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Q and APL_Q2, whereas the average values for the two remaining traits were 0.20 and 0.35, respectively. The APL_Q value for APL was lower than those for APL_Q2, which had higher reliabilities and were more highly correlated with TBA. We analyzed the correlation between trait values among individual lines within inbred and hybrid populations using correlation coefficients. We found that the highest correlation value was 0.95 in the APL_Q2 dataset, which included only the inbred lines. However, APL_Q2 had the highest average correlation value (0.51) and the highest average value (0.37) for the correlation between inbred and hybrid lines (Table [3](#Tab3){ref-type="table"}). Therefore, the APL_Q2 dataset was used to compare traits in QTL analysis.Table 3Pearson correlation coefficients between traits in individual inbred and hybrid lines within a population^a^.Pop_codeDatasetPL_Q2PL_QAPL_Q2/tbaPL_QAPL_Q2/tbaPL_Q2/taaPL_Q2/tbaPL_QAPL_Q2/taaBL_Q2BL_QAPL_Q2/tbaBL_QAPL_Q2/taaBL_Q2BL_QAPL_Q2/tbaBL_QAPL_Q2/taaAOPL_Q2AOPL_QAPL_Q2/tbaAOPL_QAPL_Q2/taaAOPL_Q2AOPL_QAPL_Q2/tbaAOPL_QAPL_Q2/taaOPL_Q2OPL_QPOP_codeDatasetBL_QPOP_codeDatasetBL_QPOP_codeDatasetBL_QPOP_codeDatasetBL_QPOP_codeDatasetBL_QPOP_codeDatasetBL_QAPL_Q2Pop_codeDatasetPop_codeDatasetPop_codeDatasetPop_codeDatasetPop_codeDatasetPop_codeDatasetPop_codeDatasetPop_codeDatasetPop_codeDatasetPop_codeDatasetPop_codeDatasetPop_codeDatasetPop_codeDatasetPop_codeDatasetPop_codeDatasetPop_codeDatasetOPL_QPOP_codeDatasetOPL_QPOP_codeDatasetOPL_QPOP_codeDatasetOPL_QPOP_codeDatasetOPL_Q2APL_Q2/taaOPL_QAPL_Q2/taaOPL_Q2APL_Q2/tbaOPL_QAPL_Q2/taaOPL_Q2/tbaOPL_Q2/tbaOPL_QAPL_Q2/taaOPL_Q2/tbaOPL_Q2/taaTaproot densityTaproot densityTaproot densityTaproot densityTaproot densityTaproot densityTaproot densityTaproot densityTaproot densityTaproot densityTaproot densityTaproot densityTaproot densityTaproot densityTaproot densityTaproot densityTaproot densityTaproot densityTaproot densityTaproot densityTaproot densityTaproot densityTaproot densityTaproot densityTaproot densityTaproot densityTaproot densityTaproot densityTaproot density^a^The numbers of inbred lines used for each dataset were as follows: POP_code and BL_Q2 (N = 48, 56, and 48); BL_Q (N = 48, 53, and 49); BL_Q2 (N = 53, 60, and 54); POP_code and APL_Q2 (N = 60, 64, and 53); APL_Q (N = 63, 71, and 60); APL_Q2 (N = 62, 69, and 55); POP_code and POP_code (N = 66, 59, and 55); BL_Q and POP_code (N = 63, 63, and 55). BL_Q and BL_Q2 (N = 49, 55, and 50); BL_Q and APL_Q2 (N = 65, 68, and 58); APL_Q and POP_code (N = 65, 63, and 55); BL_Q and BL_Q (N = 58, 54, and 56); APL_Q and APL_Q2 (N = 56, 58, and 51); APL_Q and APL_Q (N = 63, 58, and 50); BL_Q and BL_Q2 (N = 61, 62, and 59); BL_Q and APL_Q2 (N = 60, 64, and 52); BL_Q and POP_code (N = 63, 60, and 56); APL_Q and APL_Q2 (N = 58, 60, and 59); APL_Q and APL_Q (N = 56, 58, and 53); BL_Q2 and APL_Q2 (N = 55, 56, and 51); APL_Q2 and APL_Q (N = 59, 60, and 55); BL_Q2 and BL_Q (N = 54, 56, and 54); APL_Q2 and APL_Q (N = 59, 61, and 54); BL_Q2 and POP_code (N = 58, 54, and 55); POP_code and POP_code (N = 56, 59, and 54); BL_Q and POP_code (N = 55, 53, and 52); APL_Q and POP_code (N = 59, 62, and 58); POP_code and POP_code (N = 56, 56, and 54); BL_Q and POP_code (N = 55, 56, and 55); APL_Q and POP_code (N = 62, 59, and 54); BL_Q2 and POP_code (N = 61, 59, and 55); APL_Q2 and POP_code (N = 63, 59, and 55); POP_code and BL_Q (N = 58, 55, and 57). Genetic dissection of aflatoxin production and agronomic traits {#Sec7} --------------------------------------------------------------- Phenotypic correlation was observed between TBA and TAA. However, most of the significant effects of QTLs for TAA were mapped in the same QTL region as those for TBA (Table [4](#Tab4){ref-type="table"}). Significant effects of each trait were investigated using an additive genetic model and are shown in Table [5](#Tab5){ref-type="table"}. TAP and PD exhibited non-significant effects under the additive model. The total number of QTLs for TAP, PD, PL, APL_Q, and APL_Q2 was 10, 8, 6, 13, and 11, respectively. Each QTL explained a small proportion of the total phenotypic variance, and the variance explained ranged from 5.5% to 23.3%. Therefore, the QTLs we identified were of low reliability and explained a small proportion of the total variance.Table 4Mapping results of QTLs for trait phenotypic data and estimated genetic effects in QTL analysis using a mixed-model additive genetic model^a^.TraitsPhenotypic dataEstimated genetic effectsQTL namePositionCI (cM)LODR2Additive effect of positive alleleAdditive effect of negative allelePD7.08 ± 1.48APL_Q27.17PL_Q26.84 ± 1.51APL_Q26.52 ± 0.94PD6.55 ± 2.07PD_1\_19q13_1\_14144067.2920.05−0.29−0.31APL_Q24.95APL_Q2POP_code8.42 ± 2.41POP_code_1\_15_1\_15153458.4420.22−0.15−0.02AOPL_Q25.51AOPL_Q2Taproot density5.86 ± 1.75Taproot density_1\_27_1\_1612765.7322.11−0.14−0.10Taproot density_1\_27_1\_1612765.7321.96−0.15−0.11Taproot density_1\_27_1\_1612765.6921.59−0.10−0.11Taproot density_1\_27_1\_1612765.6821.50−0.13−0.16Taproot density_1\_27_1\_1612765.6921.44−0.14−0.12Taproot density_1