Lascoux Martin
Department of Forest Genetics, Swedish University of Agricultural Sciences, Uppsala Genetic Center Box 7027, 750 07, Uppsala, Sweden.
Evolution. 1997 Oct;51(5):1394-1400. doi: 10.1111/j.1558-5646.1997.tb01462.x.
In a recent paper, Gromko (1995) showed using computer simulations that pleiotropy and sampling interact to generate variation in correlated response to selection. His simulations demonstrated that different combinations of pleiotropic effects could lead to the same genetic correlation value, yet, as long as the population size and correlation value were large enough, result in significantly different variance of correlated responses after 10 generations of selection. We extended those results using Alan Roberston's "reparameterization" of selection processes in finite populations. As for direct selection response, a satisfactory description of the correlated response and its variability can be expressed in terms of Nih, t/N, and NL, where N is the effective population size, i is the selection intensity in standard units, h is the heritability of the selected and correlated traits, t is the number of generations, and L is the length of the chromosome in map units. For a given number of loci, there exits an Nih threshold under which differences between pleiotropic systems will not be detected. For values of Nih above this threshold, the higher Nih is the smaller t/N needs to be for significant differences in correlated responses between pleiotropic systems to be observed. A large number of loci or tight linkage increases the unpredictability of the correlated response, but if Nih is large enough, it does not absolutely prevent significant differences between pleiotropic systems to occur. On the other hand, a small initial allele frequency of the favorable allele would tend to cancel the differences between pleiotropic systems, even for large values of Nih and t/N. Finally, epistasis decreases the overall variability of the correlated response, but generally preserves the difference between pleiotropic systems. Thus, Gromko's conclusions on the unpredictability of correlated response due to variable pleiotropy seem fairly robust, at least in the long term.
在最近一篇论文中，格罗姆科（1995年）通过计算机模拟表明，基因多效性和抽样相互作用，从而在对选择的相关反应中产生变异。他的模拟表明，基因多效性效应的不同组合可能导致相同的遗传相关值，然而，只要种群规模和相关值足够大，经过10代选择后，相关反应的方差会有显著差异。我们利用艾伦·罗伯逊对有限种群选择过程的“重新参数化”扩展了这些结果。至于直接选择反应，相关反应及其变异性的令人满意的描述可以用Nih、t/N和NL来表示，其中N是有效种群规模，i是以标准单位表示的选择强度，h是所选性状和相关性状的遗传力，t是世代数，L是以图距单位表示的染色体长度。对于给定数量的基因座，存在一个Nih阈值，低于该阈值，基因多效性系统之间的差异将无法检测到。对于高于该阈值的Nih值，Nih越高，要观察到基因多效性系统之间相关反应的显著差异所需的t/N就越小。大量的基因座或紧密连锁会增加相关反应的不可预测性，但如果Nih足够大，它并不会绝对阻止基因多效性系统之间出现显著差异。另一方面，有利等位基因的初始频率较低往往会消除基因多效性系统之间的差异，即使对于较大的Nih和t/N值也是如此。最后，上位性会降低相关反应的总体变异性，但通常会保留基因多效性系统之间的差异。因此，格罗姆科关于由于可变基因多效性导致相关反应不可预测性的结论似乎相当可靠，至少从长远来看是这样。
