The application of massively parallel sequencing technology to the field of skeletal disorders has boosted the discovery of the underlying genetic defect for many of these diseases. It has also resulted in the delineation of new clinical entities and the identification of genes and pathways that had not previously been associated with skeletal disorders. These rapid advances have prompted the Nosology Committee of the International Skeletal Dysplasia Society to revise and update the last (2015) version of the Nosology and Classification of Genetic Skeletal Disorders. This newest and tenth version of the Nosology comprises 461 different diseases that are classified into 42 groups based on their clinical, radiographic, and/or molecular phenotypes.Remarkably, pathogenic variants affecting 437 different genes have been found in 425/461 (92%) of these disorders. By providing a reference list of recognized entities and their causal genes, the Nosology should help clinicians achieve accurate diagnoses for their patients and help scientists advance research in skeletal biology.

Malformations of the human cortex represent a major cause of disability 1 . Mouse models with mutations in known causal genes only partially recapitulate the phenotypes and are therefore not unlimitedly suited for understanding the molecular and cellular mechanisms responsible for these conditions. Here we study periventricular heterotopia (PH) by analyzing cerebral organoids derived from induced pluripotent stem cells of patients with mutations in the cadherin receptor-ligand pair DCHS1 and FAT4 or from isogenic knock-out lines 1,2 . Our results show that human cerebral organoids reproduce the cortical heterotopia associated with PH. Mutations in DCHS1 and FAT4 or knock-down of their expression cause changes in the morphology of neural progenitor cells and result in defective neuronal migration dynamics only in a subset of neurons. Single-cell RNA-sequencing data reveal a subpopulation of mutant neurons with dysregulated genes involved in axon guidance, neuronal migration and patterning. We suggest that defective neural progenitor cell (NPC) morphology and an altered navigation system in a subset of neurons underlie this form of periventricular heterotopia.

DNA methylation and Polycomb are key factors in the establishment of vertebrate cellular identity and fate. Here we report de novo missense mutations in DNMT3A, encoding the DNA methyltransferase DNMT3A, that cause microcephalic dwarfism, a hypocellular disorder of extreme global growth failure. Substitutions in the PWWP domain abrogate binding to the histone modifications H3K36me2/3, and alter DNA methylation in patient cells. Polycomb-associated DNA methylation canyons/valleys, hypomethylated domains encompassing developmental genes, become methylated with concomitant depletion of H3K27me3 and H3K4me3 bivalent marks. Such de novo DNA methylation occurs during differentiation of Dnmt3a W326R pluripotent cells in vitro, and is also evident in Dnmt3a W326R/+ dwarf mice. We therefore propose that the interaction of the DNMT3A PWWP domain with H3K36me2/3 normally limits DNA methylation of polycomb-marked regions. Our findings implicate the interplay between DNA methylation and polycomb at key developmental regulators as a determinant of organism size in mammals.

These results suggest that the Ehlers-Danlos variant of periventricular heterotopia (PH), in part, represents an overlapping syndrome with X-linked dominant PH due to filamin A mutations.

Summary Overlapping clinical phenotypes and an expanding breadth and complexity of genomic associations are a growing challenge in the diagnosis and clinical management of Mendelian disorders. The functional consequences and clinical impacts of genomic variation may involve unique, disorder-specific, genomic DNA methylation episignatures. In this study, we describe 19 novel episignature disorders and compare the findings alongside 38 previously established episignatures for a total of 57 episignatures associated with 65 genetic syndromes. We demonstrate increasing resolution and specificity ranging from protein complex, gene, sub-gene, protein domain, and even single nucleotide-level Mendelian episignatures. We show the power of multiclass modeling to develop highly accurate and disease-specific diagnostic classifiers. This study significantly expands the number and spectrum of disorders with detectable DNA methylation episignatures, improves the clinical diagnostic capabilities through the resolution of unsolved cases and the reclassification of variants of unknown clinical significance, and provides further insight into the molecular etiology of Mendelian conditions.