GenomeMUSter mouse genetic variation service enables multitrait, multipopulation data integration and analysis.

Hundreds of inbred mouse strains and intercross populations have been used to characterize the function of genetic variants that contribute to disease. Thousands of disease-relevant traits have been characterized in mice and made publicly available. New strains and populations including consomics, the collaborative cross, expanded BXD, and inbred wild-derived strains add to existing complex disease mouse models, mapping populations, and sensitized backgrounds for engineered mutations. The genome sequences of inbred strains, along with dense genotypes from others, enable integrated analysis of trait-variant associations across populations, but these analyses are hampered by the sparsity of genotypes available. Moreover, the data are not readily interoperable with other resources. To address these limitations, we created a uniformly dense variant resource by harmonizing multiple data sets. Missing genotypes were imputed using the Viterbi algorithm with a data-driven technique that incorporates local phylogenetic information, an approach that is extendable to other model organisms. The result is a web- and programmatically accessible data service called GenomeMUSter, comprising single-nucleotide variants covering 657 strains at 106.8 million segregating sites. Interoperation with phenotype databases, analytic tools, and other resources enable a wealth of applications, including multitrait, multipopulation meta-analysis. We show this in cross-species comparisons of type 2 diabetes and substance use disorder meta-analyses, leveraging mouse data to characterize the likely role of human variant effects in disease. Other applications include refinement of mapped loci and prioritization of strain backgrounds for disease modeling to further unlock extant mouse diversity for genetic and genomic studies in health and disease.

MVAR: A Mouse Variation Registry.

The Mouse Variation Registry (MVAR) resource is a scalable registry of mouse single nucleotide variants and small indels and variant annotation. The resource accepts data in standard Variant Call Format (VCF) and assesses the uniqueness of the submitted variants via a canonicalization process. Novel variants are assigned a unique, persistent MVAR identifier; variants that are equivalent to an existing variant in the resource are associated with the existing identifier. Annotations for variant type, molecular consequence, impact, and genomic region in the context of specific transcripts and protein sequences are generated using Ensembl's Variant Effect Predictor (VEP) and Jannovar. Access to the data and annotations in MVAR are supported via an Application Programming Interface (API) and web application. Researchers can search the resource by gene symbol, genomic region, variant (expressed in Human Genome Variation Society syntax), refSNP identifiers, or MVAR identifiers. Tabular search results can be filtered by variant annotations (variant type, molecular consequence, impact, variant region) and viewed according to variant distribution across mouse strains. The registry currently comprises more than 99 million canonical single nucleotide variants for 581 strains of mice. MVAR is accessible from https://mvar.jax.org.

Mouse Genome Informatics: an integrated knowledgebase system for the laboratory mouse.

Mouse Genome Informatics (MGI) is a federation of expertly curated information resources designed to support experimental and computational investigations into genetic and genomic aspects of human biology and disease using the laboratory mouse as a model system. The Mouse Genome Database (MGD) and the Gene Expression Database (GXD) are core MGI databases that share data and system architecture. MGI serves as the central community resource of integrated information about mouse genome features, variation, expression, gene function, phenotype, and human disease models acquired from peer-reviewed publications, author submissions, and major bioinformatics resources. To facilitate integration and standardization of data, biocuration scientists annotate using terms from controlled metadata vocabularies and biological ontologies (e.g. Mammalian Phenotype Ontology, Mouse Developmental Anatomy, Disease Ontology, Gene Ontology, etc.), and by applying international community standards for gene, allele, and mouse strain nomenclature. MGI serves basic scientists, translational researchers, and data scientists by providing access to FAIR-compliant data in both human-readable and compute-ready formats. The MGI resource is accessible at https://informatics.jax.org. Here, we present an overview of the core data types represented in MGI and highlight recent enhancements to the resource with a focus on new data and functionality for MGD and GXD.

Executing plans to enhance diversity across cancer centers in the United States: opportunities and challenges.

BACKGROUND: Lack of diversity in the cancer research workforce persists, which the new requirement for all National Cancer Institute (NCI)-designated cancer centers to have a Plan to Enhance Diversity (PED) seeks to address. However, it is not well understood how different cancer centers are approaching the development and execution of these plans. Our objective was to assess how cancer centers are establishing and pursuing their PED. METHODS: We conducted a cross-sectional survey of members of the Cancer Center Diversity, Equity and Inclusion Network, which includes all NCI-designated cancer centers and several emerging centers. A total of 62 cancer centers (75% of those invited), including 58 NCI-designated cancer centers (81% of those with this designation), participated and completed a questionnaire that assessed PED leadership, major challenges, implementation strategies, and approach to evaluate PED progress. RESULTS: The most common PED challenge identified is recruiting diverse faculty (68% of centers), and the most common strategy currently used to address this is reviewing and revising faculty recruitment practices (67%). The most common approach centers are using to measure PED progress is shifts in demographics (68%), and data on the demographics of faculty, leadership, and trainees are available at 79%, 81%, and 75% of centers, respectively. CONCLUSIONS: Almost all centers have established a PED leadership structure, however, there is considerable variation in the approaches used to realize PED goals and in the resources provided to support PED work. Realizing opportunities to share and implement common best practices and exemplar programs has the potential to elevate the impact of PED efforts nationally.

In vivo activity of the second-generation proteasome inhibitor ixazomib against pediatric T-cell acute lymphoblastic leukemia xenografts.

The overall survival rate of patients with T-cell acute lymphoblastic leukemia (T-ALL) is now 90%, although patients with relapsed T-ALL face poor prognosis. The ubiquitin-proteasome system maintains normal protein homeostasis, and aberrations in this pathway are associated with T-ALL. Here we demonstrate the in vitro and in vivo activity of ixazomib, a second-generation orally available, reversible, and selective proteasome inhibitor against pediatric T-ALL cell lines and patient-derived xenografts (PDXs) grown orthotopically in immunodeficient NOD.Cg-PrkdcscidIL2rgtm1Wjl/SzJAusb (NSG) mice. Ixazomib was highly potent in vitro, with half-maximal inhibitory concentration (IC50) values in the low nanomolar range. As a monotherapy, ixazomib significantly extended mouse event-free survival of five out of eight T-ALL PDXs in vivo.

Minimum information and guidelines for reporting a multiplexed assay of variant effect.

Multiplexed assays of variant effect (MAVEs) have emerged as a powerful approach for interrogating thousands of genetic variants in a single experiment. The flexibility and widespread adoption of these techniques across diverse disciplines have led to a heterogeneous mix of data formats and descriptions, which complicates the downstream use of the resulting datasets. To address these issues and promote reproducibility and reuse of MAVE data, we define a set of minimum information standards for MAVE data and metadata and outline a controlled vocabulary aligned with established biomedical ontologies for describing these experimental designs.