The complex etiology of common diseases like cardiovascular disease, diabetes, hypertension, and rheumatoid arthritis has led investigators to focus on the genetics of correlated phenotypes and risk factors. Joint analysis of multiple disease-related phenotypes may reveal genes of pleiotropic effect and increase analytical power, but at the cost of increased analytical and computational complexity. All three data sets provided for analysis at the Genetic Analysis Workshop 16 offered multiple quantitative measures of phenotypes related to underlying disease processes as well as discrete measures of affection status. Participants in Group 6 addressed the challenges and possibilities of association analysis of these data sets on multiple levels, including phenotype definition and data reduction, multivariate approaches to gene discovery, analysis of causality and data structure, and development of predictive models. These approaches included combinations of continuous and discrete phenotypes, use of repeated measures in longitudinal data, and models that included multiple phenotypic measures and multiple single-nucleotide polymorphism variants. Most research teams regarded the use of multiple related phenotypes as a tool for increasing analytical power, as well as for clarifying the underlying biology of complex diseases.
(c) 2009 Wiley-Liss, Inc.