assays (Olink® Explore 1536 and Explore Expansion) to identify disease causing genes and proteins across the human phenome. We describe 1,553 distinct credible sets of protein quantitative trait loci (pQTL), of which 256 contained cis-pQTLs not previously reported. We identify 224 cis-pQTLs shared with 578 unique health outcomes using statistical colocalization, including, gastrin releasing peptide (GRP) as a potential therapeutic target for type 2 diabetes. We observed convergence of phenotypic consequences of cis-pQTLs and rare loss-of-function gene burden for twelve protein coding genes (e.g., TIMD4 and low-density lipoprotein metabolism), highlighting the complementary nature of both approaches for drug target prioritization. Proteogenomic evidence also improved causal gene assignment at 40% (n=192) of overlapping GWAS loci, including DKKL1 as the candidate causal gene for multiple sclerosis. Our findings demonstrate the ability of broad capture, high-throughput proteomic technologies to robustly identify new gene-protein-disease links, provide mechanistic insight, and add value to existing GWASs by enabling and refining causal gene assignment.

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  • Antibody-based genetic studies have focused on narrower collections of proteins at large scale or broad collections of proteins at smaller scale.

    “Antibody-based genetic studies have focused on narrower collections of proteins at large scale 22 or broad collections of proteins at smaller scale 23 .”

    From Plasma proteomic associations with genetics and health in the UK Biobank (2023) · cites this paper as [23]

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  1. [1]Buniello A MacArthur JAL Cerezo M Harris LW Hayhurst J Malangone C The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019 Nucleic Acids Res 2019 47 D1 D1005 D12 30445434 PMC6323933
  2. [2]Barbeira AN Bonazzola R Gamazon ER Liang Y Park Y Kim-Hellmuth S Exploiting the GTEx resources to decipher the mechanisms at GWAS loci Genome Biol 2021 22 1 49 33499903 PMC7836161
  3. [3]Moore JE Purcaro MJ Pratt HE Epstein CB Shoresh N Adrian J Expanded encyclopaedias of DNA elements in the human and mouse genomes Nature 2020 583 7818 699 710 32728249 PMC7410828
  4. [4]Abell NS DeGorter MK Gloudemans MJ Greenwald E Smith KS He Z Multiple causal variants underlie genetic associations in humans Science 2022 375 6586 1247 54 35298243 PMC9725108
  5. [5]Suhre K Arnold M Bhagwat AM Cotton RJ Engelke R Raffler J Connecting genetic risk to disease end points through the human blood plasma proteome Nat Commun 2017 8 14357 28240269 PMC5333359
  6. [6]Sun BB Maranville JC Peters JE Stacey D Staley JR Blackshaw J Genomic atlas of the human plasma proteome Nature 2018 558 7708 73 9 29875488 PMC6697541
  7. [7]Folkersen L Fauman E Sabater-Lleal M Strawbridge RJ Frånberg M Sennblad B Mapping of 79 loci for 83 plasma protein biomarkers in cardiovascular disease PLoS Genet 2017 13 4 e1006706 28369058 PMC5393901
  8. [8]Yao C Chen G Song C Keefe J Mendelson M Huan T Genome-wide mapping of plasma protein QTLs identifies putatively causal genes and pathways for cardiovascular disease Nat Commun 2018 9 1 3268 30111768 PMC6093935
  9. [9]Gilly A Park YC Png G Barysenka A Fischer I Bjørnland T Whole-genome sequencing analysis of the cardiometabolic proteome Nat Commun 2020 11 1 6336 33303764 PMC7729872
  10. [11]Gudjonsson A Gudmundsdottir V Axelsson GT Gudmundsson EF Jonsson BG Launer LJ A genome-wide association study of serum proteins reveals shared loci with common diseases Nat Commun 2022 13 1 480 35078996 PMC8789779
