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目前,基因组选择（genomic selection,GS）技术已经在种猪育种中开展,但为获得较高的收益,还需研究一些应用策略,如确定仔猪基因分型个体比例和早期仔猪留种比例。本试验选择温氏集团出生于2011—2016年的大白种猪作为研究对象,共有超过4.5万条的生长测定记录,超过7万条繁殖记录,和2 090个个体的简化基因组测序（GBS）数据,其中,出生于2016年7~12月的440个体作为候选群体。研究性状包括两个生长性状（校正100 kg日龄和校正100 kg背膘厚）和一个繁殖性状（总产仔数）。为对比预测效果,在候选群体进行育种值预测时,按照是否利用其基因型或表型信息分为4种预测方案,比较不同方案的预测可靠性和个体选择指数的排名情况。结果显示,在预测候选群育种值时,利用其表型或基因型信息均比不利用时的预测结果更加可靠。对生长性状终测前、后进行基因组选择指数计算,发现,终测后指数排名前30%的个体都位于终测前指数排名前60%内。若仔猪出生后仅选择常规BLUP预测指数排名前60%的个体,会导致有接近15%的具有优秀潜力的个体被遗漏。本研究建议,对所有新生健康仔猪都进行基因分型并计算基因组选择指数,然后对指数排名靠前60%的个体进行性能测定。 相似文献
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Buch LH Sørensen MK Berg P Pedersen LD Sørensen AC 《Zeitschrift für Tierzüchtung und Züchtungsbiologie》2012,129(2):138-151
We tested the following hypotheses: (i) breeding schemes with genomic selection are superior to breeding schemes without genomic selection regarding annual genetic gain of the aggregate genotype (ΔG(AG) ), annual genetic gain of the functional traits and rate of inbreeding per generation (ΔF), (ii) a positive interaction exists between the use of genotypic information and a short generation interval on ΔG(AG) and (iii) the inclusion of an indicator trait in the selection index will only result in a negligible increase in ΔG(AG) if genotypic information about the breeding goal trait is known. We examined four breeding schemes with or without genomic selection and with or without intensive use of young bulls using pseudo-genomic stochastic simulations. The breeding goal consisted of a milk production trait and a functional trait. The two breeding schemes with genomic selection resulted in higher ΔG(AG) , greater contributions of the functional trait to ΔG(AG) and lower ΔF than the two breeding schemes without genomic selection. Thus, the use of genotypic information may lead to more sustainable breeding schemes. In addition, a short generation interval increases the effect of using genotypic information on ΔG(AG) . Hence, a breeding scheme with genomic selection and with intensive use of young bulls (a turbo scheme) seems to offer the greatest potential. The third hypothesis was disproved as inclusion of genomically enhanced breeding values (GEBV) for an indicator trait in the selection index increased ΔG(AG) in the turbo scheme. Moreover, it increased the contribution of the functional trait to ΔG(AG) , and it decreased ΔF. Thus, indicator traits may still be profitable to use even when GEBV for the breeding goal traits are available. 相似文献
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种公牛的选育是肉牛育种工作的核心。传统选育肉用种公牛需要经过后裔测定进行选择,其优点是准确性高,但存在周期长、屠宰和肉质性状难以收集、成本高等问题,致其选择效率低。自2001年全基因组选择概念提出后,该技术迅速成为动植物育种领域研究的热点。利用全基因组选择进行肉用种公牛的选育,进行早期选择从而大幅度缩短世代间隔,可以提高繁殖性状等低遗传力性状的选择准确性,加快遗传进展,并大大降低育种成本。2014年,美国安格斯协会开始应用全基因组选择技术,其他欧美发达国家也陆续使用,肉牛育种进入基因组时代。中国自2017年开始使用全基因组选择技术选择青年肉用种公牛,并于2020年在全国范围内使用该技术进行基因组遗传评估。本文综述了国内外肉牛遗传评估现状,以期为我国肉牛育种工作提供参考和借鉴。 相似文献
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A.M. Hidalgo J.W.M. Bastiaansen M.S. Lopes M.P.L. Calus D.J. de Koning 《Zeitschrift für Tierzüchtung und Züchtungsbiologie》2016,133(6):443-451
