Systematic reanalysis of genomic data improves quality of variant interpretation

Systematic reanalysis of genomic data by diagnostic laboratories: a scoping review of ethical, economic, legal and (psycho)social implications

With the introduction of Next Generation Sequencing (NGS) techniques increasing numbers of disease-associated variants are being identified. This ongoing progress might lead to diagnoses in formerly undiagnosed patients and novel insights in already solved cases. Therefore, many studies suggest introducing systematic reanalysis of NGS data in routine diagnostics. Introduction will, however, also have ethical, economic, legal and (psycho)social (ELSI) implications that Genetic Health Professionals (GHPs) from laboratories should consider before possible implementation of systematic reanalysis. To get a first impression we performed a scoping literature review. Our findings show that for the vast majority of included articles ELSI aspects were not mentioned as such. However, often these issues were raised implicitly. In total, we identified nine ELSI aspects, such as (perceived) professional responsibilities, implications for consent and cost-effectiveness. The identified ELSI aspects brought forward necessary trade-offs for GHPs to consciously take into account when considering responsible implementation of systematic reanalysis of NGS data in routine diagnostics, balancing the various strains on their laboratories and personnel while creating optimal results for new and former patients. Some important aspects are not well explored yet. For example, our study shows GHPs see the values of systematic reanalysis but also experience barriers, often mentioned as being practical or financial only, but in fact also being ethical or psychosocial. Engagement of these GHPs in further research on ELSI aspects is important for sustainable implementation.

Systematic reanalysis of copy number losses of uncertain clinical significance

BACKGROUND

Reanalysis 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 RESULTS

We 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.

CONCLUSIONS

Reanalysis of clinical aCGH data increases diagnostic yield.

Slice Testing-Considerations from Ordering to Reporting: A Joint Report of the Association for Molecular Pathology, College of American Pathologists, and National Society of Genetic Counselors

As the number of genes associated with various germline disorders continues to grow, it is becoming more difficult for clinical laboratories to maintain separate assays for interrogating disease-focused gene panels. One solution to this challenge is termed slice testing, where capture backbone is used to analyze data specific to a set of genes, and for this article, we will focus on exome. A key advantage to this strategy is greater flexibility by adding genes as they become associated with disease or the ability to accommodate specific provider requests. Here, we provide expert consensus recommendations and results from an Association for Molecular Pathology-sponsored survey of clinical laboratories performing exome sequencing to compare a slice testing approach with traditional static gene panels and comprehensive exome analysis. We explore specific considerations for slices, including gene selection, analytic performance, coverage, quality, and interpretation. Our goal is to provide comprehensive guidance for clinical laboratories interested in designing and using slice tests as a diagnostic.

Is there a duty to routinely reinterpret genomic variant classifications?

Multiple studies show that periodic reanalysis of genomic test results held by clinical laboratories delivers significant increases in overall diagnostic yield. However, while there is a widespread consensus that implementing routine reanalysis procedures is highly desirable, there is an equally widespread understanding that routine reanalysis of individual patient results is not presently feasible to perform for all patients. Instead, researchers, geneticists and ethicists are beginning to turn their attention to one part of reanalysis-reinterpretation of previously classified variants-as a means of achieving similar ends to large-scale individual reanalysis but in a more sustainable manner. This has led some to ask whether the responsible implementation of genomics in healthcare requires that diagnostic laboratories routinely reinterpret their genomic variant classifications and reissue patient reports in the case of materially relevant changes. In this paper, we set out the nature and scope of any such obligation, and analyse some of the main ethical considerations pertaining to a putative duty to reinterpret. We discern and assess three potential outcomes of reinterpretation-upgrades, downgrades and regrades-in light of ongoing duties of care, systemic error risks and diagnostic equity. We argue against the existence of any general duty to reinterpret genomic variant classifications, yet we contend that a suitably restricted duty to reinterpret ought to be recognised, and that the responsible implementation of genomics into healthcare must take this into account.

