共查询到19条相似文献，搜索用时 343 毫秒
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统计昆明犬家系3个世代兴奋性测试记录和系谱数据,对昆明犬兴奋性状不同选育方法进行模拟研究。利用家系系谱资料及F3和F4世代兴奋性状数据,估算个体育种值,依据表型和育种值,对F4犬群按照个体表型、家系表型、个体育种值、家系育种值排序,各选择30%个体,寻找其在F5代中的个体,计算各种选择方法所产生后代的表型和遗传进展,评估各种选择方法对昆明犬兴奋性状选育最优方法。研究表明:选择昆明犬个体育种值好于选择昆明犬家系育种值好于选择家系表型好于选择个体表型,选择昆明犬公犬效果好于选择昆明犬母犬效果。 相似文献
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中系马里努阿犬是以2000年进口马利诺斯犬原种优化利用开发为基础而正在选育的新品系,至2018年已完成5个世代选育。中系马里努阿犬警用性状优秀,繁殖性状优良,遗传稳定,初产、经产母犬的总产仔数分别为7.62头和8.26头。产活仔数,初生体质量,断奶头数,断奶重和断奶育成率分别达到7.40头、0.36 kg、7.39头、3.44 kg和98%。与马利诺斯犬原种相比,中系马里努阿犬不仅在警用性能方面有明显提高,在繁殖性能方面也有较大改进,且能较好地适应中国气候和地理环境,耐粗饲、繁殖性能优良,警用性能突出。 相似文献
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试验旨在进行芦花鸡H系开产性状遗传参数和育种值估计,为芦花鸡H系选育提供依据。收集芦花鸡H系2016—2020年系谱信息和开产性状表型数据,利用DMU软件以平均信息约束最大似然法（AIREML）剖分表型方差,以单性状动物模型分析各性状遗传力,以多性状动物模型分析性状间的遗传相关系数和表型相关系数,并基于系谱-加性效应模型（PBLUP＿A）对所有个体开产性状进行育种值估计,计算遗传进展。结果显示：芦花鸡H系开产日龄、开产体重和开产蛋重遗传力分别为0.42、0.40和0.27,开产日龄与开产体重、开产蛋重间的遗传相关分别为0.17、0.31,表型相关分别为0.11、0.18,开产体重和开产蛋重间遗传相关和表型相关系数分别为0.56和0.38;开产日龄、开产体重和开产蛋重年遗传进展分别为-0.014 d、1.14 g、0.018 g。结果表明：芦花鸡H系各开产性状均为中等遗传力性状,开产蛋重与开产日龄呈中等遗传正相关,与开产体重呈高遗传正相关,开产日龄与开产体重遗传相关较低,经过5个世代的选育,芦花鸡H系开产性状有了一定的遗传进展。对芦花鸡H系开产性状遗传参数和育种值的确定与分析研究结果,为... 相似文献
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本研究以１９８７年从法国引进的泰克森商品代肉鸽作育种素材，采用现代遗传育种技术，通过家系选育法，结合个体性状，表型基因的选择，进行系统的世代选育。经过四年时间的培育，缍稳定了染色体中的性别性状遗传，达到了从乳鸽开始长毛就可鉴别雌雄的目的。 相似文献
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采用多性状动物模型BLUP和非求导约束最大似然法（MTDFREML）估计了引进罗曼蛋鸡纯系配套组合中4个品系9个性状的方差组分和遗传参数,并估计了固定效应的BLUE值和动物个体的加性遗传效应值（育种值）,分析了测定性状的世代间遗传趋势和表型趋势。结果表明,4个系各性状的遗传力基本一致,体重、蛋重以及不同周期的产蛋量之间存在显著的正相关。蛋重与产蛋量存在较强的遗传负相关。六个世代选育后,各品系蛋重、产蛋量的平均育种值均有提高,表明对产蛋量和蛋重的选择是有效的。 相似文献
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采用多性状动物模型BLUP和非求导约束最大似然法（MTDFREML）估计了引进罗曼蛋鸡纯系配套组合中4个品系9个性状的方差组分和遗传参数,并估计了固定效应的BLUE值和动物个体的加性遗传效应值（育种值）,分析了测定性状的世代间遗传趋势和表型趋势。结果表明,4个系各性状的遗传力基本一致,体重、蛋重以及不同周期的产蛋量之间存在显著的正相关。蛋重与产蛋量存在较强的遗传负相关。六个世代选育后,各品系蛋重、产蛋量的平均育种值均有提高,表明对产蛋量和蛋重的选择是有效的。 相似文献
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Effects of errors in pedigree on three methods of estimating breeding value for litter size, backfat and average daily gain in swine 总被引：2，自引：0，他引：2
Estimated breeding value (EBV) was calculated based on either individual phenotype (SP), an index of individual phenotype and full- and half-sib family averages (SI) or Best Linear Unbiased Prediction (BLUP). Calculations were done with correct data or data with 5, 10, 15 or 20% of the records per generation containing pedigree errors. Traits considered were litter size (LS), backfat (BF) and average daily gain (ADG). When data were correct, BLUP resulted in an advantage in expected genetic gain over SP of 22, 7.2 or 30.8% for LS, BF and ADG, respectively, and over SI of 9.6, 3.8 or 21.4%. When sire and dam pedigrees were incorrect for 20% of the pigs each generation, genetic gain using SI was reduced by 7, 2.5 or 6.5% and genetic gain using BLUP was reduced by 9.3, 3.2 or 12.4% for LS, BF and ADG, respectively. With 20% of the pedigrees in error, the advantages in genetic gain of using BLUP over SP, the method unaffected by errors in pedigree, were 10.5, 3.8 and 14.6% for LS, BF and ADG, respectively. These results suggest that, although BLUP is affected to a greater degree by pedigree errors than SP or SI, selection of swine using BLUP still would improve response to selection over the use of SP or SI. 相似文献
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E.C. Akanno F.S. Schenkel M. Sargolzaei R.M. Friendship J.A.B. Robinson 《Zeitschrift für Tierzüchtung und Züchtungsbiologie》2014,131(5):367-378
Genetic improvement of pigs in tropical developing countries has focused on imported exotic populations which have been subjected to intensive selection with attendant high population‐wide linkage disequilibrium (LD). Presently, indigenous pig population with limited selection and low LD are being considered for improvement. Given that the infrastructure for genetic improvement using the conventional BLUP selection methods are lacking, a genome‐wide selection (GS) program was proposed for developing countries. A simulation study was conducted to evaluate the option of using 60 K SNP panel and observed amount of LD in the exotic and indigenous pig populations. Several