共查询到19条相似文献，搜索用时 213 毫秒
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多性状动物模型BLUP(Best Linear UnbiasedPrediction)法是当今世界上先进的育种值估计方法,能消除各种环境因素的影响,利用各种亲属资料,考虑选择近交及性状间遗传相关等因素,准确地同时估计出种畜各性状育种值以及综合育种值,并能比较场内,场间,地区间甚至国家间种畜优劣,可加速优良种畜的推广利用,提高种畜质量。 相似文献
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应用总性能指数(TPI)评定西门塔尔种公牛 总被引：1，自引：0，他引：1
<正> 前言应用正确的方法计算种公牛的育种值,对于准确地选择种公牛,改良牛群的遗传特性,提高牛群质量,获得更大的经济效益,无疑是十分重要的。过去对于种公牛的评定方法,只是根据单个性状育种值的高低作为唯一的标准,忽略其他一些重要的经济性状,无疑是一个很大缺点。目前应用最广泛的是总性能指数法(TPI),即多项性状估计的育种值综合为一个指数,根据指数大小选择种公牛。为消除畜群间、年季间和季度间的差异,在计算各性状育种值时,可用最佳线性无偏预期法(BLUP)或改进的同龄比较法(MCC)。本文重点以改进的同龄比较法(MCC)计算总性能指数(TPI),对我国西门塔尔种公牛评定作以研究。 相似文献
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本研究旨在为以一个初始杜泊绵羊群体为基础,建立适应多样化市场需求的商业化品系,提供可借鉴的实用方法.具体实施步骤包括:(1)用单性状动物模型BLUP法估计杜泊绵羊与品系划分有关性状的育种值；(2)利用主成分分析法,抽取所研究性状的可解释85％以上变异的主成分；(3)依据主成分值表达式的系数大小及符号,解释每个主成分的实际意义,确定各主成分可作为哪个品系的建立依据；(4)根据个体各标准化主成分值的大小及相应的分系标准,对杜泊绵羊群体进行商业化品系划分.本研究依据实际需求将现有杜泊羊群体划分为体长系、体宽系和肥羔系,其结果与依据性状估计育种值进行品系划分相似.在不考虑分系相关性状的经济权重时,BLUP法和主成分分析法适于初始群体的品系建立. 相似文献
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<正> 本文收集了四川省凉山州培育中的半细毛羊87～90年度共计2000多只羊的育种记录。用最小二乘法较正场别、年度、公羊组、出生月份、性别等固定效应后用父系半同胞方法估测了4个体重性状和6个羊毛性状的遗传参数;用BLUP方法估计了公羊各性状的育种值,所用的模型包含年度一月一性别效应(固定)、公羊组效应(固定)、公羊效应(随机)和随机误差 相似文献
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奶牛泌乳曲线数学模型的遗传分析 总被引：7，自引：3，他引：7
本文采用Wood、改进多项式、回归模型各参数估值及拟合、预报奶量进行了方差分析,遗传力、遗传相关估计及BLUP法育种值估计。方差分析结果表明,产犊季节和胎次对各性状具有较为显著的影响。各遗传相关的估值均较高,这是由环境因素及取样情况所致。实际及估计305天产奶量的遗传力估值较高,Wood模型参数c、改进多项式模型参数d及回归模型参数d、e的遗传力估值也相对较高。各估计、预报奶量及各选定模型参数的BLUP育种值排队序号与实际305天产奶量BLUP育种值排队序号均呈极显著的秩相关(p<0.001).这表明可以用估计和预报奶量及模型参数来评定种公牛的优劣。这对奶牛生产和育种实践具有一定的实践意义。 相似文献
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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. 相似文献
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《畜牧兽医学报》2020,(1)
旨在比较不同方法对遗传参数估计的差异,为未来北京油鸡胴体和肉质性状选育方法的制定提供参考依据。本研究利用传统最佳线性无偏预测(best linear unbiased prediction,BLUP)和基因组最佳线性无偏预测(genomic best linear unbiased prediction,GBLUP)两种方法对北京油鸡的胴体和肉质等性状进行了遗传参数估计。从系谱较为完整的北京油鸡群体中,选择100日龄体重相近的公鸡615只,测定其100日龄体重(BW)、屠宰率(EP)、胸肌率(BMP)、腿肌率(LMP)、腹脂率(AFP)、嫩度(T,以剪切力值表示)和肌内脂肪(IMF)等性状,并用SNP芯片(Illumina,60K)进行个体基因分型。结果表明,除IMF和剪切力(SF)遗传力基于两种方法的估值存在较大差异外,其余性状利用两种方法得到的遗传力估值差异较小;除嫩度外,GBLUP方法估计的遗传力均低于BLUP方法。所有胴体相关性状中,除屠宰率遗传力为低遗传力外,其余性状均属于中等遗传力性状。嫩度呈现低遗传力,而IMF基于BLUP法和GBLUP法的估计遗传力分别为中等(h~2=0.256)和低遗传力(h~2=0.107)。基于BLUP方法,IMF与BW、BMP和SF 3个性状间均呈高度遗传负相关(-0.572、-0.420、-0.682),与EP的遗传相关为中度负相关(-0.234),与AFP的遗传相关为中度正相关(0.420);基于GBLUP方法,IMF与BW、BMP和SF 3个性状间均呈高度遗传负相关(-0.808、-0.725、-0.784),与EP的遗传相关为高度负相关(-0.626),与AFP的遗传相关为低度正相关(0.097)。综上,对于某些性状,基于传统的BLUP方法与新的GBLUP方法得到的遗传力与遗传相关估值存在较大差异,实际育种工作中,为提高育种效率,需要综合考虑。 相似文献
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An alternative derivation method of mixed model equations from best linear unbiased prediction (BLUP) and restricted BLUP of breeding values not using maximum likelihood 
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下载免费PDF全文 Masahiro Satoh 《Animal Science Journal》2018,89(6):876-879
Two mixed model equations (MME) for best linear unbiased prediction (BLUP) of breeding values and for restricted BLUP of breeding values were derived by maximum likelihood from the joint normal probability distribution of the observations and breeding values. As a result, MME is actually more general than maximum likelihood because we can prove that each set of solutions of MME are identical to BLUP and restricted BLUP of breeding values and then it does not depend on normality. In the present study, the author shows deriving directly each MME from BLUP and restricted BLUP equations for breeding values without assuming the joint normal distribution of the data and random effects. However, if we cannot assume the multivariate normal density distribution of the estimated aggregate breeding value and each breeding value for selected traits, the response to selection by restricted BLUP may deviate from the expected values. 相似文献
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旨在将整合元共祖的一步法(single-step genomic best linear unbiased prediction with metafounders,MF-SSGBLUP)应用到基因组联合育种中,并与其他经典基因组选择方法进行比较分析。本研究使用QMSim软件模拟3个系谱相互独立的奶牛群体;分别使用广义最小二乘法(generalized least squares,GLS)和原始方法(naïve,NAI)估计不同群体间的祖先关系矩阵Γ;将MF-SSGBLUP、SSGBLUP和BLUP用于3个模拟群体的联合育种,评估各方法在遗传参数和育种值估计方面的差异。在不同遗传力下,GLS所得的Γ矩阵在对角线元素上略低于NAI法,在非对角线元素上没有明显差异,且基因组关系矩阵与基于元共祖构建的亲缘关系矩阵对角线元素相关系数(0.750~0.775)高于基因组关系矩阵与传统的亲缘关系矩阵相关系数(0.508~0.572)。MF-SSGBLUP遗传力估计值(0.138、0.140、0.297和0.298)与当代群体遗传力(0.107和0.296)的偏差小于其余两种方法(0.145、0.173、0.273和0.340),且MF-SSGBLUP估计育种值准确性(0.888~0.908)高于SSGBLUP法(0.863~0.876)和BLUP法(0.854~0.871)。表明,MF-SSGBLUP的遗传参数估计值无偏性更好,估计育种值准确性更高。根据上述模拟数据结果表明,在联合育种中,整合元共祖的基因组选择方法优于其他经典基因组选择方法。 相似文献
