DescriptionMost genome-wide association studies (GWAS) look for correlation between genetic variants and disease risk. Correlation between variants and disease progression or severity is rare. This maybe be due to the fact that progression GWAS require longitudinal data, which is much more difficult to analyze. In this thesis, I present a new method for performing GWAS with longitudinal phenotypes. Heterogeneous data is analyzed into homogeneous subgroups, and the probability of belonging to a given subgroup is used as a phenotype in association analyses. Association analyses can be performed on single SNPs or regions of the genome for both family and population data sets. Covariates can also be included in the analyses. I report that this method maintains proper type I error under all genetic scenarios, including when admixture is present. I also report that greater than 80% power is obtained for most genetic scenarios. Thus, this method is suitable for use by researchers studying longitudinal diseases