Genomic prediction for meat and carcass traits in Nellore cattle using a Markov blanket algorithm

This study was carried out to evaluate the advantage of preselecting SNP markers using Markov blanket algorithm regarding the accuracy of genomic prediction for carcass and meat quality traits in Nellore cattle. This study considered 3675, 3680, 3660 and 524 records of rib eye area (REA), back fat thickness (BF), rump fat (RF), and Warner–Bratzler shear force (WBSF), respectively, from the Nellore Brazil Breeding Program. The animals have been genotyped using low-density SNP panel (30 k), and subsequently imputed for arrays with 777 k SNPs. Four Bayesian specifications of genomic regression models, namely Bayes A, Bayes B, Bayes Cπ and Bayesian Ridge Regression methods were compared in terms of prediction accuracy using a five folds cross-validation. Prediction accuracy for REA, BF and RF was all similar using the Bayesian Alphabet models, ranging from 0.75 to 0.95. For WBSF, the predictive ability was higher using Bayes B (0.47) than other methods (0.39 to 0.42). Although the prediction accuracies using Markov blanket of SNP markers were lower than those using all SNPs, for WBSF the relative gain was lower than 13%. With a subset of informative SNPs markers, identified using Markov blanket, probably, is possible to capture a large proportion of the genetic variance for WBSF. The development of low-density and customized arrays using Markov blanket might be cost-effective to perform a genomic selection for this trait, increasing the number of evaluated animals, improving the management decisions based on genomic information and applying genomic selection on a large scale.     

Genomic Prediction of Arsenic Tolerance and Grain Yield in Rice: Contribution of Trait-Specific Markers and Multi-Environment Models

Many rice-growing areas are affected by high concentrations of arsenic(As). Rice varieties that prevent As uptake and/or accumulation can mitigate As threats to human health. Genomic selection is known to facilitate rapid selection of superior genotypes for complex traits. We explored the predictive ability(PA) of genomic prediction with single-environment models, accounting or not for trait-specific markers, multi-environment models, and multi-trait and multi-environment models, using the genotypic(1600 K SNPs) and phenotypic(grain As content, grain yield and days to flowering) data of the Bengal and Assam Aus Panel. Under the base-line single-environment model, PA of up to 0.707 and 0.654 was obtained for grain yield and grain As content, respectively; the three prediction methods(Bayesian Lasso, genomic best linear unbiased prediction and reproducing kernel Hilbert spaces) were considered to perform similarly, and marker selection based on linkage disequilibrium allowed to reduce the number of SNP to 17 K, without negative effect on PA of genomic predictions. Single-environment models giving distinct weight to trait-specific markers in the genomic relationship matrix outperformed the base-line models up to 32%. Multi-environment models, accounting for genotype × environment interactions, and multi-trait and multi-environment models outperformed the base-line models by up to 47% and 61%, respectively. Among the multi-trait and multi-environment models, the Bayesian multi-output regressor stacking function obtained the highest predictive ability(0.831 for grain As) with much higher efficiency for computing time. These findings pave the way for breeding for As-tolerance in the progenies of biparental crosses involving members of the Bengal and Assam Aus Panel. Genomic prediction can also be applied to breeding for other complex traits under multiple environments.     

Hybrid breeding for biomass yield in winter triticale: II. Combining ability and hybrid prediction

Accurate hybrid prediction and knowledge about the relative contribution of general (GCA) and specific combining ability (SCA) are of utmost importance for efficient hybrid breeding. We therefore evaluated 91 triticale single-cross hybrids in field trials at seven environments for plant height, heading time, fresh biomass, dry matter content and dry biomass. Fresh and dry biomass showed the highest proportion (23%) of variance due to SCA. Prediction accuracies based on GCA were slightly higher than based on mid-parent values. Utilizing parental kinship information yielded the highest prediction accuracies when both parental lines have been tested in other hybrid combinations, but still moderate-to-low prediction accuracies for two untested parents. Thus, hybrid prediction for biomass traits in triticale is currently promising based on mid-parent values as emphasized by our simulation study, but can be expected to shift to GCA-based prediction with an increasing importance of GCA due to selection in hybrid breeding. Moreover, the performance of potential hybrids between newly developed lines can be predicted with moderate accuracy using genomic relationship information.     