In pig breeding, as the final product is a cross bred (CB) animal, the goal is to increase the CB performance. This goal requires different strategies for the implementation of genomic selection from what is currently implemented in, for example dairy cattle breeding. A good strategy is to estimate marker effects on the basis of CB performance and subsequently use them to select pure bred (PB) breeding animals. The objective of our study was to assess empirically the predictive ability (accuracy) of direct genomic values of PB for CB performance across two traits using CB and PB genomic and phenotypic data. We studied three scenarios in which genetic merit was predicted within each population, and four scenarios where PB genetic merit for CB performance was predicted based on either CB or a PB training data. Accuracy of prediction of PB genetic merit for CB performance based on CB training data ranged from 0.23 to 0.27 for gestation length (GLE), whereas it ranged from 0.11 to 0.22 for total number of piglets born (TNB). When based on PB training data, it ranged from 0.35 to 0.55 for GLE and from 0.30 to 0.40 for TNB. Our results showed that it is possible to predict PB genetic merit for CB performance using CB training data, but predictive ability was lower than training using PB training data. This result is mainly due to the structure of our data, which had small‐to‐moderate size of the CB training data set, low relationship between the CB training and the PB validation populations, and a high genetic correlation (0.94 for GLE and 0.90 for TNB) between the studied traits in PB and CB individuals, thus favouring selection on the basis of PB data. 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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The impact of truncating data on the predictive ability for single‐step genomic best linear unbiased prediction 
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下载免费PDF全文 Jeremy T. Howard Tom A. Rathje Caitlyn E. Bruns Danielle F. Wilson‐Wells Stephen D. Kachman Matthew L. Spangler 《Zeitschrift für Tierzüchtung und Züchtungsbiologie》2018,135(4):251-262
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. 相似文献
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家兔是中国传统的畜禽品种,同时也是中国畜牧业的重要组成部分。近年来,随着产业升级,生物技术快速发展,国内外家兔遗传育种取得了较大进展。文章分别从传统育种与分子育种2个方面对2020年国内外家兔遗传育种与繁殖研究进展进行了综述,以期为家兔种质资源利用与保护提供参考。国外在传统育种与分子育种上均作了较多研究,在传统育种上主要针对选择和环境因素对家兔生长及繁殖性能的影响进行研究;在分子育种上对生长及肉质相关基因、繁殖性能相关基因及遗传多样性等方面进行研究。国内开展的家兔遗传育种与繁殖研究数量相较国外更多,研究重点集中于分子育种,包括对皮毛性状、肉质性状及繁殖性能相关基因的研究;传统育种主要包括选择和环境效应对家兔生长与繁殖性能的影响。 相似文献
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【目的】试验旨在揭示终端父本皮特兰猪与杜洛克猪在人工选择作用下重要经济性状呈现出表型趋同的基因组变化特征。【方法】利用376头皮特兰猪、451头杜洛克猪品系Ⅰ、841头杜洛克猪品系Ⅱ和497头杜洛克猪品系Ⅲ群体的50K SNP芯片数据,以100 kb窗口、50 kb步长计算综合单倍型评分(iHS)和等位基因频率差(△AF),分别取前5%作为猪群体内、群体间的基因组选择信号候选区域;利用bedtools分别对iHS、△AF按照左右200 kb进行合并,每2个群体间合并后的iHS、△AF统计量的重叠区域定义为性状趋同区域,并挖掘该区域与猪重要经济性状相关的平行选择信号。【结果】iHS结果显示,在皮特兰猪和杜洛克猪4个群体内共检测到5 112个选择信号候选区域,总长约487.51 Mb。基于△AF方法,于皮特兰猪和杜洛克猪每2个群体间共检测到9 579个选择信号显著区域,总长约913.50 Mb。基于合并后的iHS和△AF,共检测到52个性状趋同区域,总长约4.67 Mb,注释到88个与猪的繁殖、胴体和肉质等性状相关的平行选择候选基因。【结论】皮特兰猪和杜洛克猪群体间存在性状趋同的基因组选择区域有52个,平行选择信号主要涉及猪的繁殖、胴体及肉质等重要经济性状,这与瘦肉型猪种相同的育种方向相关,这些关键基因的发现可为后续商业猪品种遗传改良提供参考。 相似文献
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为了加快我国瘦肉型猪育种的研究进展,制定出符合我国瘦肉型猪育种现状的经济权重。本研究依据中国杜长大三元杂交猪育种现状,选择了适合中国当前杜长大三元杂交体系的目标性状;以生物经济模型为基础模拟猪的生产流程,计算生产周期各阶段成本和收入;先采用差额法计算目标性状的边际效益,再通过各性状的遗传标准差校正得到各育种目标性状的经济权重。结果表明,目前中国瘦肉型猪育种的繁殖、生长和胴体品质性状主要包括窝产活仔数、母猪断配间隔、饲料转化率、达100 kg体重日龄、达100 kg体重背膘厚。在我国现有生产水平和市场条件下,上述各性状的边际效益分别为：19.52、-1.07、-286.95、-8.41、-13.20元。通过计算不同品种的经济权重,得到杜洛克的饲料转化率、达100 kg体重日龄、达100 kg体重背膘厚相对经济权重分别为：50.42%、34.50%、15.08%;长白和大白群体窝产活仔数、母猪断配间隔、饲料转化率、达100 kg体重日龄、达100 kg体重背膘厚的相对经济权重分别为：16.82%、0.22%、39.56%、31.42%、11.98%和32.77%、0.41%、33.22%、24.43%、9.17%。结果显示,目前,在中国瘦肉型猪育种过程中,饲料利用效率应作为育种的主要目标性状,对于不同品种应选择最适合的性状进行育种。 相似文献
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Serenius T Sevon-Aimónen ML Kause A Mäntysaari EA Mäki-Tanila A 《Journal of animal science》2004,82(8):2301-2306
