The role of genomics in predicting neuropsychic disorders

Today, mental disorders are one of the most important challenges for modern medicine. Despite the fact that mental illnesses do not significantly contribute to the mortality of the population, they have an important impact on the quality of life of each individual patient, as well as on public health and the economy of the country. According to statistical studies, about 40% of the Russian population has symptoms of mental disorders, and 5% need treatment.  As psychiatric genomics develops, disease risk prediction models based on biological structures continue to develop. Advances in the accessibility and complexity of big data, deep phenotyping, mapping of developmental trajectories, and the inclusion of large amounts of data on a large number of individuals will contribute to understanding the factors that ultimately play an important role in determining mental health. Polygenic and polyepigenetic indicators themselves, like any other marker, have a limited ability to predict the condition for which they were generated. It should be noted, however, that the optimal selection of genetic variants and other genomic markers, as well as the aggregation of related weights, are active areas of research. Continuous improvement of the technology (increasing the sample size of GWAS and the inclusion of different pedigrees, higher genotyping resolution, etc.) entails a constant revision of the guidelines for their calculation and interpretation. Due to the recent emergence of several methods discussed in this review, there is still insufficient evidence of their clinical usefulness, but as the technologies underlying functional genomics approaches continue to improve, further research is needed to assess clinical usefulness in psychiatry. It can be assumed that some of the methods described here will be replaced by newer approaches. However, the main idea is to include functional aspects rather than being guided solely by data-driven approaches.

1. Skripov V.S., Esina K.M. Kompleksnaya otsenka zabolevayemosti psikhicheskimi rasstroystvami i rasstroystvami povedeniya v dinamike za period 2015-2019 gg. v Rossiyskoy Federatsii [Comprehensive assessment of mental disorders and behavioral disorders in the dynamics for the period 2015-2019. in Russian Federation]. Social’nye aspekty zdorov’a naselenia [Social aspects of population health [serial online]. 2021; 67(4):8. (In Russ.).

2. Oleinikova T.A., Barybina E.S. Regional’nyye razlichiya pokazateley obshchey i pervichnoy zabolevayemosti psikhicheskimi rasstroystvami v Rossii [Regional differences in the indicators of general and primary morbidity of mental disorders in Russia]. Sovremennyye problemy zdravookhraneniya i meditsinskoy statistiki [ Modern problems of healthcare and medical statistics]. 2022; (3): 679-692. (In Russ.) doi: 10.24412/2312-2935-2022-3-679-692

3. Borovkova E.I. Planirovaniye sem’i i prekontseptsionnoye konsul’tirovaniye [Family planning and preconception consultation]. RMZH. Mat’ i ditya [Russian Journal of Woman and Child Health] 2019; 2(2): 131-134. (In Russ.)

4. Marigorta U.M., Gibson G. A simulation study of gene-by-environment interactions in GWAS implies ample hidden effects. Front Genet . 2014;5:225. doi: 10.3389/fgene.2014.00225.

5. Zhang J., Yang J., Wen C. A. New SNP Genotyping Technology by Target SNP-Seq. Methods Mol Biol. 2023; 2638: 365-371. doi: 10.1007/978-1-0716-3024-2_26.

6. Duncan L.E., Ostacher M., Ballon J. How genome-wide association studies (GWAS) made traditional candidate gene studies obsolete. Neuropsychopharmacology 2019; 44(9): 1518-1523. doi: 10.1038/s41386-019-0389-5.

7. Subramanian I., Verma S., Kumar S., Jere A., Anamika K. Multiomics Data Integration, Interpretation, and Its Application. Bioinform Biol Insights 2020; 14: 1177932219899051. doi: 10.1177/1177932219899051.

8. Baranova A.N., Abramenko A.V., Smirnov T.A. Obzor polnogenomnykh issledovaniy poiska assotsiatsiy (GWAS) dlya vyyavleniya geneticheskikh faktorov razvitiya shizofrenii [Review of genome-wide association search (WAS) studies to identify genetic factors in the development of schizophrenia]. Mezhdunarodnyj nauchno-issledovatel’skij zhurnal [International Scientific Research Journal]. 2023; 2 (128): 81 (In Russ.).

9. Watanabe K., Stringer S., Frei O., et al. A global overview of pleiotropy and genetic architecture in complex traits. Nat Genet. 2019;51(9):1339-1348. doi: 10.1038/s41588-019-0481-0.

10. Buniello A., MacArthur J.A.L., Cerezo M., et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 2019;47(D1):1005-1012. doi: 10.1093/nar/gky1120.

