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1.
基因组育种值估计的贝叶斯方法   总被引:1,自引:0,他引:1  
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。  相似文献   

2.
《遗传》2017,(5)
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。  相似文献   

3.
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。  相似文献   

4.
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。  相似文献   

5.
《遗传》2017,(11)
基因组选择(genomic selection,GS)是畜禽经济性状遗传改良的重要方法。随着高密度SNP芯片和二代测序价格的下降,GS技术越来越多被应用于奶牛、猪、鸡等农业动物育种中。然而,降低全基因组SNP分型成本、提高基因组育种值(genomic estimated breeding value,GEBV)估计准确性仍然是GS研究的主要难题。本文从全基因组SNP分型策略和GEBV估计模型两个方面进行了综述,并对目前GS技术在主要畜禽品种中的应用现状进行了介绍,以期为GS在农业动物育种中的深入开展提供借鉴和参考。  相似文献   

6.
随着标记信息可以被越来越多的应用在家畜育种中,许多基因组选择(GS)方法使得育种工作者可以利用家畜早期的基因型数据提前对其进行选择。结合系谱、表型和基因型数据,我们可以缩短家畜的世代间隔,提高家畜遗传价值估计的准确性,进而加速其遗传改良速度。近年来,和广泛使用的多步基因组选择策略相比,业界更推崇基于在系谱关系矩阵中增加基因组信息的单步遗传评估方法。即使通常的基因组选择方法依然是多步方法,如GBLUP法,但是基于单步基因组模型进行的基因组评估能提供更为准确的结果。本研究的目的是引入单步贝叶斯方法,此方法可以用贝叶斯回归模型直接计算单核苷酸多态性(SNP)的效应,同时我们使用模拟方法评估模型的性能。研究结果显示:QTL数目对单步贝叶斯方法的准确性无影响,但其准确性受遗传力的影响。同时,其准确性随着测序个体数的增加而增加。我们也讨论了与使用单步贝叶斯方法相关的问题,并详细描述了一些与之有关的统计理论和算法问题。  相似文献   

7.
广义岭回归在家禽育种值估计中的应用   总被引:4,自引:1,他引:3  
讨论了岭回归方法应用于混合线性模型方程组中估计家禽育种值的方法,其实质是将传统的混合线性模型方程组理解为一种广义岭回归估计,为确定遗传参数的估计提供了一种途径;同时,以番鸭为例,考虑了一个性状和两个固定效应,采用广义岭回归法对公番鸭育种值进行了估计,并与最佳线性无偏预测法(BLUP 法)进行了比较,结果表明,广义岭回归方法和BLUP 法估计的育种值及其排序非常接近,其相关系数和秩相关系数分别达到了0.998~(**)和0.986~(**),且采用广义岭回归法预测的误差率低(在±10%以内);表明在混合线性模型方程组中使用广义岭回归估计动物育种值的方法具有可行性,并可省去估计遗传参数的过程,使BLUP 法在动物选育中的应用更具实用性.  相似文献   

8.
基因组选择(genomic selection, GS)是利用覆盖基因组的分子标记预测动物个体的估计育种值,可以提高选择的准确度和选择强度,缩短世代间隔,做到早选、准选,使动物育种发生了巨大变革。过去的10多年间,基因组选择技术应用于奶牛等动物的育种中,使种用动物的选择更为准确,遗传进展得到大幅提高。但基因组选择通常重视目标性状的遗传进展,而忽略了配种亲本个体间的遗传关系,因此也没有考虑到后代群体中近交程度的增加、遗传多样性的降低以及有害基因的纯合等问题,因此难以维持长期的遗传进展。2016年,一种具有可持续性的遗传选择方法被正式提出,称为基因组选配(genomicmating,GM)。该方法利用待选种用个体的基因组信息实施优化的选种和选配,可以控制群体近交的增长速率,实现长期且可持续的遗传进展。因此基因组选配方法比基因组选择的方法更适合于现代动物育种,尤其适用于地方品种的保护和遗传改良。本文综述了基因组选配的基本概念、方法和应用,并通过模拟的方法比较了6种选配方案的选择效果,旨在为动物育种方法的应用提供参考。  相似文献   

