闭锁群体BLUP选择下遗传方差和近交系数的变化

采用计算机随机模拟方法模拟了在一个闭锁群体内连续对单个性状进行 1 5个世代选择的情况。选择过程中世代不重叠 ,每个世代的种畜根据动物模型最佳线性无偏预测 (BLUP)法估计的育种值进行选留 ,并在此基础上系统地比较了不同群体规模、公母比例和性状遗传力对群体遗传方差和近交系数变化的影响。结果表明 ,扩大育种群规模、增加公畜比例以及对低遗传力性状进行选择时 ,群体遗传方差降低的速度和近交系数上升的速度会更慢 ,在长期选择时可望获得更大的持续进展和适宜的近交增量     

动物模型BLUP选择效果的计算机模拟研究

在采用动物模型最佳线性无偏预测(BLUP) 方法对个体育种值进行估计的基础上, 模拟了在一个闭锁群体内连续对单个性状选择10个世代的情形, 并系统地比较了群体规模、公母比例和性状遗传力对选择所获得的遗传进展和群体近交系数变化的影响。结果表明, 扩大育种群规模不仅可以获得更大的持续进展, 同时还可有效缓解近交系数的过快上升; 育种群中公畜比例过低时, 不仅会降低遗传进展, 群体近交系数的上升速度也会加快, 实际中应保证育种群具有一定的规模和适宜的公母比例。对高遗传力性状进行选择时, 可望获得更大的遗传进展, 同时近交系数的上升速度也会快一些。     

胜利白猪近交程度的分析及对主选性状的影响

胜利白猪Ⅰ、Ⅱ系经过7、8个世代的继代选育,群体平均近交系数分别达到8.36%和8.57%,随着群体纯合度的提升对主选性状有不同程度的影响。对于遗传力(h2)低的性状,如产仔数、产活仔数的影响不大,大体保持在1世代的性能水平。对于遗传力(h2)中、高的性状,如后备猪的生长速度和同胞育肥性能等性状,则随世代选育均有明显的改进,改进幅度在10%以上,显示适度近交没有影响世代选育的效果。杂交试验证明,杂交优势也未因群体近交系数的增大受到影响。     

随机留种下闭锁群体近交系数和基因杂合度的世代变

在假定不发生基因突变、群体间无个体迁移的情况下,采用Monte Carlo计算机模拟方法模拟了不同群体规模、性别比例和初始基因频率下闭锁群体近交系数和基因杂合度的变化。10个双等位基因座位分别位于10条不同的常染色体上,群体内实行随机留种,随机交配,世代不重叠,且每代参与繁殖的个体数相同。模拟试验共进行了50个世代,100次重复。结果表明,群体规模较大、公畜数较多时,群体近交系数上升缓慢,基因杂合度较高;当初始基因频率处于中等水平时,群体内可保持较高的基因杂合度;群体规模太小、公畜比例过低及基因的初始频率过低或过高,均不利于群体内遗传多样性的保持。     

随机留种下闭锁群体近交系数和基因杂合度的世代变化

在假定不发生基因突变、群体间无个体迁移的情况下,采用MonteCarlo计算机模拟方法模拟了不同群体规模、性别比例和初始基因频率下闭锁群体近交系数和基因杂合度的变化。10个双等位基因座位分别位于10条不同的常染色体上,群体内实行随机留种,随机交配,世代不重叠,且每代参与繁殖的个体数相同。模拟试验共进行了50个世代,100次重复。结果表明,群体规模较大、公畜数较多时,群体近交系数上升缓慢,基因杂合度较高;当初始基因频率处于中等水平时,群体内可保持较高的基因杂合度;群体规模太小、公畜比例过低及基因的初始频率过低或过高,均不利于群体内遗传多样性的保持。     

GBLUP技术在鸡品种选育中的应用效果研究

为揭示遗传力和标记密度对估计基因组育种值的影响和探讨基因组选择在家禽育种中的效果,运用QMSim软件分别模拟不同遗传力、不同标记数目的群体结构数据、基因组信息数据及相应的表型数据;运用基因组最佳线性无偏估计(GBLUP)方法估计基因组育种值,并计算基因组育种值的准确性;比较基因组选择与表型选择在育种成本以及遗传进展的差异。结果显示,随着遗传力和标记数目增加,估计育种值准确性明显提高,同时基因组选择在遗传进展上具有明显优势,但是在对表型选择与基因组选择进行成本分析时,基因组选择的成本并没有明显提高。因此,基因组选择育种在家禽育种过程中具有明显优势。     

