Prospects for using exome sequencing to solve problems in human reproduction (Part I)

1. Баранов В.С., ред. Эволюция предиктивной медицины. СПб.: Эко-Вектор; 2021. 359 с. [Baranov V.S., ed. Evolution of predictive medicine. St. Petersburg: Eco-Vector; 2021. 359 с. (in Russian)].
2. International Human Genome Sequencing Consortium. Finishing the euchromatic sequence of the human genome. Nature. 2004; 431(7011): 931-45. https//dx.doi.org/10.1038/nature03001.
3. Genome Reference Consortium. Human Genome Overview. Available at: https://www.ncbi.nlm.nih.gov/grc/human
4. Степанов В.А. Этногеномика населения Северной Евразии. Томск: Печатная мануфактура; 2002. 244 с. [Stepanov V.A. Ethnogenomics of the Northern Eurasia population. Tomsk: Pechatnaya Manufaktura; 2002. 244 p. (in Russian)].
5. Глотов О.С., Глотов А.С., Тарасенко О.А., Иващенко Т.Э., Баранов В.С. Исследование функционально-значимого полиморфизма ACE, AGTR1, ENOS, MTHFR, MTRR и APOE генов в популяции Северо-Западного региона России. Экологическая генетика. 2004; 2(3): 32-5. [Glotov O.S., Glotov A.S., Tarasenko O.A., Ivaschenko T.E., Baranov V.S. Analysis of ACE, AGTR1, ENOS, MTHFR, MTRR and APOE genes polymorphisms in the population of North-West of Russia. Ecological Genetics. 2004; 2(3): 32-35. (in Russian)].
6. Barbitoff Y.A., Khmelkova D.N., Pomerantseva E.A., Slepchenkov A.V., Zubashenko N.A., Mironova I.V. et al. Expanding the Russian allele frequency reference via cross-laboratory data integration: insights from 6,096 exome samples. MedRxiv. 2021. https://dx.doi.org/10.1101/2021.11.02.21265801.
7. Shikov A., Tsay V., Fedyakov M., Eismont Y., Rudnik A., Urasov S.et al. The application of nanopore sequencing for variant calling on the human mitochondrial DNA. Bio. Comm. 2021; 66(2): 109-23.https://dx.doi.org/10.21638/spbu03.2021.202.
8. Morganti S., Tarantino P., Ferraro E., D’Amico P., Viale G., Trapani D. et al. Chapter 8. Role of next-generation sequencing technologies in personalized medicine. In: Pravettoni G., Triberti S., eds. P5 eHealth: An Agenda for the Health Technologies of the Future. Springer: Cham; 2020; 125-45.https://dx.doi.org/10.1007/978-3-030-27994-3_8.
9. Glotov O.S., Romanova O.V., Eismont Y.A., Sarana A.M., Scherbak S.G., Kuzmich E.V. et al. Comparative analysis of NGS and Sanger sequencing methods for HLA typing at a Russian university clinic. Cell. Ther. Transpl. 2018; 7(4): 72-82. https//dx.doi.org/10.18620/ctt-1866-8836-2018-7-4-72-82.
10. Glotov A.S., Kazakov S.V., Zhukova E.A., Alexandrov A.V., Glotov O.S., Pakin V.S. et al. Targeted next-generation sequencing (NGS) of nine candidate genes with custom AmpliSeq in patients and a cardiomyopathy risk group. Clin. Chim. Acta. 2015; 446: 132-40. https://dx.doi.org/10.1016/j.cca.2015.04.014.
11. Majewski J., Schwartzentruber J., Lalonde E., Montpetit A., Jabado N. What can exome sequencing do for you? J. Med. Genet. 2011; 48(9): 580-9.https//dx.doi.org/10.1136/jmedgenet-2011-100223.
12. Guo Y., Dai Y., Yu H., Zhao S., Samuels D.C., Shyr Y. Affiliations expand Improvements and impacts of GRCh38 human reference on high throughput sequencing data analysis. Genomics. 2017; 109(2): 83-90. https//dx.doi.org/10.1016/j.ygeno.2017.01.005.
13. Barbitoff Y.A., Polev D.E., Shcherbakova I.V., Serebryakova E.A., Kiselev A.M., Kostareva A.A. et al. Systematic dissection of biases in whole-exome and whole-genome sequencing reveals major determinants of coding sequence coverage. Sci. Rep. 2020; 10: 2057. https://dx.doi.org/10.1038/s41598-020-59026-y.
