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。     

性别比例和性状遗传力对闭锁群体BLUP选择效果的影响

采用MonteCarlo方法模拟研究了性别比例和性状遗传力对闭锁群体动物模型BLUP选择效果的影响 ,选育过程中世代不重叠 ,共进行了 1 5个世代的选择。结果表明性别比例对群体育种值和近交系数的变化都有明显的影响。在育种值达到最大值以前 ,群体平均育种值提高的速度随着公畜比例的增加而有所减慢 ,但会使育种值达到最大值的时间后移 ,在育种值达到最大值后 ,其下降的速度则随着公畜比例的降低而加快。随着公畜比例的增加 ,群体近交系数的上升速度会明显变慢。高遗传力性状的选择效果要优于低遗传力性状     

标记辅助选择在猪育种中应用效果的研究

本研究针对猪育种中重点考虑的窝产活仔猪数（NBA）、达100 kg体重日增重（ADG）、饲料利用率（FCR）、达100 kg体重的背膘厚（BF）、肌内脂肪含量（IMF） 5个性状,利用连锁平衡（linkage equilibrium,LE）、连锁不平衡（linkage disequilibrium,LD）标记和直接标记（direct marker,DR）3种类型的分子遗传标记,设计了3个规模不同的基础群,母猪数分别为100、200、300头,公猪数都为10头,基础群个体间无亲缘关系,育种群实施闭锁繁育。用Monte Carlo方法模拟了MAS的5个世代选择试验。育种值估计采用标准BLUP（Standard BLUP,SBLUP）模型（此育种值作为对照）、QBLUP模型（使用DR标记）、MBLUP模型（使用LD和LE标记）。结果表明,利用DR标记在各种情况下都比利用LD和LE标记获得的选择效率高;5个性状中,MAS对低遗传力、限性性状NBA的选择效率最高;当性状的QTL方差占遗传方差基本相同时,中等遗传力性状FCR的选择效率比高遗传力性状BF的更高;当性状的遗传力差异不大时,QTL方差占遗传方差比例大的性状FCR的选择效率比QTL方差占遗传方差比例小的性状ADG的更高。当利用QBLUP模型时,MAS对NBA的选择效率最高,ADG的选择效率最低。     

绵羊继代选育育种规模及育种周期的简便估算

继代选育是以群体遗传和群体选择法为依据的一种品系育种方法。此法的特点是：严格按世代组群，对各世代育种群实行继代闭锁选育；要求各世代育种规模、选种目标和选种方法、育种技术措施保持一致，并且要求各世代均保持一定量的遗传进展和适宜的近交系数增量；经计定的育...     

近交与贴现对优化群体规模的影响

小群体的闭锁选育不可避免产生近交及遗传方差下降，导致近期与远期选择效果的矛盾。本文通过考察猪核心群育种方案中群体规模及公母比例对近交增量（％）及累积育种产出（元）的作用，分析遗传方差下降以及育种产出的贴现等因素对选择方案评估效果的影响。群体有效规模主要受每世代选择公猪头数的影响，为了控制群体近交增量必须维持一定的公猪头数。在所模拟的１６种方案中，１５代的贴现累积选择进展以每代选择８头公猪为最高；而母猪规模越大，累积选择进展越高。不考虑近交引起遗传方差的下降，或不进行选择进展的贴现，都造成过高估计选择方案的效果，且导致选择不适当的方案；对各世代选择进展进行贴现时，需要考虑较大的世代数，否则也会影响各选择方案的比较结果。     

BC1群体单标记分析检测QTL的效率

[目的]为QTL定位中进一步估计QTL的位置和影响效应提供必要的依据。[方法]以BC1设计为资源群体,通过计算机模拟研究了不同群体规模、标记-QTL间图距、性状遗传力和QTL效应（QTL方差占加性遗传方差的比例）对单标记分析检测QTL效率的影响。[结果]表明：资源群体规模较大,标记与QTL的间距较小（或标记与QTL连锁紧密）,目标性状的遗传力较高,且QTL效应较大时,采用单标记分析方法检测QTL的检出率较高。当所检测的标记距离QTL较近时,获得相同的QTL检出率所需的资源群体规模更小。[结论]QTL效应对QTL检出率的影响会受性状遗传力的制约,如果性状的遗传力过低,即使QTL方差占遗传方差的比例很高,也很难获得理想的QTL检出率。     

