Common pitfalls in drug target Mendelian randomization and how to avoid them.
Drug development
Drug target
Mendelian randomization
Pharmacology
Journal
BMC medicine
ISSN: 1741-7015
Titre abrégé: BMC Med
Pays: England
ID NLM: 101190723
Informations de publication
Date de publication:
15 Oct 2024
15 Oct 2024
Historique:
received:
01
08
2024
accepted:
10
10
2024
medline:
16
10
2024
pubmed:
16
10
2024
entrez:
15
10
2024
Statut:
epublish
Résumé
Drug target Mendelian randomization describes the use of genetic variants as instrumental variables for studying the effects of pharmacological agents. The paradigm can be used to inform on all aspects of drug development and has become increasingly popular over the last decade, particularly given the time- and cost-efficiency with which it can be performed even before commencing clinical studies. In this review, we describe the recent emergence of drug target Mendelian randomization, its common pitfalls, how best to address them, as well as potential future directions. Throughout, we offer advice based on our experiences on how to approach these types of studies, which we hope will be useful for both practitioners and those translating the findings from such work. Drug target Mendelian randomization is nuanced and requires a combination of biological, statistical, genetic, epidemiological, clinical, and pharmaceutical expertise to be utilized to its full potential. Unfortunately, these skillsets are relatively infrequently combined in any given study.
Sections du résumé
BACKGROUND
BACKGROUND
Drug target Mendelian randomization describes the use of genetic variants as instrumental variables for studying the effects of pharmacological agents. The paradigm can be used to inform on all aspects of drug development and has become increasingly popular over the last decade, particularly given the time- and cost-efficiency with which it can be performed even before commencing clinical studies.
MAIN BODY
METHODS
In this review, we describe the recent emergence of drug target Mendelian randomization, its common pitfalls, how best to address them, as well as potential future directions. Throughout, we offer advice based on our experiences on how to approach these types of studies, which we hope will be useful for both practitioners and those translating the findings from such work.
CONCLUSIONS
CONCLUSIONS
Drug target Mendelian randomization is nuanced and requires a combination of biological, statistical, genetic, epidemiological, clinical, and pharmaceutical expertise to be utilized to its full potential. Unfortunately, these skillsets are relatively infrequently combined in any given study.
Identifiants
pubmed: 39407214
doi: 10.1186/s12916-024-03700-9
pii: 10.1186/s12916-024-03700-9
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
473Informations de copyright
© 2024. The Author(s).
Références
Daghlas I, Gill D. Mendelian randomization as a tool to inform drug development using human genetics. Camb Prism Precis Med. 2023;1:e16.
pubmed: 38550933
pmcid: 10953771
doi: 10.1017/pcm.2023.5
Dowden H, Munro J. Trends in clinical success rates and therapeutic focus. Nat Rev Drug Discov. 2019;18(7):495–6.
pubmed: 31267067
doi: 10.1038/d41573-019-00074-z
Smith GD, Ebrahim S. “Mendelian randomization”: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol. 2003;32(1):1–22.
pubmed: 12689998
doi: 10.1093/ije/dyg070
Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014;23(R1):R89–98.
pubmed: 25064373
pmcid: 4170722
doi: 10.1093/hmg/ddu328
Hingorani A, Humphries S. Nature’s randomised trials. Lancet. 2005;366(9501):1906–8.
pubmed: 16325682
doi: 10.1016/S0140-6736(05)67767-7
Gill D, Georgakis MK, Walker VM, Schmidt AF, Gkatzionis A, Freitag DF, et al. Mendelian randomization for studying the effects of perturbing drug targets. Wellcome Open Res. 2021;6:16.
pubmed: 33644404
pmcid: 7903200
doi: 10.12688/wellcomeopenres.16544.1
King EA, Davis JW, Degner JF. 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. 2019;15(12): e1008489.
