#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

Identifying drug targets for neurological and psychiatric disease via genetics and the brain transcriptome


Autoři: Denis A. Baird aff001;  Jimmy Z. Liu aff002;  Jie Zheng aff001;  Solveig K. Sieberts aff003;  Thanneer Perumal aff003;  Benjamin Elsworth aff001;  Tom G. Richardson aff001;  Chia-Yen Chen aff002;  Minerva M. Carrasquillo aff004;  Mariet Allen aff004;  Joseph S. Reddy aff005;  Philip L. De Jager aff006;  Nilufer Ertekin-Taner aff004;  Lara M. Mangravite aff003;  Ben Logsdon aff003;  Karol Estrada aff002;  Philip C. Haycock aff001;  Gibran Hemani aff001;  Heiko Runz aff002;  George Davey Smith aff001;  Tom R. Gaunt aff001
Působiště autorů: MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, University of Bristol, Bristol, United Kingdom aff001;  Translational Biology, Research and Development, Cambridge, Massachusetts, United States of America aff002;  Sage Bionetworks, Seattle, Washington, United States of America aff003;  Department of Neuroscience, Mayo Clinic Florida, Jacksonville, Florida, United States of America aff004;  Department of Health Sciences Research, Mayo Clinic Florida, Jacksonville, Florida, United States of America aff005;  Centre for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Medical Centre, New York, New York, United States of America aff006;  Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Centre, New York, New York, United States of America aff007;  Department of Neurology, Mayo Clinic Florida, Jacksonville, Florida, United States of America aff008;  BioMarin Pharmaceuticals, San Rafael, California, United States of America aff009;  NIHR Bristol Biomedical Research Centre, Oakfield House, University of Bristol, Bristol, United Kingdom aff010
Vyšlo v časopise: Identifying drug targets for neurological and psychiatric disease via genetics and the brain transcriptome. PLoS Genet 17(1): e1009224. doi:10.1371/journal.pgen.1009224
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pgen.1009224

Souhrn

Discovering drugs that efficiently treat brain diseases has been challenging. Genetic variants that modulate the expression of potential drug targets can be utilized to assess the efficacy of therapeutic interventions. We therefore employed Mendelian Randomization (MR) on gene expression measured in brain tissue to identify drug targets involved in neurological and psychiatric diseases. We conducted a two-sample MR using cis-acting brain-derived expression quantitative trait loci (eQTLs) from the Accelerating Medicines Partnership for Alzheimer’s Disease consortium (AMP-AD) and the CommonMind Consortium (CMC) meta-analysis study (n = 1,286) as genetic instruments to predict the effects of 7,137 genes on 12 neurological and psychiatric disorders. We conducted Bayesian colocalization analysis on the top MR findings (using P<6x10-7 as evidence threshold, Bonferroni-corrected for 80,557 MR tests) to confirm sharing of the same causal variants between gene expression and trait in each genomic region. We then intersected the colocalized genes with known monogenic disease genes recorded in Online Mendelian Inheritance in Man (OMIM) and with genes annotated as drug targets in the Open Targets platform to identify promising drug targets. 80 eQTLs showed MR evidence of a causal effect, from which we prioritised 47 genes based on colocalization with the trait. We causally linked the expression of 23 genes with schizophrenia and a single gene each with anorexia, bipolar disorder and major depressive disorder within the psychiatric diseases and 9 genes with Alzheimer’s disease, 6 genes with Parkinson’s disease, 4 genes with multiple sclerosis and two genes with amyotrophic lateral sclerosis within the neurological diseases we tested. From these we identified five genes (ACE, GPNMB, KCNQ5, RERE and SUOX) as attractive drug targets that may warrant follow-up in functional studies and clinical trials, demonstrating the value of this study design for discovering drug targets in neuropsychiatric diseases.

