Cui Yuehua, Wu Rongling
Department of Statistics, University of Florida, Gainesville, FL 32611, USA.
Bioinformatics. 2005 May 15;21(10):2447-55. doi: 10.1093/bioinformatics/bti342. Epub 2005 Feb 22.
The proper development of any organ or tissue requires the coordinated expression of its underlying genes that can be located on different genomes present in an organism. For instance, each step in the development of seed for a higher plant is the consequence of gene interactions from the maternal, embryo and endosperm genomes.
We present a multivariate statistical model for mapping quantitative trait loci (QTL) by incorporating two important aspects of seed development in plants-QTL interactions derived from different genomes, the maternal, embryo and endosperm, and genetic correlations among phenotypic traits expressed in different genome-specific tissues. This model, which has a high dimensionality, is constructed within the maximum-likelihood context based on a finite mixture model. The implementation of the expectation-maximization algorithm allows for the efficient estimation of QTL positions, their action and interaction effects and pleiotropic effects. The application of this high-dimensional model to a real rice dataset has validated its usefulness.
Our model was derived for self-pollinated plants, but it can be extended to cross-pollinated plants and to animals. With the burgeoning of genetic and genomic data, this high-dimensional model will have many implications for agricultural and evolutionary genetic research.
A package of software will be provided from the corresponding author upon request.
任何器官或组织的正常发育都需要其潜在基因的协调表达，这些基因可能位于生物体中存在的不同基因组上。例如，高等植物种子发育的每一步都是母本、胚和胚乳基因组基因相互作用的结果。
我们提出了一种多变量统计模型，通过纳入植物种子发育的两个重要方面来定位数量性状基因座（QTL）——来自不同基因组（母本、胚和胚乳）的QTL相互作用，以及在不同基因组特异性组织中表达的表型性状之间的遗传相关性。这个具有高维度的模型是在基于有限混合模型的最大似然框架内构建的。期望最大化算法的实现允许对QTL位置、它们的作用和相互作用效应以及多效性效应进行有效估计。将这个高维模型应用于一个真实的水稻数据集验证了它的实用性。
我们的模型是针对自花授粉植物推导出来的，但它可以扩展到异花授粉植物和动物。随着遗传和基因组数据的迅速增加，这个高维模型将对农业和进化遗传学研究产生许多影响。
如有需要，相应作者将提供软件包。
