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This article is part of the supplement: Genetic Analysis Workshop 17: Unraveling Human Exome Data

Open Access Proceedings

Finding genes that influence quantitative traits with tree-based clustering

Ian J Wilson*, Richard AJ Howey, Darren T Houniet and Mauro Santibanez-Koref

Author Affiliations

Institute of Genetic Medicine, Newcastle University, Newcastle NE3 1NB, UK

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BMC Proceedings 2011, 5(Suppl 9):S98  doi:10.1186/1753-6561-5-S9-S98

Published: 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.