Effective population size and inbreeding depression on litter size in rabbits. A case study

The purpose of this study is to use demographic and litter size data on four Spanish maternal lines of rabbits (A, V, H and LP), as a case study, in order to: (i) estimate the effective population size of the lines, as a measure of the rate of increase of inbreeding, and (ii) study whether the inbreeding effect on litter size traits depends on the pattern of its accumulation over time. The lines are being selected for litter size at weaning and are kept closed at the same selection nucleus under the same selection and management programme. The study considered 47 794 l and a pedigree of 14 622 animals. Some practices in mating and selection management allow an increase of the inbreeding coefficient lower than 0.01 per generation in these lines of around 25 males and 125 females. Their effective population size (Ne) was around 57.3, showing that the effect of selection, increasing the inbreeding, was counterbalanced by the management practices, intended to reduce the rate of inbreeding increase. The inbreeding of each individual was broken down into three components: old, intermediate and new inbreeding. The coefficients of regression of the old, intermediate and new inbreeding on total born (TB), number born alive (NBA) and number weaned (NW) per litter showed a decreasing trend from positive to negative values. Regression coefficients significantly different from zero were those for the old inbreeding on TB (6.79 ± 2.37) and NBA (5.92 ± 2.37). The contrast between the coefficients of regression between the old and new inbreeding were significant for the three litter size traits: 7.57 ± 1.72 for TB; 6.66 ± 1.73 for NBA and 5.13 ± 1.67 for NW. These results have been interpreted as the combined action of purging unfavourable genes and artificial selection favoured by the inbreeding throughout the generations of selection.     

Is the use of formulae a reliable way to predict the accuracy of genomic selection?

We studied four formulae used to predict the accuracy of genomic selection prior to genotyping. The objectives of our study were to investigate the impact of the parameters of each formula on the values of accuracy calculated using these formulae, and to check whether the accuracies reported in the literature are in agreement with the formulae. First, we computed the marginal distribution of accuracy (by integration) for each parameter of all four formulae: heritability h², reference population size T, number of markers M and number of effective segments in the genome M_e. Then, we collected 145 accuracies and corresponding parameters reported in 13 publications on genomic selection (mainly in dairy cattle), and performed analysis of variance to test the differences between observed and predicted accuracy with effects of formulae and parameters. The variation of accuracy for different values of each parameter indicated that two parameters, T and M_e, had a significant impact and that considerable differences existed between the formulae (mean accuracies differed by up to 0.20 point). The results of our meta‐analysis showed a big formula effect on the accuracies predicted using each formula, and also a significant effect of the value obtained for M_e calculated from N_e (effective population size). Each formula can therefore be demonstrated to be optimal depending on the assumption used for M_e. In conclusion, no rules can be applied to predict the reliability of genomic selection using these formulae.     

Use of genomic recursions and algorithm for proven and young animals for single‐step genomic BLUP analyses – a simulation study

The purpose of this study was to examine accuracy of genomic selection via single‐step genomic BLUP (ssGBLUP) when the direct inverse of the genomic relationship matrix ( G ) is replaced by an approximation of G ^?1 based on recursions for young genotyped animals conditioned on a subset of proven animals, termed algorithm for proven and young animals (APY). With the efficient implementation, this algorithm has a cubic cost with proven animals and linear with young animals. Ten duplicate data sets mimicking a dairy cattle population were simulated. In a first scenario, genomic information for 20k genotyped bulls, divided in 7k proven and 13k young bulls, was generated for each replicate. In a second scenario, 5k genotyped cows with phenotypes were included in the analysis as young animals. Accuracies (average for the 10 replicates) in regular EBV were 0.72 and 0.34 for proven and young animals, respectively. When genomic information was included, they increased to 0.75 and 0.50. No differences between genomic EBV (GEBV) obtained with the regular G ^?1 and the approximated G ^?1 via the recursive method were observed. In the second scenario, accuracies in GEBV (0.76, 0.51 and 0.59 for proven bulls, young males and young females, respectively) were also higher than those in EBV (0.72, 0.35 and 0.49). Again, no differences between GEBV with regular G ^?1 and with recursions were observed. With the recursive algorithm, the number of iterations to achieve convergence was reduced from 227 to 206 in the first scenario and from 232 to 209 in the second scenario. Cows can be treated as young animals in APY without reducing the accuracy. The proposed algorithm can be implemented to reduce computing costs and to overcome current limitations on the number of genotyped animals in the ssGBLUP method.     

