Zhang-Biehn Shichen, Fritz Allan K, Zhang Guorong, Evers Byron, Regan Rebecca, Poland Jesse
Dep. of Plant Pathology, Kansas State Univ., 4024 Throckmorton Plant Sciences Center, 1712 Claflin Rd., Manhattan, KS, 66506, USA.
current address: Syngenta, 317 330th St., Stanton, MN, 55018, USA.
Plant Genome. 2021 Nov;14(3):e20164. doi: 10.1002/tpg2.20164. Epub 2021 Nov 24.
In hard-winter wheat (Triticum aestivum L.) breeding, the evaluation of end-use quality is expensive and time-consuming, being relegated to the final stages of the breeding program after selection for many traits including disease resistance, agronomic performance, and grain yield. In this study, our objectives were to identify genetic variants underlying baking quality traits through genome-wide association study (GWAS) and develop improved genomic selection (GS) models for the quality traits in hard-winter wheat. Advanced breeding lines (n = 462) from 2015-2017 were genotyped using genotyping-by-sequencing (GBS) and evaluated for baking quality. Significant associations were detected for mixograph mixing time and bake mixing time, most of which were within or in tight linkage to glutenin and gliadin loci and could be suitable for marker-assisted breeding. Candidate genes for newly associated loci are phosphate-dependent decarboxylase and lipid transfer protein genes, which are believed to affect nitrogen metabolism and dough development, respectively. The use of GS can both shorten the breeding cycle time and significantly increase the number of lines that could be selected for quality traits, thus we evaluated various GS models for end-use quality traits. As a baseline, univariate GS models had 0.25-0.55 prediction accuracy in cross-validation and from 0 to 0.41 in forward prediction. By including secondary traits as additional predictor variables (univariate GS with covariates) or correlated response variables (multivariate GS), the prediction accuracies were increased relative to the univariate model using only genomic information. The improved genomic prediction models have great potential to further accelerate wheat breeding for end-use quality.
在硬粒冬小麦（Triticum aestivum L.）育种中，对最终用途品质的评估既昂贵又耗时，在针对包括抗病性、农艺性能和籽粒产量等多种性状进行选择之后，才进入育种计划的最后阶段。在本研究中，我们的目标是通过全基因组关联研究（GWAS）鉴定烘焙品质性状潜在的遗传变异，并为硬粒冬小麦的品质性状开发改进的基因组选择（GS）模型。使用简化基因组测序（GBS）对2015 - 2017年的先进育种系（n = 462）进行基因分型，并对烘焙品质进行评估。对于粉质仪混合时间和烘焙混合时间检测到显著关联，其中大多数位于麦谷蛋白和麦醇溶蛋白基因座内或与其紧密连锁，可能适用于标记辅助育种。新关联基因座的候选基因是磷酸依赖性脱羧酶和脂质转移蛋白基因，据信它们分别影响氮代谢和面团发育。使用GS既可以缩短育种周期时间，又能显著增加可针对品质性状进行选择的品系数目，因此我们评估了用于最终用途品质性状的各种GS模型。作为基线，单变量GS模型在交叉验证中的预测准确率为0.25 - 0.55，在前瞻性预测中的准确率为0至0.41。通过将次要性状作为额外的预测变量（带协变量的单变量GS）或相关响应变量（多变量GS）纳入，相对于仅使用基因组信息的单变量模型，预测准确率有所提高。改进的基因组预测模型具有进一步加速小麦最终用途品质育种的巨大潜力。
