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

Abstract

Abstract: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.

Establishing an Efficient Way to Utilize the Drought Resistance Germplasm Population in Wheat

“…Coincidence rate of range (CR) and variable rate of coefficient of variation (VR) [16, 17] were adopted to evaluate the representativeness of core collection. Those four parameters were formulated as follows: CR = (1/ n )∑ i =1 n ( R C ( i ) / R I ( i ) ) × 100, where R C ( i ) is the range of the i th trait in the core collection; R I ( i ) is the range of the corresponding trait in the initial collection; n and is total number of traits, VR = (1/ n )∑ i =1 n (CV C ( i ) /CV I ( i ) ) × 100, where CV C ( i ) is the coefficient of variation of the i th trait in the core collection; CV I ( i ) is the coefficient of variation of the corresponding trait in the initial collection; n is total number of traits.…”

A New Regionalization Scheme for Effective Ecological Restoration on the Loess Plateau in China

“…Next, these values were used to determine whether the vegetation cover type in a given place in area R was suitable for planting; (4) In area R, the NPP value of each pixel in each year was compared with the corresponding A ij , selecting the pixel that always had a higher NPP than the A ij value from 2006 to 2013; these pixels form region RS, which was considered to have experienced proper vegetation restoration; (5) To retain more independent data for validation, only half of the samples from region RS were selected and used for training in the next step. During this procedure, the pixels in region RS were arranged from minimum to maximum NPP values, and half of the pixels were uniformly selected and marked as region U; (6) Pixels in regions N and U were used as training samples, and the Loess Plateau was divided into different vegetation restoration zones using the standardized Euclidean distance (SED) method, which was proposed by Flury and Riedwyl [27] and is considered a good classification method [28]. Each pixel within the studied domain was classified according to the nearest vegetation type.…”

Plant Genetic Resources for Food and Agriculture

“…Sampling for the development of a core collection must consider a hierarchical structure of the gene pool, that is, stratification into groups sharing common characteristics, for example, taxonomy, geographic or ecological origin, and neutral or non-neutral descriptors. Core collections are available for almost all important food crops, as well as for a few of their wild relatives and feed or fiber crops: barley (van Hintum and Haalman 1994), bean (Tohme et al 1995), cabbage (Boukema et al 1997), cassava (Chavarriaga-Aguirre et al 1999), chickpea , cotton (Xu et al 2006;Wang et al 2008), cowpea (Mahalakshmi et al 2007a), finger millet (Upadhyaya et al 2006), foxtail millet (Upadhyaya et al 2009), perennial Glycine-a wild soybean (Brown et al 1987), groundnut or peanut (Upadhyaya et al 2003), hot and sweet peppers (Thies and Fery 2002), lentil (Erskine and Muehlbauer 1991), lettuce (Jansen and van Hintum 2007), maize (Taba et al 1998;Franco et al 2007), annual and perennial Medicago-wild alfalfa or lucerne (Diwan et al 1995;Basigalup et al 1995), mungbean (Bisht et al 1998a), pea (Coyne et al 2005), pearl millet (Bhattacharjee et al 2007), pigeonpea (Reddy et al 2005), potato (Huamán et al 2000), quinoa (Ortiz et al 1998), rice , perennial ryegrass (Charmet and Balfourier 1995), sesame (Bisht et al 1998b;Xiurong et al 2000), sorghum (Grenier et al 2001), sweetpotato (Huamán et al 1999), tomato (https://www.eu-sol.wur. nl/dynamic/passport/aboutTheCC.php), bread and durum wheat (Balfourier et al 2007;Spagnoletti Zeuli and Qualset 1993), and yams (Mahalakshmi et al 2007b).…”