pubmed: 31830040
pmcid: 6907751
doi: 10.1371/journal.pgen.1008489
Nelson MR, Tipney H, Painter JL, Shen J, Nicoletti P, Shen Y, et al. The support of human genetic evidence for approved drug indications. Nat Genet. 2015;47(8):856–60.
pubmed: 26121088
doi: 10.1038/ng.3314
Hinkson IV, Madej B, Stahlberg EA. Accelerating Therapeutics for Opportunities in Medicine: A Paradigm Shift in Drug Discovery. Front Pharmacol. 2020;11:770.
pubmed: 32694991
pmcid: 7339658
doi: 10.3389/fphar.2020.00770
Heilbron K, Mozaffari SV, Vacic V, Yue P, Wang W, Shi J, et al. Advancing drug discovery using the power of the human genome. J Pathol. 2021;254(4):418–29.
pubmed: 33748968
pmcid: 8251523
doi: 10.1002/path.5664
Zhang X, Yu W, Li Y, Wang A, Cao H, Fu Y. Drug development advances in human genetics-based targets. MedComm (2020). 2024;5(2):e481.
pubmed: 38344397
pmcid: 10857782
Burgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM, et al. Guidelines for performing Mendelian randomization investigations. Wellcome Open Res. 2019;4:186.
pubmed: 32760811
doi: 10.12688/wellcomeopenres.15555.1
Davies NM, Holmes MV, Davey SG. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:k601.
pubmed: 30002074
pmcid: 6041728
doi: 10.1136/bmj.k601
Sanderson E, Davey Smith G, Windmeijer F, Bowden J. An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings. Int J Epidemiol. 2019;48(3):713–27.
pubmed: 30535378
doi: 10.1093/ije/dyy262
Larsson SC, Butterworth AS, Burgess S. Mendelian randomization for cardiovascular diseases: principles and applications. Eur Heart J. 2023;44(47):4913–24.
pubmed: 37935836
pmcid: 10719501
doi: 10.1093/eurheartj/ehad736
Burgess S, Mason AM, Grant AJ, Slob EAW, Gkatzionis A, Zuber V, et al. Using genetic association data to guide drug discovery and development: Review of methods and applications. Am J Hum Genet. 2023;110(2):195–214.
pubmed: 36736292
pmcid: 9943784
doi: 10.1016/j.ajhg.2022.12.017
Burgess S, Cronje HT. Incorporating biological and clinical insights into variant choice for Mendelian randomisation: examples and principles. eGastroenterology. 2024;2(1):e100042.
pubmed: 38362310
pmcid: 7615644
doi: 10.1136/egastro-2023-100042
Haycock PC, Burgess S, Wade KH, Bowden J, Relton C, Davey SG. Best (but oft-forgotten) practices: the design, analysis, and interpretation of Mendelian randomization studies. Am J Clin Nutr. 2016;103(4):965–78.
pubmed: 26961927
pmcid: 4807699
doi: 10.3945/ajcn.115.118216
Karhunen V, Larsson SC, Gill D. Genetically proxied growth-differentiation factor 15 levels and body mass index. Br J Clin Pharmacol. 2021;87(10):4036–9.
pubmed: 33686698
doi: 10.1111/bcp.14808
Ference BA, Ray KK, Catapano AL, Ference TB, Burgess S, Neff DR, et al. Mendelian Randomization Study of ACLY and Cardiovascular Disease. N Engl J Med. 2019;380(11):1033–42.
pubmed: 30865797
pmcid: 7612927
doi: 10.1056/NEJMoa1806747
Zhou H, Shen J, Fang W, Liu J, Zhang Y, Huang Y, et al. Mendelian randomization study showed no causality between metformin use and lung cancer risk. Int J Epidemiol. 2020;49(4):1406–7.
