Plasma proteomic associations with genetics and health in the UK Biobank.


Journal

Nature
ISSN: 1476-4687
Titre abrégé: Nature
Pays: England
ID NLM: 0410462

Informations de publication

Date de publication:
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

329-338

Investigateurs

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).

Auteurs

Benjamin B Sun (BB)

Translational Sciences, Research & Development, Biogen, Cambridge, MA, USA. bbsun92@outlook.com.

Joshua Chiou (J)

Internal Medicine Research Unit, Worldwide Research, Development and Medical, Pfizer, Cambridge, MA, USA.

Matthew Traylor (M)

Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK.

Christian Benner (C)

Genentech, San Francisco, CA, USA.

Yi-Hsiang Hsu (YH)

Amgen Research, Cambridge, MA, USA.

Tom G Richardson (TG)

Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK.
Genomic Sciences, GlaxoSmithKline, Stevenage, UK.

Praveen Surendran (P)

Genomic Sciences, GlaxoSmithKline, Stevenage, UK.

Anubha Mahajan (A)

Genentech, San Francisco, CA, USA.

Chloe Robins (C)

Genomic Sciences, GlaxoSmithKline, Collegeville, PA, USA.

Steven G Vasquez-Grinnell (SG)

Bristol Myers Squibb, Princeton, NJ, USA.

Liping Hou (L)

Population Analytics, Janssen Research & Development, Spring House, PA, USA.

Erika M Kvikstad (EM)

Bristol Myers Squibb, Princeton, NJ, USA.

Oliver S Burren (OS)

Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK.

Jonathan Davitte (J)

Genomic Sciences, GlaxoSmithKline, Collegeville, PA, USA.

Kyle L Ferber (KL)

Biostatistics, Research and Development, Biogen, Cambridge, MA, USA.

Christopher E Gillies (CE)

Regeneron Genetics Center, Tarrytown, NY, USA.

Åsa K Hedman (ÅK)

External Science and Innovation Target Sciences, Worldwide Research, Development and Medical, Pfizer, Stockholm, Sweden.

Sile Hu (S)

Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK.

Tinchi Lin (T)

Analytics and Data Sciences, Biogen, Cambridge, MA, USA.

Rajesh Mikkilineni (R)

Data Science Institute, Takeda Development Center Americas, Cambridge, MA, USA.

Rion K Pendergrass (RK)

Genentech, San Francisco, CA, USA.

Corran Pickering (C)

UK Biobank, Stockport, UK.

Bram Prins (B)

Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK.

Denis Baird (D)

Translational Sciences, Research & Development, Biogen, Cambridge, MA, USA.

Chia-Yen Chen (CY)

Translational Sciences, Research & Development, Biogen, Cambridge, MA, USA.

Lucas D Ward (LD)

Alnylam Human Genetics, Discovery & Translational Research, Alnylam Pharmaceuticals, Cambridge, MA, USA.

Aimee M Deaton (AM)

Alnylam Human Genetics, Discovery & Translational Research, Alnylam Pharmaceuticals, Cambridge, MA, USA.

Samantha Welsh (S)

UK Biobank, Stockport, UK.

Carissa M Willis (CM)

Alnylam Human Genetics, Discovery & Translational Research, Alnylam Pharmaceuticals, Cambridge, MA, USA.

Nick Lehner (N)

Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.

Matthias Arnold (M)

Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA.

Maria A Wörheide (MA)

Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.

Karsten Suhre (K)

Bioinformatics Core, Weill Cornell Medicine-Qatar, Doha, Qatar.

Gabi Kastenmüller (G)

Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.

Anurag Sethi (A)

Calico Life Sciences, San Francisco, CA, USA.

Madeleine Cule (M)

Calico Life Sciences, San Francisco, CA, USA.

Anil Raj (A)

Calico Life Sciences, San Francisco, CA, USA.

Lucy Burkitt-Gray (L)

UK Biobank, Stockport, UK.

Eugene Melamud (E)

Calico Life Sciences, San Francisco, CA, USA.

Mary Helen Black (MH)

Population Analytics, Janssen Research & Development, Spring House, PA, USA.

Eric B Fauman (EB)

Internal Medicine Research Unit, Worldwide Research, Development and Medical, Pfizer, Cambridge, MA, USA.

Joanna M M Howson (JMM)

Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK.

Hyun Min Kang (HM)

Regeneron Genetics Center, Tarrytown, NY, USA.

Mark I McCarthy (MI)

Genentech, San Francisco, CA, USA.

Paul Nioi (P)

Alnylam Human Genetics, Discovery & Translational Research, Alnylam Pharmaceuticals, Cambridge, MA, USA.

Slavé Petrovski (S)

Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK.
Department of Medicine, University of Melbourne, Austin Health, Melbourne, Victoria, Australia.

Robert A Scott (RA)

Genomic Sciences, GlaxoSmithKline, Stevenage, UK.

Erin N Smith (EN)

Takeda Development Center Americas, San Diego, CA, USA.

Sándor Szalma (S)

Takeda Development Center Americas, San Diego, CA, USA.

Dawn M Waterworth (DM)

Immunology, Janssen Research & Development, Spring House, PA, USA.

Lyndon J Mitnaul (LJ)

Regeneron Genetics Center, Tarrytown, NY, USA.

Joseph D Szustakowski (JD)

Bristol Myers Squibb, Princeton, NJ, USA.

Bradford W Gibson (BW)

Amgen Research, Cambridge, MA, USA.

Melissa R Miller (MR)

Internal Medicine Research Unit, Worldwide Research, Development and Medical, Pfizer, Cambridge, MA, USA.

Christopher D Whelan (CD)

Translational Sciences, Research & Development, Biogen, Cambridge, MA, USA. christopherdwhelan@outlook.com.
Neuroscience Data Science, Janssen Research & Development, Cambridge, MA, USA. christopherdwhelan@outlook.com.

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