Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart, Germany.
Deutsche Saatveredelung AG, Weissenburger Str. 5, 59557, Lippstadt, Germany.
Theor Appl Genet. 2017 Aug;130(8):1669-1683. doi: 10.1007/s00122-017-2917-1. Epub 2017 May 22.
Genomic prediction was evaluated in German winter barley breeding lines. In this material, prediction ability is strongly influenced by population structure and main determinant of prediction ability is the close genetic relatedness of the breeding material. To ensure breeding progress under changing environmental conditions the implementation and evaluation of new breeding methods is of crucial importance. Modern breeding approaches like genomic selection may significantly accelerate breeding progress. We assessed the potential of genomic prediction in a training population of 750 genotypes, consisting of multiple six-rowed winter barley (Hordeum vulgare L.) elite material families and old cultivars, which reflect the breeding history of barley in Germany. Crosses of parents selected from the training set were used to create a set of double-haploid families consisting of 750 genotypes. Those were used to confirm prediction ability estimates based on a cross-validation with the training set material using 11 different genomic prediction models. Population structure was inferred with dimensionality reduction methods like discriminant analysis of principle components and the influence of population structure on prediction ability was investigated. In addition to the size of the training set, marker density is of crucial importance for genomic prediction. We used genome-wide linkage disequilibrium and persistence of linkage phase as indicators to estimate that 11,203 evenly spaced markers are required to capture all QTL effects. Although a 9k SNP array does not contain a sufficient number of polymorphic markers for long-term genomic selection, we obtained fairly high prediction accuracies ranging from 0.31 to 0.71 for the traits earing, hectoliter weight, spikes per square meter, thousand kernel weight and yield and show that they result from the close genetic relatedness of the material. Our work contributes to designing long-term genetic prediction programs for barley breeding.
基因组预测在德国冬大麦育种系中进行了评估。在这种材料中，预测能力受群体结构的强烈影响，预测能力的主要决定因素是育种材料的密切遗传相关性。为了确保在不断变化的环境条件下取得育种进展，实施和评估新的育种方法至关重要。基因组选择等现代育种方法可能会显著加速育种进程。我们评估了 750 个基因型的训练群体中的基因组预测潜力，该群体由多个六棱冬大麦（Hordeum vulgare L.）优秀材料系和老品种组成，反映了德国大麦的育种历史。从训练集中选择的亲本的杂交用于创建一组由 750 个基因型组成的双单倍体家族。这些被用于通过使用 11 种不同的基因组预测模型对训练集材料进行交叉验证来确认预测能力估计。通过主成分判别分析等降维方法推断群体结构，并研究群体结构对预测能力的影响。除了训练集的大小外，标记密度对基因组预测至关重要。我们使用全基因组连锁不平衡和连锁相位的持久性作为指标来估计，需要 11,203 个均匀间隔的标记来捕获所有 QTL 效应。虽然 9k SNP 阵列不包含足够数量的多态性标记用于长期基因组选择，但我们获得了相当高的预测准确性，范围从 0.31 到 0.71，用于 ear 、 hectoliter 重量、每平方米穗数、千粒重和产量等性状，并表明这些性状是由于材料的密切遗传相关性。我们的工作有助于设计大麦育种的长期遗传预测计划。
