玉米出籽率全基因组关联分析

出籽率与玉米单穗产量密切相关,其遗传机制的解析对玉米高产育种具有重要意义。本研究利用309份玉米自交系为关联群体,利用固定和随机模型交替概率统一(FarmCPU)、压缩混合线性模型(CMLM)和多位点混合线性模型(MLMM)对2017年和2019年河南新乡原阳、周口郸城、海南三亚以及最佳线性无偏估计值(BLUE)的出籽率进行全基因组关联分析。共鉴定18个与出籽率显著关联的SNP(P<1.72E-05)。其中,FarmCPU、CMLM和MLMM方法分别检测到14个、5个和2个位点。S2_87292896利用CMLM和MLMM方法在BLUE环境和2019年原阳均检测到;在BLUE环境,S2_111319193利用FarmCPU和CMLM方法均检测到;在2017年郸城,S5_93814060利用CMLM和MLMM方法均检测到。5个位点即S1_304584425、S5_11751831、S5_93814060、S5_186385476和S8_94354503的表型变异解释率介于10.09%~15.43%之间,为出籽率的主效SNP。与前人研究结果比较发现,Bin1.08、Bin2.06、Bin4.09和Bin6.05可能是影响出籽率的重要区段。共挖掘32个候选基因,其中E3泛素蛋白连接酶UPL1、DEAD盒ATP依赖的RNA解旋酶RH52、蛋白激酶同源子4、SNARE互作蛋白KEULE和延伸因子EF1A等可能是影响出籽率的重要基因。

Genome-wide Association Studies for Kernel Ratio in Maize

Kernel ratio(KR)is closely associated with grain yield per ear in maize(Zea mays L.),and the analysis of genetic mechanism for kernel ratio is important for high yield breeding.We used 309 inbred lines of maize as an association population,and used fixed and random model circulating probability unification(FarmCPU),compressed mixed linear model(CMLM),and multiple locus mixed linear model(MLMM)to conduct genome-wide association studies for kernel ratio of maize grown in Yuanyang(Xin xiang,Henan),Dancheng(Zhoukou,Henan),and Sanya(Hainan)in 2017 and 2019,and best linear unbiased estimate(BLUE).Eighteen significant SNPs for KR were identified(P<1.72 E-05).Fourteen,five,and two SNPs were detected by FarmCPU,CMLM,and MLMM,respectively.S287292896 was detected by CMLM and MLMM in BLUE environment and Yuanyang(2019).S2111319193 was identified by FarmCPU and MLMM in BLUE environment.S593814060 was detected by CMLM and MLMM in Dancheng(2017).Five SNPs,i.e.,S1304584425,S511751831,S593814060,S5186385476,and S894354503,explained 10.09%-15.43%of phenotypic variations,and were considered as major SNPs for KR.Bin1.08,Bin2.06,Bin4.09,and Bin6.05 might be important genomic regions for KR compared with results of previous studies.A total of 32 candidate genes were identified,among which E3 ubiquitin-protein ligase UPL1,DEAD-box ATP-dependent RNA helicase RH52,protein kinase homolog4,SNARE-interacting protein KEULE,and elongation factor(EF1 A)may be important genes for KR.