家兔引种群体大小与选种方案对性能和近交的影响

与其他畜禽相比，我国兔新品种培育相对落后，工厂化饲养的主流品种还很大程度依赖国外引进。为节约引种成本和提高引种后的利用效率，本研究分析了引种群体大小与选种方案对引种后繁殖各世代群体生产性能和近交的影响。采用AlphaSimR软件进行群体模拟，共设计了3个不同引种规模、4个选种方案以及3个不同遗传力大小的数量性状，基于系谱信息和动物模型估计个体育种值。结果发现，引种后繁殖各世代群体的生产性能主要取决于性状遗传力的高低与所采用的选种方案，与引种群体大小之间没有明显的相关性。在不同选种方案中，当过分强调性状的性能提高时，群体的近交系数则会快速地上升；相反，不论群体大小如何，在采用随机选种方案时所观察到的近交增量最低。基于这些结果可以看出，在引种群规模较小时，尤其需要注意制定合适的选种方案，以避免过度近交的发生；而当引种群规模较大时，则可以适当强调性能的选育提高。与引种群的大小相比，选种方案对引种后繁殖群体生产性能和近交的影响更加显著。     

黑龙江省荷斯坦牛产奶性状的遗传统计分析

本文采用了非求导约束最大似然法 (DF -REML)估计了黑龙江省地区荷斯坦牛产奶性状的遗传参数和种畜的育种值。结果表明 :全期产奶量、3 0 5天产奶量和乳脂量为中等遗传力性状 ,而乳脂率和泌乳天数则为低遗传力性状 ,各性状间存在不同程度的相关 ,其中乳脂率与产奶量呈中等负相关。在育种值估计中 ,比较了不同遗传组公牛的效应值 ,认为美、加、德等外血公牛对该地区牛群产奶量的提高有明显改良效果。本文还分析了采用不同模型进行参数和育种值估计的效率 ,得出用动物模型估计的准确性要高于公畜模型。根据本文所得结果 ,笔者还对黑龙江省地区今后的奶牛育种工作提出了一些有益的建议。     

湘白Ⅰ系猪近效应分析

<正> 湘白Ⅰ系猪以多元杂交合成育种材料,施行群体继化选育,已进入第6世代的培育。平均以0.65278％的递增速度增加群体近交系数,使群体平均近交水平达到9.62％。不同世代存在不同程度的近交个体。本文通过对这些个体的近交水平分类,旨在了解近交对湘白Ⅰ系猪若干性状的影响和探索如何合理地利用近交。     

母系猪繁殖性状的基因组选择策略

与生长性状相比,猪的繁殖性状具有遗传力低和限性表现的特点,通过传统育种方法很难获得较高的育种值估计准确性,且无法缩短世代间隔。因此,猪的繁殖性状选育策略应与生长性状不同。基因组选择是一种基于全基因组信息的标记辅助选择。与生长性状相比,基因组选择对提高繁殖性状(如产仔数)的预测准确性更具有优势。然而,基因组选择的育种成本较高阻碍了该技术的广泛应用。本文旨在探讨母系猪繁殖性状基因组选择的参考群体构建策略,以节省基因组育种成本和加快遗传进展。     

Optimal contribution selection applied to the Norwegian and the North‐Swedish cold‐blooded trotter – a feasibility study

The aim of this study was to examine how to apply optimal contribution selection (OCS) in the Norwegian and the North‐Swedish cold‐blooded trotter and give practical recommendations for the future. OCS was implemented using the software Gencont with overlapping generations and selected a few, but young sires, as these turn over the generations faster and thus is less related to the mare candidates. In addition, a number of Swedish sires were selected as they were less related to the selection candidates. We concluded that implementing OCS is feasible to select sires (there is no selection on mares), and we recommend the number of available sire candidates to be continuously updated because of amongst others deaths and geldings. In addition, only considering sire candidates with phenotype above average within a year class would allow selection candidates from many year classes to be included and circumvent current limitation on number of selection candidates in Gencont (approx. 3000). The results showed that mare candidates can well be those being mated the previous year. OCS will, dynamically, recruit young stallions and manage the culling or renewal of annual breeding permits for stallions that had been previously approved. For the annual mating proportion per sire, a constraint in accordance with the maximum that a sire can mate naturally is recommended.     