基于基因组和系谱信息的不同选配方案效果模拟研究

基因组选配（genomic mating,GM）是利用基因组信息进行优化的选种选配,可以有效控制群体近交水平的同时实现最大化的遗传进展。但基因组选配是对群体中所有个体进行选配,这与实际的育种工作有点相悖。本研究模拟了遗传力为0.5的9 000头个体的基础群数据,每个世代根据GEBV选择30头公畜、900头母畜作为种用个体,而后使用基因组选配、同质选配、异质选配、随机交配4种不同的选配方案。其中基因组选配中分别选取遗传进展最大的解、家系间方差最大的解、近交最小的解所对应的交配方案进行选育。每种方案选育5个世代,比较其后代群体的平均GEBV、每世代的遗传进展、近交系数、遗传方差,并重复5次取平均值。结果表明,3种基因组选配方案的ΔG均显著高于随机交配和异质选配（P<0.01）,而且,选取遗传进展最大的基因组选配方案的ΔG比同质选配还高出4.3%。3种基因组选配的方案的ΔF比同质选配低22.2%~94.1%,而且选取近交最小的基因组选配方案ΔF比异质选配低11.8%。同质选配的遗传方差迅速降低,在第5世代显著低于除基因组选配中选择遗传进展最大的方案以外的所有方案（P<0.05）,3种基因组选配方案的遗传方差比同质选配高10.8%~32.2%。这表明基因组选配不仅可以获得比同质选配更高的遗传进展,同时有效的降低了近交水平,并且减缓了遗传方差降低速度,保证了一定的遗传变异。基因组选配作为一种有效的可持续育种方法,在畜禽育种中开展十分有必要。     

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。     

加性方差与遗传力

一个性状的遗传力是指该性状的遗传方差在这个群体表型方差中所占的比率。这就是广义遗传力,即: 这里,V_G=V_A+V_D+V_I,这就是说,遗传方差(V_G)包含着育种值方差(或加性方差V_A)、显性方差(V_D)和互作方差(V_I)。然而,在遗传方差中,除了在超     

Optimum contribution selection using traditional best linear unbiased prediction and genomic breeding values in aquaculture breeding schemes

The aim of this study was to compare genetic gain for a traditional aquaculture sib breeding scheme with breeding values based on phenotypic data (TBLUP) with a breeding scheme with genome-wide (GW) breeding values. Both breeding schemes were closed nuclei with discrete generations modeled by stochastic simulation. Optimum contribution selection was applied to restrict pedigree-based inbreeding to either 0.5 or 1% per generation. There were 1,000 selection candidates and a sib test group of either 4,000 or 8,000 fish. The number of selected dams and sires to create full sib families in each generation was determined from the optimum contribution selection method. True breeding values for a trait were simulated by summing the number of each QTL allele and the true effect of each of the 1,000 simulated QTL. Breeding values in TBLUP were predicted from phenotypic and pedigree information, whereas genomic breeding values were computed from genetic markers whose effects were estimated using a genomic BLUP model. In generation 5, genetic gain was 70 and 74% greater for the GW scheme than for the TBLUP scheme for inbreeding rates of 0.5 and 1%. The reduction in genetic variance was, however, greater for the GW scheme than for the TBLUP scheme due to fixation of some QTL. As expected, accuracy of selection increased with increasing heritability (e.g., from 0.77 with a heritability of 0.2 to 0.87 with a heritability of 0.6 for GW, and from 0.53 and 0.58 for TBLUP in generation 5 with sib information only). When the trait was measured on the selection candidate compared with only on sibs and the heritability was 0.4, accuracy increased from 0.55 to 0.69 for TBLUP and from 0.83 to 0.86 for GW. The number of selected sires to get the desired rate of inbreeding was in general less in GW than in TBLUP and was 33 for GW and 83 for TBLUP (rate of inbreeding 1% and heritability 0.4). With truncation selection, genetic gain for the scheme with GW breeding values was nearly twice as large as a scheme with traditional BLUP breeding values. The results indicate that the benefits of applying GW breeding values compared with TBLUP are reduced when contributions are optimized. In conclusion, genetic gain in aquaculture breeding schemes with optimized contributions can increase by as much as 81% by applying genome-wide breeding values compared with traditional BLUP breeding values.     

There is room for selection in a small local pig breed when using optimum contribution selection: a simulation study

Selection progress must be carefully balanced against the conservation of genetic variation in small populations of local breeds. Well-defined breeding programs?with specified selection traits are rare in local pig breeds. Given the small population size,?the focus is often on the management of genetic diversity. However, in local breeds, optimum contribution selection can be applied to control the rate of inbreeding and to avoid reduced performance in traits with high market value. The aim of this study was to assess the extent to which a breeding program aiming for improved product quality in a small local breed would be feasible. We used stochastic simulations to compare 25 scenarios. The scenarios differed in?size of population, selection intensity of boars, type of selection (random selection, truncation selection based on BLUP breeding values, or optimum contribution selection based on BLUP breeding values), and heritability of?the selection trait. It was assumed that the local breed is used in an extensive system for a high-meat-quality market.?The?simulations showed that in the smallest population (300 female reproducers), inbreeding increased by 0.8% when selection was performed at random. With optimum contribution selection, genetic progress can be achieved that is almost as great as that with truncation selection based on BLUP breeding values (0.2 to 0.5 vs. 0.3 to 0.5 genetic SD, P < 0.05), but at a considerably decreased rate of inbreeding (0.7 to 1.2 vs. 2.3 to 5.7%, P < 0.01). This confirmation of the potential utilization of OCS even in small populations is important in the context of sustainable management and the use of animal genetic resources.     

