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BackgroundReanalysis of exome/genome data improves diagnostic yield. However, the value of reanalysis of clinical array comparative genomic hybridisation (aCGH) data has never been investigated. Case-by-case reanalysis can be challenging in busy diagnostic laboratories.Methods and resultsWe harmonised historical postnatal clinical aCGH results from ~16 000 patients tested via our diagnostic laboratory over ~7 years with current clinical guidance. This led to identification of 37 009 copy number losses (CNLs) including 33 857 benign, 2173 of uncertain significance and 979 pathogenic. We found benign CNLs to be significantly less likely to encompass haploinsufficient genes compared with the pathogenic or CNLs of uncertain significance in our database. Based on this observation, we developed a reanalysis pipeline using up-to-date disease association data and haploinsufficiency scores and shortlisted 207 CNLs of uncertain significance encompassing at least one autosomal dominant disease-gene associated with haploinsufficiency or loss-of-function mechanism. Clinical scientist reviews led to reclassification of 15 CNLs of uncertain significance as pathogenic or likely pathogenic. This was ~0.7% of the starting cohort of 2173 CNLs of uncertain significance and 7.2% of 207 shortlisted CNLs. The reclassified CNLs included first cases of CNV-mediated disease for some genes where all previously described cases involved only point variants. Interestingly, some CNLs could not be reclassified because the phenotypes of patients with CNLs seemed distinct from the known clinical features resulting from point variants, thus raising questions about accepted underlying disease mechanisms.ConclusionsReanalysis of clinical aCGH data increases diagnostic yield.

Around 60% of individuals with neurodevelopmental disorders (NDD) remain undiagnosed after comprehensive genetic testing, primarily of protein-coding genes1. Large genome-sequenced cohorts are improving our ability to discover new diagnoses in the non-coding genome. Here we identify the non-coding RNA RNU4-2as a syndromic NDD gene. RNU4-2encodes the U4 small nuclear RNA (snRNA), which is a critical component of the U4/U6.U5 tri-snRNP complex of the major spliceosome2. We identify an 18 base pair region of RNU4-2mapping to two structural elements in the U4/U6 snRNA duplex (the T-loop and stem III) that is severely depleted of variation in the general population, but in which we identify heterozygous variants in 115 individuals with NDD. Most individuals (77.4%) have the same highly recurrent single base insertion (n.64_65insT). In 54 individuals in whom it could be determined, the de novo variants were all on the maternal allele. We demonstrate that RNU4-2is highly expressed in the developing human brain, in contrast to RNU4-1and other U4 homologues. Using RNA sequencing, we show how 5′ splice-site use is systematically disrupted in individuals with RNU4-2variants, consistent with the known role of this region during spliceosome activation. Finally, we estimate that variants in this 18 base pair region explain 0.4% of individuals with NDD. This work underscores the importance of non-coding genes in rare disorders and will provide a diagnosis to thousands of individuals with NDD worldwide.

PurposeTo investigate frequency of germline pathogenic variants (PVs) in women with ductal carcinoma in situ (DCIS) and grade 1 invasive breast cancer (G1BC).MethodsWe undertook BRCA1/2analysis in 311 women with DCIS and 392 with G1BC and extended panel testing (non-BRCA1/2) in 176/311 with DCIS and 156/392 with G1BC. We investigated PV detection by age at diagnosis, Manchester Score (MS), DCIS grade and receptor status.Results30/311 (9.6%) with DCIS and 16/392 with G1BC (4.1%) had a BRCA1/2PV (p=0.003), and 24/176-(13.6%) and 7/156-(4.5%), respectively, a non-BRCA1/2PV (p=0.004). Increasing MS was associated with increased likelihood of BRCA1/2PV in both DCIS and G1BC, although the 10% threshold was not predictive for G1GB. 13/32 (40.6%) DCIS and 0/17 with G1BC <40 years had a non-BRCA1/2 PV (p<0.001). 0/16 DCIS G1 had a PV. For G2 and G3 DCIS, PV rates were 10/98 (BRCA1/2) and 9/90 (non-BRCA1/2), and 8/47 (BRCA1/2) and 8/45 (non-BRCA1/2), respectively. 6/9 BRCA1and 3/26 BRCA2-associated DCIS were oestrogen receptor negative-(p=0.003). G1BC population testing showed no increased PV rate (OR=1.16, 95% CI 0.28 to 4.80).ConclusionDCIS is more likely to be associated with both BRCA1/2and non-BRCA1/2PVs than G1BC. Extended panel testing ought to be offered in young-onset DCIS where PV detection rates are highest.

