# Genetic correlation-guided mega-analysis of DO mice provides mechanistic insight and candidate genes for age-related pathologies

## TL;DR

Using genetic correlations in DO mice, the study identifies shared genetic factors and candidate genes for age-related health issues like aortic and lung pathologies.

## Contribution

The study introduces genetic correlation-guided mega-analysis in DO mice to uncover novel genetic links across diverse traits.

## Key findings

- 884 QTLs were detected for 383 meta-phenotypes, explaining 40.36% of genetic variance on average.
- Lifespan genetics showed limited correlation with frailty but stronger ties to immune cell composition.
- Candidate genes like Cdkn2b were linked to extracellular matrix degradation in the aorta.

## Abstract

Diversity Outbred (DO) mice are a powerful model system for mapping complex traits due to their high genetic diversity and mapping resolution. However, while there are extensive tools available for standard genetic analysis in DO mice, fewer techniques have been implemented to facilitate integrated, cross-study analysis. Here, we implement Haseman-Elston regression to estimate genetic correlations among 7,233 phenotypes measured across eleven independent DO mouse studies. We used this network of genetic correlations to cluster phenotypes according to shared genetics, which enhanced the power to detect quantitative trait loci (QTL). This approach empowered the detection of 884 QTL for 383 meta-phenotypes, explaining an average of 40.36% of the total genetic variance per mega-analysis. We leveraged this network for insights into specific areas of biology, including lifespan, frailty, immune composition, histological and functional lung phenotypes, and histological phenotypes of the aorta. We found the genetics of lifespan to share limited correlation with the genetics of frailty but stronger correlation with the genetics of immune cell composition. Additionally, mega-analyses driven by genetic correlations identified candidate genes (e.g., Cdkn2b) associated with degraded extracellular matrix in the aorta. Finally, an ensemble of genetic analyses implicated pulmonary neuroendocrine cell signaling and/or differentiation as a key driver of multiple lung pathophenotypes.

Diversity Outbred (DO) mice are a powerful system for studying the genetics of complex traits, but many of the techniques that are useful for human genetic studies have not been adapted for use with them. Here, we implement a method for DO’s – genetic correlation – that estimates whether pairs of traits are influenced by the same genetic factors. This technique allows traits with similar underlying biology to be identified, even when data on the traits was collected in different cohorts of animals. By applying this method to thousands of traits from different datasets, we gained insight into how seemingly disparate aspects of physiology are connected. We also identified ensembles of traits whose genetics can be analyzed together, empowering the discovery of shared genetic factors. We applied this technique to discover genes that influence the health of elastic tissues: aortas and lungs. We also provide the tools and resources to gain similar insights from the wider scope of DO mouse research studies, both past and future.