Genomic selection (GS) is a marker‐based selection initially suggested for livestock breeding and is being encouraged for crop breeding. Several statistical models are used to implement GS; however, none have been tested for use in lentil (Lens culinaris Medik.) breeding. This study was conducted to compare the accuracy of different GS models and prediction scenarios based on empirical data and to make recommendations for designing genomic selection strategies for lentil breeding. We evaluated nine single‐trait (ST) models, two multiple‐trait (MT) models, and a model that incorporates genotype × environment interaction (GEI) using populations from a lentil diversity panel and two recombinant inbred lines (RILs). The lines in all populations were phenotyped for five phenological traits and genotyped using a custom exome capture assay. Within‐population, across‐population, and across‐environment genomic predictions were made. Prediction accuracy varied among the evaluated models, populations, prediction scenarios, and traits. Single‐trait models showed similar accuracy in the absence of large effect quantitative trait loci (QTL) but BayesB outperformed all models when there were QTL with relatively large effects. Models that accounted for GEI and MT‐GS models increased prediction accuracy for a low heritability trait by up to 66 and 14%, respectively. Moderate to high accuracies were obtained for within‐population (range of .36–.85) and across‐environment (range of .19–.89) predictions but across‐population prediction accuracy was very low. Results suggest that GS can be implemented in lentil breeding to make predictions within populations and across environments, but across‐population prediction should not be considered when the population size is small.

中文翻译：

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扁豆育种的基因组选择：经验证据

基因组选择（GS）是一种基于标记的选择，最初建议用于牲畜育种，现在正被鼓励用于作物育种。几种统计模型用于实施GS；但是，尚未对扁豆中的任何一种进行测试（Lens culinarisMedik。）繁殖。这项研究的目的是根据经验数据比较不同GS模型和预测方案的准确性，并为设计扁豆育种的基因组选择策略提供建议。我们评估了九个单性状（ST）模型，两个多性状（MT）模型以及使用小扁豆多样性评估小组和两个重组自交系（RIL）的种群整合基因型×环境相互作用（GEI）的模型。对所有种群的品系进行五种物候特征的表型分析，并使用定制的外显子组捕获分析进行基因分型。进行了种群内，种群间和跨环境的基因组预测。预测准确性在所评估的模型，总体，预测方案和特征之间有所不同。在没有大效应数量性状基因座（QTL）的情况下，单性状模型显示出相似的准确性，但是当存在相对大效应的QTL时，BayesB优于所有模型。解释GEI和MT-GS模型的模型将低遗传性状的预测准确性分别提高了66％和14％。人群内（0.36至.85的范围）和跨环境（0.19至.89的范围）的预测获得了中度到高准确度，但是跨人群的预测准确性非常低。结果表明，可以在小扁豆育种中实施GS，以在种群内和跨环境进行预测，但是当种群较小时，不应考虑跨种群预测。解释GEI和MT-GS模型的模型将低遗传性状的预测准确性分别提高了66％和14％。对于人群内（0.36至.85的范围）和跨环境（0.19至.89的范围）的预测，获得了中等到较高的准确度，但是跨人群的预测准确性非常低。结果表明，可以在小扁豆育种中实施GS，以在种群内和跨环境进行预测，但是当种群较小时，不应考虑跨种群预测。解释GEI和MT-GS模型的模型将低遗传性状的预测准确性分别提高了66％和14％。人群内（0.36至.85的范围）和跨环境（0.19至.89的范围）的预测获得了中度到高准确度，但是跨人群的预测准确性非常低。结果表明，可以在小扁豆育种中实施GS，以在种群内和跨环境进行预测，但是当种群较小时，不应考虑跨种群预测。89）预测，但跨人群预测的准确性非常低。结果表明，可以在小扁豆育种中实施GS，以在种群内和跨环境进行预测，但是当种群较小时，不应考虑跨种群预测。89）预测，但跨人群预测的准确性很低。结果表明，可以在小扁豆育种中实施GS，以在种群内和跨环境进行预测，但是当种群较小时，不应考虑跨种群预测。