UCLA

Genome-wide association studies have identified thousands of variants implicated in dozens of complex diseases. Most studies collect individuals with and without disease and search for variants with different frequencies between the groups. For many of these studies, additional disease traits are also collected. Jointly modeling clinical phenotype and disease status is a promising way to increase power to detect true associations between genetics and disease. In particular, this approach increases the potential for discovering genetic variants that are associated with both a clinical phenotype and a disease. Standard multivariate techniques fail to effectively solve this problem, because their case-control status is discrete and not continuous. Standard approaches to estimate model parameters are biased due to the ascertainment in case-control studies. We present a novel method that resolves both of these issues for simultaneous association testing of genetic variants that have both case status and a clinical covariate. We demonstrate the utility of our method using both simulated data and the Northern Finland Birth Cohort data.