Complex traits are the features whose properties are determined by both genetic and environmental factors. Generally, complex traits include the classical quantitative traits with continuous distribution, the binary or categorical traits with discrete distribution controlled by polygene and other traits that cannot be measured exactly, such as behavior and psychology. Most human complex diseases and most economically important traits in plants and animals belong to the category. Understanding the molecular basis of complex traits plays a vital role in the genetic improvement of plant and animal breeding. In this article, the conception and research background of complex traits were summarized, and the strategies, methods and the great progress that had been made in dissecting genetic basis of complex traits were reviewed. The challenges and possible developments in future researches were also discussed.
Several typical supervised clustering methods such as Gaussian mixture model-based supervised clustering （GMM）, k- nearest-neighbor （KNN）, binary support vector machines （SVMs） and multiclass support vector machines （MC-SVMs） were employed to classify the computer simulation data and two real microarray expression datasets. False positive, false negative, true positive, true negative, clustering accuracy and Matthews＇ correlation coefficient （MCC） were compared among these methods. The results are as follows： （1） In classifying thousands of gene expression data, the performances of two GMM methods have the maximal clustering accuracy and the least overall FP＋FN error numbers on the basis of the assumption that the whole set of microarray data are a finite mixture of multivariate Gaussian distributions. Furthermore, when the number of training sample is very small, the clustering accuracy of GMM-Ⅱ method has superiority over GMM- Ⅰ method. （2） In general, the superior classification performance of the MC-SVMs are more robust and more practical, which are less sensitive to the curse of dimensionality, and not only next to GMM method in clustering accuracy to thousands of gene expression data, but also more robust to a small number of high-dimensional gene expression samples than other techniques. （3） Of the MC-SVMs, OVO and DAGSVM perform better on the large sample sizes, whereas five MC-SVMs methods have very similar performance on moderate sample sizes. In other cases, OVR, WW and CS yield better results when sample sizes are small. So, it is recommended that at least two candidate methods, choosing on the basis of the real data features and experimental conditions, should be performed and compared to obtain better clustering result.
Based on the major gene and polygene mixed inheritance model for multiple correlated quantitative traits, the authors proposed a new joint segregation analysis method of major gene controlling multiple correlated quantitative traits, which include major gene detection and its effect and variation estimation. The effect and variation of major gene are estimated by the maximum likelihood method implemented via expectation-maximization （EM） algorithm. Major gene is tested with the likelihood ratio （LR） test statistic. Extensive simulation studies showed that joint analysis not only increases the statistical power of major gene detection but also improves the precision and accuracy of major gene effect estimates. An example of the plant height and the number of tiller of F2 population in rice cross Duonieai x Zhonghua 11 was used in the illustration. The results indicated that the genetic difference of these two traits in this cross refers to only one pleiotropic major gene. The additive effect and dominance effect of the major gene are estimated as -21.3 and 40.6 cm on plant height, and 22.7 and -25.3 on number of tiller, respectively. The major gene shows overdominance for plant height and close to complete dominance for number of tillers.
XIAO Jing WANG Xue-feng HU Zhi-qiu TANG Zai-xiang SUI Jiong-ming LI Xin XU Chen-wu
四向杂交(four-way cross)设计是指4个纯系亲本参与杂交衍生分离群体的一种交配设计。尽管国际上已经提出基于四向杂交设计的迭代重新加权最小平方(iteratively reweighed least squares,IRWLS)QTL作图方法,但该方法忽略了双侧标记基因型内QTL基因型的混合分布特性,当QTL位置和标记的位置不重合时作图精度较低。本文根据四向杂交设计的数量遗传模型,发展出基于四向杂交设计和混合分布理论的QTL作图的极大似然估计方法。首先利用染色体上所有标记基因型联合计算该染色体上任一假定位置QTL的条件概率,然后根据混合分布理论建立基于EM算法实现的QTL作图的极大似然估计方法。以计算机模拟数据研究了QTL遗传力、样本容量和分子标记信息含量3个因素对方法的影响,结果表明:(1)在QTL的被发现能力上,标记信息不完全的四向杂交设计仅略低于信息完全时的四向杂交设计;(2)随着QTL遗传力、样本容量和标记信息含量的增大,QTL位置以及效应估计值的准确度和精确度逐步提高。
