Abstract

Current non-invasive prenatal screening is targeted toward the detection of chromosomal abnormalities in the fetus1,2. However, screening for many dominant monogenic disorders associated with de novo mutations is not available, despite their relatively high incidence3. Here we report on the development and validation of, and early clinical experience with, a new approach for non-invasive prenatal sequencing for a panel of causative genes for frequent dominant monogenic diseases. Cell-free DNA (cfDNA) extracted from maternal plasma was barcoded, enriched, and then analyzed by next-generation sequencing (NGS) for targeted regions. Low-level fetal variants were identified by a statistical analysis adjusted for NGS read count and fetal fraction. Pathogenic or likely pathogenic variants were confirmed by a secondary amplicon-based test on cfDNA. Clinical tests were performed on 422 pregnancies with or without abnormal ultrasound findings or family history. Follow-up studies on cases with available outcome results confirmed 20 true-positive, 127 true-negative, zero false-positive, and zero-false negative results. The initial clinical study demonstrated that this non-invasive test can provide valuable molecular information for the detection of a wide spectrum of dominant monogenic diseases, complementing current screening for aneuploidies or carrier screening for recessive disorders.

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Code availability

The customized script for the UMI-based deduplication of the NGS reads can be found at https://sourceforge.net/projects/BGNIPS.

Data availability

These authors declare that all essential data supporting the conclusion of the study as well as detailed assay protocols, analytical algorithms, and customized computational codes are within the paper and supplementary materials. All the disease-causing variants and the key phenotypes found in the subjects can be found at the ClinVar database (http://www.ncbi.nlm.nih.gov/clinvar/variation/) with accession numbers SCV000854595–SCV000854628. Subjects’ identifiable information (including their genomic sequencing data) is kept in our clinical laboratory, which is a CLIA and CAP certified laboratory and a HIPAA-compliant environment, to protect subjects’ privacy. Non-identifiable sequencing data (for example, individual variant sequencing data generated by locus-specific sequencing) can be provided on request from the authors. Source data for Fig. 1 and Extended Data Figs. 1 and 2 are available online.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Change history

  • 20 February 2019

    In the version of this article originally published, some cases that were presented in Fig. 3 should have been underlined but were not. The appropriate cases have now been underlined. The error has been corrected in the print, PDF and HTML versions of the article.

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Acknowledgements

Baylor Genetics Laboratories and Baylor College of Medicine provided funds to support this study. A.K.M. was supported by institutional funds from Baylor College of Medicine. K.W.C. was partially supported by Vice-Chancellor Discretionary Fund for CUHK-Baylor College of Medicine Joint Centre for Medical Genetics. We thank all contributing healthcare providers for their work and support on this study.

Author information

Author notes

  1. These authors contributed equally: J. Zhang, J. Li.

Affiliations

  1. Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA

    • Jinglan Zhang
    • , Hongzheng Dai
    • , Xiaoyan Ge
    • , Chad A. Shaw
    • , Hui Mei
    • , Amy Breman
    • , Fan Xia
    • , Yaping Yang
    • , Zhao Chen
    • , Xia Wang
    • , Yue Wang
    • , Shashikant Kulkarni
    • , Ignatia B. Van den Veyver
    • , Arthur Beaudet
    • , Lee-Jun Wong
    •  & Christine M. Eng
  2. Baylor Genetics, Houston, TX, USA

    • Jianli Li
    • , Yanming Feng
    • , Yanjun Jiang
    • , Jefferson Sinson
    • , Eric S. Schmitt
    • , Sandra Peacock
    • , Stella Chen
    • , Guoli Wang
    • , Anne Purgason
    •  & Alan Pourpak
  3. Natera, Inc., San Carlos, CA, USA

    • Jennifer B. Saucier
    •  & Sheetal Parmar
  4. Office of Clinical Research, Baylor College of Medicine, Houston, TX, USA

    • Anne K. McCombs
  5. Department of Statistics, Rice University, Houston, TX, USA

    • Chad A. Shaw
  6. Department of Obstetrics and Gynaecology, The Chinese University of Hong Kong, Hong Kong, China

    • Kwong Wai Choy
  7. The Chinese University of Hong Kong-Baylor College of Medicine Joint Center For Medical Genetics, Hong Kong, China