9.
Li C  Sun DX  Jiang L  Liu JF  Zhang Q  Zhang Y  Zhang SL 《遗传》2012,34(5):545-550
产奶性状是奶牛最重要的生产性状,随着平衡育种理念的提出和发展,繁殖性状、体型性状、健康性状和长寿性等功能性状也逐渐被重视并纳入育种规划中。鉴定产奶性状和功能性状主效基因或遗传标记并将之应用于奶牛标记辅助选择可望加快遗传进展。随着高密度SNP标记的高通量检测技术的发展,全基因组关联分析已成为鉴定畜禽重要经济性状基因的重要途径。文章对奶牛产奶性状和功能性状全基因组关联分析研究进展进行综述。  相似文献   

10.
本文介绍了估计阈性状育种值的贝叶斯方法的原理,演示了描述阈性状观察值、建立后验概率密度函数、以及导出非线性方程组的方法.并就这一估计方法的计算技术进行了讨论,针对动物遗传育种中方程组系数矩阵往往很大,超出计算机内存的情况,提出了不需要建立方程组,在数据上迭代求解的计算方法.本文还综述了这一非线性方法与线性方法在阈性状育种值估计上的比较.  相似文献   

11.
Genomic Selection (GS) is a new breeding method in which genome-wide markers are used to predict the breeding value of individuals in a breeding population. GS has been shown to improve breeding efficiency in dairy cattle and several crop plant species, and here we evaluate for the first time its efficacy for breeding inbred lines of rice. We performed a genome-wide association study (GWAS) in conjunction with five-fold GS cross-validation on a population of 363 elite breeding lines from the International Rice Research Institute''s (IRRI) irrigated rice breeding program and herein report the GS results. The population was genotyped with 73,147 markers using genotyping-by-sequencing. The training population, statistical method used to build the GS model, number of markers, and trait were varied to determine their effect on prediction accuracy. For all three traits, genomic prediction models outperformed prediction based on pedigree records alone. Prediction accuracies ranged from 0.31 and 0.34 for grain yield and plant height to 0.63 for flowering time. Analyses using subsets of the full marker set suggest that using one marker every 0.2 cM is sufficient for genomic selection in this collection of rice breeding materials. RR-BLUP was the best performing statistical method for grain yield where no large effect QTL were detected by GWAS, while for flowering time, where a single very large effect QTL was detected, the non-GS multiple linear regression method outperformed GS models. For plant height, in which four mid-sized QTL were identified by GWAS, random forest produced the most consistently accurate GS models. Our results suggest that GS, informed by GWAS interpretations of genetic architecture and population structure, could become an effective tool for increasing the efficiency of rice breeding as the costs of genotyping continue to decline.  相似文献   

12.

Background

Genomic selection or genome-wide selection (GS) has been highlighted as a new approach for marker-assisted selection (MAS) in recent years. GS is a form of MAS that selects favourable individuals based on genomic estimated breeding values. Previous studies have suggested the utility of GS, especially for capturing small-effect quantitative trait loci, but GS has not become a popular methodology in the field of plant breeding, possibly because there is insufficient information available on GS for practical use.

Scope

In this review, GS is discussed from a practical breeding viewpoint. Statistical approaches employed in GS are briefly described, before the recent progress in GS studies is surveyed. GS practices in plant breeding are then reviewed before future prospects are discussed.

Conclusions

Statistical concepts used in GS are discussed with genetic models and variance decomposition, heritability, breeding value and linear model. Recent progress in GS studies is reviewed with a focus on empirical studies. For the practice of GS in plant breeding, several specific points are discussed including linkage disequilibrium, feature of populations and genotyped markers and breeding scheme. Currently, GS is not perfect, but it is a potent, attractive and valuable approach for plant breeding. This method will be integrated into many practical breeding programmes in the near future with further advances and the maturing of its theory.Key words: Genomic selection, plant breeding, marker assisted selection, genetic model, linkage disequilibrium  相似文献   