11. Visscher P.M., Wray N.R., Zhang Q., Sklar P., McCarthy M.I., Brown M.A., Yang J. 10 Years of GWAS Discovery: Biology, Function, and Translation. Am J Hum Genet. 2017;101(1):5-22. doi: 10.1016/j.ajhg.2017.06.005.

12. Trubetskoy V., Pardiñas A.F., Qi T., et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature. 2022;604(7906):502-508. doi: 10.1038/s41586-022-04434-5.

13. Howard D.M., Adams M.J., Clarke T.K., et al. Genome-wide meta-analysis of depression identifies 102 independent variants and high-lights the importance of the prefrontal brain regions. Nat Neurosci. 2019;22(3):343-352. doi: 10.1038/s41593-018-0326-7.

14. Kibitov A.O., Rukavishnikov G.V., Mazo G.E., Krupitsky E. M. Sovremennyye dostizheniya i napravleniya perspektivnogo razvitiya genetiki i farmakogenetiki psikhicheskikh zabolevaniy [Modern achievements and directions of promising development of genetics and pharmacogenetics of mental diseases]. Social’naya i klinicheskaya psihiatriya [Social and clinical psychiatry]. 2020; 30(3): 100-112. (In Russ.)

15. Yu D., Sul J.H., Tsetsos F., et al. Interrogating the Genetic Determinants of Tourette’s Syndrome and Other Tic Disorders Through Genome-Wide Association Studies. Am J Psychiatry 2019; 176(3): 217-227. doi: 10.1176/appi.ajp.2018.18070857

16. Nievergelt C.M., Maihofer A.X., Klengel T., et al. International meta-analysis of PTSD genome-wide association studies identifies sex- and ancestry-specific genetic risk loci. Nat Commun. 2019; 10(1): 4558. doi: 10.1038/s41467-019-12576-w

17. Watson H.J., Yilmaz Z., Thornton L.M., et al. Genome-wide association study identifies eight risk loci and implicates metabo-psychiatric origins for anorexia nervosa. Nat Genet. 2019; 51(8): 1207-1214. doi: 10.1038/s41588-019-0439-2

18. Jansen I.E., Savage J.E.., Watanabe K., et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat Genet. 2019; 51(3): 404-413. doi: 10.1038/s41588-018-0311-9

19. Demontis D., Walters R.K., Martin J., et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat Genet. 2019; 51(1): 63-75. doi: 10.1038/s41588-018-0269-7

20. Grove J., Ripke S., Als T.D., et al. Identification of common genetic risk variants for autism spectrum disorder. Nat Genet. 2019; 51(3): 431-444. doi: 10.1038/s41588-019-0344-8.

21. Ochoa D., Karim M., Ghoussaini M., Hulcoop D.G., McDonagh E.M., Dunham I. Human genetics evidence supports two-thirds of the 2021 FDA-approved drugs. Nat Rev Drug Discov. 2022;21(8):551. doi: 10.1038/d41573-022-00120-3.

22. Limonova A.S., Ershova A.I., Kiseleva A.V., Ramensky V.E., Vyatkin Yu.V., Kutsenko V.A., Meshkov A.N., Drapkina O.M. Otsenka poligennogo riska arterial’noy gipertenzii [Assessment of polygenic risk of hypertension]. Kardiovaskulyarnaya terapiya i profilaktika [ Cardiovascular Therapy and Prevention]. 2022;21(12):3464. (In Russ.) doi:10.15829/1728-8800-2022-3464

23. Kiseleva A.V., Soplenkova A.G., Kutsenko V.A., et al. Validatsiya shkal geneticheskogo riska ozhireniya na vyborke naseleniya regionov Rossii [Validation of genetic risk scores for obesity on a sample of the population of Russian regions]. Kardiovaskulyarnaya terapiya i profilaktika [ Cardiovascular Therapy and Prevention]. 2023;22(10):3755. (In Russ.) doi:10.15829/1728-8800-2023-3755.

24. Wray N.R., Lin T., Austin J., McGrath J.J., Hickie I.B., Murray G.K., Visscher P.M. From Basic Science to Clinical Application of Polygenic Risk Scores: A Primer. JAMA Psychiatry. 2021;78(1):101-109. doi: 10.1001/jamapsychiatry.2020.3049

25. Bevilacqua L., Doly S., Kaprio J., et al. A population-specific HTR2B stop codon predisposes to severe impulsivity. Nature . 2010;468(7327):1061-6. doi: 10.1038/nature09629.

26. Kolobkova Yu.A., Vigant V.A., Shalygin A.V., Kaznacheeva E.V. Bolezn’ Khantingtona: narusheniya kal’tsiyevoy signalizatsii i modeli dlya izucheniya razvitiya patologii [Huntington’s disease: calcium signaling disorders and models for studying the development of pathology]. Acta Naturae (Russian version). 2017; 9 (2 (33): 35-49. (In Russ.).

