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Prenatal Genome-Wide Sequencing analysis (Exome or Genome) in detecting pathogenic Single Nucleotide Variants in fetal Central Nervous System Anomalies: systematic review and meta-analysis

European Journal of Human Genetics volume 32, pages 759–769 (2024)Cite this article

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Abstract

Prenatal Exome (pES) or Genome (pGS) Sequencing analysis showed a significant incremental diagnostic yield over karyotype and chromosomal microarray analysis (CMA) in fetal structural anomalies. Optimized indications and detection rates in different fetal anomalies are still under investigation. The aim of this study was to assess the incremental diagnostic yield in prenatally diagnosed Central Nervous System (CNS) anomalies. A systematic review on antenatal CNS anomalies was performed according to PRISMA guidelines, including n = 12 paper, accounting for 428 fetuses. Results were pooled in a meta‐analysis fitting a logistic random mixed-effect model. The effect of interest was the incremental diagnostic rate of pES over karyotype/CMA in detecting likely pathogenic/pathogenic Single Nucleotide Variants (SNVs). A further meta-analysis adding the available pGS studies (including diagnostic coding SNVs only) and submeta-analysis on three CNS subcategories were also performed. The pooled incremental diagnostic yield estimate of pES studies was 38% (95% C.I.: [29%;47%]) and 36% (95% C.I.: [28%;45%]) when including diagnostic SNVs of pGS studies. The point estimate of the effect resulted 22% (95% C.I.: [15%;31%]) in apparently isolated anomalies, 33% (95% C.I.: [22%;46%]) in CNS-only related anomalies (≥1) and 46% (95% C.I.: [38%;55%]) in non-isolated anomalies (either ≥ 2 anomalies in CNS, or CNS and extra-CNS). Meta-analysis showed a substantial diagnostic improvement in performing Prenatal Genome-Wide Sequencing analysis (Exome or Genome) over karyotype and CMA in CNS anomalies.

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Fig. 1: PRISMA Flow Chart.
Fig. 2: Meta-analysis - Incremental diagnostic yield of prenatal genome-wide sequencing in all fetuses with CNS anomalies (either isolated or associated, single or multiple).
Fig. 3: Influence analysis and leave-one-out analysis.
Fig. 4: Subcategory Meta-analysis Forest Plots - incremental diagnostic yield of pES/pGS in fetuses with an apparently isolated CNS anomaly, non-isolated CNS anomaly/anomalies or CNS-only anomalies (1 or more).

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All data will be available upon reasonable request at the corresponding author.

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Acknowledgements

We acknowledge Edoardo Marchionni*, for the contribution to the statistical methodology and formal meta-analysis. *Mathematical Engineering, Statistical Learning, Department of Mathematics, Politecnico di Milano University, Milan, Italy.

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The study received no funding.

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  1. Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy

    Enrica Marchionni, Daniele Guadagnolo, Gioia Mastromoro & Antonio Pizzuti

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Contributions

Study conceptualization, EM; data curation EM, DG, GM, formal analysis, EM; funding acquisition N/A; Investigation N/A; methodology, EM; DG; GM; project administration EM, AP; Resources: EM, DG, GM; writing—original draft preparation EM; DG, writing—review and editing EM, DG; GM; AP; manuscript supervision AP.

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Correspondence to Enrica Marchionni.

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Marchionni, E., Guadagnolo, D., Mastromoro, G. et al. Prenatal Genome-Wide Sequencing analysis (Exome or Genome) in detecting pathogenic Single Nucleotide Variants in fetal Central Nervous System Anomalies: systematic review and meta-analysis. Eur J Hum Genet 32, 759–769 (2024). https://doi.org/10.1038/s41431-024-01590-2

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