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植物全基因组选择育种技术原理与研究进展 总被引：1，自引：0，他引：1
《分子植物育种》2020,(16)
优势杂交育种是选育高产优质新品种的有效育种途径,该方法需要在田间选配大量的杂交组合进行试验。而作物的主要经济性状如产量等大多是数量性状,该类性状由多基因控制,受环境影响大,常规的育种选择过程耗时很长且选择能力有限。随着基因组测序技术和计算机科学的快速发展,通过高密度的分子标记准确预测作物产量等复杂性状成为可能。植物全基因组选择育种技术通过训练群体收集表型数据和基因型数据,使用特定的模型估计分子标记效应值或个体育种值,再根据待测群体的基因型数据和模型拟合结果对待测群体的表型值进行预测。全基因组选择育种技术可以对目标性状进行预测和定向选择,减少育种工作量,显著缩短育种周期,提高育种效率,具有广阔的应用前景。本研究从植物优势杂交育种预测方法研究进展、全基因组选择育种原理与模型算法研究进展、模型预测能力验证方法研究进展、植物全基因组选择育种应用、全基因组选择育种的局限性和植物全基因组选择育种展望等6个方面阐述植物全基因组选择育种的发展现状。 相似文献
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目前, 基因组选择育种主要采用线性模型估计遗传育种值指导作物遗传育种的筛选过程, 但是生物体内的基因以及遗传位点的关系主要是复杂的非线性调控。本研究将人工神经网络技术应用到作物基因组选择育种中, 对现有的作物基因组选择育种模型进行优化, 建立了高效的作物基因组选择预测系统, 并与其他线性回归预测模型进行比较。通过分析小麦的育种数据发现, 基于人工神经网络的遗传育种估计效果优于其他线性回归预测模型, 预测育种值与实际育种值间的相关系数平均值达到0.6636, 相应的岭回归BLUP、贝叶斯线性回归模型和基于系谱信息的贝叶斯回归模型的预测能力分别为0.6422、0.6294和0.6573; 最优的预测效果达到0.8379, 远高于其他2种模型的最优结果。同时, 基于人工神经网络的基因组选择模型的预测效果稳定, 与传统的统计模型相近, 因此, 利用人工神经网络技术建立基因组选择是可行的。 相似文献
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单核苷酸多态性(SNP)在植物基因组中广泛存在,基于SNP的分子标记也正越来越广泛地应用到植物基因定位、图位克隆及分子标记辅助育种等方面。模式植物拟南芥和水稻的全基因组序列测定已经完成,拟南芥有两种生态型完成了全序列测定,水稻有两个品种完成了全序列测定。许多植物有来自不同品种或不同组织器官或生长发育阶段的大量的EST序列。这些序列是植物SNP开发的重要资源。利用生物信息学手段对全基因组序列或EST序列进行分析已经形成了许多SNP位点数据库,这些数据库的建立为基于SNP的基因功能研究及分子标记开发提供了宝贵的资源。本文对植物SNP位点开发涉及的数据库资源及已经形成的SNP位点数据库进行了总结,并讨论了将SNP位点转化成CAPS或dCAPS标记的方法和相应的工具软件。 相似文献
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CRISPR/Cas9系统是一项简单、高效的基因定点编辑技术,在植物遗传改良及作物良种选育等方面具有重要的应用价值。本研究主要介绍了CRISPR/Cas9的原理及构建方法,论述了近年来CRISPR/Cas9技术在植物基因功能及基因表达调控、植物基因组的定向编辑以及作物分子育种中的应用及研究进展,分析了该基因编辑系统的主要影响因素及优化改进方式,探讨了该系统在应用中的问题及解决途径并对今后发展方向进行了展望,为该技术在植物基因组定点编辑及作物遗传育种等领域研究提供参考。 相似文献
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逆境胁迫下植物 DNA甲基化及其在抗旱育种中的研究进展 总被引：2，自引：1，他引：1
DNA甲基化作为一种重要的表观遗传现象,通过多种甲基转移酶的作用,能够在不改变DNA序列的情况下调节植物基因组的功能。此外,DNA甲基化能够对多种环境刺激做出迅速的反应,帮助植物应对不同的环境胁迫。由于DNA甲基化的变异可以遗传给后代,这种类似于经典遗传学的特性使其为植物育种中的应用提供了可能。对植物DNA甲基化的特点和变异的发生以及DNA甲基化在植物多种逆境胁迫下的研究进展等方面进行了总结和综述,并探讨了DNA甲基化在植物抗旱性育种中的应用前景。在将来的研究中可利用DNA甲基化/去甲基化抑制剂处理创造突变材料,创造抗旱性新种质;同时深入开展植物DNA甲基化与抗旱机制研究,开发新型甲基化分子标记用于抗旱分子育种实践。 相似文献
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果树2n配子研究进展 总被引：1，自引：0，他引：1
植物在进化过程中能产生低频率的未减数分裂配子,即2n配子,它在果树育种方面发挥着重要的作用。本文详述了2n配子的主要研究概况、发生途径及影响因素,为其在植物育种、种质利用和种质创新等方面提供了重要的应用基础。同时,通过近年的研究成果,总结出诱导方法和人工分离纯化方法,不同品种可根据其不同的生理生化特性选择合适的方法。另外,也叙述了2n配子在果树方面的特殊应用潜力,即在有性多倍化、稳定传递杂合性、克服自交不亲和和克服胚乳平衡数障碍方面有其他育种方法无法比拟的优势。因此,2n配子育种将会是一种重要的育种途径。 相似文献
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花生含油量对单位面积产油量至关重要。该性状受多个微效基因控制,但可用的紧密连锁标记十分有限,传统的分子标记辅助选择育种准确性不高。全基因组选择作为一种新的育种方法,可实现对数量性状的早期预测;近红外光谱分析可对作物品质性状(如含油量等)进行无损检测。通过两者优势互补,建立花生含油量全基因组选择和近红外光谱筛选联合的育种技术,探讨影响花生含油量全基因组选择预测准确性的因素,为花生分子育种奠定理论基础。本研究以216个重组自交系为材料构建训练群体;分别以139、464和505株F2、F3和F4为材料构建育种群体;利用自主开发的“PeanutGBTS40K”液相芯片进行基因分型,开展含油量全基因组选择育种模型分析;通过联合全基因组选择和近红外光谱筛选技术,开展花生含油量性状的育种应用,并评价其育种效果。结果显示,对训练群体进行基因分型后,总共获得30,355个高质量SNPs,并用于11个全基因组预测的模型选择分析。含油量预测准确性最高的模型为rrBLUP,其次是randomforest和svmrbf。以重组自交系为预测群体,F... 相似文献
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日前,由中国农业科学院作物科学研究所牵头的973项目＂玉米产量和品质性状全基因组选择育种的基础研究＂启动会在京召开。此项目的实施将有望揭开玉米高产优质背后的基因密码,实现玉米种质创新和育种效率的突破。该项目首席科学家王国英,就玉米的常规育种和分子育种、全基因组选择育种进行了解读。 相似文献
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Hiroyoshi Iwata Mai F. Minamikawa Hiromi Kajiya-Kanegae Motoyuki Ishimori Takeshi Hayashi 《Breeding Science》2016,66(1):100-115
Recent advancements in genomic analysis technologies have opened up new avenues to promote the efficiency of plant breeding. Novel genomics-based approaches for plant breeding and genetics research, such as genome-wide association studies (GWAS) and genomic selection (GS), are useful, especially in fruit tree breeding. The breeding of fruit trees is hindered by their long generation time, large plant size, long juvenile phase, and the necessity to wait for the physiological maturity of the plant to assess the marketable product (fruit). In this article, we describe the potential of genomics-assisted breeding, which uses these novel genomics-based approaches, to break through these barriers in conventional fruit tree breeding. We first introduce the molecular marker systems and whole-genome sequence data that are available for fruit tree breeding. Next we introduce the statistical methods for biparental linkage and quantitative trait locus (QTL) mapping as well as GWAS and GS. We then review QTL mapping, GWAS, and GS studies conducted on fruit trees. We also review novel technologies for rapid generation advancement. Finally, we note the future prospects of genomics-assisted fruit tree breeding and problems that need to be overcome in the breeding. 相似文献