13.
Crop improvement is a long-term, expensive institutional endeavor. Genomic selection (GS), which uses single nucleotide polymorphism (SNP) information to estimate genomic breeding values, has proven efficient to increasing genetic gain by accelerating the breeding process in animal breeding programs. As for crop improvement, with few exceptions, GS applicability remains in the evaluation of algorithm performance. In this study, we examined factors related to GS applicability in line development stage for grain yield using a hard red winter wheat (Triticum aestivum L.) doubled-haploid population. The performance of GS was evaluated in two consecutive years to predict grain yield. In general, the semi-parametric reproducing kernel Hilbert space prediction algorithm outperformed parametric genomic best linear unbiased prediction. For both parametric and semi-parametric algorithms, an upward bias in predictability was apparent in within-year cross-validation, suggesting the prerequisite of cross-year validation for a more reliable prediction. Adjusting the training population’s phenotype for genotype by environment effect had a positive impact on GS model’s predictive ability. Possibly due to marker redundancy, a selected subset of SNPs at an absolute pairwise correlation coefficient threshold value of 0.4 produced comparable results and reduced the computational burden of considering the full SNP set. Finally, in the context of an ongoing breeding and selection effort, the present study has provided a measure of confidence based on the deviation of line selection from GS results, supporting the implementation of GS in wheat variety development.  相似文献   

14.
Genomic selection (GS) has been implemented in animal and plant species, and is regarded as a useful tool for accelerating genetic gains. Varying levels of genomic prediction accuracy have been obtained in plants, depending on the prediction problem assessed and on several other factors, such as trait heritability, the relationship between the individuals to be predicted and those used to train the models for prediction, number of markers, sample size and genotype × environment interaction (GE). The main objective of this article is to describe the results of genomic prediction in International Maize and Wheat Improvement Center''s (CIMMYT''s) maize and wheat breeding programs, from the initial assessment of the predictive ability of different models using pedigree and marker information to the present, when methods for implementing GS in practical global maize and wheat breeding programs are being studied and investigated. Results show that pedigree (population structure) accounts for a sizeable proportion of the prediction accuracy when a global population is the prediction problem to be assessed. However, when the prediction uses unrelated populations to train the prediction equations, prediction accuracy becomes negligible. When genomic prediction includes modeling GE, an increase in prediction accuracy can be achieved by borrowing information from correlated environments. Several questions on how to incorporate GS into CIMMYT''s maize and wheat programs remain unanswered and subject to further investigation, for example, prediction within and between related bi-parental crosses. Further research on the quantification of breeding value components for GS in plant breeding populations is required.  相似文献   

15.
We developed a simulation study to test the efficiency of genomic selection (GS) in the case of Eucalyptus breeding. We simulated a recurrent selection scheme for clone production over four breeding cycles. Scenarios crossing broad sense heritabilities (H 2?=?0.6 and 0.1) and dominance to additive variance ratios (R?=?0.1; 0.5; and 1) were compared. GS was performed with 1,000 SNPs and 22 QTLs per Morgan and tested against phenotypic selection (PS) based on best linear unbiased prediction of parents and clones. When the training population was made up of the first cycle progeny tests and the candidate populations were the progeny tests of three successive cycles, GS accuracy decreased with breeding cycles (e.g., from 0.9 to 0.4 with H 2?=?0.6 and R?=?0.1), whereas PS presented constant performances (accuracy of 0.8 with H 2?=?0.6 and R?=?0.1). When the training population set was updated by associating data of previous cycles, GS accuracy was improved from 25 % to 418 %, especially with H 2?=?0.1. The GS model including dominance effects performed better in clone selection (genotypic value) when dominance effects were preponderant (R?=?1), heritability was high (H 2?=?0.6 and with an updated training set), but no improvement was detected for parent selection (breeding value). The genetic gains over cycles were lower with the GS method without updating the data set but, with an updated training set, were similar to PS. However, the genetic gain per unit time with GS was 1.5 to 3 times higher than with PS for breeding and clone populations. These results highlight the value of GS in Eucalyptus breeding.  相似文献   

16.
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.  相似文献   

17.
Genomic selection (GS) is of interest in breeding because of its potential for predicting the genetic value of individuals and increasing genetic gains per unit of time. To date, very few studies have reported empirical results of GS potential in the context of large population sizes and long breeding cycles such as for boreal trees. In this study, we assessed the effectiveness of marker-aided selection in an undomesticated white spruce (Picea glauca (Moench) Voss) population of large effective size using a GS approach. A discovery population of 1694 trees representative of 214 open-pollinated families from 43 natural populations was phenotyped for 12 wood and growth traits and genotyped for 6385 single-nucleotide polymorphisms (SNPs) mined in 2660 gene sequences. GS models were built to predict estimated breeding values using all the available SNPs or SNP subsets of the largest absolute effects, and they were validated using various cross-validation schemes. The accuracy of genomic estimated breeding values (GEBVs) varied from 0.327 to 0.435 when the training and the validation data sets shared half-sibs that were on average 90% of the accuracies achieved through traditionally estimated breeding values. The trend was also the same for validation across sites. As expected, the accuracy of GEBVs obtained after cross-validation with individuals of unknown relatedness was lower with about half of the accuracy achieved when half-sibs were present. We showed that with the marker densities used in the current study, predictions with low to moderate accuracy could be obtained within a large undomesticated population of related individuals, potentially resulting in larger gains per unit of time with GS than with the traditional approach.  相似文献   

