不同遗传距离聚类和抽样方法构建作物核心种质的比较

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

以棉花种质资源 16 8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、离差平方和法 ) ,3种抽样方法 (随机取样法、优先取样法、偏离度取样法 ) ,30 %的抽样比率构建作物种质资源核心种质。用均值、方差、极差和变异系数 4个指标评价不同遗传距离、聚类方法、抽样方法构建核心种质的优劣。结果表明 ,马氏距离优于欧氏距离 ,优先取样法和偏离度取样法都能显著地提高核心种质的方差和变异系数 ,前者略优于后者。最短距离法构建的核心种质能极显著地增加性状的方差和变异系数 ,使核心种质的方差和变异系数最大化 ,是构建核心种质较好的系统聚类方法 ,其次分别是中间距离法、重心法、类平均法。

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.