Detecting the undetected: Estimating the total number of loci underlying aquantitative trait

Recent studies have begun to reveal the genes underlying quantitative trait differences between closely related populations. Not all quantitative trai t loci (QTL) are, however, equally likely to be detected. QTL studies invol ve a limited number of crosses, individuals, and genetic markers and, as a result, often have little power to detect genetic factors of small to moder ate effects. In this article, we develop an estimator for the total number of fixed genetic differences between two parental lines. Like the Castle-Wr ight estimator, which is based on the observed segregation variance in clas sical crossbreeding experiments, our QTL-based estimator requires that a di stribution be specified for the expected effect sizes of the underlying loc i. We use this expected distribution and the observed mean and minimum effe ct size of the detected QTL in a likelihood model to estimate the total num ber of loci underlying the trait difference. We then test the QTL-based est imator and the Castle-Wright estimator in Monte Carlo simulations. When the assumptions or the simulations match those of the model, both estimators p erform well on average. The 95% confidence limits of the Castle-Wright esti mator, however, often excluded the true number of underlying loci, while th e confidence limits for the QTL-based estimator typically included the true value similar to 95% of the lime. Furthermore, we found that the QTL-based estimator was less sensitive to dominance and to allelic effects of opposi te 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 QT L studies.