Genomic best linear unbiased prediction method reflecting the degree of linkage disequilibrium

The degree of linkage disequilibrium (LD) between markers differs depending on the location of the genome; this difference biases genetic evaluation by genomic best linear unbiased prediction (GBLUP). To correct this bias, we used three GBLUP methods reflecting the degree of LD (GBLUP‐LD). In the three GBLUP‐LD methods, genomic relationship matrices were conducted from single nucleotide polymorphism markers weighted according to local LD levels. The predictive abilities of GBLUP‐LD were investigated by estimating variance components and assessing the accuracies of estimated breeding values using simulation data. When quantitative trait loci (QTL) were located at weak LD regions, the predictive abilities of the three GBLUP‐LD methods were superior to those of GBLUP and Bayesian lasso except when the number of QTL was small. In particular, the superiority of GBLUP‐LD increased with decreasing trait heritability. The rates of QTL at weak LD regions would increase when selection by GBLUP continues; this consequently decreases the predictive ability of GBLUP. Thus, the GBLUP‐LD could be applicable for populations selected by GBLUP for a long time. However, if QTL were located at strong LD regions, the accuracies of three GBLUP‐LD methods were lower than GBLUP and Bayesian lasso.     

天然林林木空间格局的随机性检验和调整

通过对帽儿山林场101块固定标准地的林木空间分布格局的检验发现,对天然林而言空间格局以随机格局和均匀格局为主。集聚格局一般是由林窗引起的,而有林窗不一定都是集聚格局,但择伐过程中如果人为扩大林窗则会使随机格局变为集聚格局。在择伐设计中适当保留林窗周围的林木,一般能避免对原有空间格局的改变。     

林木无性繁殖及其在林业生产中的应用进展

在对无性繁殖的特点进行总结的基础上，综述了无性繁殖在无性系林业、林木遗传改良、林木复幼等方面应用的最新进展。     

浙南地区杉木杂交组合再选择的研究

对4片定植于浙江和江西两地的全同胞试验林进行调查,评选出的若干优良杂交组合,进行重复制种和区域 化造林试验。于2001年11月份调查了遂昌试点3年生的杉木全同胞试验林,测定树高,地径,轮盘数,冠幅和成活 率5个性状,经过方差分析,方差一协方差分析和综合指数分析,初步评出9702(龙15×1339);9706(1419×1339); 9703(龙15×闽33)3个全同胞家系,其表聊突出,3年生树高超过223 cm,同时适应性也强,造林成活率在97．9％ 以上,值得在浙江省南部类似地区推广应用。     

杨树对杨干象抗性选择的研究

在杨干象危害80％以上的人工杨树林内,以抗杨干象、速生等综合指标,选出了抗虫优树。对3年生优树后代室内外人工接种和自然感染的结果表明:106号优树比周围3株优势水平均胸径高9％,树高高7％,材积高61％,室内接种其诱虫数、取食孔数都低;野外接种,虫株率低40％以上,危害指数低69％;自然感染的虫株率低54％,危害指数低50％以上。优树生长量较对照原种中东杨(Populus berolinensis)高得多,但低于小黑杨(P.×xiaohei)。其抗虫性与树皮内含物果糖和酚酸含量有关。     

桉树种间杂种的比较和选择研究

对 13个尾叶桉×细叶桉杂种、10个尾叶桉×赤桉杂种、1个尾叶桉× (尾叶桉×细叶桉 )杂种、1个尾叶桉种内控制授粉子代、5个尾叶桉母本的自由授粉子代和 1个尾叶桉×细叶桉F1自由授粉子代的比较研究表明 ,家系间树高、胸径和材积的差异均达 0 0 1显著水平。选择出优良杂种和子代 14个以及优良单株 12株。优良单株可以作为无性系比较试验的材料 ,优良杂种的优良单株可以用于建立下一世代的育种群体。这为优良杂种单株的无性系化和进一步的桉树杂交育种提供了有效的遗传材料。