Comparison of selection methods among and within full-sibling progenies in Urochloa humidicola

Hybridization in the Urochloa humidicola breeding programme allowed to explore the genetic variability of the apomictic sources and, thus, to obtain new sexual and apomictic parents with compatible ploidy and/or superior progenies. However, given the high variability arising from crosses, there is a need to adopt efficient selection strategies among and within progenies. The aim of this study was to compare the selection methods among U. humidicola progenies with (t-BLUP) and without (BLUP) the weighting based on prediction error and subsequent selection within progenies by individual simulated (BLUP_IS) or optimal individual BLUP. We evaluated seventy-one U. humidicola full-sib progenies from biparental crosses between nine sexual and ten apomictic parents. The experiment was set up in an incomplete block design with 64 blocks of 15 plots each, consisting of individual plants. Seven consecutive cuts were made and the traits total dry-matter, leaf dry-matter and regrowth scores were measured. Genetic variance was expressive for all traits. The selection procedure among progenies of U. humidicula based on t-BLUP proved to be more advantageous regarding the use of BLUP not weighted by the prediction error, resulting in higher selection gains, taking as reference the optimal procedure of the individual BLUP. Furthermore, the use of BLUP_IS allowed an optimized selection of hybrids, allowing the breeder to explore with variable selection intensity the genetic variability within the selected full-sib progenies based on relative genetic merit.     

福瑞鲤选育家系不同养殖阶段的生长差异分析

为了进一步观察最佳线性无偏预测(best linear unbiased prediction,BLUP)家系选育方法在福瑞鲤(Cyprinus carpio)继代选育中的潜力,该研究测量了继续选育第2代家系群体不同养殖阶段的体质量和形态性状。结果表明,生长快速家系群福瑞鲤早期(4月龄)生长速度较慢,到后期则生长加快,其体质量增长表现出明显的优势。在体型方面,随着养殖时间的延长,福瑞鲤各选育家系群的体厚/体长增加,体高/体长降低,逐渐呈现其体型修长的特征;同时2个越冬期的成活率均达到了94%以上。结果表明通过BLUP家系选育对福瑞鲤长期选育是可行的。在此基础上,通过主成分分析发现,福瑞鲤生长性状第一主成分是体质量;对不同生长时期的体质量进行相关性分析,发现9月龄、14月龄、21月龄鱼的体质量与24月龄的相关系数均达到极显著水平(P0.01),分别为0.851、0.897和0.957。因此,在福瑞鲤继续选育过程中,进行早期个体选择值得尝试。     

Impact of relationships between test and training animals and among training animals on reliability of genomic prediction

One of the factors affecting the reliability of genomic prediction is the relationship among the animals of interest. This study investigated the reliability of genomic prediction in various scenarios with regard to the relationship between test and training animals, and among animals within the training data set. Different training data sets were generated from EuroGenomics data and a group of Nordic Holstein bulls (born in 2005 and afterwards) as a common test data set. Genomic breeding values were predicted using a genomic best linear unbiased prediction model and a Bayesian mixture model. The results showed that a closer relationship between test and training animals led to a higher reliability of genomic predictions for the test animals, while a closer relationship among training animals resulted in a lower reliability. In addition, the Bayesian mixture model in general led to a slightly higher reliability of genomic prediction, especially for the scenario of distant relationships between training and test animals. Therefore, to prevent a decrease in reliability, constant updates of the training population with animals from more recent generations are required. Moreover, a training population consisting of less‐related animals is favourable for reliability of genomic prediction.     

动物模型BLUP法估计内蒙古白绒山羊育种值的研究

应用动物模型BLUP法估计了阿尔巴斯白绒山羊种羊场1989-1998年共10个年度3 981只个体的抓绒量和体重的单性状育种值,以及这两个性状的综合育种值.在模型中考虑的固定效应有年龄效应和性别-群体-年度效应、随机效应有个体的加性效应和个体永久性环境效应.比较育种值选择与表型值选择的结果表明:①公羔依据断乳重选择与依据育种值选择的结果差异较大;②育成母羊依据表型值选择与依据综合育种值选择的结果差异极显著(P<0.01);③育成公羊依据表型值选择与依据综合育种值选择的结果差异极显著(P<0.01);④种公羊依据抓绒量和体重的表型值选择结果分别与依据各自性状育种值选择结果的秩相关均未达到显著水平(P>0.05).研究表明:内蒙古白绒山羊应用个体表型值选种存在准确性较差的缺点;动物模型BLUP法适合用于内蒙古白绒山羊的选种.并根据生产实际情况提出了一套选择种公羊的具体方法.     

