PDF(Pubmed)
Abstract：
Given the universality of autopolyploid species in nature, it is crucial to develop genomic selection methods that consider different allele dosages for autopolyploid breeding. However, no method has been developed to deal with autopolyploid data regardless of the ploidy level. In this study, we developed a modified genomic best linear unbiased prediction (GBLUP) model (polyGBLUP) through constructing additive and dominant genomic relationship matrices based on different allele dosages. polyGBLUP could carry out genomic prediction for autopolyploid species regardless of the ploidy level. Through comprehensive simulations and analysis of real data of autotetraploid blueberry and guinea grass and autohexaploid sweet potato, the results showed that polyGBLUP achieved higher prediction accuracy than GBLUP and its superiority was more obvious when the ploidy level of autopolyploids is high. Furthermore, when the dominant effect was added to polyGBLUP (polyGDBLUP), the greater the dominance degree, the more obvious the advantages of polyGDBLUP over the diploid models in terms of prediction accuracy, bias, mean squared error and mean absolute error. For real data, the superiority of polyGBLUP over GBLUP appeared in blueberry and sweet potato populations and a part of the traits in guinea grass population due to the high correlation coefficients between diploid and polyploidy genomic relationship matrices. In addition, polyGDBLUP did not produce higher prediction accuracy than polyGBLUP for most traits of real data as dominant genetic variance was not captured for these traits. Our study will be a significant promising method for genomic prediction of autopolyploid species.
摘要：
鉴于同源多倍体物种在自然界中的普遍性，开发考虑不同等位基因剂量的基因组选择方法对于自体多倍体育种至关重要。然而,无论倍性水平如何，都没有开发处理自体多倍体数据的方法。在这项研究中,我们通过构建基于不同等位基因剂量的加性和显性基因组关系矩阵,建立了改良的基因组最佳线性无偏预测(GBLUP)模型(polyGBLUP).无论倍性水平如何，polyGBLUP都可以对自倍体物种进行基因组预测。通过对同源四倍体蓝莓和豚鼠草以及自六倍体甘薯的真实数据进行综合模拟和分析，结果表明，polyGBLUP比GBLUP具有更高的预测精度，当自倍体的倍性水平较高时，其优越性更加明显。此外，当显性效应被添加到polyGBLUP(polyGDBLUP)时，优势程度越大，polyGDBLUP在预测精度方面比二倍体模型的优势越明显，偏见，均方误差和平均绝对误差。对于真实数据，由于二倍体和多倍体基因组关系矩阵之间的相关系数高，polyGBLUP优于GBLUP的优势出现在蓝莓和甘薯种群中，部分性状出现在几内亚草种群中。此外,对于实际数据的大多数性状，polyGDBLUP没有产生比polyGBLUP更高的预测准确性，因为这些性状没有捕获显性遗传变异。我们的研究将是自倍体物种基因组预测的重要有前途的方法。
