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1.
种猪企业在实际育种中会面临单个品种（例如杜洛克）数据量稀少的情况，利用其他品种数据量的优势，将品种作为固定效应可以提升育种值估计准确率。此外，多性状一步法可以利用性状间的遗传联系提升育种值计算的准确率，也被企业视作重要的育种方法。本研究以大白、长白和杜洛克猪为研究对象，分析多性状一步法（ssGBLUP）与单性状一步法育种值预测的准确率，以及多品种联合计算与单品种独立计算育种值预测的准确率。研究包括3个品种、5个生长性状和3个繁殖性状以及超过8 000头的芯片数据，利用BLUPF90+软件计算育种值以及标准误差（SE）。结果表明，对于生长性状和繁殖性状，多性状在ssGBLUP、GBLUP和BLUP方法上的育种值准确率均优于单性状3%～15%；当参考群数量<500头时，多品种联合计算能获得比单品种独立计算更高的育种值准确率。表明在实际育种中，单品种数据量小于500头时，可以采用多品种联合计算育种值，随着数据量增大到1 000头时，应该切换回单品种独立计算；在算法和算力满足的情况下，多性状在育种值准确性上优于单性状。 相似文献
2.
我国荷斯坦青年公牛基因组选择效果分析 总被引：2，自引：2，他引：0
本研究基于我国荷斯坦奶牛基因组遗传评估和生产性能测定（DHI）结果,旨在分析我国荷斯坦公牛基因组选择的效果。选择1 686头既有基因组遗传评估成绩又有后裔测定成绩的荷斯坦公牛,利用2019年12月基因组遗传评估结果及其女儿的产奶和体型性状数据,通过R软件与Excel计算公牛基因组评估结果与公牛女儿表型数据间的相关性,对我国荷斯坦青年公牛基因组选择效果进行分析。相关性分析结果表明,荷斯坦公牛的基因组性能指数（GCPI）与后裔测定性能指数（CPI）呈正相关（rs>0.3）,其中产奶量和体细胞评分的基因组育种值（GEBV）与估计育种值（EBV）呈较强的正相关（0.4 < rs < 0.8）。对公牛女儿表型数据分析结果表明,女儿产奶量、乳蛋白率、乳脂率与肢蹄评分的表型值与公牛GEBV分组趋势一致,且公牛不同产奶性状GEBV组间的女儿性状表型值大部分达到极显著差异（P<0.01）;北京及上海地区公牛产奶性状、体细胞评分和肢蹄评分的GEBV分组与女儿表型值趋势较其他省市（地区）更一致,且GEBV高组与低组之间差值均高于其他省市（地区）。基于1 686头荷斯坦公牛基因组选择及其女儿表型数据的分析结果表明,我国荷斯坦公牛的基因组遗传评估准确性较好,其中产奶量、乳蛋白率、体细胞评分和肢蹄评分的表型数据更好地反映了基因组选择的效果;北京及上海地区较其他省市（地区）更能反映我国荷斯坦公牛基因组选择的效果。 相似文献
3.
在基因组选择中(GS),相比单性状模型，多性状模型有诸多优势：可以利用到性状间的遗传相关信息，提高预测可靠性；当直接选择主要性状效果不佳时，可通过选择与其遗传相关较高的次要性状来提高主要性状的预测可靠性；当个体缺失某个表型的记录时，可以参与到遗传评估中。本研究旨在评估最佳无偏线性预测(BLUP)与一步法基因组选择(ssGBLUP)两性状模型相比单性状模型的选择实施效果，为应对现实中个体可能缺失部分性状观察值提供参考依据。选择出生于2012—2019年的2 132头杜洛克猪为研究对象，利用DMU(v 6.0)软件中的单性状与多性状模型(BLUP,ssGBLUP)基因组选择方法探讨了验证群中不同比例的表型个体对生长性状(达100 kg体重时的日龄、背膘厚和眼肌面积)的预测可靠性的影响。结果：不同的验证群表型个体比例，单性状ssGBLUP对性状的预测可靠性均要优于单性状BLUP,两性状ssGBLUP模型的预测可靠性均要优于单性状模型；随着验证群中有表型的个体比例变化，两性状ssGBLUP与两性状BLUP模型对性状的预测可靠性均呈现提高的趋势。随着验证群中有表型的个体比例变化，两性状ssGBL... 相似文献
4.