Sync for Genes Phase 5: Computable artifacts for sharing dynamically annotated FHIR-formatted genomic variants

Introduction

Variant annotation is a critical component in next-generation sequencing, enabling a sequencing lab to comb through a sea of variants in order to hone in on those likely to be most significant, and providing clinicians with necessary context for decision-making. But with the rapid evolution of genomics knowledge, reported annotations can quickly become out-of-date. Under the ONC Sync for Genes program, our team sought to standardize the sharing of dynamically annotated variants (e.g., variants annotated on demand, based on current knowledge). The computable biomedical knowledge artifacts that were developed enable a clinical decision support (CDS) application to surface up-to-date annotations to clinicians.

Methods

The work reported in this article relies on the Health Level 7 Fast Healthcare Interoperability Resources (FHIR) Genomics and Global Alliance for Genomics and Health (GA4GH) Variant Annotation (VA) standards. We developed a CDS pipeline that dynamically annotates patient's variants through an intersection with current knowledge and serves up the FHIR-encoded variants and annotations (diagnostic and therapeutic implications, molecular consequences, population allele frequencies) via FHIR Genomics Operations. ClinVar, CIViC, and PharmGKB were used as knowledge sources, encoded as per the GA4GH VA specification.

Results

Primary public artifacts from this project include a GitHub repository with all source code, a Swagger interface that allows anyone to visualize and interact with the code using only a web browser, and a backend database where all (synthetic and anonymized) patient data and knowledge are housed.

Conclusions

We found that variant annotation varies in complexity based on the variant type, and that various bioinformatics strategies can greatly improve automated annotation fidelity. More importantly, we demonstrated the feasibility of an ecosystem where genomic knowledge bases have standardized knowledge (e.g., based on the GA4GH VA spec), and CDS applications can dynamically leverage that knowledge to provide real-time decision support, based on current knowledge, to clinicians at the point of care.

A holistic approach to maximise diagnostic output in trio exome sequencing

Introduction

Rare genetic diseases are a major cause for severe illness in children. Whole exome sequencing (WES) is a powerful tool for identifying genetic causes of rare diseases. For a better and faster assessment of the vast number of variants that are identified in the index patient in WES, parental sequencing can be applied ("trio WES").

Methods

We assessed the diagnostic rate of routine trio WES including analysis of copy number variants in 224 pediatric patients during an evaluation period of three years.

Results

Trio WES provided a diagnosis in 67 (30%) of all 224 analysed children. The turnaround time of trio WES analysis has been reduced significantly from 41 days in 2019 to 23 days in 2021. Copy number variants could be identified to be causative in 10 cases (4.5%), underlying the importance of copy number variant analysis. Variants in three genes which were previously not associated with a clinical condition (GAD1, TMEM222 and ZNFX1) were identified using the matching tool GeneMatcher and were part of the first description of a new syndrome.

Discussion

Trio WES has proven to have a high diagnostic yield and to shorten the process of identifying the correct diagnosis in paediatric patients. Re-evaluation of all 224 trio WES 1-3 years after initial analysis did not establish new diagnoses. Initiating (trio) WES as a first-tier diagnostics including copy number variant detection should be considered as early as possible, especially for children treated in ICU, if a monogenetic disease is suspected.

A concurrent dual analysis of genomic data augments diagnoses: Experiences of 2 clinical sites in the Undiagnosed Diseases Network

PURPOSE

Next-generation sequencing (NGS) has revolutionized the diagnostic process for rare/ultrarare conditions. However, diagnosis rates differ between analytical pipelines. In the National Institutes of Health-Undiagnosed Diseases Network (UDN) study, each individual's NGS data are concurrently analyzed by the UDN sequencing core laboratory and the clinical sites. We examined the outcomes of this practice.

