IRC-SET 2018 pp 263-273 | Cite as

Resolving the Diagnostic Odyssey of a Patient with an Undefined Neuromuscular Disorder Using Massively Parallel Sequencing Approaches

  • Yu Yiliu
  • Ong Hui JuanEmail author
  • Swati Tomar
  • Grace Tan Li Xuan
  • Raman Sethi
  • Tay Kiat Hong
  • Lai Poh San
Conference paper


We investigated the effectiveness of DNA-based next-generation sequencing (NGS) targeting different genomic region and coverage from five platforms in resolving the diagnostic odyssey of a patient with an unidentified neuromuscular disorder. There were advantages and limitations associated with the different platform approaches. On average, over 22,000 rare protein effecting single nucleotide variants (SNVs), 1955 structural variants (SVs), and 229 copy number variants (CNVs) were identified and analyzed. Seven candidate SNVs fulfilled filtration criteria but were likely to be non-causative due to their classification or disease phenotype. Assessment of an intronic event through IGV and PCR suggested a region of structural rearrangement in DMD gene, which mismatched to two other genes on chromosome X hinting towards a possible novel mechanism of gene inactivation. Our results show that NGS platforms detect candidate variants but some disease mechanisms may remain undetected and points for need for caution when applying NGS for diagnostic purposes.


Next generation sequencing Targeted sequencing Whole exome sequencing Whole genome sequencing Neuromuscular disorder 



We would like to thank our mentors: Professor Lai Poh San, Dr. Swati Tomar, Ms. Grace Tan, Mr. Raman and Mr. Tay Kiat Hong for their invaluable guidance and help throughout the entire course of the project.


