Mouse Phenome Database: towards a more FAIR-compliant and TRUST-worthy data repository and tool suite for phenotypes and genotypes

Abstract

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.

Figure 1.

Study Intake Platform screenshot. SIP is set up to be a step-by-step form with tabs (upper red arrow) for Project Details, Animal Profile, Procedures, Data Upload, Genotype Intake and Data Validation. The screenshot shows some of the fields which are available for Project Details (lower red arrow), including information about the contributor (German Mouse Clinic) and participants. The Data Upload tab provides the ability to define data types and study types, and it provides fields for annotating with ontology terms and methods used for each measure uploaded.

Figure 2.

GenomeMUSter search results page. The results table includes chromosome, location (bp), rsID, observed alleles, functional annotation, gene and known and imputed SNP calls for, in this case, Collaborative Cross founder strains. The reference strain is B6Eve (12). Note the option to ‘Show confidence level data’ which provides a heat map. Users can select cut-offs using a handy slider (not shown), and the table will automatically update.

Figure 3.

Manhattan plot of GWAS meta-analysis results. Bone mineral density measures (n = 10 from seven studies) were chosen for analysis (see accession numbers). Genomic location is displayed along the x-axis and the negative logarithm (base 10) of the association P-value for each SNP is shown on the y-axis. Note that data points ≥3 are highlighted and results can be filtered based on –log₁₀(P-value) (see slider). The circled SNP is further analyzed in Figure 4.

Figure 4.

P–M plot (left) and Forest plot (right) for SNP rs30281400, circled in Figure 3. For a selected SNP, the P–M plot displays the individual measures’ P-values versus the m-values (posterior probability the effect exists in the measure) (22). The m-value distinguishes measures where the effect exists (m-value ≥ 0.9), measures where the effect does not exist (m-value ≤ 0.1), and measures where the effect is uncertain (0.1 < m-value < 0.9). The Forest plot shows effect size (midline), standard error (bars), and study size (size of box) on a log odds ratio axis for all measures in the analysis. Data are color coded based on sex.