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Background As whole exome sequencing (WES) and whole genome sequencing (WGS) transition from research tools to clinical diagnostic tests, it is increasingly critical for sequencing methods and analysis pipelines to be technically accurate. The Genome in a Bottle Consortium has recently published a set of benchmark SNV, indel, and homozygous reference genotypes for the pilot whole genome NIST Reference Material based on the NA12878 genome. Methods We examine the relationship between human genome complexity and genes/variants reported to be associated with human disease. Specifically, we map regions of medical relevance to benchmark regions of high or low confidence. We use benchmark data to assess the sensitivity and positive predictive value of two representative sequencing pipelines for specific classes of variation. Results We observe that the accuracy of a variant call depends on the genomic region, variant type, and read depth, and varies by analytical pipeline. We find that most false negative WGS calls result from filtering while most false negative WES variants relate to poor coverage. We find that only 74.6 % of the exonic bases in ClinVar and OMIM genes and 82.1 % of the exonic bases in ACMG-reportable genes are found in high-confidence regions. Only 990 genes in the genome are found entirely within high-confidence regions while 593 of 3,300 ClinVar/OMIM genes have less than 50 % of their total exonic base pairs in high-confidence regions. We find greater than 77 % of the pathogenic or likely pathogenic SNVs currently in ClinVar fall within high-confidence regions. We identify sites that are prone to sequencing errors, including thousands present in publicly available variant databases. Finally, we examine the clinical impact of mandatory reporting of secondary findings, highlighting a false positive variant found in BRCA2 . Conclusions Together, these data illustrate the importance of appropriate use and continued improvement of technical benchmarks to ensure accurate and judicious interpretation of next-generation DNA sequencing results in the clinical setting.

Whole-genome sequencing (WGS) is positioned to become one of the most robust strategies for achieving timely diagnosis of rare genomic diseases. Despite its favorable diagnostic performance compared to conventional testing strategies, routine use and reimbursement of WGS are hampered by inconsistencies in the definition and measurement of clinical utility. For example, what constitutes clinical utility for WGS varies by stakeholder’s perspective (physicians, patients, families, insurance companies, health-care organizations, and society), clinical context (prenatal, pediatric, critical care, adult medicine), and test purpose (diagnosis, screening, treatment selection). A rapidly evolving technology landscape and challenges associated with robust comparative study design in the context of rare disease further impede progress in this area of empiric research. To address this challenge, an expert working group of the Medical Genome Initiative was formed. Following a consensus-based process, we align with a broad definition of clinical utility and propose a conceptually-grounded and empirically-guided measurement toolkit focused on four domains of utility: diagnostic thinking efficacy, therapeutic efficacy, patient outcome efficacy, and societal efficacy. For each domain of utility, we offer specific indicators and measurement strategies. While we focus on diagnostic applications of WGS for rare germline diseases, this toolkit offers a flexible framework for best practices around measuring clinical utility for a range of WGS applications. While we expect this toolkit to evolve over time, it provides a resource for laboratories, clinicians, and researchers looking to characterize the value of WGS beyond the laboratory.

Pharmacogenomics has the potential to inform drug dosing and selection, reduce adverse events, and improve medication efficacy; however, provider knowledge of pharmacogenomic testing varies across provider types and specialties. Given that many actionable pharmacogenomic genes are implicated in cardiovascular medication response variability, this study aimed to evaluate cardiology providers' knowledge and attitudes on implementing clinical pharmacogenomic testing. Sixty‐one providers responded to an online survey, including pharmacists (46%), physicians (31%), genetic counselors (15%), and nurses (8%). Most respondents (94%) reported previous genetics education; however, only 52% felt their genetics education prepared them to order a clinical pharmacogenomic test. In addition, most respondents (66%) were familiar with pharmacogenomics, with genetic counselors being most likely to be familiar (p < 0.001). Only 15% of respondents had previously ordered a clinical pharmacogenomic test and a total of 36% indicated they are likely to order a pharmacogenomic test in the future; however, the vast majority of respondents (89%) were interested in pharmacogenomic testing being incorporated into diagnostic cardiovascular genetic tests. Moreover, 84% of providers preferred pharmacogenomic panel testing compared to 16% who preferred single gene testing. Half of the providers reported being comfortable discussing pharmacogenomic results with their patients, but the majority (60%) expressed discomfort with the logistics of test ordering. Reported barriers to implementation included uncertainty about the clinical utility and difficulty choosing an appropriate test. Taken together, cardiology providers have moderate familiarity with pharmacogenomics and limited experience with test ordering; however, they are interested in incorporating pharmacogenomics into diagnostic genetic tests and ordering pharmacogenomic panels.

Phenotypic heterogeneity is apparent among individuals with putative monogenic disease, such as familial hypertrophic cardiomyopathy. Genome sequencing (GS) allows interrogation of the full spectrum of inborn genetic variation in an individual and RNA profiling provides a snapshot of the cardiac-specific pathogenic effects on gene expression. Identify candidate genetic modifiers of hypertrophic cardiomyopathy phenotype. We performed GS of 48 individuals with variants in , the gene encoding beta myosin heavy chain, and a personal or family history of cardiomyopathy. The genome sequences were annotated with a custom pipeline optimized for cardiovascular gene variant detection. We utilized multiple lines of evidence to prioritize genes together with rare variant gene-based association testing to identify candidate genetic modifiers. GS identified the variant in all 48 cases. Several variants were reclassified based on best available data. We identified known disease-associated genes ( ), candidate modifiers ( , and novel candidate modifiers of cardiomyopathy including and . We identified regulatory variants and intergenic regions associated with the phenotypes. Using RNA profiling, we show that several genes identified through gene-based association testing are differentially regulated in human hypertrophic cardiomyopathy, and in models of disease. Evaluation of the whole genome, even in the case of alleged monogenic disease, leads to important new insights. The identified variants, regions, and genes are candidates to modify disease presentation in cardiomyopathy.

