Accuracy of genomic prediction using imputed whole‐genome sequence data in white layers

There is an increasing interest in using whole‐genome sequence data in genomic selection breeding programmes. Prediction of breeding values is expected to be more accurate when whole‐genome sequence is used, because the causal mutations are assumed to be in the data. We performed genomic prediction for the number of eggs in white layers using imputed whole‐genome resequence data including ~4.6 million SNPs. The prediction accuracies based on sequence data were compared with the accuracies from the 60 K SNP panel. Predictions were based on genomic best linear unbiased prediction (GBLUP) as well as a Bayesian variable selection model (BayesC). Moreover, the prediction accuracy from using different types of variants (synonymous, non‐synonymous and non‐coding SNPs) was evaluated. Genomic prediction using the 60 K SNP panel resulted in a prediction accuracy of 0.74 when GBLUP was applied. With sequence data, there was a small increase (~1%) in prediction accuracy over the 60 K genotypes. With both 60 K SNP panel and sequence data, GBLUP slightly outperformed BayesC in predicting the breeding values. Selection of SNPs more likely to affect the phenotype (i.e. non‐synonymous SNPs) did not improve the accuracy of genomic prediction. The fact that sequence data were based on imputation from a small number of sequenced animals may have limited the potential to improve the prediction accuracy. A small reference population (n = 1004) and possible exclusion of many causal SNPs during quality control can be other possible reasons for limited benefit of sequence data. We expect, however, that the limited improvement is because the 60 K SNP panel was already sufficiently dense to accurately determine the relationships between animals in our data.     

Genomic prediction for meat and carcass traits in Nellore cattle using a Markov blanket algorithm

This study was carried out to evaluate the advantage of preselecting SNP markers using Markov blanket algorithm regarding the accuracy of genomic prediction for carcass and meat quality traits in Nellore cattle. This study considered 3675, 3680, 3660 and 524 records of rib eye area (REA), back fat thickness (BF), rump fat (RF), and Warner–Bratzler shear force (WBSF), respectively, from the Nellore Brazil Breeding Program. The animals have been genotyped using low-density SNP panel (30 k), and subsequently imputed for arrays with 777 k SNPs. Four Bayesian specifications of genomic regression models, namely Bayes A, Bayes B, Bayes Cπ and Bayesian Ridge Regression methods were compared in terms of prediction accuracy using a five folds cross-validation. Prediction accuracy for REA, BF and RF was all similar using the Bayesian Alphabet models, ranging from 0.75 to 0.95. For WBSF, the predictive ability was higher using Bayes B (0.47) than other methods (0.39 to 0.42). Although the prediction accuracies using Markov blanket of SNP markers were lower than those using all SNPs, for WBSF the relative gain was lower than 13%. With a subset of informative SNPs markers, identified using Markov blanket, probably, is possible to capture a large proportion of the genetic variance for WBSF. The development of low-density and customized arrays using Markov blanket might be cost-effective to perform a genomic selection for this trait, increasing the number of evaluated animals, improving the management decisions based on genomic information and applying genomic selection on a large scale.     

黑龙江省水稻单产增长潜力预测方法研究

水稻是黑龙江省的主要粮食作物。加入WTO后,水稻生产、贸易、进出口等方面都面临新的形势,同时水稻产量的稳定和增长对于国家粮食安全、人民生活水平提高以及农民奔小康都有着重要意义。因此,探讨了应用Peal曲线函数模型对黑龙江省水稻单产进行预测的方法。     

Prediction of the Economic Development for the Circumjacent Reservoir Region of the Longtan Hydropower Project Using Grey Quantitative Analog

In order to overcome some technical difficulties,we adopt ingenously the grey models combined with the quantitative analog method,and select the Dan Jiangkou Project which has accumulated practical operation data for a long time as the analog project to predict the economic influence for the circumjacent reservoir region of the Longtan hydropower project.On the basis of analog we have built various industrial economic grey models,and compare them with the corresponding predictions for the unconstructed reservoir case.The predictions of the former are greatly larger than the later ones.and hence provides a certain scientific basis for the central leaders to make a decision for constructing the huge hydropower project ultimateiy.     

A Kind of Directing Operation Expert Systems Based on Self-adaptive Predictor

In the general directing-operation expert systems.the information of directingoperation is often shown after the subprocess has finished,The aim of the thesis is to enhance thefunction of directing-operation,and suggest that we add the self-tuning predictor in the expertsystems and use it to direct the present operation.We have shown the case oftemperature-predicting experiment made in the small wine distillatorv so that we can illustrate thatit is feasible to use the predictor to direct the operation.     

煤与瓦斯突出综合预测指标临界值的优化

为了提高煤与瓦斯突出预测的准确性、保障采掘工作面的安全、高效推进和降低防突工程成本,根据“三率”(预测突出率、突出预测准确率、不突出预测准确率)各自具有的合理区间属性和权重,以张集煤矿采掘工作面为例,采用区间数关联决策方法,得出综合预测指标 F ′的最优化区间为［370,380］;根据加权灰靶决策方法,得出预测指标临界值排序为400、390、380等;取最优化区间和排序靠前的临界值的交集,得到煤与瓦斯突出综合预测指标 F ′的临界值为380,并开展现场应用。结果表明,考虑评价指标的区间属性,并取预测指标区间与排序靠前的临界值的交集作为煤与瓦斯突出预测综合指标 F ′的最优临界值方法是合理的。     

基于LS-SVM的小水电站年发电量智能预测模型

针对小水电站年发电量序列的特点,将最小二乘支持向量机(least squares support vector machine,LS-SVM)回归模型引入年发电量预测领域,并给出了相应的过程和算法。与常规基于人工神经网络(artificial neural net-works,ANN)的智能预测方法比较,该模型优点是明显的:①将神经网络迭代学习问题转化为直接求解多元线性方程;②整个训练过程中有且仅有一个全局极值点,确定了预测的稳定性。最后,一个实际的预测例子表明:该模型实现容易、预测准确,适用于小水电站预测。     

基于最小二乘支持向量机模型预测田面糙率

田面糙率是影响地面灌溉质量的重要参数。基于最小二乘支持向量机建立了两类4个田面糙率预测模型,并进行了验证。结果表明第一类模型预测值(即作物地采用LSSVM-N-I3、裸地采用LSSVM-N-I1,翻耕地采用LSSVM-N-I2)相对误差最大值为9.7%;第二类模型预测值(即LSSVM-N-II模型)相对误差最大值为10.5%,由此可见两类模型都具有较高的预测精度,可以用于田面糙率的预测。     

两种有效的预测方法理论分析及实践

预算实际上是一种建模,即根据过去和现在已有的信息建立一个从过去引伸到未来的模型。而时序分析法和灰色系统理论是两种有效的预测方法。为此,介绍了这两种预测方法的原理,并预测了新疆兵团的农机情况。其结果表明:未来10年,新疆农机总动力和投入将会有较大幅度的增长,而农业机械化程度将趋于最高值。