Improving replicability using interaction with laboratories: a multi-lab experimental assessment

Phenotyping inbred and genetically-engineered mouse lines has become a central strategy for discovering mammalian gene function and evaluating pharmacological treatment. Yet the utility of any findings critically depends on their replicability in other laboratories. In previous publications we proposed a statistical approach for estimating the inter-laboratory replicability of novel discoveries in a single laboratory, and demonstrated that previous phenotyping results from multi-lab databases can be used to derive a Genotype-by-Lab (GxL) adjustment factor to ensure the replicability of single-lab results, for similarly measured phenotypes, even before making the effort of replicating the new finding in additional laboratories.The demonstration above, however, still raised several important questions that could only be answered by an additional large-scale prospective experiment: Does GxL-adjustment works in single-lab experiments that were not intended to be standardized across laboratories? With genotypes that were not included in the previous experiments? And can it be used to adjust the results of pharmacological treatment experiments? We replicated results from five studies in the Mouse Phenome Database (MPD), in three behavioral tests, across three laboratories, offering 212 comparisons including 60 involving a pharmacological treatment: 18 mg/kg/day fluoxetine. In addition, we define and use a dimensionless GxL factor, derived from dividing the GxL variance by the standard deviation between animals within groups, as the more robust vehicle to transfer the adjustment from the multi-lab analysis to very different labs and genotypes.For genotype comparisons, GxL-adjustment reduced the rate of non-replicable discoveries from 60% to 12%, for the price of reducing the power to make replicable discoveries from 87% to 66%. Another way to look at these results is noting that the adjustment could have prevented 23 failures to replicate, for the price of missing only three replicated ones. The tools and data needed for deployment of this method across other mouse experiments are publicly available in the Mouse Phenome Database.Our results further point at some phenotypes as more prone to produce non-replicable results, while others, known to be more difficult to measure, are as likely to produce replicable results (once adjusted) as the physiological body weight is.

Analysis of the sex-specific variability of blood parameters in data sets of the Mouse Phenome Database

Objective The use of mice as animal models in biomedical research allows the standardization of genetic background and environmental conditions, which both affect phenotypic variability. As the use of both sexes in experiments is strongly recommended, sex-specific phenotypic variability is discussed with regard to putative consequences on the group size which is necessary for achieving valid and reproducible results. In this study, the sex-specific variability of 25 clinical chemical and hematological parameters which represent a comprehensive blood screen of laboratory mice, was analyzed in data sets which have been submitted to the Mouse Phenome Database. Results The overall analysis comprising all 25 clinical chemical and hematological parameters showed no evidence for substantial and robust general sex-specific variability. A large range of the ratio of the female and male coefficient of variation (CV) was found for every parameter among the respective strain data sets. This clearly demonstrated the appearance of unpredictable major interactions between genotype and environment regarding the sex-specific variability of the blood parameters analyzed.

Identification of a blood test-based biomarker of aging through deep learning of aging trajectories in large phenotypic datasets of mice

We proposed and characterized a novel biomarker of aging and frailty in mice trained from the large set of the most conventional, easily measured blood parameters such as Complete Blood Counts (CBC) from the open-access Mouse Phenome Database (MPD). Instead of postulating the existence of an aging clock associated with any particular subsystem of an aging organism, we assumed that aging arises cooperatively from positive feedback loops spanning across physiological compartments and leading to an organism-level instability of the underlying regulatory network. To analyze the data, we employed a deep artificial neural network including auto-encoder (AE) and auto-regression (AR) components. The AE was used for dimensionality reduction and denoising the data. The AR was used to describe the dynamics of an individual mouse’s health state by means of stochastic evolution of a single organism state variable, the “dynamic frailty index” (dFI), that is the linear combination of the latent AE features and has the meaning of the total number of regulatory abnormalities developed up to the point of the measurement or, more formally, the order parameter associated with the instability. We used neither the chronological age nor the remaining lifespan of the animals while training the model. Nevertheless, dFI fully described aging on the organism level, that is it increased exponentially with age and predicted remaining lifespan. Notably, dFI correlated strongly with multiple hallmarks of aging such as physiological frailty index, indications of physical decline, molecular markers of inflammation and accumulation of senescent cells. The dynamic nature of dFI was demonstrated in mice subjected to aging acceleration by placement on a high-fat diet and aging deceleration by treatment with rapamycin.

Mouse Phenome Database: a data repository and analysis suite for curated primary mouse phenotype data

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.