Abstract
Purpose: We developed a new workflow design which included results from both biochemical and targeted gene sequencing analysis interpreted comprehensively. We then conducted a pilot study to evaluate the benefit of this new approach in newborn screening (NBS) and demonstrated the efficiency of this workflow in detecting causative genetic variants. Materials and Methods: Ten patients in Group 1 were diagnosed clinically using biochemical assays only, and 10 newborns in Group 2 were diagnosed with suspected inherited metabolic disease (IMD) in NBS. We applied NewbornDiscovery (SD Genomics), an integrated workflow design that encompasses analyte-phenotype-gene, single nucleotide variant/small insertion and dele-tion/copy number variation analyses along with clinical interpretation of genetic variants related to each participant’s condition. Results: A molecular genetic diagnosis was established in 95% (19/20) of individuals. In Group 1, 13 and 7 of 20 alleles were classified as pathogenic and likely pathogenic, respectively. In Group 2, 11 and 6 of 17 alleles with identified causative variants were pathogenic and likely pathogenic, respectively. There were no variants of uncertain significance. For each individual, the NewbornDiscovery and biochemical analysis results reached 100% concordance, since the single newborn testing negative for causative genetic variant in Group 2 showed a benign clinical course. Conclusion: This integrated diagnostic workflow resulted in a high yield. This approach not only enabled early confirmation of specific IMD, but also detected conditions not included in the current NBS.
Original language | English |
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Pages (from-to) | 652-661 |
Number of pages | 10 |
Journal | Yonsei medical journal |
Volume | 59 |
Issue number | 5 |
DOIs | |
Publication status | Published - 2018 Jul |
Bibliographical note
Funding Information:The study was supported by grant A120030 from the Korean Health Technology R&D Project, Ministry of Health and Welfare, Republic of Korea.
Publisher Copyright:
© Yonsei University College of Medicine 2018.
All Science Journal Classification (ASJC) codes
- Medicine(all)