家畜基因组选择研究进展

近年来,随着基因芯片技术的发展与育种技术的进步,动植物的基因组选择成为研究热点。在家畜育种中,基因组选择凭借其准确性高、世代间隔短和育种成本低等优势被应用于各种经济动物的种畜选择中。本文详细介绍了基因分型技术和基因组育种值估计方法(最小二乘法、RR-BLUP法、GBLUP法、ssGBLUP法、贝叶斯A法、贝叶斯B法等),并对这些育种方法选用的标记范围、准确性以及计算速度进行了比较,总结了我国和其他国家基因组选择在种畜选择中的应用情况及存在的问题,展望了目前国内外在基因组选择上的最新研究动态及进展,以期为其他育种工作者进一步了解基因组选择提供参考。     

植物全基因组选择技术的研究进展及其在玉米育种上的应用

全基因组选择技术通过全基因组中大量的单核苷酸多态性标记（SNP）和参照群体的表型数据建立 BLUP 模型估计出每一标记的育种值,称为估计育种值（GEBV）,然后仅利用同样的分子标记估计出后代个体育种值并进行选择。该文就近年来国内外有关影响基因组选择效率的主要因素——参考群体的类型与大小、模型的建立方法、标记的类型及其数目、性状遗传力,以及对基因组选择效率的影响等方面的研究进展进行综述,并介绍了全基因组选择技术在玉米育种上应用概况以及对未来的展望。     

基因组重排技术在酵母菌育种中的应用

基因组重排(genome shuffling)技术是在传统诱变育种的基础上与细胞原生质体融合技术相结合一种新兴微生物菌种改良手段,由于该技术高效的正向突变效率和频率,近年来被广泛应用于酵母菌种的选育和改良。本文主要对基因组重排技术在酵母菌育种中的应用进行了综述。     

一种新的线粒体基因组DNA捕获探针的制备及初步应用

目的:建立新的线粒体基因组DNA杂交捕获探针制备方法并用进行初步应用。方法:通过PCR技术扩增特异线粒体DNA片段,并与生物素偶联,最后与标记磁珠的亲和素混合获得捕获探针。并自行制备的线粒体基因组DNA文库捕获探针与肝癌全基因组测序文库进行液相杂交。分离捕获产物后PCR扩增并进行测序分析。结果:成功建立了线粒体基因组杂交捕获探针制备方法并成功分离线粒体基因组DNA;对测序数据的分析显示:90%以上测序数据来自线粒体基因组DNA,且覆盖率达到100%,且均一性良好。检测到的同质性变异位点数量和异质性变异位点数量与全基因组测序数据产生的结果接近(P=0.9152,P=0.8409)。结论:新方法制备的线粒体基因组DNA杂交捕获探针可以从全基因组文库中高效捕获线粒体基因组DNA测序文库。     

基因组选择及其应用

品种选育在农业生产中占有十分重要的地位,育种值估计是品种选育的核心。随着遗传标记的发展,尤其是高通量的基因分型技术,使得从基因组水平估计育种值成为可能,即基因组选择。文章将基因组选择的方法分为两类:一是基于估计等位基因效应来预测基因组估计育种值(GEBV),如最小二乘法,随机回归-最佳线性无偏预测(RR-BLUP)、Bayes、主成分分析等方法;二是基于遗传关系矩阵来预测GEBV,通过采用高通量标记构建个体间的遗传关系矩阵,然后用线性混合模型来预测育种值,即GBLUP法,并以这两种分类简要介绍了基因组选择各种方法的大致原理。影响基因组选择准确性的因素主要有标记类型和密度、单倍型长度、参考群体大小和标记-数量性状基因座(QTL)连锁不平衡(LD)大小等;在基因组选择的各种方法中,一般说来Bayes方法和GBLUP方法具有较高的准确性,最小二乘法最差;GBLUP计算速度快,能够将标记和系谱结合起来,因而比其他方法更具优势。尽管基因组选择取得了很大进展,但在理论方面还面临着一些挑战,如联合育种、长期选择的遗传进展及如何解析与性状有关和无关的标记等。基因组选择在一些动植物育种上已经开始应用,在人类遗传倾向预测和进化动力学研究中也有潜在的应用前景。基因组选择在个体间亲缘关系的量化上有了突破,比传统方法更加精确,因此,基因组选择将会是动植物育种史上革命性的事件。     

