Eleven evaluating parameters for rice core collection were assessed based on genotypic values and molecular marke＇ information. Monte Carlo simulation combined with mixed linear model was used to eliminate the interf...Eleven evaluating parameters for rice core collection were assessed based on genotypic values and molecular marke＇ information. Monte Carlo simulation combined with mixed linear model was used to eliminate the interference from environment in order to draw more reliable results. The coincidence rate of range （CR） was the optimal parameter. Mean Simpson index （MD）, mean Shannon-Weaver index of genetic diversity （M1） and mean polymorphism information content （MPIC） were important evaluating parameters. The variable rate of coefficient of variation （VR） could act as an important reference parameter for evaluating the variation degree of core collection. Percentage of polymorphic loci （p） could be used as a determination parameter for the size of core collection. Mean difference percentage （MD） was a determination parameter for the reliability judgment of core collection. The effective evaluating parameters for core collection selected in the research could be used as criteria for sampling percentage in different plant germplasm populations.展开更多
Two mixed linear models are proposed for grouping populations by a dissimilarity coefficent which has two parameters for squared difference of marginal mean and variance component of interaction.Cluster trees can be c...Two mixed linear models are proposed for grouping populations by a dissimilarity coefficent which has two parameters for squared difference of marginal mean and variance component of interaction.Cluster trees can be constructed by the mixed linear model approaches for experimental data with sampling errors within populations or with some missing values.Unweighted pair-group method ( UPGM ) is suggested as fusion method. Sampling variances of estimated dissimilarity coefficient can be obtained by the jackknife procedure.A one-tail t-test is applicable for detecting significance of dissimilarity of populaions within specific group.Unbiasedness and efficiency for estimation of dissimilarity coefficients are proved by Monte Carolo simulations.Worked example from cotton yield data is given for demonstration of the use of these cluster methods.展开更多
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 be...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.展开更多
基金the National Natural Science Foundation of China （Grant No. 30270759） the Science and Technology Department of Zhejiang Province （Grant No. 2005C32001）.
文摘Eleven evaluating parameters for rice core collection were assessed based on genotypic values and molecular marke＇ information. Monte Carlo simulation combined with mixed linear model was used to eliminate the interference from environment in order to draw more reliable results. The coincidence rate of range （CR） was the optimal parameter. Mean Simpson index （MD）, mean Shannon-Weaver index of genetic diversity （M1） and mean polymorphism information content （MPIC） were important evaluating parameters. The variable rate of coefficient of variation （VR） could act as an important reference parameter for evaluating the variation degree of core collection. Percentage of polymorphic loci （p） could be used as a determination parameter for the size of core collection. Mean difference percentage （MD） was a determination parameter for the reliability judgment of core collection. The effective evaluating parameters for core collection selected in the research could be used as criteria for sampling percentage in different plant germplasm populations.
文摘Two mixed linear models are proposed for grouping populations by a dissimilarity coefficent which has two parameters for squared difference of marginal mean and variance component of interaction.Cluster trees can be constructed by the mixed linear model approaches for experimental data with sampling errors within populations or with some missing values.Unweighted pair-group method ( UPGM ) is suggested as fusion method. Sampling variances of estimated dissimilarity coefficient can be obtained by the jackknife procedure.A one-tail t-test is applicable for detecting significance of dissimilarity of populaions within specific group.Unbiasedness and efficiency for estimation of dissimilarity coefficients are proved by Monte Carolo simulations.Worked example from cotton yield data is given for demonstration of the use of these cluster methods.
基金Project supported by the Special Foundation for Agro-Scientific Research in the Public Interest of China(No.201203052)the China Postdoctoral Science Foundation(No.2012M521184)the Shandong Provincial Natural Science Foundation of China (No.ZR2010CQ016)
文摘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.
