This article is part of the supplement: Genetic Analysis Workshop 17: Unraveling Human Exome Data
Case-control association testing by graphical modeling for the Genetic Analysis Workshop 17 mini-exome sequence data
1 Division of Statistical Genomics, Washington University School of Medicine, Saint Louis, MO, USA
2 Division of Genetic Epidemiology, University of Utah, 391 Chipeta Way, Salt Lake City, UT 84105, USA
BMC Proceedings 2011, 5(Suppl 9):S62 doi:10.1186/1753-6561-5-S9-S62Published: 29 November 2011
We generalize recent work on graphical models for linkage disequilibrium to estimate the conditional independence structure between all variables for individuals in the Genetic Analysis Workshop 17 unrelated individuals data set. Using a stepwise approach for computational efficiency and an extension of our previously described methods, we estimate a model that describes the relationships between the disease trait, all quantitative variables, all covariates, ethnic origin, and the loci most strongly associated with these variables. We performed our analysis for the first 50 replicate data sets. We found that our approach was able to describe the relationships between the outcomes and covariates and that it could correctly detect associations of disease with several loci and with a reasonable false-positive detection rate.