Leveraging genetic association data to investigate the polygenic architecture of human traits and diseases

Many human traits and diseases have a polygenic architecture, where phenotype is partially determined by variation in many genes. These complex traits or diseases can be highly heritable and genome-wide association studies (GWAS) have been relatively successful in the identification of associated variants. However, these variants typically do not account for most of the heritability and thus, the genetic architecture remains uncertain.;This dissertation describes analytical approaches to look for evidence of models of genetic architecture that could explain the remaining heritability. We develop methods to make predictions under various models, and compare the expected results from these predictions against the observed data for several traits and diseases. First, in studies of height (a classical polygenic trait), we modeled the expected cumulative effect of common variants identified from GWAS and compared the model with empirical data in individuals from the tails of the height distribution. We found that these common variants are predictive of stature, but have less than expected effects specifically at the short end of the height distribution. This result is consistent with models where rare variants with moderate effect, influence stature only in the shortest individuals. Second, we showed that under genetic models where low frequency variants make polygenic contributions to disease, there will be an excess of low frequency risk-increasing variants detected in GWAS. As such, by comparing the number of detected risk-increasing to risk-decreasing variants, one can detect a signal of the contribution to polygenic inheritance from low frequency variants. Finally, we examine the genetic architecture of sitting height ratio (SHR), a measure of body proportion that varies dramatically between individuals of African and European ancestry. We find that the SHR difference between populations is largely due to polygenic architecture; there is no evidence for any major locus accounting for most of this difference.;These results show that, with the appropriate computational and genetic models, one can use empirical results of genetics studies to make inferences regarding genetic architecture of human traits and diseases. Doing so can help investigators prioritize strategies for uncovering the remaining unexplained heritability.