scenarios were evaluated including different size and structure of training and validation populations, different selection methods and long‐term accuracy of GS in different population/breeding structures and traits. The training set included previously selected exotic population, unselected indigenous population and their crossbreds. Traits studied included number born alive (NBA), average daily gain (ADG) and back fat thickness (BFT). The ridge regression method was used to train the prediction model. The results showed that accuracies of genomic breeding values (GBVs) in the range of 0.30 (NBA) to 0.86 (BFT) in the validation population are expected if high density marker panels are utilized. The GS method improved accuracy of breeding values better than pedigree‐based approach for traits with low heritability and in young animals with no performance data. Crossbred training population performed better than purebreds when validation was in populations with similar or a different structure as in the training set. Genome‐wide selection holds promise for genetic improvement of pigs in the tropics. 相似文献
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旨在估计山东省荷斯坦奶牛体型性状遗传参数,为育种方案制定提供参考。本研究收集了山东省2010—2020年间的144个牛场31 963头头胎中国荷斯坦母牛的20个体型性状记录,其中性状评分由线性分转为功能分,将场、泌乳月、产犊月龄、鉴定员效应为固定效应,以个体的加性遗传效应作为随机效应,利用 DMU 软件,采用AI-REML结合EM算法并配合动物模型进行遗传参数估计。结果表明,体型性状的遗传力属于中等偏低水平,其估计值变化范围为0.049(后肢侧视)到 0.282(棱角性),性状间的遗传相关范围为-0.558(前乳头位置与乳房深度)至0.717(蹄踵深度与蹄角度)。体躯容量各性状间的遗传相关范围为0.118 (体深与体高)至0.461(胸宽与腰强度);尻角度与尻宽的遗传相关为-0.251;肢蹄各性状间的遗传相关范围为-0.035(蹄踵深度与后肢后视)到 0.717(蹄踵深度与蹄角度);泌乳系统各性状间的遗传相关范围为-0.558 (前乳头位置与乳房深度)至0.587(悬韧带与前乳房附着)。另外,体型性状的遗传力估计标准误在查找的系谱世代数为3的情况下为最小,这可能是由于系谱数据完整性的限制导致了该种情况,具体还需要进一步验证。加强对体型性状中遗传力较高且与泌乳系统遗传相关较强性状的选择,有利于奶牛生产性能的提高。另外,在本研究数据中,使用前3代系谱估计的遗传力标准误最小,因此,利用前3代系谱估算遗传参数可能较佳。 相似文献
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The study of population structure by pedigree analysis is useful to identify important circumstances that affect the genetic history of populations. The intensive use of a small number of superior individuals may reduce the genetic diversity of populations. This situation is very common for the beef cattle breeds. Therefore, the objectives of the present study were to analyze the pedigree and possible inbreeding depression on traits of economic interest in the Marchigiana and Bonsmara breeds and to test the inclusion of the individual inbreeding coefficient (F(i)) or individual increases in inbreeding coefficient (ΔF(i)) in the genetic evaluation model for the quantification of inbreeding depression. The complete pedigree file of the Marchigiana breed included 29,411 animals born between 1950 and 2003. For the Bonsmara breed, the pedigree file included 18,695 animals born between 1988 and 2006. Only animals with at least 2 equivalent generations of known pedigree were kept in the analyses of inbreeding effect on birth weight, weaning weight measured at about 205 d, and BW at 14 mo in the Marchigiana breed, and on birth weight, weaning weight, and scrotal circumference measured at 12 mo in the Bonsmara breed. The degree of pedigree knowledge was greater for Marchigiana than for Bonsmara animals. The average generation interval was 7.02 and 3.19 for the Marchigiana and Bonsmara breed, respectively. The average inbreeding coefficient was 1.33% for Marchigiana and 0.26% for Bonsmara. The number of ancestors explaining 50% of the gene pool and effective population size computed via individual increase in coancestry were 13 and 97.79 for Marchigiana and 41 and 54.57 for Bonsmara, respectively. These estimates indicate reduction in genetic variability in both breeds. Inbreeding depression was observed for most of the growth traits. The model including ΔF(i) can be considered more adequate to quantify inbreeding depression. The inclusion of F(i) or ΔF(i) in the genetic evaluation model may not result in better fit to the data. A genetic evaluation with simultaneous estimation of inbreeding depression can be performed in Marchigiana and Bonsmara breeds, providing additional information to producers and breeders. 相似文献
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Comparison of restricted maximum likelihood and method R for estimating heritability and predicting breeding value under selection 总被引：3，自引：0，他引：3
