Integrating genomic additive relationship matrices improves the efficiency in diploid banana breeding

This study investigated if integrating molecular information into the clonal model could improve the partitioning of the variance components and yield more accurate estimates of genetic parameters and prediction accuracy of breeding values of 14 key traits in diploid banana. A set of 2,792 informative single nucleotide polymorphism markers were used to construct the genomic-based relationship matrices. We compared the partitioning of genetic variance into additive and non-additive components using the pedigree-based best linear unbiased prediction (P-BLUP) model, the genomic best linear unbiased prediction (G-BLUP) method, which integrates the genetic relationship established through molecular marker information and a combination of the P-BLUP and G-BLUP information, which sources to create a hybrid matrix that estimates hybrid best linear unbiased prediction (H-BLUP).

本研究探讨了将分子信息整合至无性系模型（clonal model）中，能否优化方差组分的划分，并更精准地估计二倍体香蕉14个关键性状的遗传参数与育种值预测准确性。本研究使用2792个信息性单核苷酸多态性（single nucleotide polymorphism, SNP）标记，构建了基于基因组的亲缘关系矩阵。本研究通过三种策略比较了遗传方差划分为加性与非加性组分的效果：其一为基于系谱的最佳线性无偏预测（pedigree-based best linear unbiased prediction, P-BLUP）模型；其二为基因组最佳线性无偏预测（genomic best linear unbiased prediction, G-BLUP）方法，该方法整合了通过分子标记信息构建的遗传关系；其三为混合最佳线性无偏预测（hybrid best linear unbiased prediction, H-BLUP），该方法结合系谱与基因组信息，通过构建混合矩阵实现估计。