Modern Pediatrics. Ukraine. (2026).1(153): 82-89. doi: 10.15574/SP.2026.1(153).8289
Grechanin Y. R.¹, Fadieieva A. L.², Shkolnikova D. V.^3
1Kharkiv National Medical University, Ukraine
²Mitochondrial and Epigenomic Medicine of Kharkiv National Medical University, Ukraine
³MNCE of Kharkiv Regional Council "Interregional Specialized Medical-Genetic Center for Rare (Orphan) Diseases", Ukraine

For citation: Grechanin YR, Fadieieva AL, Shkolnikova DV. (2026). Molecular genetics of neurodevelopmental disorders in Ukrainian children. Modern Pediatrics. Ukraine. 1(153): 82-89. doi: 10.15574/SP.2026.1(153).8289.
Article received: Dec 10, 2025. Accepted for publication: Feb 08, 2026.

Neurodevelopmental disorders make a significant contribution to pediatric pathologies. This group of diseases varies symptomatically and has various underlying molecular reasons, which result in the necessity of personalized diagnostic and treatment protocols
Aim – to detect metabolic pathways and molecular markers potentially contributing to the development of the pathological phenotype in pediatric patients with neural pathology.
Materials and methods. Selection of the patient’s cohort for the pilot study was done during consultations provided by pediatricians, geneticists, and neurologists following principles of an interdisciplinary approach in diagnostic and management of children with developmental abnormalities. Further analysis of detected DNA variations, along with structural and functional predictions, was done using the SNP-NEXUS tool.
Results. The work presented in this manuscript enabled the identification of all detected genomic variations that may be associated with the chosen clinical phenotype in the small cohort of patients. Further functional enrichment analysis allowed us to identify various biological cellular processes that may be affected by the identified genomic alterations. Particularly, we found metabolic pathways with high enrichment score for possibly pathogenic and probably pathogenic amino acid substitutions in proteins, with lipid metabolism having the highest enrichment score.
Conclusions. Predictive and functional enrichment analysis of SNV (Single Nucleotide Variation) patterns detected by DNA sequencing on a small cohort of pediatric patients with neurodevelopmental pathology allowed us to identify cellular processes and proteins that potentially play a role in neural pathology. Further investigation in a higher number of patients and search for statistically significant associations of the preselected targets with the phenotype may help to improve the diagnostic and treatment approach for the selected pathology.
The study was conducted in accordance with the principles of the Declaration of Helsinki. The research protocol was approved by the Local Ethics Committee of the institution. Informed parental consent was obtained for participation in the study.
The authors declare no conflict of interest.
Keywords: neurodevelopmental disorders, pediatrics, molecular diagnostics, functional enrichment analysis, lipid metabolism.

REFERENCES

1. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P et al. (2010). A method and server for predicting damaging missense mutations. Nature Methods. 7(4): 248-249. https://doi.org/10.1038/nmeth0410-248; PMid:20354512 PMCid:PMC2855889

2. Arnett AB, Wang T, Eichler EE, Bernier RA. (2021). Reflections on the genetics-first approach to advancements in molecular genetic and neurobiological research on neurodevelopmental disorders. Journal of Neurodevelopmental Disorders. 13(1): 24. https://doi.org/10.1186/s11689-021-09371-4; PMid:34148555 PMCid:PMC8215789

3. Asdaq SMB. (2025). Learning disabilities in the 21st century: Integrating neuroscience, education, and technology for better outcomes. SAGE Open. 15(3): 21582440251365483. https://doi.org/10.1177/21582440251365483

4. Bui DT, Ton ANV, Nguyen CTD, Nguyen SH, Tran HK, Nguyen XT et al. (2024). Pathogenic/likely pathogenic mutations identified in Vietnamese children diagnosed with autism spectrum disorder using high-resolution SNP genotyping platform. Scientific Reports. 14(1): 2360. https://doi.org/10.1038/s41598-024-52777-y; PMid:38287090 PMCid:PMC10825208

5. Chen AK, Wang X, McCluskey LP, Morgan JC, Switzer JA, Mehta R et al. (2022). Neurodevelopmental sequelae of long COVID-19: Pilot results from the COVID-19 neurological and molecular prospective cohort study in Georgia, USA. Brain, Behavior, & Immunity Health. 24: 100491. https://doi.org/10.1016/j.bbih.2022.100491; PMid:35873350 PMCid:PMC9290328