基因组育种值估计的贝叶斯方法

基因组育种值估计是基因组选择的重要环节,基因组育种值的准确性是基因组选择成功应用的关键,而其准确性在很大程度上取决于估计方法。目前研究和应用最多的基因组育种值估计方法是贝叶斯(Bayes)和最佳线性无偏预测(BLUP)两大类方法。文章系统介绍了目前已提出的各种Bayes方法,并总结了该类方法的估计效果和各方面的改进。模拟数据和实际数据研究结果都表明,Bayes类方法估计基因组育种值的准确性优于BLUP类方法,特别对于存在较大效应QTL的性状其优势更明显。由于Bayes方法的理论和计算过程相对复杂,目前其在实际育种中的运用不如BLUP类方法普遍,但随着快速算法的开发和计算机硬件的改进,计算问题有望得到解决;另外,随着对基因组和性状遗传结构研究的深入开展,能为Bayes方法提供更为准确的先验信息,从而使Bayes方法估计基因组育种值准确性的优势更加突出,应用将会更加广泛。     

二代测序技术在结核分枝杆菌研究中的应用进展

结核病是由结核分枝杆菌引起的全球第二大传染病。二代测序技术为从基因组水平研究结核分枝杆菌提供了重要的研究方法。本文从结核病流行病学、结核分枝杆菌耐药和进化及相关生物信息学等方面,介绍二代测序技术在结核分枝杆菌研究中的应用进展。     

基因组改组技术及其在微生物育种中的应用研究进展

Genome shuffling(基因组改组)技术是借助递归式原生质体融合策略对微生物基因组进行遗传改良的一种新兴微生物育种方法。自2002年首次被用来培育tylosin高产菌株以来,目前已为育种工作者广泛采用。对Genome shuffling技术的原理及最近的应用研究进展进行了综述,探讨了其局限性,并展望了其发展的趋势。     

CRISPR/Cas9基因组编辑技术在农业动物中的应用

CRISPR(Clustered regularly interspaced short palindromic repeats)/Cas(CRISPR associated proteins)是在细菌和古细菌中发现的一种用来抵御病毒或质粒入侵的获得性免疫系统.目前已发现的CRISPR/Cas系统包括Ⅰ,Ⅱ和Ⅲ型,其中Ⅱ型系统的组成较简单,由其改造成的CRISPR/Cas9技术已成为一种高效的基因组编辑工具.自2013年CRISPR/Cas9技术成功用于哺乳动物基因组定点编辑以来,应用该技术进行基因组编辑的报道呈现出爆发式的增长.农业动物不仅是重要的经济动物,也是人类疾病和生物医药研究的重要模式动物.本文综述了CRISPR/Cas9技术在农业动物中的研究和应用进展,简述了该技术的脱靶效应及减少脱靶的主要方法,并展望了该技术的应用前景.     

