Abstract
AbstractA better understanding of the role of sex in studies of genetic architecture for complex traits and diseases will help translate genetic data into improved precision-based medicine and clinical care. Towards this end, we explored the use of sex-stratified versus sex-combined analyses for several metabolic and blood traits in the Hybrid Mouse Diversity Panel (HMDP). Traits such as body weight and glucose levels exhibited a high degree of genetic correlation between males and females whereas other traits such as HDL levels and white blood count did not. Nonetheless, even with the high genetic correlation between males and females for body weight, the use of sex-stratified analyses enabled the identification of dozens of loci regulating adiposity not identified in sex-combined analyses or sex-stratified analyses in the opposite sex. In addition, comparisons of the direction of allelic effects in males and females detected in the sex-stratified analyses demonstrated a high concordance, even among loci that were not statistically significant using a conventional p-value threshold. Simulation studies indicated that these elevated concordance rates were consistent with a genetic architecture consisting of hundreds of additive loci regulating every trait analyzed, including those for which no statistically significant loci were identified. These findings demonstrate the importance of stratifying by sex and suggest a method for identifying biologically rather than statistically significant associations. Applying these methods to GWAS data broadly may result in the identification of many additional loci contributing to the genetic architecture of complex traits that were missed using conventional sex-adjusted GWAS methods.
Publisher
Cold Spring Harbor Laboratory