METHODS

A retrospective review was performed at 2 UDN clinical sites to compare the variants and diagnoses/candidate genes identified with the dual analyses of the NGS data.

RESULTS

In total, 95 individuals had 100 diagnoses/candidate genes. There was 59% concordance between the UDN sequencing core laboratories and the clinical sites in identifying diagnoses/candidate genes. The core laboratory provided more diagnoses, whereas the clinical sites prioritized more research variants/candidate genes (P < .001). The clinical sites solely identified 15% of the diagnoses/candidate genes. The differences between the 2 pipelines were more often because of variant prioritization disparities than variant detection.

CONCLUSION

The unique dual analysis of NGS data in the UDN synergistically enhances outcomes. The core laboratory provided a clinical analysis with more diagnoses and the clinical sites prioritized more research variants/candidate genes. Implementing such concurrent dual analyses in other genomic research studies and clinical settings can improve both variant detection and prioritization.

Merged Testing for Colorectal Cancer Syndromes and Re‐evaluation of Genetic Variants Improve Diagnostic Yield: results from a nation‐wide prospective cohort

Approximately 5% of patients with colorectal cancer (CRC) have a Mendelian predisposition for the disease. Identification of the disease‐causing genetic variant enables carrier testing and tailored cancer prevention within affected families. To determine the panorama and genetic variation of Mendelian CRC syndromes among referrals at the cancer genetics clinics in Sweden, 850 patients clinically selected for CRC genetic investigation were included in a prospective study that tested for all major hereditary polyposis and nonpolyposis CRC conditions. Genetically defined syndromes were diagnosed in 11% of the patients. Lynch syndrome was predominant (n = 73) followed by familial adenomatous polyposis (n = 12) and MUTYH‐associated polyposis (n = 8); the latter of which two patients presented with CRC before polyposis was evident. One patient with a history of adolescent‐onset CRC and polyposis had biallelic disease‐causing variants diagnostic for constitutional mismatch repair deficiency syndrome. Post‐study review of detected variants of unknown clinical significance (n = 129) resulted in the reclassification of variants as likely benign (n = 59) or as diagnostic for Lynch syndrome (n = 2). Our results reveal the panorama of Mendelian CRC syndromes at the cancer genetics clinics in Sweden and show that unified testing for polyposis and nonpolyposis CRC conditions as well as regular reexamination of sequence data improve the diagnostic yield.

Re-analysis of genomic data: An overview of the mechanisms and complexities of clinical adoption

Re-analyzing genomic information from a patient suspected of having an underlying genetic condition can improve the diagnostic yield of sequencing tests, potentially providing significant benefits to the patient and to the health care system. Although a significant number of studies have shown the clinical potential of re-analysis, less work has been performed to characterize the mechanisms responsible for driving the increases in diagnostic yield. Complexities surrounding re-analysis have also emerged. The terminology itself represents a challenge because "re-analysis" can refer to a range of different concepts. Other challenges include the increased workload that re-analysis demands of curators, adequate reimbursement pathways for clinical and diagnostic services, and the development of systems to handle large volumes of data. Re-analysis also raises ethical implications for patients and families, most notably when re-classification of a variant alters diagnosis, treatment, and prognosis. This review highlights the possibilities and complexities associated with the re-analysis of existing clinical genomic data. We propose a terminology that builds on the foundation presented in a recent statement from the American College of Medical Genetics and Genomics and describes each re-analysis process. We identify mechanisms for increasing diagnostic yield and provide perspectives on the range of challenges that must be addressed by health care systems and individual patients.