Cantet RJ Birchmeier AN Santos-Cristal MG de Avila VS 《Journal of animal science》2000,78(10):2554-2560
Method R and Restricted Maximum Likelihood (REML) were compared for estimating heritability (h2) and subsequent prediction of breeding values (a) with data subject to selection. A single-trait animal model was used to generate the data and to predict breeding values. The data originated from 10 sires and 100 dams and simulation progressed for 10 overlapping generations. In simulating the data, genetic evaluation used the underlying parameter values and sires and dams were chosen by truncation selection for greatest predicted breeding values. Four alternative pedigree structures were evaluated: complete pedigree information, 50% of phenotypes with sire identities missing, 50% of phenotypes with dam identities missing, and 50% of phenotypes with sire and dams identities missing. Under selection and with complete pedigree data, Method R was a slightly less consistent estimator of h2 than REML. Estimates of h2 by both methods were biased downward when there was selection and loss of pedigree information and were unbiased when no selection was practiced. The empirical mean square error (EMSE) of Method R was several times larger than the EMSE of REML. In a subsequent analysis, different combinations of generations selected and generations sampled were simulated in an effort to disentangle the effects of both factors on Method R estimates of h2. It was observed that Method R overestimated h2 when both the sampling that is intrinsic in the method and the selection occurred in generations 6 to 10. In a final experiment, BLUP(a) were predicted with h2 estimated by either Method R or REML. Subsequently, five more generations of selection were practiced, and the mean square error of prediction (MSEP) of BLUP(a) was calculated with estimated h2 by either method, or the true value of the parameter. The MSEP of empirical BLUP(a) using Method R was greater than the MSEP of empirical BLUP(a) using REML. The latter statistic was closer to prediction error variance of BLUP(a) than the MSEP of empirical BLUP(a) using Method R, indicating that empirical BLUP(a) calculated using REML produced accurate predictions of breeding values under selection. In conclusion, the variability of h2 estimates calculated with Method R was greater than the variability of h2 estimates calculated with REML, with or without selection. Also, the MSEP of EBLUP(a) calculated using estimates of h2 by Method R was larger than MSEP of EBLUP(a) calculated with REML estimates of h2. 相似文献
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畜禽的选种选育在生产中至关重要,育种值估计是选种选育的核心。基因组选择（genomic selection,GS）是利用全基因组范围内的高密度标记估计个体基因组育种值的一种新型分子育种方法,目前已在牛、猪、鸡等畜禽育种中得到应用并取得了良好的效果。该方法可实现畜禽育种早期选择,降低测定费用,缩短世代间隔,提高育种值估计准确性,加快遗传进展。基因组选择主要是通过参考群体中每个个体的表型性状信息和单核苷酸多态性（single nucleotide polymorphism,SNP）基因型估计出每个SNP的效应值,然后测定候选群体中每个个体的SNP基因型,计算候选个体的基因组育种值,根据基因组育种值的高低对候选群体进行合理的选择。随着基因分型技术快速发展和检测成本不断降低,以及基因组选择方法不断优化,基因组选择已成为畜禽选种选育的重要手段。作者对一些常用的基因组选择方法进行了综述,比较了不同方法之间的差异,分析了基因组选择存在的问题与挑战,并展望了其在畜禽育种中的应用前景。 相似文献
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Selection and breeding are very important in production of livestock and poultry,and breeding value estimation is the core of selection and breeding.Genomic selection (GS) is a novel molecular breeding method to estimate genomic breeding value using high-density markers across the whole genome.At present,GS has been successfully applied in cattle,pig,chicken and so on,and made significant progress.This method can achieve early selection,decrease the testing costs,shorten generation interval,improve the accuracy of breeding value estimation and accelerate genomic progress.GS estimates the effect of SNP by phenotype information and SNP genotype of each individual in the reference population,and measures the SNP genotype to calculate the genomic estimated breeding value in the candidate population,then selects the best individuals according to the genomic estimated breeding value.With the rapid development of genotyping technology and the decrease of detection cost,and the continuous optimization and high efficiency of genomic selection methods,genomic selection has become an important research method in the selection and breeding of livestock and poultry.The authors reviewed some of the widely used genomic selection methods,compared the differences between different methods,analyzed the problems and challenges of genomic selection,and looked forward to its application prospects in breeding. 相似文献