BackgroundGenomic variant prioritisation is one of the most significant bottlenecks to mainstream genomic testing in healthcare. Tools to improve precision while ensuring high recall are critical to successful mainstream clinical genomic testing, in particular for whole genome sequencing where millions of variants must be considered for each patient.MethodsWe developed EyeG2P, a publicly available database and web application using the Ensembl Variant Effect Predictor. EyeG2P is tailored for efficient variant prioritisation for individuals with inherited ophthalmic conditions. We assessed the sensitivity of EyeG2P in 1234 individuals with a broad range of eye conditions who had previously received a confirmed molecular diagnosis through routine genomic diagnostic approaches. For a prospective cohort of 83 individuals, we assessed the precision of EyeG2P in comparison with routine diagnostic approaches. For 10 additional individuals, we assessed the utility of EyeG2P for whole genome analysis.ResultsEyeG2P had 99.5% sensitivity for genomic variants previously identified as clinically relevant through routine diagnostic analysis (n=1234 individuals). Prospectively, EyeG2P enabled a significant increase in precision (35% on average) in comparison with routine testing strategies (p<0.001). We demonstrate that incorporation of EyeG2P into whole genome sequencing analysis strategies can reduce the number of variants for analysis to six variants, on average, while maintaining high diagnostic yield.ConclusionAutomated filtering of genomic variants through EyeG2P can increase the efficiency of diagnostic testing for individuals with a broad range of inherited ophthalmic disorders.

BackgroundInherited retinal diseases (IRDs) can be caused by variants in >270 genes. The Bardet-Biedl syndrome 1 (BBS1) gene is one of these genes and may be associated with syndromic and non-syndromic autosomal recessive retinitis pigmentosa (RP). Here, we identified a branchpoint variant in BBS1and assessed its pathogenicity by in vitro functional analysis.MethodsWhole genome sequencing was performed for three unrelated monoallelic BBS1cases with non-syndromic RP. A fourth case received MGCM 105 gene panel analysis. Functional analysis using a midigene splice assay was performed for the putative pathogenic branchpoint variant in BBS1. After confirmation of its pathogenicity, patients were clinically re-evaluated, including assessment of non-ocular features of Bardet-Biedl syndrome.ResultsClinical assessments of probands showed that all individuals displayed non-syndromic RP with macular involvement. Through detailed variant analysis and prioritisation, two pathogenic variants in BBS1, the most common missense variant, c.1169T>G (p.(Met390Arg)), and a branchpoint variant, c.592-21A>T, were identified. Segregation analysis confirmed that in all families, probands were compound heterozygous for c.1169T>G and c.592-21A>T. Functional analysis of the branchpoint variant revealed a complex splicing defect including exon 8 and exon 7/8 skipping, and partial in-frame deletion of exon 8.ConclusionA putative severe branchpoint variant in BBS1, together with a mild missense variant, underlies non-syndromic RP in four unrelated individuals. To our knowledge, this is the first report of a pathogenic branchpoint variant in IRDs that results in a complex splice defect. In addition, this research highlights the importance of the analysis of non-coding regions in order to provide a conclusive molecular diagnosis.

PurposeThe increased adoption of genomic strategies in the clinic makes it imperative for diagnostic laboratories to improve the efficiency of variant interpretation. Clinical exome sequencing (CES) is becoming a valuable diagnostic tool, capable of meeting the diagnostic demand imposed by the vast array of different rare monogenic disorders. We have assessed a clinician-led and phenotype-based approach for virtual gene panel generation for analysis of targeted CES in patients with rare disease in a single institution.MethodsRetrospective survey of 400 consecutive cases presumed by clinicians to have rare monogenic disorders, referred on singleton basis for targeted CES. We evaluated diagnostic yield and variant workload to characterise the usefulness of a clinician-led approach for generation of virtual gene panels that can incorporate up to three different phenotype-driven gene selection methods.ResultsAbnormalities of the nervous system (54.5%), including intellectual disability, head and neck (19%), skeletal system (16%), ear (15%) and eye (15%) were the most common clinical features reported in referrals. Combined phenotype-driven strategies for virtual gene panel generation were used in 57% of cases. On average, 7.3 variants (median=5) per case were retained for clinical interpretation. The overall diagnostic rate of proband-only CES using personalised phenotype-driven virtual gene panels was 24%.ConclusionsOur results show that personalised virtual gene panels are a cost-effective approach for variant analysis of CES, maintaining diagnostic yield and optimising the use of resources for clinical genomic sequencing in the clinic.

