For more than a decade, risk stratification for hypertrophic cardiomyopathy has been enhanced by targeted genetic testing. Using sequencing results, clinicians routinely assess the risk of ...hypertrophic cardiomyopathy in a patient's relatives and diagnose the condition in patients who have ambiguous clinical presentations. However, the benefits of genetic testing come with the risk that variants may be misclassified.
Using publicly accessible exome data, we identified variants that have previously been considered causal in hypertrophic cardiomyopathy and that are overrepresented in the general population. We studied these variants in diverse populations and reevaluated their initial ascertainments in the medical literature. We reviewed patient records at a leading genetic-testing laboratory for occurrences of these variants during the near-decade-long history of the laboratory.
Multiple patients, all of whom were of African or unspecified ancestry, received positive reports, with variants misclassified as pathogenic on the basis of the understanding at the time of testing. Subsequently, all reported variants were recategorized as benign. The mutations that were most common in the general population were significantly more common among black Americans than among white Americans (P<0.001). Simulations showed that the inclusion of even small numbers of black Americans in control cohorts probably would have prevented these misclassifications. We identified methodologic shortcomings that contributed to these errors in the medical literature.
The misclassification of benign variants as pathogenic that we found in our study shows the need for sequencing the genomes of diverse populations, both in asymptomatic controls and the tested patient population. These results expand on current guidelines, which recommend the use of ancestry-matched controls to interpret variants. As additional populations of different ancestry backgrounds are sequenced, we expect variant reclassifications to increase, particularly for ancestry groups that have historically been less well studied. (Funded by the National Institutes of Health.).
Next-generation sequencing technologies have been and continue to be deployed in clinical laboratories, enabling rapid transformations in genomic medicine. These technologies have reduced the cost of ...large-scale sequencing by several orders of magnitude, and continuous advances are being made. It is now feasible to analyze an individual’s near-complete exome or genome to assist in the diagnosis of a wide array of clinical scenarios. Next-generation sequencing technologies are also facilitating further advances in therapeutic decision making and disease prediction for at-risk patients. However, with rapid advances come additional challenges involving the clinical validation and use of these constantly evolving technologies and platforms in clinical laboratories. To assist clinical laboratories with the validation of next-generation sequencing methods and platforms, the ongoing monitoring of next-generation sequencing testing to ensure quality results, and the interpretation and reporting of variants found using these technologies, the American College of Medical Genetics and Genomics has developed the following professional standards and guidelines.
Genet Med15 9, 733–747.
The accurate interpretation of variation in Mendelian disease genes has lagged behind data generation as sequencing has become increasingly accessible. Ongoing large sequencing efforts present huge ...interpretive challenges, but they also provide an invaluable opportunity to characterize the spectrum and importance of rare variation.
We analyzed sequence data from 7,855 clinical cardiomyopathy cases and 60,706 Exome Aggregation Consortium (ExAC) reference samples to obtain a better understanding of genetic variation in a representative autosomal dominant disorder.
We found that in some genes previously reported as important causes of a given cardiomyopathy, rare variation is not clinically informative because there is an unacceptably high likelihood of false-positive interpretation. By contrast, in other genes, we find that diagnostic laboratories may be overly conservative when assessing variant pathogenicity.
We outline improved analytical approaches that evaluate which genes and variant classes are interpretable and propose that these will increase the clinical utility of testing across a range of Mendelian diseases.
Dilated cardiomyopathy is characterized by substantial locus, allelic, and clinical heterogeneity that necessitates testing of many genes across clinically overlapping diseases. Few studies have ...sequenced sufficient individuals; thus, the contributions of individual genes and the pathogenic variant spectrum are still poorly defined. We analyzed 766 dilated cardiomyopathy patients tested over 5 years in our molecular diagnostics laboratory.
