DataSheet_1_Genomic Prediction of Agronomic Traits in Common Bean (Phaseolus vulgaris L.) Under Environmental Stress.pdf

In plant and animal breeding, genomic prediction models are established to select new lines based on genomic data, without the need for laborious phenotyping. Prediction models can be trained on recent or historic phenotypic data and increasingly available genotypic data. This enables the adoption of genomic selection also in under-used legume crops such as common bean. Beans are an important staple food in the tropics and mainly grown by smallholders under limiting environmental conditions such as drought or low soil fertility. Therefore, genotype-by-environment interactions (G × E) are an important consideration when developing new bean varieties. However, G × E are often not considered in genomic prediction models nor are these models implemented in current bean breeding programs. Here we show the prediction abilities of four agronomic traits in common bean under various environmental stresses based on twelve field trials. The dataset includes 481 elite breeding lines characterized by 5,820 SNP markers. Prediction abilities over all twelve trials ranged between 0.6 and 0.8 for yield and days to maturity, respectively, predicting new lines into new seasons. In all four evaluated traits, the prediction abilities reached about 50–80% of the maximum accuracies given by phenotypic correlations and heritability. Predictions under drought and low phosphorus stress were up to 10 and 20% improved when G × E were included in the model, respectively. Our results demonstrate the potential of genomic selection to increase the genetic gain in common bean breeding. Prediction abilities improved when more phenotypic data was available and G × E could be accounted for. Furthermore, the developed models allowed us to predict genotypic performance under different environmental stresses. This will be a key factor in the development of common bean varieties adapted to future challenging conditions.

在植物与动物育种领域，基于基因组数据的预测模型被建立起来，以选育新种系，无需进行繁琐的表型鉴定。这些预测模型可以基于近期或历史表型数据以及日益可得的基因型数据进行训练。这使基因组选择在诸如普通豆类等利用率较低的豆科作物中得以应用。豆类是热带地区的重要主食，主要在限制性环境条件下，如干旱或土壤肥力低下时，由小农户种植。因此，基因型与环境相互作用（G×E）在培育新豆种时是一个重要的考虑因素。然而，G×E往往未被纳入基因组预测模型中，这些模型也未在当前的豆类育种计划中得到实施。在本研究中，我们展示了基于十二个田间试验，在普通豆类中针对四种农业性状在不同环境压力下的预测能力。数据集包括481个由5,820个SNP标记表征的精英育种线。在所有十二个试验中，产量和成熟天数预测能力分别介于0.6和0.8之间，预测新种系进入新季节。在所有四个评估的性状中，预测能力达到了表型相关性和遗传力给出的最大准确度的50-80%。在干旱和低磷胁迫下，当将G×E纳入模型时，预测能力分别提高了10%和20%。我们的结果表明，基因组选择在普通豆类育种中提高遗传增益的潜力。当可用的表型数据更多且G×E可被考虑时，预测能力得到了提升。此外，开发的模型使我们能够预测在不同环境压力下的基因型表现。这将成为适应未来挑战性条件的普通豆类品种开发的关键因素。