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玉米穗轴粗和出籽率全基因组预测分析
马 娟, 曹言勇, 朱卫红
0
(河南省农业科学院粮食作物研究所)
摘要:
穗轴粗和出籽率均是典型的数量性状,在不同程度上影响玉米产量。全基因组选择整合全基因组关联分析(GWAS,genome-wide association study)的先验信息是提高性状预测准确性的有效方法。本研究利用309份玉米自交系穗轴粗和出籽率表型和基因分型测序技术获得的基因型数据,研究基因组最佳线性无偏预测(GBLUP,genomic best linear unbiased prediction)、贝叶斯A(Bayes A)和再生核希尔伯特空间(RKHS,reproducing kernel Hilbert space)模型对2种GWAS方法即固定和随机模型交替概率统一(FarmCPU,fixed and random model circulating probability unification)和压缩混合线性模型(CMLM,compressed mixed linear model)衍生的不同密度标记集、随机选择标记集和所有标记对预测准确性的影响。对于2个性状FarmCPU和CMLM衍生标记集,3个预测模型间的预测准确性差异较小,变异范围介于0-0.03。对于随机标记集,相比其他2个模型的预测准确性,RKHS对穗轴粗可提高3.57%-15.91%,而3个预测模型对出籽率具有相似的预测效果。除了50和100个标记,3个模型利用CMLM衍生标记对2个性状的预测效果均优于FarmCPU。相比随机标记集,穗轴粗GWAS衍生标记的预测准确性可提高15.52%-88.37%;出籽率利用衍生标记可提高1-5.89倍。所有衍生标记集的预测准确性均高于所有标记。这些结果均表明,全基因组选择整合GWAS衍生标记有利于提高穗轴粗和出籽率的预测准确性。
关键词:  全基因组关联分析衍生标记  全基因组预测  基因组最佳线性无偏预测  再生核希尔伯特空间  贝叶斯A
DOI:10.13430/j.cnki.jpgr. 20210405002
投稿时间:2021-04-05修订日期:2021-06-03
基金项目:河南省科技攻关项目(192102110008;212102110279);河南省农业科学院优秀青年基金(2020YQ04)
Genome-Wide Prediction Analysis for Ear Cob Diameter and Kernel Ratio in Maize
MA Juan, CAO Yan-yong, ZHU Wei-hong
(Institute of Cereal Crops, Henan Academy of Agricultural Sciences)
Abstract:
Maize ear cob diameter (CD) and kernel ratio (KR) are controlled by multiple quantitative loci and both traits associate with the yield production. The genomic selection in conjugation with information identified by genome-wide association study (GWAS) is an effective method to improve the prediction accuracy. By taking use of the phenotypic datasets of CD and KR and the genotypic data derived from genotyping-by-sequencing in 309 maize inbred lines, here we investigated the genomic prediction accuracy using three GS models (genomic best linear unbiased prediction, GBLUP; Bayes A; reproducing kernel Hilbert space, RKHS) and different marker subsets (GWAS-derived markers: fixed and random model circulating probability unification, FarmCPU; compressed mixed linear model, CMLM; randomly selected markers, and all markers). By taking use of FarmCPU- and CMLM-derived markers at both traits, only slight difference (0 to 0.03) on the prediction accuracy using three prediction models was observed. For random markers, GS using RKHS model represented higher prediction accuracy of CD (3.57-15.91%) if compared to two other models, whereas no difference for KR was detected. Except for 50 and 100 markers, the prediction accuracy of CMLM-derived marker using three models were higher than that of FarmCPU-derived markers. Compared to random markers, GWAS-derived markers were able to increase the prediction accuracy (15.52%-88.37% for CD; 1 to 5.89-fold for KR). The prediction accuracy by deployment of GWAS-derived marker subsets was higher than that of all markers. Collectively, these results indicated that genomic selection using GWAS-derived markers could improve the prediction accuracy of CD and KR in maize.
Key words:  markers derived from genome-wide association study  genome-wide prediction  genomic best linear unbiased prediction  reproducing kernel Hilbert space  Bayes A

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