本研究对11头蒙贝利亚母牛和9头蒙贝利亚公牛的生长发育进行了跟踪测定,并与同期荷斯坦母牛的生长发育、产奶性能进行了对比分析。结果表明,蒙贝利亚母牛与荷斯坦母牛初生重无显著差异;4～18月龄,蒙贝利亚母牛体重均明显高于荷斯坦母牛同期体重,差异极显著（P〈0．01）。蒙贝利亚母牛与荷斯坦母牛日增重变化规律相似,lOB龄平均日增重最大,17月龄平均日增重最小。蒙贝利亚母牛各阶段体高、体长与荷斯坦母牛无显著差异,14月龄体高可达127cm,达到配种体高。蒙贝利亚母牛头胎305d产奶量为7241kg,对照组荷斯坦母牛头胎305d产奶量为9589kg,二者差异极显著（P〈0．01）。蒙贝利亚公牛初生重较大,至24月龄体重接近荷斯坦公牛同期体重。12~24月龄,荷斯坦公牛体高、体斜长、胸围均明显高于蒙贝利亚公牛,差异板显著（P〈0．01）;蒙贝利亚公牛初生重48kg,蒙贝利亚母牛初生重42kg,二者差异显著（P〈0．05）。蒙贝利亚公牛各阶段睾丸周径均显著高于荷斯坦公牛,差异极显著（P〈0．01）。 相似文献
5.
旨在探究快速型黄羽肉鸡饲料利用效率性状的遗传参数,评估不同方法所得估计育种值的准确性。本研究以自主培育的快速型黄羽肉鸡E系1 923个个体（其中公鸡1 199只,母鸡724只）为研究素材,采用"京芯一号"鸡55K SNP芯片进行基因分型。分别利用传统最佳线性无偏预测（BLUP）、基因组最佳线性无偏预测（GBLUP）和一步法（SSGBLUP）3种方法,基于加性效应模型进行遗传参数估计,通过10倍交叉验证比较3种方法所得估计育种值的准确性。研究性状包括4个生长性状和4个饲料利用效率性状：42日龄体重（BW42D）、56日龄体重（BW56D）、日均增重（ADG）、日均采食量（ADFI）和饲料转化率（FCR）、剩余采食量（RFI）、剩余增长体重（RG）、剩余采食和增长体重（RIG）。结果显示,4个饲料利用效率性状均为低遗传力（0.08~0.20）,其他生长性状为中等偏低遗传力（0.11~0.35）;4个饲料利用效率性状间均为高度遗传相关,RFI、RIG与ADFI间为中度遗传相关,RFI与ADG间无显著相关性,RIG与ADG间为低度遗传相关。本研究在获得SSGBLUP方法的最佳基因型和系谱矩阵权重比基础上,比较8个性状的估计育种值准确性,SSGBLUP方法获得的准确性分别比传统BLUP和GBLUP方法提高3.85%~14.43%和5.21%~17.89%。综上,以RIG为选择指标能够在降低日均采食量的同时保持日均增重,比RFI更适合快速型黄羽肉鸡的选育目标;采用最佳权重比进行SSGBLUP分析,对目标性状估计育种值的预测性能最优,建议作为快速型黄羽肉鸡基因组选择方法。 相似文献
6.
