PDF(Pubmed)
Abstract：
CONCLUSIONS: A Bayesian linkage disequilibrium-based multiple-locus mixed model identified QTLs for fibre, seed and oil traits and predicted breeding worthiness of test lines, enabling their simultaneous improvement in cotton. Improving cotton seed and oil yields has become increasingly important while continuing to breed for higher lint yield. In this study, a novel Bayesian linkage disequilibrium-based multiple-locus mixed model was developed for QTL identification and genomic prediction (GP). A multi-parent population consisting of 256 recombinant inbred lines, derived from four elite cultivars with distinct combinations of traits, was used in the analysis of QTLs for lint percentage, seed index, lint index and seed oil content and their interrelations. All four traits were moderately heritable and correlated but with no large influence of genotype × environment interactions across multiple seasons. Seven to ten major QTLs were identified for each trait with many being adjacent or overlapping for different trait pairs. A fivefold cross-validation of the model indicated prediction accuracies of 0.46-0.62. GP results based on any two-season phenotypes were strongly correlated with phenotypic means of a pooled analysis of three-season experiments (r = 0.83-0.92). When used for selection of improvement in lint, seed and oil yields, GP captured 40-100% of individuals with comparable lint yields of those selected based on the three-season phenotypic results. Thus, this quantitative genomics-enabled approach can not only decipher the genomic variation underlying lint, seed and seed oil traits and their interrelations, but can provide predictions for their simultaneous improvement. We discuss future breeding strategies in cotton that will enhance the entire value of the crop, not just its fibre.
摘要：
结论：基于贝叶斯连锁不平衡的多位点混合模型确定了纤维的QTL，种子和油性状以及测试品系的预测育种价值，使他们同时改善棉花。在继续繁殖以获得更高的皮棉产量的同时，提高棉籽和油的产量变得越来越重要。在这项研究中,一种新的基于贝叶斯连锁不平衡的多基因座混合模型被开发用于QTL识别和基因组预测（GP）。由256个重组自交系组成的多亲群体，来自四个具有不同性状组合的优良品种，用于皮棉百分比的QTL分析，种子指数，皮棉指数和种子油含量及其相互关系。所有四个性状均具有中等遗传性和相关性，但在多个季节中对基因型×环境相互作用的影响不大。对于每个性状，确定了7到10个主要QTL，对于不同的性状对，许多QTL是相邻或重叠的。模型的五倍交叉验证表明预测精度为0.46-0.62。基于任何两个季节表型的GP结果与三个季节实验的汇总分析的表型平均值密切相关（r=0.83-0.92）。当用于选择棉绒的改进时，种子和油的产量，GP捕获了40-100％的个体，其皮棉产量与根据三季表型结果选择的个体相当。因此，这种定量基因组学的方法不仅可以破译皮棉潜在的基因组变异，种子和种子油性状及其相互关系，但可以为它们的同步改进提供预测。我们讨论了棉花未来的育种策略，这些策略将提高作物的整体价值，不仅仅是它的纤维。