27. Kuznetsov K.O., Khaidarova R.R., Khabibullina R.H., et al. Testosteron i bolezn’ Al’tsgeymera. [Testosterone and Alzheimer’s disease]. Problemy Endokrinologii [Problems of Endocrinology]. 2022;68(5):97-107. (In Russ.) doi:10.14341/probl13136

28. Duncan L.E., Ostacher M., Ballon J. How genome-wide association studies (GWAS) made traditional candidate gene studies obsolete. Neuropsychopharmacology 2019; 44(9): 1518-1523. doi: 10.1038/s41386-019-0389-5.

29. Zhang Y., Qi G., Park J.H., Chatterjee N. Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits. Nat Genet. 2018; 50(9): 1318-1326. doi: 10.1038/s41588-018-0193-x.

30. Boyle E.A., Li Y.I., Pritchard J.K. An Expanded View of Complex Traits: From Polygenic to Omnigenic. Cell 2017; 169(7): 1177-1186. doi: 10.1016/j.cell.2017.05.038.

31. International Schizophrenia Consortium; Purcell S.M., Wray N.R., Stone J.L., Visscher P.M., O’Donovan M.C., Sullivan P.F., Sklar P. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 2009; 460(7256): 748-52. doi: 10.1038/nature08185.

32. Chatterjee N., Shi .J, García-Closas M. Developing and evaluating polygenic risk prediction models for stratified disease prevention. Nat Rev Genet. 2016; 17(7): 392-406. doi: 10.1038/nrg.2016.27.

33. Batra A., Chen L.M., Wang Z., et al. Early Life Adversity and Polygenic Risk for High Fasting Insulin Are Associated With Childhood Impulsivity. Front Neurosci. 2021; 15: 704785. doi: 10.3389/fnins.2021.704785.

34. Ge T., Chen C.Y., Ni Y., Feng Y.A., Smoller J.W. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat Commun. 2019; 10(1): 1776. doi: 10.1038/s41467-019-09718-5.

35. Ni G., Zeng J., Revez J.A., et al. A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts. Biol Psychiatry 2021; 90(9): 611-620. doi: 10.1016/j.biopsych.2021.04.018.

36. Lloyd-Jones L.R., Zeng J., Sidorenko J., et al. Improved polygenic prediction by Bayesian multiple regression on summary statistics. Nat Commun. 2019; 10(1): 5086. doi: 10.1038/s41467-019-12653-0.

37. Kondrateva O.A., Karpulevich E.A. Modifikatsiya metoda rascheta poli-gennykh riskov s ispol’zovaniyem grafa variatsii [Modification of the Method for Calculating Polygenic Risks With Variation Graph]. Trudy Instituta sistemnogo programmirovaniya RAN [Proceedings of the Institute for System Programming of the RAS (Proceedings of ISP RAS)] . 2022;34(2):191-200. (In Russ.) doi:10.15514/ISPRAS-2022-34(2)-15

38. Ripke S., PGC SCZ WORKGROUP GWAS with over 70.000 cases and 100,000 controls. Eur Neuropsychopharmacol. 2019; 29: S814

39. Desikan R.S., Fan C.C., Wang Y., et al. Genetic assessment of age-associated Alzheimer disease risk: Development and validation of a polygenic hazard score. PLoS Med. 2017; 14(3): e1002258. doi: 10.1371/journal.pmed.1002258

40. Mester R., Hou K., Ding Y., et al. Impact of cross-ancestry genetic architecture on GWASs in admixed populations. Am J Hum Genet. 2023; 110(6): 927-939. doi: 10.1016/j.ajhg.2023.05.001

41. Perkins D.O., Olde Loohuis L., Barbee J., et al. Polygenic Risk Score Contribution to Psychosis Prediction in a Target Population of Persons at Clinical High Risk. Am J Psychiatry 2020; 177(2): 155-163. doi: 10.1176/appi.ajp.2019.18060721.

