Результаты пилотного полногеномного ассоциативного исследования (GWAS) и полигенные шкалы риска депрессии в российской популяции с использованием онлайн-фенотипирования

В поперечном ассоциативном исследовании типа «случай–контроль» с целью разработки генетической тест-системы на основе полигенных шкал риска (PRS) развития депрессии проведено тестирование метода онлайн-фенотипирования депрессии и тревоги с использованием опросника «Госпитальная шкала тревоги и депрессии» (HADS) и оригинального опросника на основе критериев диагностического интервью DSM-5. В популяционной выборке (n=2540) были проведены полногеномные ассоциативные исследования (GWAS) и анализ по PRS с использованием сводной статистики крупнейших GWAS депрессии и ряда других психических заболеваний. Наиболее значимый маркер протективного характера выявлен для фенотипа «Суммарный балл HADS-D (депрессия)» rs9517934 (β=–0,4041, р=2,277*10^–7) в гене длинных некодирующих РНК. Полученные PRS были протестированы в клинической выборке (n=336). Наибольшей предиктивной мощностью в отношении депрессии обладали PRS на основе GWAS метаанализа депрессии для 71% исследовательских фенотипов, c максимальной мощностью PRS для количественного фенотипа «Суммарный балл HADS-A (тревога)» (β=0,36561, р=0,0031, AUC=0,6137).

1. Kasyanov ED, Rukavishnikov GV, Kibitov AA, et al. [Modern approaches to the genetics of depression: scopes and limitations]. Zhurnal Nevrologii i Psikhiatrii imeni SS Korsakova [Zh Nevrol Psihiatr Im SS Korsakova]. 2021;121(5–2):61–6. (In Russ.) DOI: https://doi.org/10.17116/jnevro202112105261 2. World Health Organization. The Global Burden of Disease. 2008. URL: https://www.who.int/healthinfo/global_burden_disease/estimates/en/index1.html (accessed on: 02.09.2022). 3. Lewinsohn PM, Shankman SA, Gau JM, Klein DN. The prevalence and co-morbidity of subthreshold psychiatric conditions. Psychol Med. 2004;34(4):613–22. DOI: https://doi.org/10.1017/S0033291703001466 4. Neznanov NG, Kibitov AO, Rukavishnikov GV, Mazo GE. [The prognostic role of depression as a predictor of chronic somatic diseases manifestation]. Therapeutic archive. 2018;90(12):122–32. (In Russ.) DOI: https://doi.org/10.26442/00403660.2018.12.000019 5. Chang CK, Hayes RD, Perera G, et al. Life expectancy at birth for people with serious mental illness and other major disorders from a secondary mental health care case register in London. PLoS One. 2011;6(5):e19590. DOI: https://doi.org/10.1371/journal.pone.0019590 6. Bachmann S. Epidemiology of Suicide and the Psychiatric Perspective. Int J Environ Res Public Health. 2018;15(7):1425. DOI: https://doi.org/10.3390/ijerph15071425 7. Kendler KS, Gatz M, Gardner CO, Pedersen NL. A Swedish national twin study of lifetime major depression. Am J Psychiatry. 2006;163(1):109–14. DOI: https://doi.org/10.1176/appi.ajp.163.1.109 8. Bienvenu OJ, Davydow DS, Kendler KS. Psychiatric 'diseases' versus behavioral disorders and degree of genetic influence. Psychol Med. 2011;41(1):33–40. DOI: https://doi.org/10.1017/S003329171000084X 9. Psaty BM, Dekkers OM, Cooper RS. Comparison of 2 Treatment Models: Precision Medicine and Preventive Medicine. JAMA. 2018;320(8):751–2. DOI: https://doi.org/10.1001/jama.2018.8377 10. US Preventive Services Task Force, Curry SJ, Krist AH, et al. Risk Assessment for Cardiovascular Disease with Nontraditional Risk Factors: US Preventive Services Task Force Recommendation Statement. JAMA. 2018;320(3):272–80. DOI: https://doi.org/10.1001/jama.2018.8359 11. Smoller JW, Andreassen OA, Edenberg HJ, et al. Psychiatric genetics and the structure of psychopathology. Molecular Psychiatry. 