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Shreeve N, Sproule C, Choy KW, Dong Z, Gajewska-Knapik K, Kilby MD, Mone F. Incremental yield of whole-genome sequencing over chromosomal microarray analysis and exome sequencing for congenital anomalies in prenatal period and infancy: systematic review and meta-analysis. ULTRASOUND IN OBSTETRICS & GYNECOLOGY : THE OFFICIAL JOURNAL OF THE INTERNATIONAL SOCIETY OF ULTRASOUND IN OBSTETRICS AND GYNECOLOGY 2024; 63:15-23. [PMID: 37725747 DOI: 10.1002/uog.27491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 08/08/2023] [Accepted: 09/08/2023] [Indexed: 09/21/2023]
Abstract
OBJECTIVES First, to determine the incremental yield of whole-genome sequencing (WGS) over quantitative fluorescence polymerase chain reaction (QF-PCR)/chromosomal microarray analysis (CMA) with and without exome sequencing (ES) in fetuses, neonates and infants with a congenital anomaly that was or could have been detected on prenatal ultrasound. Second, to evaluate the turnaround time (TAT) and quantity of DNA required for testing using these pathways. METHODS This review was registered prospectively in December 2022. Ovid MEDLINE, EMBASE, MEDLINE (Web of Science), The Cochrane Library and ClinicalTrials.gov databases were searched electronically (January 2010 to December 2022). Inclusion criteria were cohort studies including three or more fetuses, neonates or infants with (i) one or more congenital anomalies; (ii) an anomaly which was or would have been detectable on prenatal ultrasound; and (iii) negative QF-PCR and CMA. In instances in which the CMA result was unavailable, all cases of causative pathogenic copy number variants > 50 kb were excluded, as these would have been detectable on standard prenatal CMA. Pooled incremental yield was determined using a random-effects model and heterogeneity was assessed using Higgins' I2 test. Subanalyses were performed based on pre- or postnatal cohorts, cases with multisystem anomalies and those meeting the NHS England prenatal ES inclusion criteria. RESULTS A total of 18 studies incorporating 902 eligible cases were included, of which eight (44.4%) studies focused on prenatal cohorts, incorporating 755 cases, and the remaining studies focused on fetuses undergoing postmortem testing or neonates/infants with congenital structural anomalies, constituting the postnatal cohort. The incremental yield of WGS over QF-PCR/CMA was 26% (95% CI, 18-36%) (I2 = 86%), 16% (95% CI, 9-24%) (I2 = 85%) and 39% (95% CI, 27-51%) (I2 = 53%) for all, prenatal and postnatal cases, respectively. The incremental yield increased in cases in which sequencing was performed in line with the NHS England prenatal ES criteria (32% (95% CI, 22-42%); I2 = 70%) and in those with multisystem anomalies (30% (95% CI, 19-43%); I2 = 65%). The incremental yield of WGS for variants of uncertain significance (VUS) was 18% (95% CI, 7-33%) (I2 = 74%). The incremental yield of WGS over QF-PCR/CMA and ES was 1% (95% CI, 0-4%) (I2 = 47%). The pooled median TAT of WGS was 18 (range, 1-912) days, and the quantity of DNA required was 100 ± 0 ng for WGS and 350 ± 50 ng for QF-PCR/CMA and ES (P = 0.03). CONCLUSION While WGS in cases with congenital anomaly holds great promise, its incremental yield over ES is yet to be demonstrated. However, the laboratory pathway for WGS requires less DNA with a potentially faster TAT compared with sequential QF-PCR/CMA and ES. There was a relatively high rate of VUS using WGS. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.
