Models to estimate genetic parameters in crossbred dairy cattle populations under selection
Models to estimate genetic parameters in crossbred dairy cattle populations under selection
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估计选择下的杂交奶牛群体遗传参数的模型
DOI：
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发表时间：
1990
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影响因子：
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通讯作者：
J. Werf
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作者：
J. Werf
由于环境的变化以及种群的持续选择和杂交，控制育种计划所需的遗传参数的估计必须定期更新。假设使用的统计遗传模型是正确的，限制最大似然方法可以最佳地提供这些估计。一般来说，用于分析牛奶产量数据的模型仅假设加性遗传效应和随机抽样。这些假设很少得到满足。在许多动物种群中，使用来自其他种群的遗传物质。品系或品种的杂交常常会产生非加性效应。此外，用于遗传分析的大部分数据来自选择的群体。本论文的主题是确定乳制品种群遗传评估模型是否应考虑非加性效应和选择，以及如何进行。第 2 章研究了非加性效应对遗传力和育种值估计的影响。模拟了具有来自两个品种的不同基因片段的父系和母系后代的群体。模拟了品种杂交的加性品种效应和非加性效应。使用混合模型分析性能数据，该模型考虑了固定加性遗传组和随机父系效应。将三个加性模型与非加性模型进行比较，其中遗传组根据 1) 后代的品种组成，2) 父本和母本的品种组成，或 3) 品种分数的线性回归来定义，非加性模型对后代基因组中的品种分数、杂合性和重组进行线性回归。使用限制最大似然估计方差分量。对于具有后代群体的加性模型，加性遗传方差和遗传力被高估。累加模型对品种差异、群体效应和育种价值的估计存在偏差。当使用父系群体时，品种差异被高估。使用非加性模型对每个参数进行无偏估计。在第三章中，相同的模型应用于具有不同比例的荷兰弗里斯兰和荷斯坦弗里斯兰（HF）群体基因的奶牛的数据。该数据集包含来自 675 名年轻公牛的 92,333 条首次泌乳记录（305 天产奶量）和来自 202 名经过验证的公牛的 307,050 条奶牛记录。杂种优势的估计值从 2.5%（脂肪产量）到 0%（蛋白质百分比）不等。重组效果从-1.9%（蛋白质产量）到1.5%（脂肪百分比）不等。后代组的累加模型将遗传方差高估了 6%。具有父系群体的模型将进口 HF 父系的附加遗传值高估了 33%。使用非加性模型，产奶量的遗传力估计值为 0.38，脂肪百分比的遗传力估计为 0.80，蛋白质百分比的遗传力估计为 0.70。结论是，非加性模型更适合估计杂交奶牛群体的遗传方差和预测育种值。第四章研究了选择对加性遗传方差估计的影响。规模为 40 的种群被模拟 100 次，共 10 代。每代从二十只雄性中选出五只，每只雄性与四只雌性交配并生育两个后代。选择前的加性遗传方差(σ 2 a )为10，初始遗传力为0.5。由于动物之间的协方差、近交和配子不平衡，经过十代选择后，遗传方差减少到6.72。在另一组规模为 400 且所选男性为 10% 的模拟群体中，方差的减少较低。使用动物模型，使用限制最大似然来估计 σ 2 a。当使用所有数据和所有关系时，σ 2 a 的估计在经验上是无偏的。由于并未考虑到所有配子不平衡，因此省略选定祖先的数据会导致 σ 2 a 的估计出现偏差。当考虑假设的基础动物之间的其他关系时，对近交和协方差进行了调整。随着使用更多关系信息，配子不平衡引起的偏差略有减少。仅基于后代数据的估计因选择而产生偏差。遗传方差的平均估计取决于假设的基础群体，并且对具有数据的后续世代的数量不敏感。第 5 章研究了一种估计遗传参数的方法，该方法以在基础群体形成之前发生的选择为条件。为此，使用了来自与第 4 章相同群体的模拟数据。该方法假设基础亲本是固定的，并且条件方差基于来自基础亲本的配子的孟德尔采样。选择是针对五代的，但只有第四代和第五代的动物被认为有表现记录和已知的父母。加性遗传方差和残差方差假设为 10。当每代从 200 只公牛中选择 20 只时，假设基础动物是随机的，估计遗传方差为 8.58，固定时估计遗传方差为 6.03。在后一种情况下，残差方差被高估了。当第 4 代雄性没有被选择产生后代时，估计的遗传方差为 9.91。结论是，条件模型对遗传参数的估计不会因基础动物的选择而产生偏差。然而，当选择基础动物的后代来产生后代时，固定基础父母的程序是有偏差的。用条件模型估计产奶性状的遗传方差，以解释公牛的选择。在经过更密集选择的 HF 亚群中，与假设不进行选择的随机模型相比，产奶量的遗传方差估计高出约 8%。在校正非加性效应（第 3 章）和亲本选择（第 5 章）后，发现父系模型对产奶性状遗传力的估计较高。考虑到公牛非随机交配的动物模型的初步结果并未显示出较低的估计值。建议进行更多研究以确定高遗传力的原因是遗传还是环境。主要结论 - 通过不考虑杂交群体遗传评估中的非加性效应，发现了杂交群体之间育种值和加性遗传差异的有偏差估计。因此，应根据系统的加性和非加性品种效应调整杂交奶牛的记录。 - 从现场数据估计杂交参数可以提供低标准误差，尽管对于某些交配设计采样相关性可能很高。 -仅基于选定世代的数据的遗传方差估计因选择而产生偏差。遗传方差的平均估计主要取决于假设的基础群体，并且对具有数据的后续世代的数量不敏感。其他关系调整了动物间协方差和某些配子不平衡的遗传方差估计。 - 使用条件模型对遗传参数的估计不会因基础动物的选择而产生偏差，但当选择基础动物的后代来产生后代时，就会引入偏差。 - 发现杂交奶牛数据中产奶性状的遗传力估计值作为目前遗传评估假设的参数较高。
Estimates of genetic parameters needed to control breeding programs, have to be regularly updated, due to changing environments and ongoing selection and crossing of populations. Restricted maximum likelihood methods optimally provide these estimates, assuming that the statisticalgenetic model used is correct. Generally, a model for analysis of milk production data assumes only additive genetic effects and random sampling. These assumptions are rarely met. In many animal populations genetic material from other populations is used. Crossing of lines or breeds often gives rise to non-additive effects. Furthermore, most of the data used for genetic analysis come from populations under selection. The subject of this thesis was to determine whether