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Background: Cancer health research relies on large-scale cohorts to derive generalizable results for different populations. While traditional epidemiological cohorts often use costly random sampling or self-motivated, preselected groups, a shift toward health system-based cohorts has emerged. However, such cohorts depend on participants remaining within a single system. Recent consumer engagement models using smartphone-based communication, driving projects, and social media have begun to upend these paradigms. Methods: We initiated the Healthy Oregon Project (HOP) to support basic and clinical cancer research. HOP study employs a novel, cost-effective remote recruitment approach to effectively establish a large-scale cohort for population-based studies. The recruitment leverages the unique email account, the HOP website, and social media platforms to direct smartphone users to the study app, which facilitates saliva sample collection and survey administration. Monthly newsletters further facilitate engagement and outreach to broader communities. Results: By the end of 2022, the HOP has enrolled approximately 35,000 participants aged 18-100 years (median = 44.2 years), comprising more than 1% of the Oregon adult population. Among those who have app access, ∼87% provided consent to genetic screening. The HOP monthly email newsletters have an average open rate of 38%. Efforts continue to be made to improve survey response rates. Conclusion: This study underscores the efficacy of remote recruitment approaches in establishing large-scale cohorts for population-based cancer studies. The implementation of the study facilitates the collection of extensive survey and biological data into a repository that can be broadly shared and supports collaborative clinical and translational research.

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The Healthy Oregon Project (HOP) is a statewide effort that aims to build a large research repository and influence the health of Oregonians through providing no-cost genetic screening to participants for a next-generation sequencing 32-gene panel comprising genes related to inherited cancers and familial hypercholesterolemia. This type of unbiased population screening can detect at-risk individuals who may otherwise be missed by conventional medical approaches. However, challenges exist for this type of high-throughput testing in an academic setting, including developing a low-cost high-efficiency test and scaling up the clinical laboratory for processing large numbers of samples. Modifications to our academic clinical laboratory including efficient test design, robotics, and a streamlined analysis approach increased our ability to test more than 1,000 samples per month for HOP using only one dedicated HOP laboratory technologist. Additionally, enrollment using a HIPAA-compliant smartphone app and sample collection using mouthwash increased efficiency and reduced cost. Here, we present our experience three years into HOP and discuss the lessons learned, including our successes, challenges, opportunities, and future directions, as well as the genetic screening results for the first 13,670 participants tested. Overall, we have identified 730 pathogenic/likely pathogenic variants in 710 participants in 24 of the 32 genes on the panel. The carrier rate for pathogenic/likely pathogenic variants in the inherited cancer genes on the panel for an unselected population was 5.0% and for familial hypercholesterolemia was 0.3%. Our laboratory experience described here may provide a useful model for population screening projects in other states.

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The Association for Molecular Pathology Variant Interpretation Testing Among Laboratories (VITAL) Working Group convened to evaluate the Standards and Guidelines for the Interpretation of Sequence Variants implementation into clinical practice, identify problematic classification rules, and define implementation challenges. Variants and associated clinical information were provided to volunteer respondents. Participant variant classifications were compared with intended consensus-derived classifications of the Working Group. The 24 variant challenges received 1379 responses; 1119 agreed with the intended response (81%; 95% CI, 79% to 83%). Agreement ranged from 44% to 100%, with 16 challenges (67%; 47% to 82%) reaching consensus (≥80% agreement). Participant classifications were also compared to a calculated interpretation of the ACMG Guidelines using the participant-reported criteria as input. The 24 variant challenges had 1368 responses with specific evidence provided and 1121 (82%; 80% to 84%) agreed with the calculated interpretation. Agreement for challenges ranged from 63% to 98%; 15 (63%; 43% to 79%) reaching consensus. Among 81 individual participants, 32 (40%; 30% to 50%) reached agreement with at least 80% of the intended classifications and 42 (52%; 41% to 62%) with the calculated classifications. This study demonstrated that although variant classification remains challenging, published guidelines are being utilized and adapted to improve variant calling consensus. This study identified situations where clarifications are warranted and provides a model for competency assessment.