BackgroundImproving the clinical interpretation of missense variants can increase the diagnostic yield of genomic testing and lead to personalised management strategies. Currently, due to the imprecision of bioinformatic tools that aim to predict variant pathogenicity, their role in clinical guidelines remains limited. There is a clear need for more accurate prediction algorithms and this study aims to improve performance by harnessing structural biology insights. The focus of this work is missense variants in a subset of genes associated with X linked disorders.MethodsWe have developed a protein-specific variant interpreter(ProSper) that combines genetic and protein structural data. This algorithm predicts missense variant pathogenicity by applying machine learning approaches to the sequence and structural characteristics of variants.ResultsProSper outperformed seven previously described tools, including meta-predictors, in correctly evaluating whether or not variants are pathogenic; this was the case for 11 of the 21 genes associated with X linked disorders that met the inclusion criteria for this study. We also determined gene-specific pathogenicity thresholds that improved the performance of VEST4, REVEL and ClinPred, the three best-performing tools out of the seven that were evaluated; this was the case in 11, 11 and 12 different genes, respectively.ConclusionProSper can form the basis of a molecule-specific prediction tool that can be implemented into diagnostic strategies. It can allow the accurate prioritisation of missense variants associated with X linked disorders, aiding precise and timely diagnosis. In addition, we demonstrate that gene-specific pathogenicity thresholds for a range of missense prioritisation tools can lead to an increase in prediction accuracy.

BackgroundWhile the likelihood of identifying constitutional breast cancer-associated BRCA1, BRCA2and TP53pathogenic variants (PVs) increases with earlier diagnosis age, little is known about the correlation with age at diagnosis in other predisposition genes. Here, we assessed the contribution of known breast cancer-associated genes to very early onset disease.MethodsSequencing of BRCA1, BRCA2, TP53and CHEK2c.1100delC was undertaken in women with breast cancer diagnosed ≤30 years. Those testing negative were screened for PVs in a minimum of eight additional breast cancer-associated genes. Rates of PVs were compared with cases ≤30 years from the Prospective study of Outcomes in Sporadic vs Hereditary breast cancer (POSH) study.ResultsTesting 379 women with breast cancer aged ≤30 years identified 75 PVs (19.7%) in BRCA1, 35 (9.2%) in BRCA2, 22 (5.8%) in TP53and 2 (0.5%) CHEK2c.1100delC. Extended screening of 184 PV negative women only identified eight additional actionable PVs. BRCA1/2PVs were more common in women aged 26–30 years than in younger women (p=0.0083) although the younger age group had rates more similar to those in the POSH cohort. Out of 26 women with ductal carcinoma in situ(DCIS) alone, most were high-grade and 11/26 (42.3%) had a PV (TP53=6, BRCA2=2, BRCA1=2, PALB2=1). This PV yield is similar to the 61 (48.8%) BRCA1/2PVs identified in 125 women with triple-negative breast cancer. The POSH cohort specifically excluded pure DCIS which may explain lower TP53PV rates in this group (1.7%).ConclusionThe rates of BRCA1, BRCA2and TP53PVs are high in very early onset breast cancer, with limited benefit from testing of additional breast cancer-associated genes.

BackgroundPrimary ciliopathies represent a group of inherited disorders due to defects in the primary cilium, the ‘cell’s antenna’. The 100,000 Genomes Project was launched in 2012 by Genomics England (GEL), recruiting National Health Service (NHS) patients with eligible rare diseases and cancer. Sequence data were linked to Human Phenotype Ontology (HPO) terms entered by recruiting clinicians.MethodsEighty-three prescreened probands were recruited to the 100,000 Genomes Project suspected to have congenital malformations caused by ciliopathies in the following disease categories: Bardet-Biedl syndrome (n=45), Joubert syndrome (n=14) and ‘Rare Multisystem Ciliopathy Disorders’ (n=24). We implemented a bespoke variant filtering and analysis strategy to improve molecular diagnostic rates for these participants.ResultsWe determined a research molecular diagnosis for n=43/83 (51.8%) probands. This is 19.3% higher than previously reported by GEL (n=27/83 (32.5%)). A high proportion of diagnoses are due to variants in non-ciliopathy disease genes (n=19/43, 44.2%) which may reflect difficulties in clinical recognition of ciliopathies. n=11/83 probands (13.3%) had at least one causative variant outside the tiers 1 and 2 variant prioritisation categories (GEL’s automated triaging procedure), which would not be reviewed in standard 100,000 Genomes Project diagnostic strategies. These include four structural variants and three predicted to cause non-canonical splicing defects. Two unrelated participants have biallelic likely pathogenic variants in LRRC45, a putative novel ciliopathy disease gene.ConclusionThese data illustrate the power of linking large-scale genome sequence to phenotype information. They demonstrate the value of research collaborations in order to maximise interpretation of genomic data.