42. Byrne J.F., Mongan D., Murphy J., et al. Prognostic models predicting transition to psychotic disorder using blood-based biomarkers: a systematic review and critical appraisal. Transl Psychiatry 2023; 13(1): 333. doi: 10.1038/s41398-023-02623-y

43. Hernandez L.M., Kim M., Hoftman G.D., et al. Transcriptomic Insight Into the Polygenic Mechanisms Underlying Psychiatric Disorders. Biol Psychiatry 2021; 89(1): 54-64. doi: 10.1016/j.biopsych.2020.06.005

44. Uffelmann E., Posthuma D. Emerging Methods and Resources for Biological Interrogation of Neuropsychiatric Polygenic Signal. Biol Psychiatry 2021; 89(1): 41-53. doi: 10.1016/j.biopsych.2020.05.022

45. Bhattacharya A., Vo D.D., Jops C. et al. Isoform-level transcriptome-wide association uncovers genetic risk mechanisms for neuropsychiatric disorders in the human brain. Nat Genet. 2023. doi: 10.1038/s41588-023-01560-2

46. Girgenti M.J., Wang J., Ji D., et al. Transcriptomic organization of the human brain in post-traumatic stress disorder. Nat Neurosci. 2021; 24(1): 24-33. doi: 10.1038/s41593-020-00748-7

47. Zhang Y., Quick C., Yu K., et al. PTWAS: investigating tissue-relevant causal molecular mechanisms of complex traits using probabilistic TWAS analysis. Genome Biol. 2020; 21(1): 232. doi: 10.1186/s13059-020-02026-y

48. Alpay B.A., Demetci P., Istrail S., Aguiar D. Combinatorial and statistical prediction of gene expression from haplotype sequence. Bioinformatics 2020; 36(1): 194-202. doi: 10.1093/bioinformatics/btaa318

49. Koch L. Predicting mRNA levels from genome sequence. Nat Rev Genet. 2020; 21(8): 446-447. doi: 10.1038/s41576-020-0253-9.

50. Li B., Verma S.S., Veturi Y.C., Verma A., Bradford Y., Haas D.W., Ritchie M.D. Evaluation of PrediXcan for prioritizing GWAS associations and predicting gene expression. Pac Symp Biocomput. 2018; 23: 448-459.

51. Barbeira A.N., Pividori M., Zheng J., Wheeler H.E., Nicolae D.L., Im H.K. Integrating predicted transcriptome from multiple tissues improves association detection. PLoS Genet. 2019; 15(1): e1007889. doi: 10.1371/journal.pgen.1007889

52. Shah K.P., Wheeler H.E., Gamazon E.R., Nicolae D.L., Cox N.J., Im H.K. Genetic predictors of gene expression associated with risk of bipolar disorder. bioRxiv. 2016 doi: 10.1101/043752

53. Yates D. Gene networking. Nat Rev Neurosci. 2021; 22(10): 589. doi: 10.1038/s41583-021-00522-z

54. Johnson M.R., Shkura K., Langley S.R., et al. Systems genetics identifies a convergent gene network for cognition and neurodevelopmental disease. Nat Neurosci. 2016; 19(2): 223-232. doi: 10.1038/nn

55. Pergola G., Di Carlo P., D’Ambrosio E., et al. DRD2 co-expression network and a related polygenic index predict imaging, behavioral and clinical phenotypes linked to schizophrenia. Transl Psychiatry 2017; 7(1): e1006. doi: 10.1038/tp.2016.253

56. Gerring Z.F., Gamazon E.R., Derks E.M., Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium. A gene co-expression network-based analysis of multiple brain tissues reveals novel genes and molecular pathways underlying major depression. PLoS Genet. 2019; 15(7): e1008245. doi: 10.1371/journal.pgen.1008245

57. Restrepo-Lozano J.M., Pokhvisneva I., Wang Z., Patel S., Meaney M.J., Silveira P.P., Flores C. Corticolimbic DCC gene co-expression networks as predictors of impulsivity in children. Mol Psychiatry 2022; 27(6): 2742-2750. doi: 10.1038/s41380-022-01533-7

58. Mucignat-Caretta C., Soravia G. Positive or negative environmental modulations on human brain development: the morphofunctional outcomes of music training or stress. Front Neurosci. 2023; 17: 1266766. doi: 10.3389/fnins.2023.1266766

59. McGill M.G., Pokhvisneva I., Clappison A.S., et al. Maternal Prenatal Anxiety and the Fetal Origins of Epigenetic Aging. Biol Psychiatry 2022; 91(3): 303-312. doi: 10.1016/j.biopsych.2021.07.025

60. Belsky D.W., Domingue B.W., Wedow R., et al. Genetic analysis of social-class mobility in five longitudinal studies. Proc Natl Acad Sci USA 2018; 115(31): 7275-7284. doi: 10.1073/pnas.1801238115

61. Provençal N., Arloth J., Cattaneo A., et al. Glucocorticoid exposure during hippocampal neurogenesis primes future stress response by inducing changes in DNA methylation. Proc Natl Acad Sci USA 2020; 117(38): 23280-23285. doi: 10.1073/pnas.1820842116