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Genomic selection employs genome‐wide marker data to predict genomic breeding values. In this study, a population consisting of 391 lines of elite winter oilseed rape derived from nine families was used to evaluate the prospects of genomic selection in rapeseed breeding. All lines have been phenotyped for six morphological, quality‐ and yield‐related traits and genotyped with genome‐wide SNP markers. We used ridge regression best linear unbiased prediction in combination with cross‐validation and obtained medium to high prediction accuracies for the studied traits. Our results illustrate that among‐family variance contributes to the prediction accuracy and can lead to an overestimation of the prospects of genomic selection within single segregating families. We also tested a scenario where estimation of effects was carried out without individuals from the family in which breeding values were predicted, which yielded lower but nevertheless attractive prediction accuracies. Taken together, our results suggest that genomic selection can be a valuable genomic approach for complex agronomic traits towards a knowledge‐based breeding in rapeseed. 相似文献
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Tobias Würschum Hans Peter Maurer Sigrid Weissmann Volker Hahn Willmar L. Leiser 《Plant Breeding》2017,136(2):230-236
Genomic prediction has emerged as a powerful genomic tool to assist breeding of complex traits. In this study, we employed a population of 647 triticale doubled haploid lines derived from four families to assess the potential of this approach for triticale breeding. All lines were phenotyped for grain yield, thousand‐kernel weight, biomass yield, plant height, frost tolerance and Fusarium head blight resistance. The obtained prediction accuracies were moderate to high and consisted to varying degrees of within‐ and among‐family variance, in line with the different degrees of phenotypic differences between family means. The prediction accuracy within individual families also varied with the genetic complexity of the traits and was generally highest based on effect estimation with lines from the respective family, whereas the prediction accuracy decreased with decreasing relatedness among the families. Taken together, our results illustrate the potential of genomic prediction to increase selection gain in triticale breeding, but the composition of the training set is of utmost importance, and consequently, the implementation of this approach in applied breeding programmes is not straightforward. 相似文献
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Hanna Haikka Timo Knürr Outi Manninen Leena Pietilä Mika Isolahti Esa Teperi Esa A. Mäntysaari Ismo Strandén 《Plant Breeding》2020,139(3):550-561
Genomic selection has been adopted in many plant breeding programmes. In this paper, we cover some aspects of information necessary before starting genomic selection. Spring oat and barley breeding data sets from commercial breeding programmes were studied using single, multitrait and trait-assisted models for predicting grain yield. Heritabilities were higher when estimated using multitrait models compared to single-trait models. However, no corresponding increase in prediction accuracy was observed in a cross-validation scenario. On the other hand, forward prediction showed a slight, but not significant, increase in accuracy of genomic estimated breeding values for breeding cohorts when a multitrait model was applied. When a correlated trait was used in a trait-assisted model, on average the accuracies increased by 9%–14% for oat and by 11%–28% for barley compared with a single-trait model. Overall, accuracies in forward validation varied between breeding cohorts and years for grain yield. Forward prediction accuracies for multiple cohorts and multiple years’ data are reported for oat for the first time. 相似文献