pubmed: 31628798
doi: 10.1093/ije/dyz218
Yarmolinsky J, Bull CJ, Walker VM, Nounu A, Davey SG. Mendelian randomization applied to pharmaceutical use: the case of metformin and lung cancer. Int J Epidemiol. 2020;49(4):1410–1.
pubmed: 32356895
pmcid: 7660135
doi: 10.1093/ije/dyaa059
Rena G, Hardie DG, Pearson ER. The mechanisms of action of metformin. Diabetologia. 2017;60(9):1577–85.
pubmed: 28776086
pmcid: 5552828
doi: 10.1007/s00125-017-4342-z
Davis RL. Mechanism of action and target identification: a matter of timing in drug discovery. iScience. 2020;23(9):101487.
pubmed: 32891054
pmcid: 7479624
doi: 10.1016/j.isci.2020.101487
Woolf B, Cronjé HT, Zagkos L, Larsson SC, Gill D, Burgess S. Comparison of caffeine consumption behavior with plasma caffeine levels as exposure measures in drug-target Mendelian randomization. Am J Epidemiol. 2024:kwae143.
Yuan S, Larsson SC, Gill D, Burgess S. Concerns about instrumental variable selection for biological effect versus uptake of proton pump inhibitors in Mendelian randomisation analysis. Gut. 2024:gutjnl-2024-332280.
Zagkos L, Cronje HT, Woolf B, de La Harpe R, Burgess S, Mantzoros CS, et al. Genetic investigation into the broad health implications of caffeine: evidence from phenome-wide, proteome-wide and metabolome-wide Mendelian randomization. BMC Med. 2024;22(1):81.
pubmed: 38378567
pmcid: 10880284
doi: 10.1186/s12916-024-03298-y
Gill D, Georgakis MK, Koskeridis F, Jiang L, Feng Q, Wei WQ, et al. Use of genetic variants related to antihypertensive drugs to inform on efficacy and side effects. Circulation. 2019;140(4):270–9.
pubmed: 31234639
pmcid: 6687408
doi: 10.1161/CIRCULATIONAHA.118.038814
Ye C, Wang T, Wang H, Lian G, Xie L. Causal relationship between genetic proxies for calcium channel blockers and the risk of depression: a drug-target Mendelian randomization study. Front Psychiatry. 2024;15:1377705.
pubmed: 38800057
pmcid: 11117141
doi: 10.3389/fpsyt.2024.1377705
Fan B, Schooling CM, Zhao JV. Genetic proxies for calcium channel blockers and cancer: a Mendelian randomization study. J Hum Hypertens. 2023;37(11):1028–32.
pubmed: 37117874
doi: 10.1038/s41371-023-00835-9
Sae-Jie W, Supasai S, Kivimaki M, Price JF, Wong A, Kumari M, et al. Triangulating evidence from observational and Mendelian randomization studies of ketone bodies for cognitive performance. BMC Med. 2023;21(1):340.
pubmed: 37667256
pmcid: 10478491
doi: 10.1186/s12916-023-03047-7
Rusina PV, Falaguera MJ, Romero JMR, McDonagh EM, Dunham I, Ochoa D. Genetic support for FDA-approved drugs over the past decade. Nat Rev Drug Discov. 2023;22(11):864.
pubmed: 37803084
doi: 10.1038/d41573-023-00158-x
Gaziano L, Giambartolomei C, Pereira AC, Gaulton A, Posner DC, Swanson SA, et al. Actionable druggable genome-wide Mendelian randomization identifies repurposing opportunities for COVID-19. Nat Med. 2021;27(4):668–76.
pubmed: 33837377
doi: 10.1038/s41591-021-01310-z
Gabe MBN, van der Velden WJC, Gadgaard S, Smit FX, Hartmann B, Brauner-Osborne H, et al. Enhanced agonist residence time, internalization rate and signalling of the GIP receptor variant [E354Q] facilitate receptor desensitization and long-term impairment of the GIP system. Basic Clin Pharmacol Toxicol. 2020;126 Suppl 6(Suppl 6):122–32.
