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BMC Med Genomics. 2021 Nov 17;14:212. doi: 10.1186/s12920-021-01063-1.

Development and validation of an expanded targeted sequencing panel for non-invasive prenatal diagnosis of sporadic skeletal dysplasia.

BMC medical genomics

Ching-Yuan Wang, Yen-An Tang, I-Wen Lee, Fong-Ming Chang, Chun-Wei Chien, Hsien-An Pan, H Sunny Sun

Affiliations

  1. Institute of Molecular Medicine, College of Medicine, National Cheng Kung University, 1 University Road, Tainan, 70101, Taiwan.
  2. Center for Genomic Medicine, Innovation Headquarters, National Cheng Kung University, Tainan, Taiwan.
  3. FMC Fetal Medicine Center, Tainan, Taiwan.
  4. AN-AN Women and Children Clinic, Tainan, Taiwan.
  5. Institute of Molecular Medicine, College of Medicine, National Cheng Kung University, 1 University Road, Tainan, 70101, Taiwan. [email protected].
  6. Center for Genomic Medicine, Innovation Headquarters, National Cheng Kung University, Tainan, Taiwan. [email protected].

PMID: 34789231 PMCID: PMC8600686 DOI: 10.1186/s12920-021-01063-1

Abstract

BACKGROUND: Skeletal dysplasia (SD) is one of the most common inherited neonatal disorders worldwide, where the recurrent pathogenic mutations in the FGFR2, FGFR3, COL1A1, COL1A2 and COL2A1 genes are frequently reported in both non-lethal and lethal SD. The traditional prenatal diagnosis of SD using ultrasonography suffers from lower accuracy and performed at latter gestational stage. Therefore, it remains in desperate need of precise and accurate prenatal diagnosis of SD in early pregnancy. With the advancements of next-generation sequencing (NGS) technology and bioinformatics analysis, it is feasible to develop a NGS-based assay to detect genetic defects in association with SD in the early pregnancy.

METHODS: An ampliseq-based targeted sequencing panel was designed to cover 87 recurrent hotspots reported in 11 common dominant SD and run on both Ion Proton and NextSeq550 instruments. Thirty-six cell-free and 23 genomic DNAs were used for assay developed. Spike-in DNA prepared from standard sample harboring known mutation and normal sample were also employed to validate the established SD workflow. Overall performances of coverage, uniformity, and on-target rate, and the detecting limitations on percentage of fetal fraction and read depth were evaluated.

RESULTS: The established targeted-seq workflow enables a single-tube multiplex PCR for library construction and shows high amplification efficiency and robust reproducibility on both Ion Proton and NextSeq550 platforms. The workflow reaches 100% coverage and both uniformity and on-target rate are > 96%, indicating a high quality assay. Using spike-in DNA with different percentage of known FGFR3 mutation (c.1138 G > A), the targeted-seq workflow demonstrated the ability to detect low-frequency variant of 2.5% accurately. Finally, we obtained 100% sensitivity and 100% specificity in detecting target mutations using established SD panel.

CONCLUSIONS: An expanded panel for rapid and cost-effective genetic detection of SD has been developed. The established targeted-seq workflow shows high accuracy to detect both germline and low-frequency variants. In addition, the workflow is flexible to be conducted in the majority of the NGS instruments and ready for routine clinical application. Taken together, we believe the established panel provides a promising diagnostic or therapeutic strategy for prenatal genetic testing of SD in routine clinical practice.

© 2021. The Author(s).

Keywords: Amplicon-based targeted sequencing; Noninvasive prenatal testing (NIPT); Precision medicine; Skeletal dysplasia

