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Probable digenic inheritance of Diamond-Blackfan anemia.

Yutaka FurutaRory J TinkerAlican GulsevinSerena M NeumannRizwan HamidJoy D CoganLynette RivesQi LiuHua-Chang ChenKaren M JoosJohn A Phillipsnull null
Published in: American journal of medical genetics. Part A (2023)
A 26-year-old female proband with a clinical diagnosis and consistent phenotype of Diamond-Blackfan anemia (DBA, OMIM 105650) without an identified genotype was referred to the Undiagnosed Diseases Network. DBA is classically associated with monoallelic variants that have an autosomal-dominant or -recessive mode of inheritance. Intriguingly, her case was solved by a detection of a digenic interaction between non-allelic RPS19 and RPL27 variants. This was confirmed with a machine learning structural model, co-segregation analysis, and RNA sequencing. This is the first report of DBA caused by a digenic effect of two non-allelic variants demonstrated by machine learning structural model. This case suggests that atypical phenotypic presentations of DBA may be caused by digenic inheritance in some individuals. We also conclude that a machine learning structural model can be useful in detecting digenic models of possible interactions between products encoded by alleles of different genes inherited from non-affected carrier parents that can result in DBA with an unrealized 25% recurrence risk.
Keyphrases
  • machine learning
  • copy number
  • mitochondrial dna
  • artificial intelligence
  • chronic kidney disease
  • big data
  • genome wide
  • iron deficiency
  • autism spectrum disorder
  • transcription factor
  • quantum dots
  • sensitive detection