不同遗传距离聚类和抽样方法构建作物核心种质的比较

以棉花种质资源168个基因型，5个纤维性状2.5%跨长(mm)、整齐度(%)、强度(gf/tex)、伸长度(%)、麦克隆值]数据为例，用混合线性模型分析方法无偏地预测基因型值，利用得到的基因型值，分别采用2种遗传距离(马氏距离和欧氏距离)和7种系统聚类方法(最短距离法、最长距离法、中间距离法、重心法、类平均法、加权配对算术平均法

Methods of Constructing Core Collection of Crop Germplasm by Comparing Different Genetic Distances,Cluster Methods and Sampling Strategies

genotypes of cotton germplasm were used as an example for comparing the efficiency of establishing core collection based on five quantitative fiber traits, which were the 2.5% length (mm), uniformity (%), strength (gf/tex), elongation (%), micronaire. Genotypic values were predicted unbiasedly by AUP (Adjusted Unbiased Prediction) of mixed linear model. Based on the genotypic values, core collections were constructed at 30% sampling proportion by using two genetic distances (Mahalanobis distance, Euclidean distance), seven hierarchical cluster methods (Single linkage, Complete linkage, Median method, Centroid method, Unweighted pair-group method using arithmetic averages (UPGMA), Weighted pair-group average (WPGMA), Ward's method and three sampling strategies (random sampling, preferred sampling, deviation sampling), respectively. The genetic variation of quantitative traits among core collections was compared by evaluating the means, variances, ranges and coefficients of variation of the traits. The results showed that the Mahalanobis distance was much better than Euclidean distance in constructing core collection; generally, the preferred sampling strategy was better than the deviation sampling, although the deviation could also significantly increase the variance and coefficient of variation; among the seven hierarchical cluster methods, single linkage was the best linkage rules for constructing core collection, which could capture the most genetic diversity of quantitative traits, the succeeding methods were the median method, centroid method and UPGMA.