title: Detecting the undetected: estimating the total number of loci underlying a quantitative trait.
abstract: Recent studies have begun to reveal the genes underlying quantitative trait differences between closely related populations. Not all quantitative trait loci (QTL) are, however, equally likely to be detected. QTL studies involve a limited number of crosses, individuals, and genetic markers and, as a result, often have little power to detect genetic factors of small to moderate effects. In this article, we develop an estimator for the total number of fixed genetic differences between two parental lines. Like the Castle-Wright estimator, which is based on the observed segregation variance in classical crossbreeding experiments, our QTL-based estimator requires that a distribution be specified for the expected effect sizes of the underlying loci. We use this expected distribution and the observed mean and minimum effect size of the detected QTL in a likelihood model to estimate the total number of loci underlying the trait difference. We then test the QTL-based estimator and the Castle-Wright estimator in Monte Carlo simulations. When the assumptions of the simulations match those of the model, both estimators perform well on average. The 95% confidence limits of the Castle-Wright estimator, however, often excluded the true number of underlying loci, while the confidence limits for the QTL-based estimator typically included the true value approximately 95% of the time. Furthermore, we found that the QTL-based estimator was less sensitive to dominance and to allelic effects of opposite sign than the Castle-Wright estimator. We therefore suggest that the QTL-based estimator be used to assess how many loci may have been missed in QTL studies.
authors: Department of Zoology, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada. otto@zoology.ubc.ca
authors: S P
authors: SP
authors: Otto
authors: C D
authors: CD
authors: Jones
mesh_terms: D000465:Algorithms; D000483:Alleles; D000818:Animals; D003198:Computer Simulation; D003433:Crosses, Genetic; D005796:Genes; D005799:Genes, Dominant; D005838:Genotype; D008957:Models, Genetic; D010641:Phenotype; D019655:Quantitative Trait, Heritable