Obesity and its comorbidities such as type 2 diabetes are a common health problem in the U.S. and throughout the developed world. Approximately one-third of the U.S population is currently obese. There is strong evidence that genetic factors contribute to an individual's propensity for weight gain, however identifying these factors has proven difficult. Part of the difficulty is likely due to complex interactions between genes that modify the phenotypic effects of each gene. Identifying interacting genes is difficult in both humans and in model organisms using traditional genetic techniques. We will to take a novel approach based on using mouse chromosome substitutions strains to characterize and identify these interacting genes. These strains have many advantages that facilitate the identification of genes underlying complex traits including improved reproducibility and simplified genetic crosses and data interpretation due to the partitioning of the genome into non-overlapping segments. We propose to study gene expression in pairwise combinations of the chromosome substitution strains. This will allow us to identify genes with expression patterns that are dependent on non-additive interactions between unlinked loci. This data will then be applied to existing human datasets for which gene expression and genotyping data are available. The mouse epistasis data will be used as a guide to improve statistical power in human studies, and therefore better detect epistasis in humans. Dr. Buchner's training prior to his K01 award had focused on the genetics of complex disease. During the K01 award period, he has complemented this training with additional activities in the fields of physiology, cell biology, an metabolic disease. This R03 proposal now seeks to combine Dr. Buchner's expertise in genetics, physiology, and metabolic disease to distinguish his research from that of his current and former mentors. This proposal will provide novel insights into the genetic architecture of complex traits while laying the groundwork for Dr. Buchner's work as an independent investigator.
Understanding the genetics of complex diseases such as obesity and type 2 diabetes requires more than simply a list of susceptibility factors to determine an individual's risk. It requires understanding how these genes work and interact together to collectively determine risk. Identifying interacting genes is simpler in a model system. Therefore, we will test whether the interactions identified using mouse models can be used to improve the detection of interacting genes in humans.