14. Suwinski P., Ong C.K., Ling M.H.T., Poh Y.M., Khan A.M., Ong H.S. Advancing personalized medicine through the application of whole exome sequencing and big data analytics. Front. Genet. 2019; 10: 49. https//dx.doi.org/10.3389/fgene.2019.00049.
15. Boycott K.M., Rath A., Chong J.X., Hartley T., Alkuraya F.S., Baynam G. et al. International cooperation to enable the diagnosis of all rare genetic diseases. Am. J. Hum. Genet. 2017; 100(5): 695-705. https//dx.doi.org/10.1016/j.ajhg.2017.04.003.
16. Ferreira C.R. The burden of rare diseases. Am. J. Med. Genet. A. 2019; 179(6): 885-92. https//dx.doi.org/10.1002/ajmg.a.61124.
17. Turro E., Astle W.J., Megy K., Gräf S., Greene D., Shamardina O. et al. Whole-genome sequencing of patients with rare diseases in a national health system. Nature. 2020; 583(7814): 96-102. https//dx.doi.org/10.1038/s41586-020-2434-2.
18. Федяков М.А., Кенис В.М., Мельченко Е.В., Федякова Н.С., Щербак С.Г., Федяков Д.А. и др. Анаукcетическая дисплазия: клиника, молекулярно-генетическая диагностика и лечение. В кн.: Масленникова А.Б., ред. Молекулярно-биологические технологии в медицинской практике. Новосибирск: Академиздат; 2021; вып. 32: 81-92. [Fedyakov M.A., Kenis V.M.,Melchenko E.V., Fedyakova N.S., Shcherbak S.G., Fedyakov D.A. et al. Anauxtetic dysplasia: clinic, molecular genetic diagnosis and treatment. In: Maslennikova A.B., ed. Molecular biological technologies in medical practice. Novosibirsk: Akademizdat; 2021; 32: 81-92. (in Russian)].
19. Lightbody G., Haberland V., Browne F., Taggart L., Zheng H., Parkes E. et al.Review of applications of high-throughput sequencing in personalized medicine: barriers and facilitators of future progress in research and clinical application. Brief Bioinform. 2019; 20(5): 1795-811. https//dx.doi.org/10.1093/bib/bby051.
20. Hofmann A.L., Behr J., Singer J., Kuipers J., Beisel C., Schraml P. et al. Detailed simulation of cancer exome sequencing data reveals differences and common limitations of variant callers. BMC Bioinformatics. 2017; 18: 8.https//dx.doi.org/10.1186/s12859-016-1417-7.
21. 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. https//dx.doi.org/10.1038/s41598-017-01005-x.
22. Richards S., Aziz N., Bale S., Bick D., Das S., Gastier-Foster J. et al. 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): 405-24. https//dx.doi.org/10.1038/gim.2015.30.
23. Рыжкова О.П., Кардымон О.Л., Прохорчук Е.Б., Коновалов Ф.А.,Масленников А.Б., Степанов В.А. и др. Руководство по интерпретации данных, полученных методами массового параллельного секвенирования (MPS). Медицинская генетика. 2017; 16(7): 4-17. [Ryzhkova O.P., Kardymon O.L., Prohorchuk E.B., Konovalov F.A., Maslennikov A.B., Stepanov V.A. et al. Guidelines for the interpretation of massive parallel sequencing variants. Medical Genetics. 2017;16(7):4-17.(in Russian)].
24. Рыжкова О.П., Кардымон О.Л., Прохорчук Е.Б., Коновалов Ф.А., Масленников А.Б., Степанов В.А. и др. Руководство по интерпретации данных последовательности ДНК человека, полученных методами массового параллельного секвенирования (MPS) (редакция 2018, версия 2). Медицинская генетика. 2019; 18(2): 3-23. https://dx.doi.org/10.25557/2073-7998.2019.02.3-23. [Ryzhkova O.P., Kardymon O.L., Prohorchuk E.B., Konovalov F.A., Maslennikov A.B., Stepanov V.A. et al. Guidelines for the interpretation of data on human DNA sequencing obtained by methods of massive parallel sequencing (MPS) (Ed. 2018, version 2). Medical Genetics. 2019; 18(2):3-23. (in Russian)]. https://dx.doi.org/10.25557/2073-7998.2019.02.3-23.