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PURPOSE: Artificial intelligence (AI) and variant prioritization tools for genomic variant analysis are being rapidly developed for use in clinical diagnostic testing. However, their clinical utility and reliability are currently limited. Therefore, we performed a validation of a commercial AI tool (Moon) and a comprehensive reanalysis of previously collected clinical exome sequencing cases using an open-source variant prioritization tool (Exomiser) and the now-validated AI tool to test their feasibility in clinical diagnostics. METHODS: A validation study of Moon was performed with 29 positive cases determined by previous manual analysis. After validation, reanalysis was performed on 80 previously manually analyzed nondiagnostic exome cases using Moon. Finally, a comparison between Moon and Exomiser was completed regarding their ability to identify previously completed positive cases and to identify new positive cases. RESULTS: Moon correctly selected the causal variant(s) in 97% of manually analyzed positive cases and identified 7 new positive cases. Exomiser correctly identified the causal gene in 85% of positive cases and agreed with Moon by ranking the new gene in its top 10 list 43% of the time. CONCLUSION: The use of AI in diagnostic laboratories greatly enhances exome sequencing analysis by reducing analysis time and increasing the diagnostic rate.

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The originally published version of this Article contained errors in Fig. 2. The numbers below the black arrowheads were incorrect; please see incorrect Figure in associated Correction. These errors have now been corrected in the PDF and HTML versions of the Article.

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PURPOSE: Clinical sequencing emerging in health care may result in secondary findings (SFs). METHODS: Seventy-four of 6240 (1.2%) participants who underwent genome or exome sequencing through the Clinical Sequencing Exploratory Research (CSER) Consortium received one or more SFs from the original American College of Medical Genetics and Genomics (ACMG) recommended 56 gene-condition pair list; we assessed clinical and psychosocial actions. RESULTS: The overall adjusted prevalence of SFs in the ACMG 56 genes across the CSER consortium was 1.7%. Initially 32% of the family histories were positive, and post disclosure, this increased to 48%. The average cost of follow-up medical actions per finding up to a 1-year period was $128 (observed, range: $0-$678) and $421 (recommended, range: $141-$1114). Case reports revealed variability in the frequency of and follow-up on medical recommendations patients received associated with each SF gene-condition pair. Participants did not report adverse psychosocial impact associated with receiving SFs; this was corroborated by 18 participant (or parent) interviews. All interviewed participants shared findings with relatives and reported that relatives did not pursue additional testing or care. CONCLUSION: Our results suggest that disclosure of SFs shows little to no adverse impact on participants and adds only modestly to near-term health-care costs; additional studies are needed to confirm these findings.

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A research study utilizing whole-genome sequence analysis for preconception carrier screening provided a genome-first detection of a severe de novo Factor VIII mutation in a woman with implications for pregnancy management and life-saving interventions of her newborn son, and a challenge to the existing paradigm regarding carrier testing.

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Factor V Leiden and factor II c.*97G>A (formerly referred to as prothrombin 20210G>A) are the two most common genetic variants associated with venous thromboembolism (VTE). Testing for these variants is one of the most common referrals in clinical genetics laboratories. While the methodologies for testing these two variants are relatively straightforward, the clinical implementation can be complicated with regard to test indications, risk assessment of occurrence and recurrence of VTE, and related genetic counseling. This document provides an overview of VTE, information about the variants and their influence on risk, considerations before initiating genetic testing, and the clinical and analytical sensitivity and specificity of the tests. Key information that should be included in the laboratory report is also provided. Disease-specific statements are intended to augment the general American College of Medical Genetics and Genomics (ACMG) technical standards for clinical genetics laboratories. Individual laboratories are responsible for meeting the Clinical Laboratory Improvement Amendments (CLIA)/College of American Pathologists (CAP) quality assurance standards with respect to appropriate sample documentation, assay validation, general proficiency testing, and quality control measures. This 2018 edition of the ACMG technical standard updates and supersedes the 2005 edition on this topic. It is designed to be a checklist for genetic testing professionals who are already familiar with the disease and the methods of analysis.

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Advances in sequencing technologies permit the analysis of a larger selection of genes for preconception carrier screening. The study was designed as a sequential carrier screen using genome sequencing to analyze 728 gene-disorder pairs for carrier and medically actionable conditions in 131 women and their partners (n = 71) who were planning a pregnancy. We report here on the clinical laboratory results from this expanded carrier screening program. Variants were filtered and classified using the latest American College of Medical Genetics and Genomics (ACMG) guideline; only pathogenic and likely pathogenic variants were confirmed by orthologous methods before being reported. Novel missense variants were classified as variants of uncertain significance. We reported 304 variants in 202 participants. Twelve carrier couples (12/71 couples tested) were identified for common conditions; eight were carriers for hereditary hemochromatosis. Although both known and novel variants were reported, 48% of all reported variants were missense. For novel splice-site variants, RNA-splicing assays were performed to aid in classification. We reported ten copy-number variants and five variants in non-coding regions. One novel variant was reported in F8, associated with hemophilia A; prenatal testing showed that the male fetus harbored this variant and the neonate suffered a life-threatening hemorrhage which was anticipated and appropriately managed. Moreover, 3% of participants had variants that were medically actionable. Compared with targeted mutation screening, genome sequencing improves the sensitivity of detecting clinically significant variants. While certain novel variant interpretation remains challenging, the ACMG guidelines are useful to classify variants in a healthy population.

