DOI QR코드

DOI QR Code

Multi-omics techniques for the genetic and epigenetic analysis of rare diseases

  • Yeonsong Choi (Department of Biomedical Engineering, Ulsan National Institute of Science and Technology) ;
  • David Whee-Young Choi (Department of Biomedical Engineering, Ulsan National Institute of Science and Technology) ;
  • Semin Lee (Department of Biomedical Engineering, Ulsan National Institute of Science and Technology)
  • Received : 2022.11.23
  • Accepted : 2023.02.22
  • Published : 2023.06.30

Abstract

Until now, rare disease studies have mainly been carried out by detecting simple variants such as single nucleotide substitutions and short insertions and deletions in protein-coding regions of disease-associated gene panels using diagnostic next-generation sequencing in association with patient phenotypes. However, several recent studies reported that the detection rate hardly exceeds 50% even when whole-exome sequencing is applied. Therefore, the necessity of introducing whole-genome sequencing is emerging to discover more diverse genomic variants and examine their association with rare diseases. When no diagnosis is provided by whole-genome sequencing, additional omics techniques such as RNA-seq also can be considered to further interrogate causal variants. This paper will introduce a description of these multi-omics techniques and their applications in rare disease studies.

Keywords

Acknowledgement

This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2020M3E5D7115320). This research was also partly supported by Basic Science Research Program through the NRF funded by the Ministry of Education (NRF-2021R1A6A3A13045998 to Y.C.).

