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Same performance of exome sequencing before and after fetal autopsy for congenital abnormalities: toward a paradigm shift in prenatal diagnosis?

Nicolas BourgonAurore GardeAnge-Line BruelMathilde LefebvreFrederic Tran Mau-ThemSebastien MouttonArthur SorlinSophie NambotJulian DelanneMartin ChevarinCharlotte PöeJulien ThevenonDaphné LehalleNolween Jean-MarçaisPaul KuentzLaetitia LambertSalima El ChehadehElise SchaeferMarjolaine WillemsFanny LaffargueChristine FrancannetMélanie FradinDominique GaillardSophie BlessonAlice GoldenbergYline CapriPaul SagotThierry RousseauEmmanuel SimonChristine BinquetMarie-Laure AscencioYannis DuffourdChristophe PhilippeLaurence FaivreAntonio VitobelloChristel Thauvin-Robinet
Published in: European journal of human genetics : EJHG (2022)
Prenatal exome sequencing could be complex because of limited phenotypical data compared to postnatal/portmortem phenotype in fetuses affected by multiple congenital abnormalities (MCA). Here, we investigated limits of prenatal phenotype for ES interpretation thanks to a blindly reanalysis of postmortem ES data using prenatal data only in fetuses affected by MCA and harboring a (likely)pathogenic variant or a variant of unknown significance (VUS). Prenatal ES identified all causative variant previously reported by postmortem ES (22/24 (92%) and 2/24 (8%) using solo-ES and trio-ES respectively). Prenatal ES identified 5 VUS (in four fetuses). Two of them have been previously reported by postmortem ES. Prenatal ES were negative for four fetuses for which a VUS were diagnosed after autopsy. Our study suggests that prenatal phenotype is not a limitation for implementing pES in the prenatal assessment of unsolved MCA to personalize fetal medicine and could influence indication of postmortem examination.
Keyphrases
  • pregnant women
  • electronic health record
  • gestational age
  • big data
  • single cell
  • machine learning
  • copy number
  • dna methylation
  • genome wide
  • quality improvement
  • deep learning