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Genomics-based carrier screening is one of many opportunities to use genomic information to inform medical decision making, but clinicians, health care delivery systems, and payers need to determine whether to offer screening and how to do so in an efficient, ethical way. To shed light on this issue, we conducted a study in the period 2014-17 to inform the design of clinical screening programs and guide further health services research. Many of our results have been published elsewhere; this article summarizes the lessons we learned from that study and offers policy insights. Our experience can inform understanding of the potential impact of expanded carrier screening services on health system workflows and workforces-impacts that depend on the details of the screening approach. We found limited patient or health system harms from expanded screening. We also found that some patients valued the information they learned from the process. Future policy discussions should consider the value of offering such expanded carrier screening in health delivery systems with limited resources.

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Disclaimer: This Points to Consider document is designed as an educational resource to provide best practices for medical genetic clinicians, laboratories, and journals regarding the provision, publication, and dissemination of patient phenotypes in the context of genomic testing, clinical genetic practice, and research. While the goal of the document is the improvement of patient care, the considerations and practices described should not be considered inclusive of all proper considerations and practices or exclusive of others that are reasonably directed to obtaining the same goal. In determining the value of any practice, clinicians, laboratories, and journals should apply their own professional standards and judgment to the specific circumstances presented.The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the authors' affiliated institutions.

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PURPOSE: We investigated the use of genome sequencing for preconception carrier testing. Genome sequencing could identify one or more of thousands of X-linked or autosomal recessive conditions that could be disclosed during preconception or prenatal counseling. Therefore, a framework that helps both clinicians and patients understand the possible range of findings is needed to respect patient preferences by ensuring that information about only the desired types of genetic conditions are provided to a given patient. METHODS: We categorized gene-condition pairs into groups using a previously developed taxonomy of genetic conditions. Patients could elect to receive results from these categories. A Return of Results Committee (RORC) developed inclusion and exclusion criteria for each category. RESULTS: To date, the RORC has categorized 728 gene-condition pairs: 177 are categorized as life span-limiting, 406 are categorized as serious, 93 are categorized as mild, 41 are categorized as unpredictable, and 11 are categorized as adult-onset. An additional 64 gene-condition pairs were excluded from reporting to patients or put on a watch list, generally because evidence that a gene and condition were associated was limited. CONCLUSION: Categorization of gene-condition pairs using our taxonomy simplifies communication regarding patient preferences for carrier information from a genomic test.Genet Med advance online publication 12 January 2017.

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Disclaimer: These recommendations are designed primarily as an educational resource for medical geneticists and other healthcare providers to help them provide quality medical services. Adherence to these recommendations is completely voluntary and does not necessarily assure a successful medical outcome. These recommendations should not be considered inclusive of all proper procedures and tests or exclusive of other procedures and tests that are reasonably directed toward obtaining the same results. In determining the propriety of any specific procedure or test, the clinician should apply his or her own professional judgment to the specific clinical circumstances presented by the individual patient or specimen. Clinicians are encouraged to document the reasons for the use of a particular procedure or test, whether or not it is in conformance with this statement. Clinicians also are advised to take notice of the date this statement was adopted and to consider other 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.To promote standardized reporting of actionable information from clinical genomic sequencing, in 2013, the American College of Medical Genetics and Genomics (ACMG) published a minimum list of genes to be reported as incidental or secondary findings. The goal was to identify and manage risks for selected highly penetrant genetic disorders through established interventions aimed at preventing or significantly reducing morbidity and mortality. The ACMG subsequently established the Secondary Findings Maintenance Working Group to develop a process for curating and updating the list over time. We describe here the new process for accepting and evaluating nominations for updates to the secondary findings list. We also report outcomes from six nominations received in the initial 15 months after the process was implemented. Applying the new process while upholding the core principles of the original policy statement resulted in the addition of four genes and removal of one gene; one gene did not meet criteria for inclusion. The updated secondary findings minimum list includes 59 medically actionable genes recommended for return in clinical genomic sequencing. We discuss future areas of focus, encourage continued input from the medical community, and call for research on the impact of returning genomic secondary findings.Genet Med 19 2, 249-255.

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