| Autor: |
Wilson IJ; Institute of Genetic Medicine, Newcastle University, Newcastle NE3 1NB, UK. ian.wilson@ncl.ac.uk., Howey RA, Houniet DT, Santibanez-Koref M |
| Jazyk: |
angličtina |
| Zdroj: |
BMC proceedings [BMC Proc] 2011 Nov 29; Vol. 5 Suppl 9, pp. S98. Date of Electronic Publication: 2011 Nov 29. |
| DOI: |
10.1186/1753-6561-5-S9-S98 |
| Abstrakt: |
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. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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