荷斯坦公牛泌乳量育种值的BLUP评价及分析

以公牛女儿数大于10头为限制因子，建立线性模型yijk=μ hi sj eijk，对安徽荷斯坦公牛泌乳量的育种值进行BLUP评定。分析结果表明：估计育种值排序在前的公牛女儿数并不是最多的，在泌乳量这一生产性状的选择育种上表现优秀的种公牛并没有得到充分利用；对部分种公牛在泌乳量估计育种值中排序与其在体型评定中排序进行比较，有一定的不一致性。     

基于B/S模式的肉牛选育评估系统的研究与实现

[目的]通过对肉牛选育评估方法的研究,基于B/S模式,设计并实现一个肉牛选育评估系统.[方法]研究选用发达国家广泛应用的动物模型BLUP方法作为选育依据,采用MVC(Model View Controller)开发模式,基于浏览器/服务器(Browser/Server,B/S)模式收集牛只的基本资料,从而计算遗传参数,...     

山西瘦肉型猪SD-Ⅱ系6月龄体重遗传趋势分析

用混合模型BLUP法对山西瘦肉型猪SD-系选育过程中O～4世代53头公猪的603头后裔6月龄体重进行了遗传趋势评估分析。结果表明，遗传传递力与后代平均表型值的相关及其名次的秩相关均迭极显著水平；遗传趋势、表型趋势和年遗传改进量较高，．且遗传趋势与表型趋势较一致，说明SD-Ⅱ系选育过程中所要取的育种措施是有效的，继续进行选育还有提高的可能。     

人工神经网络在作物基因组选择中的应用

目前, 基因组选择育种主要采用线性模型估计遗传育种值指导作物遗传育种的筛选过程, 但是生物体内的基因以及遗传位点的关系主要是复杂的非线性调控。本研究将人工神经网络技术应用到作物基因组选择育种中, 对现有的作物基因组选择育种模型进行优化, 建立了高效的作物基因组选择预测系统, 并与其他线性回归预测模型进行比较。通过分析小麦的育种数据发现, 基于人工神经网络的遗传育种估计效果优于其他线性回归预测模型, 预测育种值与实际育种值间的相关系数平均值达到0.6636, 相应的岭回归BLUP、贝叶斯线性回归模型和基于系谱信息的贝叶斯回归模型的预测能力分别为0.6422、0.6294和0.6573; 最优的预测效果达到0.8379, 远高于其他2种模型的最优结果。同时, 基于人工神经网络的基因组选择模型的预测效果稳定, 与传统的统计模型相近, 因此, 利用人工神经网络技术建立基因组选择是可行的。     

应用BLUP方法对猪繁殖性状和生长性状遗传评估的研究

【目的】估计杜洛克猪(Duroc,DD)、长白猪(Landrace,LL)、大白猪(Yorkshire,YY)繁殖性状和生长性状的遗传参数,分析不同年份育种值变化的遗传趋势,为制定合理的育种方案提供理论依据。【方法】以杜洛克猪、长白猪、大白猪繁殖性状和生长性状的性能测定数据为研究材料,其中繁殖性状数据10 963条,包括总产仔数(total number born,TNB)、产活仔数(number born alive,NBA)、出生窝重(litter born weight,LBW)和21日龄窝重(litter weight at 21 days,LW21);生长性状数据25 257条,包括达100 kg体重日龄(age at 100 kg live weight,AGE)和达100 kg体重背膘厚(backfat adjusted to 100 kg,BF)。采用基于动物模型的最佳线性无偏预测(best linear unbiased prediction,BLUP)方法,使用ASReml统计分析软件进行遗传力、遗传相关和育种值估计。【结果】TNB、NBA和LBW的遗传力在0.08~0.20之间,LW21的遗传力在0.02~0.05之间;AGE和BF的遗传力在0.22~0.37之间。繁殖性状TNB、NBA、LBW、LW21的遗传相关系数总体分布在0.20~0.97之间,呈中等偏上正相关;生长性状AGE和BF的遗传相关系数分布在-0.07~-0.03之间,呈微弱的负相关。杜洛克猪繁殖性状的遗传趋势上升幅度较大,长白猪、大白猪繁殖性状的遗传趋势上升幅度较小;生长性状中AGE的遗传趋势均呈下降趋势,且下降幅度较大,BF的遗传趋势变化幅度较小。【结论】本研究对杜洛克猪、长白猪、大白猪繁殖性状和生长性状的遗传参数和遗产趋势进行了准确的评估,结果可为该育种场的育种工作提供参考。     