pubmed: 31299132
doi: 10.1111/bcpt.13289
Greenland S. An introduction to instrumental variables for epidemiologists. Int J Epidemiol. 2000;29(6):1102.
pubmed: 11101554
doi: 10.1093/oxfordjournals.ije.a019909
Martens EP, Pestman WR, de Boer A, Belitser SV, Klungel OH. Instrumental variables: application and limitations. Epidemiology. 2006;17(3):260–7.
pubmed: 16617274
doi: 10.1097/01.ede.0000215160.88317.cb
Carithers LJ, Moore HM. The Genotype-Tissue Expression (GTEx) Project. Biopreserv Biobank. 2015;13(5):307–8.
pubmed: 26484569
doi: 10.1089/bio.2015.29031.hmm
Consortium GT. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science. 2015;348(6235):648–60.
doi: 10.1126/science.1262110
Consortium GT. The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013;45(6):580–5.
doi: 10.1038/ng.2653
Ferkingstad E, Sulem P, Atlason BA, Sveinbjornsson G, Magnusson MI, Styrmisdottir EL, et al. Large-scale integration of the plasma proteome with genetics and disease. Nat Genet. 2021;53(12):1712–21.
pubmed: 34857953
doi: 10.1038/s41588-021-00978-w
Sun BB, Chiou J, Traylor M, Benner C, Hsu YH, Richardson TG, et al. Plasma proteomic associations with genetics and health in the UK Biobank. Nature. 2023;622(7982):329–38.
pubmed: 37794186
pmcid: 10567551
doi: 10.1038/s41586-023-06592-6
Liu Y, Wang Q, Zhao Y, Liu L, Hu J, Qiao Y, et al. Identification of novel drug targets for multiple sclerosis by integrating plasma genetics and proteomes. Exp Gerontol. 2024;194:112505.
Liu M, Chen M, Tan J, Chen A, Guo J. Plasma proteins and inflammatory dermatoses: proteome-wide Mendelian randomization and colocalization analyses. Arch Dermatol Res. 2024;316(7):443.
pubmed: 38951247
doi: 10.1007/s00403-024-03191-x
Fan Q, Wen S, Zhang Y, Feng X, Zheng W, Liang X, et al. Assessment of circulating proteins in thyroid cancer: proteome-wide Mendelian randomization and colocalization analysis. iScience. 2024;27(6):109961.
pubmed: 38947504
pmcid: 11214373
doi: 10.1016/j.isci.2024.109961
Tian J, Keller MP, Broman AT, Kendziorski C, Yandell BS, Attie AD, et al. The dissection of expression quantitative trait locus hotspots. Genetics. 2016;202(4):1563–74.
pubmed: 26837753
pmcid: 4905536
doi: 10.1534/genetics.115.183624
Larsson SC, Michaelsson K, Mola-Caminal M, Hoijer J, Mantzoros CS. Genome-wide association and Mendelian randomization study of fibroblast growth factor 21 reveals causal associations with hyperlipidemia and possibly NASH. Metabolism. 2022;137: 155329.
pubmed: 36208799
doi: 10.1016/j.metabol.2022.155329
Larsson SC, Michaelsson K, Mola-Caminal M, Hoijer J, Mantzoros CS. Corrigendum to “Genome-wide association and Mendelian randomization study of fibroblast growth factor 21 reveals causal associations with hyperlipidemia and possibly NASH” [Metab Volume 137, December 2022, 155329]. Metabolism. 2023;143: 155555.
pubmed: 37023631
doi: 10.1016/j.metabol.2023.155555
Ongen H, Brown AA, Delaneau O, Panousis NI, Nica AC, Consortium G. Estimating the causal tissues for complex traits and diseases. Nat Genet. 2017;49(12):1676–83.