To investigate the contribution of PALB2pathogenic gene variants (PGVs, PALB2_PGV) and the CHEK2c.1100delC (CHEK2_1100delC) PGV to familial breast and ovarian cancer, and PALB2_PGV associated breast cancer pathology.

Advances in DNA sequencing technologies have revolutionised rare disease diagnostics and have led to a dramatic increase in the volume of available genomic data. A key challenge that needs to be overcome to realise the full potential of these technologies is that of precisely predicting the effect of genetic variants on molecular and organismal phenotypes. Notably, despite recent progress, there is still a lack of robust in silico tools that accurately assign clinical significance to variants. Genetic alterations in the CACNA1Fgene are the commonest cause of X-linked incomplete Congenital Stationary Night Blindness (iCSNB), a condition associated with non-progressive visual impairment. We combined genetic and homology modelling data to produce CACNA1F-vp, an in silico model that differentiates disease-implicated from benign missense CACNA1Fchanges. CACNA1F-vp predicts variant effects on the structure of the CACNA1Fencoded protein (a calcium channel) using parameters based upon changes in amino acid properties; these include size, charge, hydrophobicity, and position. The model produces an overall score for each variant that can be used to predict its pathogenicity. CACNA1F-vp outperformed four other tools in identifying disease-implicated variants (area under receiver operating characteristic and precision recall curves = 0.84; Matthews correlation coefficient = 0.52) using a tenfold cross-validation technique. We consider this protein-specific model to be a robust stand-alone diagnostic classifier that could be replicated in other proteins and could enable precise and timely diagnosis.

Thirty percent of all inherited retinal disease (IRD) is accounted for by conditions with extra-ocular features. This study aimed to establish the genetic diagnostic pick-up rate for IRD patients with one or more extra-ocular features undergoing panel-based screening in a clinical setting. One hundred and six participants, tested on a gene panel which contained both isolated and syndromic IRD genes, were retrospectively ascertained from the Manchester Genomic Diagnostics Laboratory database spanning 6 years (2012–2017). Phenotypic features were extracted from the clinical notes and classified according to Human Phenotype Ontology; all identified genetic variants were interpreted in accordance to the American College of Medical Genetics and Genomics guidelines. Overall, 49% (n?=?52) of patients received a probable genetic diagnosis. A further 6% (n?=?6) had a single disease-associated variant in an autosomal recessive disease-relevant gene. Fifty-two percent (n?=?55) of patients had a clinical diagnosis at the time of testing. Of these, 71% (n?=?39) received a probable genetic diagnosis. By contrast, for those without a provisional clinical diagnosis (n?=?51), only 25% (n?=?13) received a probable genetic diagnosis. The clinical diagnosis of Usher (n?=?33) and Bardet–Biedl syndrome (n?=?10) was confirmed in 67% (n?=?22) and 80% (n?=?8), respectively. The testing diagnostic rate in patients with clinically diagnosed multisystemic IRD conditions was significantly higher than those without one (71% versus 25%; pvalue?

Purpose: A key property to consider in all genetic tests is clinical utility, the ability of the test to influence patient management and health outcomes. Here we assess the current clinical utility of genetic testing in diverse pediatric inherited eye disorders (IEDs). Methods: Two hundred one unrelated children (0–5 years old) with IEDs were ascertained through the database of the North West Genomic Laboratory Hub, Manchester, UK. The cohort was collected over a 7-year period (2011–2018) and included 74 children with bilateral cataracts, 8 with bilateral ectopia lentis, 28 with bilateral anterior segment dysgenesis, 32 with albinism, and 59 with inherited retinal disorders. All participants underwent panel-based genetic testing. Results: The diagnostic yield of genetic testing for the cohort was 64% (ranging from 39% to 91% depending on the condition). The test result led to altered management (including preventing additional investigations or resulting in the introduction of personalized surveillance measures) in 33% of probands (75% for ectopia lentis, 50% for cataracts, 33% for inherited retinal disorders, 7% for anterior segment dysgenesis, 3% for albinism). Conclusion: Genetic testing helped identify an etiological diagnosis in the majority of preschool children with IEDs. This prevented additional unnecessary testing and provided the opportunity for anticipatory guidance in significant subsets of patients.

