Replication Data for: Genomic prediction of agronomic traits in common bean under environmental stress

These datasets contain phenotypic and genotypic data of a panel of elite Andean breeding lines of common bean (Phaseolus vulgaris L.) from CIAT. This population has been tested in twelve yield trials carried out in Palmira and Darien (Colombia) between 2013 and 2018 to assess its performance under irrigated, drought and variable soil P conditions. These trials were laid out in the field with an alpha-lattice experimental design using two to three replicates, and a non-replicated trial in 2016. Meteorological data for these trials is also provided. Different agronomic traits were evaluated including Days to Flowering (DF), Days to Physiological Maturity (DPM), 100 seed weight (100SdW) and Yield (Yd). The agronomic performance of the population was modeled using linear mixed models with spatial correction. From these models, best linear unbiased estimators (BLUEs) and their corresponding standard errors (SE) were obtained. This population was genotyped by sequencing (GBS) using the ApeKI-based restriction digestion. The genotypic data is presented in a variant call format (VCF) file of 5,820 SNPs and 481 lines. The genotypic matrix was imputed using Beagle (v4.1). These datasets were used to test genomic prediction models on the panel in order to assess their prediction ability under different scenarios and parameter settings.

这些数据集包含来自CIAT的一组优良安第斯菜豆（Phaseolus vulgaris L.）育种系的表型和基因型数据。该群体于2013年至2018年间在哥伦比亚的Palmira和Darien两地开展了12次产量试验，以评估其在灌溉、干旱及土壤磷含量可变条件下的表现。这些田间试验采用α-格子试验设计（alpha-lattice experimental design），重复次数为2至3次，其中2016年的试验为非重复设计。同时提供了这些试验的气象数据。评估的农艺性状包括开花天数（DF）、生理成熟天数（DPM）、百粒重（100SdW）及产量（Yd）。采用带空间校正的线性混合模型（linear mixed models with spatial correction）对该群体的农艺表现进行建模，通过这些模型获得了最佳线性无偏估计量（best linear unbiased estimators, BLUEs）及其对应的标准误差（SE）。采用基于ApeKI酶切的基因型测序（genotyping by sequencing, GBS）技术对该群体进行基因分型，基因型数据以变异检测格式（variant call format, VCF）文件呈现，包含5820个单核苷酸多态性位点（SNPs）和481个育种系。基因型矩阵采用Beagle软件（v4.1版本）进行填充。这些数据集用于在该群体上测试基因组预测模型，以评估其在不同场景和参数设置下的预测能力。