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.     

Estimates of genetic variance and variance of predicted genetic merits using pedigree or genomic relationship matrices in six Brown Swiss cattle populations for different traits

The amount of variance captured in genetic estimations may depend on whether a pedigree‐based or genomic relationship matrix is used. The purpose of this study was to investigate the genetic variance as well as the variance of predicted genetic merits (PGM) using pedigree‐based or genomic relationship matrices in Brown Swiss cattle. We examined a range of traits in six populations amounting to 173 population‐trait combinations. A main aim was to determine how using different relationship matrices affect variance estimation. We calculated ratios between different types of estimates and analysed the impact of trait heritability and population size. The genetic variances estimated by REML using a genomic relationship matrix were always smaller than the variances that were similarly estimated using a pedigree‐based relationship matrix. The variances from the genomic relationship matrix became closer to estimates from a pedigree relationship matrix as heritability and population size increased. In contrast, variances of predicted genetic merits obtained using a genomic relationship matrix were mostly larger than variances of genetic merit predicted using pedigree‐based relationship matrix. The ratio of the genomic to pedigree‐based PGM variances decreased as heritability and population size rose. The increased variance among predicted genetic merits is important for animal breeding because this is one of the factors influencing genetic progress.     

Efficient selection against categorically scored hip dysplasia in dogs is possible using best linear unbiased prediction and optimum contribution selection: a simulation study

Breeding to reduce the prevalence of categorically scored hip dysplasia (HD), based on phenotypic assessment of radiographic hip status, has had limited success. The aim of this study was to evaluate two selection strategies for improved hip status: truncation selection based on phenotypic record versus best linear unbiased prediction (BLUP), using stochastic simulation and selection scenarios resembling those in real dog populations. In addition, optimum contribution selection (OCS) was evaluated. Two traits were considered: HD (as a categorical trait with five classes and a heritability of 0.45 on the liability scale) and a continuous trait (with a heritability of 0.25) intended to represent other characteristics in the breeding goal. A population structure mimicking that in real dog populations was modelled. The categorical nature of HD caused a considerably lower genetic gain compared to simulating HD as a continuous trait. Genetic gain was larger for BLUP selection than for phenotypic selection in all scenarios. However, BLUP selection resulted in higher rates of inbreeding. By applying OCS, the rate of inbreeding was lowered to about the same level as phenotypic selection but with increased genetic improvement. For efficient selection against HD, use of BLUP breeding values should be prioritized. In small populations, BLUP should be used together with OCS or similar strategy to maintain genetic variation.     

Accuracy of breeding values when using and ignoring the polygenic effect in genomic breeding value estimation with a marker density of one SNP per cM

Genomic selection is based on breeding values that are estimated using genome-wide dense marker maps. The objective of this paper was to investigate the effect of including or ignoring the polygenic effect on the accuracy of total genomic breeding values, when there is coverage of the genome with approximately one SNP per cM. The importance of the polygenic effect might differ for high and low heritability traits, and might depend on the design of the reference dataset. Hence, different scenarios were evaluated using stochastic simulation. Accuracies of the total breeding value of juvenile selection candidates depended on the number of animals included in the reference data. When excluding polygenic effects, those accuracies ranged from 0.38 to 0.55 and from 0.73 to 0.79 for traits with heritabilities of 10 and 50%, respectively. Accuracies were improved by including a polygenic effect in the model for the low heritability trait, when the LD-measure r2 between adjacent markers became smaller than approximately 0.10, while for the high heritability trait there was already a small improvement at r2 between adjacent markers of 0.14. In all situations, the estimated total genetic variance was underestimated, particularly when the polygenic effect was excluded from the model. The haplotype variance was less underestimated when more animals were added in the reference dataset.     

Genetic heteroscedasticity of teat count in pigs

The genetic improvement in pig litter size has been substantial. The number of teats on the sow must thus increase as well to meet the needs of the piglets, because each piglet needs access to its own teat. We applied a genetic heterogeneity model to teat counts in pigs, and estimated a medium heritability for teat counts (0.35), but found a low heritability for residual variance (0.06), indicating that selection for reduced residual variance might have a limited effect. A numerically positive correlation (0.8) was estimated between the breeding values for the mean and the residual variance. However, because of the low heritability of the residual variance, the residual variance will probably increase very slowly with the mean.