Massive parallel sequencing in individuals with multiple primary tumours reveals the benefit of re-analysis

Multiple primary cancers, defined as three or more primary tumours, are rare, and there are few genetic studies concerning them. There is a need for increased knowledge on the heritability of multiple primary cancers and genotype-phenotype correlations. We have performed whole-genome/exome sequencing (WGS/WES) in ten individuals with three or more primary tumours, with no previous findings on standard clinical genetic investigations. In one individual with a clinical diagnosis of MEN1, a likely pathogenic cryptic splice site variant was detected in the MEN1 gene. The variant (c.654C > A) is synonymous but we showed in a cDNA analysis that it affects splicing and leads to a frameshift, with the theoretical new amino acid sequence p.(Gly219Glufs*13). In one individual with metachronous colorectal cancers, ovarian cancer, endometrial cancer and chronic lymphocytic leukaemia, we found a likely pathogenic variant in the MLH1 gene (c.27G > A), and two risk factor variants in the genes CHEK2 and HOXB13. The MLH1 variant is synonymous but has previously been shown to be associated to constitutional low-grade hypermethylation of the MLH1 promoter, and segregates with disease in families with colorectal and endometrial cancer. No pathogenic single nucleotide or structural variants were detected in the remaining eight individuals in the study. The pathogenic variants found by WGS/WES were in genes already sequenced by Sanger sequencing and WES in the clinic, without any findings. We conclude that, in individuals with an unequivocal clinical diagnosis of a specific hereditary cancer syndrome, where standard clinical testing failed to detect a causative variant, re-analysis may lead to a diagnosis.

Genetic health professionals' experiences with initiating reanalysis of genomic sequence data

Despite the increased diagnostic yield associated with genomic sequencing (GS), a sizable proportion of patients do not receive a genetic diagnosis at the time of the initial GS analysis. Systematic data reanalysis leads to considerable increases in genetic diagnosis rates yet is time intensive and leads to questions of feasibility. Few policies address whether laboratories have a duty to reanalyse and it is unclear how this impacts clinical practice. To address this, we interviewed 31 genetic health professionals (GHPs) across Europe, Australia and Canada about their experiences with data reanalysis and variant reinterpretation practices after requesting GS for their patients. GHPs described a range of processes required to initiate reanalysis of GS data for their patients and often practices involved a combination of reanalysis initiation methods. The most common mechanism for reanalysis was a patient-initiated model, where they instruct patients to return to the genetic service for clinical reassessment after a period of time or if new information comes to light. Yet several GHPs expressed concerns about patients' inabilities to understand the need to return to trigger reanalysis, or advocate for themselves, which may exacerbate health inequities. Regardless of the reanalysis initiation model that a genetic service adopts, patients' and clinicians' roles and responsibilities need to be clearly outlined so patients do not miss the opportunity to receive ongoing information about their genetic diagnosis. This requires consensus on the delineation of these roles for clinicians and laboratories to ensure clear pathways for reanalysis and reinterpretation to be performed to improve patient care.

Data sharing to improve concordance in variant interpretation across laboratories: results from the Canadian Open Genetics Repository

BACKGROUND

This study aimed to identify and resolve discordant variant interpretations across clinical molecular genetic laboratories through the Canadian Open Genetics Repository (COGR), an online collaborative effort for variant sharing and interpretation.

METHODS

Laboratories uploaded variant data to the Franklin Genoox platform. Reports were issued to each laboratory, summarising variants where conflicting classifications with another laboratory were noted. Laboratories could then reassess variants to resolve discordances. Discordance was calculated using a five-tier model (pathogenic (P), likely pathogenic (LP), variant of uncertain significance (VUS), likely benign (LB), benign (B)), a three-tier model (LP/P are positive, VUS are inconclusive, LB/B are negative) and a two-tier model (LP/P are clinically actionable, VUS/LB/B are not). We compared the COGR classifications to automated classifications generated by Franklin.

RESULTS

Twelve laboratories submitted classifications for 44 510 unique variants. 2419 variants (5.4%) were classified by two or more laboratories. From baseline to after reassessment, the number of discordant variants decreased from 833 (34.4% of variants reported by two or more laboratories) to 723 (29.9%) based on the five-tier model, 403 (16.7%) to 279 (11.5%) based on the three-tier model and 77 (3.2%) to 37 (1.5%) based on the two-tier model. Compared with the COGR classification, the automated Franklin classifications had 94.5% sensitivity and 96.6% specificity for identifying actionable (P or LP) variants.