不同基因辅助选择方法对QTL的选择效率

 在假定目标性状由一个QTL和多基因共同控制的基础上,采用计算机模拟方法研究了在一个闭锁群体内分别采用基因型选择（GTS）、基因辅助BLUP（GBLUP）和常规动物模型BLUP方法对单个性状实施连续选择的效果,并以常规动物模型BLUP为参照,分析了GTS和GBLUP方法对目标性状QTL的选择效率。结果表明：与常规动物模型BLUP相比,GTS和GBLUP选择均可获得更大的QTL基因型值进展,QTL增效等位基因频率上升的速度也更快;与GBLUP相比,GTS方法对QTL的选择效率更高。GTS和GBLUP对QTL的选择优势在短期选择时较为明显,随着世代的递增,这种优势将逐渐降低甚至消失。     

Estimation of total genetic effects for survival time in crossbred laying hens showing cannibalism,using pedigree or genomic information

Mortality of laying hens due to cannibalism is a major problem in the egg‐laying industry. Survival depends on two genetic effects: the direct genetic effect of the individual itself (DGE) and the indirect genetic effects of its group mates (IGE). For hens housed in sire‐family groups, DGE and IGE cannot be estimated using pedigree information, but the combined effect of DGE and IGE is estimated in the total breeding value (TBV). Genomic information provides information on actual genetic relationships between individuals and might be a tool to improve TBV accuracy. We investigated whether genomic information of the sire increased TBV accuracy compared with pedigree information, and we estimated genetic parameters for survival time. A sire model with pedigree information (BLUP) and a sire model with genomic information (ssGBLUP) were used. We used survival time records of 7290 crossbred offspring with intact beaks from four crosses. Cross‐validation was used to compare the models. Using ssGBLUP did not improve TBV accuracy compared with BLUP which is probably due to the limited number of sires available per cross (~50). Genetic parameter estimates were similar for BLUP and ssGBLUP. For both BLUP and ssGBLUP, total heritable variance (T²), expressed as a proportion of phenotypic variance, ranged from 0.03 ± 0.04 to 0.25 ± 0.09. Further research is needed on breeding value estimation for socially affected traits measured on individuals kept in single‐family groups.     

采用动物模型BLUP法对不同模型进行筛选

运用动物模型BLUP法时敖汉细毛羊的早期性状和产毛性状进行分析得出:模型(1)适合估计周岁重、断乳毛长度和断乳毛细度;模型(4)适合估计初生重和断乳重;模型(5)适合估计产毛量、体重和毛长度;模型(7)适合估计毛细度、毛弯曲分、毛密度分和毛油汗分.     

Estimation of genetic parameters from pedigreed populations: lessons from analysis of alevin weight in Coho salmon (Oncorhynchus kisutch)

The purpose of the study was to assess the impact of various model structures on REML estimates of variance components using data on alevin weight from two replicate populations from the Genetic Improvement Program for Coho salmon (Chile). Data consisted of 130 d alevin weight from a dams-nested-within-sires mating design over two consecutive generations. Relationship information included direct and collateral relatives but parental individuals lacked records. The construction of a range of animal models considered random effects of direct additive genetic, maternal additive genetic and full-sib family effects as well as the covariance of direct and maternal genetic effects. Fixed effects of year (generation) and spawn date of dams within year were considered and also evaluated. The relative effectiveness of various models in describing the data set were assessed using likelihood ratio tests. The results demonstrated the importance of the correct interpretation of effects in the data set, particularly those effects that can influence the resemblance between relatives. The data structure, as well as the animal model applied, markedly influenced the magnitude of variance component estimates. Models based on year as the only fixed effect did not describe the data nearly as effectively as models containing both year and spawn data of dams within year. Simple models based on animal as the only random effect gave upward biased estimates of additive genetic variance. The most appropriate model for the data set was one based on both year and spawn date as fixed effects, and animal and full-sib family as random effects. The results from models combining maternal genetic and full-sib family effects to exploit the full covariance structure of the data showed that there was confounding between these variance component estimates. The most consistent interpretation of this result was that common environmental effects and non-additive genetic effects were more important sources of variability than maternal genetic effects. The study also demonstrated high variability in parameter estimates for replicate populations.     

应用布拉普法选择南阳公牛的研究(上)

应用单性状布拉普(BLUP)育种值及方差组分的约束极大似然估计,评价了9头南阳黄牛种公牛的优劣秩次,估出1.5岁体高、体斜长、胸围、管围及体重的遗传力为0.5297、0.6239、0.3929、0.1242和0.4257,1979～1983年5年间各性状年平均遗传进展分别为0.3009、0.1225、—2.1225、O.0494和1.5514。