Episignature analysis of moderate effects and mosaics

DNA methylation classifiers ("episignatures") help to determine the pathogenicity of variants of uncertain significance (VUS). However, their sensitivity is limited due to their training on unambiguous cases with strong-effect variants so that the classification of variants with reduced effect size or in mosaic state may fail. Moreover, episignature evaluation of mosaics as a function of their degree of mosaicism has not been developed so far. We improved episignatures with respect to three categories. Applying (i) minimum-redundancy-maximum-relevance feature selection we reduced their length by up to one order of magnitude without loss of accuracy. Performing (ii) repeated re-training of a support vector machine classifier by step-wise inclusion of cases in the training set that reached probability scores larger than 0.5, we increased the sensitivity of the episignature-classifiers by 30%. In the newly diagnosed patients we confirmed the association between DNA methylation aberration and age at onset of KMT2B-deficient dystonia. Moreover, we found evidence for allelic series, including KMT2B-variants with moderate effects and comparatively mild phenotypes such as late-onset focal dystonia. Retrained classifiers also can detect mosaics that previously remained below the 0.5-threshold, as we showed for KMT2D-associated Kabuki syndrome. Conversely, episignature-classifiers are able to revoke erroneous exome calls of mosaicism, as we demonstrated by (iii) comparing presumed mosaic cases with a distribution of artificial in silico-mosaics that represented all the possible variation in degree of mosaicism, variant read sampling and methylation analysis.