CONCLUSIONS

The COGR provides a standardised mechanism for laboratories to identify discordant variant interpretations and reduce discordance in genetic test result delivery. Such quality assurance programmes are important as genetic testing is implemented more widely in clinical care.

Long-read genome sequencing for the molecular diagnosis of neurodevelopmental disorders

Exome and genome sequencing have proven to be effective tools for the diagnosis of neurodevelopmental disorders (NDDs), but large fractions of NDDs cannot be attributed to currently detectable genetic variation. This is likely, at least in part, a result of the fact that many genetic variants are difficult or impossible to detect through typical short-read sequencing approaches. Here, we describe a genomic analysis using Pacific Biosciences circular consensus sequencing (CCS) reads, which are both long (>10 kb) and accurate (>99% bp accuracy). We used CCS on six proband-parent trios with NDDs that were unexplained despite extensive testing, including genome sequencing with short reads. We identified variants and created de novo assemblies in each trio, with global metrics indicating these datasets are more accurate and comprehensive than those provided by short-read data. In one proband, we identified a likely pathogenic (LP), de novo L1-mediated insertion in CDKL5 that results in duplication of exon 3, leading to a frameshift. In a second proband, we identified multiple large de novo structural variants, including insertion-translocations affecting DGKB and MLLT3, which we show disrupt MLLT3 transcript levels. We consider this extensive structural variation likely pathogenic. The breadth and quality of variant detection, coupled to finding variants of clinical and research interest in two of six probands with unexplained NDDs, support the hypothesis that long-read genome sequencing can substantially improve rare disease genetic discovery rates.

Clinical sites of the Undiagnosed Diseases Network: unique contributions to genomic medicine and science

PURPOSE

The NIH Undiagnosed Diseases Network (UDN) evaluates participants with disorders that have defied diagnosis, applying personalized clinical and genomic evaluations and innovative research. The clinical sites of the UDN are essential to advancing the UDN mission; this study assesses their contributions relative to standard clinical practices.

METHODS

We analyzed retrospective data from four UDN clinical sites, from July 2015 to September 2019, for diagnoses, new disease gene discoveries and the underlying investigative methods.

RESULTS

Of 791 evaluated individuals, 231 received 240 diagnoses and 17 new disease-gene associations were recognized. Straightforward diagnoses on UDN exome and genome sequencing occurred in 35% (84/240). We considered these tractable in standard clinical practice, although genome sequencing is not yet widely available clinically. The majority (156/240, 65%) required additional UDN-driven investigations, including 90 diagnoses that occurred after prior nondiagnostic exome sequencing and 45 diagnoses (19%) that were nongenetic. The UDN-driven investigations included complementary/supplementary phenotyping, innovative analyses of genomic variants, and collaborative science for functional assays and animal modeling.

CONCLUSION

Investigations driven by the clinical sites identified diagnostic and research paradigms that surpass standard diagnostic processes. The new diagnoses, disease gene discoveries, and delineation of novel disorders represent a model for genomic medicine and science.

Genetic landscape of congenital disorders in patients from Southeast Asia: results from sequencing using a gene panel for Mendelian phenotypes

OBJECTIVE

To test the utility and diagnostic yield of a medical-exome gene panel for identifying pathogenic variants in Mendelian disorders.

METHODS

Next-generation sequencing was performed with the TruSight One gene panel (targeting 4813 genes) followed by MiSeq sequencing on 216 patients who presented with suspected genetic disorders as assessed by their attending physicians.