Klíčová slova:

Alzheimer's disease – Amyotrophic lateral sclerosis – Drug discovery – Gene expression – Genetics of disease – Medical risk factors – Parkinson disease – Schizophrenia


Zdroje

1. Plenge RM, Scolnick EM, Altshuler D. Validating therapeutic targets through human genetics. Nat Rev Drug Discov. 2013;12: 581. doi: 10.1038/nrd4051 23868113

2. Cummings JL, Morstorf T, Zhong K. Alzheimer’s disease drug-development pipeline: few candidates, frequent failures. Alzheimers Res Ther. 2014;6: 37–37. doi: 10.1186/alzrt269 25024750

3. Brainstorm Consortium, Anttila V, Bulik-Sullivan B, Finucane HK, Walters RK, Bras J, et al. Analysis of shared heritability in common disorders of the brain. Science. 2018;360: eaap8757. doi: 10.1126/science.aap8757 29930110

4. Horwitz T, Lam K, Chen Y, Xia Y, Liu C. A decade in psychiatric GWAS research. Mol Psychiatry. 2019;24: 378–389. doi: 10.1038/s41380-018-0055-z 29942042

5. Tan M-S, Jiang T, Tan L, Yu J-T. Genome-wide association studies in neurology. Ann Transl Med. 2014;2: 124–124. doi: 10.3978/j.issn.2305-5839.2014.11.12 25568877

6. 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: 856–860. doi: 10.1038/ng.3314 26121088

7. 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. bioRxiv. 2019; 513945. doi: 10.1371/journal.pgen.1008489 31830040

8. Wu Y, Zeng J, Zhang F, Zhu Z, Qi T, Zheng Z, et al. Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits. Nat Commun. 2018;9: 918. doi: 10.1038/s41467-018-03371-0 29500431

9. Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet. 2016;48: 481–487. doi: 10.1038/ng.3538 27019110

10. Qi T, Wu Y, Zeng J, Zhang F, Xue A, Jiang L, et al. Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood. Nat Commun. 2018;9: 2282. doi: 10.1038/s41467-018-04558-1 29891976

11. Richardson TG, Haycock PC, Zheng J, Timpson NJ, Gaunt TR, Davey Smith G, et al. Systematic Mendelian randomization framework elucidates hundreds of CpG sites which may mediate the influence of genetic variants on disease. Hum Mol Genet. 2018;27: 3293–3304. doi: 10.1093/hmg/ddy210 29893838

12. Zheng J, Haberland V, Baird D, Walker V, Haycock PC, Hurle MR, et al. Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases. Nat Genet. 2020;52: 1122–1131. doi: 10.1038/s41588-020-0682-6 32895551

13. Millard LAC, Davies NM, Timpson NJ, Tilling K, Flach PA, Smith GD. MR-PheWAS: hypothesis prioritization among potential causal effects of body mass index on many outcomes, using Mendelian randomization. Sci Rep. 2015;5: 16645. doi: 10.1038/srep16645 26568383

14. Diogo D, Tian C, Franklin CS, Alanne-Kinnunen M, March M, Spencer CCA, et al. Phenome-wide association studies across large population cohorts support drug target validation. Nat Commun. 2018;9: 4285. doi: 10.1038/s41467-018-06540-3 30327483

15. Sieberts SK, Perumal T, Carrasquillo MM, Allen M, Reddy JS, Hoffman GE, et al. Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions. bioRxiv. 2019; 638544. doi: 10.1101/638544

16. Võsa U, Claringbould A, Westra H-J, Bonder MJ, Deelen P, Zeng B, et al. Unraveling the polygenic architecture of complex traits using blood eQTL metaanalysis. bioRxiv. 2018; 447367. doi: 10.1101/447367

17. Hemani G, Bowden J, Davey Smith G. Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum Mol Genet. 2018;27: R195–R208. doi: 10.1093/hmg/ddy163 29771313

18. Paaby AB, Rockman MV. The many faces of pleiotropy. Trends Genet TIG. 2013;29: 66–73. doi: 10.1016/j.tig.2012.10.010 23140989

19. Jordan VK, Fregeau B, Ge X, Giordano J, Wapner RJ, Balci TB, et al. Genotype-phenotype correlations in individuals with pathogenic RERE variants. Hum Mutat. 2018;39: 666–675. doi: 10.1002/humu.23400 29330883

20. Bosch DGM, Boonstra FN, de Leeuw N, Pfundt R, Nillesen WM, de Ligt J, et al. Novel genetic causes for cerebral visual impairment. Eur J Hum Genet EJHG. 2016;24: 660–665. doi: 10.1038/ejhg.2015.186 26350515

21. Fregeau B, Kim BJ, Hernández-García A, Jordan VK, Cho MT, Schnur RE, et al. De Novo Mutations of RERE Cause a Genetic Syndrome with Features that Overlap Those Associated with Proximal 1p36 Deletions. Am J Hum Genet. 2016;98: 963–970. doi: 10.1016/j.ajhg.2016.03.002 27087320