Because a genetic diagnosis can guide clinical management and improve prognosis in critically ill patients, much effort has gone into developing methods that result in rapid, reliable results. The authors describe extremely rapid sequencing and analysis of the genomes of 12 patients, 5 of whom received a diagnosis.

Whole-genome sequencing (WGS) can identify variants that cause genetic disease, but the time required for sequencing and analysis has been a barrier to its use in acutely ill patients. In the present study, we develop an approach for ultra-rapid nanopore WGS that combines an optimized sample preparation protocol, distributing sequencing over 48 flow cells, near real-time base calling and alignment, accelerated variant calling and fast variant filtration for efficient manual review. Application to two example clinical cases identified a candidate variant in <8 h from sample preparation to variant identification. We show that this framework provides accurate variant calls and efficient prioritization, and accelerates diagnostic clinical genome sequencing twofold compared with previous approaches. A streamlined sequencing process enables identification of disease-causing variants in the clinic within 8 hours.

High throughput sequencing has facilitated a precipitous drop in the cost of genomic sequencing, prompting predictions of a revolution in medicine via genetic personalization of diagnostic and therapeutic strategies. There are significant barriers to realizing this goal that are related to the difficult task of interpreting personal genetic variation. A comprehensive, widely accessible application for interpretation of whole genome sequence data is needed. Here, we present a series of methods for identification of genetic variants and genotypes with clinical associations, phasing genetic data and using Mendelian inheritance for quality control, and providing predictive genetic information about risk for rare disease phenotypes and response to pharmacological therapy in single individuals and father-mother-child trios. We demonstrate application of these methods for disease and drug response prognostication in whole genome sequence data from twelve unrelated adults, and for disease gene discovery in one father-mother-child trio with apparently simplex congenital ventricular arrhythmia. In doing so we identify clinically actionable inherited disease risk and drug response genotypes in pre-symptomatic individuals. We also nominate a new candidate gene in congenital arrhythmia, ATP2B4, and provide experimental evidence of a regulatory role for variants discovered using this framework.

We sought to delineate the genetic test review and interpretation practices of clinical cardiovascular genetic counselors. A one-time anonymous online survey was taken by 46 clinical cardiovascular genetic counselors recruited through the National Society of Genetic Counselors Cardiovascular Special Interest Group. Nearly all (95.7%) gather additional information on variants reported on clinical genetic test reports and most (81.4%) assess the classification of such variants. Clinical cardiovascular genetic counselors typically (81.0%) classify variants in collaboration with cardiologist and/or geneticist colleagues, with the genetic counselor as the team member who is primarily responsible. Variant classification is a relatively recent (mean 3.2 years) addition to practice. Most genetic counselors learned classification skills on the job from clinical and laboratory colleagues. Recent graduates were more likely to have learned this in graduate school ( p  < 0.001). Genetic counselors are motivated to take responsibility for the classification of variants because of prior experiences with variant reclassification, inconsistencies between laboratories, and incomplete laboratory reports. They are also driven by a sense of professional duty and their proximity to the clinical context. This practice represents a broadening of the skill set of clinical cardiovascular genetic counselors and a unique expertise that they contribute to the interdisciplinary teams in which they work.

Purpose To describe the frequency and nature of differences in variant classifications between clinicians and genetic testing laboratories. Methods Retrospective review of variants identified through genetic testing ordered in routine clinical care by clinicians in the Stanford Center for Inherited Cardiovascular Disease. We compared classifications made by clinicians, the testing laboratory, and other laboratories in ClinVar. Results Of 688 laboratory classifications, 124 (18%) differed from the clinicians’ classifications. Most differences in classification would probably affect clinical care of the patient and/or family (83%, 103/124). The frequency of discordant classifications differed depending on the testing laboratory ( P  < 0.0001) and the testing laboratory’s classification ( P  < 0.00001). For the majority (82/124, 66%) of discordant classifications, clinicians were more conservative (less likely to classify a variant pathogenic or likely pathogenic). The clinicians’ classification was discordant with one or more submitter in ClinVar in 49.1% (28/57) of cases, while the testing laboratory’s classification was discordant with a ClinVar submitter in 82.5% of cases (47/57, P  = 0.0002). Conclusion The clinical team disagreed with the laboratory’s classification at a rate similar to that of reported disagreements between laboratories. Most of this discordance was clinically significant, with clinicians tending to be more conservative than laboratories in their classifications.

As exome and whole genome sequencing become clinically available, the potential to receive a large number of clinically relevant but incidental results is a significant challenge in the provision of genomic counseling. We conducted three focus groups of a total of 35 individuals who were members of ASHG and/or NSGC, assessing views towards the return of genomic results. Participants stressed that patient autonomy was primary. There was consensus that a mechanism to return results to the healthcare provider, rather than patient, and to streamline integration into the electronic health record would ensure these results had the maximal impact on patient management. All three focus groups agreed that pharmacogenomic results were reasonable to return and that they were not felt to be stigmatizing. With regard to the return of medically relevant results, there was much debate. Participants had difficulty in consistently assigning specific diseases to ‘bins’ that were considered obligatory versus optional for disclosure. Consensus was reached regarding the importance of informed consent and pretest counseling visits to clarify what the return of results process would entail. Evidence based professional guidelines should continue to be developed and regularly revised to assist in consistently and appropriately providing genomic results to patients.