  11. [12]Katz DH Tahir UA Bick AG Pampana A Ngo D Benson MD Whole Genome Sequence Analysis of the Plasma Proteome in Black Adults Provides Novel Insights Into Cardiovascular Disease Circulation 2022 145 5 357 70 34814699 PMC9158509
  12. [13]Png G Barysenka A Repetto L Navarro P Shen X Pietzner M Mapping the serum proteome to neurological diseases using whole genome sequencing Nat Commun 2021 12 1 7042 34857772 PMC8640022
  13. [15]Pietzner M Wheeler E Carrasco-Zanini J Cortes A Koprulu M Wörheide MA Mapping the proteo-genomic convergence of human diseases Science 2021 374 6569 eabj1541 34648354 PMC9904207
  14. [16]Zhang J Dutta D Köttgen A Tin A Schlosser P Grams ME Plasma proteome analyses in individuals of European and African ancestry identify cis-pQTLs and models for proteome-wide association studies Nat Genet 2022 54 5 593 602 35501419 PMC9236177
  15. [17]Sun BB Chiou J Traylor M Benner C Hsu Y-H Richardson TG Genetic regulation of the human plasma proteome in 54,306 UK Biobank participants bioRxiv 2022:2022.06.17.496443
  16. [18]Folkersen L Gustafsson S Wang Q Hansen DH Hedman A Schork A Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals Nat Metab 2020 2 10 1135 48 33067605 PMC7611474
  17. [19]Dhindsa RS Burren OS Sun BB Prins BP Matelska D Wheeler E Influences of rare protein-coding genetic variants on the human plasma proteome in 50,829 UK Biobank participants bioRxiv 2022:2022.10.09.511476
  18. [20]Enroth S Johansson A Enroth SB Gyllensten U Strong effects of genetic and lifestyle factors on biomarker variation and use of personalized cutoffs Nat Commun 2014 5 4684 25147954 PMC4143927
  19. [21]Day N Oakes S Luben R Khaw KT Bingham S Welch A EPIC-Norfolk: study design and characteristics of the cohort. European Prospective Investigation of Cancer Br J Cancer 1999 80 Suppl 1 95 103 10466767
  20. [22]Vujkovic M Keaton JM Lynch JA Miller DR Zhou J Tcheandjieu C Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multiancestry meta-analysis Nat Genet 2020 52 7 680 91 32541925 PMC7343592
  21. [23]Spracklen CN Horikoshi M Kim YJ Lin K Bragg F Moon S Identification of type 2 diabetes loci in 433,540 East Asian individuals Nature 2020 582 7811 240 5 32499647 PMC7292783
  22. [24]Mahajan A Spracklen CN Zhang W Ng MCY Petty LE Kitajima H Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation Nat Genet 2022 54 5 560 72 35551307 PMC9179018
  23. [25]Gaziano JM Concato J Brophy M Fiore L Pyarajan S Breeling J Million Veteran Program: A mega-biobank to study genetic influences on health and disease J Clin Epidemiol 2016 70 214 23 26441289
  24. [26]McDonald TJ Nilsson G Vagne M Ghatei M Bloom SR Mutt V A gastrin releasing peptide from the porcine nonantral gastric tissue Gut 1978 19 9 767 74 361511 PMC1412172
  25. [27]McDonald TJ Jörnvall H Nilsson G Vagne M Ghatei M Bloom SR Characterization of a gastrin releasing peptide from porcine non-antral gastric tissue Biochem Biophys Res Commun 1979 90 1 227 33 496973
  26. [28]Ladenheim EE Taylor JE Coy DH Moore KA Moran TH Hindbrain GRP receptor blockade antagonizes feeding suppression by peripherally administered GRP Am J Physiol 1996 271 1 Pt 2 R180 4 8760218
  27. [29]Ladenheim EE Hampton LL Whitney AC White WO Battey JF Moran TH Disruptions in feeding and body weight control in gastrin-releasing peptide receptor deficient mice J Endocrinol 2002 174 2 273 81 12176666
  28. [30]Persson K Gingerich RL Nayak S Wada K Wada E Ahrén B Reduced GLP-1 and insulin responses and glucose intolerance after gastric glucose in GRP receptor-deleted mice Am J Physiol Endocrinol Metab 2000 279 5 E956 62 11052949
  29. [31]Gutzwiller JP Drewe J Hildebrand P Rossi L Lauper JZ Beglinger C Effect of intravenous human gastrin-releasing peptide on food intake in humans Gastroenterology 1994 106 5 1168 73 8174879