or not models for genetic evaluation of dairy populations should account for non-additive effects and selection, and how this should be done. The influence of non-additive effects on the estimation of heritabilities and breeding values was studied in Chapter 2. A population having progeny that descended from sires and dams with various fractions of genes from two breeds was simulated. Additive breed effects and non-additive effects from breed crosses, were simulated. Data on performance were analyzed using mixed models, that accounted for fixed additive genetic group and random sire effects. Three additive models, with genetic groups defined according to 1) breed composition of the progeny, 2) breed composition of the sire and dam, or 3) linear regression on breed fraction, were compared with a non-additive model, with a linear regression on breed fraction, heterozygosity and recombination in the genome of the progeny. Variance components were estimated using restricted maximum likelihood. Additive genetic variance and heritability were overestimated for an additive model with progeny groups. Additive models gave biased estimates for breed differences, group effects and breeding values. Breed differences were overestimated when sire groups were used. Estimates for each parameter were unbiased using the non-additive model. In Chapter 3, the same models were applied to data of cows with variable proportions of genes from the Dutch Friesian and the Holstein Friesian (HF) populations. The data set contained 92,333 first lactation records (305 days milk production) of cows from 675 young sires and 307,050 records of cows from 202 proven sires. Estimates for heterosis varied from 2.5% (fat yield) to 0% (protein percentage). Recombination effects varied from -1.9% (protein yield) to 1.5% (fat percentage). Additive models with progeny groups overestimated genetic variance by 6%. Models with sire groups overestimated additive genetic values of imported HF sires by 33%. Using a nonadditive model, heritability estimates were .38 for milk yield, .80 for fat percentage and .70 for protein percentage. It was concluded that a nonadditive model was preferable for estimation of genetic variance and prediction of breeding values in crossbred dairy populations. In the fourth chapter, the effect of selection on estimation of additive genetic variance was studied. A population of size 40 was simulated 100 times, for ten generations. Five out of twenty males were selected at each generation and each male was mated to four females and had two progeny. The additive genetic variance (σ 2 a ) before selection was 10 and the initial heritability was .5. The genetic variance was reduced to 6.72 after ten generations of selection, due to covariances among animals, inbreeding and gametic disequilibrium. Reduction of variance was lower in another population simulated with size 400 and ten percent of the males selected. Restricted Maximum Likelihood was used to estimate σ 2 a using an animal model. The estimate of σ 2 a was empirically unbiased, when all data and all relationships were used. Omitting data from selected ancestors caused biased estimates of σ 2 a due to the fact that not all gametic disequilibrium was accounted for. Inbreeding and covariances were adjusted for, when additional relationships between assumed base animals were considered. Bias from gametic