RAPD技术及其在动物遗传育种中的应用

ＲＡＰＤ技术是在ＰＣＲ基础上发展起来的一种ＤＮＡ多态性检测技术,已广泛应用于基因组研究的种个领域。本文概述了ＲＡＰＤ反应的原理、特点,总结了其在遗传多样性检测、亲缘关系鉴定、遗传连锁分析和数量性状的辅助标记选择等方面的应用,并肯定了ＲＡＰＤ在动物遗传户种领域的应用前景。     

基因组选择在猪杂交育种中的应用

基因组选择是指在全基因组范围内通过基因组中大量的标记信息估计出个体全基因组范围的育种值,可进一步提升育种效率和准确性,目前在猪纯繁育种中得到广泛应用。但有研究表明,现有的基因组选择方法在猪杂交育种上的应用效果并不理想,在跨群体条件下预测准确性极低。杂交作为养猪业中最为广泛的育种手段之一,通过结合基因组选择理论进一步提升猪的生产性能,具有重要的经济和研究价值。本文综述了基因组选择的发展及其在猪育种中的应用现状,并结合国内外猪杂交育种的方式,分析了目前基因组选择方法在猪杂交育种应用方面的不足,旨在为未来基因组选择在猪杂交育种中的合理应用提供参考。     

Economic evaluation of genomic selection in small ruminants: a sheep meat breeding program

Recent genomic evaluation studies using real data and predicting genetic gain by modeling breeding programs have reported moderate expected benefits from the replacement of classic selection schemes by genomic selection (GS) in small ruminants. The objectives of this study were to compare the cost, monetary genetic gain and economic efficiency of classic selection and GS schemes in the meat sheep industry. Deterministic methods were used to model selection based on multi-trait indices from a sheep meat breeding program. Decisional variables related to male selection candidates and progeny testing were optimized to maximize the annual monetary genetic gain (AMGG), that is, a weighted sum of meat and maternal traits annual genetic gains. For GS, a reference population of 2000 individuals was assumed and genomic information was available for evaluation of male candidates only. In the classic selection scheme, males breeding values were estimated from own and offspring phenotypes. In GS, different scenarios were considered, differing by the information used to select males (genomic only, genomic+own performance, genomic+offspring phenotypes). The results showed that all GS scenarios were associated with higher total variable costs than classic selection (if the cost of genotyping was 123 euros/animal). In terms of AMGG and economic returns, GS scenarios were found to be superior to classic selection only if genomic information was combined with their own meat phenotypes (GS-Pheno) or with their progeny test information. The predicted economic efficiency, defined as returns (proportional to number of expressions of AMGG in the nucleus and commercial flocks) minus total variable costs, showed that the best GS scenario (GS-Pheno) was up to 15% more efficient than classic selection. For all selection scenarios, optimization increased the overall AMGG, returns and economic efficiency. As a conclusion, our study shows that some forms of GS strategies are more advantageous than classic selection, provided that GS is already initiated (i.e. the initial reference population is available). Optimizing decisional variables of the classic selection scheme could be of greater benefit than including genomic information in optimized designs.     

Integrating genomic selection into dairy cattle breeding programmes: a review

Extensive genetic progress has been achieved in dairy cattle populations on many traits of economic importance because of efficient breeding programmes. Success of these programmes has relied on progeny testing of the best young males to accurately assess their genetic merit and hence their potential for breeding. Over the last few years, the integration of dense genomic information into statistical tools used to make selection decisions, commonly referred to as genomic selection, has enabled gains in predicting accuracy of breeding values for young animals without own performance. The possibility to select animals at an early stage allows defining new breeding strategies aimed at boosting genetic progress while reducing costs. The first objective of this article was to review methods used to model and optimize breeding schemes integrating genomic selection and to discuss their relative advantages and limitations. The second objective was to summarize the main results and perspectives on the use of genomic selection in practical breeding schemes, on the basis of the example of dairy cattle populations. Two main designs of breeding programmes integrating genomic selection were studied in dairy cattle. Genomic selection can be used either for pre-selecting males to be progeny tested or for selecting males to be used as active sires in the population. The first option produces moderate genetic gains without changing the structure of breeding programmes. The second option leads to large genetic gains, up to double those of conventional schemes because of a major reduction in the mean generation interval, but it requires greater changes in breeding programme structure. The literature suggests that genomic selection becomes more attractive when it is coupled with embryo transfer technologies to further increase selection intensity on the dam-to-sire pathway. The use of genomic information also offers new opportunities to improve preservation of genetic variation. However, recent simulation studies have shown that putting constraints on genomic inbreeding rates for defining optimal contributions of breeding animals could significantly reduce achievable genetic gain. Finally, the article summarizes the potential of genomic selection to include new traits in the breeding goal to meet societal demands regarding animal health and environmental efficiency in animal production.     

