Clinical Variant Reclassification in Hereditary Disease Genetic Testing.

Importance: Because accurate and consistent classification of DNA sequence variants is fundamental to germline genetic testing, understanding patterns of initial variant classification (VC) and subsequent reclassification from large-scale, empirical data can help improve VC methods, promote equity among race, ethnicity, and ancestry (REA) groups, and provide insights to inform clinical practice.

Objectives: To measure the degree to which initial VCs met certainty thresholds set by professional guidelines and quantify the rates of, the factors associated with, and the impact of reclassification among more than 2 million variants.

Design, Setting, And Participants: This cohort study used clinical multigene panel and exome sequencing data from diagnostic testing for hereditary disorders, carrier screening, or preventive genetic screening from individuals for whom genetic testing was performed between January 1, 2015, and June 30, 2023.

Variable effects of transient Wolbachia infections on alphaviruses in Aedes aegypti.

Wolbachia pipientis (= Wolbachia) has promise as a tool to suppress virus transmission by Aedes aegypti mosquitoes. However, Wolbachia can have variable effects on mosquito-borne viruses. This variation remains poorly characterized, yet the multimodal effects of Wolbachia on diverse pathogens could have important implications for public health.

Olfactory receptor coexpression and co-option in the dengue mosquito.

The olfactory sensory neurons of vinegar flies and mice tend to express a single ligand-specific receptor. While this 'one neuron-one receptor' motif has long been expected to apply broadly across insects, recent evidence suggests it may not extend to mosquitoes. We sequenced and analyzed the transcriptomes of 46,000 neurons from antennae of the dengue mosquito to resolve all olfactory, thermosensory, and hygrosensory neuron subtypes and identify the receptors expressed therein.

Applications of artificial intelligence in clinical laboratory genomics.

The transition from analog to digital technologies in clinical laboratory genomics is ushering in an era of "big data" in ways that will exceed human capacity to rapidly and reproducibly analyze those data using conventional approaches. Accurately evaluating complex molecular data to facilitate timely diagnosis and management of genomic disorders will require supportive artificial intelligence methods. These are already being introduced into clinical laboratory genomics to identify variants in DNA sequencing data, predict the effects of DNA variants on protein structure and function to inform clinical interpretation of pathogenicity, link phenotype ontologies to genetic variants identified through exome or genome sequencing to help clinicians reach diagnostic answers faster, correlate genomic data with tumor staging and treatment approaches, utilize natural language processing to identify critical published medical literature during analysis of genomic data, and use interactive chatbots to identify individuals who qualify for genetic testing or to provide pre-test and post-test education.