Assessment of different genetic distances in constructing cotton core subset by genotypic values
文献类型： 外文期刊
作者： Wang, Jian-cheng 1 ; Hu, Jin 1 ; Huang, Xin-xian 1 ; Xu, Sheng-chun 1 ;
作者机构： 1.Zhejiang Univ, Coll Agr & Biotechnol, Seed Sci Ctr, Hangzhou 310029, Zhejiang, Peoples R China
2.Shandong Acad Agr Sci, Crop Res Inst, Jinan 250100, Peoples R China
关键词： core subset;mixed linear model;least distance stepwise sampling (LDSS) method;standardized Euclidean distance;Mahalanobis distance
期刊名称：JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE B （ 影响因子：3.066； 五年影响因子：3.057 ）
ISSN： 1673-1581
年卷期： 2008 年 9 卷 5 期
页码：
收录情况： SCI
摘要： 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.
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