  30. [32]Mhalhal TR Washington MC Newman KD Heath JC Sayegh AI Combined gastrin releasing peptide-29 and glucagon like peptide-1 reduce body weight more than each individual peptide in diet-induced obese male rats Neuropeptides 2018 67 71 8 29180139
  31. [33]Frullanti E Berking C Harbeck N Jézéquel P Haugen A Mawrin C Meta and pooled analyses of FGFR4 Gly388Arg polymorphism as a cancer prognostic factor Eur J Cancer Prev 2011 20 4 340 7 21412156
  32. [34]Chou CH Hsieh MJ Chuang CY Lin JT Yeh CM Tseng PY Functional FGFR4 Gly388Arg polymorphism contributes to oral squamous cell carcinoma susceptibility Oncotarget 2017 8 56 96225 38 29221201 PMC5707095
  33. [35]Xiong SW Ma J Feng F Fu W Shu SR Ma T Functional FGFR4 Gly388Arg polymorphism contributes to cancer susceptibility: Evidence from meta-analysis Oncotarget 2017 8 15 25300 9 28445975 PMC5421931
  34. [36]Ulaganathan VK Sperl B Rapp UR Ullrich A Germline variant FGFR4 p.G388R exposes a membrane-proximal STAT3 binding site Nature 2015 528 7583 570 4 26675719
  35. [37]Shin DJ Osborne TF FGF15/FGFR4 integrates growth factor signaling with hepatic bile acid metabolism and insulin action J Biol Chem 2009 284 17 11110 20 19237543 PMC2670116
  36. [38]Ge H Zhang J Gong Y Gupte J Ye J Weiszmann J Fibroblast growth factor receptor 4 (FGFR4) deficiency improves insulin resistance and glucose metabolism under diet-induced obesity conditions J Biol Chem 2014 289 44 30470 80 25204652 PMC4215229
  37. [39]Wu X Ge H Lemon B Weiszmann J Gupte J Hawkins N Selective activation of FGFR4 by an FGF19 variant does not improve glucose metabolism in ob/ob mice Proc Natl Acad Sci U S A 2009 106 34 14379 84 19706524 PMC2726038
  38. [40]Huang X Yang C Luo Y Jin C Wang F McKeehan WL FGFR4 prevents hyperlipidemia and insulin resistance but underlies high-fat diet induced fatty liver Diabetes 2007 56 10 2501 10 17664243
  39. [41]Lutz SZ Hennige AM Peter A Kovarova M Totsikas C Machann J The Gly385(388)Arg Polymorphism of the FGFR4 Receptor Regulates Hepatic Lipogenesis Under Healthy Diet J Clin Endocrinol Metab 2019 104 6 2041 53 30541128
  40. [42]Micha R Khatibzadeh S Shi P Fahimi S Lim S Andrews KG Global, regional, and national consumption levels of dietary fats and oils in 1990 and 2010: a systematic analysis including 266 country-specific nutrition surveys BMJ 2014 348 g2272 24736206 PMC3987052
  41. [43]Lappalainen T MacArthur DG From variant to function in human disease genetics Science 2021 373 6562 1464 8 34554789
  42. [44]Cencioni MT Mattoscio M Magliozzi R Bar-Or A Muraro PA B cells in multiple sclerosis - from targeted depletion to immune reconstitution therapies Nat Rev Neurol 2021 17 7 399 414 34075251
  43. [45]Granqvist M Boremalm M Poorghobad A Svenningsson A Salzer J Frisell T Comparative Effectiveness of Rituximab and Other Initial Treatment Choices for Multiple Sclerosis JAMA Neurol 2018 75 3 320 7 29309484 PMC5885857
  44. [46]Morris JA Kemp JP Youlten SE Laurent L Logan JG Chai RC An atlas of genetic influences on osteoporosis in humans and mice Nat Genet 2019 51 2 258 66 30598549 PMC6358485
  45. [47]Medina-Gomez C Kemp JP Trajanoska K Luan J Chesi A Ahluwalia TS Life-Course Genome-wide Association Study Meta-analysis of Total Body BMD and Assessment of Age-Specific Effects Am J Hum Genet 2018 102 1 88 102 29304378 PMC5777980
  46. [48]Lotta LA Wittemans LBL Zuber V Stewart ID Sharp SJ Luan J Association of Genetic Variants Related to Gluteofemoral vs Abdominal Fat Distribution With Type 2 Diabetes, Coronary Disease, and Cardiovascular Risk Factors JAMA 2018 320 24 2553 63 30575882 PMC6583513
