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PURPOSE: Genomic test results, regardless of laboratory variant classification, require clinical practitioners to judge the applicability of a variant for medical decisions. Teaching and standardizing clinical interpretation of genomic variation calls for a methodology or tool. METHODS: To generate such a tool, we distilled the Clinical Genome Resource framework of causality and the American College of Medical Genetics/Association of Molecular Pathology and Quest Diagnostic Laboratory scoring of variant deleteriousness into the Clinical Variant Analysis Tool (CVAT). Applying this to 289 clinical exome reports, we compared the performance of junior practitioners with that of experienced medical geneticists and assessed the utility of reported variants. RESULTS: CVAT enabled performance comparable to that of experienced medical geneticists. In total, 124 of 289 (42.9%) exome reports and 146 of 382 (38.2%) reported variants supported a diagnosis. Overall, 10.5% (1 pathogenic [P] or likely pathogenic [LP] variant and 39 variants of uncertain significance [VUS]) of variants were reported in genes without established disease association; 20.2% (23 P/LP and 54 VUS) were in genes without sufficient phenotypic concordance; 7.3% (15 P/LP and 13 VUS) conflicted with the known molecular disease mechanism; and 24% (91 VUS) had insufficient evidence for deleteriousness. CONCLUSION: Implementation of CVAT standardized clinical interpretation of genomic variation and emphasized the need for collaborative and transparent reporting of genomic variation.

Study objectives As cannabis is increasingly used to treat sleep disorders, we performed a systematic review to examine the effects of cannabis on sleep and to guide cannabis prescribers in their recommendations to patients, specifically focusing on dosing. Methods We searched EMBASE, Medline, and Web of Science and identified 4550 studies for screening. Five hundred sixty-eight studies were selected for full-text review and 31 were included for analysis. Study results were considered positive based on improvements in sleep architecture or subjective sleep quality. Bias in randomized controlled trials was assessed using Cochrane Risk of Bias tool 2.0. Results Sleep improvements were seen in 7 out of 19 randomized studies and in 7 out of 12 uncontrolled trials. There were no significant differences between the effects of tetrahydrocannabinol and cannabidiol. Cannabis showed most promise at improving sleep in patients with pain-related disorders, as compared to those with neurologic, psychiatric, or sleep disorders, and showed no significant effects on healthy participants’ sleep. While subjective improvements in sleep quality were often observed, diagnostic testing showed no improvements in sleep architecture. Adverse events included headaches, sedation, and dizziness, and occurred more frequently at higher doses, though no serious adverse events were observed. Conclusion High-quality evidence to support cannabis use for sleep remains limited. Heterogeneity in cannabis types, doses, timing of administration, and sleep outcome measures limit the ability to make specific dosing recommendations.