奶牛繁殖性状的育种值估计 总被引：1，自引：0，他引：1
利用北京市8个荷斯坦奶牛群的头胎繁殖性状资料，用BLUP法估计了公牛繁殖力的育种值。利用20头公牛的2299头与配母牛头胎繁殖性状资料．估计了公牛雄性繁殖力的育种值。利用19头公牛的2001头女儿的头胎繁殖性状资料．估计了公牛雌性繁殖力的育种值。分别比较了考虑场、年、季之间互作效应的和未考虑场、年、季之间互作效应的育种值估计准确性．分析了不同场、不同输精年份、不同输精月份对公牛繁殖力的影响。结果表明，考虑场、年、季之间互作效应时的准确性最高．未考虑时的准确性最低；本文中不同场、年、季水平对公牛繁殖力的影响是显著的。结论：估计公牛繁殖性能的育种值时，应事先或模型中加以剔除这些固定效应。 相似文献
7.
目的:估计新疆乌鲁木齐种牛场荷斯坦牛产奶性状的遗传参数及育种值,为该场荷斯坦种公牛的选种选配计划提供理论依据。方法:以新疆乌鲁木齐种牛场2009~2015年间荷斯坦泌乳母牛的产奶性能记录资料为基础,运用DMUAI-REML算法及动物模型BLUP法,对荷斯坦牛产奶量及乳成分进行单性状方差组分及育种值估计。结果:牛场荷斯坦种公牛后代母牛的产奶量、乳脂率、乳蛋白率、乳糖率及非脂固形物的遗传力分别为0.14、0.13、0.14、0.09、0.11。6头种公牛里估计得到产奶量育种值最高的是65206012号种公牛,乳脂率、乳蛋白率、乳糖率、非脂固形物育种值最高的是11101917号种公牛。结论:使种公牛生产性能评定的更准确,需综合考虑产奶量和乳成分等性状,最终对种公牛进行选育选配。 相似文献
8.
北京地区荷斯坦青年牛繁殖性状影响因素分析 总被引：1，自引：0，他引：1
本研究利用北京地区23个奶牛场105头公牛的25705个女儿繁殖记录,对中国荷斯坦青年牛的4个繁殖性状,包括青年牛初配日龄（AFI）、青年牛妊娠日龄（AFP）、青年牛初产日龄（AFC）和输精次数（NS）,采用固定模型通过SAS9.1软件GLM过程进行统计分析。结果表明,牛场、出生年份、出生季节以及公牛等效应对青年牛繁殖性状具有显著（P〈0.05）或极显著影响（P〈0.01）。通过提高饲养管理水平,给母牛创造良好的生存环境,选用具有优秀繁殖性能的种公牛,可有效提高母牛繁殖效率。 相似文献
9.
基于BLUP和GBLUP方法估计北京油鸡胴体和肉质性状遗传参数的差异 总被引：1，自引：1，他引：0
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（h2 =0.256）和低遗传力（h2 =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
10.
以新疆呼图壁种牛场中国荷斯坦牛为研究对象,利用动物模型BLUP法对中国荷斯坦牛各生产性能性状进行育种值估计并制定总性能指数。结果表明:在群母牛305d产奶量、乳脂率、乳蛋白率育种值排名第一的分别是732210233号、724409179号和119870098号,体细胞评分育种值最低的是766963365号母牛;种公牛305d产奶量、乳脂率、乳蛋白率育种值分别是133412077号、60755967号和13号,体细胞评分育种值最低的是12000167号。根据305天产奶量、乳脂率、乳蛋白率和体细胞评分育种值,制定了总性能指数公式,选留390头母牛为核心群母牛。 相似文献
11.