  47. [49]Acosta-Herrera M Kerick M González-Serna D Wijmenga C Franke A Gregersen PK Genome-wide meta-analysis reveals shared new loci in systemic seropositive rheumatic diseases Ann Rheum Dis 2019 78 3 311 9 30573655 PMC6800208
  48. [51]Clarke LA Giugliani R Guffon N Jones SA Keenan HA Munoz-Rojas MV Genotypephenotype relationships in mucopolysaccharidosis type I (MPS I): Insights from the International MPS I Registry Clin Genet 2019 96 4 281 9 31194252 PMC6852151
  49. [52]Kuehn SC Koehne T Cornils K Markmann S Riedel C Pestka JM Impaired bone remodeling and its correction by combination therapy in a mouse model of mucopolysaccharidosis-I Hum Mol Genet 2015 24 24 7075 86 26427607
  50. [53]Backman JD Li AH Marcketta A Sun D Mbatchou J Kessler MD Exome sequencing and analysis of 454,787 UK Biobank participants Nature 2021 599 7886 628 34 34662886 PMC8596853
  51. [54]Szustakowski JD Balasubramanian S Kvikstad E Khalid S Bronson PG Sasson A Advancing human genetics research and drug discovery through exome sequencing of the UK Biobank Nat Genet 2021 53 7 942 8 34183854
  52. [55]Plenge RM Scolnick EM Altshuler D Validating therapeutic targets through human genetics Nat Rev Drug Discov 2013 12 8 581 94 23868113
  53. [56]Kathiresan S Willer CJ Peloso GM Demissie S Musunuru K Schadt EE Common variants at 30 loci contribute to polygenic dyslipidemia Nat Genet 2009 41 1 56 65 19060906 PMC2881676
  54. [57]Lemke G How macrophages deal with death Nat Rev Immunol 2019 19 9 539 49 31019284 PMC6733267
  55. [59]Kuchroo VK Dardalhon V Xiao S Anderson AC New roles for TIM family members in immune regulation Nat Rev Immunol 2008 8 8 577 80 18617884
  56. [60]Fazio S Hasty AH Carter KJ Murray AB Price JO Linton MF Leukocyte low density lipoprotein receptor (LDL-R) does not contribute to LDL clearance in vivo: bone marrow transplantation studies in the mouse J Lipid Res 1997 38 2 391 400 9162757
  57. [61]Magalhaes MS Smith P Portman JR Jackson-Jones LH Bain CC Ramachandran P Role of Tim4 in the regulation of ABCA1 Nat Commun 2021 12 1 4434 34290249 PMC8295389
  58. [62]Alsheikh AJ Wollenhaupt S King EA Reeb J Ghosh S Stolzenburg LR The landscape of GWAS validation; systematic review identifying 309 validated non-coding variants across 130 human diseases BMC Med Genomics 2022 15 1 74 35365203 PMC8973751
  59. [63]Porcu E Rüeger S Lepik K Santoni FA Reymond A Kutalik Z Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits Nat Commun 2019 10 1 3300 31341166 PMC6656778
  60. [64]Võsa U Claringbould A Westra HJ Bonder MJ Deelen P Zeng B Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression Nat Genet 2021 53 9 1300 10 34475573 PMC8432599
  61. [65]Gudmundsson S Karczewski KJ Francioli LC Tiao G Cummings BB Alföldi J Addendum: The mutational constraint spectrum quantified from variation in 141,456 humans Nature 2021 597 7874 E3 E4 34373650 PMC8410591
  62. [66]Mostafavi H Spence JP Naqvi S Pritchard JK Limited overlap of eQTLs and GWAS hits due to systematic differences in discovery bioRxiv 2022:2022.05.07.491045
  63. [67]Akbari P Gilani A Sosina O Kosmicki JA Khrimian L Fang YY Sequencing of 640,000 exomes identifies GPR75 variants associated with protection from obesity Science 2021 373 6550 PMC10275396 34210852
  64. [68]Zhong W Edfors F Gummesson A Bergström G Fagerberg L Uhlén M Next generation plasma proteome profiling to monitor health and disease Nat Commun 2021 12 1 2493 33941778 PMC8093230
  65. [69]Assarsson E Lundberg M Holmquist G Björkesten J Thorsen SB Ekman D Homogenous 96-plex PEA immunoassay exhibiting high sensitivity, specificity, and excellent scalability PLoS One 2014 9 4 e95192 24755770 PMC3995906