pubmed: 29058715
doi: 10.1038/ng.3981
Patel A, Gill D, Shungin D, Mantzoros CS, Knudsen LB, Bowden J, et al. Robust use of phenotypic heterogeneity at drug target genes for mechanistic insights: Application of cis-multivariable Mendelian randomization to GLP1R gene region. Genet Epidemiol. 2024;48(4):151–63.
pubmed: 38379245
pmcid: 7616158
doi: 10.1002/gepi.22551
Gill D, Burgess S. The evolution of mendelian randomization for investigating drug effects. PLoS Med. 2022;19(2):e1003898.
pubmed: 35113864
pmcid: 8812877
doi: 10.1371/journal.pmed.1003898
Rhodes B, Merriman ME, Harrison A, Nissen MJ, Smith M, Stamp L, et al. A genetic association study of serum acute-phase C-reactive protein levels in rheumatoid arthritis: implications for clinical interpretation. PLoS Med. 2010;7(9):e1000341.
pubmed: 20877716
pmcid: 2943443
doi: 10.1371/journal.pmed.1000341
Lemmelä S, Wigmore EM, Benner C, Havulinna AS, Ong RMY, Kempf T, et al. Integrated analyses of growth differentiation factor-15 concentration and cardiometabolic diseases in humans. Elife. 2022;11:e76272.
Gill D, Arvanitis M, Carter P, Hernandez Cordero AI, Jo B, Karhunen V, et al. ACE inhibition and cardiometabolic risk factors, lung ACE2 and TMPRSS2 gene expression, and plasma ACE2 levels: a Mendelian randomization study. R Soc Open Sci. 2020;7(11):200958.
pubmed: 33391794
pmcid: 7735342
doi: 10.1098/rsos.200958
Woolf B, Rajasundaram S, Cronje HT, Yarmolinsky J, Burgess S, Gill D. A drug target for erectile dysfunction to help improve fertility, sexual activity, and wellbeing: mendelian randomisation study. BMJ. 2023;383:e076197.
pubmed: 38086555
pmcid: 10716676
doi: 10.1136/bmj-2023-076197
Karhunen V, Daghlas I, Zuber V, Vujkovic M, Olsen AK, Knudsen LB, et al. Leveraging human genetic data to investigate the cardiometabolic effects of glucose-dependent insulinotropic polypeptide signalling. Diabetologia. 2021;64(12):2773–8.
pubmed: 34505161
pmcid: 8563538
doi: 10.1007/s00125-021-05564-7
Torekov SS, Harslof T, Rejnmark L, Eiken P, Jensen JB, Herman AP, et al. A functional amino acid substitution in the glucose-dependent insulinotropic polypeptide receptor (GIPR) gene is associated with lower bone mineral density and increased fracture risk. J Clin Endocrinol Metab. 2014;99(4):E729–33.
pubmed: 24446656
doi: 10.1210/jc.2013-3766
Rogers M, Gill D, Ahlqvist E, Robinson T, Mariosa D, Johansson M, et al. Genetically proxied impaired GIPR signaling and risk of 6 cancers. iScience. 2023;26(6):106848.
pubmed: 37250804
pmcid: 10209536
doi: 10.1016/j.isci.2023.106848
Veniant MM, Lu SC, Atangan L, Komorowski R, Stanislaus S, Cheng Y, et al. A GIPR antagonist conjugated to GLP-1 analogues promotes weight loss with improved metabolic parameters in preclinical and phase 1 settings. Nat Metab. 2024;6(2):290–303.
pubmed: 38316982
pmcid: 10896721
doi: 10.1038/s42255-023-00966-w
Georgakis MK, Malik R, Gill D, Franceschini N, Sudlow CLM, Dichgans M, et al. Interleukin-6 Signaling Effects on Ischemic Stroke and Other Cardiovascular Outcomes: A Mendelian Randomization Study. Circ Genom Precis Med. 2020;13(3): e002872.
