Lund Mogens S, Sorensen Peter, Madsen Per, Jaffrézic Florence
Faculty of Agricultural Sciences, Department of Genetics and Biotechnology, University of Aarhus, Research Center Foulum, P.O. Box 50 8830 Tjele, Denmark.
Genet Sel Evol. 2008 Mar-Apr;40(2):177-94. doi: 10.1186/1297-9686-40-2-177. Epub 2008 Feb 27.
A longitudinal approach is proposed to map QTL affecting function-valued traits and to estimate their effect over time. The method is based on fitting mixed random regression models. The QTL allelic effects are modelled with random coefficient parametric curves and using a gametic relationship matrix. A simulation study was conducted in order to assess the ability of the approach to fit different patterns of QTL over time. It was found that this longitudinal approach was able to adequately fit the simulated variance functions and considerably improved the power of detection of time-varying QTL effects compared to the traditional univariate model. This was confirmed by an analysis of protein yield data in dairy cattle, where the model was able to detect QTL with high effect either at the beginning or the end of the lactation, that were not detected with a simple 305 day model.
本文提出了一种纵向分析方法，用于定位影响功能值性状的数量性状基因座（QTL）并估计其随时间的效应。该方法基于拟合混合随机回归模型。QTL等位基因效应通过随机系数参数曲线并利用配子关系矩阵进行建模。为了评估该方法拟合不同时间模式QTL的能力，进行了一项模拟研究。结果发现，与传统单变量模型相比，这种纵向分析方法能够充分拟合模拟的方差函数，并显著提高检测随时间变化的QTL效应的功效。对奶牛蛋白质产量数据的分析证实了这一点，该模型能够检测到泌乳开始或结束时具有高效应的QTL，而简单的305天模型则无法检测到这些QTL。
