Including an additional systematic environmental effect within a generation in an evaluation model improves accuracy of prediction of breeding values in a closed herd of pigs

The present study evaluated the advantage of mixed‐model techniques over a selection index under different magnitudes of an additional systematic environmental effect (ASEE) in terms of accuracy of prediction and expected genetic gain. The data attempted to simulate a closed herd in a pig breeding program. The base population (G₀) consisted of 10 males and 50 females. Six generations (G₀ to G₅) were selected by using a selection index of three traits without overlapping. Additional systematic environmental constants with four levels in a generation were assigned from a uniform distribution at different ranges. Breeding values of animals in the last generation (G₅) were estimated on the basis of an index of individual phenotype (SI‐U), SI‐U adjusted for ASEE using a least‐squares mean (SI‐A), best linear unbiased prediction using an animal model excluding ASEE (AM‐E), and an animal model including ASEE (AM‐I). Accuracy of prediction and expected genetic gain were larger by the animal model than by the selection index, even if heritability of the traits selected was high and ASEE was set to zero. When ASEE was zero, the accuracy of prediction and expected genetic gain given by SI‐U and AM‐I were similar to those given by SI‐A and AM‐E, respectively. However, the differences in accuracy and expected gain between SI‐U and AI‐A and between AM‐I and AM‐E increased as the range of ASEE increased. It was concluded that selection based on an animal model was more effective than index selection, even if the herd environment was uniform and traits with high heritability were selected, and that it should be always included in an evaluation model, however slight any systematic environmental effect may be in a closed herd.     

Comparison of genetic improvement for litter size at birth by direct and indirect selection in swine herd

Responses to selection for number of piglets born alive (NBA) by the total number of piglets born (TNB), the NBA, and the NBA plus number of piglets born dead (NBD) were compared using the accuracy of selection and expected genetic gain calculated from the selection index with family information and the real response to selection, using data generated by Monte Carlo computer simulation. The accuracy of selection for NBA selected by TNB was higher than that by NBA only if the genetic correlation between TNB and NBA was close to 1.0, or the value of heritability for the TNB was much larger than that for the NBA. The accuracy of selection for the NBA selected by the combination of the TNB and the NBA was generally highest in the three selection methods in each family structure. Selection by the TNB resulted in the greatest expected genetic gain for the TNB among the selection methods. In the best linear unbiased prediction (BLUP) selection, the genetic gain for the NBA accumulated by the NBA tended to be similar to that accumulated by the combination of the NBA and the NBD, and both genetic gains at generation 10 were significantly larger than that by the TNB (P < 0.001). The accumulated responses selected by the two‐trait animal model BLUP estimated from genetic parameters with errors were similar to those estimated from the true parameters, and there was no significant difference between them. These results indicate that selection by the NBA or by the NBA and the NBD gives more genetic improvement in the NBA than that by the TNB.     

2000-2050年中国草地资源综合生产能力预测分析

分析了中国草地资源的面积变化动态和生产力现状及主要问题，探讨了未来50年中国草地资源综合生产能力增长的主要途径和措施。在此基础上预测分析了至2010年、2030年和2050年中国草地资源生产能力的增长趋势和潜力空间及其地域分布状况，并简要论述了中国草地畜牧业在未来50年国民经济发展、西部开发和解决人民吃饭问题中所应具有的地位。     

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.     

快速型黄羽肉鸡饲料利用效率性状的基因组选择研究

旨在探究快速型黄羽肉鸡饲料利用效率性状的遗传参数,评估不同方法所得估计育种值的准确性.本研究以自主培育的快速型黄羽肉鸡E系1923个个体(其中公鸡1199只,母鸡724只)为研究素材,采用"京芯一号"鸡55K SNP芯片进行基因分型.分别利用传统最佳线性无偏预测(BLUP)、基因组最佳线性无偏预测(GBLUP)和一步法...     

