【摘要】 一般配合力（GCA）是评价亲本自交系利用价值的一个重要指标。为解析玉米配合力遗传机理，以NCII遗传交配设计获得的537份杂交组合为材料，结合玉米5.5K液相育种芯片的11 734个高质量单核苷酸多态性（SNP）标记，采用7种多位点全基因组关联分析（MGWAS）方法挖掘新乡、周口和综合环境穗行数、粒长和粒宽GCA显著关联位点，并在MGWAS研究基础上利用5种基因组选择方法对GCA效应开展预测研究。结果表明，共检测到46个SNPs与穗行数以及2个籽粒性状GCA显著关联（P<8.52×10-7），其中10个位点被2～5种MGWAS方法同时检测到，8个SNPs被至少2个环境共定位。6个SNPs(1＿43440622、2＿69742504、2＿71037706、2＿197716855、5＿219239213和8＿134634317)为环境稳定和MGWAS方法稳定重叠的位点，是控制穗行数和籽粒性状GCA效应的重要位点。穗行数和粒宽GCA利用5种随机效应模型取得较高的预测准确性，为0.62～0.74，粒长GCA基因组预测精度较低，为0.28～0.45。3个环境中，多数情况下将不同MGWAS挖掘的显著SNPs作为固定效应加入最佳线性无偏估预测（GBLUP）和再生核希尔伯特空间（RKHS）会提高穗行数和2个籽粒性状GCA的预测准确性，穗行数和粒宽的提高率为0.66%～15.96%，粒长的提高率为9.26%～83.05%。本研究结果为后续基因功能验证以及关键位点的基因组选择辅助育种提供了重要基因信息和技术指导。更多还原

【Abstract】 General combining ability(GCA) is an important index to evaluate the utilization value of parental inbred lines. In order to analyze the genetic mechanism of combining ability of maize, 537 hybrid combinations obtained from NCII genetic mating design were used as materials, seven multi-locus genome-wide association study(MGWAS) methods were used to identify significant loci for GCA of kernel row number, kernel length, and kernel width in Xinxiang, Zhoukou, and combined environment, combining with 11 734 high-quality single nucleotide polymorphism(SNP) markers obtained from the maize 5. 5K liquid breeding chip. Based on MGWAS, five genomic selection methods were used to predicting the GCA effects. The results showed that 46SNPs were detected and significantly associated with GCA of kernel row number and two kernel traits(P<8. 52×10-7). Among them, ten loci were detected using two-to-five MGWAS methods simultaneously, and eight SNPs were co-located in at least two environments. Six SNPs(1＿43440622, 2＿69742504, 2＿71037706, 2＿197716855, 5＿219239213, and 8＿134634317) were both environment-stable and MGWAS method-stable loci, which were important loci controlling the GCA effects of kernel row number and kernel traits. The prediction accuracy of GCA for kernel row number and kernel width was high when using five random effect models, with a value of 0. 62～0. 74, and the prediction accuracy of GCA for kernel length was low, with a value of 0. 28～0. 45. In most cases, adding significant SNPs identified from different MGWAS as fixed effects into genomic best linear unbiased prediction(GBLUP) and reproducing kernel Hilbert space(RKHS) improved the prediction accuracy of GCA for kernel row number and two kernel traits in the three environments, with the percentage increase of 0. 66%～15. 96% for kernel row number and kernel width and 9. 26%～83. 05% for kernel length.The results of this study provide important gene information and technical guidance for subsequent gene function verification and genomic selection-assisted breeding of key loci.更多还原