2019;24(3):409–20. DOI: https://doi.org/10.1038/s41380-017-0010-4 12. Domschke K. Clinical and Molecular Genetics of Psychotic Depression. Schizophr Bull. 2013;39(4):766–75. DOI: https://doi.org/10.1093/schbul/sbt040 13. Flint J, Kendler KS. The genetics of major depression. Neuron. 2014;81(3):484–503. DOI: https://doi.org/10.1016/j.neuron.2014.01.027 14. Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium, Ripke S, Wray NR, et al. A mega-analysis of genome-wide association studies for major depressive disorder. Molecular Psychiatry. 2013;18(4):497–511. DOI: https://doi.org/10.1038/mp.2012.21 15. Hyde CL, Nagle MW, Tian C. Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nat Genet. 2016;48(9):1031–6. DOI: https://doi.org/10.1038/ng.3623 16. Chatterjee N, Shi J, García-Closas M. Developing and evaluating polygenic risk prediction models for stratified disease prevention. Nature Reviews Genetics. 2016;17(7):392–406. DOI: https://doi.org/10.1038/nrg.2016.27 17. Martin AR, Daly MJ, Robinson EB, et al. Predicting Polygenic Risk of Psychiatric Disorders. Biol Psychiatry. 2019;86(2):97–109. DOI: https://doi.org/10.1016/j.biopsych.2018.12.015 18. Khera AV, Chaffin M, Aragam KG, et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nature Genetics. 2018;50(9):1219–24. DOI: https://doi.org/10.1038/s41588-018-0183-z 19. Polimanti R. Not Only Gene Discovery: Genome-wide Association Studies and Polygenic Risk Scores as Tools to Dissect the Heterogeneity of Major Depressive Disorder. Biol Psychiatry. 2022;92(3):177–8. DOI: https://doi.org/10.1016/j.biopsych.2022.05.002 20. Jermy BS, Glanville KP, Coleman JRI, et al. Exploring the genetic heterogeneity in major depression across diagnostic criteria. Mol Psychiatry. 2021;26(12):7337–45. DOI: https://doi.org/10.1038/s41380-021-01231-w 21. Nguyen TD, Harder A, Xiong Y, et al. Genetic heterogeneity and subtypes of major depression. Mol Psychiatry. 2022;27(3):1667–75. DOI: https://doi.org/10.1038/s41380-021-01413-6 22. Ingram WM, Baker AM, Bauer CR, et al. Defining Major Depressive Disorder Cohorts Using the EHR: Multiple Phenotypes Based on ICD-9 Codes and Medication Orders. Neurol Psychiatry Brain Res. 2020;36:18–26. DOI: https://doi.org/10.1016/j.npbr.2020.02.002 23. Cai N, Revez JA, Adams MJ, et al. Minimal phenotyping yields genome-wide association signals of low specificity for major depression. Nat Genet. 2020;52(4):437–47. DOI: https://doi.org/10.1038/s41588-020-0594-5 24. Kasyanov ED, Rakitko AS, Rukavishnikov GV, et al. [Contemporary GWAS studies of depression: the critical role of phenotyping]. Zhurnal Nevrologii i Psikhiatrii imeni SS Korsakova [Zh Nevrol Psihiatr Im SS Korsakova]. 2022;122(1):50–61. (In Russ.) DOI: https://doi.org/10.17116/jnevro202212201150 25. Howard DM, Adams MJ, Shirali M, et al. Genome-wide association study of depression phenotypes in UK Biobank identifies variants in excitatory synaptic pathways. Nature Communications. 2018;9(1):1470. DOI: https://doi.org/10.1038/s41467-018-03819-3 26. Mitchell BL, Thorp JG, Wu Y, et al. Polygenic Risk Scores Derived from Varying Definitions of Depression and Risk of Depression. JAMA Psychiatry. 2021;78(10):1152–60. DOI: https://doi.org/10.1001/jamapsychiatry.2021.1988 27. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). American Psychiatric Publishing, 2013. 992 p. 28. Zigmond AS, Snaith RP. The hospital anxiety and depression scale. Acta Psychiatrica Scandinavica. 1983;67(6):361–70. DOI: https://doi.org/10.1111/j.1600-0447.1983.tb09716.x 29. Kibitov AA, Rakitko AS, Kasyanov ED, et al. Screening of Depressive Symptoms in a Russian General Population Sample: A Web-based Cross-sectional Study. Clin Pract Epidemiol Ment Health. 