Mayer M
Research Unit Genetics and Biometry, Research Institute for the Biology of Farm Animals, Dummerstorf D-18196, Germany.
Heredity (Edinb). 2005 Jun;94(6):599-605. doi: 10.1038/sj.hdy.6800667.
Regression interval mapping and multiple interval mapping are compared with regard to mapping linked quantitative trait loci (QTL) in inbred-line cross experiments. For that purpose, a simulation study was performed using genetic models with two linked QTL. Data were simulated for F(2) populations of different sizes and with all QTL and marker alleles fixed for alternative alleles in the parental lines. The criteria for comparison are power of QTL identification and the accuracy of the QTL position and effect estimates. Further, the estimates of the relative QTL variance are assessed. There are distinct differences in the QTL position estimates between the two methods. Multiple interval mapping tends to be more powerful as compared to regression interval mapping. Multiple interval mapping further leads to more accurate QTL position and QTL effect estimates. The superiority increased with wider marker intervals and larger population sizes. If QTL are in repulsion, the differences between the two methods are very pronounced. For both methods, the reduction of the marker interval size from 10 to 5 cM increases power and greatly improves QTL parameter estimates. This contrasts with findings in the literature for single QTL scenarios, where a marker density of 10 cM is generally considered as sufficient. The use of standard (asymptotic) statistical theory for the computation of the standard errors of the QTL position and effect estimates proves to give much too optimistic standard errors for regression interval mapping as well as for multiple interval mapping.
在近交系杂交实验中，就连锁数量性状基因座（QTL）定位而言，对回归区间作图法和多重区间作图法进行了比较。为此，使用具有两个连锁QTL的遗传模型进行了一项模拟研究。针对不同大小的F(2)群体进行数据模拟，并将所有QTL和标记等位基因固定为亲本系中的替代等位基因。比较的标准是QTL识别的功效以及QTL位置和效应估计的准确性。此外，还评估了相对QTL方差的估计值。两种方法在QTL位置估计上存在明显差异。与回归区间作图法相比，多重区间作图法往往更具功效。多重区间作图法还能得到更准确的QTL位置和QTL效应估计值。随着标记区间变宽和群体规模增大，其优势更加明显。如果QTL处于相斥状态，两种方法之间的差异会非常显著。对于这两种方法，将标记区间大小从10 cM减小到5 cM会提高功效，并大大改善QTL参数估计。这与文献中关于单个QTL情况的研究结果形成对比，在单个QTL情况下，通常认为10 cM的标记密度就足够了。事实证明，使用标准（渐近）统计理论来计算QTL位置和效应估计值的标准误差，对于回归区间作图法和多重区间作图法来说都过于乐观了。