    • Kwong Wai Choy
  8. Department of Obstetrics and Gynecology, Columbia University, New York, NY, USA

    • Ronald J. Wapner
  9. Department of Obstetrics and Gynecology, Baylor College of Medicine, Houston, TX, USA

    • Ignatia B. Van den Veyver

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Contributions

A.B., J.Z., C.M.E., and L.-J.W. designed the study; J.L., Y.F., J.S., S.C., H.D., X.G., and G.W. conducted the experiments; J.Z., J.L., Y.F., Y.J., E.S.S., S.P., S.C., H.D., X.G., G.W., C.A.S., H.M., A.B., F.X., Y.Y., A. Purgason, A. Pourpak, Z.C., X.W., Y.W., S.K., K.W.C., R.J.W., J.B.S., S.P., A.K.M., I.B.V.d.V., A.B., L.-J.W., and C.M.E. conducted the validation and/or clinical data analyses; C.A.S., J.L., Y.F., and J.Z. conducted the statistical analyses; J.Z. and J.L. wrote the manuscript; J.Z., L.-J.W., and C.M.E. supervised the project.

Competing interests

The joint venture of Department of Molecular and Human Genetics at Baylor College of Medicine (BCM) and Baylor Genetics Laboratories (BG) derives revenue from the clinical sequencing offered at BG and the authors who are BCM faculty members or BG employees are indicated in the affiliation section. J.B.S. and S.P. are employees of Natera who provided samples for validating fetal fraction calculation and contributed to the clinical data collection and analysis for the outcome study. The patent application related to this work has been filed (WO2018049049A1) by BCM and BG Laboratories, including laboratory methods of non-invasive prenatal testing to detect dominant monogenic disorders.

Corresponding author

Correspondence to Jinglan Zhang.

Extended data

  1. Extended Data Fig. 1 Validation of a SNP-based fetal fraction calculation method.

    a, The comparison of fetal fraction calculation between our SNP-based method and a Y chromosome marker method. A total of 31 samples from pregnancies with male fetuses were used. b, The comparison of the calculated and expected fetal fraction in the spike-in experiment. Five sets of spike-in samples were created by mixing a proband’s DNA with maternal DNA to mimic fetal fraction ranging from 1 to 20%. A total of 43 spike-in samples were used. c, Comparison of the fetal fraction calculated for 67 samples collected from pregnant women (fetal fraction ranging 5.0–27.6%) by our method and an independent method developed by another laboratory. Pearson correlation coefficient is shown as the R value calculated by Microsoft Excel. Source data

  2. Extended Data Fig. 2 Z-value plots for three representative samples which had de novo, paternally inherited, and false analytical variants with UMI deduplication and consolidation for NGS reads and variant calling/filtering.

    The paternally inherited variants and confirmed de novo variants deemed true positives had Z > −0.6. Source data

Supplementary information

Source data

  1. Source data Fig. 1

    Figure 1c. The original data to demonstrate the distribution of duplicated NGS reads with the same UMIs in four samples.

    Figure 1d. The original data to show the number of background noise variant calls (analytical false variant calls) is reduced by the application UMIs as shown in these four samples.

    Figure 1e. The original variant calls and their calculated Z-values for de novo, paternally inherited, and false analytical variants in a representative validation sample.

  2. Source data Extended Data Fig. 1

    Extended Data Fig 1a. The comparison of fetal fraction calculation between our SNP-based method and Y chromosome marker method. A total of 31 samples from pregnancies with male fetuses were used.

    Extended Data Fig1b. The comparison of the calculated and expected fetal fraction in the spike-in experiment. Five sets of spike-in samples were created by mixing a poband’s DNA to the maternal DNA which mimicked fetal fraction ranging from 1 to 20%. A total of 20 spike-in samples were used. Pearson correlation coefficient is shown as the R value.

    Extended Data Fig1c. The comparison of the fetal fraction calculated for 67 samples collected from pregnant women (F.F. ranging 5.0-27.6%) by our method and Natera method.

  3. Source data Extended Data Fig. 2

    Z value plots for three representative samples which had de novo, paternally inherited, and false analytical variants with UMI deduplication and consolidation for NGS reads and variant calling/filtering. The paternally inherited variants and confirmed de novo variants deemed true positives had Z>-0.6.

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DOI

https://doi.org/10.1038/s41591-018-0334-x