Kaarina Matilainen Ismo Strandén Gert Pedersen Aamand Esa A. Mäntysaari 《Zeitschrift für Tierzüchtung und Züchtungsbiologie》2018,135(5):337-348
Joint Nordic (Denmark, Finland, Sweden) genetic evaluation of female fertility is currently based on the multiple trait multilactation animal model (BLUP). Here, single step genomic model (ssGBLUP) was applied for the Nordic Red dairy cattle fertility evaluation. The 11 traits comprised of nonreturn rate and days from first to last insemination in heifers and first three parities, and days from calving to first insemination in the first three parities. Traits had low heritabilities (0.015–0.04), but moderately high genetic correlations between the parities (0.60–0.88). Phenotypic data included 4,226,715 animals with records and pedigree 5,445,392 animals. Unknown parents were assigned into 332 phantom parent groups (PPG). In mixed model equations animals were associated with PPG effects through the pedigree or both the pedigree and genomic information. Genotype information of 46,914 SNPs was available for 33,969 animals in the pedigree. When PPG used pedigree information only, BLUP converged after 2,420 iterations whereas the ssGBLUP evaluation needed over ten thousand iterations. When the PPG effects were solved accounting both the pedigree and the genomic information, the ssGBLUP model converged after 2,406 iterations. Also, with the latter model breeding values by ssGBLUP and BLUP became more consistent and genetic trends followed each other well. Models were validated using forward prediction of the young bulls. Reliabilities and variance inflation of predicted genomic breeding values (values for parent averages in brackets) for the 11 traits ranged 0.22–0.31 (0.10–0.27) and 0.81–0.95 (0.83–1.06), respectively. The ssGBLUP model gave always higher validation reliabilities than BLUP, but largest increases were for the cow fertility traits. 相似文献
12.
Natlia Galoro Leite Egbert Frank Knol Andr Luiz Seccatto Garcia Marcos Soares Lopes Louisa Zak Shogo Tsuruta Fabyano Fonseca e Silva Daniela Lourenco 《Journal of animal science》2021,99(8)
Pig survival is an economically important trait with relevant social welfare implications, thus standing out as an important selection criterion for the current pig farming system. We aimed to estimate (co)variance components for survival in different production phases in a crossbred pig population as well as to investigate the benefit of including genomic information through single-step genomic best linear unbiased prediction (ssGBLUP) on the prediction accuracy of survival traits compared with results from traditional BLUP. Individual survival records on, at most, 64,894 crossbred piglets were evaluated under two multi-trait threshold models. The first model included farrowing, lactation, and combined postweaning survival, whereas the second model included nursery and finishing survival. Direct and maternal breeding values were estimated using BLUP and ssGBLUP methods. Furthermore, prediction accuracy, bias, and dispersion were accessed using the linear regression validation method. Direct heritability estimates for survival in all studied phases were low (from 0.02 to 0.08). Survival in preweaning phases (farrowing and lactation) was controlled by the dam and piglet additive genetic effects, although the maternal side was more important. Postweaning phases (nursery, finishing, and the combination of both) showed the same or higher direct heritabilities compared with preweaning phases. The genetic correlations between survival traits within preweaning and postweaning phases were favorable and strong, but correlations between preweaning and postweaning phases were moderate. The prediction accuracy of survival traits was low, although it increased by including genomic information through ssGBLUP compared with the prediction accuracy from BLUP. Direct and maternal breeding values were similarly accurate with BLUP, but direct breeding values benefited more from genomic information. Overall, a slight increase in bias was observed when genomic information was included, whereas dispersion of breeding values was greatly reduced. Combined postweaning survival presented higher direct heritability than in the preweaning phases and the highest prediction accuracy among all evaluated production phases, therefore standing out as a candidate trait for improving survival. Survival is a complex trait with low heritability; however, important genetic gains can still be obtained, especially under a genomic prediction framework. 相似文献
13.
L. Zhou M.S. Lund Y. Wang G. Su 《Zeitschrift für Tierzüchtung und Züchtungsbiologie》2014,131(4):249-257
This study investigated genomic predictions across Nordic Holstein and Nordic Red using various genomic relationship matrices. Different sources of information, such as consistencies of linkage disequilibrium (LD) phase and marker effects, were used to construct the genomic relationship matrices (G‐matrices) across these two breeds. Single‐trait genomic best linear unbiased prediction (GBLUP) model and two‐trait GBLUP model were used for single‐breed and two‐breed genomic predictions. The data included 5215 Nordic Holstein bulls and 4361 Nordic Red bulls, which was composed of three populations: Danish Red, Swedish Red and Finnish Ayrshire. The bulls were genotyped with 50 000 SNP chip. Using the two‐breed predictions with a joint Nordic Holstein and Nordic Red reference population, accuracies increased slightly for all traits in Nordic Red, but only for some traits in Nordic Holstein. Among the three subpopulations of Nordic Red, accuracies increased more for Danish Red than for Swedish Red and Finnish Ayrshire. This is because closer genetic relationships exist between Danish Red and Nordic Holstein. Among Danish Red, individuals with higher genomic relationship coefficients with Nordic Holstein showed more increased accuracies in the two‐breed predictions. Weighting the two‐breed G‐matrices by LD phase consistencies, marker effects or both did not further improve accuracies of the two‐breed predictions. 相似文献
14.