2021;17:205–11. DOI: https://doi.org/10.2174/1745017902117010205 30. Kibitov AO, Mazo GE, Rakitko AS, et al. [GWAS-based polygenic risk scores for depression with clinical validation: methods and study design in the Russian population]. Zhurnal Nevrologii i Psikhiatrii imeni SS Korsakova [Zh Nevrol Psihiatr Im SS Korsakova]. 2020;120(11):131–40. (In Russ.) DOI: https://doi.org/10.17116/jnevro2020120111131 31. Fedorenko OY, Golimbet VE, Ivanova SА, et al. Opening up new horizons for psychiatric genetics in the Russian Federation: moving toward a national consortium. Mol Psychiatry. 2019;24(8):1099–111. DOI: https://doi.org/10.1038/s41380-019-0354-z 32. Andrjushhenko AV, Drobizhev MJu, Dobrovol'skij AV. [Sravnitel'naja ocenka shkal CES-D, BDI i HADS v diagnostike depressij v obshhemedicinskoj praktike]. Zhurnal Nevrologii i Psikhiatrii imeni SS Korsakova [Zh Nevrol Psihiatr Im SS Korsakova]. 2003;103(5):11–8. (In Russ.) PMID: 12789819 33. Kasyanov ED, Verbitskaya EV, Rakitko AS, et al. [Validation of a DSM-5-based screening test using digital phenotyping in the Russian population]. Zhurnal Nevrologii i Psikhiatrii imeni SS Korsakova [Zh Nevrol Psihiatr Im SS Korsakova]. 2022;122(6–2):64–70. (In Russ.) DOI: https://doi.org/10.17116/jnevro202212206264 34. Brennan C, Worrall-Davies A, McMillan D, et al. The Hospital Anxiety and Depression Scale: a diagnostic meta-analysis of case-finding ability. J Psychosom Res. 2010;69(4):371–8. DOI: https://doi.org/10.1016/j.jpsychores.2010.04.006 35. Browning SR, Browning BL. Rapid and Accurate Haplotype Phasing and Missing-Data Inference for Whole-Genome Association Studies by Use of Localized Haplotype Clustering. Am. J. Hum. Genet. 2007;81(5):1084–97. DOI: https://doi.org/10.1086/521987 36. 1000 Genomes Project Consortium; Auton A, Brooks LD, Durbin RM, et al. A Global Reference for Human Genetic Variation. Nature. 2015;526(7571):68–74. DOI: https://doi.org/10.1038/nature15393 37. McCarthy S, Das S, Kretzschmar W, et al. A Reference Panel of 64,976 Haplotypes for Genotype Imputation. Nature Genetics. 2016;48(10):1279–83. DOI: https://doi.org/10.1038/ng.3643 38. Andries TM, de Kluiver H, Stringer S. A Tutorial on Conducting Genome-Wide Association Studies: Quality Control and Statistical Analysis. Int J Methods Psychiatr Res. 2018;27(2):e1608. DOI: https://doi.org/10.1002/mpr.1608 39. Staples J, Qiao D, Cho MH, et al. PRIMUS: Rapid Reconstruction of Pedigrees from Genome-Wide Estimates of Identity by Descent. Am J Hum Genet. 2014;95(5):553–64. DOI: https://doi.org/10.1016/j.ajhg.2014.10.005 40. Michael H, Piekenbrock M, Doran D. Dbscan: Fast Density-Based Clustering with R. J Stat Softw. 2019;91(Iss 1):1–30. DOI: https://doi.org/10.18637/jss.v091.i01 41. Chang CC, Carson CC, Cam Tellier L, et al. Second-Generation PLINK: Rising to the Challenge of Larger and Richer Datasets. Gigascience. 2015;4:7. DOI: https://doi.org/10.1186/s13742-015-0047-8 42. Cingolani P, Platts A, Wang L, et al. A Program for Annotating and Predicting the Effects of Single Nucleotide Polymorphisms, SnpEff: SNPs in the Genome of Drosophila Melanogaster Strain w1118; Iso-2; Iso-3. Fly (Austin). 2012;6(2):80–92. DOI: https://doi.org/10.4161/fly.19695 43. Electronic database dbSNP. URL: https://ldlink.nci.nih.gov/?tab=ldpair (accessed on: 02.09.2022). 44. Electronic database GeneCards. URL: https://www.genecards.org/ (accessed on: 02.09.2022). 45. Electronic database GWAS central. URL: https://www.gwascentral.org (accessed on: 02.09.2022). 46. Electronic database GWAS Catalog. URL: https://www.ebi.ac.uk/gwas (accessed on: 02.09.2022). 