Diagnostic efficiency of whole exome sequencing in the search for genetic causes of hereditary diseases in Yugra (West Siberia, Russia)

Whole-exome sequencing (WES) has revolutionized the diagnostics of hereditary diseases, yet its efficacy varies across populations. Data on the genetic architecture of rare hereditary disorders in many Russian regions, including the ethnically diverse Khanty-Mansi Autonomous Okrug (Yugra) are scarce. The aim of this study was to evaluate the diagnostic yield of WES for identifying genetic variants associated with hereditary disorders in this ethnically heterogeneous population. The study involved 286 probands with suspected hereditary disorders observed by regional geneticists in the years 2021–2024. WES was performed on the DNBSEQ-G50 platform (MGI, China). Bioinformatic analysis included variant calling and annotation using population databases and pathogenicity prediction tools. Identified variants were classified according to ACMG/Russian Medical Genetics Society guidelines and correlated with clinical phenotypes. Molecular genetic diagnoses were categorized as definitive, partial, potential (based on variants of unknown significance), or unknown. The examined cohort was predominantly pediatric, the most common clinical indications were neurological, dysmorphic, and metabolic disorders. Definitive molecular diagnoses were established in 24.8 % of patients. Inclusion of potential diagnoses increased the total yield to 48.6 %. Diagnostic efficacy varied significantly among disease categories ranging from 58.3 % for renal disorders to 0 % for neurodevelopmental disorders. A total of 420 unique variants were analyzed, and missense changes were the most frequent among clinically significant findings. The most commonly implicated genes were ATP7B, GJB2, ABCA4, and GALT. The study results indicate that WES is an effective first-tier molecular tool for a wide range of suspected hereditary diseases in the Yugra population, with a diagnostic yield comparable to similar studies abroad. The findings support the utility of WES in diverse populations and highlight the potential for increasing yield through trio-WES and periodic data reanalysis.

1. Adams D.R., Eng C.M. Next-generation sequencing to diagnose suspected genetic disorders. N Engl J Med. 2018;379(14):1353-1362. doi 10.1056/NEJMra1711801

2. Adzhubei I.A., Schmidt S., Peshkin L., Ramensky V.E., Gerasimova A., Bork P., Kondrashov A.S., Sunyaev S.R. A method and server for predicting damaging missense mutations. Nat Methods. 2010;7(4): 248-249. doi 10.1038/nmeth0410-248

3. Arteche-López A., Ávila-Fernández A., Riveiro Álvarez R., Almoguera B., Bustamante Aragonés A., Martin-Merida I., López Martínez M.A., … Blanco-Kelly F., Tahsin Swafiri S., Lorda Sánchez I., Trujillo Tiebas M.J., Ayuso C. Five years’ experience of the clinical exome sequencing in a Spanish single center. Sci Rep. 2022;12(1): 19209. doi 10.1038/s41598-022-23786-6

4. Azzollini J., Scuvera G., Bruno E., Pasanisi P., Zaffaroni D., Calvello M., Pasini B., Ripamonti C.B., Colombo M., Pensotti V., Radice P., Peissel B., Manoukian S. Mutation detection rates associated with specific selection criteria for BRCA1/2 testing in 1854 high-risk families: a monocentric Italian study. Eur J Intern Med. 2016;32: 65-71. doi 10.1016/j.ejim.2016.03.010

5. Barbitoff Y.A., Khmelkova D.N., Pomerantseva E.A., Slepchenkov A.V., Zubashenko N.A., Mironova I.V., Kaimonov V.S., … Aseev M.V., Shcherbak S.G., Glotov O.S., Isaev A.A., Predeus A.V. Expanding the Russian allele frequency reference via cross-laboratory data integration: insights from 7452 exome samples. Natl Sci Rev. 2024; 11(10):nwae326. doi 10.1093/nsr/nwae326

6. Cingolani P., Platts A., Wang le L., Coon M., Nguyen T., Wang L., Land S.J., Lu X., Ruden D.M. 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 10.4161/fly.19695

7. Davydov E.V., Goode D.L., Sirota M., Cooper G.M., Sidow A., Batzoglou S. Identifying a high fraction of the human genome to be under selective constraint using GERP++. PLoS Comput Biol. 2010; 6(12):e1001025. doi 10.1371/journal.pcbi.1001025

