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Clinical sequencing tests that focus on genes linked to specific diseases or phenotypes are increasingly widely being used. This article discusses how disease-targeting tests retain several advantages despite moves towards the clinical application of whole-genome or exome sequencing. With the declining cost of sequencing and the ongoing discovery of disease genes, it is now possible to examine hundreds of genes in a single disease-targeted test. Although exome- and genome-sequencing approaches are beginning to compete, disease-targeted testing retains certain advantages and still holds a firm place in the diagnostic evaluation. Here I examine the current state of clinical disease-targeted sequencing and evaluate the benefits and challenges of incorporating sequencing tests into patient care.

The advent of genomic technologies is changing health care systems, with genomic data increasingly being applied to guide individual patient care. In this Essay, Rehm discusses how genomics is becoming an essential part of clinical care and the existing challenges that must be surmounted to take full advantage of personal genomic information. 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.

Studies have demonstrated the value of genomic analysis for the diagnosis of rare diseases, but accessibility is still in its infancy; global data sharing is needed to further advance our knowledge of all causes of rare disease.

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.

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.

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. 1 , 2 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. 3 In more than one third of patients, causal genetic lesions are identified, which enables clinicians to assess risk among the patient’s relatives 4 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.

Reporting and sharing pharmacogenetic test results across clinical laboratories and electronic health records is a crucial step toward the implementation of clinical pharmacogenetics, but allele function and phenotype terms are not standardized. Our goal was to develop terms that can be broadly applied to characterize pharmacogenetic allele function and inferred phenotypes. Terms currently used by genetic testing laboratories and in the literature were identified. The Clinical Pharmacogenetics Implementation Consortium (CPIC) used the Delphi method to obtain a consensus and agree on uniform terms among pharmacogenetic experts. Experts with diverse involvement in at least one area of pharmacogenetics (clinicians, researchers, genetic testing laboratorians, pharmacogenetics implementers, and clinical informaticians; n = 58) participated. After completion of five surveys, a consensus (>70%) was reached with 90% of experts agreeing to the final sets of pharmacogenetic terms. The proposed standardized pharmacogenetic terms will improve the understanding and interpretation of pharmacogenetic tests and reduce confusion by maintaining consistent nomenclature. These standard terms can also facilitate pharmacogenetic data sharing across diverse electronic health care record systems with clinical decision support.