This article is part of the supplement: Genetic Analysis Workshop 17: Unraveling Human Exome Data
Finding genes that influence quantitative traits with tree-based clustering
Institute of Genetic Medicine, Newcastle University, Newcastle NE3 1NB, UK
BMC Proceedings 2011, 5(Suppl 9):S98 doi:10.1186/1753-6561-5-S9-S98Published: 29 November 2011
We present a new statistical method to identify genes in which one or more variants influence quantitative traits. We use the Genetic Analysis Workshop 17 (GAW17) data set of unrelated individuals as a test of the method on the raw GAW17 phenotypes and on residuals after fitting linear models to individual-based covariates. By performing appropriate randomization tests, we found many significant results for a proportion of the genes that contain variants that directly contribute to disease but that have an increased type I error for analyses of raw phenotypes. Power calculations show that our methods have the ability to reliably identify a subset of the loci contributing to disease. When we applied our method to derived phenotypes, we removed many false positives, giving appropriate type I error rates at little cost to power. The correlation between genome-wide heterozygosity and the value of the trait Q1 appears to drive much of the type I error in this data set.