The objective of this study was to estimate genetic associations of prolificacy traits with other traits under selection in the Finnish Landrace and Large White populations. The prolificacy traits evaluated were total number of piglets born, number of stillborn piglets, piglet mortality during suckling, age at first farrowing, and first farrowing interval. Genetic correlations were estimated with two performance traits (ADG and feed:gain ratio), with two carcass traits (lean percent and fat percent), with four meat quality traits (pH and L* values in longissimus dorsi and semimembranosus muscles), and with two leg conformation traits (overall leg action and buck-kneed forelegs). The data contained prolificacy information on 12,525 and 10,511 sows in the Finnish litter recording scheme and station testing records on 10,372 and 9,838 pigs in Landrace and Large White breeds, respectively. The genetic correlations were estimated by the restricted maximum likelihood method. The most substantial correlations were found between age at first farrowing and lean percent (0.19 in Landrace and 0.27 in Large White), and fat percent (-0.26 in Landrace and -0.18 in Large White), and between number of stillborn piglets and ADG (-0.38 in Landrace and -0.25 in Large White) and feed:gain (0.27 in Landrace and 0.12 in Large White). The correlations are indicative of the benefits of superior growth for piglets already at birth. Similarly, the correlations indicate that age at first farrowing is increasing owing to selection for carcass lean content. There was also clear favorable correlation between performance traits and piglet mortality from birth to weaning in Large White (r(g) was -0.43 between piglet mortality and ADG, and 0.42 between piglet mortality and feed:gain), but not in Landrace (corresponding correlations were 0.26 and -0.22). There was a general tendency that prolificacy traits were favorably correlated with performance traits, and unfavorably with carcass lean and fat percents, whereas there were no clear associations between prolificacy and meat quality or leg conformation. In conclusion, accuracy of estimated breeding values may be improved by accounting for genetic associations between prolificacy, carcass, and performance traits in a multitrait analysis. 相似文献
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东北梅花鹿优化育种规划中育种目标的确定 总被引：1，自引：0，他引：1
讨论了东北梅花鹿依据经济指标确定育种目标的问题 ,以及在东北梅花鹿育种规划中 ,应如何挑选在综合育种目标中的生产性状及其对应的选择性状。并着重介绍了应用综合育种值作为数量化育种目标的方法。指出在确定以茸用为主的东北梅花鹿育种目标时 ,除了主要考虑茸用性状外 ,还应适当地考虑繁殖和使用寿命等性状 ,同时对于直接影响茸鹿生产效益的直接性状和次级性状不容忽视。 相似文献
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旨在通过测定基因组选择选留的大白公猪的后裔生产性能,探究基因组选择实际育种效果。本研究选用913头大白猪构建参考群体,利用ssGBLUP对新出生的823头大白公猪在去势前进行第一次基因组评估,待生产性能测定后进行第二次基因组评估,最终选留10头性能差异显著的公猪留种,比较其后代生长性状表型和育种值及综合选择指数差异。结果表明,两次基因组遗传评估,达100 kg体重日龄、100 kg活体背膘厚和总产仔数3个性状基因组育种值（GEBV）估计准确性分别由0.56、0.67和0.64提高至0.73、0.73和0.67,两次基因组选择基因组母系指数相关系数为0.82,表明在去势前进行公猪基因组选择具有较高的准确性,可实现种猪早期选择。根据各性状GEBV和基因组母系指数,10头公猪被划分为高、低生产性能组,后裔测定成绩表明,两组公猪后代100 kg体重日龄表型均值之差为2.58 d,育种值之差为3.08 d,100 kg活体背膘厚表型均值之差为1.15 mm,育种值之差为1.03 mm,综合母系指数均值之差为9.3,除后代100 kg体重日龄表型均值之差外,其他差异均达到极显著水平。本研究证明,在基因组评估中具有显著差异的公猪其后代在表型值和育种值等方面均存在显著差异,通过基因组选择能够挑选出优秀种公猪,可将其遗传优势传递给后代。 相似文献
