Background: The majority of clinical genetic testing focuses almost exclusively on regions of the genome that directly encode proteins. The important role of variants in non-coding regions in penetrant disease is, however, increasingly being demonstrated, and the use of whole genome sequencing in clinical diagnostic settings is rising across a large range of genetic disorders. Despite this, there is no existing guidance on how current guidelines designed primarily for variants in protein-coding regions should be adapted for variants identified in other genomic contexts. Methods: We convened a panel of nine clinical and research scientists with wide-ranging expertise in clinical variant interpretation, with specific experience in variants within non-coding regions. This panel discussed and refined an initial draft of the guidelines which were then extensively tested and reviewed by external groups. Results: We discuss considerations specifically for variants in non-coding regions of the genome. We outline how to define candidate regulatory elements, highlight examples of mechanisms through which non-coding region variants can lead to penetrant monogenic disease, and outline how existing guidelines can be adapted for the interpretation of these variants. Conclusions: These recommendations aim to increase the number and range of non-coding region variants that can be clinically interpreted, which, together with a compatible phenotype, can lead to new diagnoses and catalyse the discovery of novel disease mechanisms.

Basement membranes (BMs) are ubiquitous extracellular matrices whose composition remains elusive, limiting our understanding of BM regulation and function. By developing a bioinformatic and in vivo discovery pipeline, we define a network of 222 human proteins and their animal orthologs localized to BMs. Network analysis and screening in C. elegans and zebrafish uncovered BM regulators, including ADAMTS, ROBO, and TGFβ. More than 100 BM network genes associate with human phenotypes, and by screening 63,039 genomes from families with rare disorders, we found loss-of-function variants in LAMA5 , MPZL2 , and MATN2 and show that they regulate BM composition and function. This cross-disciplinary study establishes the immense complexity of BMs and their impact on in human health.

To compare the efficacy of whole genome sequencing (WGS) with targeted next-generation sequencing (NGS) in the diagnosis of inherited retinal disease (IRD). Case series. A total of 562 patients diagnosed with IRD. We performed a direct comparative analysis of current molecular diagnostics with WGS. We retrospectively reviewed the findings from a diagnostic NGS DNA test for 562 patients with IRD. A subset of 46 of 562 patients (encompassing potential clinical outcomes of diagnostic analysis) also underwent WGS, and we compared mutation detection rates and molecular diagnostic yields. In addition, we compared the sensitivity and specificity of the 2 techniques to identify known single nucleotide variants (SNVs) using 6 control samples with publically available genotype data. Diagnostic yield of genomic testing. Across known disease-causing genes, targeted NGS and WGS achieved similar levels of sensitivity and specificity for SNV detection. However, WGS also identified 14 clinically relevant genetic variants through WGS that had not been identified by NGS diagnostic testing for the 46 individuals with IRD. These variants included large deletions and variants in noncoding regions of the genome. Identification of these variants confirmed a molecular diagnosis of IRD for 11 of the 33 individuals referred for WGS who had not obtained a molecular diagnosis through targeted NGS testing. Weighted estimates, accounting for population structure, suggest that WGS methods could result in an overall 29% (95% confidence interval, 15-45) uplift in diagnostic yield. We show that WGS methods can detect disease-causing genetic variants missed by current NGS diagnostic methodologies for IRD and thereby demonstrate the clinical utility and additional value of WGS.

Background Inherited retinal diseases (IRDs) are a clinically and genetically heterogeneous set of disorders, for which diagnostic second-generation sequencing (next-generation sequencing, NGS) services have been developed worldwide. Methods We present the molecular findings of 537 individuals referred to a 105-gene diagnostic NGS test for IRDs. We assess the diagnostic yield, the spectrum of clinical referrals, the variant analysis burden and the genetic heterogeneity of IRD. We retrospectively analyse disease-causing variants, including an assessment of variant frequency in Exome Aggregation Consortium (ExAC). Results Individuals were referred from 10 clinically distinct classifications of IRD. Of the 4542 variants clinically analysed, we have reported 402 mutations as a cause or a potential cause of disease in 62 of the 105 genes surveyed. These variants account or likely account for the clinical diagnosis of IRD in 51% of the 537 referred individuals. 144 potentially disease-causing mutations were identified as novel at the time of clinical analysis, and we further demonstrate the segregation of known disease-causing variants among individuals with IRD. We show that clinically analysed variants indicated as rare in dbSNP and the Exome Variant Server remain rare in ExAC, and that genes discovered as a cause of IRD in the post-NGS era are rare causes of IRD in a population of clinically surveyed individuals. Conclusions Our findings illustrate the continued powerful utility of custom-gene panel diagnostic NGS tests for IRD in the clinic, but suggest clear future avenues for increasing diagnostic yields.

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.

Background Diagnostic 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. Methods Three 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. Results We 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. Conclusion Incorporation 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.