| High-oil maize has a vast range of prospects with its unique comprehensive utilization and high-added value. Plant traits are important agronomic traits related with grain yield in high-oil maize. They not only play an important role in grain yield production, but are also closely related with yield stability. In this study, 284 and 265 F2:3 family lines (P1F2:3, P2F2:3) were respectively developed from two crosses between the same high-oil corn inbred GY220 and two normal corn inbreds, 8984 and 8622. GY220 was derived from ASK high-oil germplasm. Two genetic maps were constructed using SSR markers. Using composite interval mapping (CIM) method, QTL associated with eight plant traits were detected for the two environments individually and combined according to the LOD thresholds after 1000 permutations. The interactions of detected QTL were identified using multiple interval mapping (MIM) method according to the result of CIM method. Conditional QTL mapping and joint QTL analysis was introduced to reveal the genetic correlations among main plant-height traits, between plant height and two main yield component traits, and between plant height and two kernel nutritional quality traits. Our objectives were to reflect the genetic nature for plant traits, and to detect QTL with stable expression across different populations and environments and their linked molecular markers. These results will do great help in fine mapping QTL associated with plant traits and their map-based cloning, and in marker-assisted selection in high-oil maize breeding. The main results in this study were as follows:1. Totally, 665 SSR primers were employed to screen polymorphism between two pairs of parents for the two crosses, including 8984 with GY220, 8622 with GY220. 212 and 205 polymorphism markers were selected, respectively. The SSR markers that showed segregation distortion were excluded from the analysis. Finally, 185 and 173 pairs of SSR markers were selected respectively to construct the maize genetic linkage maps with the genetic distance of 2111.7 cM and 2298.5 cM (centimorgan) and an average of 11.41 cM and 13.29 cM using Mapmaker 3.0b.2. 84 QTL were detected for eight plant traits using the two F2:3 populations under the two environments individually and combined analysis. 50 QTL and 34 QTL were detected in the two populations, respectively. No common QTL were detected across both populations. Contribution of single QTL to phenotypic variation varied from 3.1% to 39.0%. Partially dominance, over-dominance effects played main role in the heredity of plant traits. Interaction between detected QTL were less, and their effects were small. qlPH1-10-1 (qxPH1-10-1, qPH1-10-1), qlTL1-7-1 (qxTL1-7-1, qTL1-7-1), qlTB2-2-1 (qxTB2-2-1,qTB2-2-1), qlTB2-6-1 (qxTB2-6-1, qTB2-6-1) showed high stability among different environments. Their contribution to phenotypic variation varied from 12.4% to 27.9%. These QTL could be used as the main objevtive QTL in further studies and in MAS.3. There were highly significant positive correlations between plant height (PH) and ear height (EH), top height (TH), while plant high was significant or highly significantly negatively correlated with top height/plant height (THPH) under the two environments and combined analysis. The result of single trait QTL mapping and multiple traits analysis all showed that QTL for 2-3 plant-height traits were all detected at chromosomes 3 (umc1320-bnlg1754), 5 (umc1162- bnlg2323), 8 (bnlg2082-umc1360), 10 (umc1506-umc2122). The graph of LOD curve peaks for PH and EH, TH, THPH on chromosomes 3 (umc1320-bnlg1754) and 5 (umc1162- bnlg2323), for PH and TH, THPH on chromosome 10 (umc1506-umc2122), and for PH and THPH on chromosome 8 changed simultaneously and in the same directions, suggesting the existence of pleiotropic QTL controlling these respective plant-height traits simultaneously. The graph of LOD curve peaks of PH and EH for marker intervals bnlg2082-umc1360 on chromosomes 8, and umc1506-umc2122 on chromosomes 10 changed in the same close direction, suggesting that QTL controlling PH and EH might be tightly linked in these marker intervals.4. There were the significant or highly significant positive correlations between PH and grain weight per plant (GWP) for the two populations at Luoyang (LY) location, and between PH and 100 grain weight (100GW) for P1F2:3 in combined analysis. Conditional QTL mapping showed that the expression of PH QTL was greatly affected by 100GW, and then GWP. However, qxPH1-10-1(qPH1-10-1) was not affected by 100GW and GWP, which was also detected at LY location. Therefore, qxPH1-10-1(qPH1-10-1) had stability across different environments. While lowering PH by MAS through negative selection for this QTL, 100GW and GWP could not be influenced.5. In P1F2:3 population, there were significant or highly significant negative correlations between PH and starch concentration under both environments and combined analysis. Most correlations between PH and oil concentration were significant or highly significantly positive. The correlations for between PH and protein concentration were all insignificant. The results of conditional QTL mapping showed that both oil and starch had the great effect on the expression of PH QTL. But qlPH1-10-1 (qPH1-10-1) was not affected by oil and starch concentration, which was also detected at Xuchang location. Therefore, this QTL had stability across different environments. While lowering PH by MAS through negative selection for this QTL, both oil and starch concentration could not be influenced greatly.
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