Huilin Wang1,2, Zirui Dong2,3, Rui Zhang1, Matthew Hoi Kin Chau2,3, Zhenjun Yang2, Kathy Yin Ching Tsang2, Hoi Kin Wong2, Baoheng Gui3, Zhuo Meng1, Kelin Xiao1, Xiaofan Zhu2,3, Yanfang Wang1, Shaoyun Chen1, Tak Yeung Leung2,3,4, Sau Wai Cheung4,5, Yvonne K Kwok2, Cynthia C Morton6,7,8,9,10, Yuanfang Zhu11, Kwong Wai Choy12,13,14. 1. Maternal-Fetal Medicine Institute, Bao'an Maternity and Child Health Hospital Affiliated to Jinan University School of Medicine, Key Laboratory of Birth Defects Research, Birth Defects Prevention Research and Transformation Team, Shenzhen, China. 2. Department of Obstetrics & Gynaecology, The Chinese University of Hong Kong, Hong Kong, China. 3. Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China. 4. The Chinese University of Hong Kong-Baylor College of Medicine Joint Center For Medical Genetics, Hong Kong, China. 5. Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA. 6. Department of Obstetrics and Gynecology, Brigham and Women's Hospital, Boston, MA, USA. cmorton@bwh.harvard.edu. 7. Harvard Medical School, Boston, MA, USA. cmorton@bwh.harvard.edu. 8. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. cmorton@bwh.harvard.edu. 9. Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA. cmorton@bwh.harvard.edu. 10. Manchester Center for Audiology and Deafness, University of Manchester, Manchester Academic Health Science Center, Manchester, UK. cmorton@bwh.harvard.edu. 11. Maternal-Fetal Medicine Institute, Bao'an Maternity and Child Health Hospital Affiliated to Jinan University School of Medicine, Key Laboratory of Birth Defects Research, Birth Defects Prevention Research and Transformation Team, Shenzhen, China. zhuyf1027@163.com. 12. Department of Obstetrics & Gynaecology, The Chinese University of Hong Kong, Hong Kong, China. richardchoy@cuhk.edu.hk. 13. Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China. richardchoy@cuhk.edu.hk. 14. The Chinese University of Hong Kong-Baylor College of Medicine Joint Center For Medical Genetics, Hong Kong, China. richardchoy@cuhk.edu.hk.
Abstract
PURPOSE: Emerging studies suggest that low-pass genome sequencing (GS) provides additional diagnostic yield of clinically significant copy-number variants (CNVs) compared with chromosomal microarray analysis (CMA). However, a prospective back-to-back comparison evaluating accuracy, efficacy, and incremental yield of low-pass GS compared with CMA is warranted. METHODS: A total of 1023 women undergoing prenatal diagnosis were enrolled. Each sample was subjected to low-pass GS and CMA for CNV analysis in parallel. CNVs were classified according to guidelines of the American College of Medical Genetics and Genomics. RESULTS: Low-pass GS not only identified all 124 numerical disorders or pathogenic or likely pathogenic (P/LP) CNVs detected by CMA in 121 cases (11.8%, 121/1023), but also defined 17 additional and clinically relevant P/LP CNVs in 17 cases (1.7%, 17/1023). In addition, low-pass GS significantly reduced the technical repeat rate from 4.6% (47/1023) for CMA to 0.5% (5/1023) and required less DNA (50 ng) as input. CONCLUSION: In the context of prenatal diagnosis, low-pass GS identified additional and clinically significant information with enhanced resolution and increased sensitivity of detecting mosaicism as compared with the CMA platform used. This study provides strong evidence for applying low-pass GS as an alternative prenatal diagnostic test.
PURPOSE: Emerging studies suggest that low-pass genome sequencing (GS) provides additional diagnostic yield of clinically significant copy-number variants (CNVs) compared with chromosomal microarray analysis (CMA). However, a prospective back-to-back comparison evaluating accuracy, efficacy, and incremental yield of low-pass GS compared with CMA is warranted. METHODS: A total of 1023 women undergoing prenatal diagnosis were enrolled. Each sample was subjected to low-pass GS and CMA for CNV analysis in parallel. CNVs were classified according to guidelines of the American College of Medical Genetics and Genomics. RESULTS: Low-pass GS not only identified all 124 numerical disorders or pathogenic or likely pathogenic (P/LP) CNVs detected by CMA in 121 cases (11.8%, 121/1023), but also defined 17 additional and clinically relevant P/LP CNVs in 17 cases (1.7%, 17/1023). In addition, low-pass GS significantly reduced the technical repeat rate from 4.6% (47/1023) for CMA to 0.5% (5/1023) and required less DNA (50 ng) as input. CONCLUSION: In the context of prenatal diagnosis, low-pass GS identified additional and clinically significant information with enhanced resolution and increased sensitivity of detecting mosaicism as compared with the CMA platform used. This study provides strong evidence for applying low-pass GS as an alternative prenatal diagnostic test.
Authors: Mengmeng Shi; Xinyi Leng; Ying Li; Zihan Chen; Ye Cao; Tiffany Chung; Bonaventure Ym Ip; Vincent Hl Ip; Yannie Oy Soo; Florence Sy Fan; Sze Ho Ma; Karen Ma; Anne Y Y Chan; Lisa Wc Au; Howan Leung; Alexander Y Lau; Vincent Ct Mok; Kwong Wai Choy; Zirui Dong; Thomas W Leung Journal: Stroke Vasc Neurol Date: 2021-12-08
Authors: Xiya Zhou; Xiangbin Chen; Yulin Jiang; Qingwei Qi; Na Hao; Chengkun Liu; Mengnan Xu; David S Cram; Juntao Liu Journal: Life (Basel) Date: 2021-01-28