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Conventional and contemporary approaches for drought tolerance rice breeding: Progress and prospects
C Anilkumar Rameswar Prasad Sah Radha Beena Muhammed Azharudheen TP Awadesh Kumar Sasmita Behera NC Sunitha SK Pradhan KR Reshmi Raj Parameswaran C BC Marndi Anil Kumar Singh 《Plant Breeding》2023,142(4):418-438
Drought is becoming a major threat to rice farming across the globe owing to the depletion of water tables in rice-growing belts. Drought affects rice plants at multiple stages, causing damage at morphological and physio-biochemical levels, leading to severe losses that exceed losses from all other stresses. The amalgamation of conventional breeding methods with modern molecular biology tools and biometrical methods could help accelerate the genetic gain for drought tolerance in rice. Many drought-tolerance traits with genetic determinants have been identified and exploited for tolerance rice variety breeding. The integration of genome-wide association study and genomic selection tools with speed breeding shortened the breeding cycle and aided in rapid improvement of genetic gain. In this review, we emphasized the progress made through classical breeding as well as the limitations and usefulness of current genomic methods in improving drought tolerance. We briefly addressed methods for identifying genetic determinants for drought tolerance and deploying them through genomics-assisted breeding programmes to develop high-yielding drought-tolerant rice cultivars. 相似文献
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Torben Schulz‐Streeck Joseph O. Ogutu Andrés Gordillo Zivan Karaman Carsten Knaak Hans‐Peter Piepho 《Plant Breeding》2013,132(6):532-538
Genomic selection has been routinely implemented in plant breeding in two stages. The first stage usually omits the marker information and estimates adjusted means of genotypes across environments. The second stage uses the adjusted means to predict genomic breeding values. However, if the effects of markers vary substantially between different environments, it may be important to account for this variation for varieties adapted to different environments. Using two maize data sets, we investigated whether modelling the marker‐by‐environment interaction can improve the predictive ability of genomic selection relative to modelling genotype‐by‐environment interaction alone. Modelling the marker‐by‐environment interaction did not substantially increase the predictive ability relative to modelling only the genotype‐by‐environment interaction for the two tested data sets. Thus, genomic selection, carried out in a stagewise fashion, such that the marker information is omitted until the last stage of the process, may suffice for most practical purposes. Moreover, predictive ability did not reduce substantially even when the number of markers with consistent effects across environments used for genomic prediction was reduced to about 50. 相似文献
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Conventional genomic selection approaches use breeding values to evaluate individual plants or animals and to make selection decisions. Multiple variants of breeding values and selection approaches have been proposed, but they suffer two major limitations. First, selection decisions are not responsive to changes in time and resource availability. Second, selection decisions are not coordinated with related decisions such as mating and resource allocation. We present three new genomic selection approaches that attempt to address these two limitations, which were designed by engineering students in a class project at Iowa State University. Compared with previous approaches using the same data set from the literature, two of these engineering approaches were found to be comparable to the state of the art, and the third one significantly dominated all the previous approaches. 相似文献
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Wenxin Liu Tobias Würschum Hans P. Maurer Friedrich H. Longin Nicolas Ranc Hans P. Piepho Jochen C. Reif 《Plant Breeding》2013,132(1):99-106