A key property to consider in all genetic tests is clinical utility, the ability of the test to influence patient management and health outcomes. Here we assess the current clinical utility of genetic testing in diverse pediatric inherited eye disorders (IEDs).

BackgroundDiagnostic use of gene panel next-generation sequencing (NGS) techniques is commonplace for individuals with inherited retinal dystrophies (IRDs), a highly genetically heterogeneous group of disorders. However, these techniques have often failed to capture the complete spectrum of genomic variation causing IRD, including CNVs. This study assessed the applicability of introducing CNV surveillance into first-tier diagnostic gene panel NGS services for IRD.MethodsThree read-depth algorithms were applied to gene panel NGS data sets for 550 referred individuals, and informatics strategies used for quality assurance and CNV filtering. CNV events were confirmed and reported to referring clinicians through an accredited diagnostic laboratory.ResultsWe confirmed the presence of 33 deletions and 11 duplications, determining these findings to contribute to the confirmed or provisional molecular diagnosis of IRD for 25 individuals. We show that at least 7% of individuals referred for diagnostic testing for IRD have a CNV within genes relevant to their clinical diagnosis, and determined a positive predictive value of 79% for the employed CNV filtering techniques.ConclusionIncorporation of CNV analysis increases diagnostic yield of gene panel NGS diagnostic tests for IRD, increases clarity in diagnostic reporting and expands the spectrum of known disease-causing mutations.

Although a common cause of disease, copy number variants (CNVs) have not routinely been identified from next-generation sequencing (NGS) data in a clinical context. This study aimed to examine the sensitivity and specificity of a widely used software package, ExomeDepth, to identify CNVs from targeted NGS data sets. We benchmarked the accuracy of CNV detection using ExomeDepth v1.1.6 applied to targeted NGS data sets by comparison to CNV events detected through whole-genome sequencing for 25 individuals and determined the sensitivity and specificity of ExomeDepth applied to these targeted NGS data sets to be 100% and 99.8%, respectively. To define quality assurance metrics for CNV surveillance through ExomeDepth, we undertook simulation of single-exon (n=1000) and multiple-exon heterozygous deletion events (n=1749), determining a sensitivity of 97% (n=2749). We identified that the extent of sequencing coverage, the inter- and intra-sample variability in the depth of sequencing coverage and the composition of analysis regions are all important determinants of successful CNV surveillance through ExomeDepth. We then applied these quality assurance metrics during CNV surveillance for 140 individuals across 12 distinct clinical areas, encompassing over 500 potential rare disease diagnoses. All 140 individuals lacked molecular diagnoses after routine clinical NGS testing, and by application of ExomeDepth, we identified 17 CNVs contributing to the cause of a Mendelian disorder. Our findings support the integration of CNV detection using ExomeDepth v1.1.6 with routine targeted NGS diagnostic services for Mendelian disorders. Implementation of this strategy increases diagnostic yields and enhances clinical care.

Routine testing for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in health care workers (HCWs) is critical. Group testing strategies to increase capacity facilitate mass population testing but do not prioritize turnaround time, an important consideration for HCW screening. We propose a nonadaptive combinatorial (NAC) group testing strategy to increase throughput while facilitating rapid turnaround. NAC matrices were constructed for sample sizes of 700, 350, and 250. Matrix performance was tested by simulation under different SARS-CoV-2 prevalence scenarios of 0.1% to 10%. NAC matrices were compared versus Dorfman sequential (DS) group testing approaches. NAC matrices performed well at low prevalence levels, with an average of 97% of samples resolved after a single round of testing via the n= 700 matrix at a prevalence of 1%. In simulations of low to medium (0.1% to 3%) prevalence, all NAC matrices were superior to the DS strategy, measured by fewer repeated tests required. At very high prevalence levels (10%), the DS matrix was marginally superior, although both group testing approaches performed poorly at high prevalence levels. This strategy maximizes the proportion of samples resolved after a single round of testing, allowing prompt return of results to HCWs. This methodology may allow laboratories to adapt their testing scheme based on required throughput and the current population prevalence, facilitating a data-driven testing strategy.