Kabuki syndrome DNA methylation data. Kabuki syndrome DNA methylation data

Disease-specific DNA methylation patterns (DNAm signatures) have been established for an increasing number of genetic disorders and represent a valuable tool for classification of genetic variants of uncertain significance (VUS). Sample size and batch effects are critical issues for establishing DNAm signatures, but their impact on the sensitivity and specificity of an already established DNAm signature as a predictive tool of variant pathogenicity has not previously been tested. Here, we assessed whether publicly available DNAm data can be employed to generate a binary machine learning classifier for VUS classification, and used variants in KMT2D, the gene associated with Kabuki syndrome, together with an existing DNAm signature as proof-of-concept. Using publicly available data for training, a classifier for KMT2D variants that correctly discriminated DNA samples from individuals with molecularly confirmed Kabuki syndrome and unaffected individuals was generated. These data document the clinical utility of a robust DNAm signature even with small numbers of patients to be tested. This study further underlines the importance of data sharing in the field of rare genetic disorders. Overall design: Bisulphite converted DNA samples were hybridized to the Illumina Infinium Human MethylationEPIC Beadchip.