A Systems Genetics Approach to the Identification of Causal Genes in Heart Failure Using a Large Mouse Panel
- Author(s): Rau, Christoph Daniel
- Advisor(s): Lusis, Aldons J
- et al.
Heart failure (HF) accounts for 1 in 9 deaths in the United States and is the leading cause of hospitalization for people over the age of 65 and the incidence of HF is predicted to rise over the coming years. The complexity which underlies common forms of HF has hindered the study of the disease in humans, and approaches, such as genome-wide association studies (GWAS), have had only modest success in identifying genes which are related to this disease. Here we describe the use of a panel of mice to facilitate the study of this complex disorder, reducing heterogeneity and facilitating systems-level approaches.
We used the beta-adrenergic agonist isoproterenol to induce HF in 105 unique strains drawn from the Hybrid Mouse Diversity Panel, a novel mouse resource population for the analysis of complex traits. Our first study reports the results of a GWAS on heart weights, cardiac fibrosis and other surrogate traits relevant to HF. Among the 32 significant loci, we identified several strong candidates which had previously been shown to contribute to mendelian forms of cardiomyopathy. We were also able to validate two novel candidate genes, the orphan transporter Abcc6, and the long noncoding RNA Miat, using gene targeting, transgenic and in vitro approaches.
As part of our systems genetics approach, we developed a novel gene network construction algorithm, which improves on prior methods by allowing non-linear interactions and the ability for genes to operate in multiple modules at once. We were able to demonstrate using previously published data that our results either matched or exceeded another well-known network construction algorithm. In a subsequent study, we applied this method to transcriptomes taken from the HF study. We identified a module of 41 genes which significantly regulates the response of the heart to isoproterenol and HF and which contains several genes of interest such as Lgals3, a diagnostic marker for human HF.
Our results provide a valuable resource toward a better understanding of the pathways and gene-by-environment interactions influencing heart failure.