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Nazneen Rahman, Geert Kops and colleagues report the identification of biallelic loss-of-function mutations in TRIP13 in six individuals with Wilms tumor who presented with features of mosaic variegated aneuploidy. They show that TRIP13 -mutant cells show spindle assembly checkpoint defects and suggest that mechanisms leading to aneuploidy may contribute directly to increased cancer risk. Through exome sequencing, we identified six individuals with biallelic loss-of-function mutations in TRIP13 . All six developed Wilms tumor. Constitutional mosaic aneuploidies, microcephaly, developmental delay and seizures, which are features of mosaic variegated aneuploidy (MVA) syndrome 1 , 2 , were more variably present. Through functional studies, we show that TRIP13 -mutant patient cells have no detectable TRIP13 and have substantial impairment of the spindle assembly checkpoint (SAC), leading to a high rate of chromosome missegregation. Accurate segregation, as well as SAC proficiency, is rescued by restoring TRIP13 function. Individuals with biallelic TRIP13 or BUB1B mutations have a high risk of embryonal tumors 3 , and here we show that their cells display severe SAC impairment. MVA due to biallelic CEP57 mutations 4 , or of unknown cause, is not associated with embryonal tumors and cells from these individuals show minimal SAC deficiency. These data provide insights into the complex relationships between aneuploidy and carcinogenesis.

Clare Turnbull and colleagues identify nine new susceptibility loci for testicular germ-cell tumor. The newly identified risk regions include variants near DAZL and PRDM14 , genes known to be important for germ cell development. Testicular germ cell tumor (TGCT) is the most common cancer in young men and is notable for its high familial risks 1 , 2 . So far, six loci associated with TGCT have been reported 3 , 4 , 5 , 6 , 7 . From genome-wide association study (GWAS) analysis of 307,291 SNPs in 986 TGCT cases and 4,946 controls, we selected for follow-up 694 SNPs, which we genotyped in a further 1,064 TGCT cases and 10,082 controls from the UK. We identified SNPs at nine new loci (1q22, 1q24.1, 3p24.3, 4q24, 5q31.1, 8q13.3, 16q12.1, 17q22 and 21q22.3) showing association with TGCT ( P < 5 × 10 −8 ), which together account for an additional 4–6% of the familial risk of TGCT. The loci include genes plausibly related to TGCT development. PRDM14 , at 8q13.3, is essential for early germ cell specification 8 , and DAZL , at 3p24.3, is required for the regulation of germ cell development 9 . Furthermore, PITX1 , at 5q31.1, regulates TERT expression and is the third TGCT-associated locus implicated in telomerase regulation 10 .

Nazneen Rahman and colleagues identify inactivating germline mutations in the gene encoding the transcriptional repressor REST in familial and non-familial cases of Wilms tumor. The mutations cluster in the DNA-binding domain of REST and compromise REST transcriptional repression. Wilms tumor is the most common childhood renal cancer 1 . To identify mutations that predispose to Wilms tumor, we are conducting exome sequencing studies. Here we describe 11 different inactivating mutations in the REST gene (encoding RE1-silencing transcription factor) in four familial Wilms tumor pedigrees and nine non-familial cases. Notably, no similar mutations were identified in the ICR1000 control series 2 (13/558 versus 0/993; P < 0.0001) or in the ExAC series (13/558 versus 0/61,312; P < 0.0001). We identified a second mutational event in two tumors, suggesting that REST may act as a tumor-suppressor gene in Wilms tumor pathogenesis. REST is a zinc-finger transcription factor that functions in cellular differentiation and embryonic development 3 , 4 . Notably, ten of 11 mutations clustered within the portion of REST encoding the DNA-binding domain, and functional analyses showed that these mutations compromise REST transcriptional repression. These data establish REST as a Wilms tumor predisposition gene accounting for ∼2% of Wilms tumor.

