Institute of Crop Science, The National Key Facility for Crop Gene Resources and Genetic Improvement and CIMMYT China, Chinese Academy of Agricultural Sciences, Beijing, PR China.
Heredity (Edinb). 2010 Sep;105(3):257-67. doi: 10.1038/hdy.2010.56. Epub 2010 May 12.
Quantitative trait gene or locus (QTL) mapping is routinely used in genetic analysis of complex traits. Especially in practical breeding programs, questions remain such as how large a population and what level of marker density are needed to detect QTLs that are useful to breeders, and how likely it is that the target QTL will be detected with the data set in hand. Some answers can be found in studies on conventional interval mapping (IM). However, it is not clear whether the conclusions obtained from IM are the same as those obtained using other methods. Inclusive composite interval mapping (ICIM) is a useful step forward that highlights the importance of model selection and interval testing in QTL linkage mapping. In this study, we investigate the statistical properties of ICIM compared with IM through simulation. Results indicate that IM is less responsive to marker density and population size (PS). The increase in marker density helps ICIM identify independent QTLs explaining >5% of phenotypic variance. When PS is >200, ICIM achieves unbiased estimations of QTL position and effect. For smaller PS, there is a tendency for the QTL to be located toward the center of the chromosome, with its effect overestimated. The use of dense markers makes linked QTL isolated by empty marker intervals and thus improves mapping efficiency. However, only large-sized populations can take advantage of densely distributed markers. These findings are different from those previously found in IM, indicating great improvements with ICIM.
数量性状基因或位点(QTL)作图是遗传分析复杂性状的常用方法。特别是在实际的育种计划中,仍然存在一些问题,例如需要多大的群体和多少标记密度来检测对育种者有用的 QTL,以及手头的数据组检测到目标 QTL 的可能性有多大。一些答案可以在常规区间作图(IM)的研究中找到。然而,尚不清楚从 IM 获得的结论是否与使用其他方法获得的结论相同。包容性复合区间作图(ICIM)是向前迈出的有用一步,强调了模型选择和区间测试在 QTL 连锁作图中的重要性。在这项研究中,我们通过模拟研究了与 IM 相比,ICIM 的统计特性。结果表明,IM 对标记密度和群体大小(PS)的响应较低。标记密度的增加有助于 ICIM 识别独立的 QTL,这些 QTL可解释>5%的表型方差。当 PS >200 时,ICIM 可实现对 QTL 位置和效应的无偏估计。对于较小的 PS,QTL 有位于染色体中心的趋势,其效应被高估。使用密集标记可以将由空标记间隔隔开的连锁 QTL 分离,从而提高作图效率。但是,只有大规模群体才能利用密集分布的标记。这些发现与在 IM 中发现的结果不同,表明 ICIM 有很大的改进。