Estimation of genome-wide haplotype effects in half-sib designs

Genome-wide estimated breeding values can be computed from the simultaneous estimates of the effects of small intervals of DNA throughout the genome on a trait or traits of interest. Small intervals or segments of DNA can be created by the use of thousands of single nucleotide polymorphisms (SNP) available in panels of 10, 25 and 50 thousand SNP. A simulation study was conducted to compare factors that could influence the accuracy of genome-wide selection. Factors studied were the heritability of the trait, dispersion of quantitative trait loci (QTL) across the genome and size of the QTL effects. A 100-cM genome was assumed with 100 equally spaced SNP markers and 10 QTL. A granddaughter design was constructed with 20 sires and 100 sons per sire. Population-wide linkage disequilibrium was assumed to be sufficient after 25 generations of random mating starting with 30 sires and 400 dams. Best linear unbiased prediction was used to simultaneously estimate the effects of 99 SNP intervals, based on determining the SNP haplotype of each son inherited from the sire. Indicator variables were used in the model to indicate haplotype transmission. A genome-wide estimated breeding value was calculated as the sum of the appropriate haplotype interval estimates for each son. Correlations between estimated and true breeding values ranged from 0.60 to 0.79. Situations with unequally sized QTL effects and randomly dispersed QTL gave higher correlations. QTL positions could be estimated to within 2 cM or less.     

Effects of genotype x environment interaction on genetic gain in breeding programs

Genotype x environment interaction (G x E) is increasingly important, because breeding programs tend to be more internationally oriented. The aim of this theoretical study was to investigate the effects of G x E on genetic gain in sib-testing and progeny-testing schemes. Loss of genetic gain due to G x E was predicted for different values of heritability, number of progeny per dam, number of progeny per sire, proportion of selected sires, and population size in the selection environment. Two environments were considered: a selection environment (SLE) and a production environment (PDE). The breeding goal was only for performance in PDE. A pseudo-BLUP selection index was used to predict genetic gain. Recording of half-sibs or progeny in PDE limited the loss in genetic gain in PDE due to G x E between SLE and PDE. Progeny-testing schemes had less loss in genetic gain than sib-testing schemes. Higher heritability increased the loss in genetic gain, whereas increasing the number of progeny per sire in PDE decreased the loss in genetic gain. The number of progeny per sire required to minimize loss in genetic gain due to G x E was greater for sib-testing schemes than for progeny-testing schemes. More progeny per dam slightly increased the loss in genetic gain. Genetic gains for sex-limited and carcass traits were less affected by G x E than traits measured on both sexes. Loss in genetic gain was due to decreased accuracy of selection in most situations, but it was due to decreased selection intensity in situations with small population size and a low proportion of selected sires. It was concluded that recording performance of relatives in PDE minimizes loss in genetic gain due to G x E, and that progeny-testing schemes rather than sib-testing schemes are preferable in situations with low to moderate heritability (h(2) 相似文献   

Genotyping of groups of cows to improve genomic breeding values of new traits

Genotyping females and including them into the reference set for genomic predictions in dairy cattle is considered to provide gains in reliabilities of estimated breeding values for selection candidates. This should especially be true for low heritability traits. By the use of simulation, we extended a genomic reference set for an existing trait by including a fixed number of genotyped first‐crop daughters for one or two generations of reference sires. Moreover, we calculated results for the effects of a similar strategy in a situation where for a new trait the recording of phenotypes has recently started. For this case, we compared the effect of two different genotyping strategies: first, to phenotype cows but to genotype their sires only, and second, to collect phenotypes and genotypes on the same cows. We studied the effects on validation reliabilities and unbiasedness of predicted values for selection candidates. We found that by extending the reference set with genotyped daughters it is possible to increase the validation reliability of genomic breeding values. In the case of a new trait, it is always better to collect and use genotypes and phenotypes on the same animals instead of using only sire genotypes. We found that the benefits that can be achieved are sensitive to the sampling strategy used when selecting females for genotyping.     