disequilibrium decreased slightly with the use of more relationship information. Estimates from data based on later generations only, were biased by selection. Mean estimates of genetic variance depended on the assumed base population and were insensitive to the number of subsequent generations with data. A method to estimate genetic parameters conditional to selection occurring before formation of the base population was investigated in Chapter 5. For this, simulated data from the same populations as in Chapter 4 was used. The method assumes base parents as fixed and a conditional variance is based upon the Mendelian sampling of gametes from the base parents. Selection was for five generations but only animals of generations 4 and 5 were assumed to have performance records and parents known. Additive genetic and residual variance were assumed to be 10. When 20 out of 200 sires were selected per generation, estimated genetic variance was 8.58 when base animals were assumed random, and it was 6.03 when they were fixed. Residual variance was overestimated in the latter case. When males of generation 4 were not selected to have progeny, estimated genetic variance was 9.91. It was concluded that estimates for genetic parameters with the conditional model were not biased by selection of base animals. However, the procedure with fixed base parents was biased when descendants of base animals were selected to have progeny. Genetic variance of milk production traits was estimated with a conditional model to account for selection of sires. In the HF subpopulation, which had been selected more intensively, genetic variance for milk yield was estimated about 8% higher compared to a random models that assumes no selection. Estimates of heritability for milk production traits were found to be high with a sire model, after correction for non-additive effects (Chapter 3) and selection of parents (Chapter 5). Preliminary results with an animal model, which accounted for non- random mating of sires, did not show lower estimates. More research is suggested to determine whether the cause for high heritabilities is genetic or environmental. Main conclusions - By not accounting for non-additive effects in genetic evaluation of crossbred populations, biased estimates of breeding values and additive genetic differences between crossbred groups are found. Records of crossbred dairy cattle should therefore be adjusted for systematic additive and non-additive breed effects. - Estimation of crossbreeding parameters from field data can provide low standard errors, although sampling correlation may be high for certain mating designs. - Estimates of genetic variance based on data from selected generations only were biased by selection. Mean estimates of genetic variance depended mostly on the assumed base population and were insensitive to the number of subsequent generations with data. Additional relationships adjust genetic variance estimates for covariances among animals, and for some of the gametic disequilibrium. - Estimates for genetic parameters with a conditional model are not biased by selection of base animals, but a bias will be introduced when descendants of base animals have been selected to have progeny. - Heritability estimates of milk production traits in crossbred dairy cattle data were found to be higher as parameters currently assumed for genetic evaluation.