| Kernel related traits are important determinant factors for maize yield. Kernel weight(HKW), a complex quantitative trait and largely determined by kernel size, is a key component of grain yield in maize. Thus the dissection of the genetic basis of traits related to HKW and kernel size is of great theoretical importance for high yield breeding. In our previous studies, a pleiotropic QTL of qKW7, associated with both HKW and kernel width(KW), was detected within the region of bin 7.02 using both F2:3 and recombinant inbred line(RIL) populations from the cross of Ye478 and Huangzaosi(HZS). Therefore, a series of backcross populations from the same cross were developed for the fine mapping of qKW7. Further, an association panel(AP) with 627 diverse inbred lines was used in regional association analysis to explore the candidate genes for HKW and KW. The main results were as follows:1. Seven major QTL for HKW and for KW expressing in different environments were selected respectively based on the mapping result of F2:3 and RIL populations from the cross of Ye478 and HZS. Their effects were validated in BC3F1 population with Ye478 as the donor parent and HZS as the recurrent parent. Among the seven major QTL for HKW and for KW, qKW7 showed the greatest genetic effect for both HKW and KW with R2 of 12.24% for HKW and 6.18% for KW. Furthermore, we selected 13 SNPs on Chromosome 7 to give linkage analysis for these two traits in RIL. The major QTL qKW7 was mapped within an interval of 35 Mb, between SNP markers MZA7374-161 and MZA8541-27. The logarithms of odds(LOD) scores of qKW7 was 3.46 for HKW and 6.60 for KW, and the phenotypic variance explained(PVE) by this QTL was 10.77% for HKW and 20.22% for KW respectively.2. Using the BC3F2 population, qKW7 was narrowed to a chromosome segment of 10 Mb, with two flanking markers of IDP872 and 7C-6 within bin 7.02, which could explain 11.41% of the phenotypic variance for KW. Single marker-based t-test showed that the two neighbored markers of IDP872 and 7C-6 were also significantly correlated with HKW with the P values of 1.19×10-3 and 7.16×10-3, respectively. In order to narrow the target region of qKW7 further, using BC3F3 generation, by which qKW7 was mapped to a region between the two markers of 7B-7 and 7B-6, with an interval of less than 5 Mb and PVE to be 15.55% for HKW. Meanwhile, the two flanking markers of 7B-7 and 7B-6 were also significant correlated with KW at the P value of 5.48×10-7 and 3.03×10-7, respectively. With a large BC3F4 segregating population, qKW7 was remapped within the region of 647 Kb by the analysis of recombinants, which had four candidate genes in the region.3. Regional association mapping about HKW and KW was performed in AP with 627 diverse inbred lines based on the result of linkage analysis of qKW7. Phenotype analysis indicated that 100-kernel volume(HKV) significantly correlated with HKW and could explained 78% phenotypic variation for HKW. Regression analysis among different heterotic groups indicated that KW contributed the most to HKV in the heterotic groups of Reid, LRC, Lancaster and P with the PVE varied from 54% to 71%. While in TSPT, kernel thickness(KT) and kernel length(KL) made great contribution to HKV with the PVE of 45% and 22%, respectively. Association mapping indicated that three significant QTNs associated with both HKW and KW had been detected. The peak signal SNP of PZE-107058976 showed the strongest association with both HKW(P = 1.95×10-6) and KW(P = 1.63×10-7), which could explain 3.4% of HKW and 4.2% of KW phenotypic variance, respectively. Meanwhile, LD analysis and bioinformatics analysis proposed two candidate genes for qKW7, i.e. GRMZM2G114706(ankyrin protein kinase) and GRMZM2G029153(sugar carrier protein A). |
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