Background: Targeted next generation sequencing (NGS) panels are increasingly being used in clinical genomics to increase capacity, throughput and affordability of gene testing. Identifying whole exon deletions or duplications (termed exon copy number variants, ‘exon CNVs’) in exon-targeted NGS panels has proved challenging, particularly for single exon CNVs.  Methods: We developed a tool for the D etection of E xon Co py N umber variants (DECoN), which is optimised for analysis of exon-targeted NGS panels in clinical settings. We evaluated DECoN performance using 96 samples with independently validated exon CNV data. We performed simulations to evaluate DECoN detection performance of single exon CNVs and evaluate performance using different coverage levels and sample numbers. Finally, we implemented DECoN in a clinical laboratory that tests BRCA1 and BRCA2 with the TruSight Cancer Panel (TSCP). We used DECoN to analyse 1,919 samples, validating exon CNV detections by multiplex ligation-dependent probe amplification (MLPA).  Results: In the evaluation set, DECoN achieved 100% sensitivity and 99% specificity for BRCA exon CNVs, including identification of 8 single exon CNVs. DECoN also identified 14/15 exon CNVs in 8 other genes. Simulations of all possible BRCA single exon CNVs gave a mean sensitivity of 98% for deletions and 95% for duplications. DECoN performance remained excellent with different levels of coverage and sample numbers; sensitivity and specificity was >98% with the typical NGS run parameters. In the clinical pipeline, DECoN automatically analyses pools of 48 samples at a time, taking 24 minutes per pool, on average. DECoN detected 24 BRCA exon CNVs, of which 23 were confirmed by MLPA, giving a false discovery rate of 4%. Specificity was 99.7%.  Conclusions: DECoN is a fast, accurate, exon CNV detection tool readily implementable in research and clinical NGS pipelines. It has high sensitivity and specificity and acceptable false discovery rate. DECoN is freely available at www.icr.ac.uk/decon .

Background Next-generation sequencing (NGS) offers unprecedented opportunities to expand clinical genomics. It also presents challenges with respect to integration with data from other sequencing methods and historical data. Provision of consistent, clinically applicable variant annotation of NGS data has proved difficult, particularly of indels, an important variant class in clinical genomics. Annotation in relation to a reference genome sequence, the DNA strand of coding transcripts and potential alternative variant representations has not been well addressed. Here we present tools that address these challenges to provide rapid, standardized, clinically appropriate annotation of NGS data in line with existing clinical standards. Methods We developed a clinical sequencing nomenclature (CSN), a fixed variant annotation consistent with the principles of the Human Genome Variation Society (HGVS) guidelines, optimized for automated variant annotation of NGS data. To deliver high-throughput CSN annotation we created CAVA (Clinical Annotation of VAriants), a fast, lightweight tool designed for easy incorporation into NGS pipelines. CAVA allows transcript specification, appropriately accommodates the strand of a gene transcript and flags variants with alternative annotations to facilitate clinical interpretation and comparison with other datasets. We evaluated CAVA in exome data and a clinical BRCA1 / BRCA2 gene testing pipeline. Results CAVA generated CSN calls for 10,313,034 variants in the ExAC database in 13.44 hours, and annotated the ICR1000 exome series in 6.5 hours. Evaluation of 731 different indels from a single individual revealed 92 % had alternative representations in left aligned and right aligned data. Annotation of left aligned data, as performed by many annotation tools, would thus give clinically discrepant annotation for the 339 (46 %) indels in genes transcribed from the forward DNA strand. By contrast, CAVA provides the correct clinical annotation for all indels. CAVA also flagged the 370 indels with alternative representations of a different functional class, which may profoundly influence clinical interpretation. CAVA annotation of 50 BRCA1 / BRCA2 gene mutations from a clinical pipeline gave 100 % concordance with Sanger data; only 8/25 BRCA2 mutations were correctly clinically annotated by other tools. Conclusions CAVA is a freely available tool that provides rapid, robust, high-throughput clinical annotation of NGS data, using a standardized clinical sequencing nomenclature.

To enhance knowledge of gene variation in outbred populations, and to provide a dataset with utility in research and clinical genomics, we performed exome sequencing of 1,000 UK individuals from the general population and applied a high-quality analysis pipeline that includes high sensitivity and specificity for indel detection. Each UK individual has, on average, 21,978 gene variants including 160 rare (0.1%) variants not present in any other individual in the series. These data provide a baseline expectation for gene variation in an outbred population. Summary data of all 295,391 variants we detected are included here and the individual exome sequences are available from the European Genome-phenome Archive as the ICR1000 UK exome series. Furthermore, samples and other phenotype and experimental data for these individuals are obtainable through application to the 1958 Birth Cohort committee.

