Couto Evellyn G O, Chaves Saulo F S, Dias Kaio Olimpio G, Morales-Marroquín Jonathan A, Alves-Pereira Alessandro, Motoike Sérgio Yoshimitsu, Colombo Carlos Augusto, Zucchi Maria Imaculada
Deparment of Agronomy, Federal University of Viçosa, Viçosa, Brazil.
Deparment of General Biology, Federal University of Viçosa, Viçosa, Brazil.
Front Plant Sci. 2024 Sep 10;15:1441683. doi: 10.3389/fpls.2024.1441683. eCollection 2024.
Orphan perennial native species are gaining importance as sustainability in agriculture becomes crucial to mitigate climate change. Nevertheless, issues related to the undomesticated status and lack of improved germplasm impede the evolution of formal agricultural initiatives. - neotropical palm with potential for oil production - is an example. Breeding efforts can aid the species to reach its full potential and increase market competitiveness. Here, we present genomic information and training set optimization as alternatives to boost orphan perennial native species breeding using as an example. Furthermore, we compared three SNP calling methods and, for the first time, presented the prediction accuracies of three yield-related traits. We collected data for two years from 201 wild individuals. These trees were genotyped, and three references were used for SNP calling: the oil palm genome, sequencing, and the transcriptome. The traits analyzed were fruit dry mass (FDM), pulp dry mass (PDM), and pulp oil content (OC). We compared the predictive ability of GBLUP and BayesB models in cross- and real validation procedures. Afterwards, we tested several optimization criteria regarding consistency and the ability to provide the optimized training set that yielded less risk in both targeted and untargeted scenarios. Using the oil palm genome as a reference and GBLUP models had better results for the genomic prediction of FDM, OC, and PDM (prediction accuracies of 0.46, 0.45, and 0.39, respectively). Using the criteria PEV, r-score and core collection methodology provides risk-averse decisions. Training set optimization is an alternative to improve decision-making while leveraging genomic information as a cost-saving tool to accelerate plant domestication and breeding. The optimized training set can be used as a reference for the characterization of native species populations, aiding in decisions involving germplasm collection and construction of breeding populations.
随着农业可持续发展对于缓解气候变化至关重要,孤儿多年生本土物种正变得愈发重要。然而,与未驯化状态和缺乏改良种质相关的问题阻碍了正规农业举措的发展。以一种有产油潜力的新热带棕榈为例。育种工作有助于该物种充分发挥其潜力并提高市场竞争力。在此,我们以[具体物种]为例,介绍基因组信息和训练集优化,作为促进孤儿多年生本土物种育种的替代方法。此外,我们比较了三种单核苷酸多态性(SNP)检测方法,并首次展示了三个产量相关性状的预测准确性。我们从201个野生个体中收集了两年的数据。对这些树木进行了基因分型,并使用了三个参考序列进行SNP检测:油棕基因组、[具体测序方式]测序和[具体转录组]转录组。分析的性状包括果实干重(FDM)、果肉干重(PDM)和果肉含油率(OC)。我们在交叉验证和实际验证程序中比较了GBLUP和BayesB模型的预测能力。之后,我们测试了几个关于一致性的优化标准以及提供在目标和非目标场景中风险较小的优化训练集的能力。以油棕基因组作为参考,GBLUP模型在FDM、OC和PDM的基因组预测方面取得了更好的结果(预测准确性分别为0.46、0.45和0.39)。使用预测误差方差(PEV)、r分数和核心种质方法等标准可做出规避风险的决策。训练集优化是在利用基因组信息作为节省成本的工具来加速植物驯化和育种的同时改善决策的一种替代方法。优化后的训练集可作为本土物种种群特征描述的参考,有助于涉及种质收集和育种群体构建的决策。