Pregnancy rate and first-service conception rate in Angus heifers

The objective of this project was to determine the genetic control of conception rate, or pregnancy percentage in Angus beef heifers. Producers from 6 herds in 5 states provided 3,144 heifer records that included breeding dates, breeding contemporary groups, service sires, and pregnancy check information. Two hundred fourteen sires of the heifers were represented; with 104 sires having less than 5 progeny, and 14 sires having greater than 50 progeny. These data were combined with performance and pedigree information, including actual and adjusted birth weights, weaning weights, and yearling weights, from the American Angus Association database. Heifer pregnancy rate varied from 75 to 95% between herds, and from 65 to 100% between sires, with an overall pregnancy rate of 93%, measured as the percentage of heifers pregnant at pregnancy check after the breeding season. Pregnancy was analyzed as a threshold trait with an underlying continuous distribution. A generalized linear animal model, using a relationship matrix, was fitted. This model included the fixed effects of contemporary group, age of dam, and first AI service sire, and the covariates of heifer age at the beginning of breeding, adjusted birth weight, adjusted weaning weight, and adjusted yearling weight. The relationship matrix included 4 generations of pedigree. The heritability of pregnancy and first-service conception rates on the underlying scale was 0.13 +/- 0.07 and 0.03 +/- 0.03, respectively. Estimated breeding values for pregnancy rate on the observed scale ranged from -0.02 to 0.05 for sires of heifers. Including growth traits with pregnancy rate as 2-trait analyses did not change the heritability of pregnancy rate. As expected for a reproductive trait, the heritability of pregnancy rate was low. Because of its low heritability, genetic improvement in fertility by selection on heifer pregnancy rate would be expected to be slow.     

Impacts of genotyping strategies on long‐term genetic response in genomic selection

The present study investigated the effects of the choices of animals of reference populations on long‐term responses to genomic selection. Simulated populations comprised 300 individuals and 10 generations of selection practiced for a trait with heritability of 0.1, 0.3 or 0.5. Thirty individuals were randomly selected in the first five generations and selected by estimated breeding values from best linear unbiased prediction (BLUP) and genomic BLUP in the subsequent five generations. The reference populations comprise all animals for all generations (scenario 1), all animals for 6‐10 generations (scenario 2) and 2‐6 generations (scenario 3), and half of the animals for all generations (scenario 4). For all heritability levels, the genetic gains in generation 10 were similar in scenarios 1 and 2. Among scenarios 2 to 4, the highest genetic gains were obtained in scenario 2, with heritabilities of 0.1 and 0.3 as well as scenario 4 with heritability of 0.5. The inbreeding coefficients in scenarios 1, 2 and 4 were lower than those in BLUP, especially within cases with low heritability. These results indicate an appropriate choice of reference population can improve genetic gain and restrict inbreeding even when the reference population size is limited.     

公母畜间有不同的年龄组数目条件下控制近交最大化选择反应

一种扩展的动态选择规则能够在公母畜间有不同的年龄组数目的世代重叠群体内约束年近交速率为一个预定义值，逐年最大化遗传反应。该规则考虑在世代重叠群体中按性别一年龄分组，通过限制父母亲群体性别一年龄组的平均加性遗传相关的增加，从而限制新生后代平均近交系数的增加。动态选择程序通过输入候选个体的BLUP估计育种值、所有个体的加性遗传相关矩阵和所有性别一年龄组的长期遗传贡献，给出最适宜的选留个体数及其每个选留个体最适宜的后代数。猪核心群随机模拟结果显示该动态选择规则能够获得预定义的近交速率。在相同的近交速率条件下，动态选择比截断选择获得高达10％的更多年遗传进展。     

Comparison of genomic and traditional BLUP-estimated breeding value accuracy and selection response under alternative trait and genomic parameters

Accuracy of prediction of estimated breeding values based on genome-wide markers (GEBV) and selection based on GEBV as compared with traditional Best Linear Unbiased Prediction (BLUP) was examined for a number of alternatives, including low heritability, number of generations of training, marker density, initial distributions, and effective population size (Ne). Results show that the more the generations of data in which both genotypes and phenotypes were collected, termed training generations (TG), the better the accuracy and persistency of accuracy based on GEBV. GEBV excelled for traits of low heritability regardless of initial equilibrium conditions, as opposed to traditional marker-assisted selection, which is not useful for traits of low heritability. Effective population size is critical for populations starting in Hardy-Weinberg equilibrium but not for populations started from mutation-drift equilibrium. In comparison with traditional BLUP, GEBV can exceed the accuracy of BLUP provided enough TG are included. Unfortunately selection rapidly reduces the accuracy of GEBV. In all cases examined, classic BLUP selection exceeds what was possible for GEBV selection. Even still, GEBV could have an advantage over traditional BLUP in cases such as sex-limited traits, traits that are expensive to measure, or can only be measured on relatives. A combined approach, utilizing a mixed model with a second random effect to account for quantitative trait loci in linkage equilibrium (the polygenic effect) was suggested as a way to capitalize on both methodologies.