18.
The genome sequence of apple (Malus×domestica Borkh.) was published more than a year ago, which helped develop an 8K SNP chip to assist in implementing genomic selection (GS). In apple breeding programmes, GS can be used to obtain genomic breeding values (GEBV) for choosing next-generation parents or selections for further testing as potential commercial cultivars at a very early stage. Thus GS has the potential to accelerate breeding efficiency significantly because of decreased generation interval or increased selection intensity. We evaluated the accuracy of GS in a population of 1120 seedlings generated from a factorial mating design of four females and two male parents. All seedlings were genotyped using an Illumina Infinium chip comprising 8,000 single nucleotide polymorphisms (SNPs), and were phenotyped for various fruit quality traits. Random-regression best liner unbiased prediction (RR-BLUP) and the Bayesian LASSO method were used to obtain GEBV, and compared using a cross-validation approach for their accuracy to predict unobserved BLUP-BV. Accuracies were very similar for both methods, varying from 0.70 to 0.90 for various fruit quality traits. The selection response per unit time using GS compared with the traditional BLUP-based selection were very high (>100%) especially for low-heritability traits. Genome-wide average estimated linkage disequilibrium (LD) between adjacent SNPs was 0.32, with a relatively slow decay of LD in the long range (r(2)?=?0.33 and 0.19 at 100 kb and 1,000 kb respectively), contributing to the higher accuracy of GS. Distribution of estimated SNP effects revealed involvement of large effect genes with likely pleiotropic effects. These results demonstrated that genomic selection is a credible alternative to conventional selection for fruit quality traits.  相似文献   

19.
DNA Fingerprints Applied to Gene Introgression in Breeding Programs   总被引:17,自引:5,他引:12       下载免费PDF全文
An application of DNA fingerprints (DFP) for gene introgression in breeding programs of both farm animals and plants is proposed. DFP loci, detectable by minisatellite probes, are extremely polymorphic. Individuals have unique patterns of DFP and thus can be selected for maximal genomic similarity to the recipient line, and minimal similarity to the donor line, using their DFP patterns as the criterion for similarity. This genomic selection (GS) can be performed at generations BC1, BC2 or both, and thus significantly reduce the required number of backcross generations in introgression breeding programs. The association between genomic and DFP similarity is demonstrated. Theoretical distributions and variances of the relative percentages of the donor and recipient genomes as the basis for the GS approach are presented.  相似文献   

20.
In genomic selection (GS), genome-wide SNP markers are used to generate genomic estimated breeding values for selection candidates. The application of GS in shellfish looks promising and has the potential to help in dealing with one of the main issues currently affecting Pacific oyster production worldwide, which is the ‘summer mortality syndrome’. This causes periodic mass mortality in farms worldwide and has mainly been attributed to a specific variant of the ostreid herpesvirus (OsHV-1). In the current study, we evaluated the potential of genomic selection for host resistance to OsHV-1 in Pacific oysters, and compared it with pedigree-based approaches. An OsHV-1 disease challenge was performed using an immersion-based virus exposure treatment for oysters for 7 days. A total of 768 samples were genotyped using the medium-density SNP array for oysters. A GWAS was performed for the survival trait using a GBLUP approach in blupf 90 software. Heritability ranged from 0.25 ± 0.05 to 0.37 ± 0.05 (mean ± SE) based on pedigree and genomic information respectively. Genomic prediction was more accurate than pedigree prediction, and SNP density reduction had little impact on prediction accuracy until marker densities dropped below approximately 500 SNPs. This demonstrates the potential for GS in Pacific oyster breeding programmes, and importantly, demonstrates that a low number of SNPs might suffice to obtain accurate genomic estimated breeding values, thus potentially making the implementation of GS more cost effective.  相似文献   

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