pubmed: 32397738
pmcid: 7299212
doi: 10.1161/CIRCGEN.119.002872
Cupido AJ, Asselbergs FW, Natarajan P, Group CIW, Ridker PM, Hovingh GK, et al. Dissecting the IL-6 pathway in cardiometabolic disease: a Mendelian randomization study on both IL6 and IL6R. Br J Clin Pharmacol. 2022;88(6):2875–84.
pubmed: 34931349
doi: 10.1111/bcp.15191
Ziaeian B, Fonarow GC. Epidemiology and aetiology of heart failure. Nat Rev Cardiol. 2016;13(6):368–78.
pubmed: 26935038
pmcid: 4868779
doi: 10.1038/nrcardio.2016.25
Lumbers RT, Shah S, Lin H, Czuba T, Henry A, Swerdlow DI, et al. The genomics of heart failure: design and rationale of the HERMES consortium. ESC Heart Fail. 2021;8(6):5531–41.
pubmed: 34480422
pmcid: 8712846
doi: 10.1002/ehf2.13517
Schlosser P, Tin A, Matias-Garcia PR, Thio CHL, Joehanes R, Liu H, et al. Meta-analyses identify DNA methylation associated with kidney function and damage. Nat Commun. 2021;12(1):7174.
pubmed: 34887417
pmcid: 8660832
doi: 10.1038/s41467-021-27234-3
Bassett E, Broadbent J, Gill D, Burgess S, Mason AM. Inconsistency in UK biobank event definitions from different data sources and its impact on bias and generalizability: a case study of venous thromboembolism. Am J Epidemiol. 2024;193(5):787–97.
pubmed: 37981722
doi: 10.1093/aje/kwad232
Cho Y, Rau A, Reiner A, Auer PL. Mendelian randomization analysis with survival outcomes. Genet Epidemiol. 2021;45(1):16–23.
pubmed: 32918779
doi: 10.1002/gepi.22354
Suzuki K, Hatzikotoulas K, Southam L, Taylor HJ, Yin X, Lorenz KM, et al. Genetic drivers of heterogeneity in type 2 diabetes pathophysiology. Nature. 2024;627(8003):347–57.
pubmed: 38374256
pmcid: 10937372
doi: 10.1038/s41586-024-07019-6
Chen J, Spracklen CN, Marenne G, Varshney A, Corbin LJ, Luan J, et al. The trans-ancestral genomic architecture of glycemic traits. Nat Genet. 2021;53(6):840–60.
pubmed: 34059833
pmcid: 7610958
doi: 10.1038/s41588-021-00852-9
Soderholm M, Pedersen A, Lorentzen E, Stanne TM, Bevan S, Olsson M, et al. Genome-wide association meta-analysis of functional outcome after ischemic stroke. Neurology. 2019;92(12):e1271–83.
pubmed: 30796134
pmcid: 6511098
doi: 10.1212/WNL.0000000000007138
Walker VM, Kehoe PG, Martin RM, Davies NM. Repurposing antihypertensive drugs for the prevention of Alzheimer’s disease: a Mendelian randomization study. Int J Epidemiol. 2020;49(4):1132–40.
pubmed: 31335937
doi: 10.1093/ije/dyz155
Cronje HT, Karhunen V, Hovingh GK, Coppieters K, Lagerstedt JO, Nyberg M, et al. Genetic evidence implicating natriuretic peptide receptor-3 in cardiovascular disease risk: a Mendelian randomization study. BMC Med. 2023;21(1):158.
pubmed: 37101178
pmcid: 10134514
doi: 10.1186/s12916-023-02867-x
Myocardial Infarction G, Investigators CAEC, Stitziel NO, Stirrups KE, Masca NG, Erdmann J, et al. Coding variation in ANGPTL4, LPL, and SVEP1 and the risk of coronary disease. N Engl J Med. 2016;374(12):1134–44.