References

  1. Am J Med Genet A. 2008 Sep 15;146A(18):2385-9 - PubMed
  2. Nat Biotechnol. 2013 Mar;31(3):213-9 - PubMed
  3. PLoS One. 2016 Mar 22;11(3):e0151664 - PubMed
  4. Prog Clin Biol Res. 1982;104:441-9 - PubMed
  5. Gigascience. 2018 Jan 1;7(1):1-4 - PubMed
  6. Lancet. 1997 Aug 16;350(9076):485-7 - PubMed
  7. J Med Genet. 1986 Aug;23(4):328-32 - PubMed
  8. Prenat Diagn. 2016 Apr;36(4):312-20 - PubMed
  9. Congenit Anom (Kyoto). 2019 Jan;59(1):4-10 - PubMed
  10. Bioinformatics. 2009 Jul 15;25(14):1754-60 - PubMed
  11. Hum Genomics. 2019 Dec 4;13(1):62 - PubMed
  12. Prenat Diagn. 2013 Jul;33(7):662-6 - PubMed
  13. Prenat Diagn. 2016 Jul;36(7):636-42 - PubMed
  14. Clin Perinatol. 2015 Jun;42(2):301-19, viii - PubMed
  15. Am J Med Genet. 1989 Apr;32(4):484-9 - PubMed
  16. Am J Hum Genet. 2002 Feb;70(2):472-86 - PubMed
  17. PLoS One. 2016 Jul 19;11(7):e0159355 - PubMed
  18. Am J Med Genet A. 2019 Dec;179(12):2393-2419 - PubMed
  19. J Neurosurg. 1999 Mar;90(3):443-7 - PubMed
  20. Mol Genet Genomic Med. 2019 Sep;7(9):e843 - PubMed
  21. Prenat Diagn. 2015 Jul;35(7):656-62 - PubMed
  22. J Mol Diagn. 2019 Jul;21(4):572-579 - PubMed
  23. Am J Med Genet. 1996 Jan 2;61(1):49-58 - PubMed
  24. Science. 2003 Aug 1;301(5633):643-6 - PubMed
  25. Dev Dyn. 2017 Apr;246(4):291-309 - PubMed
  26. Nucleic Acids Res. 1998 Jan 1;26(1):253-5 - PubMed
  27. J Oral Biol Craniofac Res. 2019 Jan-Mar;9(1):37-39 - PubMed
  28. Eur J Hum Genet. 2006 Mar;14(3):289-98 - PubMed
  29. Prenat Diagn. 2013 May;33(5):416-23 - PubMed
  30. Am J Hum Genet. 1998 Apr;62(4):768-75 - PubMed
  31. Can Assoc Radiol J. 1999 Jun;50(3):185-97 - PubMed
  32. J Ultrasound Med. 2003 Mar;22(3):255-8; quiz 259-61 - PubMed
  33. Mol Genet Genomic Med. 2014 Nov;2(6):497-503 - PubMed
  34. Nat Genet. 2009 Nov;41(11):1247-52 - PubMed
  35. Genet Med. 2009 Feb;11(2):127-33 - PubMed
  36. Nat Med. 2019 Mar;25(3):439-447 - PubMed
  37. Am J Med Genet A. 2008 Aug 1;146A(15):1917-24 - PubMed
  38. Ann Genet. 2000 Jul-Dec;43(3-4):163-9 - PubMed
  39. Prenat Diagn. 2018 Jan;38(1):44-51 - PubMed
  40. Paediatr Respir Rev. 2001 Dec;2(4):365-71 - PubMed
  41. Genet Mol Biol. 2018 Jul/Sept.;41(3):545-554 - PubMed
  42. Ultrasound Obstet Gynecol. 2015 Jan;45(1):36-41 - PubMed
  43. Mol Genet Genomic Med. 2019 Apr;7(4):e00597 - PubMed
  44. Am J Hum Genet. 2012 Feb 10;90(2):175-200 - PubMed
  45. Int J Epidemiol. 1993 Feb;22(1):107-15 - PubMed
  46. Genet Med. 2010 Jun;12(6):327-41 - PubMed
  47. PLoS One. 2016 Apr 14;11(4):e0153258 - PubMed
  48. Hum Mol Genet. 2013 Oct 15;22(20):4117-26 - PubMed
  49. Front Genet. 2020 Aug 14;11:897 - PubMed
  50. Am J Med Genet. 1985 Oct;22(2):243-53 - PubMed
  51. Am J Med Genet. 1998 Feb 17;75(5):518-22 - PubMed
  52. J Clin Endocrinol Metab. 2014 Jun;99(6):E1022-30 - PubMed
  53. Bioinformatics. 2009 Aug 15;25(16):2078-9 - PubMed
  54. Clin Genet. 1989 Feb;35(2):88-92 - PubMed
  55. Cancer Res Treat. 2020 Jan;52(1):41-50 - PubMed
  56. J Ultrasound Med. 1994 Dec;13(12):977-85 - PubMed
  57. Nucleic Acids Res. 1997 Jan 1;25(1):181-7 - PubMed
  58. Am J Hum Genet. 2016 Jan 7;98(1):34-44 - PubMed
  59. Am J Obstet Gynecol. 2017 Dec;217(6):691.e1-691.e6 - PubMed
  60. J Med Genet. 1986 Jun;23(3):227-30 - PubMed

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