Matias Bermann Andres Legarra Mary Kate Hollifield Yutaka Masuda Daniela Lourenco Ignacy Misztal 《Zeitschrift für Tierzüchtung und Züchtungsbiologie》2021,138(1):4-13
The objective of this study was to determine whether the linear regression (LR) method could be used to validate genomic threshold models. Statistics for the LR method were computed from estimated breeding values (EBVs) using the whole and truncated data sets with variances from the reference and validation populations. The method was tested using simulated and real chicken data sets. The simulated data set included 10 generations of 4,500 birds each; genotypes were available for the last three generations. Each animal was assigned a continuous trait, which was converted to a binary score assuming an incidence of failure of 7%. The real data set included the survival status of 186,596 broilers (mortality rate equal to 7.2%) and genotypes of 18,047 birds. Both data sets were analysed using best linear unbiased predictor (BLUP) or single‐step GBLUP (ssGBLUP). The whole data set included all phenotypes available, whereas in the partial data set, phenotypes of the most recent generation were removed. In the simulated data set, the accuracies based on the LR formulas were 0.45 for BLUP and 0.76 for ssGBLUP, whereas the correlations between true breeding values and EBVs (i.e. true accuracies) were 0.37 and 0.65, respectively. The gain in accuracy by adding genomic information was overestimated by 0.09 when using the LR method compared to the true increase in accuracy. However, when the estimated ratio between the additive variance computed based on pedigree only and on pedigree and genomic information was considered, the difference between true and estimated gain was <0.02. Accuracies of BLUP and ssGBLUP with the real data set were 0.41 and 0.47, respectively. This small improvement in accuracy when using ssGBLUP with the real data set was due to population structure and lower heritability. The LR method is a useful tool for estimating improvements in accuracy of EBVs due to the inclusion of genomic information when traditional validation methods as k‐fold validation and predictive ability are not applicable. 相似文献
15.
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
16.
Impact of fitting dominance and additive effects on accuracy of genomic prediction of breeding values in layers 
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下载免费PDF全文 M. Heidaritabar A. Wolc J. Arango J. Zeng P. Settar J.E. Fulton N.P. O'Sullivan J.W.M. Bastiaansen R.L. Fernando D.J. Garrick J.C.M. Dekkers 《Zeitschrift für Tierzüchtung und Züchtungsbiologie》2016,133(5):334-346
Most genomic prediction studies fit only additive effects in models to estimate genomic breeding values (GEBV). However, if dominance genetic effects are an important source of variation for complex traits, accounting for them may improve the accuracy of GEBV. We investigated the effect of fitting dominance and additive effects on the accuracy of GEBV for eight egg production and quality traits in a purebred line of brown layers using pedigree or genomic information (42K single‐nucleotide polymorphism (SNP) panel). Phenotypes were corrected for the effect of hatch date. Additive and dominance genetic variances were estimated using genomic‐based [genomic best linear unbiased prediction (GBLUP)‐REML and BayesC] and pedigree‐based (PBLUP‐REML) methods. Breeding values were predicted using a model that included both additive and dominance effects and a model that included only additive effects. The reference population consisted of approximately 1800 animals hatched between 2004 and 2009, while approximately 300 young animals hatched in 2010 were used for validation. Accuracy of prediction was computed as the correlation between phenotypes and estimated breeding values of the validation animals divided by the square root of the estimate of heritability in the whole population. The proportion of dominance variance to total phenotypic variance ranged from 0.03 to 0.22 with PBLUP‐REML across traits, from 0 to 0.03 with GBLUP‐REML and from 0.01 to 0.05 with BayesC. Accuracies of GEBV ranged from 0.28 to 0.60 across traits. Inclusion of dominance effects did not improve the accuracy of GEBV, and differences in their accuracies between genomic‐based methods were small (0.01–0.05), with GBLUP‐REML yielding higher prediction accuracies than BayesC for egg production, egg colour and yolk weight, while BayesC yielded higher accuracies than GBLUP‐REML for the other traits. In conclusion, fitting dominance effects did not impact accuracy of genomic prediction of breeding values in this population. 相似文献
17.
