Buchanan, Adam H.

Importance Completion of the Human Genome Project prompted predictions that genomics would transform medicine, including through genomic screening that identifies potentially medically actionable findings that could prevent disease, detect it earlier, or treat it better. However, genomic screening remains anchored in research and largely unavailable as part of routine care. Objective To summarize 11 years of experience with genomic screening and explore the landscape of genomic screening efforts. Design, Setting, and Participants This cohort study was based in Geisinger’s MyCode Community Health Initiative, a genomic screening program in a rural Pennsylvania health care system in which patient-participants exomes are analyzed. Main Outcomes and Measures Genomic screen-positive rates were evaluated and stratified by condition type (cancer, cardiovascular, other) and US Centers for Disease Control and Prevention (CDC) Tier 1 designation. The proportion of participants previously unaware of their genomic result was assessed. Other large-scale population-based genomic screening efforts with genomic results disclosure were compiled from public resources. Results A total of 354 957 patients participated in Geisinger’s genomic screening program (median [IQR] age, 54 [36-69] years; 194 037 [59.7%] assigned female sex at birth). As of June 2024, 175 500 participants had exome sequencing available for analysis, and 5934 participants (3.4%) had a pathogenic variant in 81 genes known to increase risk for disease. Between 2013 and July 2024, 5119 results were disclosed to 5052 eligible participants, with 2267 (44.2%) associated with risk for cardiovascular disease, 2031 (39.7%) with risk for cancer, and 821 (16.0%) with risk for other conditions. Most results (3040 [59.4%]) were in genes outside of those with a CDC Tier 1 designation. Nearly 90% of participants (4425 [87.6%]) were unaware of their genomic risk prior to disclosure. In a survey of large-scale biobanks with genomic and electronic health record (EHR) data, only 25.0% (6 of 24) disclosed potentially actionable genomic results. Conclusions and Relevance In this large, genomics-informed cohort study from a single health system, 1 in 30 participants had a potentially actionable genomic finding. However, nearly 90% were unaware of their risk prior to screening, demonstrating the utility of genomic screening in identifying at-risk individuals. Most large-scale biobanks with genomic and EHR data did not return genomic results with potential medical relevance, missing opportunities to significantly improve genomic risk ascertainment for these individuals and to perform longitudinal studies of clinical and implementation outcomes in diverse settings.

A challenge in returning genomic test results to research participants is how best to communicate complex and clinically nuanced findings to participants in a manner that is scalable to the large numbers of participants enrolled. The purpose of this study was to examine the features of genetic results letters produced at each Electronic Medical Records and Genomics (eMERGE3) Network site to assess their readability and content. Letters were collected from each site, and a qualitative analysis of letter content and a quantitative analysis of readability statistics were performed. Because letters were produced independently at each eMERGE site, significant heterogeneity in readability and content was found. The content of letters varied widely from a baseline of notifying participants that results existed to more detailed information about positive or negative results, as well as materials for sharing with family members. Most letters were significantly above the Centers for Disease Control-suggested reading level for health communication. While continued effort should be applied to make letters easier to understand, the ongoing challenge of explaining complex genomic information, the implications of negative test results, and the uncertainty that comes with some types of test and result makes simplifying letter text challenging.

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.