8. Glotov O.S., Donnikov M.Yu., Glotov A.S., Popova E.A., Suchko P.A., Kolbasin L.N., Belotserkovtseva L.D., Kovalenko L.V. Genetic variants identified in patients with rare and multifactorial diseases in the Khanty-Mansi Autonomous Okrug – Yugra based on exome sequencing: registered database No. 2025622641. 2025. Available: https://new.fips.ru/publication-web/publications/document?type=doc&tab=PrEVM&id=60EDD49E-F82F-481E-BFB7-474B01087E34, accessed on 28.09.2025 (in Russian)

9. Han H., Seo G.H., Hyun S.I., Kwon K., Ryu S.W., Khang R., Lee E., … Yang S., Lee S., Jang S., Lee J., Lee H. Exome sequencing of 18,994 ethnically diverse patients with suspected rare Mendelian disorders. NPJ Genom Med. 2025;10(1):6. doi 10.1038/s41525-024-00455-3

10. Ioannidis N.M., Rothstein J.H., Pejaver V., Middha S., McDonnell S.K., Baheti S., Musolf A., … Bailey-Wilson J.E., Radivojac P., Thibodeau S.N., Whittemore A.S., Sieh W. REVEL: an ensemble method for predicting the pathogenicity of rare missense variants. Am J Hum Genet. 2016;99(4):877-885. doi 10.1016/j.ajhg.2016.08.016

11. Kumar P., Henikoff S., Ng P.C. Predicting the effects of coding nonsynonymous variants on protein function using the SIFT algorithm. Nat Protoc. 2009;4(7):1073-1081. doi 10.1038/nprot.2009.86

12. Lai G., Gu Q., Lai Z., Chen H., Chen J., Huang J. The application of whole-exome sequencing in the early diagnosis of rare genetic diseases in children: a study from Southeastern China. Front Pediatr. 2024;12:1448895. doi 10.3389/fped.2024.1448895

13. Li H. Exploring single-sample SNP and INDEL calling with wholegenome de novo assembly. Bioinformatics. 2011;28(14):1838-1844. doi 10.1093/bioinformatics/bts280

14. Moey L.H., Seo G.H., Cheah B.E., Keng W.T., Lee H., Ch’ng G.S. A first large study of whole-exome sequencing (WES) in 489 patients with suspected rare genetic disorders at a tertiary centre in Malaysia. Rare. 2025;3:100102. doi 10.1016/j.rare.2025.100102

15. Okuneva E.G., Kozina A.A., Baryshnikova N.V., Krasnenko A.Yu., Klimchuk O.I., Stetsenko I.F., Plotnikov N.A., Surkova E.I., Ilinsky V.V. The utility of exome sequencing in diagnosis of hereditary diseases. Med Genet. 2020;19(12):18-24. doi 10.25557/2073-7998.2020.12.18-24 (in Russian)

16. Pashaei E., Yilmaz A., Ozen M., Aydin N. Prediction of splice site using AdaBoost with a new sequence encoding approach. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), Budapest, Hungary. IEEE, 2016;003853-003858. doi 10.1109/SMC.2016.7844835

17. Petersen B.S., Fredrich B., Hoeppner M.P., Ellinghaus D., Franke A. Opportunities and challenges of whole-genome and -exome sequencing. BMC Genet. 2017;18(1):14. doi 10.1186/s12863-017-0479-5

18. Quang D., Chen Y., Xie X. DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics. 2015; 31(5):761-763. doi 10.1093/bioinformatics/btu703

19. Retterer K., Juusola J., Cho M.T., Vitazka P., Millan F., Gibellini F., Vertino-Bell A., … Richard G., Brandt T., Haverfield E., Chung W.K., Bale S. Clinical application of whole-exome sequencing across clinical indications. Genet Med. 2016;18(7):696-704. doi 10.1038/gim.2015.148

20. Richards S., Aziz N., Bale S., Bick D., Das S., Gastier-Foster J., Grody W.W., Hegde M., Lyon E., Spector E., Voelkerding K., Rehm H.L.; ACMG Laboratory Quality Assurance Committee. 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. Genet Med. 2015;17(5):405-424. doi 10.1038/gim.2015.30

