Whole-genome sequencing (WGS) permits comprehensive cancer genome analyses, revealing mutational signatures, imprints of DNA damage, and repair processes that have arisen in each patient’s cancer. We performed mutational signature analyses on 12,222 whole-genome–sequenced tumor-normal matched pairs from patients recruited via the UK National Health Service (NHS). We contrasted our results with two independent cancer WGS datasets—from the International Cancer Genome Consortium (ICGC) and the Hartwig Medical Foundation (HMF)—involving 18,640 whole-genome–sequenced cancers in total. Our analyses add 40 single and 18 double substitution signatures to the current mutational signature tally. We show for each organ that cancers have a limited number of common signatures and a long tail of rare signatures, and we provide a practical solution for applying this concept of common versus rare signatures to future analyses.

BackgroundLong QT syndrome (LQTS) is an autosomal dominant condition predisposing to sudden death from malignant arrhythmia. Genetic testing identifies many missense single nucleotide variants of uncertain pathogenicity. Establishing genetic pathogenicity is an essential prerequisite to family cascade screening. Many laboratories use in silico prediction tools, either alone or in combination, or metaservers, in order to predict pathogenicity; however, their accuracy in the context of LQTS is unknown. We evaluated the accuracy of five in silico programs and two metaservers in the analysis of LQTS 1–3 gene variants.MethodsThe in silico tools SIFT, PolyPhen-2, PROVEAN, SNPs&GO and SNAP, either alone or in all possible combinations, and the metaservers Meta-SNP and PredictSNP, were tested on 312 KCNQ1, KCNH2 and SCN5A gene variants that have previously been characterised by either in vitro or co-segregation studies as either “pathogenic” (283) or “benign” (29). The accuracy, sensitivity, specificity and Matthews Correlation Coefficient (MCC) were calculated to determine the best combination of in silico tools for each LQTS gene, and when all genes are combined.ResultsThe best combination of in silico tools for KCNQ1 is PROVEAN, SNPs&GO and SIFT (accuracy 92.7%, sensitivity 93.1%, specificity 100% and MCC 0.70). The best combination of in silico tools for KCNH2 is SIFT and PROVEAN or PROVEAN, SNPs&GO and SIFT. Both combinations have the same scores for accuracy (91.1%), sensitivity (91.5%), specificity (87.5%) and MCC (0.62). In the case of SCN5A, SNAP and PROVEAN provided the best combination (accuracy 81.4%, sensitivity 86.9%, specificity 50.0%, and MCC 0.32). When all three LQT genes are combined, SIFT, PROVEAN and SNAP is the combination with the best performance (accuracy 82.7%, sensitivity 83.0%, specificity 80.0%, and MCC 0.44). Both metaservers performed better than the single in silico tools; however, they did not perform better than the best performing combination of in silico tools.ConclusionsThe combination of in silico tools with the best performance is gene-dependent. The in silico tools reported here may have some value in assessing variants in the KCNQ1 and KCNH2 genes, but caution should be taken when the analysis is applied to SCN5A gene variants.Electronic supplementary materialThe online version of this article (doi:10.1186/s12881-015-0176-z) contains supplementary material, which is available to authorized users.

The identification of causal variants in sequencing studies remains a considerable challenge that can be partially addressed by new gene-specific knowledge. Here, we integrate measures of how essential a gene is to supporting life, as inferred from viability and phenotyping screens performed on knockout mice by the International Mouse Phenotyping Consortium and essentiality screens carried out on human cell lines. We propose a cross-species gene classification across the Full Spectrum of Intolerance to Loss-of-function (FUSIL) and demonstrate that genes in five mutually exclusive FUSIL categories have differing biological properties. Most notably, Mendelian disease genes, particularly those associated with developmental disorders, are highly overrepresented among genes non-essential for cell survival but required for organism development. After screening developmental disorder cases from three independent disease sequencing consortia, we identify potentially pathogenic variants in genes not previously associated with rare diseases. We therefore propose FUSIL as an efficient approach for disease gene discovery.

The mutational landscape is shaped by many processes. Genic regions are vulnerable to mutation but are preferentially protected by transcription-coupled repair1. In microorganisms, transcription has been demonstrated to be mutagenic2,3; however, the impact of transcription-associated mutagenesis remains to be established in higher eukaryotes4. Here we show that ID4—a cancer insertion–deletion (indel) mutation signature of unknown aetiology5 characterized by short (2 to 5 base pair) deletions —is due to a transcription-associated mutagenesis process. We demonstrate that defective ribonucleotide excision repair in mammals is associated with the ID4 signature, with mutations occurring at a TNT sequence motif, implicating topoisomerase 1 (TOP1) activity at sites of genome-embedded ribonucleotides as a mechanistic basis. Such TOP1-mediated deletions occur somatically in cancer, and the ID-TOP1 signature is also found in physiological settings, contributing to genic de novo indel mutations in the germline. Thus, although topoisomerases protect against genome instability by relieving topological stress6, their activity may also be an important source of mutations in the human genome.

Purpose: Determining the role of DYNC2H1 variants in nonsyndromic inherited retinal disease (IRD). Methods: Genome and exome sequencing were performed for five unrelated cases of IRD with no identified variant. In vitro assays were developed to validate the variants identified (fibroblast assay, induced pluripotent stem cell [iPSC] derived retinal organoids, and a dynein motility assay). Results: Four novel DYNC2H1 variants (V1, g.103327020_10332 7021dup; V2, g.103055779A>T; V3, g.103112272C>G; V4, g.103070104A>C) and one previously reported variant (V5, g.103339363T>G) were identified. In proband 1 (V1/V2), V1 was predicted to introduce a premature termination codon (PTC), whereas V2 disrupted the exon 41 splice donor site causing incomplete skipping of exon 41. V1 and V2 impaired dynein-2 motility in vitro and perturbed IFT88 distribution within cilia. V3, homozygous in probands 2-4, is predicted to cause a PTC in a retinapredominant transcript. Analysis of retinal organoids showed that this new transcript expression increased with organoid differentiation. V4, a novel missense variant, was in trans with V5, previously associated with Jeune asphyxiating thoracic dystrophy (JATD). Conclusion: The DYNC2H1 variants discussed herein were either hypomorphic or affecting a retina-predominant transcript and caused nonsyndromic IRD. Dynein variants, specifically DYNC2H1 variants are reported as a cause of non syndromic IRD.