With the rapid evolution of next-generation DNA sequencing technologies, the cost of sequencing a human genome has plummeted, and genomics has started to pervade health care across all stages of life ...- from preconception to adult medicine. Challenges to fully embracing genomics in a clinical setting remain, but some approaches are starting to overcome these barriers, such as community-driven data sharing to improve the accuracy and efficiency of applying genomics to patient care.
Precision medicine has the potential to profoundly improve the practice of medicine. However, the advances required will take time to implement. Genetics is already being used to direct clinical ...decision-making and its contribution is likely to increase. To accelerate these advances, fundamental changes are needed in the infrastructure and mechanisms for data collection, storage and sharing. This will create a continuously learning health-care system with seamless cycling between clinical care and research. Patients must be educated about the benefits of sharing data. The building blocks for such a system are already forming and they will accelerate the adoption of precision medicine.
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DOBA, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, SBMB, SIK, UILJ, UKNU, UL, UM, UPUK
This article is based on the address given by the author at the 2022 meeting of The American Society of Human Genetics (ASHG) in Los Angeles, CA. The video of the original address can be found at the ...ASHG website.
The 2015 ACMG/AMP sequence variant interpretation guideline provided a framework for classifying variants based on several benign and pathogenic evidence criteria, including a pathogenic criterion ...(PVS1) for predicted loss of function variants. However, the guideline did not elaborate on specific considerations for the different types of loss of function variants, nor did it provide decision‐making pathways assimilating information about variant type, its location, or any additional evidence for the likelihood of a true null effect. Furthermore, this guideline did not take into account the relative strengths for each evidence type and the final outcome of their combinations with respect to PVS1 strength. Finally, criteria specifying the genes for which PVS1 can be applied are still missing. Here, as part of the ClinGen Sequence Variant Interpretation (SVI) Workgroup's goal of refining ACMG/AMP criteria, we provide recommendations for applying the PVS1 criterion using detailed guidance addressing the above‐mentioned gaps. Evaluation of the refined criterion by seven disease‐specific groups using heterogeneous types of loss of function variants (n = 56) showed 89% agreement with the new recommendation, while discrepancies in six variants (11%) were appropriately due to disease‐specific refinements. Our recommendations will facilitate consistent and accurate interpretation of predicted loss of function variants.
We provide guidance for PVS1 usage that takes into consideration all aspects of putative loss of function (LoF) variants, including type, location, and annotation, and the disease mechanism of the genes they affect. We demonstrate how the combination of these variant and gene attributes can lead to varied PVS1 strength levels. Finally, we evaluate the refined criterion using > 50 LoF variants in several genes and diseases.
Disclaimer: These ACMG Standards and Guidelines were developed primarily as an educational resource for clinical laboratory geneticists to help them provide quality clinical laboratory services. ...Adherence to these standards and guidelines is voluntary and does not necessarily assure a successful medical outcome. These Standards and Guidelines should not be considered inclusive of all proper procedures and tests or exclusive of other procedures and tests that are reasonably directed to obtaining the same results. In determining the propriety of any specific procedure or test, the clinical laboratory geneticist should apply his or her own professional judgment to the specific circumstances presented by the individual patient or specimen. Clinical laboratory geneticists are encouraged to document in the patient’s record the rationale for the use of a particular procedure or test, whether or not it is in conformance with these Standards and Guidelines. They also are advised to take notice of the date any particular guideline was adopted and to consider other relevant medical and scientific information that becomes available after that date. It also would be prudent to consider whether intellectual property interests may restrict the performance of certain tests and other procedures.
