at variance

at variance的基本解释

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有分歧, 不和

与at 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％。

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。

AMOVAresults showed there were 95.99%genetic variance within subpopulations,4.01% genetic variance between subpopulationsfor Schima superba;there were 75.36% genetic variance within subpopulation,24.64%genetic variance between subpopulations for Castanopsis chinensis;there were 89.55%genetic variance within subpopuiations,10.45% genetic variance betweensubpopulations for Cryptocarya chinensis.

AMOVA分析结果表明，荷木有95.99％的遗传变异表现在亚种群内，有4.01％的遗传变异表现在亚种群间，即95.99％的遗传变异是由于个体与个体之间的差异造成的，有4.01％的遗传变异是由于个体在不同的群落造成的；锥栗有75.36％的遗传变异表现在亚种群内，有24.64％的遗传变异表现在亚种群间，即75.36％的遗传变异是由于个体与个体之间的差异造成的，有24.64％的遗传变异是由于个体在不同的群落造成的；厚壳桂有89.55％的遗传变异表现在亚种群内，有10.45％的遗传变异表现在亚种群间，即89.55％的遗传变异是由于个体与个体之间的差异造成的，有10.45％的遗传变异是由于个体在不同的群落造成的。

与at variance相关的网络解释 [注：此内容来源于网络，仅供参考]

at variance：有分歧

at usance 在习惯期支付 | at variance 有分歧 | at war with 作战

at variance：有分歧, 不和

at variance with | 和不和 和不符 | at variance | 有分歧, 不和 | at war with | 和作战

at variance：分歧;不一致

at the expense of 归...负担/付费;在损害...的情况下 | at variance 分歧;不一致 | attachment 扣押财产;逮捕;附属品

set at variance：使不和, 离间

backplan 底视图 | set at variance 使不和, 离间 | catch thetide 趁机

set at variance：不和

set at naught 蔑视 | set at variance 不和 | set at 设置成