Department of Agronomy, University of Wisconsin-Madison, Madison, WI 53706
Great Lakes Bioenergy Research Center, Michigan State University, East Lansing, MI 48824.
G3 (Bethesda). 2019 Jun 5;9(6):1921-1931. doi: 10.1534/g3.119.400094.
The lowland ecotype of switchgrass has generated considerable interest because of its higher biomass yield and late flowering characteristics compared to the upland ecotype. However, lowland ecotypes planted in northern latitudes exhibit very low winter survival. Implementation of genomic selection could potentially enhance switchgrass breeding for winter survival by reducing generation time while eliminating the dependence on weather. The objectives of this study were to assess the potential of genomic selection for winter survival in lowland switchgrass by combining multiple populations in the training set and applying the selected model in two independent testing datasets for validation. Marker data were generated using exome capture sequencing. Validation was conducted using (1) indirect indicators of winter adaptation based on geographic and climatic variables of accessions from different source locations and (2) winter survival estimates of the phenotype. The prediction accuracies were significantly higher when the training dataset comprising all populations was used in fivefold cross validation but its application was not useful in the independent validation dataset. Nevertheless, modeling for population heterogeneity improved the prediction accuracy to some extent but the genetic relationship between the training and validation populations was found to be more influential. The predicted winter survival of lowland switchgrass indicated latitudinal and longitudinal variability, with the northeast USA the region for most cold tolerant lowland populations. Our results suggested that GS could provide valuable opportunities for improving winter survival and accelerate the lowland switchgrass breeding programs toward the development of cold tolerant cultivars suitable for northern latitudes.
与旱地生态型相比,柳枝稷低地生态型具有更高的生物量产量和晚花特性,因此引起了广泛关注。然而,在高纬度地区种植的低地生态型柳枝稷冬季存活率非常低。基因组选择的实施有可能通过缩短世代时间并消除对天气的依赖,从而提高柳枝稷的冬季存活率育种。本研究的目的是通过在训练集中组合多个群体,并在两个独立的测试数据集中应用选择模型进行验证,评估基因组选择在柳枝稷低地生态型冬季存活率中的潜力。使用外显子捕获测序生成标记数据。验证是基于不同来源地点的品系的地理和气候变量的间接指标(1)和表型的冬季存活率估计值(2)进行的。当在五重交叉验证中使用包含所有群体的训练数据集时,预测准确性显著提高,但在独立验证数据集中的应用没有用处。尽管如此,对群体异质性的建模在一定程度上提高了预测准确性,但发现训练和验证群体之间的遗传关系更具影响力。预测的柳枝稷低地生态型冬季存活率表明存在纬度和经度的可变性,美国东北部是最耐寒的低地种群的区域。我们的研究结果表明,GS 可以为提高冬季存活率提供有价值的机会,并加速柳枝稷低地生态型的育种计划,开发适合高纬度地区的耐寒品种。