RESULTS

There were 56 pathogenic and 36 likely pathogenic variants across 57 genes identified in 87 patients. Causal mutations were more likely to be truncating and from patients with a prior clinical diagnosis. Another 18 promising variants need further evaluation for more evidence to meet the requirement for potential upgrade to pathogenic. Forty-five of the 92 clinically significant variants were novel.

CONCLUSION

The 40.3% positive yield compares favourably with similar studies using either this panel or whole exome sequencing, demonstrating that large gene panels could be a good alternative to whole exome sequencing for quick genetic confirmation of Mendelian disorders.

Evaluating systematic reanalysis of clinical genomic data in rare disease from single center experience and literature review

Background

Our primary aim was to evaluate the systematic reanalysis of singleton exome sequencing (ES) data for unsolved cases referred for any indication. A secondary objective was to undertake a literature review of studies examining the reanalysis of genomic data from unsolved cases.

Methods

We examined data from 58 unsolved cases referred between June 2016 and March 2017. First reanalysis at 4–13 months after the initial report considered genes newly associated with disease since the original analysis; second reanalysis at 9–18 months considered all disease‐associated genes. At 25–34 months we reviewed all cases and the strategies which solved them.

Results

Reanalysis of existing ES data alone at two timepoints did not yield new diagnoses. Over the same timeframe, 10 new diagnoses were obtained (17%) from additional strategies, such as microarray detection of copy number variation, repeat sequencing to improve coverage, and trio sequencing. Twenty‐seven peer‐reviewed articles were identified on the literature review, with a median new diagnosis rate via reanalysis of 15% and median reanalysis timeframe of 22 months.

Conclusion

Our findings suggest that an interval of greater than 18 months from the original report may be optimal for reanalysis. We also recommend a multi‐faceted strategy for cases remaining unsolved after singleton ES.

The Clinical Genome and Ancestry Report: An interactive web application for prioritizing clinically implicated variants from genome sequencing data with ancestry composition

Genome sequencing is positioned as a routine clinical work-up for diverse clinical conditions. A commonly used approach to highlight candidate variants with potential clinical implication is to search over locus- and gene-centric knowledge databases. Most web-based applications allow a federated query across diverse databases for a single variant; however, sifting through a large number of genomic variants with combination of filtering criteria is a substantial challenge. Here we describe the Clinical Genome and Ancestry Report (CGAR), an interactive web application developed to follow clinical interpretation workflows by organizing variants into seven categories: (1) reported disease-associated variants, (2) rare- and high-impact variants in putative disease-associated genes, (3) secondary findings that the American College of Medical Genetics and Genomics recommends reporting back to patients, (4) actionable pharmacogenomic variants, (5) focused reports for candidate genes, (6) de novo variant candidates for trio analysis, and (7) germline and somatic variants implicated in cancer risk, diagnosis, treatment and prognosis. For each variant, a comprehensive list of external links to variant-centric and phenotype databases are provided. Furthermore, genotype-derived ancestral composition is used to highlight allele frequencies from a matched population since some disease-associated variants show a wide variation between populations. CGAR is an open-source software and is available at https://tom.tch.harvard.edu/apps/cgar/.

Clinical utility of genomic sequencing

PURPOSE OF REVIEW

Identifying pathogenic variation underlying pediatric developmental disease is critical for medical management, therapeutic development, and family planning. This review summarizes current genetic testing options along with their potential benefits and limitations. We also describe results from large-scale genomic sequencing projects in pediatric and neonatal populations with a focus on clinical utility.

RECENT FINDINGS

Recent advances in DNA sequencing technology have made genomic sequencing a feasible and effective testing option in a variety of clinical settings. These cutting-edge tests offer much promise to both medical providers and patients as it has been demonstrated to detect causal genetic variation in ∼25% or more of previously unresolved cases. Efforts aimed at promoting data sharing across clinical genetics laboratories and systematic reanalysis of existing genomic sequencing data have further improved diagnostic rates and reduced the number of unsolved cases.