doi: 10.1056/NEJMoa1507652
Gobeil E, Bourgault J, Mitchell PL, Houessou U, Gagnon E, Girard A, et al. Genetic inhibition of angiopoietin-like protein-3, lipids, and cardiometabolic risk. Eur Heart J. 2024;45(9):707–21.
pubmed: 38243829
doi: 10.1093/eurheartj/ehad845
Dewey FE, Gusarova V, Dunbar RL, O’Dushlaine C, Schurmann C, Gottesman O, et al. Genetic and pharmacologic inactivation of ANGPTL3 and cardiovascular disease. N Engl J Med. 2017;377(3):211–21.
pubmed: 28538136
pmcid: 5800308
doi: 10.1056/NEJMoa1612790
Kersten S. Role and mechanism of the action of angiopoietin-like protein ANGPTL4 in plasma lipid metabolism. J Lipid Res. 2021;62: 100150.
pubmed: 34801488
pmcid: 8666355
doi: 10.1016/j.jlr.2021.100150
Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife. 2018;7:7.
doi: 10.7554/eLife.34408
Zuber V, Grinberg NF, Gill D, Manipur I, Slob EAW, Patel A, et al. Combining evidence from Mendelian randomization and colocalization: review and comparison of approaches. Am J Hum Genet. 2022;109(5):767–82.
pubmed: 35452592
doi: 10.1016/j.ajhg.2022.04.001
Bowker N, Hansford R, Burgess S, Foley CN, Auyeung VPW, Erzurumluoglu AM, et al. Genetically predicted Glucose-Dependent Insulinotropic Polypeptide (GIP) levels and cardiovascular disease risk are driven by distinct causal variants in the GIPR region. Diabetes. 2021;70(11):2706–19.
pubmed: 34426508
doi: 10.2337/db21-0103
Schmidt AF, Hunt NB, Gordillo-Maranon M, Charoen P, Drenos F, Kivimaki M, et al. Cholesteryl ester transfer protein (CETP) as a drug target for cardiovascular disease. Nat Commun. 2021;12(1):5640.
pubmed: 34561430
doi: 10.1038/s41467-021-25703-3
Group HTRC, Bowman L, Hopewell JC, Chen F, Wallendszus K, Stevens W, et al. Effects of anacetrapib in Patients with Atherosclerotic Vascular Disease. N Engl J Med. 2017;377(13):1217–27.
doi: 10.1056/NEJMoa1706444
Piwecka M, Rajewsky N, Rybak-Wolf A. Single-cell and spatial transcriptomics: deciphering brain complexity in health and disease. Nat Rev Neurol. 2023;19(6):346–62.
pubmed: 37198436
pmcid: 10191412
doi: 10.1038/s41582-023-00809-y
Ottaviani D, Ter Huurne M, Elliott DA, Bellin M, Mummery CL. Maturing differentiated human pluripotent stem cells in vitro: methods and challenges. Development. 2023;150(11):dev201103.
pubmed: 37260361
doi: 10.1242/dev.201103
May LT, Bartolo BA, Harrison DG, Guzik T, Drummond GR, Figtree GA, et al. Translating atherosclerosis research from bench to bedside: navigating the barriers for effective preclinical drug discovery. Clin Sci (Lond). 2022;136(23):1731–58.
pubmed: 36459456
doi: 10.1042/CS20210862
Trajanoska K, Bherer C, Taliun D, Zhou S, Richards JB, Mooser V. From target discovery to clinical drug development with human genetics. Nature. 2023;620(7975):737–45.
pubmed: 37612393
doi: 10.1038/s41586-023-06388-8
Kim GB, Kim JY, Lee JA, Norsigian CJ, Palsson BO, Lee SY. Functional annotation of enzyme-encoding genes using deep learning with transformer layers. Nat Commun. 2023;14(1):7370.
pubmed: 37963869
pmcid: 10645960
doi: 10.1038/s41467-023-43216-z