Detecting the undetected: estimating the total number of loci underlying a quantitative trait.
Detecting the undetected: estimating the total number of loci underlying a quantitative trait.
复制标题
作者：
S. Otto;Corbin D. Jones
最近的研究已经开始揭示亲缘关系密切的群体之间数量性状差异的基因。然而，并不是所有的数量性状基因座(QTL)都有同样的可能性被检测到。QTL研究涉及数量有限的杂交、个体和遗传标记，因此，通常几乎没有能力检测到小到中等影响的遗传因素。在这篇文章中，我们开发了两个亲本之间固定遗传差异总数的估计器。与Castle-Wright估计器一样，Castle-Wright估计器基于经典杂交实验中观察到的分离方差，我们基于QTL的估计器要求为潜在基因座的预期效应大小指定分布。我们使用这一预期分布以及在似然模型中检测到的QTL的观察到的平均和最小效应大小来估计导致性状差异的基因座总数。然后，我们在蒙特卡罗模拟中检验了基于QTL的估计量和Castle-Wright估计量。当模拟的假设与模型的假设匹配时，两个估计器的平均表现都很好。然而，Castle-Wright估计器的95%置信限通常排除了潜在基因座的真实数量，而基于QTL的估计器的置信限通常包括真实值约95%的时间。此外，我们发现基于QTL的估计器对显性和对相反符号的等位基因效应的敏感性低于Castle-Wright估计器。因此，我们建议使用基于QTL的估计器来评估在QTL研究中可能遗漏了多少个座位。
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.