22. Lerche C, Scherer CR, Seebohm G, Derst C, Wei AD, Busch AE, et al. Molecular cloning and functional expression of KCNQ5, a potassium channel subunit that may contribute to neuronal M-current diversity. J Biol Chem. 2000;275: 22395–22400. doi: 10.1074/jbc.M002378200 10787416

23. Schroeder BC, Hechenberger M, Weinreich F, Kubisch C, Jentsch TJ. KCNQ5, a novel potassium channel broadly expressed in brain, mediates M-type currents. J Biol Chem. 2000;275: 24089–24095. doi: 10.1074/jbc.M003245200 10816588

24. Lehman A, Thouta S, Mancini GMS, Naidu S, van Slegtenhorst M, McWalter K, et al. Loss-of-Function and Gain-of-Function Mutations in KCNQ5 Cause Intellectual Disability or Epileptic Encephalopathy. Am J Hum Genet. 2017;101: 65–74. doi: 10.1016/j.ajhg.2017.05.016 28669405

25. Dunn J, Blight A. Dalfampridine: a brief review of its mechanism of action and efficacy as a treatment to improve walking in patients with multiple sclerosis. Curr Med Res Opin. 2011;27: 1415–1423. doi: 10.1185/03007995.2011.583229 21595605

26. Ott PA, Hamid O, Pavlick AC, Kluger H, Kim KB, Boasberg PD, et al. Phase I/II study of the antibody-drug conjugate glembatumumab vedotin in patients with advanced melanoma. J Clin Oncol Off J Am Soc Clin Oncol. 2014;32: 3659–3666. doi: 10.1200/JCO.2013.54.8115 25267741

27. Bendell J, Saleh M, Rose AAN, Siegel PM, Hart L, Sirpal S, et al. Phase I/II study of the antibody-drug conjugate glembatumumab vedotin in patients with locally advanced or metastatic breast cancer. J Clin Oncol Off J Am Soc Clin Oncol. 2014;32: 3619–3625. doi: 10.1200/JCO.2013.52.5683 25267761

28. Baker M, Mackenzie IR, Pickering-Brown SM, Gass J, Rademakers R, Lindholm C, et al. Mutations in progranulin cause tau-negative frontotemporal dementia linked to chromosome 17. Nature. 2006;442: 916–919. doi: 10.1038/nature05016 16862116

29. Piaceri I, Imperiale D, Ghidoni E, Atzori C, Bagnoli S, Ferrari C, et al. Novel GRN Mutations in Alzheimer’s Disease and Frontotemporal Lobar Degeneration. J Alzheimers Dis JAD. 2018;62: 1683–1689. doi: 10.3233/JAD-170989 29614680

30. Ibanez L, Dube U, Davis AA, Fernandez MV, Budde J, Cooper B, et al. Pleiotropic Effects of Variants in Dementia Genes in Parkinson Disease. Front Neurosci. 2018;12: 230. doi: 10.3389/fnins.2018.00230 29692703

31. 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. 2019. doi: 10.1093/ije/dyz155 31335937

32. Koronyo-Hamaoui M, Shah K, Koronyo Y, Bernstein E, Giani JF, Janjulia T, et al. ACE overexpression in myelomonocytic cells: effect on a mouse model of Alzheimer’s disease. Curr Hypertens Rep. 2014;16: 444–444. doi: 10.1007/s11906-014-0444-x 24792094

33. Maric G, Rose AA, Annis MG, Siegel PM. Glycoprotein non-metastatic b (GPNMB): A metastatic mediator and emerging therapeutic target in cancer. OncoTargets Ther. 2013;6: 839–852. doi: 10.2147/OTT.S44906 23874106

34. Neal ML, Boyle AM, Budge KM, Safadi FF, Richardson JR. The glycoprotein GPNMB attenuates astrocyte inflammatory responses through the CD44 receptor. J Neuroinflammation. 2018;15: 73. doi: 10.1186/s12974-018-1100-1 29519253

35. Moloney EB, Moskites A, Ferrari EJ, Isacson O, Hallett PJ. The glycoprotein GPNMB is selectively elevated in the substantia nigra of Parkinson’s disease patients and increases after lysosomal stress. Neurobiol Dis. 2018;120: 1–11. doi: 10.1016/j.nbd.2018.08.013 30149180