猕猴桃溃疡病菌biovar 3群体MLVA分型技术的建立与应用

 丁香假单胞菌猕猴桃致病变种生物型3(Pseudomonas syringae pv. actinidiae biovar 3,Psa3)是猕猴桃溃疡病菌的世界流行群体,但仅在中国存在复杂的遗传多样性。开发适于Psa3群体分型的MLVA(multilocus variable-number tandem-repeat analysis)技术是探索中国Psa3起源与流行学特性的基础。本研究对7个Psa3菌株进行了全基因组测序,结合已公布的86个全基因组数据,进行比较分析发现,中国Psa3至少存在7个亚群;在各亚群间存在多态性的24个串联重复序列中,其中10个可以通过琼脂糖凝胶电泳区分开且变异指数合适,据此建立了适于Psa3的MLVA技术。采用该技术对分别来自贵州和陕西的62和9个Psa3菌株进行群体分型,分型结果与全基因组分析高度一致,证明该MLVA体系分型准确。MLVA分型结果表明:贵州主产区修文县Psa3有3个MLVA群体,其中亚群4的组内分化明显,代表最早发生的群体;而亚群 1和3的结构单一,且多在新果园发现,是新传入群体。总之,本研究建立了一套可用于Psa3群体分型的MLVA技术,将有助于解析中国各猕猴桃产区Psa3群体结构,以及探索中国Psa3的起源、传播和流行学特征。     

全基因组选择育种策略及在水产动物育种中的应用前景

全基因组选择的概念自2001年由Meuwissen等提出后便引起了动物育种工作者的广泛关注.目前,澳大利亚、新西兰、荷兰、美国的研究小组已经应用该方法进行了优质种牛的选择育种,并取得了很好的效果.此外在鸡和猪的选择育种中也有该方法的应用,但在水产动物选育中尚未见该方法使用的报道.本文对“全基因组选择育种”的概念和提出背景进行了归纳,对全基因组选择育种的优势进行了阐述,并详细介绍了其具体的策略,总结了目前全基因组育种所广泛采用的方法以及取得的成果,旨在为该方法在水产动物育种方面的应用研究提供科学参考.     

基于不完全双列杂交设计的水稻农艺性状配合力基因组预测

[目的]在亲本一般配合力的基础上优选特殊配合力高的杂交种,是水稻杂种育种的关键。基因组选择基于覆盖全基因组的分子标记和样本的表型数据建立预测模型,实现对品种更加可靠的选择。[方法]本研究利用一组基于不完全双列杂交(NCII 设计)的水稻数据集,考查其多个农艺性状配合力的基因组预测能力。并比较了不同训练群体构建方法对杂交种表型预测能力的影响。[结果]8个农艺性状一般配合力的预测能力由其遗传率主导,从0.3888 到0.7367。杂交种特殊配合力的预测能力较低,但是直接预测杂交种的表型可以获得较高的预测能力。[结论]基因组预测水稻亲本一般配合力是有效的,能够帮助育种家实现对亲本的科学选择。如果要选配杂交种,直接预测杂交种表型是最有效的手段。此时让更多的亲本均衡地参与杂交种训练集的组配,有利于获得更高的预测能力。     

鱼糜制品中基因组DNA提取方法的比较

将分别采用普通酚-氯仿抽提法和试剂盒(柱吸附法)提取DNA的两种方法加以比较,以确定最适提取鱼糜制品DNA的方法.将鱼肉和鱼糜制品作为原料,用两种方法提取基因组DNA,利用分光光度计测定其A260与A280的吸光值,计算比率估计核酸的纯度,并利用琼脂糖凝胶电泳观察DNA片段在凝胶中的位置;并用线粒体16S rRNA基因PCR扩增产物鉴定所提取的基因组DNA.     

Assessment of EST- and genomic microsatellite markers for variety discrimination and genetic diversity studies in wheat

It is likely that in the near future sequence information from sequencing programmes and EST libraries will generate an abundance of genic microsatellite markers. This study is focused on the assessment of their likely impact and performance vis-à-vis their genomic counterparts. Microsatellites from two sources were used to assess the genetic diversity in 56 old and new varieties of bread wheat on the UK Recommended List. A set of 12 microsatellite markers generated from genomic libraries and 20 expressed sequence tag (EST)-derived microsatellites were used in the study, and the performance of both marker sets assessed. The EST-derived or genic microsatellites delivered fingerprints of superior quality, amplifying clear products with few stutter bands. Diversity levels as revealed bygenic microsatellites are similar to the few published results. The PIC values for the genic markers were generally lower than those calculated for the genomic microsatellites, though advantages of both marker classes for variety identification applications are discussed. This revised version was published online in July 2006 with corrections to the Cover Date.