Abstract：
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.
摘要：
一名26岁的女性先证者，具有临床诊断和一致的Diamond-Blackfan贫血表型（DBA，没有确定的基因型的OMIM105650)被称为未诊断疾病网络。DBA通常与具有常染色体显性或隐性遗传模式的单等位基因变体相关。有趣的是，通过检测非等位基因RPS19和RPL27变异体之间的双基因相互作用,解决了她的病例.这得到了机器学习结构模型的证实，共分离分析，和RNA测序。这是由机器学习结构模型证明的两个非等位基因变体的双基因效应引起的DBA的第一份报告。这种情况表明DBA的非典型表型表现可能是由某些个体的双基因遗传引起的。我们还得出结论，机器学习结构模型可用于检测由从未受影响的携带者父母遗传的不同基因的等位基因编码的产物之间可能相互作用的双基因模型，这可能导致DBA具有未实现的25％复发风险。