山西瘦肉型猪新品系(SD-Ⅰ系)6月龄体重遗传趋势分析

用混合模型ＢＬＵＰ法对山西瘦肉型猪新品系（ＳＤ Ⅰ系）选育过程中０～４世代４０头公猪的４４７头后裔６月龄体重进行了遗传趋势评估。结果表明：群体平均育种值随世代的增加而增加，说明ＳＤ Ⅰ系猪选育过程中所采取的育种措施是有效的。各世代间环境条件基本一致，且除０世代外，对表型值都有正的效应。继续进行选育，６月龄体重尚有提高的可能。     

Marker-based screening of maize inbred lines using support vector machine regression

The phenomenon of heterosis is widely used in hybrid breeding programmes, despite the fact that no satisfactory molecular explanation is available. Estimators of quantitative genetic components like GCA and SCA values are tools used by the plant breeder to identify superior parental individuals and to search for high heterosis combinations. Obtaining these estimators usually requires the creation of new parental combinations and testing their offspring in multi-environment field trials. In this study we explore the use of ɛ-insensitive Support Vector Machine Regression (ɛ-SVR) for the prediction of GCA and SCA values from the molecular marker scores of parental inbred lines as an alternative to these field trials. Prediction accuracies are obtained by means of cross-validation on a grain maize data set from the private breeding company RAGT R2n. Results indicate that the proposed method allows the routine screening of new inbred lines despite the fact that predicting the SCA value of an untested hybrid remains problematic with the available molecular marker information and standard kernel functions. The genotypical performance of a testcross hybrid, originating from a cross between an untested inbred line and a well-known complementary tester, can be predicted with moderate to high accuracy while this cannot be said for a cross between two untested inbred lines.     

基于Web的陕北白绒山羊辅助育种管理系统的设计与实现

针对陕北白绒山羊多性状选择育种研究处于起步阶段的现状,以动物模型BLUP法为核心的多性状复合育种技术体系为依托,在Windows XP平台下利用ASP技术和SQL Server 2000数据库开发了一套基于Web的陕北白绒山羊辅助育种管理系统-ABMS。该系统能实时地管理分布在不同地域种羊场的生产性能测定信息,并能方便地导入和导出选择性状的育种数据。ABMS在陕北白绒山羊种羊场实际应用中体现了技术先进、易于操作、网络化管理等特点,其推广和应用对实现种羊场育种工作和生产管理的自动化,具有极大的促进作用。     

利用SAS估计动物BLUP育种值

本文简要介绍了BLUP的数学背景、用于BLUP计算的SAS程序模块使用方法和2个计算BLUP的程序示例。     

Multiple trait genetic evaluation of ewe traits in Icelandic sheep

The prolificacy of the ewes was measured as the number of lambs born per ewe mated (NLB) when the ewes were 1–4 years of age. The ewe productivity related to the same age interval was measured by special ewe production indices (EPI). The genetic parameters for these traits were estimated by a series of bivariate REML analyses using animal models. The material used for the genetic analysis contained records on 193 213 ewes. The heritability estimates for NLB were h² = 0.17, 0.13, 0.11, 0.10 for the four respective age classes. Corresponding estimates for EPI were h² = 0.16, 0.17, 0.17, 0.15. The genetic correlations among NLB at different ages ranged from 0.63 to 0.98 and among EPI from 0.82 to 0.99. The genetic correlations between NLB and EPI were generally low. The material used for estimating the breeding values by the MT‐BLUP Animal Model consisted of 1.5 million individuals in the pedigree file. In total 815 782 ewes had records for the NLB and 763 491 ewes had production index (at least 1 year). The records were registered in the years 1990–2006. All possible missing patterns were present in the data. In the iteration process expected values for missing traits were generated and solutions were obtained on canonical transformed scale. The genetic evaluations were run independently for NLB and EPI for computational convenience given the correlations between these traits were negligible.     

The impact of truncating data on the predictive ability for single‐step genomic best linear unbiased prediction

Simulated and swine industry data sets were utilized to assess the impact of removing older data on the predictive ability of selection candidate estimated breeding values (EBV) when using single‐step genomic best linear unbiased prediction (ssGBLUP). Simulated data included thirty replicates designed to mimic the structure of swine data sets. For the simulated data, varying amounts of data were truncated based on the number of ancestral generations back from the selection candidates. The swine data sets consisted of phenotypic and genotypic records for three traits across two breeds on animals born from 2003 to 2017. Phenotypes and genotypes were iteratively removed 1 year at a time based on the year an animal was born. For the swine data sets, correlations between corrected phenotypes (Cp) and EBV were used to evaluate the predictive ability on young animals born in 2016–2017. In the simulated data set, keeping data two generations back or greater resulted in no statistical difference (p‐value > 0.05) in the reduction in the true breeding value at generation 15 compared to utilizing all available data. Across swine data sets, removing phenotypes from animals born prior to 2011 resulted in a negligible or a slight numerical increase in the correlation between Cp and EBV. Truncating data is a method to alleviate computational issues without negatively impacting the predictive ability of selection candidate EBV.