Despite the potential advantages noted above, high-dimensional intermediate phenotypes pose considerable analytic challenges. Here we describe the results of an innovative strategy for analyzing these data, which includes methods to normalize phenotypes and application of sparse factor analysis (SFA) [31], [32] to provide interpretable summary phenotypes. Our approach provides a model for the analysis of other high-dimensional intermediate phenotypes in the context of genetic association studies.
