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The Mouse Phenome Database (MPD; https://phenome.jax.org; RRID:SCR_003212), supported by the US National Institutes of Health, is a Biomedical Data Repository listed in the Trans-NIH Biomedical Informatics Coordinating Committee registry. As an increasingly FAIR-compliant and TRUST-worthy data repository, MPD accepts phenotype and genotype data from mouse experiments and curates, organizes, integrates, archives, and distributes those data using community standards. Data are accompanied by rich metadata, including widely used ontologies and detailed protocols. Data are from all over the world and represent genetic, behavioral, morphological, and physiological disease-related characteristics in mice at baseline or those exposed to drugs or other treatments. MPD houses data from over 6000 strains and populations, representing many reproducible strain types and heterogenous populations such as the Diversity Outbred where each mouse is unique but can be genotyped throughout the genome. A suite of analysis tools is available to aggregate, visualize, and analyze these data within and across studies and populations in an increasingly traceable and reproducible manner. We have refined existing resources and developed new tools to continue to provide users with access to consistent, high-quality data that has translational relevance in a modernized infrastructure that enables interaction with a suite of bioinformatics analytic and data services.

Visualizing regions of conserved synteny between two genomes is supported by numerous software applications. However, none of the current applications allow researchers to select genome features to display or highlight in blocks of synteny based on the annotated biological properties of the features (e.g., type, function, and/or phenotype association). To address this usability gap, we developed an interactive web-based conserved synteny browser, The Jackson Laboratory (JAX) Synteny Browser. The browser allows researchers to highlight or selectively display genome features in the reference and/or the comparison genome according to the biological attributes of the features. Although the current implementation for the browser is limited to the reference genomes for the laboratory mouse and human, the software platform is intentionally genome agnostic. The JAX Synteny Browser software can be deployed for any two genomes where genome coordinates for syntenic blocks are defined and for which biological attributes of the features in one or both genomes are available in widely used standard bioinformatics file formats. The JAX Synteny Browser is available at: http://syntenybrowser.jax.org/. The code base is available from GitHub: https://github.com/TheJacksonLaboratory/syntenybrowser and is distributed under the Creative Commons Attribution license (CC BY).

It is well understood that variation in relatedness among individuals, or kinship, can lead to false genetic associations. Multiple methods have been developed to adjust for kinship while maintaining power to detect true associations. However, relatively unstudied are the effects of kinship on genetic interaction test statistics. Here, we performed a survey of kinship effects on studies of six commonly used mouse populations. We measured inflation of main effect test statistics, genetic interaction test statistics, and interaction test statistics reparametrized by the Combined Analysis of Pleiotropy and Epistasis (CAPE). We also performed linear mixed model (LMM) kinship corrections using two types of kinship matrix: an overall kinship matrix calculated from the full set of genotyped markers, and a reduced kinship matrix, which left out markers on the chromosome(s) being tested. We found that test statistic inflation varied across populations and was driven largely by linkage disequilibrium. In contrast, there was no observable inflation in the genetic interaction test statistics. CAPE statistics were inflated at a level in between that of the main effects and the interaction effects. The overall kinship matrix overcorrected the inflation of main effect statistics relative to the reduced kinship matrix. The two types of kinship matrices had similar effects on the interaction statistics and CAPE statistics, although the overall kinship matrix trended toward a more severe correction. In conclusion, we recommend using an LMM kinship correction for both main effects and genetic interactions and further recommend that the kinship matrix be calculated from a reduced set of markers in which the chromosomes being tested are omitted from the calculation. This is particularly important in populations with substantial population structure, such as recombinant inbred lines in which genomic replicates are used.

