Results of a pilot genome-wide association study (GWAS) and polygenic depression risk scales in the Russian population using online phenotyping

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Kibitov AO, Rakit'ko AS, Kas'yanov ED, et al. [Results of a pilot genome-wide association study (GWAS) and polygenic depression risk scales in the Russian population using online phenotyping]. Rossiiskii psikhiatricheskii zhurnal [Russian Journal of Psychiatry]. 2022;(5):13-29. Russian

Abstract

In this cross-sectional association study of the case-control type with the aim of developing a genetic test system based on polygenic risk scores (PRS) for major depression, the method of online phenotyping of depression and anxiety was tested using the Hospital Anxiety and Depression Scale (HADS) questionnaire and the original questionnaire based on the DSM-5 diagnostic interview criteria. In a population sample (n=2540), genome-wide association studies (GWAS) and analysis of PRSs were performed using summary statistics of the largest GWAS of depression and a number of other mental disorders. The most significant protective marker was identified for the HADS-D total score (depression) phenotype rs9517934 (β=–0.4041, р=2.277*10–7) in the long noncoding RNA gene. The resulting PRSs were tested in a clinical sample (n=336). The PRS based on the GWAS meta-analysis of depression for 71% of the study phenotypes had the highest predictive power for depression, with the highest PRS power for the HADS-A total Score (anxiety) quantitative phenotype (β=0.36561, p=0.0031, AUC=0.6137).

Keywords depression; anxiety; GWAS; HADS; DSM; polygenic risk scores; prophylaxis

References

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



DOI: http://dx.doi.org/10.47877/1560-957Х-2022-10502

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