Congruence between selection on breeding values and farmers' selection criteria in sheep breeding under conventional nucleus breeding schemes

Designing breeding schemes suitable for smallholder livestock production systems in developing regions has hitherto been a challenge. The suggested schemes either do not address farmers' breeding goals (centralized station-based nucleus schemes) or yield slow genetic progress (village-based schemes). A new breeding scheme that integrates the merits of previously suggested schemes has been designed for Menz sheep improvement in Ethiopia. It involves selection based on breeding values in nucleus flocks to produce elite rams, a one-time only provision of improved rams to villagers and a follow-up village-based selection to sustain genetic progress in village flocks. Here, we assessed whether conventional selection of breeding rams based on breeding values for production traits, which is the practice in station-based nucleus flocks, meets farmers' breeding objectives. We also elicited determinants of farmers' ram choice. Low but significant correlations were found between rankings of rams based on farmers' selection criteria, estimated breeding values (EBV) and body weight (BW). Appearance traits (such as color and horn) and meat production traits (BW and linear size traits) significantly determined farmers' breeding ram choice. The results imply that conventional selection criteria based solely on EBV for production traits do not address farmers' trait preferences fully, but only partially. Thus, a two-stage selection procedure involving selection on breeding values in nucleus centers followed by farmers' selection among top- ranking candidate rams is recommended. This approach accommodates farmers' preferences and speeds up genetic progress in village-based selection. The Menz sheep scheme could be applied elsewhere with similar situations to transform conventional station-based nucleus breeding activities into participatory breeding programs.     

Genome-wide association and genomic selection in animal breeding

Results from genome-wide association studies in livestock, and humans, has lead to the conclusion that the effect of individual quantitative trait loci (QTL) on complex traits, such as yield, are likely to be small; therefore, a large number of QTL are necessary to explain genetic variation in these traits. Given this genetic architecture, gains from marker-assisted selection (MAS) programs using only a small number of DNA markers to trace a limited number of QTL is likely to be small. This has lead to the development of alternative technology for using the available dense single nucleotide polymorphism (SNP) information, called genomic selection. Genomic selection uses a genome-wide panel of dense markers so that all QTL are likely to be in linkage disequilibrium with at least one SNP. The genomic breeding values are predicted to be the sum of the effect of these SNPs across the entire genome. In dairy cattle breeding, the accuracy of genomic estimated breeding values (GEBV) that can be achieved and the fact that these are available early in life have lead to rapid adoption of the technology. Here, we discuss the design of experiments necessary to achieve accurate prediction of GEBV in future generations in terms of the number of markers necessary and the size of the reference population where marker effects are estimated. We also present a simple method for implementing genomic selection using a genomic relationship matrix. Future challenges discussed include using whole genome sequence data to improve the accuracy of genomic selection and management of inbreeding through genomic relationships.     

Integration of genomic information into sport horse breeding programs for optimization of accuracy of selection