It is well understood that variation in relatedness among individuals, or kinship, can lead to false genetic associations. Multiple methods have been developed to adjust for kinship while maintaining power to detect true associations. However, relatively unstudied, are the effects of kinship on genetic interaction test statistics. Here we performed a survey of kinship effects on studies of six commonly used mouse populations. We measured inflation of main effect test statistics, genetic interaction test statistics, and interaction test statistics reparametrized by the Combined Analysis of Pleiotropy and Epistasis (CAPE). We also performed linear mixed model (LMM) kinship corrections using two types of kinship matrix: an overall kinship matrix calculated from the full set of genotyped markers, and a reduced kinship matrix, which left out markers on the chromosome(s) being tested. We found that test statistic inflation varied across populations and was driven largely by linkage disequilibrium. In contrast, there was no observable inflation in the genetic interaction test statistics. CAPE statistics were inflated at a level in between that of the main effects and the interaction effects. The overall kinship matrix overcorrected the inflation of main effect statistics relative to the reduced kinship matrix. The two types of kinship matrices had similar effects on the interaction statistics and CAPE statistics, although the overall kinship matrix trended toward a more severe correction. In conclusion, we recommend using a LMM kinship correction for both main effects and genetic interactions and further recommend that the kinship matrix be calculated from a reduced set of markers in which the chromosomes being tested are omitted from the calculation. This is particularly important in populations with substantial population structure, such as recombinant inbred lines in which genomic replicates are used.

The Mouse Phenome Database (MPD; https://phenome.jax.org) is a widely used resource that provides access to primary experimental trait data, genotypic variation, protocols and analysis tools for mouse genetic studies. Data are contributed by investigators worldwide and represent a broad scope of phenotyping endpoints and disease-related traits in naïve mice and those exposed to drugs, environmental agents or other treatments. MPD houses individual animal data with detailed, searchable protocols, and makes these data available to other resources via API. MPD provides rigorous curation of experimental data and supporting documentation using relevant ontologies and controlled vocabularies. Most data in MPD are from inbreds and other reproducible strains such that the data are cumulative over time and across laboratories. The resource has been expanded to include the QTL Archive and other primary phenotype data from mapping crosses as well as advanced high-diversity mouse populations including the Collaborative Cross and Diversity Outbred mice. Furthermore, MPD provides a means of assessing replicability and reproducibility across experimental conditions and protocols, benchmarking assays in users’ own laboratories, identifying sensitized backgrounds for making new mouse models with genome editing technologies, analyzing trait co-inheritance, finding the common genetic basis for multiple traits and assessing sex differences and sex-by-genotype interactions.

The Mouse Phenome Database (MPD; https://phenome.jax.org) is a widely accessed and highly functional data repository housing primary phenotype data for the laboratory mouse accessible via APIs and providing tools to analyze and visualize those data. Data come from investigators around the world and represent a broad scope of phenotyping endpoints and disease-related traits in naïve mice and those exposed to drugs, environmental agents or other treatments. MPD houses rigorously curated per-animal data with detailed protocols. Public ontologies and controlled vocabularies are used for annotation. In addition to phenotype tools, genetic analysis tools enable users to integrate and interpret genome–phenome relations across the database. Strain types and populations include inbred, recombinant inbred, F1 hybrid, transgenic, targeted mutants, chromosome substitution, Collaborative Cross, Diversity Outbred and other mapping populations. Our new analysis tools allow users to apply selected data in an integrated fashion to address problems in trait associations, reproducibility, polygenic syndrome model selection and multi-trait modeling. As we refine these tools and approaches, we will continue to provide users a means to identify consistent, quality studies that have high translational relevance.

As bioinformatic workflows become increasingly complex and involve multiple specialized tools, so does the difficulty of reliably reproducing those workflows. Cytoscape is a critical workflow component for executing network visualization, analysis, and publishing tasks, but it can be operated only manually via a point-and-click user interface. Consequently, Cytoscape-oriented tasks are laborious and often error prone, especially with multistep protocols involving many networks.In this paper, we present the new cyREST Cytoscape app and accompanying harmonization libraries. Together, they improve workflow reproducibility and researcher productivity by enabling popular languages (e.g., Python and R, JavaScript, and C#) and tools (e.g., IPython/Jupyter Notebook and RStudio) to directly define and query networks, and perform network analysis, layouts and renderings. We describe cyREST’s API and overall construction, and present Python- and R-based examples that illustrate how Cytoscape can be integrated into large scale data analysis pipelines.cyREST is available in the Cytoscape app store (http://apps.cytoscape.org) where it has been downloaded over 1900 times since its release in late 2014.