  66. [70]Wang G Sarkar A Car bonetto P Stephens M A simple new approach to variable selection in regression, with application to genetic fine mapping Royal Statistical Society 2020 1273 300 PMC10201948 37220626
  67. [71]Jiang L Zheng Z Qi T Kemper KE Wray NR Visscher PM A resource-efficient tool for mixed model association analysis of large-scale data Nat Genet 2019 51 12 1749 55 31768069
  68. [72]Willer CJ Li Y Abecasis GR METAL: fast and efficient meta-analysis of genomewide association scans Bioinformatics 2010 26 17 2190 1 20616382 PMC2922887
  69. [73]McLaren W Gil L Hunt SE Riat HS Ritchie GR Thormann A The Ensembl Variant Effect Predictor Genome Biol 2016 17 1 122 27268795 PMC4893825
  70. [74]Consortium U UniProt: the universal protein knowledgebase in 2021 Nucleic Acids Res 2021 49 D1 D480 D9 33237286 PMC7778908
  71. [75]Elsworth B Lyon M Alexander T Liu Y Matthews P Hallett J The MRC IEU OpenGWAS data infrastructure bioRxiv 2020:2020.08.10.244293
  72. [76]Hemani G Zheng J Elsworth B Wade KH Haberland V Baird D The MR-Base platform supports systematic causal inference across the human phenome Elife 2018 7 PMC5976434 29846171
  73. [78]Consortium G The GTEx Consortium atlas of genetic regulatory effects across human tissues Science 2020 369 6509 1318 30 32913098 PMC7737656
  74. [79]Foley CN Staley JR Breen PG Sun BB Kirk PDW Burgess S A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits Nat Commun 2021 12 1 764 33536417 PMC7858636
  75. [80]Loh PR Tucker G Bulik-Sullivan BK Vilhjálmsson BJ Finucane HK Salem RM Efficient Bayesian mixed-model analysis increases association power in large cohorts Nat Genet 2015 47 3 284 90 25642633 PMC4342297
  76. [81]Pulit SL Stoneman C Morris AP Wood AR Glastonbury CA Tyrrell J Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry Hum Mol Genet 2019 28 1 166 74 30239722 PMC6298238

Cited by

Sorted by citations

Authors
Koprulu, M., Carrasco-Zanini, J., Wheeler, E., Lockhart, S., Kerrison, N. D., Wareham, N. J., Pietzner, M., & Langenberg, C.
Affiliations
  • University of Cambridge (GB)
  • Queen Mary University of London (GB)
Corresponding author
Langenberg, Claudia
ORCID
Venue
Nature Metabolism
Publisher
Springer Science and Business Media LLC
Published
2023-02-23
Volume
5
Issue
3
Pages
516-528
ISSN
2522-5812 (ISSN-L)
DOI
10.1038/s42255-023-00753-7
PMC
PMC7614946
OpenAlex
W4321618012
Cited by
57
References
6
Citations by year
2026: 13 · 2025: 23 · 2024: 15 · 2023: 6
FWCI
9.05
Type
journal article
Language
en
Update notices
correction (2023-03-19)
Open access
Licence
Locations
  • Nature Metabolism (publishedVersion, cc-by)
  • medRxiv (acceptedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion, cc-by)
  • Queen Mary Research Online (Queen Mary University of London) (submittedVersion, cc-by)
Funders
  • Wellcome Trust
  • UK Research and Innovation (MR/V033867/1)
  • Cancer Research UK (MC_PC_13048, C864/A14136)
  • National Institute for Health and Care Research
  • University of Cambridge (MR/N003284/1)
  • Cambridge Trust
  • Gates Cambridge Trust
  • Medical Research Council (MC_UU_12015/1, MC_PC_21036, MC_PC_13048, C864/A14136, MR/N003284/1, MC_UU_00006/1), MR/V033867/1, G107794)
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 › Molecular Biology › Bioinformatics and Genomic Networks
Keywords
proteogenomics, plasma proteome, protein quantitative trait loci, cis-pQTL, metabolic disorders, type 2 diabetes, gastrin-releasing peptide, rare variants, TIMD4, lipoprotein metabolism, therapeutic target
MeSH terms
Proteogenomics, Blood Proteins, Diabetes Mellitus, Type 2, Humans, Quantitative Trait Loci, Proteomics

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