47. Shing Wan C, Shin-Heng Mak T, O’Reilly PF. Tutorial: A Guide to Performing Polygenic Risk Score Analyses. Nature Protocols. 2020;15(9):2759–72. DOI: https://doi.org/10.1038/s41596-020-0353-1 48. Shing Wan C, O’Reilly PF. PRSice-2: Polygenic Risk Score Software for Biobank-Scale Data. Gigascience. 2019;8(7):giz082. DOI: https://doi.org/10.1093/gigascience/giz082 49. Stahl EA, Breen G, Forstner AJ, et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat Genet. 2019;51(5):793–803. DOI: https://doi.org/10.1038/s41588-019-0397-8 50. Trubetskoy V, Pardiñas AF, Qi T, et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature. 2022;604(7906):502–8. DOI: https://doi.org/10.1038/s41586-022-04434-5 51. Otowa T, Hek K, Lee M, et al. Meta-analysis of genome-wide association studies of anxiety disorders. Mol Psychiatry. 2016;21(10):1391–9. DOI: https://doi.org/10.1038/mp.2015.197 52. van den Berg SM, de Moor MH, Verweij KJ, et al. Meta-analysis of Genome-Wide Association Studies for Extraversion: Findings from the Genetics of Personality Consortium. Behav Genet. 2016;46(2):170–82. DOI: https://doi.org/10.1007/s10519-015-9735-5 53. Jaeger M, Matzaraki V, Aguirre-Gamboa R, et al. A Genome-Wide Functional Genomics Approach Identifies Susceptibility Pathways to Fungal Bloodstream Infection in Humans. J Infect Dis. 2019;220(5):862–72. DOI: https://doi.org/10.1093/infdis/jiz206 54. Liu J, Zhou Y, Liu S, et al. The coexistence of copy number variations (CNVs) and single nucleotide polymorphisms (SNPs) at a locus can result in distorted calculations of the significance in associating SNPs to disease. Hum Genet. 2018;137(6–7):553–67. DOI: https://doi.org/10.1007/s00439-018-1910-3 55. Howard DM, Adams MJ, Clarke TK, et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat Neurosci. 2019;22(3):343–52. DOI: https://doi.org/10.1038/s41593-018-0326-7 56. Moshfeghy Z, Tahari S, Janghorban R, et al. Association of sexual function and psychological symptoms including depression, anxiety and stress in women with recurrent vulvovaginal candidiasis. J Turk Ger Gynecol Assoc. 2020;2(2):90–6. DOI: https://doi.org/10.4274/jtgga.galenos.2019.2019.0077 57. Lin T, Meng Y, Ji Z, et al. Extent of Depression in Juvenile and Adolescent Patients with Idiopathic Scoliosis During Treatment with Braces. World Neurosurg. 2019;126:e27–32. DOI: https://doi.org/10.1016/j.wneu.2019.01.095 58. Wang Y, Guo J, Ni G, et al. Theoretical and empirical quantification of the accuracy of polygenic scores in ancestry divergent populations. Nat Commun. 2020;11(1):3865. DOI: https://doi.org/10.1038/s41467-020-17719-y 59. Thorp JG, Campos AI, Grotzinger AD, et al. Symptom-level modelling unravels the shared genetic architecture of anxiety and depression. Nat Hum Behav. 2021;5(10):1432–42. DOI: https://doi.org/10.1038/s41562-021-01094-9 60. Fusar-Poli L, Rutten BPF, van Os J, et al. Polygenic risk scores for predicting outcomes and treatment response in psychiatry: hope or hype? Int Rev Psychiatry. 2022. URL: https://www.researchgate.net/publication/326914747_httpsdoiorg101016jcej201808019 (accessed on: 02.09.2022). 61. Sun J, Wang Y, Folkersen L, et al. Translating polygenic risk scores for clinical use by estimating the confidence bounds of risk prediction. Nat Commun. 2021;12(1):5276. DOI: https://doi.org/10.1038/s41467-021-25014-7 62. Wray NR, Lin T, Austin J, et al. From Basic Science to Clinical Application of Polygenic Risk Scores: A Primer. JAMA Psychiatry. 2021;78(1):101–9. DOI: https://doi.org/10.1001/jamapsychiatry.2020.3049 63. Rees E, Owen MJ. Translating insights from neuropsychiatric genetics and genomics for precision psychiatry. Genome Med. 2020;12(1):43. DOI: https://doi.org/10.1186/s13073-020-00734-5