Role of polymerase β in DNA metabolism and genomic instability

The major role of DNA polymerase β was thought to be limited in its involvement in short patch base excision repair by removing 5’-deoxyribose phosphate and base insertion. However, the recent researches indicate that polymerase β might take part in a wide spectrum of DNA metabolism reactions, including long patch base excision repair, DNA replication, recombination, meiosis and transleisional DNA synthesis. Because of its wide and important cellular function, an inappropriate intracellular polymerase β level might be associated with genomic instability. Down-regulation or mutation of polymerase β is mutagenic due to deficient in DNA repair, while overexpression of this error-prone β polymerase might perturb the normal function of other accurate polymerases and cause genomic instability as well.     

国外生猪育种体系简析及对我国生猪育种的几点思考

选择是生猪育种的核心,而准确地选择和选种依赖于完善和成熟的育种体系。国际养猪发达国家(国际育种企业)对育种体系不断完善,长期进行生长、繁殖和肉品质等各类性状的表型测定,运用多场联合评估和基因组选择等方法不断提高遗传评估的准确性,不断挖掘新的育种性状,并进行测定、评估和选育,实现了种猪群体持续的遗传改良和综合性能的不断提升。本文对欧美发达国家(国际育种企业)的生猪育种技术体系进行了综述,以期为我国的生猪育种工作提供参考,并促进我国生猪种业的创新发展。     

Accuracy of genomic prediction using imputed whole‐genome sequence data in white layers

There is an increasing interest in using whole‐genome sequence data in genomic selection breeding programmes. Prediction of breeding values is expected to be more accurate when whole‐genome sequence is used, because the causal mutations are assumed to be in the data. We performed genomic prediction for the number of eggs in white layers using imputed whole‐genome resequence data including ~4.6 million SNPs. The prediction accuracies based on sequence data were compared with the accuracies from the 60 K SNP panel. Predictions were based on genomic best linear unbiased prediction (GBLUP) as well as a Bayesian variable selection model (BayesC). Moreover, the prediction accuracy from using different types of variants (synonymous, non‐synonymous and non‐coding SNPs) was evaluated. Genomic prediction using the 60 K SNP panel resulted in a prediction accuracy of 0.74 when GBLUP was applied. With sequence data, there was a small increase (~1%) in prediction accuracy over the 60 K genotypes. With both 60 K SNP panel and sequence data, GBLUP slightly outperformed BayesC in predicting the breeding values. Selection of SNPs more likely to affect the phenotype (i.e. non‐synonymous SNPs) did not improve the accuracy of genomic prediction. The fact that sequence data were based on imputation from a small number of sequenced animals may have limited the potential to improve the prediction accuracy. A small reference population (n = 1004) and possible exclusion of many causal SNPs during quality control can be other possible reasons for limited benefit of sequence data. We expect, however, that the limited improvement is because the 60 K SNP panel was already sufficiently dense to accurately determine the relationships between animals in our data.     

Molecular cloning of a Mycoplasma meleagridis-specific antigenic domain endowed with a serodiagnostic potential

A recombinant phage library harbouring Mycoplasma meleagridis (MM) genomic DNA fragments was generated in the bacteriophage lambda gt11 expression vector. The library was screened for expression of MM specific antigens with a polyclonal antiserum that had been preadsorbed with antigens of the most common unrelated avian mycoplasma species. A 49-amino acid antigenic domain unique to MM was isolated, expressed in Escherichia coli, and its serodiagnostic potential was demonstrated. An antiserum raised against this MM-specific antigenic domain recognized a cluster of seven membrane-associated MM proteins with molecular masses ranging from 34 to 75 kDa. Overall, this study resulted in the identification of a potent serodiagnostic tool and revealed the complex antigenic nature of MM.     

一种用于桑RAD-Seq高通量测序的基因组DNA提取方法

[目的]筛选一种能够得到高质量桑树基因组DNA的方法。[方法]采用天根试剂盒法(Plant Genomic DNA Kit离心柱型),并稍加改进,从11个桑种的新鲜嫩叶中提取DNA。[结果]该方法简单快速,不需要复杂仪器,且能有效地除去桑叶中的多糖、多酚、蛋白质等杂质,得到的DNA能够满足RAD-Seq高通量的测序要求。[结论]建立了一种用于桑RAD-Seq高通量测序的基因组DNA提取方法。     

快速大量提取甜菜DNA的新方法

以甜菜幼嫩叶片为材料,通过冷冻真空干燥将材料变成干粉,利用CTAB法对DNA提取程序进行了研究,建立了一种简单、快速的甜菜DNA提取程序,既节省时间,又可获得高质量的DNA,可以满足SSR及SRAP扩增的需要。     

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.     

Model averaging for genome‐enabled prediction with reproducing kernel Hilbert spaces: a case study with pig litter size and wheat yield

Predictive ability of yet‐to‐be observed litter size (pig) grain yield (wheat) records of several reproducing kernel Hilbert spaces (RKHS) regression models combining different number of Gaussian or t kernels was evaluated. Predictive performance was assessed as the average (over 50 replicates) predictive correlation in the testing set. Predictions from these models were combined using three different types of model averaging: (i) mean of predicted phenotypes obtained in each model, (ii) weighted average using mean squared error as weight or (iii) using the marginal likelihood as weight. (ii) and (iii) were obtained in a validation set with 5% of the data. Phenotypes consisted of 2598, 1604 and 1879 average litter size records from three commercial pig lines and wheat grain yield of 599 lines evaluated in four macro‐environments. SNPs from the PorcineSNP60 BeadChip and 1447 DArT markers were used as predictors for the pig and wheat data analyses, respectively. Gaussian and univariate t kernels led to same predictive performance. Multikernel RKHS regression models overcame shortcomings of single kernel models (increasing the predictive correlation of RKHS models by 0.05 where 3 Gaussian or t kernels were fitted in the RKHS models simultaneously). None of the proposed averaging strategies improved the predictive correlations attained with single models using multiple kernel fitting.