Assessment of different genetic distances in constructing cotton core subset by genotypic values

One hundred and sixty-eight genotypes of cotton from the same growing region were used as a germplasm group to study the validity of different genetic distances in constructing cotton core subset. Mixed linear model approach was employed to unbiasedly predict genotypic values of 20 traits for eliminating the environmental effect. Six commonly used genetic distances(Euclidean,standardized Euclidean,Mahalanobis,city block,cosine and correlation distances) combining four commonly used hierarchical cluster methods(single distance,complete distance,unweighted pair-group average and Ward's methods) were used in the least distance stepwise sampling(LDSS) method for constructing different core subsets. The analyses of variance(ANOVA) of different evaluating parameters showed that the validities of cosine and correlation distances were inferior to those of Euclidean,standardized Euclidean,Mahalanobis and city block distances. Standardized Euclidean distance was slightly more effective than Euclidean,Mahalanobis and city block distances. The principal analysis validated standardized Euclidean distance in the course of constructing practical core subsets. The covariance matrix of accessions might be ill-conditioned when Mahalanobis distance was used to calculate genetic distance at low sampling percentages,which led to bias in small-sized core subset construction. The standardized Euclidean distance is recommended in core subset construction with LDSS method.

菜用豌豆核心种质构建的关键策略

利用已搜集的180份菜用豌豆材料进行核心种质构建策略研究。分别对所有种质材料进行单株荚数、每荚粒数、荚长、荚宽、荚厚、百荚鲜质量、百粒鲜质量及产量等性状进行调查,结果表明,搜集的材料具有丰富的遗传多样性。利用上述数据,采用最小距离逐步取样(minimum distance stepwise sampling,LDSS)法,分别选择4种遗传距离、8种取样比例进行核心种质构建策略研究,并采用极差符合率(coincidence rate of range,CR)和变异系数变化率(variable rate of coefficient of variation,VR)2个参数对构建策略进行评价;同时,利用主成分分析法和聚类分析法对构建的核心种质代表性进行鉴定。结果表明,采用LDSS法构建菜用豌豆核心种质的最佳遗传距离为欧式距离,最佳取样比例为25%。该构建策略将为菜用豌豆核心种质构建与高效利用奠定基础。

菜用大豆核心种质构建策略研究

核心种质是种质资源研究的新领域,构建核心种质是研究和利用植物遗传资源的便捷途径。本研究采用基于田间实际数据的计算机模拟方法构建两个菜用大豆种质资源群体,应用最小距离逐步取样法研究菜用大豆核心种质的构建策略,采用极差符合率和变异系数变化率两个参数对构建策略进行评价,利用主成分分析确认核心种质的代表性。研究表明,采用最小距离逐步取样法构建菜用大豆核心种质的最佳遗传距离为欧式距离,最佳取样比例为95%。

Effect of the scale of quantitative trait data on the representativeness of a cotton germplasm sub-core collection

A cotton germplasm collection with data for 20 quantitative traits was used to investigate the effect of the scale of quantitative trait data on the representativeness of plant sub-core collections.The relationship between the representativeness of a sub-core collection and two influencing factors,the number of traits and the sampling percentage,was studied.A mixed linear model approach was used to eliminate environmental errors and predict genotypic values of accessions.Sub-core collections were constructed using a least distance stepwise sampling(LDSS) method combining standardized Euclidean distance and an unweighted pair-group method with arithmetic means(UPGMA) cluster method.The mean difference percentage(MD),variance difference percentage(VD),coincidence rate of range(CR),and variable rate of coefficient of variation(VR) served as evaluation parameters.Monte Carlo simulation was conducted to study the relationship among the number of traits,the sampling percentage,and the four evaluation parameters.The results showed that the representativeness of a sub-core collection was affected greatly by the number of traits and the sampling percentage,and that these two influencing factors were closely connected.Increasing the number of traits improved the representativeness of a sub-core collection when the data of genotypic values were used.The change in the genetic diversity of sub-core collections with different sampling percentages showed a linear tendency when the number of traits was small,and a logarithmic tendency when the number of traits was large.However,the change in the genetic diversity of sub-core collections with different numbers of traits always showed a strong logarithmic tendency when the sampling percentage was changing.A CR threshold method based on Monte Carlo simulation is proposed to determine the rational number of traits for a relevant sampling percentage of a sub-core collection.