References

  1. Nguengang Wakap S, Lambert DM, Olry A, Rodwell C, Gueydan C, Lanneau V, et al. Estimating cumulative point prevalence of rare diseases: analysis of the Orphanet database. Eur J Hum Genet 2020;28:165-73. https://doi.org/10.1038/s41431-019-0508-0
  2. U.S. Food and Drug Administration. An Act to amend the Federal Food, Drug, and Cosmetic Act to facilitate the development of drugs for rare diseases and conditions, and for other purposes. Public Law 97-414. 96 STAT. 2049. Washington, D.C.: U.S. Congress; 1983.
  3. European Commission. Directorate-General for Research and Innovation. Collaboration: a key to unlock the challenges of rare diseases research. Luxembourg: Publications Office of the European Union; 2021.
  4. Global Genes. RARE disease facts. [https://globalgenes.org/learn/rare-disease-facts/]
  5. Ko KP. Analyzing the status of rare diseases and ways to improve support for rare diseases patients in Korea. Cheongju: Korea Centers for Disease Control and Prevention; 2018 Nov.
  6. Amberger J, Bocchini CA, Scott AF, Hamosh A. McKusick's Online Mendelian Inheritance in Man (OMIM). Nucleic Acids Res 2009;37(Database issue):D793-6. https://doi.org/10.1093/nar/gkn665
  7. Amberger JS, Bocchini CA, Schiettecatte F, Scott AF, Hamosh A. OMIM.org: Online Mendelian Inheritance in Man (OMIM®), an online catalog of human genes and genetic disorders. Nucleic Acids Res 2015;43(Database issue):D789-98. https://doi.org/10.1093/nar/gku1205
  8. Zastrow DB, Kohler JN, Bonner D, Reuter CM, Fernandez L, Grove ME, et al. A toolkit for genetics providers in follow-up of patients with non-diagnostic exome sequencing. J Genet Couns 2019;28:213-28. https://doi.org/10.1002/jgc4.1119
  9. Pervez MT, Hasnain MJU, Abbas SH, Moustafa MF, Aslam N, Shah SSM. A comprehensive review of performance of next-generation sequencing platforms. Biomed Res Int 2022;2022:3457806.
  10. Burdick KJ, Cogan JD, Rives LC, Robertson AK, Koziura ME, Brokamp E, et al.; Undiagnosed Diseases Network. Limitations of exome sequencing in detecting rare and undiagnosed diseases. Am J Med Genet A 2020;182:1400-6. https://doi.org/10.1002/ajmg.a.61558
  11. Turro E, Astle WJ, Megy K, Graf S, Greene D, Shamardina O, et al. Whole-genome sequencing of patients with rare diseases in a national health system. Nature 2020;583:96-102. https://doi.org/10.1038/s41586-020-2434-2
  12. Freson K, Devriendt K, Matthijs G, Van Hoof A, De Vos R, Thys C, et al. Platelet characteristics in patients with X-linked macrothrombocytopenia because of a novel GATA1 mutation. Blood 2001;98:85-92. https://doi.org/10.1182/blood.V98.1.85
  13. Chu Y, Corey DR. RNA sequencing: platform selection, experimental design, and data interpretation. Nucleic Acid Ther 2012;22:271-4. https://doi.org/10.1089/nat.2012.0367
  14. Brahamsha B, Haselkorn R. Isolation and characterization of the gene encoding the principal sigma factor of the vegetative cell RNA polymerase from the cyanobacterium Anabaena sp. strain PCC 7120. J Bacteriol 1991;173:2442-50. https://doi.org/10.1128/jb.173.8.2442-2450.1991
  15. Fresard L, Smail C, Ferraro NM, Teran NA, Li X, Smith KS, et al. Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts. Nat Med 2019;25:911-9. https://doi.org/10.1038/s41591-019-0457-8
  16. Ferraro NM, Strober BJ, Einson J, Abell NS, Aguet F, Barbeira AN, et al. Transcriptomic signatures across human tissues identify functional rare genetic variation. Science 2020;369:eaaz5900.
  17. Oliver GR, Tang X, Schultz-Rogers LE, Vidal-Folch N, Jenkinson WG, Schwab TL, et al. A tailored approach to fusion transcript identification increases diagnosis of rare inherited disease. PLoS One 2019;14:e0223337.
  18. Fraga MF, Esteller M. DNA methylation: a profile of methods and applications. Biotechniques 2002;33:632, 634, 636-49. https://doi.org/10.2144/02333rv01_11833a
  19. Sun Z, Wu Y, Ordog T, Baheti S, Nie J, Duan X, et al. Aberrant signature methylome by DNMT1 hot spot mutation in hereditary sensory and autonomic neuropathy 1E. Epigenetics 2014;9:1184-93. https://doi.org/10.4161/epi.29676
  20. Gatto S, Gagliardi M, Franzese M, Leppert S, Papa M, Cammisa M, et al. ICF-specific DNMT3B dysfunction interferes with intragenic regulation of mRNA transcription and alternative splicing. Nucleic Acids Res 2017;45:5739-56. https://doi.org/10.1093/nar/gkx163
  21. Klemm SL, Shipony Z, Greenleaf WJ. Chromatin accessibility and the regulatory epigenome. Nat Rev Genet 2019;20:207-20. https://doi.org/10.1038/s41576-018-0089-8
  22. Boyle AP, Davis S, Shulha HP, Meltzer P, Margulies EH, Weng Z, et al. High-resolution mapping and characterization of open chromatin across the genome. Cell 2008;132:311-22. https://doi.org/10.1016/j.cell.2007.12.014
  23. Giresi PG, Kim J, McDaniell RM, Iyer VR, Lieb JD. FAIRE (formaldehyde-assisted isolation of regulatory elements) isolates active regulatory elements from human chromatin. Genome Res 2007;17:877-85. https://doi.org/10.1101/gr.5533506
  24. Buenrostro JD, Giresi PG, Zaba LC, Chang HY, Greenleaf WJ. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat Methods 2013;10:1213-8. https://doi.org/10.1038/nmeth.2688
  25. Luperchio TR, Boukas L, Zhang L, Pilarowski G, Jiang J, Kalinousky A, et al. Leveraging the Mendelian disorders of the epigenetic machinery to systematically map functional epigenetic variation. Elife 2021;10:e65884.
  26. Suwinski P, Ong C, Ling MHT, Poh YM, Khan AM, Ong HS. Advancing personalized medicine through the application of whole exome sequencing and big data analytics. Front Genet 2019;10:49.
  27. Rabbani B, Tekin M, Mahdieh N. The promise of whole-exome sequencing in medical genetics. J Hum Genet 2014;59:5-15. https://doi.org/10.1038/jhg.2013.114
  28. Austin-Tse CA, Jobanputra V, Perry DL, Bick D, Taft RJ, Venner E, et al.; Medical Genome Initiative. Best practices for the interpretation and reporting of clinical whole genome sequencing. NPJ Genom Med 2022;7:27.
  29. Ng PC, Kirkness EF. Whole genome sequencing. Methods Mol Biol 2010;628:215-26. https://doi.org/10.1007/978-1-60327-367-1_12
  30. Stark R, Grzelak M, Hadfield J. RNA sequencing: the teenage years. Nat Rev Genet 2019;20:631-56. https://doi.org/10.1038/s41576-019-0150-2
  31. Hong M, Tao S, Zhang L, Diao LT, Huang X, Huang S, et al. RNA sequencing: new technologies and applications in cancer research. J Hematol Oncol 2020;13:166.
  32. Feng L, Lou J. DNA methylation analysis. Methods Mol Biol 2019;1894:181-227. https://doi.org/10.1007/978-1-4939-8916-4_12
  33. Wreczycka K, Gosdschan A, Yusuf D, Gruning B, Assenov Y, Akalin A. Strategies for analyzing bisulfite sequencing data. J Biotechnol 2017;261:105-15. https://doi.org/10.1016/j.jbiotec.2017.08.007
  34. Yan F, Powell DR, Curtis DJ, Wong NC. From reads to insight: a hitchhiker's guide to ATAC-seq data analysis. Genome Biol 2020;21:22.
  35. Grandi FC, Modi H, Kampman L, Corces MR. Chromatin accessibility profiling by ATAC-seq. Nat Protoc 2022;17:1518-52. https://doi.org/10.1038/s41596-022-00692-9