25. Инге-Вечтомов С.Г. Генетика с основами селекции. СПб.: Изд-во Н-Л; 2010. 720 с. [Inge-Vechtomov S.G. Genetics with the basics of breeding. SPb.: N-L Publishing House; 2010. 720 p (in Russian)].
26. Barbitoff Y.A., Bezdvornykh I.V., Polev D.E., Serebryakova E.A., Glotov A.S., Glotov O.S. et al. Catching hidden variation: systematic correction of reference minor alleles in clinical variant calling. Genet. Med. 2018; 20(3): 360-4.https://dx.doi.org/10.1038/gim.2017.168.
27. Barbitoff Y.A., Serebryakova E.A., Nasykhova Y.A., Predeus A.V., Polev D.E., Shuvalova A.R. et al. Identification of novel candidate markers of type 2 diabetes and obesity in Russia by exome sequencing with a limited sample size. Genes. 2018; 9(8): 415. https://dx.doi.org/10.3390/genes9080415.
28. Glotov O.S., Serebryakova E.A., Turkunova M.S., Efimova O.A., Glotov A.S.,Barbitoff Y.A. et al. Whole-exome sequencing for monogenic diabetes in Russian children reveals wide spectrum of genetic variants in MODY-related and unrelated genes. Mol. Med. Rep. 2019; 20(6): 4905-14.https//dx.doi.org/10.3892/mmr.2019.10751.
29. Fedyakov M.A., Veleslavova O.E., Romanova O.V., Shubik Yu.V., Urazov S.P., Rud S.D., Sarana A.M., Scherbak S.G., Glotov O.S. New frameshift mutation found in PKP2 gene in arrhythmogenic right ventricular cardiomyopathy/dysplasia: a family case study. Vestnik of Saint Petersburg University. Medicine. 2019; 14(1): 3-13. https://dx.doi.org/10.21638/11701/spbu10.2019.101.
30. Miroshnikova V.V., Romanova O.V., Ivanova O.N., Fedyakov M.A., Panteleeva A.A., Barbitoff Y.A. et al. Identification of novel variants in the LDLR gene in Russian patients with familial hypercholesterolemia using targeted sequencing. Biomed. Rep. 2021; 14(1): 15. https://dx.doi.org/10.3892/BR.2020.1391.
31. Баранов В.С., Баранова Е.В., Иващенко Т.Э., Асеев М.В. Геном человека и гены «предрасположенности» (Введение в предиктивную медицину). СПб.: Интермедика; 2000. 272 с. [Baranov V.S., Baranova E.V., Ivaschenko T.E.,Aseev M.V. Human genome and "predisposition" genes (Introduction to predictive medicine). SPb.: Intermedica; 2000. 272 p. (in Russian)].
32. Agarwal S., Moorchung N. Modifier genes and oligogenic disease. J. Nippon Med. Sch. 2005; 72(6): 326-34. https//dx.doi.org/10.1272/jnms.72.326.
33. Kousi M., Katsanis N. Genetic modifiers and oligogenic inheritance. Cold Spring Harb. Perspect. Med. 2015; 5(6): a017145. https//dx.doi.org/10.1101/cshperspect.a017145.
34. Alaverdian D.A., Fedyakov M., Polennikova E., Ivashchenko T., Shcherbak S.,Urasov S. et al. X-linked and autosomal dominant forms of the ichthyosis in coinheritance. Drug Metab. Pers. Ther. 2019; 34(4): /j/dmdi.2019.34.issue-4/dmpt-2019-0008/dmpt-2019-0008.xml. https//dx.doi.org/10.1515/dmpt-2019-0008.
35. Abul-Husn N.S., Manickam K., Jones L.K., Wright E.A., Hartzel D.N., Gonzaga-Jauregui C. et al. Genetic identification of familial hypercholesterolemia within a single U.S. health care system. Science. 2016; 354(6319): aaf7000.https//dx.doi.org/10.1126/science.aaf7000.
36. Khera A.V., Chaffin M., Aragam K.G., Haas M.E., Roselli C., Choi S.H. et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet. 2018; 50(9): 1219-24. https//dx.doi.org/10.1038/s41588-018-0183-z.

Received 19.09.2022

Accepted 11.11.2022