Data & Statistical Core
Core Leader: Gary Churchill
Mouse studies sponsored by The Jackson Laboratory Nathan Shock Center (JAX NSC) produce an enormous amount of data, including phenotypic, genetic, epigenetic, expression profiling and microbiome profiling data. Our focus on genetic diversity using Collaborative Cross (CC) inbred and Diversity Outbred (DO) mice requires the analysis of hundreds of mice. With new ‘omics’ approaches constantly being developed, the scope of data collection and analysis continues to grow. Thus, a well-functioning and efficient data management and statistical analysis operation is necessary. The overall goal of the JAX NSC Data and Statistical Core is to provide support for the management, quality control, analysis, and dissemination of data for JAX NSC projects. The Specific Aims of the Data and Statistical Core are to: Aim 1. Develop and maintain the data management, quality control, and analysis infrastructure needed to support JAX NSC projects. We will continue to develop this system to implement automated data collection and quality control procedures for our growing portfolio of pilot projects and collaborations. Aim 2. Disseminate JAX NSC data and analytical tools to provide greater access to these and other data resources related to mouse models in aging research. This aim will support development of new data types and web-based interactive tools. We will mine public repositories for aging-related transcriptional profiling data from mouse studies to update and integrate with JAX NSC data. Aim 3. Provide study design and statistical analysis support for the aging research community. The Core will engage in collaborative research with members of the aging research community to support the broader adoption of genetic diversity in aging studies that use mouse models. This outreach activity will be managed and tracked in parallel with projects in the other JAX NSC Cores but with an emphasis on working with collaborators who have or who plan to generate their own primary experimental data. The Data and Statistical Core is significant because it will provide efficient, comprehensive and cutting-edge management, analysis, and dissemination of data generated from aging mouse studies focused on understanding the genetic factors that underlie lifespan and healthspan. Improved data management and quality control methods, greater emphasis on active outreach to promote use of JAX NSC data, and deeper engagement with collaborative projects, will accelerate the pace of aging research, enhance the impact of the Core on the geroscience research community and increase the visibility of the JAX NSC data and other resources.
The Statistical Core provides data analysis support for ongoing studies conducted within the JAX NSC including the development of new analysis methods and software necessary to support these projects. This core also disseminates JAX NSC data in conjunction with the Mouse Phenome Database and through the development of web services and interfaces to provide access to large-scale data resources. In addition the core also provide experimental design and analysis support to the aging research community.
In collaboration with Dr. Steven Gygi (Harvard University), we carried out proteomic profiling of 188 heart and kidney samples from our DO cross-sectional study tissue bank. We identified functional categories of genes that change with age and mapped genetic associations that are age-dependent. We demonstrated that effects of age on the proteome are not mediated through corresponding changes in mRNA. In contrast, proteins that differ between the sexes are primarily mediated through RNA. Our results suggest that the effects of age on the proteome are not genetically programmed but rather reflect accumulation of post-translational modifications over time. The data have been released with interactive analysis tools on the JAX NSC website. This study is in preparation for publication.
We have compiled lifespan data on more than 2200 DO mice from our own JAX NSC studies and from two independent studies carried out at JAX (by Drs. Karen Svenson and David Harrison) to investigate the responses to dietary restriction and rapamycin in DO mice (Figure 1). We carried out genetic mapping of lifespan as a meta-analysis across these studies and established that lifespan is heritable in a range similar to human studies (15-25%). Our findings suggest that many, perhaps hundreds, of individual loci influence lifespan. However, unlike human studies, the combined statistically significant loci in DO mice explain up to 50% of the genetic variation in lifespan in DO mice.
We have established sample size guidelines for intervention studies using DO mice. We computed sample sizes required to detect a change in lifespan between groups of treated and control animals using data from our DO longitudinal study (Figure 2). Due to the added genetic variability in lifespan, DO studies require approximately twice as many animals compared to a C57BL/6J study to detect the same magnitude of change. However, if we consider the possibility that the intervention effect may vary across strains, it makes economic sense to carry out initial studies on the outbred population rather than testing multiple inbred strains.
We are evaluating phenotypic (healthspan) data from our longitudinal study to identify potential predictors of lifespan. To date, our findings suggest that long-term predictions (beyond six months) are not better than chance expectation, despite many parameters that show trends with age. However, we can accurately predict death within the next six months (Fig. 3). Findings from the ongoing project suggest that while healthspan para-meters provide reliable indicators of “death’s door,” we have yet to discover the key to long-range prediction of lifespan.