Genomic selection (GS) is a promising alternative to marker‐assisted selection particularly for quantitative traits. In this study, we examined the prediction accuracy of genomic breeding values by using ridge regression best linear unbiased prediction in combination with fivefold cross‐validation based on empirical data of a commercial maize breeding programme. The empirical data is composed of 930 testcross progenies derived from 11 segregating families evaluated at six environments for grain yield and grain moisture. Accuracy to predict genomic breeding values was affected by the choice of the shrinkage parameter λ2, by unbalanced family size, by size of the training population and to a lower extent by the number of markers. Accuracy of genomic breeding values was high suggesting that the selection gain can be improved implementing GS in elite maize breeding programmes. 相似文献
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Maize is a commodity crop providing millions of people with food, feed, industrial raw material and economic opportunities. However, maize yields in Africa are relatively stagnant and low, at a mean of 1.7 t ha−1 compared with the global average of 6 t ha−1. The yield gap can be narrowed with accelerated and precision breeding strategies that are required to develop and deploy high-yielding and climate-resilient maize varieties. Genomic and phenotypic selections are complementary methods that offer opportunities for the speedy choice of contrasting parents and derived progenies for hybrid breeding and commercialization. Genomic selection (GS) will shorten the crop breeding cycle by identifying and tracking desirable genotypes and aid the timeous commercialization of market-preferred varieties. The technology, however, has not yet been fully embraced by most public and private breeding programmes, notably in Africa. This review aims to present the importance, current status, challenges and opportunities of GS to accelerate genetic gains for economic traits to speed up the breeding of high-yielding maize varieties. The first section summarizes genomic selection and the contemporary phenotypic selection and phenotyping platforms as a foundation for GS and trait integration in maize. This is followed by highlights on the reported genetic gains and progress through phenotypic selection and GS for grain yield and yield components. Training population development, genetic design and statistical models used in GS in maize breeding are discussed. Lastly, the review summarizes the challenges of GS, including prediction accuracy, and integrates GS with speed breeding, doubled haploid breeding and genome editing technologies to increase breeding efficiency and accelerate cultivar release. The paper will guide breeders in selection and trait introgression using GS to develop cultivars preferred by the marketplace. 相似文献
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This study compared three strategies to develop new recipients for quantitative trait loci (QTL) introgression (background recovery [BG], selective sweep [SS] and breeding value [BV]) in a short-term rice breeding programme (over five breeding cycles). Furthermore, we evaluated two different numbers of recipients (10 and 20) in the introgression process and how they influence the population performance and the QTL fixation over cycles. Finally, we used the International Rice Research Institute (IRRI) rice breeding framework as the model to perform the stochastic simulations. Each strategy was simulated and replicated 100 times. Regardless of the selection strategy used, the QTL introgression resulted in substantial penalties in yield performance. However, introducing fewer new parents to the augmentation process minimized this effect. Conversely, the time required to achieve fixation of target QTLs showed substantial differences, with selection for BV during augmentation outperforming other methods. Overall, the BV_10 strategy (10 parents selected based on genomic estimated BV) displayed the best trade-off between reduced penalty from introducing new QTLs with a reasonable speed at which those QTLs can achieve fixation over subsequent breeding cycles. 相似文献