component of variance

component of variance的基本解释

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方差分量

与component of variance相关的网络例句 [注：此内容来源于网络，仅供参考]

Thecompare of genetic map between Lowes and ours showed 26 homology marker situ,which occupied 21.1% of the marker situ in the experiment. 81 QTLs were detected for 11 agronomic traits. 4 QTLs were detected for plantheight, which explained 10.3%～28.9% of trait variance; 2 QTLs were detected forNo. of effective 1-st branches, which explained 22.1%～47% of trait variance; 16QTLs were detected for effective branches height, which explained 12.2%～51.8% oftrait variance; 15 QTLs were detected for length of main inflorenscence, whichexplained 7.4%～26.6% of trait variance; 5 QTLs were detected for effective siliquesof main inflorenscence, which explained 11.2%～25% of trait variance; 1 QTLs weredetected for density of main infiorenscence, which explained 17.3% of trait variance;12 QTLs were detected for length of silique, which explained 24%～36.7% of traitvariance; 2 QTLs were detected for seed per sillique, which explained 9.6% and16.9% of trait variance; 2 QTLs were detected for 1000 seed weight, which explained26%～13.7% of trait variance; 11 QTLs were detected for Total effective siliques perplant, which explained 14.8%～47.2% of trait variance; 11 QTLs were detected forplant height, which explained 14.3%～32.8% of trait variance.

其中，株高检测到4个QTLs，解释性状表型变异的10.3％～28.9％；一次有效分枝数检测到2个QTLs，解释性状表型变异的22.1％和47％；有效分枝部位检测到16个QTLs，解释性状表型变异的12.2％～51.8％；主花序长度检测到15个QTLs，解释性状表型变异的7.4％～26.6％；主花序有效角数检测到5个QTLs，解释性状表型变异的11.2％～25％；主花序角密度检测到1个QTLs，解释性状表型变异的17.3％；角果长度检测到12个QTLs，解释性状表型变异的24％～36.7％；每角粒数检测到2个QTLs，解释性状表型变异的9.6％和16.9％；千粒重检测到2个QTLs，解释性状表型变异的26％和13.7％；单株有效角果总数检测到11个QTLs，解释性状表型变异的14.8％～47.2％；单株产量检测到11个QTLs，解释性状表型变异的14.3％～32.8％。

In order to reduce calculation error, the frequency distribution of average values is used to compute the mixed distribution's digital features of each component distribution, thereinto, the number of the component distribution is determined by AIC, choose the number that meets the minimum value of AIC as the component number of mixed distribution, and the other parameters are estimated by EM algorithm; Secondly, because each component distribution is corresponding to a kind of major gene genotype, according to the values of the average and variance of the each component distribution, we can use the limit error of the normal distribution to plot each individual into the correspondent component distribution, namely into correspondent major gene genotype. Then we regard each major gene genotype as a treatment level of one-way analysis of variances, and the one-way multivariate analysis of variance is carried out to calculate the covariance matrix of major gene effect, covariance matrix of polygene effect, covariance matrix of environment effect and so on; At last, combining the weights of the each component distribution of mixed distribution, we can calculate the variance of major gene effect, the variance of polygene effect, environmental variance and the genetic gain of the quantitative trait.

为减小计算误差，本研究采用均值的频数分布来计算各成分分布的数字特征，其中成分分布个数根据AIC准则，选择使AIC值达到最小的成分分布个数作为混合分布的成分分布数，分布中其它参数的确定利用EM算法来估计；其次，每个成分分布对应一种主基因基因型，根据各个成分分布的均值和方差，利用正态分布的极限误差将每个个体划分到相应的成分分布中，即相应的主基因基因型中，将每种主基因型作为单因素方差分析的一个处理水平，对其进行单因素的多元方差分析，分别计算主基因效应协方差阵、多基因效应协方差阵、环境协方差阵等参数；最后结合混合分布中各成分分布的权重即各主基因基因型的分离比例，计算主基因效应方差，多基因效应方差和环境方差，以及遗传力等参数，进而计算该数量性状的遗传进展。

The results showed that:(1) we can mapping quantitative trait locus while estimating the variance component of QTL;(2) granddaughter design is better than daughter design when mapping QTL;(3) it is easy to map a QTL for trait with a high heriability and a large QTL variance contribution;(4) we can estimate the variance component of a QTL by TM-BLUP based on ML method whether the QTL has only 2 alleles or QTL has normal distributed alleles effects;(5) the estimation accuracy of variance component contributed by QTL was improved by using of grand daughter design;(6) the higher the heritability and the QTL variance contribution was, the more accurate estimation of QTL variance component.

结果表明：（1）采用随机QTL效应模型和最大似然法，在估计QTL方差组分的同时，能够定位QTL；（2）孙女设计与女儿设计相比，在其它因素相同时，容易检出QTL；（3）遗传力高，QTL方差贡献较大的性状，QTL检出的效果优于遗传力低，QTL方差贡献较小的性状；（4）无论QTL上有2个等位基因，还是QTL上等位基因的效应服从正态分布，都可将其看作随机效应，采用基于TM-BLUP的ML法，估计其方差组分和定位QTL；（5）QTL方差组分估计的准确性，孙女设计高于女儿设计；（6）遗传力高的性状，QTL方差贡献大的QTL，QTL方差组分估计的准确性高。

与component of variance相关的网络解释 [注：此内容来源于网络，仅供参考]

component of variance：方差的分量

component 分量 | component of variance 方差的分量 | componentwise convergence 分量方式收敛

component of variance：方差分量

方差分布 distribution of variance; variance distribution | 方差分量 component of variance | 方差分析 variance analysis; varince analysis; analysis of vari....

negative estimate of variance component：变异数成分的负估计值

负相关 negative correlation | 变异数成分的负估计值 negative estimate of variance component | 负指数分布;负指数分配 negative exponential distribution