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Affiliation(s)
- N Shreeve
- Department of Obstetrics & Gynaecology, University of Cambridge, Cambridge, UK
| | - C Sproule
- Department of Obstetrics & Gynaecology, South Eastern Health and Social Care Trust, Belfast, UK
| | - K W Choy
- Department of Obstetrics & Gynaecology, The Chinese University of Hong Kong, Hong Kong, China
| | - Z Dong
- Department of Obstetrics & Gynaecology, The Chinese University of Hong Kong, Hong Kong, China
| | - K Gajewska-Knapik
- Department of Obstetrics & Gynaecology, Cambridge University Hospitals, Cambridge, UK
| | - M D Kilby
- Fetal Medicine Centre, Birmingham Women's and Children's Foundation Trust, Birmingham, UK
- College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
- Medical Genomics Research Group, Illumina, Cambridge, UK
| | - F Mone
- Centre for Public Health, Queen's University Belfast, Belfast, UK
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Lowther C, Valkanas E, Giordano JL, Wang HZ, Currall BB, O'Keefe K, Pierce-Hoffman E, Kurtas NE, Whelan CW, Hao SP, Weisburd B, Jalili V, Fu J, Wong I, Collins RL, Zhao X, Austin-Tse CA, Evangelista E, Lemire G, Aggarwal VS, Lucente D, Gauthier LD, Tolonen C, Sahakian N, Stevens C, An JY, Dong S, Norton ME, MacKenzie TC, Devlin B, Gilmore K, Powell BC, Brandt A, Vetrini F, DiVito M, Sanders SJ, MacArthur DG, Hodge JC, O'Donnell-Luria A, Rehm HL, Vora NL, Levy B, Brand H, Wapner RJ, Talkowski ME. Systematic evaluation of genome sequencing for the diagnostic assessment of autism spectrum disorder and fetal structural anomalies. Am J Hum Genet 2023; 110:1454-1469. [PMID: 37595579 PMCID: PMC10502737 DOI: 10.1016/j.ajhg.2023.07.010] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 07/25/2023] [Accepted: 07/25/2023] [Indexed: 08/20/2023] Open
Abstract
Short-read genome sequencing (GS) holds the promise of becoming the primary diagnostic approach for the assessment of autism spectrum disorder (ASD) and fetal structural anomalies (FSAs). However, few studies have comprehensively evaluated its performance against current standard-of-care diagnostic tests: karyotype, chromosomal microarray (CMA), and exome sequencing (ES). To assess the clinical utility of GS, we compared its diagnostic yield against these three tests in 1,612 quartet families including an individual with ASD and in 295 prenatal families. Our GS analytic framework identified a diagnostic variant in 7.8% of ASD probands, almost 2-fold more than CMA (4.3%) and 3-fold more than ES (2.7%). However, when we systematically captured copy-number variants (CNVs) from the exome data, the diagnostic yield of ES (7.4%) was brought much closer to, but did not surpass, GS. Similarly, we estimated that GS could achieve an overall diagnostic yield of 46.1% in unselected FSAs, representing a 17.2% increased yield over karyotype, 14.1% over CMA, and 4.1% over ES with CNV calling or 36.1% increase without CNV discovery. Overall, GS provided an added diagnostic yield of 0.4% and 0.8% beyond the combination of all three standard-of-care tests in ASD and FSAs, respectively. This corresponded to nine GS unique diagnostic variants, including sequence variants in exons not captured by ES, structural variants (SVs) inaccessible to existing standard-of-care tests, and SVs where the resolution of GS changed variant classification. Overall, this large-scale evaluation demonstrated that GS significantly outperforms each individual standard-of-care test while also outperforming the combination of all three tests, thus warranting consideration as the first-tier diagnostic approach for the assessment of ASD and FSAs.
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Affiliation(s)
- Chelsea Lowther
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Elise Valkanas
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Program in Biological and Biomedical Sciences, Division of Medical Sciences, Harvard Medical School, Boston, MA, USA
| | - Jessica L Giordano
- Department of Obstetrics & Gynecology, Columbia University Medical Center, New York, NY, USA
| | - Harold Z Wang
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Benjamin B Currall
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Kathryn O'Keefe
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Emma Pierce-Hoffman
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nehir E Kurtas
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Christopher W Whelan
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Stephanie P Hao
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ben Weisburd
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Vahid Jalili
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jack Fu
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Isaac Wong
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ryan L Collins
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Program in Bioinformatics and Integrative Genomics, Division of Medical Sciences, Harvard Medical School, Boston, MA, USA
| | - Xuefang Zhao
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Christina A Austin-Tse
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Department of Pathology, Harvard Medical School, Boston, MA, USA
| | - Emily Evangelista
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Gabrielle Lemire
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Vimla S Aggarwal
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY, USA
| | - Diane Lucente
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Laura D Gauthier
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Data Science Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Charlotte Tolonen
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Data Science Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nareh Sahakian
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Data Science Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Christine Stevens
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joon-Yong An
- School of Biosystem and Biomedical Science, Korea University, Seoul, South Korea
| | - Shan Dong
- Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Mary E Norton
- Center for Maternal-Fetal Precision Medicine, University of California, San Francisco, San Francisco, CA, USA; Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Francisco, San Francisco, California, USA
| | - Tippi C MacKenzie
- Center for Maternal-Fetal Precision Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Bernie Devlin
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Kelly Gilmore
- Department of Obstetrics and Gynecology, Division of Maternal-Fetal Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Bradford C Powell
- Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Alicia Brandt
- Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Francesco Vetrini
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Michelle DiVito
- Department of Obstetrics & Gynecology, Columbia University Medical Center, New York, NY, USA
| | - Stephan J Sanders
- Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Daniel G MacArthur
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Centre for Population Genomics, Garvan Institute of Medical Research, and University of New South Wales Sydney, Sydney, NSW, Australia; Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, VIC, Australia
| | - Jennelle C Hodge
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Anne O'Donnell-Luria
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA
| | - Heidi L Rehm
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Neeta L Vora
- Department of Obstetrics and Gynecology, Division of Maternal-Fetal Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Brynn Levy
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY, USA
| | - Harrison Brand
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Ronald J Wapner
- Department of Obstetrics & Gynecology, Columbia University Medical Center, New York, NY, USA
| | - Michael E Talkowski
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA; Program in Biological and Biomedical Sciences, Division of Medical Sciences, Harvard Medical School, Boston, MA, USA; Program in Bioinformatics and Integrative Genomics, Division of Medical Sciences, Harvard Medical School, Boston, MA, USA.