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《Livestock Production Science》1986,14(2):123-138
In sire evaluation using the best linear unbiased prediction (BLUP) method a relationship matrix including only the relationships among sires can be used. The aim of the present study was to show properties of this method when models with or without grouping of sires are applied. The examination consisted of Monte-Carlo simulation of special situations occurring in dairy sire evaluation. Up to 20 repetitions were generated for each data set. Sire effects were equal for all repetitions of a particular set, cow and residual effects being sampled afresh. Thus, in addition to the correlation between true and estimated breeding value, the true standard deviation of estimated breeding value could be calculated from the results of repeated sire evaluation.With respect to these parameters, the results show that models which do not contain sire grouping but include relationships were superior to grouping models even when relationships are considered in addition. This superiority increases with decreasing number of daughters per sire.The hypothesis that a model without sire grouping but including a relationship matrix leads to a bias in the estimation of breeding values when dams of bulls are selected, could not be confirmed. On condition that the frequency of sires which are related to other sires is high, a model including a relationship matrix but without grouping can be recommended. 相似文献
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Selection of animals based on their BLUP evaluations from an animal model results in animals that are closely related which leads to increased rates of inbreeding. The tendency for higher inbreeding rates is greater at low heritability values. Several attempts have been made to reduce the impact of parent average breeding values from animals evaluations in order to reduce inbreeding while not sacrificing genetic response. A method that modifies the rules for forming the inverse of the additive genetic relationship matrix for use in best linear unbiased estimation of breeding values via an animal model was developed. This method and several others were compared analytically and empirically, from the perspective of partitioning the animal solutions into contributions from the data, from progeny, and from the parent average. The ratio of genetic progress to average level of inbreeding showed that the modified relationship matrix method was superior to the other methods. Similar results could be obtained by using artificially high heritability in a usual BLUP analysis. 相似文献
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Breeding and Genetics Symposium: really big data: processing and analysis of very large data sets 总被引：1，自引：0，他引：1
Modern animal breeding data sets are large and getting larger, due in part to recent availability of high-density SNP arrays and cheap sequencing technology. High-performance computing methods for efficient data warehousing and analysis are under development. Financial and security considerations are important when using shared clusters. Sound software engineering practices are needed, and it is better to use existing solutions when possible. Storage requirements for genotypes are modest, although full-sequence data will require greater storage capacity. Storage requirements for intermediate and results files for genetic evaluations are much greater, particularly when multiple runs must be stored for research and validation studies. The greatest gains in accuracy from genomic selection have been realized for traits of low heritability, and there is increasing interest in new health and management traits. The collection of sufficient phenotypes to produce accurate evaluations may take many years, and high-reliability proofs for older bulls are needed to estimate marker effects. Data mining algorithms applied to large data sets may help identify unexpected relationships in the data, and improved visualization tools will provide insights. Genomic selection