title: Finding genes that influence quantitative traits with tree-based clustering.
abstract: 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.
authors.affiliation: Institute of Genetic Medicine, Newcastle University, Newcastle NE3 1NB, UK. ian.wilson@ncl.ac.uk.
authors.forename: Ian J
authors.initials: IJ
authors.lastname: Wilson
authors.forename: Richard Aj
authors.initials: RA
authors.lastname: Howey
authors.forename: Darren T
authors.initials: DT
authors.lastname: Houniet
authors.forename: Mauro
authors.initials: M
authors.lastname: Santibanez-Koref