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This study aimed to evaluate the actual genetic improvement effect of genomic selection in Large White boars through progeny testing in production performance. Nine hundred and thirteen Large White pigs were used to construct a reference group, and 823 new-born Large White boars were used to implement the first genomic selection through ssGBLUP before castration. The second genomic selection were carried out after performance testing, then 10 boars with significant difference in production performance were selected and their offsprings were compared in phenotypic values, estimated breeding values of growth traits and selection index. The results showed that the accuracies of genomic prediction on age at 100 kg body weight, 100 kg backfat thickness and total number born increased from 0.56, 0.67 and 0.64 in the first genomic selection to 0.73, 0.73 and 0.67 in the second genomic selection, respectively. The correlation coefficient of maternal selection index between the two genomic selection before castration and after performance testing was 0.82, which indicated that the first genomic selection before castration was accurate enough to make early selection on boars. According to the genomic breeding values and maternal selection index of 10 selected boars, two groups with high and low production performance were set up. The progeny testing showed that the difference of average phenotypic value between groups was 2.58 days, and the difference of average evaluated breeding value(EBV) between groups was 3.08 days in age at 100 kg body weight, those were 1.15 mm and 1.03 mm in 100 kg backfat thickness, respectively, and the difference in the mean of the comprehensive maternal index was 9.3, all the differences(except age at 100 kg body weight) were extremely significant. This study prove that the offspring of boars with significant differences in genomic evaluation have significant differences in phenotypic values and breeding values, which indicate that, through genomic selection, excellent breeding boars can be selected and their genetic superiority can be passed to their offsprings. 相似文献
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凉山半细毛羊新品种的育成,是采用表型选择和基因型选择相结合产生的结果。在初生期、断奶期、育成期和成年期采取表型鉴定、逐步选择淘汰的制度。根据羔羊断奶期性状与育成期性状之间的显著相关性对羔羊单性状进行早期选择。应用同期发情与后裔测验相结合的方法,对种公羊基因型值用BLUP法进行选择。同时,加强育种工作的组织领导,组建独具特色的三级繁育体系,改善育种羊群的饲养管理条件,圆满完成了国家下达的半细毛羊新品种选育的任务,填补了国内粗档半细毛羊品种的空白。 相似文献
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Genomic selection has been adopted nationally and internationally in different livestock and plant species. However, understanding whether genomic selection has been effective or not is an essential question for both industry and academia. Once genomic evaluation started being used, estimation of breeding values with pedigree best linear unbiased prediction (BLUP) became biased because this method does not consider selection using genomic information. Hence, the effective starting point of genomic selection can be detected in two possible ways including the divergence of genetic trends and Realized Mendelian sampling (RMS) trends obtained with BLUP and single-step genomic BLUP (ssGBLUP). This study aimed to find the start date of genomic selection for a set of economically important traits in three livestock species by comparing trends obtained using BLUP and ssGBLUP. Three datasets were used for this purpose: 1) a pig dataset with 117k genotypes and 1.3M animals in pedigree, 2) an Angus cattle dataset consisted of ~842k genotypes and 11.5M animals in pedigree, and 3) a purebred broiler chicken dataset included ~154k genotypes and 1.3M birds in pedigree were used. The genetic trends for pigs diverged for the genotyped animals born in 2014 for average daily gain (ADG) and backfat (BF). In beef