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Exome sequencing efficacy and phenotypic expansions involving esophageal atresia/tracheoesophageal fistula plus.

Mary R SyJaynee ChauhanKatrina PrescottAliza ImamAlison KrausAna BelezaLee SalkeldSaraswati HosdurgaMichael ParkerPradeep VasudevanLily IslamHimanshu GoelNicole BainSoo-Mi ParkShehla MohammedKlaus DieterichCharles CouttonVéronique SatreGaëlle VievilleAlan DonaldsonClaire BeneteauJamal GhoumidKris Van Den BogaertAnneleen BoogaertsElise BoudryClémence VanlerbergheFlorence PetitLaura BernardiniBarbara TorresTeresa MattinaDiana CarliGiorgia MandrileMichele PinelliNicola Brunetti-PierriKatherine NeasRachel BeddowPernille M TørringFlavio FaletraBeatrice SpedicatiPaolo GaspariniAlessandro MussaGiovanni Battista FerreroAnne LampeWayne LamWeimin BiCarlos A BacinoAkela KuwaharaJeffrey O BushXiaonan ZhaoPamela N LunaChad A ShawJill A RosenfeldDaryl A Scott
Published in: American journal of medical genetics. Part A (2022)
Esophageal atresia/tracheoesophageal fistula (EA/TEF) is a life-threatening birth defect that often occurs with other major birth defects (EA/TEF+). Despite advances in genetic testing, a molecular diagnosis can only be made in a minority of EA/TEF+ cases. Here, we analyzed clinical exome sequencing data and data from the DECIPHER database to determine the efficacy of exome sequencing in cases of EA/TEF+ and to identify phenotypic expansions involving EA/TEF. Among 67 individuals with EA/TEF+ referred for clinical exome sequencing, a definitive or probable diagnosis was made in 11 cases for an efficacy rate of 16% (11/67). This efficacy rate is significantly lower than that reported for other major birth defects, suggesting that polygenic, multifactorial, epigenetic, and/or environmental factors may play a particularly important role in EA/TEF pathogenesis. Our cohort included individuals with pathogenic or likely pathogenic variants that affect TCF4 and its downstream target NRXN1, and FANCA, FANCB, and FANCC, which are associated with Fanconi anemia. These cases, previously published case reports, and comparisons to other EA/TEF genes made using a machine learning algorithm, provide evidence in support of a potential pathogenic role for these genes in the development of EA/TEF.
Keyphrases
  • machine learning
  • copy number
  • single cell
  • genome wide
  • gene expression
  • dna methylation
  • big data
  • gestational age
  • healthcare
  • systematic review
  • radiation therapy
  • pregnant women
  • transcription factor
  • case report