Genomic prediction applied to high-biomass sorghum for bioenergy production
Genomic prediction applied to high-biomass sorghum for bioenergy production
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DOI：
10.1007/s11032-018-0802-5
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发表时间：
2018-04-01
影响因子：
3.1
通讯作者：
Alves Margarido, Gabriel Rodrigues
中科院分区：
文献类型：
--
作者：
de Oliveira, Amanda Avelar;Pastina, Maria Marta;Alves Margarido, Gabriel Rodrigues
能源成本的上升和有限的石油和天然气储量使得人们需要从可再生能源中开发替代燃料。高生物量高粱（sorghum bicolor L. Moench）由于其耐非生物胁迫和一年生栽培的特点，显示出作为生物能源作物的潜力。基因组选择是加速遗传增益的有用工具，可以通过早期选择和缩短育种周期来重组植物育种计划。本研究旨在通过基因组选择模型预测200个高粱基因型的育种价值，这些基因型包括地方品种和来自生物质和糖精类群的选育品系。这些基因型根据育种目的分为两个亚组。我们评估了以下表型生物量性状：开花天数、株高、鲜物质和干物质产量、纤维、纤维素、半纤维素和木质素比例。基因分型测序得到了超过258,000个单核苷酸多态性标记，揭示了亚面板之间的群体结构。然后，我们拟合并比较了基因组选择模型BayesA、BayesB、BayesC pi、BayesLasso、Bayes Ridge回归和随机回归最佳线性无偏预测器。所得的预测能力在不同模型之间差别不大，但在性状之间差别很大。不同情景的预测显示了使用不同亚组和年份的基因组选择结果的潜力，尽管环境相互作用的基因型对准确性有负面影响。利用标记预测效应进行的功能富集分析显示了一些有趣的关联，有可能揭示与所研究的数量性状相关的生物学过程。本研究表明，基因组选择可以成功地应用于生物质高粱育种计划。
The increasing cost of energy and finite oil and gas reserves have created a need to develop alternative fuels from renewable sources. Due to its abiotic stress tolerance and annual cultivation, high-biomass sorghum (Sorghum bicolor L. Moench) shows potential as a bioenergy crop. Genomic selection is a useful tool for accelerating genetic gains and could restructure plant breeding programs by enabling early selection and reducing breeding cycle duration. This work aimed at predicting breeding values via genomic selection models for 200 sorghum genotypes comprising landrace accessions and breeding lines from biomass and saccharine groups. These genotypes were divided into two sub-panels, according to breeding purpose. We evaluated the following phenotypic biomass traits: days to flowering, plant height, fresh and dry matter yield, and fiber, cellulose, hemicellulose, and lignin proportions. Genotyping by sequencing yielded more than 258,000 single-nucleotide polymorphism markers, which revealed population structure between subpanels. We then fitted and compared genomic selection models BayesA, BayesB, BayesC pi, BayesLasso, Bayes Ridge Regression and random regression best linear unbiased predictor. The resulting predictive abilities varied little between the different models, but substantially between traits. Different scenarios of prediction showed the potential of using genomic selection results between sub-panels and years, although the genotype by environment interaction negatively affected accuracies. Functional enrichment analyses performed with the marker-predicted effects suggested several interesting associations, with potential for revealing biological processes relevant to the studied quantitative traits. This work shows that genomic selection can be successfully applied in biomass sorghum breeding programs.