Moreau L, Charcosset A, Hospital F, Gallais A
Station de Génétique Végétale (Institut National de la Recherche Agronomique, Université Paris-Sud, Institut National Agronomique Paris-Grignon), Gif Sur Yvette, France.
Genetics. 1998 Mar;148(3):1353-65. doi: 10.1093/genetics/148.3.1353.
The efficiency of marker-assisted selection (MAS) based on an index incorporating both phenotypic and molecular information is evaluated with an analytical approach that takes into account the size of the experiment. We consider the case of a population derived from a cross between two homozygous lines, which is commonly used in plant breeding, and we study the relative efficiency of MAS compared with selection based only on phenotype in the first cycle of selection. It is shown that the selection of the markers included in the index leads to an overestimation of the effects associated with these markers. Taking this bias into account, we study the influence of several parameters, including experiment size and heritability, on MAS efficiency. Even if MAS appears to be most interesting for low heritabilities, we point out the existence of an optimal heritability (approximately 0.2) below which the low power of quantitative trait loci detection and the bias caused by the selection of markers reduce the efficiency. In this situation, increasing the power of detection by using a higher probability of type I error can improve MAS efficiency. This approach, validated by simulations, gives results that are generally consistent with those previously obtained by simulations using a more sophisticated biological model than ours. Thus, though developed from a simple genetic model, our approach may be a useful tool to optimize the experimental means for more complex genetic situations.
基于结合了表型和分子信息的指数的标记辅助选择（MAS）效率，采用一种考虑了实验规模的分析方法进行评估。我们考虑了两个纯合品系杂交产生的群体的情况，这在植物育种中常用，并且我们研究了在选择的第一个周期中，与仅基于表型的选择相比，MAS的相对效率。结果表明，对指数中包含的标记的选择会导致对与这些标记相关效应的高估。考虑到这种偏差，我们研究了几个参数，包括实验规模和遗传力，对MAS效率的影响。即使对于低遗传力，MAS似乎最具吸引力，但我们指出存在一个最优遗传力（约为0.2），低于该遗传力时，数量性状位点检测的低功效以及标记选择导致的偏差会降低效率。在这种情况下，通过使用更高的I型错误概率来提高检测功效可以提高MAS效率。这种方法通过模拟得到验证，其结果通常与之前使用比我们更复杂的生物学模型进行模拟所得到的结果一致。因此，尽管我们的方法是基于一个简单的遗传模型开发的，但它可能是优化更复杂遗传情况下实验手段的有用工具。