应用SPC分析蛋白质原料配料保真加工现状及误差产生原因

试验应用统计过程控制（SPC）分析饲料生产中蛋白质原料配料保真现状,并对配料误差存在原因进行探讨,旨在为饲料企业配料质量管理提供借鉴。收集两家饲料厂（饲料厂A、B）2015年仔猪料中豆粕、鱼粉和膨化大豆3种蛋白质原料的配料设定值和实际称量值,应用单值-移动极差控制图和生产过程能力评价方法对蛋白质原料的配料保真现状进行分析。结果显示,饲料厂A豆粕、鱼粉和膨化大豆生产过程能力指数C_PK分别为0.20、0.11和0.01,膨化大豆的配料保真度最差,且3种原料的配料实际称量值远高于设定值;饲料厂B豆粕、鱼粉和膨化大豆生产过程能力指数C_PK分别为0.24、0.12和0.24,鱼粉的配料保真度最差,膨化大豆的配料过程存在失控情况;两家饲料厂的蛋白质原料配料误差均超过了饲料行业动态配料精度±0.3% FS的要求,配方保真性较差,需采取措施进行改进。综合分析两家饲料厂配方保真性差的原因是,配料秤工艺参数配置不合理,空中落料量和快慢进料量参数需进一步优化,饲料厂B存在人为操作错误。结果提示,应用SPC方法可较为直观的监控饲料配料过程,提升蛋白质原料的配料精度,减少蛋白质原料的浪费,降低饲料生产成本,从而提高饲料厂配料质量管理水平。     

一种基于花授粉算法的WRF风速集合预报新模型

基于不同水平分辨率和边界层参数化方案的集合预报思路,应用花授粉算法与不限制负值的约束理论(FPA-NNCT)进行权重平均,提出一种新的风速集合预报模型(FPA-NNCT-WRF-E).利用山东省代表山地和海滨下垫面的2个风电场风速实测数据,将新模型与传统算术集合模型(M-WRF-E)以及FPA模型(FPA-WRF-E)的风速预报结果进行对比评估.结果表明:FPA-NNCT-WRF-E预报明显优于M-WRF-E和FPA-WRF-E的风速预报,与M-WRF-E相比, FPA-WRF-E将风速平均绝对误差(MAE)减小了20%以上,而新模型FPA-NNCT-WRF-E将MAE减小了38%以上.预报的准确性得到了提高.     

猪主要经济性状的基因组选择研究

旨在系统比较GBLUP、SSGBLUP、BayesA、BayesB、BayesC、BayesLASSO、BSLMM和BayesR等8种方法对猪重要经济性状基因组选择的准确性。本研究以本实验室收集的2 585头大白猪达100kg日龄、达100kg背膘厚和母猪乳头数3个性状为分析对象,结合猪50K基因芯片分型数据,以加性模型为基础,利用5倍交叉验证比较8种方法的基因组选择准确性。研究发现,基因组选择的准确性与不同性状估计遗传力呈正相关。交叉验证结果表明,预测准确性最高的性状为达100kg日龄,但不同方法在不同性状中表现并不完全相同,在达100kg日龄和达100kg背膘厚中SSGBLUP基因组预测准确性均为最高,而在母猪乳头数中BayesA的基因组预测准确性最高。综上表明,对小样本开展基因组预测时,中、高等遗传力性状可以选择SSGBLUP方法,低等遗传力性状可以选择BayesA方法。如何优化和选择一种广泛适用于所有性状的方法,可能是未来研究的方向。     

Accuracy of imputation of single nucleotide polymorphism marker genotypes from low‐density panels in Japanese Black cattle

Using target and reference fattened steer populations, the performance of genotype imputation using lower‐density marker panels in Japanese Black cattle was evaluated. Population imputation was performed using BEAGLE software. Genotype information for approximately 40 000 single nucleotide polymorphism (SNP) markers by Illumina BovineSNP50 BeadChip was available, and imputation accuracy was assessed based on the average concordance rates of the genotypes, varying equally spaced SNP densities, and the number of individuals in the reference population. Two additional statistics were also calculated as indicators of imputation performance. The concordance rates tended to be lower for SNPs with greater minor allele frequencies, or those located near the ends of the chromosomes. Longer autosomes yielded greater imputation accuracies than shorter ones. When SNPs were selected based on linkage disequilibrium information, relative imputation accuracy was slightly improved. When 3000 and 10 000 equally spaced SNPs were used, the imputation accuracies were greater than 90% and approximately 97%, respectively. These results indicate that combining genotyping using a lower‐density SNP chip with genotype imputation based on a population of individuals genotyped using a higher‐density SNP chip is a cost‐effective and valid approach for genomic prediction.