using large data requires a lot of computing power, particularly when large fractions of the population are genotyped. Theoretical improvements have made possible the inversion of large numerator relationship matrices, permitted the solving of large systems of equations, and produced fast algorithms for variance component estimation. Recent work shows that single-step approaches combining BLUP with a genomic relationship (G) matrix have similar computational requirements to traditional BLUP, and the limiting factor is the construction and inversion of G for many genotypes. A na?ve algorithm for creating G for 14,000 individuals required almost 24 h to run, but custom libraries and parallel computing reduced that to 15 m. Large data sets also create challenges for the delivery of genetic evaluations that must be overcome in a way that does not disrupt the transition from conventional to genomic evaluations. Processing time is important, especially as real-time systems for on-farm decisions are developed. The ultimate value of these systems is to decrease time-to-results in research, increase accuracy in genomic evaluations, and accelerate rates of genetic improvement. 相似文献
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Absorption of equations for non-parents for an animal model with maternal effects and genetic groups
L D Van Vleck 《Journal of animal science》1990,68(12):4014-4025
Rules for forming the mixed-model equations for the reduced animal model with all relationships and including maternal effects have been set out by Quaas and Pollak. They also have shown how to simplify the mixed-model equations when genetic group effects are included in the model with what has become known as the Q-P transformation. Westell has given rules for calculating the coefficients for the Q-P transformed equations that are associated with the inverse of the numerator relationship matrix and genetic group effects. Those rules can be extended to include maternal effects and genetic groups for maternal as well as direct effects. As with the rules of Quaas and Pollak for the equations for the reduced animal model, a similar set of rules can be obtained for the genetic groups model after the Q-P transformation. The rules are derived easily by examining the algebraic results of absorbing the direct and maternal breeding value equations for non-parents into the parent breeding value, group and fixed effects equations. These rules involve Westell's rules and the inverse elements of the genetic (co)variance matrix for direct and maternal additive genetic effects. The rules make calculation of breeding values for parents for models including direct and maternal genetic group effects nearly as easy as for models without genetic group effects. Back solution for direct and maternal breeding values of non-parents similarly is as simple as when genetic group effects are not in the model. 相似文献
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Estimated breeding value was calculated based on individual phenotype (SP), an index of individual phenotype and full- and half-sib family averages (SI), or Best Linear Unbiased Prediction (BLUP). Traits considered were litter size (LS), backfat (BF), and ADG. Estimated breeding values were calculated using all data and after deletion of the poorest 5, 10, 15, or 20% of the records for BF and ADG, or 4.8, 8, 13, or 21% of the records for LS. When all data were used, expected genetic gain from BLUP was greater than for SP by 22, 7, and 31% and greater than for SI by 10, 4, and 21% for LS, BF, and ADG, respectively. Expected genetic gain was 4, 0, and 3% lower for LS, BF, and ADG, respectively, for selection on breeding values estimated by SI after the poorest 20% of the records were deleted compared with selection on estimates by SI using all the data. Genetic gain using BLUP on data with the poorest 20% of the records deleted was reduced by 5, 2, and 8% for LS, BF, and ADG, respectively, compared with genetic gain using BLUP on all the data. The advantage in genetic gain of BLUP, with 20% of the poorest records deleted, over SP was 15, 5, and 21% for LS, BF, and ADG, respectively. Although BLUP is affected to a greater degree by deletion of records than is SP or SI, selection of swine using BLUP on field data would improve response to selection over the use of SP or SI. 相似文献