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, BayesCπ, 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.

中文翻译：

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基因组预测应用于高生物量高粱用于生物能源生产。

能源成本的不断增加以及石油和天然气储量的有限导致了开发可再生能源替代燃料的需求。由于其非生物胁迫耐受性和每年种植，高生物量高粱（Sorghum bicolor L. Moench）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，可以通过实现早期选择和缩短育种周期持续时间来重组植物育种计划。这项工作旨在通过 200 个高粱基因型的基因组选择模型预测育种价值，其中包括地方品种和来自生物质和糖精群体的育种系。根据育种目的，这些基因型分为两个子组。我们评估了以下表型生物量性状：开花天数、植物高度、新鲜和干物质产量以及纤维、纤维素、半纤维素和木质素比例。通过测序进行基因分型产生了超过 258,000 个单核苷酸多态性标记，揭示了子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、BayesCπ、BayesLasso、Bayes Ridge Regression 和随机回归最佳线性无偏预测器。由此产生的预测能力在不同模型之间变化不大，但在特征之间变化很大。不同的预测场景显示了使用子面板和年份之间的基因组选择结果的潜力，尽管环境相互作用的基因型会对准确性产生负面影响。对标记预测效应进行的功能富集分析表明了一些有趣的关联，有可能揭示与所研究的数量性状相关的生物过程。这项工作表明基因组选择可以成功应用于生物质高粱育种计划。