Haipeng Xu

Methods of constructing core collections by stepwise clustering with three sampling strategies based on the genotypic values of crops.

Abstract A genetic model with genotype×environment (GE) interactions for controlling systematical errors in the field can be used for predicting genotypic values by an adjusted unbiased prediction (AUP) method. Mahalanobis distance, calculated based on the genotypic values, is then applied to measure the genetic distance among accessions. The unweighted pair-group average, Ward’s and the complete linkage methods of hierarchical clustering combined with three sampling strategies are proposed to construct core collections in a procedure of stepwise clustering. A homogeneous test and t-tests are suggested for use in testing variances and means, respectively. The coincidence rate (CR%) for range and the variable rate (VR%) for the coefficient of variation are designed to evaluate the property of core collections. A worked example of constructing core collections in cotton with 21 traits was conducted. Random sampling can represent the genetic diversity structure of the initial collection. Preferred sampling can keep the accessions with special or valuable characteristics in the initial collection. Deviation sampling can retain the larger genetic variability of the initial collection. For better representation of the core collection, cluster methods should be combined with different sampling strategies. The core collections based on genotypic values retained larger genetic variability and had superior representatives than those based on phenotypic values.

A strategy on constructing core collections by least distance stepwise sampling

A strategy was proposed for constructing core collections by least distance stepwise sampling (LDSS) based on genotypic values. In each procedure of cluster, the sampling is performed in the subgroup with the least distance in the dendrogram during constructing a core collection. Mean difference percentage (MD), variance difference percentage (VD), coincidence rate of range (CR) and variable rate of coefficient of variation (VR) were used to evaluate the representativeness of core collections constructed by this strategy. A cotton germplasm collection of 1,547 accessions with 18 quantitative traits was used to construct core collections. Genotypic values of all quantitative traits of the cotton collection were unbiasedly predicted based on mixed linear model approach. By three sampling percentages (10, 20 and 30%), four genetic distances (city block distance, Euclidean distance, standardized Euclidean distance and Mahalanobis distance) combining four hierarchical cluster methods (nearest distance method, furthest distance method, unweighted pair-group average method and Ward’s method) were adopted to evaluate the property of this strategy. Simulations were conducted in order to draw consistent, stable and reproducible results. The principal components analysis was performed to validate this strategy. The results showed that core collections constructed by LDSS strategy had a good representativeness of the initial collection. As compared to the control strategy (stepwise clusters with random sampling strategy), LDSS strategy could construct more representative core collections. For LDSS strategy, cluster methods did not need to be considered because all hierarchical cluster methods could give same results completely. The results also suggested that standardized Euclidean distance was an appropriate genetic distance for constructing core collections in this strategy.

Identification of endosperm and maternal plant QTLs for protein and lysine contents of rice across different environments

Using a newly developed mapping model with endosperm and maternal main effects and QTL × environment interaction effects on quantitative quality traits of seed in cereal crops, the investigation of quantitative trait loci (QTLs) located on triploid endosperm and diploid maternal plant genomes for protein content and lysine content of rice grain under different environments was carried out with two backcross (BC1F1 and BC2F1) populations from a set of 241 recombinant inbred lines derived from an elite hybrid cross of Shanyou 63. The results showed a total of 18 QTLs to be associated with these two quality traits of rice, which were subsequently mapped on chromosomes 2, 3, 5, 6, 7, 10, 11 and 12. Three of these QTLs were also found having QTL × environment interaction effects. Therefore, the genetic main effects from QTLs located on chromosomes in endosperm and maternal plant genomes and their QTL × environment interaction effects in different environments were all important for protein and lysine contents in rice. The influence of environmental factors on the expression of some QTLs located in different genetic systems could not be ignored for both nutrient quality traits.