Patients were tested using gene panels of increasing size from 5 to 46 genes, including 121 cases tested with a multiple-cardiomyopathy next-generation panel covering 46 genes. All variants were reassessed using our current clinical-grade scoring system to eliminate false-positive disease associations that afflict many older analyses.
Up to 37% of dilated cardiomyopathy cases carry a clinically relevant variant in one of 20 genes, titin (TTN) being the largest contributor (up to 14%). Desmoplakin (DSP), an arrhythmogenic right ventricular cardiomyopathy gene, contributed 2.4%, illustrating the utility of multidisease testing. The clinical sensitivity increased from 10 to 37% as gene panel sizes increased. However, the number of inconclusive cases also increased from 4.6 to 51%.
Our data illustrate the utility of broad gene panels for genetically and clinically heterogeneous diseases but also highlight challenges as molecular diagnostics moves toward genome-wide testing.
Standardized benchmarking approaches are required to assess the accuracy of variants called from sequence data. Although variant-calling tools and the metrics used to assess their performance ...continue to improve, important challenges remain. Here, as part of the Global Alliance for Genomics and Health (GA4GH), we present a benchmarking framework for variant calling. We provide guidance on how to match variant calls with different representations, define standard performance metrics, and stratify performance by variant type and genome context. We describe limitations of high-confidence calls and regions that can be used as truth sets (for example, single-nucleotide variant concordance of two methods is 99.7% inside versus 76.5% outside high-confidence regions). Our web-based app enables comparison of variant calls against truth sets to obtain a standardized performance report. Our approach has been piloted in the PrecisionFDA variant-calling challenges to identify the best-in-class variant-calling methods within high-confidence regions. Finally, we recommend a set of best practices for using our tools and evaluating the results.
PurposeWhole-exome and whole-genome sequencing have transformed the discovery of genetic variants that cause human Mendelian disease, but discriminating pathogenic from benign variants remains a ...daunting challenge. Rarity is recognized as a necessary, although not sufficient, criterion for pathogenicity, but frequency cutoffs used in Mendelian analysis are often arbitrary and overly lenient. Recent very large reference datasets, such as the Exome Aggregation Consortium (ExAC), provide an unprecedented opportunity to obtain robust frequency estimates even for very rare variants.MethodsWe present a statistical framework for the frequency-based filtering of candidate disease-causing variants, accounting for disease prevalence, genetic and allelic heterogeneity, inheritance mode, penetrance, and sampling variance in reference datasets.ResultsUsing the example of cardiomyopathy, we show that our approach reduces by two-thirds the number of candidate variants under consideration in the average exome, without removing true pathogenic variants (false-positive rate<0.001).ConclusionWe outline a statistically robust framework for assessing whether a variant is "too common" to be causative for a Mendelian disorder of interest. We present precomputed allele frequency cutoffs for all variants in the ExAC dataset.
Hypertrophic cardiomyopathy (HCM) exhibits genetic heterogeneity that is dominated by variation in eight sarcomeric genes. Genetic variation in a large number of non-sarcomeric genes has also been ...implicated in HCM but not formally assessed. Here we used very large case and control cohorts to determine the extent to which variation in non-sarcomeric genes contributes to HCM.
We sequenced known and putative HCM genes in a new large prospective HCM cohort (n = 804) and analysed data alongside the largest published series of clinically genotyped HCM patients (n = 6179), previously published HCM cohorts and reference population samples from the exome aggregation consortium (ExAC, n = 60 706) to assess variation in 31 genes implicated in HCM. We found no significant excess of rare (minor allele frequency < 1:10 000 in ExAC) protein-altering variants over controls for most genes tested and conclude that novel variants in these genes are rarely interpretable, even for genes with previous evidence of co-segregation (e.g. ACTN2). To provide an aid for variant interpretation, we integrated HCM gene sequence data with aggregated pedigree and functional data and suggest a means of assessing gene pathogenicity in HCM using this evidence.