cattle, the trends started diverging in 2009 for weaning weight (WW) and in 2016 for postweaning gain (PWG), with little divergence for birth weight (BTW). In broiler chickens, the genetic trends estimated by ssGBLUP and BLUP diverged at breeding cycle 6 for two out of the three production traits. The RMS trends for the genotyped pigs diverged for animals born in 2014, more for ADG than for BF. In beef cattle, the RMS trends started diverging in 2009 for WW and in 2016 for PWG, with a trivial trend for BTW. In broiler chickens, the RMS trends from ssGBLUP and BLUP diverged strongly for two production traits at breeding cycle 6, with a slight divergence for another trait. Divergence of the genetic trends from ssGBLUP and BLUP indicates the onset of the genomic selection. The presence of trends for RMS indicates selective genotyping, with or without the genomic selection. The onset of genomic selection and genotyping strategies agrees with industry practices across the three species. In summary, the effective start of genomic selection can be detected by the divergence between genetic and RMS trends from BLUP and ssGBLUP. 相似文献
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Genetic parameters for within-litter variation in piglet birth weight and change in within-litter variation during suckling 总被引：10，自引：0，他引：10
The objective of this study was to ascertain whether maternal additive genetic variance exists for within-litter variation in birth weight and for change in within-litter variation in piglet weight during suckling. A further objective was to estimate maternal genetic correlations of these two traits with mortality, birth weight, growth, and number of piglets born alive. Data were obtained from L?vsta research station, Swedish University of Agricultural Sciences, and included 22,521 piglets born in 2,003 litters by 1,074 Swedish Yorkshire sows. No cross fostering was used in the herd. The following seven traits were analysed in a multivariate animal (sow) model: number of piglets born alive, within-litter SD in birth weight, within-litter SD in piglet weight at 3 wk of age, mean weight at birth, mean weight at 3 wk of age, proportion of stillborn piglets, and proportion of dead piglets during suckling. Maternal genetic variance for the change in within-litter SD in piglet weight during suckling was assessed from the estimated additive genetic covariance components by conditioning on within-litter SD in birth weight. Similarly, mean growth of piglets during suckling was assessed from the additive genetic covariance components by conditioning on mean weight at birth. The heritability for within-litter SD in birth weight was 0.08 and 0.06 for within-litter SD in piglet weight at 3 wk. The genetic correlation between these two traits was 0.71. Little maternal genetic variance was found for the change in within-litter SD in piglet weight during suckling, and opportunity for genetic improvement of this trait by selective breeding seems limited. The genetic correlation of within-litter SD in birth weight with proportion of dead piglets during suckling was 0.25 and of within-litter SD in birth weight with mean growth of piglets was -0.31. The maternal genetic variance and heritability found for within-litter SD in birth weight indicates that genetic improvement of this trait by selective breeding is possible. In addition, selection for sows' capacity to give birth to homogeneous litters may be advantageous for piglet survival, piglet growth, and litter homogeneity at weaning. 相似文献