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茧丝量性状和生命力性状的综合选择方法是家蚕新品种选育的关键技术。采用杂交和系统分离为基本育种手段,结合春秋自然交替的培育环境,蛾区选择始终把生命力性状作为重要指标,同时兼顾茧丝质的选择,入选蛾区内严格精选全茧量和茧层量性状在平均值偏上的个体留种并控制茧层率,从而达到了茧丝量性状和生命力性状的综合选择目的。按以上方法育成2个优良中、日系统品种"芙菁"和"湘8H,"F1代杂交种经过春秋2次实验室初交测试表现为生命力强、茧丝质好、茧丝量和产茧量高,综合性状优良,从而克服了茧丝量与生命力难以兼顾的矛盾。 相似文献
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Selection on the best estimate of the breeding value of individuals should, in large populations, provide the maximal response in breeding value. However, many breeders deal with the selection of small numbers of animals from relatively small populations and therefore there is a trend for inbreeding to rise because of genetic drift. Moreover, as the evaluation of candidates is traditionally based on methodologies including information from relatives [selection indices, best linear unbiased predictor (BLUP)] more individuals are selected from the best families and so closely related individuals will generate most of the offspring. This effect is more important for traits with low heritability as phenotype gives little information on the breeding value of the individuals and more weight is given to relatives’ data. The need for controlling inbreeding refers not only to a better use of the genetic variability available and to a reduced inbreeding depression in the selected trait, but also to a reduced depression of fitness-related traits, which may be the most serious drawback at present due to the increase in inbreeding in domestic populations (M euwissen and W oolliams 1994). In recent years considerable work has been carried out on the design of strategies to maintain genetic diversity in selection programmes. These strategies are aimed at simultaneously optimizing genetic gain and inbreeding, either by reducing the rate of inbreeding (or variance of response) while keeping genetic gains at a predetermined level, or by increasing selection response under a restriction on inbreeding (or on variance of response). Following T oro and P& eacute ; rez -E nciso (1990) the different strategies can be classified according to the factor on which they act: (i) the selection criterion used; (ii) the mating system imposed; (iii) the number of selected individuals and their contribution to the next generation. The first group of strategies proposes the use of a suboptimal selection criterion that reduces the weight given to family information or the use of an upward-biased heritability in BLUP evaluation (T oro and P& eacute ; rez -E nciso 1990; see G rundy et al. 1998a for the latest development of this idea). The second group of strategies proposes action on the mating system including factorial mating designs, minimum co-ancestry mating (using linear programming) or compensatory mating (see review by C aballero et al. 1996). The third group of strategies includes the ones considered in the present work. The first possibility is to modify the contribution of the selected individuals of generation t to the selected individuals of generation t + 1, by practising some form of within-family selection with respect to BLUP values. Two strategies of this type were considered: modified within-family selection (MWFS) and restricted co-ancestry selection (RCS). The second possibility is to modify the contribution of the selected individuals of generation t to the evaluated individuals of generation t + 1 (instead of to the selected individuals) by a strategy called weighted selection (T oro and N ieto 1984). Three strategies were considered in this case: weighted selection (WS), restricted co-ancestry weighted selection (RCWS) and pair weighted selection (PWS). More specifically, the aim of the present paper is to show how these five strategies can be implemented using mathematical programming techniques. A small example comparing all of these strategies with standard truncation selection (TS) is also given for illustration. 相似文献