  1. 1.
    Thevenon, J., Duffourd, Y., Masurel-Paulet, A., Lefebvre, M., Feillet, F., El Chehadeh-Djebbar, S., et al. (2016). Diagnostic odyssey in severe neurodevelopmental disorders: Toward clinical whole-exome sequencing as a first-line diagnostic test. Clinical Genetics, 89(6), 700–707. Scholar
  2. 2.
    Zurynski, Y., Deverell, M., Dalkeith, T., Johnson, S., Christodoulou, J., Leonard, H., et al. (2017). Australian children living with rare diseases: Experiences of diagnosis and perceived consequences of diagnostic delays. Orphanet Journal of Rare Diseases, 12(1), 68.
  3. 3.
    Wong, S. H., McClaren, B. J., Archibald, A. D., Weeks, A., Langmaid, T., Ryan, M. M., et al. (2015). A mixed methods study of age at diagnosis and diagnostic odyssey for duchenne muscular dystrophy. European Journal of Human Genetics, 23(10), 1294–1300.
  4. 4.
    O’Donnell-Luria, A. H., & Miller, D. T. (2016). A clinician’s perspective on clinical exome sequencing. Human Genetics, 135(6), 643–654.
  5. 5.
    Gilissen, C., Hehir-Kwa, J. Y., Thung, D. T., van de Vorst, M., van Bon, B. W. M., Willemsen, M. H., et al. (2014). Genome sequencing identifies major causes of severe intellectual disability. Nature, 511(7509), 344–347.
  6. 6.
    Savarese, M., Sarparanta, J., Vihola, A., Udd, B., & Hackman, P. (2016). Increasing role of titin mutations in neuromuscular disorders. Journal of Neuromuscular Diseases, 3(3), 293–308. Scholar
  7. 7.
    Dixon-Salazar, T. J., Silhavy, J. L., Udpa, N., Schroth, J., Schaffer, A. E., Olvera, J., et al. (2012). Exome sequencing can improve diagnosis and alter patient management. Science Translational Medicine, 4(138).
  8. 8.
    Zhang, X. (2014). Exome sequencing greatly expedites the progressive research of mendelian diseases. Frontiers of Medicine in China, 8(1), 42–57. Scholar
  9. 9.
    Pal, L. R., Kundu, K., Yin, Y., & Moult, J. (2017). CAGI4 SickKids clinical genomes challenge: A pipeline for identifying pathogenic variants. Human Mutation, 38(9), 1169–1181. Scholar
  10. 10.
    Marian, A. J. (2014). Sequencing your genome: What does it mean? Methodist DeBakey Cardiovascular Journal, 10(1), 3–6. Scholar
  11. 11.
    Liew, W. K. M., Ben-Omran, T., Darras, B. T., Prabhu, S. P., De Vivo, D. C., Vatta, M., et al. (2013). Clinical application of whole-exome sequencing. JAMA Neurology, 70(6), 788. Scholar
  12. 12.
    Belkadi, A., Bolze, A., Itan, Y., Cobat, A., Vincent, Q. B., Antipenko, A., et al. (2015). Whole-genome sequencing is more powerful than whole-exome sequencing for detecting exome variants. Proceedings of the National Academy of Sciences, 112(17), 5473–5478. Scholar
  13. 13.
    Fumagalli, M. (2013). Assessing the effect of sequencing depth and sample size in population genetics inferences. PLoS One, 8(11), e79667.
  14. 14.
    Desai, A., Marwah, V. S., Yadav, A., Jha, V., Dhaygude, K., Bangar, U., et al. (2013). Identification of optimum sequencing depth especially for de novo genome assembly of small genomes using next generation sequencing data. PLoS One, 8(4), e60204.
  15. 15.
    Laing, N. G. (2012). Genetics of neuromuscular disorders. Neuromuscular Disorders of Infancy, Childhood, and Adolescence: A Clinician’s Approach, 49, 17–31. Scholar
  16. 16.
    Timmerman, V., Strickland, A. V., & Züchner, S. (2014). Genetics of Charcot-Marie-Tooth (CMT) disease within the frame of the human genome project success. Genes, 5(1), 13–32. Scholar
  17. 17.
    Nigro, V., & Savarese, M. (2014). Genetic basis of limb-girdle muscular dystrophies: The 2014 update. Acta Myologica, 33(1), 1–12.PubMedPubMedCentralGoogle Scholar
  18. 18.
    Bönnemann, C. G., Wang, C. H., Quijano-Roy, S., Deconinck, N., Bertini, E., Ferreiro, A., et al. (2014). Diagnostic approach to the congenital muscular dystrophies. Neuromuscular Disorders, 24(4), 289–311.
  19. 19.
    Li, Q., & Wang, K. (2017). InterVar: Clinical interpretation of genetic variants by the 2015 ACMG-AMP guidelines. American Journal of Human Genetics, 100(2), 267–280.
  20. 20.
    Lek, M., Karczewski, K. J., Minikel, E. V., Samocha, K. E., Banks, E., Fennell, T., et al. (2016). Analysis of protein-coding genetic variation in 60,706 humans. Nature, 536(7616), 285–291.
  21. 21.
    NHLBI grand opportunity exome sequencing project (ESP). (2017). Accessed December 26.
  22. 22.
    Auton, A., Abecasis, G. R., Altshuler, D. M., Durbin, R. M., Abecasis, G. R., Bentley, D. R., et al. (2015). A global reference for human genetic variation. Nature, 526(7571), 68–74.
  23. 23.
    Glusman, G., Caballero, J., Mauldin, D. E., Hood, L., & Roach, J. C. (2011). Kaviar: An accessible system for testing SNV novelty. Bioinformatics, 27(22), 3216–3217. Scholar
  24. 24.
    Hamosh, A., Scott, A. F., Amberger, J. S., Bocchini, C. A., & McKusick, V. A. (2004). Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Research, 33(Database issue), D514–D517.
  25. 25.
    Quinlan, A. R. (2014). BEDTools: The Swiss-army tool for genome feature analysis. Current Protocols in Bioinformatics, 47, 11.12.1–11.12.34.
  26. 26.
    MacDonald, J. R., Ziman, R., Yuen, R. K. C., Feuk, L., & Scherer, S. W. (2014). The database of genomic variants: A curated collection of structural variation in the human genome. Nucleic Acids Research, 42(Database issue), D986–D992.
  27. 27.
    Lappalainen, I., Lopez, J., Skipper, L., Hefferon, T., Spalding, J. D., Garner, J., et al. (2013). DbVar and DGVa: Public archives for genomic structural variation. Nucleic Acids Research, 41(Database issue), D936–D941.
  28. 28.
    Genomics Incorporated, Complete. (2013). Standard sequencing service data file formats.
  29. 29.
    Stelzer, G., Plaschkes, I., Oz-Levi, D., Alkelai, A., Olender, T., Zimmerman, S., et al. (2016). VarElect: The phenotype-based variation prioritizer of the GeneCards Suite. BMC Genomics, 17(Suppl 2), 444.
  30. 30.
    Rehm, H. L., Bale, S. J., Bayrak-Toydemir, P., Berg, J. S., Brown, K. K., Deignan, J. L., et al. (2013). ACMG clinical laboratory standards for next-generation sequencing. Genetics in Medicine, 15(9), 733–747. Scholar
  31. 31.
    Thorvaldsdóttir, H., Robinson, J. T., & Mesirov, J. P. (2013). Integrative Genomics Viewer (IGV): High-performance genomics data visualization and exploration. Briefings in Bioinformatics, 14(2), 178–192.
  32. 32.
    Chen, X., Schulz-Trieglaff, O., Shaw, R., Barnes, B., Schlesinger, F., Cox, A. J., et al. (2015). Manta: Rapid detection of structural variants and indels for clinical sequencing applications. bioRxiv, 32, 24232.
  33. 33.
    Kronenberg, Z. N., Osborne, E. J., Cone, K. R., Kennedy, B. J., Domyan, E. T., Shapiro, M. D., et al. (2015). Wham: Identifying structural variants of biological consequence. PLoS Computational Biology, 11(12), 1–19. Scholar
  34. 34.
    Reference SNP (refSNP) Cluster Report: rs143187236. (2018). Accessed January 3.
  35. 35.
    Rouillon, J., Poupiot, J., Zocevic, A., Amor, F., Leger, T., Garcia, C., et al. (2015). Serum proteomic profiling reveals fragments of MYOM3 as potential biomarkers for monitoring the outcome of therapeutic interventions in muscular dystrophies.
  36. 36.
    Boeva, V., Popova, T., Bleakley, K., Chiche, P., Cappo, J., Schleiermacher, G., et al. (2012). Control-FREEC: A tool for assessing copy number and allelic content using next-generation sequencing data. Bioinformatics (Oxford, England), 28(3), 423–425.

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Yu Yiliu
    • 1
  • Ong Hui Juan
    • 1
    Email author
  • Swati Tomar
    • 2
  • Grace Tan Li Xuan
    • 2
  • Raman Sethi
    • 2
  • Tay Kiat Hong
    • 2
  • Lai Poh San
    • 2
  1. 1.Raffles InstitutionSingaporeSingapore
  2. 2.Department of Paediatrics, YYL School of MedicineNational University of SingaporeSingaporeSingapore

Personalised recommendations