SUMMARY

Genomic sequencing is a powerful and increasingly cost-effective alternative to current genetic tests and will continue to grow in clinical utility as more of the genome is understood and as analytical methods are improved. The evolution of genomic sequencing is changing the landscape of clinical testing and requires medical professionals who are adept at understanding and returning genomic results to patients.

The clinical imperative for inclusivity: Race, ethnicity, and ancestry (REA) in genomics

The Clinical Genome Resource (ClinGen) Ancestry and Diversity Working Group highlights the need to develop guidance on race, ethnicity, and ancestry (REA) data collection and use in clinical genomics. We present quantitative and qualitative evidence to characterize: (1) acquisition of REA data via clinical laboratory requisition forms, and (2) information disparity across populations in the Genome Aggregation Database (gnomAD) at clinically relevant sites ascertained from annotations in ClinVar. Our requisition form analysis showed substantial heterogeneity in clinical laboratory ascertainment of REA, as well as marked incongruity among terms used to define REA categories. There was also striking disparity across REA populations in the amount of information available about clinically relevant variants in gnomAD. European ancestral populations constituted the majority of observations (55.8%), allele counts (59.7%), and private alleles (56.1%) in gnomAD at 550 loci with "pathogenic" and "likely pathogenic" expert-reviewed variants in ClinVar. Our findings highlight the importance of implementing and supporting programs to increase diversity in genome sequencing and clinical genomics, as well as measuring uncertainty around population-level datasets that are used in variant interpretation. Finally, we suggest the need for a standardized REA data collection framework to be developed through partnerships and collaborations and adopted across clinical genomics.

Genomic testing in pediatric epilepsy

Genomic testing has become routine in the diagnosis and management of pediatric patients with epilepsy. In a single test, hundreds to thousands of genes are examined for DNA changes that may not only explain the etiology of the patient's condition but may also inform management and seizure control. Clinical genomic testing has been in clinical practice for less than a decade, and because of this short period of time, the appropriate clinical use and interpretation of genomic testing is still evolving. Compared to the previous era of single-gene testing in epilepsy, which yielded a diagnosis in <5% of cases, many clinical genomic studies of epilepsy have demonstrated a clinically significant diagnosis in 30% or more of patients tested. This review will examine key studies of the past decade and indicate the clinical scenarios in which genomic testing should be considered standard of care.

Case for genome sequencing in infants and children with rare, undiagnosed or genetic diseases

Up to 350 million people worldwide suffer from a rare disease, and while the individual diseases are rare, in aggregate they represent a substantial challenge to global health systems. The majority of rare disorders are genetic in origin, with children under the age of five disproportionately affected. As these conditions are difficult to identify clinically, genetic and genomic testing have become the backbone of diagnostic testing in this population. In the last 10 years, next-generation sequencing technologies have enabled testing of multiple disease genes simultaneously, ranging from targeted gene panels to exome sequencing (ES) and genome sequencing (GS). GS is quickly becoming a practical first-tier test, as cost decreases and performance improves. A growing number of studies demonstrate that GS can detect an unparalleled range of pathogenic abnormalities in a single laboratory workflow. GS has the potential to deliver unbiased, rapid and accurate molecular diagnoses to patients across diverse clinical indications and complex presentations. In this paper, we discuss clinical indications for testing and historical testing paradigms. Evidence supporting GS as a diagnostic tool is supported by superior genomic coverage, types of pathogenic variants detected, simpler laboratory workflow enabling shorter turnaround times, diagnostic and reanalysis yield, and impact on healthcare.

RNA-Seq detects a SAMD12-EXT1 fusion transcript and leads to the discovery of an EXT1 deletion in a child with multiple osteochondromas

Background

We describe a patient presenting with pachygyria, epilepsy, developmental delay, short stature, failure to thrive, facial dysmorphisms, and multiple osteochondromas.