H.R. de Oliveira F.F. Silva L.F. Brito A.R. Guarini J. Jamrozik F.S. Schenkel 《Zeitschrift für Tierzüchtung und Züchtungsbiologie》2018,135(2):97-106
We aimed to investigate the performance of three deregression methods (VanRaden, VR; Wiggans, WG; and Garrick, GR) of cows’ and bulls’ breeding values to be used as pseudophenotypes in the genomic evaluation of test‐day dairy production traits. Three scenarios were considered within each deregression method: (i) including only animals with reliability of estimated breeding value (RELEBV ) higher than the average of parent reliability (RELPA ) in the training and validation populations; (ii) including only animals with RELEBV higher than 0.50 in the training and RELEBV higher than RELPA in the validation population; and (iii) including only animals with RELEBV higher than 0.50 in both training and validation populations. Individual random regression coefficients of lactation curves were predicted using the genomic best linear unbiased prediction (GBLUP), considering either unweighted or weighted residual variances based on effective records contributions. In summary, VR and WG deregression methods seemed more appropriate for genomic prediction of test‐day traits without need for weighting in the genomic analysis, unless large differences in RELEBV between training population animals exist. 相似文献
18.
Chen CY Misztal I Aguilar I Tsuruta S Meuwissen TH Aggrey SE Wing T Muir WM 《Journal of animal science》2011,89(1):23-28
Data of broiler chickens for 2 pure lines across 3 generations were used for genomic evaluation. A complete population (full data set; FDS) consisted of 183,784 and 164,246 broilers for the 2 lines. The genotyped subsets (SUB) consisted of 3,284 and 3,098 broilers with 57,636 SNP. Genotyped animals were preselected based on more than 20 traits with different index applied to each line. Three traits were analyzed: BW at 6 wk (BW6), ultrasound measurement of breast meat (BM), and leg score (LS) coded 1 = no and 2 = yes for leg defect. Some phenotypes were missing for BM. The training population consisted of the first 2 generations including all animals in FDS or only genotyped animals in SUB. The validation data set contained only genotyped animals in the third generation. Genetic evaluations were performed using 3 approaches: 1) phenotypic BLUP, 2) extending BLUP methodologies to utilize pedigree and genomic information in a single step (ssGBLUP), and 3) Bayes A. Whereas BLUP and ssGBLUP utilized all phenotypic data, Bayes A could use only those of the genotyped subset. Heritabilities were 0.17 to 0.20 for BW6, 0.30 to 0.35 for BM, and 0.09 to 0.11 for LS. The average accuracies of the validation population with BLUP for BW6, BM, and LS were 0.46, 0.30, and <0 with SUB and 0.51, 0.34, and 0.28 with FDS. With ssGBLUP, those accuracies were 0.60, 0.34, and 0.06 with SUB and 0.61, 0.40, and 0.37 with FDS, respectively. With Bayes A, the accuracies were 0.60, 0.36, and 0.09 with SUB. With SUB, Bayes A and ssGBLUP had similar accuracies. For traits of high heritability, the accuracy of Bayes A/SUB and ssGBLUP/FDS were similar, and up to 50% better than BLUP/FDS. However, with low heritability, ssGBLUP/FDS was 4 to 6 times more accurate than Bayes A/SUB and 50% better than BLUP/FDS. An optimal genomic evaluation would be multi-trait and involve all traits and records on which selection is based. 相似文献