36. Li YI, Wong G, Humphrey J, Raj T. Prioritizing Parkinson’s disease genes using population-scale transcriptomic data. Nat Commun. 2019;10: 994–994. doi: 10.1038/s41467-019-08912-9 30824768

37. Nalls MA, Pankratz N, Lill CM, Do CB, Hernandez DG, Saad M, et al. Large-scale meta-analysis of genome-wide association data identifies six new risk loci for Parkinson’s disease. Nat Genet. 2014;46: 989–993. doi: 10.1038/ng.3043 25064009

38. Imbrici P, Camerino DC, Tricarico D. Major channels involved in neuropsychiatric disorders and therapeutic perspectives. Front Genet. 2013;4: 76–76. doi: 10.3389/fgene.2013.00076 23675382

39. Wang X, Su Y, Yan H, Huang Z, Huang Y, Yue W. Association Study of KCNH7 Polymorphisms and Individual Responses to Risperidone Treatment in Schizophrenia. Front Psychiatry. 2019;10: 633. doi: 10.3389/fpsyt.2019.00633 31543842

40. Ramasamy A, Trabzuni D, Guelfi S, Varghese V, Smith C, Walker R, et al. Genetic variability in the regulation of gene expression in ten regions of the human brain. Nat Neurosci. 2014;17: 1418–1428. doi: 10.1038/nn.3801 25174004

41. Giambartolomei C, Vukcevic D, Schadt EE, Franke L, Hingorani AD, Wallace C, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 2014;10: e1004383. doi: 10.1371/journal.pgen.1004383 24830394

42. Hatcher C, Relton CL, Gaunt TR, Richardson TG. Leveraging brain cortex-derived molecular data to elucidate epigenetic and transcriptomic drivers of complex traits and disease. Transl Psychiatry. 2019;9: 105. doi: 10.1038/s41398-019-0437-2 30820025

43. 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. doi: 10.7554/eLife.34408 29846171

44. Duncan L, Yilmaz Z, Gaspar H, Walters R, Goldstein J, Anttila V, et al. Significant Locus and Metabolic Genetic Correlations Revealed in Genome-Wide Association Study of Anorexia Nervosa. Am J Psychiatry. 2017;174: 850–858. doi: 10.1176/appi.ajp.2017.16121402 28494655

45. Demontis D, Walters RK, Martin J, Mattheisen M, Als TD, Agerbo E, et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat Genet. 2019;51: 63–75. doi: 10.1038/s41588-018-0269-7 30478444

46. Autism Spectrum Disorders Working Group of The Psychiatric Genomics Consortium. Meta-analysis of GWAS of over 16,000 individuals with autism spectrum disorder highlights a novel locus at 10q24.32 and a significant overlap with schizophrenia. Mol Autism. 2017;8: 21. doi: 10.1186/s13229-017-0137-9 28540026

47. Psychiatric GWAS Consortium Bipolar Disorder Working Group. Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4. Nat Genet. 2011;43: 977–983. doi: 10.1038/ng.943 21926972

48. Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM, Abdellaoui A, et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat Genet. 2018;50: 668–681. doi: 10.1038/s41588-018-0090-3 29700475

49. International Obsessive Compulsive Disorder Foundation Genetics Collaborative (IOCDF-GC) and OCD Collaborative Genetics Association Studies (OCGAS). Revealing the complex genetic architecture of obsessive-compulsive disorder using meta-analysis. Mol Psychiatry. 2018;23: 1181–1188. doi: 10.1038/mp.2017.154 28761083

50. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511: 421–427. doi: 10.1038/nature13595 25056061

51. Nicolas A, Kenna KP, Renton AE, Ticozzi N, Faghri F, Chia R, et al. Genome-wide Analyses Identify KIF5A as a Novel ALS Gene. Neuron. 2018;97: 1268–1283.e6. doi: 10.1016/j.neuron.2018.02.027 29566793

52. Ferrari R, Hernandez DG, Nalls MA, Rohrer JD, Ramasamy A, Kwok JBJ, et al. Frontotemporal dementia and its subtypes: a genome-wide association study. Lancet Neurol. 2014;13: 686–699. doi: 10.1016/S1474-4422(14)70065-1 24943344

