Seong-Keun Yoo1, Byung Chan Lim2, Jiyoung Byeun3, Hee Hwang3, Ki Joong Kim3, Yong Seung Hwang3, JoonHo Lee4, Joong Shin Park4, Yong-Sun Lee5, Junghyun Namkung5, Jungsun Park5, Seungbok Lee6, Jong-Yeon Shin7, Jeong-Sun Seo8, Jong-Il Kim9, Jong Hee Chae10. 1. Genomic Medicine Institute (GMI), Medical Research Center, Seoul National University, Seoul, Korea; Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea; 2. Department of Pediatrics, Seoul National University College of Medicine, Seoul National University Children's Hospital, Seoul, Korea; Institute of Reproductive Medicine and Population, Medical Research Center, Seoul National University, Seoul, Korea; 3. Department of Pediatrics, Seoul National University College of Medicine, Seoul National University Children's Hospital, Seoul, Korea; 4. Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul National University Children's Hospital, Seoul, Korea; 5. Bioinformatics Technology Lab, Healthcare Group, Future Technology R&D Division, SK Telecom, Sungnam, Korea; 6. Genomic Medicine Institute (GMI), Medical Research Center, Seoul National University, Seoul, Korea; 7. Genomic Medicine Institute (GMI), Medical Research Center, Seoul National University, Seoul, Korea; Macrogen, Seoul, Korea; 8. Genomic Medicine Institute (GMI), Medical Research Center, Seoul National University, Seoul, Korea; Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea; Macrogen, Seoul, Korea; Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, Korea; Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, Korea. 9. Genomic Medicine Institute (GMI), Medical Research Center, Seoul National University, Seoul, Korea; Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, Korea; Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, Korea. jongil@snu.ac.kr chaeped1@snu.ac.kr. 10. Department of Pediatrics, Seoul National University College of Medicine, Seoul National University Children's Hospital, Seoul, Korea; Institute of Reproductive Medicine and Population, Medical Research Center, Seoul National University, Seoul, Korea; jongil@snu.ac.kr chaeped1@snu.ac.kr.
Abstract
BACKGROUND: Noninvasive prenatal diagnosis of monogenic disorders using maternal plasma and targeted massively parallel sequencing is being investigated actively. We previously demonstrated that comprehensive genetic diagnosis of a Duchenne muscular dystrophy (DMD) patient is feasible using a single targeted sequencing platform. Here we demonstrate the applicability of this approach to carrier detection and noninvasive prenatal diagnosis. METHODS: Custom solution-based target enrichment was designed to cover the entire dystrophin (DMD) gene region. Targeted massively parallel sequencing was performed using genomic DNA from 4 mother and proband pairs to test whether carrier status could be detected reliably. Maternal plasma DNA at varying gestational weeks was collected from the same families and sequenced using the same targeted platform to predict the inheritance of the DMD mutation by their fetus. Overrepresentation of an inherited allele was determined by comparing the allele fraction of 2 phased haplotypes after examining and correcting for the recombination event. RESULTS: The carrier status of deletion/duplication and point mutations was detected reliably through using a single targeted massively parallel sequencing platform. Whether the fetus had inherited the DMD mutation was predicted correctly in all 4 families as early as 6 weeks and 5 days of gestation. In one of these, detection of the recombination event and reconstruction of the phased haplotype produced a correct diagnosis. CONCLUSIONS: Noninvasive prenatal diagnosis of DMD is feasible using a single targeted massively parallel sequencing platform with tiling design.
BACKGROUND: Noninvasive prenatal diagnosis of monogenic disorders using maternal plasma and targeted massively parallel sequencing is being investigated actively. We previously demonstrated that comprehensive genetic diagnosis of a Duchenne muscular dystrophy (DMD) patient is feasible using a single targeted sequencing platform. Here we demonstrate the applicability of this approach to carrier detection and noninvasive prenatal diagnosis. METHODS: Custom solution-based target enrichment was designed to cover the entire dystrophin (DMD) gene region. Targeted massively parallel sequencing was performed using genomic DNA from 4 mother and proband pairs to test whether carrier status could be detected reliably. Maternal plasma DNA at varying gestational weeks was collected from the same families and sequenced using the same targeted platform to predict the inheritance of the DMD mutation by their fetus. Overrepresentation of an inherited allele was determined by comparing the allele fraction of 2 phased haplotypes after examining and correcting for the recombination event. RESULTS: The carrier status of deletion/duplication and point mutations was detected reliably through using a single targeted massively parallel sequencing platform. Whether the fetus had inherited the DMD mutation was predicted correctly in all 4 families as early as 6 weeks and 5 days of gestation. In one of these, detection of the recombination event and reconstruction of the phased haplotype produced a correct diagnosis. CONCLUSIONS: Noninvasive prenatal diagnosis of DMD is feasible using a single targeted massively parallel sequencing platform with tiling design.
Authors: Soo Heon Kwak; Camille E Powe; Se Song Jang; Michael J Callahan; Sarah N Bernstein; Seung Mi Lee; Sunyoung Kang; Kyong Soo Park; Hak C Jang; Jose C Florez; Jong-Il Kim; Jong Hee Chae Journal: J Clin Endocrinol Metab Date: 2021-08-18 Impact factor: 5.958
Authors: Peiyong Jiang; Xianlu Peng; Xiaoxi Su; Kun Sun; Stephanie C Y Yu; Weng In Chu; Tak Y Leung; Hao Sun; Rossa W K Chiu; Yuk Ming Dennis Lo; Kwan Chee Allen Chan Journal: NPJ Genom Med Date: 2016-05-11 Impact factor: 8.617