Reliable selection criteria are required for young riding horses to increase genetic gain by increasing accuracy of selection and decreasing generation intervals. In this study, selection strategies incorporating genomic breeding values (GEBVs) were evaluated. Relevant stages of selection in sport horse breeding programs were analyzed by applying selection index theory. Results in terms of accuracies of indices (r_TI) and relative selection response indicated that information on single nucleotide polymorphism (SNP) genotypes considerably increases the accuracy of breeding values estimated for young horses without own or progeny performance. In a first scenario, the correlation between the breeding value estimated from the SNP genotype and the true breeding value (= accuracy of GEBV) was fixed to a relatively low value of r_mg = 0.5. For a low heritability trait (h² = 0.15), and an index for a young horse based only on information from both parents, additional genomic information doubles r_TI from 0.27 to 0.54. Including the conventional information source ‘own performance’ into the before mentioned index, additional SNP information increases r_TI by 40%. Thus, particularly with regard to traits of low heritability, genomic information can provide a tool for well-founded selection decisions early in life. In a further approach, different sources of breeding values (e.g. GEBV and estimated breeding values (EBVs) from different countries) were combined into an overall index when altering accuracies of EBVs and correlations between traits. In summary, we showed that genomic selection strategies have the potential to contribute to a substantial reduction in generation intervals in horse breeding programs.     

高通量测序技术在动植物研究领域中的应用

高通量测序是核酸测序研究的一次革命性技术创新, 该技术以极低的单碱基测序成本和超高的数据产出量为特征, 为基因组学和后基因组学研究带来了新的科研方法和解决方案. 在动植物研究领域, 高通量测序引领了一次具有里程碑意义的科学研究模式革新, 科研人员可利用该技术在基因组、转录组和表观基因组等领域展开多层次多方面多水平研究. 本文就高通量测序技术应用于动植物基因组学和功能基因组学研究进展进行了系统阐述, 并对当前高通量测序技术的现状和热点及未来的发展趋势作了深入剖析和讨论.     

A global barley panel revealing genomic signatures of breeding in modern Australian cultivars

The future of plant cultivar improvement lies in the evaluation of genetic resources from currently available germplasm. Today’s gene pool of crop genetic diversity has been shaped during domestication and more recently by breeding. Recent efforts in plant breeding have been aimed at developing new and improved varieties from poorly adapted crops to suit local environments. However, the impact of these breeding efforts is poorly understood. Here, we assess the contributions of both historical and recent breeding efforts to local adaptation and crop improvement in a global barley panel by analysing the distribution of genetic variants with respect to geographic region or historical breeding category. By tracing the impact that breeding had on the genetic diversity of Hordeum vulgare (barley) released in Australia, where the history of barley production is relatively young, we identify 69 candidate regions within 922 genes that were under selection pressure. We also show that modern Australian barley varieties exhibit 12% higher genetic diversity than historical cultivars. Finally, field-trialling and phenotyping for agriculturally relevant traits across a diverse range of Australian environments suggests that genomic regions under strong breeding selection and their candidate genes are closely associated with key agronomic traits. In conclusion, our combined data set and germplasm collection provide a rich source of genetic diversity that can be applied to understanding and improving environmental adaptation and enhanced yields.     

Goals and hurdles for a successful implementation of genomic selection in breeding programme for selected annual and perennial crops

Genomic Selection is an important topic in quantitative genetics and breeding. Not only does it allow the full use of current molecular genetic technologies, it stimulates also the development of new methods and models. Genomic selection, if fully implemented in commercial farming, should have a major impact on the productivity of various agricultural systems. But suggested approaches need to be applicable in commercial breeding populations. Many of the published research studies focus on methodologies. We conclude from the reviewed publications, that a stronger focus on strategies for the implementation of genomic selection in advanced breeding lines, introduction of new varieties, hybrids or multi-line crosses is needed. Efforts to find solutions for a better prediction and integration of environmental influences need to continue within applied breeding schemes. Goals of the implementation of genomic selection into crop breeding should be carefully defined and crop breeders in the private sector will play a substantial part in the decision-making process. However, the lack of published results from studies within, or in collaboration with, private companies diminishes the knowledge on the status of genomic selection within applied breeding programmes. Studies on the implementation of genomic selection in plant breeding need to evaluate models and methods with an enhanced emphasis on population-specific requirements and production environments. Adaptation of methods to breeding schemes or changes to breeding programmes for a better integration of genomic selection strategies are needed across species. More openness with a continuous exchange will contribute to successes.