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Genetic mosaicism is defined as the presence of two or more cell lineages with different genotypes arising from a single zygote. Mosaicism has been implicated in hundreds of genetic diseases with diverse genetic etiologies affecting every organ system. Mosaic genetic disease (MDG) is a spectrum that, on the extreme ends, enables survival from genetic severe disorders that would be lethal in a non-mosaic form. On the milder end of the spectrum, mosaicism can result in little if any phenotypic effects but increases the risk of transmitting a pathogenic genotype. In the middle of the spectrum, mosaicism has been implicated in reducing the phenotypic severity of genetic disease. In this review will describe the spectrum of mosaic genetic disease whilst discussing the status of the detection and prevalence of mosaic genetic disease. [ABSTRACT FROM AUTHOR]

Mosaic variants (MVs) reflect mutagenic processes during embryonic development and environmental exposure, accumulate with aging and underlie diseases such as cancer and autism. The detection of noncancer MVs has been computationally challenging due to the sparse representation of nonclonally expanded MVs. Here we present DeepMosaic, combining an image-based visualization module for single nucleotide MVs and a convolutional neural network-based classification module for control-independent MV detection. DeepMosaic was trained on 180,000 simulated or experimentally assessed MVs, and was benchmarked on 619,740 simulated MVs and 530 independent biologically tested MVs from 16 genomes and 181 exomes. DeepMosaic achieved higher accuracy compared with existing methods on biological data, with a sensitivity of 0.78, specificity of 0.83 and positive predictive value of 0.96 on noncancer whole-genome sequencing data, as well as doubling the validation rate over previous best-practice methods on noncancer whole-exome sequencing data (0.43 versus 0.18). DeepMosaic represents an accurate MV classifier for noncancer samples that can be implemented as an alternative or complement to existing methods. A deep learning model detects low frequency mosaic variants with improved accuracy. [ABSTRACT FROM AUTHOR]

The technological progress of massively parallel sequencing (MPS) has triggered a remarkable development in the research on postzygotic mutations. Although the overwhelming majority of studies in the field focus on oncogenesis, non-neoplastic diseases are attracting more and more attention. The aim of this review was to summarize some of the most recent findings in the field of somatic mosaicism in diseases other than neoplastic events. We discuss the abundance and role of postzygotic mutations, with a special emphasis on disorders which occur only in a mosaic form (obligatory mosaic diseases; OMDs). Based on the list of OMDs compiled from the published literature and three databases (OMIM, Orphanet and MosaicBase), we demonstrate the prevalence of cancer-related genes across OMDs and suggest other sources to further explore OMDs and OMD-related genes. Additionally, we comment on some practical aspects related to mosaic diseases, such as approaches to tissue sampling, the MPS coverage required to detect variants at a very low frequency, as well as on bioinformatic and molecular tools dedicated to detect somatic mutations in MPS data. [ABSTRACT FROM AUTHOR]