6. Cortese S, Bellgrove MA, Brikell I, Franke B, Goodman DW, Hartman CA et al. (2025). Attention-deficit/hyperactivity disorder (ADHD) in adults: Evidence base, uncertainties and controversies. World Psychiatry. 24(3): 347-371. https://doi.org/10.1002/wps.21374; PMid:40948064 PMCid:PMC12434367

7. Geistlinger L, Csaba G, Santarelli M, Ramos M, Schiffer L, Turaga N et al. (2021). Toward a gold standard for benchmarking gene set enrichment analysis. Briefings in Bioinformatics. 22(1): 545-556. https://doi.org/10.1093/bib/bbz158; PMid:32026945 PMCid:PMC7820859

8. Gillespie M, Jassal B, Stephan R, Milacic M, Rothfels K, Senff-Ribeiro A et al. (2022). The Reactome pathway knowledgebase 2022. Nucleic Acids Research. 50(D1): D687-D692. https://doi.org/10.1093/nar/gkab1028; PMid:34788843 PMCid:PMC8689983

9.Haque UM, Kabir E, Khanam R. (2023). Early detection of paediatric and adolescent obsessive-compulsive, separation anxiety and attention deficit hyperactivity disorder using machine learning algorithms. Health Information Science and Systems. 11(1): 31. https://doi.org/10.1007/s13755-023-00232-z; PMid:37489154 PMCid:PMC10363094

10. He J-L, Xu X-M, Wang W, Chen JM, Zhang Q, Gan Y et al. (2025). Study pressure and self-harm in Chinese primary school students: The effect of depression and parent-child relationships. Frontiers in Psychiatry. 16: 1580527. https://doi.org/10.3389/fpsyt.2025.1580527; PMid:40370592 PMCid:PMC12076088

11. He J, Zhong X, Cheng C, Dong D, Zhang B et al. (2023). Characteristics of white matter structural connectivity in healthy adults with childhood maltreatment. European Journal of Psychotraumatology. 14(1): 2179278. https://doi.org/10.1080/20008066.2023.2179278; PMid:37052100 PMCid:PMC9970228

12. Herrero-Roldán S, Martín-Rodríguez A. (2025). Neglect and neurodevelopment: A narrative review understanding the link between child neglect and executive function deficits. Biomedicines. 13(7): 1565. https://doi.org/10.3390/biomedicines13071565; PMid:40722641 PMCid:PMC12292309

13. Kim S, Lee H, Lee J, Lee SW, Kwon R, Kim MS et al. (2024). Short- and long-term neurodevelopmental outcomes in long COVID in South Korea and Japan. Nature Human Behaviour. 8(8): 1530-1544. https://doi.org/10.1038/s41562-024-01895-8; PMid:38918517

14. Kuo P-C, Yao Z-F. (2025). Amygdala hyperactivation in childhood maltreatment: An ALE-based meta-analysis on emotion-related processing. Neuroscience & Biobehavioral Reviews. 174: 106180. https://doi.org/10.1016/j.neubiorev.2025.106180; PMid:40311771

15. Lu Y, Tong J, Zhang D, Chen J, Li L, Lei Y et al. (2025). Risk of neurodevelopmental and related conditions associated with SARS-CoV-2 infection: A difference-in-differences analysis. Nature Communications. 16(1): 6829. https://doi.org/10.1038/s41467-025-61961-1; PMid:40707478 PMCid:PMC12290120

16. Makhlynets NP, Pyuryk MV. (2025). The impact of stress on the quality of life of modern children. International Medical Herald. 1(1): 22-30. https://doi.org/10.64108/imh.2025.1.1.22

17. Makhlynets NP, Pyuryk MV, Kokoshko MV, Mytsak L. (2025). Social stress in children and its influence on the development of bad habits. International Medical Herald. 1(2): 14-18. https://doi.org/10.64108/imh.2025.2.2.14

