Citations

Abstract Background Genetic testing has traditionally been divided into molecular genetics and cytogenetics, originally driven by the use of different assays and their associated limitations. Cytogenetic technologies such as karyotyping, fluorescent in situ hybridization or chromosomal microarrays are used to detect large “megabase level” copy number variants and other structural variants such as inversions or translocations. In contrast, molecular methodologies are heavily biased toward subgenic “small variants” such as single nucleotide variants, insertions/deletions, and targeted detection of intragenic, exon level deletions or duplications. The boundaries between these approaches are now increasingly blurred as next-generation sequencing technologies and their use for genome-wide analysis are used by both disciplines, therefore eliminating the historic and somewhat artificial separation driven by variant type. Content This review discusses the history of genomic nomenclature across both fields, summarizes implementation challenges for the clinical genetics community, and identifies key considerations for enabling a seamless connection of the stakeholders that consume variant descriptions. Summary Standardization is naturally a lengthy and complex process that requires consensus building between different stakeholders. Developing a standard that not only fits the multitude of needs across the entities that consume genetic variant information but also works equally well for all genetic variant types is an ambitious goal that calls for revisiting this vision.

Context.— Next-generation sequencing (NGS)–based assays are used for diagnosis of diverse inherited disorders. Limited data are available pertaining to interlaboratory analytical performance of these assays. Objective.— To report on the College of American Pathologists (CAP) NGS Germline Program, which is methods based, and explore the evolution in laboratory testing practices. Design.— Results from the NGS Germline Program from 2016–2020 were analyzed for interlaboratory analytical performance. Self-reported laboratory testing practices were also evaluated. Results.— From 2016–2020, a total of 297 laboratories participated in at least 1 program mailing. Of the 289 laboratories that provided information on tests offered, 138 (47.8%) offered only panel testing throughout their enrollment, while 35 (12.1%) offered panels and exome testing, 30 (10.4%) offered only exomes, 9 (3.1%) offered only genomes, and 15 (5.2%) offered panels, exomes, and genomes. The remainder (62 laboratories, 21.4%) changed their test offerings during the 2016–2020 timeframe. Considering each genomic position/interval, the median detection percentage at variant positions across the 2016–2020 mailings ranged from 94.3% to 100%, while at reference positions (no variant detected), the median correct response percentage was 100% across all mailings. When considering performance of individual laboratories, 89.5% (136 of 152) to 98.0% (149 of 152) of laboratories successfully met the detection threshold (≥90% of the variants present), while 94.6% (87 of 92) to 100% (163 of 163) of laboratories met the 95% specificity threshold across mailings. Conclusions.— Since the inception of this program, laboratories have consistently performed well. The median sensitivity and specificity of detection of sequence variants included in this program (eg, single nucleotide variants, insertions, and deletions) were 100.0%.

Testing asymptomatic individuals for unsuspected conditions is not new to the medical and public health communities and protocols to develop screening tests are well-established. However, the application of screening principles to inherited diseases presents unique challenges. Unlike most screening tests, the natural history and disease prevalence of most rare inherited diseases in an unselected population are unknown. It is difficult or impossible to obtain a “truth set” cohort for clinical validation studies. As a result, it is not possible to accurately calculate clinical positive and negative predictive values for “likely pathogenic” genetic variants, which are commonly returned in genetic screening assays. In addition, many of the genetic conditions included in screening panels do not have clinical confirmatory tests. All of these elements are typically required to justify the development of a screening test, according to the World Health Organization screening principles. Nevertheless, as the cost of DNA sequencing continues to fall, more individuals are opting to undergo genomic testing in the absence of a clinical indication. Despite the challenges, reasonable estimates can be deduced and used to inform test design strategies. Here, we review test design principles and apply them to genetic screening.

Testing asymptomatic individuals for unsuspected conditions is not new to the medical and public health communities and protocols to develop screening tests are well-established. However, the application of screening principles to inherited diseases presents unique challenges. Unlike most screening tests, the natural history and disease prevalence of most rare inherited diseases in an unselected population are unknown. It is difficult or impossible to obtain a “truth set” cohort for clinical validation studies. As a result, it is not possible to accurately calculate clinical positive and negative predictive values for “likely pathogenic” genetic variants, which are commonly returned in genetic screening assays. In addition, many of the genetic conditions included in screening panels do not have clinical confirmatory tests. All of these elements are typically required to justify the development of a screening test, according to the World Health Organization screening principles. Nevertheless, as the cost of DNA sequencing continues to fall, more individuals are opting to undergo genomic testing in the absence of a clinical indication. Despite the challenges, reasonable estimates can be deduced and used to inform test design strategies. Here, we review test design principles and apply them to genetic screening.

Testing asymptomatic individuals for unsuspected conditions is not new to the medical and public health communities and protocols to develop screening tests are well-established. However, the application of screening principles to inherited diseases presents unique challenges. Unlike most screening tests, the natural history and disease prevalence of most rare inherited diseases in an unselected population are unknown. It is difficult or impossible to obtain a “truth set” cohort for clinical validation studies. As a result, it is not possible to accurately calculate clinical positive and negative predictive values for “likely pathogenic” genetic variants, which are commonly returned in genetic screening assays. In addition, many of the genetic conditions included in screening panels do not have clinical confirmatory tests. All of these elements are typically required to justify the development of a screening test, according to the World Health Organization screening principles. Nevertheless, as the cost of DNA sequencing continues to fall, more individuals are opting to undergo genomic testing in the absence of a clinical indication. Despite the challenges, reasonable estimates can be deduced and used to inform test design strategies. Here, we review test design principles and apply them to genetic screening.

As the cost of DNA sequencing continues to fall, more individuals are opting to undergo genomic testing in the absence of a clinical indication. Testing asymptomatic individuals for unsuspected conditions is not new to the medical and public health communities. However, the application of screening principles to inherited diseases with unknown prevalence in an unselected (low-risk) test population raises unique challenges. This paper examines the effect of disease prevalence on the positive predictive value of a test result. Many inherited conditions have very low prevalence in an unselected population, which increases the probability that some likely pathogenic variants may be false positives. In situations where the adverse impact of a false positive result is significant, laboratories should address this issue by either increasing the interpretive specificity of the test, performing a clinical confirmatory test to establish the presence of disease, or restricting the test to a population with increased disease prevalence. Here, we review the statistical concepts relevant to screening tests, apply these concepts to genetic disease screening, create a model to estimate prevalence and positive predictive value, and provide a framework for further discussion.