Background: The existing risk of procedure-related miscarriage following invasive sampling for prenatal diagnosis is higher for twin pregnancies and some women are reluctant to test these typically difficultly obtained pregnancies invasively. Therefore, there is a need for noninvasive testing options that can test twin pregnancies at an early gestational age and ideally test the twins individually.Case presentation: A pregnant woman opted for cell-based NIPT at GA 10 + 5. As cell-based NIPT is not established for use in twins, the test was provided in a research setting only, when an ultrasound scan showed that she carried dichorionic twins.Materials and Methods: Fifty mL of peripheral blood was sampled, and circulating fetal cells were enriched and isolated. Individual cells were subject to whole-genome amplification and STR analysis. Three fetal cells were analyzed by chromosomal microarray (aCGH).Results: We identified 20 fetal cells all sharing the same genetic profile, which increased the likelihood of monozygotic twins. aCGH of three fetal cells showed the presence of two X chromosomes and a gain of chromosome Y. CVS from both placentae confirmed the sex chromosomal anomaly, 47,XXY and that both fetuses were affected.Conclusion: NIPT options can provide valuable genetic information to twin pregnancies that help the couples in their decision-making on prenatal testing. Little has been published about the use of cell-based NIPT in twin pregnancies, but the method may offer the possibility to obtain individual cell-based NIPT results in dizygotic twins.

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 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 methylation data for training, a classifier for KMT2D variants was generated, and individuals with molecularly confirmed Kabuki syndrome and unaffected individuals could be correctly classified. The present study documents the clinical utility of a robust DNAm signature even for few affected individuals, and most importantly, underlines the importance of data sharing for improved diagnosis of rare genetic disorders.

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