18. Morris-Rosendahl DJ, Crocq MA. (2020). Neurodevelopmental disorders – the history and future of a diagnostic concept. Dialogues in Clinical Neuroscience. 22(1): 65-72. https://doi.org/10.31887/DCNS.2020.22.1/macrocq; PMid:32699506 PMCid:PMC7365295

19. Oscanoa J, Sivapalan L, Gadaleta E, Dayem Ullah AZ, Lemoine NR, Chelala C. (2020). SNPnexus: A web server for functional annotation of human genome sequence variation (2020 update). Nucleic Acids Research. 48(W1): W185-W192. https://doi.org/10.1093/nar/gkaa420; PMid:32496546 PMCid:PMC7319579

20. Parenti I, Garcia Rabaneda LE, Schön H, Novarino G. (2020). Neurodevelopmental disorders: From genetics to functional pathways. Trends in Neurosciences. 43(8): 608-621. https://doi.org/10.1016/j.tins.2020.05.004; PMid:32507511

21. Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J et al. (2015). Standards and guidelines for the interpretation of sequence variants: A joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genetics in Medicine. 17(5): 405-423. https://doi.org/10.1038/gim.2015.30; PMid:25741868 PMCid:PMC4544753

22. Rosero-Pahi M, Andoh J, Shields GS, Acosta-Ortiz A, Serrano-Gomez S, Slavich GM. (2024). Cumulative lifetime stressor exposure impairs stimulus-response but not contextual learning. Scientific Reports. 14(1): 13080. https://doi.org/10.1038/s41598-024-62595-x; PMid:38844465 PMCid:PMC11156921

23. Santos IMF, Ralin VLO, Menezes E da C, Medeiros JA. (2023). Anxiety and its relationship with learning disorders in childhood: A systematic review. Revista Contemporânea. 3(5): 3539-3561. https://doi.org/10.56083/RCV3N5-008

24. Sheridan MA, Mukerji CE, Wade M, Humphreys KL, Garrisi K, Goel S et al. (2022). Early deprivation alters structural brain development from middle childhood to adolescence. Science Advances. 8(40): eabn4316. https://doi.org/10.1126/sciadv.abn4316; PMid:36206331 PMCid:PMC9544316

25. Tarani L, Fiore M. (2024). Disclosing the complexities of childhood neurodevelopmental disorders. Children. 12(1): 16. https://doi.org/10.3390/children12010016; PMid:39857847 PMCid:PMC11763363

26. Uffelmann E, Huang, QQ, Munung NS et al. (2021). Genome-wide association studies. Nature Reviews Methods Primers. 1: 59. https://doi.org/10.1038/s43586-021-00056-9

27. Wijesooriya K, Jadaan SA, Perera KL, Kaur T, Ziemann M. (2021). Guidelines for reliable and reproducible functional enrichment analysis. bioRxiv. https://doi.org/10.1101/2021.09.06.459114

28. Yang M, Xu B, Wang J, Zhang Z, Xie H, Wang H et al. (2021). Genetic diagnoses in pediatric patients with epilepsy and comorbid intellectual disability. Epilepsy Research. 170: 106552. https://doi.org/10.1016/j.eplepsyres.2021.106552; PMid:33486335

29. Zhao K, Rhee SY. (2023). Interpreting omics data with pathway enrichment analysis. Trends in Genetics. 39(4): 308-319. https://doi.org/10.1016/j.tig.2023.01.003; PMid:36750393

30. Alfardan J, Mohsen AW, Copeland S, Ellison J, Keppen-Davis L, Rohrbach M, Powell BR, Gillis J, Matern D, Kant J, Vockley J. (2010). Characterization of new ACADSB gene sequence mutations and clinical implications in patients with 2-methylbutyrylglycinuria identified by newborn screening. Mol Genet Metab. 100(4):333-8. https://doi.org/10.1016/j.ymgme.2010.04.014; PMid:20547083 PMCid:PMC2906669

31. Zhong J, Jiang F, Yang H, Li J, Cheng J, Zeng Q, Xu Q. Novel GALC Mutations Cause Adult-Onset Krabbe Disease With Myelopathy in Two Chinese Families: Case Reports and Literature Review. Front Neurol. (2020) 11:830. https://doi.org/10.3389/fneur.2020.00830; PMid:32973651 PMCid:PMC7473299