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

• Published in: Journal of Zhejiang University. B. Science Vol. 9; no. 5; pp. 356 - 362
• Main Authors: Wang, Jian-cheng, Hu, Jin, Huang, Xin-xian, Xu, Sheng-chun
• Format: Journal Article
• Language: English
• Published: Heidelberg SP Zhejiang University Press 01.05.2008; Springer Nature B.V; Seed Science Center, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310029, China; Crop Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China%Seed Science Center, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310029, China; Zhejiang University Press
• Subjects: Biomedical and Life Sciences; Biomedicine; Cluster Analysis; Genetic Variation; Genetics; Genotype; Gossypium - genetics; Linear Models; Plant Sciences; Principal Component Analysis; Studies; 最短距离逐步取样; 混合线性模型; 遗传距离; Least distance stepwise sampling (LDSS) method; S32; Standardized Euclidean distance; Core subset; Mixed linear model; S56; Mahalanobis distance
• ISSN: 1673-1581; 1862-1783
• DOI: 10.1631/jzus.B0710615

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.