Evaluating diabetes and hypertension disease causality using mouse phenotypes

Background Genome-wide association studies (GWAS) have found hundreds of single nucleotide polymorphisms (SNPs) associated with common diseases. However, it is largely unknown what genes linked with the SNPs actually implicate disease causality. A definitive proof for disease causality can be demonstration of disease-like phenotypes through genetic perturbation of the genes or alleles, which is obviously a daunting task for complex diseases where only mammalian models can be used. Results Here we tapped the rich resource of mouse phenotype data and developed a method to quantify the probability that a gene perturbation causes the phenotypes of a disease. Using type II diabetes (T2D) and hypertension (HT) as study cases, we found that the genes, when perturbed, having high probability to cause T2D and HT phenotypes tend to be hubs in the interactome networks and are enriched for signaling pathways regulating metabolism but not metabolic pathways, even though the genes in these metabolic pathways are often the most significantly changed in expression levels in these diseases. Conclusions Compared to human genetic disease-based predictions, our mouse phenotype based predictors greatly increased the coverage while keeping a similarly high specificity. The disease phenotype probabilities given by our approach can be used to evaluate the likelihood of disease causality of disease-associated genes and genes surrounding disease-associated SNPs.