The American College of Medical Genetics and Genomics (ACMG) previously developed guidance for the interpretation of sequence variants.1 In the past decade, sequencing technology has evolved rapidly with the advent of high-throughput next-generation sequencing. By adopting and leveraging next-generation sequencing, clinical laboratories are now performing an ever-increasing catalogue of genetic testing spanning genotyping, single genes, gene panels, exomes, genomes, transcriptomes, and epigenetic assays for genetic disorders. By virtue of increased complexity, this shift in genetic testing has been accompanied by new challenges in sequence interpretation. In this context the ACMG convened a workgroup in 2013 comprising representatives from the ACMG, the Association for Molecular Pathology (AMP), and the College of American Pathologists to revisit and revise the standards and guidelines for the interpretation of sequence variants. The group consisted of clinical laboratory directors and clinicians. This report represents expert opinion of the workgroup with input from ACMG, AMP, and College of American Pathologists stakeholders. These recommendations primarily apply to the breadth of genetic tests used in clinical laboratories, including genotyping, single genes, panels, exomes, and genomes. This report recommends the use of specific standard terminology—“pathogenic,” “likely pathogenic,” “uncertain significance,” “likely benign,” and “benign”—to describe variants identified in genes that cause Mendelian disorders. Moreover, this recommendation describes a process for classifying variants into these five categories based on criteria using typical types of variant evidence (e.g., population data, computational data, functional data, segregation data). Because of the increased complexity of analysis and interpretation of clinical genetic testing described in this report, the ACMG strongly recommends that clinical molecular genetic testing should be performed in a Clinical Laboratory Improvement Amendments–approved laboratory, with results interpreted by a board-certified clinical molecular geneticist or molecular genetic pathologist or the equivalent.
Genet Med17 5, 405–423.
Reference population databases are an essential tool in variant and gene interpretation. Their use guides the identification of pathogenic variants amidst the sea of benign variation present in every ...human genome, and supports the discovery of new disease–gene relationships. The Genome Aggregation Database (gnomAD) is currently the largest and most widely used publicly available collection of population variation from harmonized sequencing data. The data is available through the online gnomAD browser (https://gnomad.broadinstitute.org/) that enables rapid and intuitive variant analysis. This review provides guidance on the content of the gnomAD browser, and its usage for variant and gene interpretation. We introduce key features including allele frequency, per‐base expression levels, constraint scores, and variant co‐occurrence, alongside guidance on how to use these in analysis, with a focus on the interpretation of candidate variants and novel genes in rare disease.
Reference population databases are critical in the interpretation of genomic variation for diagnosing rare disease, and supports the discovery of new disease–gene relationships. This review provides guidance for using the Genome Aggregation Database (gnomAD) browser and key features like allele frequency, per‐base expression levels, constraint scores, and variant co‐occurrence, for variant and gene interpretation in clinical and research analysis.
In 2015, professional guidelines defined the term 'likely pathogenic' to mean with a 90% chance of pathogenicity. To determine whether current practice reflects this definition, ClinVar ...classifications were tracked from 2016 to 2019. During that period, between 83.8 and 99.1% of likely pathogenic classifications were reclassified as pathogenic, depending on whether LP to VUS reclassifications are included and on how these classifications are categorized.
This study shows that for variants initially classified as pathogenic that were later reclassified as benign, the misclassification would have been prevented had racially diverse populations been ...considered in the original studies of the variants.
Although hypertrophic cardiomyopathy is best known as a fatal disease of young athletes, it causes considerable morbidity and mortality among patients of all ages and lifestyles.
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The defining feature of hypertrophic cardiomyopathy is unexplained left ventricular hypertrophy, but its clinical presentation is variable; it can manifest as severe heart failure in some patients yet be asymptomatic in others.
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In more than one third of patients, causal genetic lesions are identified, which enables clinicians to assess risk among the patient’s relatives
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and, in rare circumstances, to tailor therapy for a patient who is found to have a tractable disorder, such . . .
Precision medicine is predicted to revolutionize the clinical practice of medicine, in part by using molecular biomarkers to assess patients' risk, prognosis, and therapeutic response more precisely. ...However, reliance on biomarkers could present challenges for diverse populations that are not equitably represented in precision medicine research. We examined the populations included in genomic studies whose data were available in the following two public databases: the Genome-Wide Association Study Catalog and the database of Genotypes and Phenotypes. We found significantly fewer studies of African, Latin American, and Asian ancestral populations in comparison to European populations. These patterns were consistent across both data types and disease areas. While the number of genomic research studies that include non-European populations is modestly improving, the overall numbers are still low, and decisive action is needed now to implement the changes necessary for realizing the promise of precision medicine for all.
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