53. Sawcer S, Hellenthal G, Pirinen M, Spencer CCA, Patsopoulos NA, Moutsianas L, et al. Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature. 2011;476: 214–219. doi: 10.1038/nature10251 21833088

54. Lambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bellenguez C, et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat Genet. 2013;45: 1452–1458. doi: 10.1038/ng.2802 24162737

55. Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562: 203–209. doi: 10.1038/s41586-018-0579-z 30305743

56. Loh P-R, Tucker G, Bulik-Sullivan BK, Vilhjálmsson BJ, Finucane HK, Salem RM, et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat Genet. 2015;47: 284–290. doi: 10.1038/ng.3190 25642633

57. Liu JZ, Erlich Y, Pickrell JK. Case-control association mapping by proxy using family history of disease. Nat Genet. 2017;49: 325–331. doi: 10.1038/ng.3766 28092683

58. Zeng P, Wang T, Zheng J, Zhou X. Causal association of type 2 diabetes with amyotrophic lateral sclerosis: new evidence from Mendelian randomization using GWAS summary statistics. BMC Med. 2019;17: 225. doi: 10.1186/s12916-019-1448-9 31796040

59. Cragg JG, Donald SG. Testing Identifiability and Specification in Instrumental Variable Models. Econom Theory. 1993;9: 222–240.

60. Pruim RJ, Welch RP, Sanna S, Teslovich TM, Chines PS, Gliedt TP, et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinforma Oxf Engl. 2010;26: 2336–2337. doi: 10.1093/bioinformatics/btq419 20634204

61. GTEx Consortium, Aguet F, Brown AA, Castel SE, Davis JR, He Y, et al. Genetic effects on gene expression across human tissues. Nature. 2017;550: 204. doi: 10.1038/nature24277 29022597

62. Zheng J, Haberland V, Baird D, Walker V, Haycock P, Gutteridge A, et al. Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases. bioRxiv. 2019; 627398. doi: 10.1101/627398

63. Magi R, Morris AP. GWAMA: software for genome-wide association meta-analysis. BMC Bioinformatics. 2010;11: 288. doi: 10.1186/1471-2105-11-288 20509871

64. Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014;23: R89–R98. doi: 10.1093/hmg/ddu328 25064373

65. Hemani G, Tilling K, Davey Smith G. Correction: Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genet. 2017;13: e1007149. doi: 10.1371/journal.pgen.1007149 29287073

66. Hemani G, Tilling K, Davey Smith G. Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genet. 2017;13: e1007081. doi: 10.1371/journal.pgen.1007081 29149188

67. Hamosh A, Scott AF, Amberger JS, Bocchini CA, McKusick VA. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 2005;33: D514–D517. doi: 10.1093/nar/gki033 15608251

68. Koscielny G, An P, Carvalho-Silva D, Cham JA, Fumis L, Gasparyan R, et al. Open Targets: a platform for therapeutic target identification and validation. Nucleic Acids Res. 2017;45: D985–D994. doi: 10.1093/nar/gkw1055 27899665


Článek vyšel v časopise

PLOS Genetics


2021 Číslo 1
Nejčtenější tento týden
Nejčtenější v tomto čísle
Kurzy

Zvyšte si kvalifikaci online z pohodlí domova

Hypertenze a hypercholesterolémie – synergický efekt léčby
nový kurz
Autoři: prof. MUDr. Hana Rosolová, DrSc.

Multidisciplinární zkušenosti u pacientů s diabetem
Autoři: Prof. MUDr. Martin Haluzík, DrSc., prof. MUDr. Vojtěch Melenovský, CSc., prof. MUDr. Vladimír Tesař, DrSc.

Úloha kombinovaných preparátů v léčbě arteriální hypertenze
Autoři: prof. MUDr. Martin Haluzík, DrSc.

Halitóza
Autoři: MUDr. Ladislav Korábek, CSc., MBA

Terapie roztroušené sklerózy v kostce
Autoři: MUDr. Dominika Šťastná, Ph.D.

Všechny kurzy
Přihlášení
Zapomenuté heslo

Zadejte e-mailovou adresu, se kterou jste vytvářel(a) účet, budou Vám na ni zaslány informace k nastavení nového hesla.

Přihlášení

Nemáte účet?  Registrujte se

#ADS_BOTTOM_SCRIPTS#