Azevedo Peixoto Leonardo de, Laviola Bruno Galvêas, Alves Alexandre Alonso, Rosado Tatiana Barbosa, Bhering Leonardo Lopes
Biology Department, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil.
Empresa Brasileira de Pesquisa Agropecuária, Embrapa Agroenergia, Parque Estação Biológica-PqEB s/n, Asa Norte, Brasília, Brazil.
PLoS One. 2017 Mar 15;12(3):e0173368. doi: 10.1371/journal.pone.0173368. eCollection 2017.
Genomic wide selection is a promising approach for improving the selection accuracy in plant breeding, particularly in species with long life cycles, such as Jatropha. Therefore, the objectives of this study were to estimate the genetic parameters for grain yield (GY) and the weight of 100 seeds (W100S) using restricted maximum likelihood (REML); to compare the performance of GWS methods to predict GY and W100S; and to estimate how many markers are needed to train the GWS model to obtain the maximum accuracy. Eight GWS models were compared in terms of predictive ability. The impact that the marker density had on the predictive ability was investigated using a varying number of markers, from 2 to 1,248. Because the genetic variance between evaluated genotypes was significant, it was possible to obtain selection gain. All of the GWS methods tested in this study can be used to predict GY and W100S in Jatropha. A training model fitted using 1,000 and 800 markers is sufficient to capture the maximum genetic variance and, consequently, maximum prediction ability of GY and W100S, respectively. This study demonstrated the applicability of genome-wide prediction to identify useful genetic sources of GY and W100S for Jatropha breeding. Further research is needed to confirm the applicability of the proposed approach to other complex traits.
全基因组选择是提高植物育种选择准确性的一种有前景的方法，特别是对于生命周期长的物种，如麻疯树。因此，本研究的目的是使用限制最大似然法（REML）估计籽粒产量（GY）和百粒重（W100S）的遗传参数；比较全基因组选择（GWS）方法预测GY和W100S的性能；并估计训练GWS模型以获得最大准确性所需的标记数量。比较了八个GWS模型的预测能力。使用从2到1248不等数量的标记研究了标记密度对预测能力的影响。由于评估基因型之间的遗传方差显著，因此有可能获得选择增益。本研究中测试的所有GWS方法均可用于预测麻疯树的GY和W100S。分别使用1000个和800个标记拟合的训练模型足以捕获最大遗传方差，从而分别捕获GY和W100S的最大预测能力。本研究证明了全基因组预测在识别麻疯树育种中GY和W100S有用遗传资源方面的适用性。需要进一步研究以确认所提出方法对其他复杂性状的适用性。
