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. Where individual authors are listed, the views expressed may not reflect those of authors’ employers or affiliated institutions.
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.
Significance The conventional parametric approach to modeling relies on hypothesized equations to approximate mechanistic processes. Although there are known limitations in using an assumed set of ...equations, parametric models remain widely used to test for interactions, make predictions, and guide management decisions. Here, we show that these objectives are better addressed using an alternative equation-free approach, empirical dynamic modeling (EDM). Applied to Fraser River sockeye salmon, EDM models ( i ) recover the mechanistic relationship between the environment and population biology that fisheries models dismiss as insignificant, ( ii ) produce significantly better forecasts compared with contemporary fisheries models, and ( iii ) explicitly link control parameters (spawning abundance) and ecosystem objectives (future recruitment), producing models that are suitable for current management frameworks.
It is well known that current equilibrium-based models fall short as predictive descriptions of natural ecosystems, and particularly of fisheries systems that exhibit nonlinear dynamics. For example, model parameters assumed to be fixed constants may actually vary in time, models may fit well to existing data but lack out-of-sample predictive skill, and key driving variables may be misidentified due to transient (mirage) correlations that are common in nonlinear systems. With these frailties, it is somewhat surprising that static equilibrium models continue to be widely used. Here, we examine empirical dynamic modeling (EDM) as an alternative to imposed model equations and that accommodates both nonequilibrium dynamics and nonlinearity. Using time series from nine stocks of sockeye salmon ( Oncorhynchus nerka ) from the Fraser River system in British Columbia, Canada, we perform, for the the first time to our knowledge, real-data comparison of contemporary fisheries models with equivalent EDM formulations that explicitly use spawning stock and environmental variables to forecast recruitment. We find that EDM models produce more accurate and precise forecasts, and unlike extensions of the classic Ricker spawner–recruit equation, they show significant improvements when environmental factors are included. Our analysis demonstrates the strategic utility of EDM for incorporating environmental influences into fisheries forecasts and, more generally, for providing insight into how environmental factors can operate in forecast models, thus paving the way for equation-free mechanistic forecasting to be applied in management contexts.
ACMG previously developed recommendations for standards for interpretation of sequence variations. We now present the updated revised recommendations. Here, we describe six interpretative categories ...of sequence variations: (1) sequence variation is previously reported and is a recognized cause of the disorder; (2) sequence variation is previously unreported and is of the type which is expected to cause the disorder; (3) sequence variation is previously unreported and is of the type which may or may not be causative of the disorder; (4) sequence variation is previously unreported and is probably not causative of disease; (5) sequence variation is previously reported and is a recognized neutral variant; and (6) sequence variation is previously not known or expected to be causative of disease, but is found to be associated with a clinical presentation. We emphasize the importance of appropriate reporting of sequence variations using standardized terminology and established databases, and of clearly reporting the limitations of sequence-based testing. We discuss follow-up studies that may be used to ascertain the clinical significance of sequence variations, including the use of additional tools (such as predictive software programs) that may be useful in variant classification. As more information becomes available allowing the interpretation of a new sequence variant, it is recommended that the laboratory amend previous reports and provide updated results to the physician. The ACMG strongly recommends that the clinical and technical validation of sequence variation detection be performed in a CLIA-approved laboratory and interpreted by a board-certified clinical molecular geneticist or equivalent.
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.
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.
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.
The use of AI in diagnostic laboratories greatly enhances exome sequencing analysis by reducing analysis time and increasing the diagnostic rate.
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.
Clinical sequencing emerging in health care may result in secondary findings (SFs).
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.
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.
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.
PDF
