Mixed modeling of Meta-Analysis P-values (MixMAP) is a recently described analytic framework for using publicly-available SNP-level summary data from candidate-gene association studies to characterize gene or locus-level associations with a measured trait. The underlying premise of this approach, similar to many clustered data methods, is that SNP-level effects are influenced by latent locus or gene level variables. In this presentation, we describe MixMAP, including a formal hypothesis testing framework with appropriate error control for genome-wide association studies (GWAS). We also describe a mixture model extension for further data exploration and characterization of gene-level associations that has the advantage of providing for more flexible underlying model assumptions. Application of MixMAP and its extensions to the Global Lipids Gene Consortium (GLGC) and the Meta-Analysis of Glucose and Insulin-related Traits Consortium (MAGIC) publicly-available GWAS metadata are presented for illustration. All statistical analysis is performed using R version 2.15.2 and the open-source, publicly-available MixMAP package (http://cran.r-project.org/web/packages/MixMAP/index.html).