Methods

The patient underwent extensive genetic testing and analysis in an attempt to diagnose the cause of his condition. Clinical testing included metaphase karyotyping, array comparative genomic hybridization, direct sequencing and multiplex ligation‐dependent probe amplification and trio‐based exome sequencing. Subsequently, research‐based whole transcriptome sequencing was conducted to determine whether it might shed light on the undiagnosed phenotype.

Results

Clinical exome sequencing of patient and parent samples revealed a maternally inherited splice‐site variant in the doublecortin (DCX) gene that was classified as likely pathogenic and diagnostic of the patient's neurological phenotype. Clinical array comparative genome hybridization analysis revealed a 16p13.3 deletion that could not be linked to the patient phenotype based on affected genes. Further clinical testing to determine the cause of the patient's multiple osteochondromas was unrevealing despite extensive profiling of the most likely causative genes, EXT1 and EXT2, including mutation screening by direct sequence analysis and multiplex ligation‐dependent probe amplification. Whole transcriptome sequencing identified a SAMD12‐EXT1 fusion transcript that could have resulted from a chromosomal deletion, leading to the loss of EXT1 function. Re‐review of the clinical array comparative genomic hybridization results indicated a possible unreported mosaic deletion affecting the SAMD12 and EXT1 genes that corresponded precisely to the introns predicted to be affected by a fusion‐causing deletion. The existence of the mosaic deletion was subsequently confirmed clinically by an increased density copy number array and orthogonal methodologies

Conclusions

While mosaic mutations and deletions of EXT1 and EXT2 have been reported in the context of multiple osteochondromas, to our knowledge, this is the first time that transcriptomics technologies have been used to diagnose a patient via fusion transcript analysis in the congenital disease setting.

Clinical Utility of Reinterpreting Previously Reported Genomic Epilepsy Test Results for Pediatric Patients

IMPORTANCE

Clinical genomic tests that examine the DNA sequence of large numbers of genes are commonly used in the diagnosis and management of epilepsy in pediatric patients. The permanence of genomic test result interpretations is not known.

OBJECTIVE

To investigate the value of reinterpreting previously reported genomic test results.

DESIGN, SETTING, AND PARTICIPANTS

This study retrospectively reviewed and reinterpreted genomic test results from July 1, 2012, to August 31, 2015, for pediatric patients who previously underwent genomic epilepsy testing at a single tertiary care pediatric health care facility. Reinterpretation of previously reported variants was conducted in May 2017.

MAIN OUTCOMES AND MEASURES

Patient reports from clinical genomic epilepsy tests were reviewed, and all reported genetic variants were reinterpreted using 2015 consensus standards and guidelines for interpreting hereditary genetic variants. Three classification tiers were used in the reinterpretation: pathogenic or likely pathogenic variant, variant of uncertain significance (VUS), or benign or likely benign variant.

RESULTS

A total of 309 patients had genomic epilepsy tests performed (mean [SD] age, 5.6 [0.8] years; 163 [52.8%] male), and 185 patients had a genetic variant reported. The reported variants resulted in 61 patients with and 124 patients without a genetic diagnosis (VUS variants only). On reinterpretation of all reported variants, 67 of the 185 patients (36.2%) had a change in variant classification. Of the 67 patients with a genetic variant change in interpretation, 21 (31.3%) experienced a change in diagnosis. During the 5 years of the study, 19 of 61 patients (31.1%) with a genetic diagnosis and 48 of 124 patients (38.7%) with undiagnosed conditions (VUS only) had their results reclassified. Review of genomic reports issued during the final 2 years of the study identified reclassification of variants in 4 of 16 patients (25.0%) with a pathogenic or likely pathogenic variant and 11 of 41 patients (26.8%) with a VUS.

CONCLUSIONS AND RELEVANCE

The identified high rate of reinterpretation in this study suggests that interpretation of genomic test results has rapidly evolved during the past 5 years. These findings suggest that reinterpretation of genomic test results should be performed at least every 2 years.