Han Lide, Xu Haiming, Zhu Jun, Lou Xiangyang
Institute of Bioinformatics, Zhejiang University, Hangzhou, Zhejiang, 310029, People's Republic of China.
Theor Appl Genet. 2008 Apr;116(6):769-76. doi: 10.1007/s00122-008-0709-3. Epub 2008 Feb 19.
Two Genetic models (an embryo model and an endosperm model) were proposed for analyzing genetic effects of nuclear genes, cytoplasmic genes, maternal genes, and nuclear-cytoplasmic interaction (NCI) as well as their genotype by environment interaction for quantitative traits of plant seed. In these models, the NCI effects were partitioned into direct additive and dominance NCI components. Mixed linear model approaches were employed for statistical analysis. For both balanced and unbalanced diallel cross designs, Monte Carlo simulations were conducted to evaluate unbiasedness and precision of estimated variance components of these models. The results showed that the proposed methods work well. Random genetic effects were predicted with an adjusted unbiased prediction method. Seed traits (protein content and oil content) of Upland cotton (Gossypium hirsutum L.) were analyzed as worked examples to demonstrate the use of the models.
提出了两种遗传模型(胚胎模型和胚乳模型),用于分析核基因、细胞质基因、母体基因以及核质互作(NCI)对植物种子数量性状的遗传效应及其基因型与环境的互作。在这些模型中,NCI效应被划分为直接加性和显性NCI成分。采用混合线性模型方法进行统计分析。对于平衡和不平衡双列杂交设计,进行了蒙特卡罗模拟,以评估这些模型估计方差成分的无偏性和精确性。结果表明,所提出的方法效果良好。采用调整后的无偏预测方法预测随机遗传效应。以陆地棉(Gossypium hirsutum L.)的种子性状(蛋白质含量和油含量)为例进行分析,以说明模型的应用。