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Validation and depth evaluation of low-pass genome sequencing in prenatal diagnosis using 387 amniotic fluid samples
  1. Yeqing Qian1,2,
  2. Yan Sun3,
  3. Xueqin Guo4,
  4. Lijie Song5,6,7,
  5. Yixi Sun1,2,
  6. Xiaoyang Gao1,2,
  7. Bei Liu1,2,
  8. Yuqing Xu1,2,
  9. Na Chen1,2,
  10. Min Chen1,2,
  11. Yuqin Luo1,2,
  12. Zhihong Qiao5,6,
  13. Linlin Fan5,6,
  14. Jianfen Man4,
  15. Kang Zhang4,
  16. Xiaoli Wang8,
  17. Tingting Rong8,
  18. Zhonghua Wang5,6,
  19. Fengxia Liu5,6,
  20. Jing Zhao3,
  21. Xiaoming Wei4,
  22. Minfeng Chen8,
  23. Zhiyu Peng3,
  24. Huanhuan Peng8,
  25. Jun Sun5,6,
  26. Minyue Dong1,2
  1. 1 Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
  2. 2 Key Laboratory of Reproductive Genetics, Ministry of Education, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
  3. 3 BGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China
  4. 4 BGI-Wuhan Clinical Laboratories, BGI-Shenzhen, Wuhan, 430074, China
  5. 5 Tianjin Medical Laboratory, BGI-Tianjin, BGI-Shenzhen, Tianjin 300308, China
  6. 6 BGI-Tianjin, BGI-Shenzhen, Tianjin 300308, China
  7. 7 DTU Bioengineering, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
  8. 8 Clinical laboratory of BGI Health, BGI-Shenzhen, Shenzhen 518083, China
  1. Correspondence to Jun Sun; sunjun{at}bgi.com; Minyue Dong; dongmy{at}zju.edu.cn

Abstract

Background Low-pass genome sequencing (LP GS) is an alternative to chromosomal microarray analysis (CMA). However, validations of LP GS as a prenatal diagnostic test for amniotic fluid are rare. Moreover, sequencing depth of LP GS in prenatal diagnosis has not been evaluated.

Objective The diagnostic performance of LP GS was compared with CMA using 375 amniotic fluid samples. Then, sequencing depth was evaluated by downsampling.

Results CMA and LP GS had the same diagnostic yield (8.3%, 31/375). LP GS showed all copy number variations (CNVs) detected by CMA and six additional variant of uncertain significance CNVs (>100 kb) in samples with negative CMA results; CNV size influenced LP GS detection sensitivity. CNV detection was greatly influenced by sequencing depth when the CNV size was small or the CNV was located in the azoospermia factor c (AZFc) region of the Y chromosome. Large CNVs were less affected by sequencing depth and more stably detected. There were 155 CNVs detected by LP GS with at least a 50% reciprocal overlap with CNVs detected by CMA. With 25 M uniquely aligned high-quality reads (UAHRs), the detection sensitivity for the 155 CNVs was 99.14%. LP GS using samples with 25 M UAHRs showed the same performance as LP GS using total UAHRs. Considering the detection sensitivity, cost and interpretation workload, 25 M UAHRs are optimal for detecting most aneuploidies and microdeletions/microduplications.

Conclusion LP GS is a promising, robust alternative to CMA in clinical settings. A total of 25 M UAHRs are sufficient for detecting aneuploidies and most microdeletions/microduplications.

  • Molecular Diagnostic Techniques

Data availability statement

Data are available upon reasonable request. The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive for Human (GSA-Human: HRA003179) at https://ngdc.cncb.ac.cn/gsa-human and are available on reasonable request.

https://doi.org/10.1136/jmg-2022-109112

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    Data availability statement

    Data are available upon reasonable request. The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive for Human (GSA-Human: HRA003179) at https://ngdc.cncb.ac.cn/gsa-human and are available on reasonable request.

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    Footnotes

    • YQ, YS, XG and LS contributed equally.

    • Contributors MD and JS are the guarantors for this study and are responsible for the overall content. MD, JS, YQ, YS, XG and LS contributed to the conception and design of the study. YS wrote the first draft of the article. LS, ZQ, LF and XW designed and performed the experiments. YQ, YS, XG, LS, YS, XG, BL, YX, NC, MC, YL, JM, KZ, TR, ZW, FL, JZ, XW, MC, ZP and HP performed data analysis. MD, JS, YQ, YS and XG contributed to revising the manuscript. All authors reviewed the manuscript and approved the submitted version.

    • Funding This work was supported by the Key Research and Development Program of Zhejiang Province (2019C03025) and the Special Foundation for High-level Talents of Guangdong (grant 2016TX03R171).

    • Competing interests None declared.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.