We show that genetic variation in the majority of non-sarcomeric genes implicated in HCM is not associated with the condition, reinforce the fact that the sarcomeric gene variation is the primary cause of HCM known to date and underscore that the aetiology of HCM is unknown in the majority of patients.
Next-generation sequencing (NGS) is now routinely used to interrogate large sets of genes in a diagnostic setting. Regions of high sequence homology continue to be a major challenge for short-read ...technologies and can lead to false-positive and false-negative diagnostic errors. At the scale of whole-exome sequencing (WES), laboratories may be limited in their knowledge of genes and regions that pose technical hurdles due to high homology. We have created an exome-wide resource that catalogs highly homologous regions that is tailored toward diagnostic applications.
This resource was developed using a mappability-based approach tailored to current Sanger and NGS protocols.
Gene-level and exon-level lists delineate regions that are difficult or impossible to analyze via standard NGS. These regions are ranked by degree of affectedness, annotated for medical relevance, and classified by the type of homology (within-gene, different functional gene, known pseudogene, uncharacterized noncoding region). Additionally, we provide a list of exons that cannot be analyzed by short-amplicon Sanger sequencing.
This resource can help guide clinical test design, supplemental assay implementation, and results interpretation in the context of high homology.
Genet Med18 12, 1282–1289.
PurposeIntegrating genomic sequencing in clinical care requires standardization of variant interpretation practices. The Clinical Genome Resource has established expert panels to adapt the American ...College of Medical Genetics and Genomics/Association for Molecular Pathology classification framework for specific genes and diseases. The Cardiomyopathy Expert Panel selected MYH7, a key contributor to inherited cardiomyopathies, as a pilot gene to develop a broadly applicable approach.MethodsExpert revisions were tested with 60 variants using a structured double review by pairs of clinical and diagnostic laboratory experts. Final consensus rules were established via iterative discussions.ResultsAdjustments represented disease-/gene-informed specifications (12) or strength adjustments of existing rules (5). Nine rules were deemed not applicable. Key specifications included quantitative frameworks for minor allele frequency thresholds, the use of segregation data, and a semiquantitative approach to counting multiple independent variant occurrences where fully controlled case-control studies are lacking. Initial inter-expert classification concordance was 93%. Internal data from participating diagnostic laboratories changed the classification of 20% of the variants (n = 12), highlighting the critical importance of data sharing.ConclusionThese adapted rules provide increased specificity for use in MYH7-associated disorders in combination with expert review and clinical judgment and serve as a stepping stone for genes and disorders with similar genetic and clinical characteristics.
Hypertrophic cardiomyopathy (HCM) is caused primarily by pathogenic variants in genes encoding sarcomere proteins. We report genetic testing results for HCM in 2,912 unrelated individuals with ...nonsyndromic presentations from a broad referral population over 10 years.
Genetic testing was performed by Sanger sequencing for 10 genes from 2004 to 2007, by HCM CardioChip for 11 genes from 2007 to 2011 and by next-generation sequencing for 18, 46, or 51 genes from 2011 onward.
The detection rate is ~32% among unselected probands, with inconclusive results in an additional 15%. Detection rates were not significantly different between adult and pediatric probands but were higher in females compared with males. An expanded gene panel encompassing more than 50 genes identified only a very small number of additional pathogenic variants beyond those identifiable in our original panels, which examined 11 genes. Familial genetic testing in at-risk family members eliminated the need for longitudinal cardiac evaluations in 691 individuals. Based on the projected costs derived from Medicare fee schedules for the recommended clinical evaluations of HCM family members by the American College of Cardiology Foundation/American Heart Association, our data indicate that genetic testing resulted in a minimum cost savings of about $0.7 million.
Clinical HCM genetic testing provides a definitive molecular diagnosis for many patients and provides cost savings to families. Expanded gene panels have not substantively increased the clinical sensitivity of HCM testing, suggesting major additional causes of HCM still remain to be identified.
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