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9
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Hu P, Zhang Q, Cheng Q, Luo C, Zhang C, Zhou R, Meng L, Huang M, Wang Y, Wang Y, Qiao F, Xu Z. Whole genome sequencing vs chromosomal microarray analysis in prenatal diagnosis. Am J Obstet Gynecol 2023; 229:302.e1-302.e18. [PMID: 36907537 DOI: 10.1016/j.ajog.2023.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 02/27/2023] [Accepted: 03/03/2023] [Indexed: 03/12/2023]
Abstract
BACKGROUND Emerging studies suggest that whole genome sequencing provides additional diagnostic yield of genomic variants when compared with chromosomal microarray analysis in the etiologic diagnosis of infants and children with suspected genetic diseases. However, the application and evaluation of whole genome sequencing in prenatal diagnosis remain limited. OBJECTIVE This study aimed to evaluate the accuracy, efficacy, and incremental yield of whole genome sequencing in comparison with chromosomal microarray analysis for routine prenatal diagnosis. STUDY DESIGN In this prospective study, a total of 185 unselected singleton fetuses with ultrasound-detected structural anomalies were enrolled. In parallel, each sample was subjected to whole genome sequencing and chromosomal microarray analysis. Aneuploidies and copy number variations were detected and analyzed in a blinded fashion. Single nucleotide variations and insertions and deletions were confirmed by Sanger sequencing, and trinucleotide repeats expansion variants were verified using polymerase chain reaction plus fragment-length analysis. RESULTS Overall, genetic diagnoses using whole genome sequencing were obtained for 28 (15.1%) cases. Whole genome sequencing not only detected all these aneuploidies and copy number variations in the 20 (10.8%) diagnosed cases identified by chromosomal microarray analysis, but also detected 1 case with an exonic deletion of COL4A2 and 7 (3.8%) cases with single nucleotide variations or insertions and deletions. In addition, 3 incidental findings were detected including an expansion of the trinucleotide repeat in ATXN3, a splice-sites variant in ATRX, and an ANXA11 missense mutation in a case of trisomy 21. CONCLUSION Compared with chromosomal microarray analysis, whole genome sequencing increased the additional detection rate by 5.9% (11/185). Using whole genome sequencing, we detected not only aneuploidies and copy number variations, but also single nucleotide variations and insertions and deletions, trinucleotide repeat expansions, and exonic copy number variations with high accuracy in an acceptable turnaround time (3-4 weeks). Our results suggest that whole genome sequencing has the potential to be a new promising prenatal diagnostic test for fetal structural anomalies.
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Affiliation(s)
- Ping Hu
- Department of Prenatal Diagnosis, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, China
| | - Qinxin Zhang
- Department of Prenatal Diagnosis, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, China
| | - Qing Cheng
- Department of Prenatal Diagnosis, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, China
| | - Chunyu Luo
- Department of Prenatal Diagnosis, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, China
| | - Cuiping Zhang
- Department of Prenatal Diagnosis, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, China
| | - Ran Zhou
- Department of Prenatal Diagnosis, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, China
| | - Lulu Meng
- Department of Prenatal Diagnosis, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, China
| | - Mingtao Huang
- Department of Prenatal Diagnosis, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, China
| | - Yuguo Wang
- Department of Prenatal Diagnosis, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, China
| | - Yan Wang
- Department of Prenatal Diagnosis, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, China.
| | - Fengchang Qiao
- Department of Prenatal Diagnosis, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, China.
| | - Zhengfeng Xu
- Department of Prenatal Diagnosis, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, China.
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