Next generation sequencing (NGS) is routinely used in clinical genetic testing. Quality management of NGS testing is essential to ensure performance is consistently and rigorously evaluated. Three primary metrics are used in NGS quality evaluation: depth of coverage, base quality and mapping quality. To provide consistency and transparency in the utilisation of these metrics we present the Quality Sequencing Minimum (QSM). The QSM defines the minimum quality requirement a laboratory has selected for depth of coverage (C), base quality (B) and mapping quality (M) and can be applied per base, exon, gene or other genomic region, as appropriate. The QSM format is CX_BY(P Y )_MZ(P Z ). X is the parameter threshold for C, Y the parameter threshold for B, P Y the percentage of reads that must reach Y, Z the parameter threshold for M, P Z the percentage of reads that must reach Z. The data underlying the QSM is in the BAM file, so a QSM can be easily and automatically calculated in any NGS pipeline. We used the QSM to optimise cancer predisposition gene testing using the TruSight Cancer Panel (TSCP). We set the QSM as C50_B10(85)_M20(95). Test regions falling below the QSM were automatically flagged for review, with 100/1471 test regions QSM-flagged in multiple individuals. Supplementing these regions with 132 additional probes improved performance in 85/100. We also used the QSM to optimise testing of genes with pseudogenes such as PTEN and PMS2 . In TSCP data from 960 individuals the median number of regions that passed QSM per sample was 1429 (97%).  Importantly, the QSM can be used at an individual report level to provide succinct, comprehensive quality assurance information about individual test performance. We believe many laboratories would find the QSM useful. Furthermore, widespread adoption of the QSM would facilitate consistent, transparent reporting of genetic test performance by different laboratories.

Advances in DNA sequencing have made genetic testing fast and affordable, but limitations of testing processes are impeding realisation of patient benefits. Ovarian cancer exemplifies the potential value of genetic testing and the shortcomings of current pathways to access testing. Approximately 15% of ovarian cancer patients have a germline BRCA1 or BRCA2 mutation which has substantial implications for their personal management and that of their relatives. Unfortunately, in most countries, routine implementation of BRCA testing for ovarian cancer patients has been inconsistent and largely unsuccessful. We developed a rapid, robust, mainstream genetic testing pathway in which testing is undertaken by the trained cancer team with cascade testing to relatives performed by the genetics team. 207 women with ovarian cancer were offered testing through the mainstream pathway. All accepted. 33 (16%) had a BRCA mutation. The result informed management of 79% (121/154) women with active disease. Patient and clinician feedback was very positive. The pathway offers a 4-fold reduction in time and 13-fold reduction in resource requirement compared to the conventional testing pathway. The mainstream genetic testing pathway we present is effective, efficient and patient-centred. It can deliver rapid, robust, large-scale, cost-effective genetic testing of BRCA1 and BRCA2 and may serve as an exemplar for other genes and other diseases.

Quality assurance and quality control are essential for robust next generation sequencing (NGS). Here we present CoverView, a fast, flexible, user-friendly quality evaluation tool for NGS data. CoverView processes mapped sequencing reads and user-specified regions to report depth of coverage, base and mapping quality metrics with increasing levels of detail from a chromosome-level summary to per-base profiles. CoverView can flag regions that do not fulfil user-specified quality requirements, allowing suboptimal data to be systematically and automatically presented for review. It also provides an interactive graphical user interface (GUI) that can be opened in a web browser and allows intuitive exploration of results. We have integrated CoverView into our accredited clinical cancer predisposition gene testing laboratory that uses the TruSight Cancer Panel (TSCP). CoverView has been invaluable for optimisation and quality control of our testing pipeline, providing transparent, consistent quality metric information and automatic flagging of regions that fall below quality thresholds. We demonstrate this utility with TSCP data from the Genome in a Bottle reference sample, which CoverView analysed in 13 seconds. CoverView uses data routinely generated by NGS pipelines, reads standard input formats, and rapidly creates easy-to-parse output text (.txt) files that are customised by a simple configuration file. CoverView can therefore be easily integrated into any NGS pipeline. CoverView and detailed documentation for its use are freely available at github.com/RahmanTeamDevelopment/CoverView/releases and www.icr.ac.uk/CoverView