21. Ross J.P., Dion P.A., Rouleau G.A. Exome sequencing in genetic disease: recent advances and considerations. F1000Res. 2020;9(F1000 Faculty Rev):336. doi 10.12688/f1000research.19444.1

22. Ryzhkova O.P., Kardymon O.L., Prohorchuk E.B., Konovalov F.A., Maslennikov A.B., Stepanov V.A., Afanas’ev A.A., … Kostareva A.A., Pavlov A.E., Golubenko M.V., Polyakov A.V., Kucev S.I. Guidelines for the interpretation of massive parallel sequencing variants (update 2018, v2). Med Genet. 2019;18(2):3-24. doi 10.25557/2073-7998.2019.02.3-23 (in Russian)

23. Shchagina O.A., Ryzhkova O.P., Chuhrova A.L., Milovidova T.B., Gundorova P., Mironovich O.L., Orlova A.A., Orlova M.D., Polyakov A.V. Diagnostic utility of exome sequencing for inherited peripheral neuropathies. Neuromuscular Disord. 2020;10(4):12-26. doi 10.17650/2222-8721-2020-10-4-12-26 (in Russian)

24. Stankiewicz P., Lupski J.R. Structural variation in the human genome and its role in disease. Annu Rev Med. 2010;61:437-455. doi 10.1146/annurev-med-100708-204735

25. Stranneheim H., Lagerstedt-Robinson K., Magnusson M., Kvarnung M., Nilsson D., Lesko N., Engvall M., … Soller M.J., Nordgren A., Wirta V., Lindstrand A., Wedell A. Integration of whole genome sequencing into a healthcare setting: high diagnostic rates across multiple clinical entities in 3219 rare disease patients. Genome Med. 2021;13(1):40. doi 10.1186/s13073-021-00855-5

26. Strauch Y., Lord J., Niranjan M., Baralle D. CI-SpliceAI-improving machine learning predictions of disease causing splicing variants using curated alternative splice sites. PLoS One. 2022;17(6):e0269159. doi 10.1371/journal.pone.0269159

27. Sundaram L., Gao H., Padigepati S.R., McRae J.F., Li Y., KosmickiJ.A., Fritzilas N., Hakenberg J., Dutta A., Shon J., Xu J., Batzoglou S., Li X., Farh K.K. Predicting the clinical impact of human mutation with deep neural networks. Nat Genet. 2018;50(8):1161-1170. doi 10.1038/s41588-018-0167-z

28. Tan T.Y., Lunke S., Chong B., Phelan D., Fanjul-Fernandez M., Marum J.E., Kumar V.S., … Martyn M., Goranitis I., Thorne N., Gaff C.L., White S.M. A head-to-head evaluation of the diagnostic efficacy and costs of trio versus singleton exome sequencing analysis. Eur J Hum Genet. 2019;27(12):1791-1799. doi 10.1038/s41431-019-0471-9

29. Van der Auwera G.A., O’Connor B.D. Genomics in the Cloud: Using Docker, GATK, and WDL in Terra. O’Reilly Media, 2020

30. Wang Q., Shashikant C.S., Jensen M., Altman N.S., Girirajan S. Novel metrics to measure coverage in whole exome sequencing datasets reveal local and global non-uniformity. Sci Rep. 2017;7(1):885. doi 10.1038/s41598-017-01005-x

31. Xiang J., Sun X., Song N., Ramaswamy S., Abou Tayoun A.N., Peng Z. Comprehensive interpretation of single-nucleotide substitutions in GJB2 reveals the genetic and phenotypic landscape of GJB2-related hearing loss. Hum Genet. 2023;142(1):33-43. doi 10.1007/s00439-022-02479-0

32. Zinchenko R.A., Kutsev S.I., Aleksandrova O.Yu., Ginter E.K. Main methodological approaches to the identification and diagnosis of monogenic hereditary diseases and problems in the organization of medical care and unified preventive programs. Probl Social Hyg Public Health Hist Med. 2019;27(5):865-877. doi 10.32687/0869-866X-2019-27-5-865-877 (in Russian)