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Objective Our goal was to perform detailed clinical and genomic analysis of a large multigenerational Chinese family with 21 individuals showing symptoms of Familial Cortical Myoclonic Tremor with Epilepsy (FCMTE) that we have followed for over 20 years. Methods Patients were subjected to clinical evaluation, routine EEG, and structural magnetic resonance imaging. Whole exome sequencing, repeat‐primed PCR, long‐range PCR, and PacBio sequencing were performed to characterize the disease‐causing mutation in this family. Results All evaluated patients manifested adult‐onset seizures and presented with progressive myoclonic postural tremors starting after the third or fourth decade of life. Seizures typically diminished markedly in frequency with implementation of antiseizure medications but did not completely cease. The electroencephalogram of affected individuals showed generalized or multifocal spikes and slow wave complexes. An expansion of TTTTA motifs with addition of TTTCA motifs in intron 4 of SAMD12 was identified to segregate with the disease phenotype in this family. Furthermore, we found that the mutant allele is unstable and can undergo both contraction and expansion by changes in the number of repeat motifs each time it is passed to the next generation. The size of mutant allele varied from 5 to 5.5 kb with 549‐603 copies of TTTTA and 287‐343 copies of TTTCA repeat motifs in this family. Significance Our study provides a detailed description of clinical progression of FCMTE symptoms and its management with antiseizure medications. Our method of repeat analysis by PacBio sequencing of long‐range PCR products does not require high‐quality DNA and hence can be easily applied to other families to elucidate any correlation between the repeat size and phenotypic variables, such as, age of onset, and severity of symptoms.

Many of the sequenced bacterial and archaeal genomes encode regions of viral provenance. Yet, not all of these regions encode bona fide viruses. Gene transfer agents (GTAs) are thought to be former viruses that are now maintained in genomes of some bacteria and archaea and are hypothesized to enable exchange of DNA within bacterial populations. In Alphaproteobacteria, genes homologous to the ‘head-tail’ gene cluster that encodes structural components of the Rhodobacter capsulatus GTA (RcGTA) are found in many taxa, even if they are only distantly related to Rhodobacter capsulatus. Yet, in most genomes available in GenBank RcGTA-like genes have annotations of typical viral proteins, and therefore are not easily distinguished from their viral homologs without additional analyses. Here, we report a ‘support vector machine’ classifier that quickly and accurately distinguishes RcGTA-like genes from their viral homologs by capturing the differences in the amino acid composition of the encoded proteins. Our open-source classifier is implemented in Python and can be used to scan homologs of the RcGTA genes in newly sequenced genomes. The classifier can also be trained to identify other types of GTAs, or even to detect other elements of viral ancestry. Using the classifier trained on a manually curated set of homologous viruses and GTAs, we detected RcGTA-like ‘head-tail’ gene clusters in 57.5% of the 1,423 examined alphaproteobacterial genomes. We also demonstrated that more than half of the in silico prophage predictions are instead likely to be GTAs, suggesting that in many alphaproteobacterial genomes the RcGTA-like elements remain unrecognized.Data depositionSequence alignments and phylogenetic trees are available in a FigShare repository at DOI 10.6084/m9.figshare.8796419. The Python source code of the described classifier and additional scripts used in the analyses are available via a GitHub repository at https://github.com/ecg-lab/GTA-Hunter-v1

Worldwide, hundreds of thousands of humans have had their genomes or exomes sequenced, and access to the resulting data sets can provide valuable information for variant interpretation and understanding gene function. Here, we present a lightweight, flexible browser framework to display large population datasets of genetic variation. We demonstrate its use for exome sequence data from 60 706 individuals in the Exome Aggregation Consortium (ExAC). The ExAC browser provides gene- and transcript-centric displays of variation, a critical view for clinical applications. Additionally, we provide a variant display, which includes population frequency and functional annotation data as well as short read support for the called variant. This browser is open-source, freely available at http://exac.broadinstitute.org, and has already been used extensively by clinical laboratories worldwide.

The interpretation of genetic variants identified during clinical sequencing has come to rely heavily on reference population databases such as the Exome Aggregation Consortium (ExAC). Genuinely pathogenic variants, particularly in genes associated with severe autosomal dominant conditions, are assumed to be absent or extremely rare in these databases. Clinical exome sequencing of a six-year-old female patient with seizures, global developmental delay, dysmorphic features and failure to thrive identified an ASXL1 variant that was previously reported as causative of Bohring-Opitz syndrome (BOS). Surprisingly, the variant was observed seven times in the ExAC database, presumably in individuals without BOS. Although the BOS phenotype matched the presentation of the patient, the presence of the variant in reference population databases introduced ambiguity in result interpretation. Interrogation of the literature revealed that acquired somatic mosaicism of ASXL1 variants (including known pathogenic variants) during hematopoietic clonal expansion may be concomitant with aging in healthy individuals. We examined all high quality ASXL1 predicted truncating variant calls in the ExAC database and determined the majority could be attributed to this phenomenon. Failure to consider somatic mosaicism may lead to the inaccurate assumption that conditions like Bohring-Opitz syndrome have reduced penetrance, or the misclassification of potentially pathogenic variants.

SummaryLarge-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) sequence data for 60,706 individuals of diverse ethnicities generated as part of the Exome Aggregation Consortium (ExAC). The resulting catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We show that this catalogue can be used to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; we identify 3,230 genes with near-complete depletion of truncating variants, 72% of which have no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human “knockout” variants in protein-coding genes.

Exome and whole-genome sequencing are becoming increasingly routine approaches in Mendelian disease diagnosis. Despite their success, the current diagnostic rate for genomic analyses across a variety of rare diseases is approximately 25 to 50%. We explore the utility of transcriptome sequencing [RNA sequencing (RNA-seq)] as a complementary diagnostic tool in a cohort of 50 patients with genetically undiagnosed rare muscle disorders. We describe an integrated approach to analyze patient muscle RNA-seq, leveraging an analysis framework focused on the detection of transcript-level changes that are unique to the patient compared to more than 180 control skeletal muscle samples. We demonstrate the power of RNA-seq to validate candidate splice-disrupting mutations and to identify splice-altering variants in both exonic and deep intronic regions, yielding an overall diagnosis rate of 35%. We also report the discovery of a highly recurrent de novo intronic mutation in COL6A1 that results in a dominantly acting splice-gain event, disrupting the critical glycine repeat motif of the triple helical domain. We identify this pathogenic variant in a total of 27 genetically unsolved patients in an external collagen VI-like dystrophy cohort, thus explaining approximately 25% of patients clinically suggestive of having collagen VI dystrophy in whom prior genetic analysis is negative. Overall, this study represents a large systematic application of transcriptome sequencing to rare disease diagnosis and highlights its utility for the detection and interpretation of variants missed by current standard diagnostic approaches. 2017, National Institutes of Health (Grant GM096911), National Institute of General Medical Sciences (Grants F32GM115208 and 4T32GM007748)