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首页> 外文期刊>PLoS One >Advancing crested wheatgrass [ Agropyron cristatum (L.) Gaertn.] breeding through genotyping-by-sequencing and genomic selection
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Advancing crested wheatgrass [ Agropyron cristatum (L.) Gaertn.] breeding through genotyping-by-sequencing and genomic selection

机译:通过逐个测序和基因组选择,推进冠状鸟草[Agropyron Cristatum(L.)Gaertn]育种

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摘要

Crested wheatgrass [ Agropyron cristatum (L.) Gaertn.] provides high quality, highly palatable forage for early season grazing. Genetic improvement of crested wheatgrass has been challenged by its complex genome, outcrossing nature, long breeding cycle, and lack of informative molecular markers. Genomic selection (GS) has potential for improving traits of perennial forage species, and genotyping-by-sequencing (GBS) has enabled the development of genome-wide markers in non-model polyploid plants. An attempt was made to explore the utility of GBS and GS in crested wheatgrass breeding. Sequencing and phenotyping 325 genotypes representing 10 diverse breeding lines were performed. Bioinformatics analysis identified 827, 3,616, 14,090 and 46,136 single nucleotide polymorphism markers at 20%, 30%, 40% and 50% missing marker levels, respectively. Four GS models (BayesA, BayesB, BayesCπ, and rrBLUP) were examined for the accuracy of predicting nine agro-morphological and three nutritive value traits. Moderate accuracy (0.20 to 0.32) was obtained for the prediction of heading days, leaf width, plant height, clump diameter, tillers per plant and early spring vigor for genotypes evaluated at Saskatoon, Canada. Similar accuracy (0.29 to 0.35) was obtained for predicting fall regrowth and plant height for genotypes evaluated at Swift Current, Canada. The Bayesian models displayed similar or higher accuracy than rrBLUP. These findings show the feasibility of GS application for a non-model species to advance plant breeding.
机译:凤头·麦克斯[Agropyron Cristatum(L.)Gaertn。]为初季放牧提供高品质,高度可口的牧草。冠状疱疹的遗传改善是由其复杂的基因组,令人满意的性质,长期繁殖循环和缺乏信息分子标记造成的挑战。基因组选择(GS)具有改善多年生饲料物种的特征的可能性,并且逐序列(GBS)能够在非模型多倍体植物中的基因组标记的发展。试图探索GBS和GS在凤头小雌性滋生中的效用。进行测序和表型三种基因型,代表10种不同的育种线。生物信息学分析分别鉴定了827,3,616,14,090和46,136个单核苷酸多态性标记物,分别为20%,30%,40%和50%缺失的标志物水平。检查了四个GS模型(Bayesa,Bayesb,Bayescπ和Rrblup),用于预测九个农业形态学和三种营养价值特征的准确性。获得中等精度(0.20至0.32),用于预测出标题,叶宽,植物高度,丛生,耕作,每株植物和早春活力,在加拿大萨斯卡通评估的基因型中进行基因型。获得类似的准确度(0.29至0.35),以预测在加拿大Swift Current评估的基因型的秋季再生和植物高度。贝叶斯型号比RRBLUP显示出类似或更高的精度。这些发现表明GS应用于非模型物种推进植物育种的可行性。

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