玉米新选自交系产量相关性状配合力和遗传基础分析及GY220/1145组合QTL定位
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摘要
中国的玉米种植面积2463.4万hm2,总产12131万t,平均单产4924.5kg/hm2;世界玉米种植面积13750万hm2,总产为60220万t,平均单产为4380kg/hm2;美国玉米平均单产10000kg/hm2.我国玉米单产水平,高于世界平均水平,与美国比还有相当大的差距,杂交玉米生产能力的提高还有很大的发展空间。20世纪90年代以来,我国玉米单产增速渐缓一直处于徘徊状态。玉米产量徘徊的根本原因是所利用的种质基础狭窄。江苏沿江地区农业科学研究所近年来利用热带亚热带等外来种质进行玉米种质创新,选育了一批新的自交系。为了明确新选自交系的利用价值和进一步改良的潜力,本研究首先选择其中的9个自交系(代号s1至s9),配制双列杂交组合,在江苏南通和南京两个地点对小区产量、穗行数等11个性状进行了配合力分析和综合评价；然后选用综合评价较好的3个自交系S1、S3和S7配成S1×S3、S3xS7两个组合的P1、P2、F1、B1、B2、F2六个世代,运用主基因+多基因遗传模型和6个世代联合分析的方法,对玉米穗粒重、百粒重、行粒数、穗行数、穗长、穗粗、轴粗、穗总重性状进行了遗传分析；最后调查玉米自交系GY220与自交系1145杂交衍生的RIL群体109个家系(F10；11)及其亲本在2个环境下10个穗部性状、2个植株性状和1个粗缩病抗性的表型值,利用该群体的分子标记连锁图谱,运用WinQTLCartographer2.5软件的复合区间作图法(CIM)和基于混合线性模型的QTLNetwork2.0软件的复合区间作图法,分别对这13个性状进行了QTL检测。主要研究结果如下：
1.9个新选自交系间杂种F1小区产量、行粒数、穗长、单株粒重的变异中,非加性遗传方差大于加性遗传方差；穗行数、千粒重、穗粗、百粒体积、单株杆重、生育期的变异中,加性遗传方差大于非加性遗传方差。株高的加性遗传方差占总遗传方差的比重在2试点间不一致。小区产量、千粒重、百粒体积、单株粒重性状一般配合力好的自交系是S7和S3。穗行数、穗粗一般配合力较好的自交系是S6和S3。行粒数、穗长一般配合力较好的自交系是S9和S2。株高高杆一般配合力好的是S7和S2,矮杆一般配合力好的是S4和S1。早熟性一般配合力最好的是S3。综合11个性状评价,s3最好,其次为s7。导入了热带亚热带种质的s9和s2等行粒数一般配合力有所提高。除行粒数和南通点株高外,其它性状的反交效应均不显著。
2.玉米穗粒重、穗总重性状在s1×s3和s3×s72个组合中均表现为2对加性-显性-上位性主基因+加性-显性-上位性多基因遗传,以主基因遗传为主。行粒数性状在2个组合中均表现为1对加性主基因+加性-显性多基因混合遗传,主基因遗传为主；多基因位点显性总效应大于加性总效应。百粒重性状在s1×S3组合中表现为2对加性-显性-上位性主基因+加性-显性-上位性多基因混合遗传,在S3×S7组合中表现为1对加性-显性主基因+加性-显性多基因混合遗传,均以主基因遗传为主。玉米穗行数性状在S1×S3组合中表现为1对加性-显性主基因+加性-显性-上位性多基因遗传,以多基因遗传为主；在S3×S7组合中表现为2对加性-显性-上位性主基因+加性-显性-上位性多基因混合遗传,以主基因遗传为主。轴粗性状在组合S1×s3中表现为2对加性-显性-上位性主基因+加性-显性多基因混合遗传,主基因遗传为主；在S3×S7组合中表现为2对加性-显性-上位性主基因+加性-显性-上位性多基因混合遗传,多基因遗传为主。穗长性状在组合s1×s3中表现为加性-显性-上位性多基因遗传；在s3xS7组合中表现为1对加性-显性主基因+加性-显性-上位性多基因混合遗传。穗粗性状在组合S1×S3中表现为1对加性-显性主基因+加性-显性-上位性多基因遗传；在S3xS7组合中表现为1对完全显性主基因+加性-显性多基因混合遗传。穗长、穗粗性状均表现为多基因遗传为主。百粒重、穗粒重、穗总重、行粒数性状以主基因遗传为主。
3.(1)运用WinQTLCartographer软件中复合区间作图法,13个性状共检测到63个位点。穗粒重共检测到4个QTL,解释表型变异的7.8%～25.8%,其中有2个在两个环境中被共同检测到,增效等位基因来自1145。穗行数检测到4个QTL,解释表型变异的6.4%～11.7%,其中1个2环境都存在。行粒数检测到4个位点,解释表型变异的8.4%-17.2%,其中1个2环境共同检测到。百粒重检测到6个QTL,解释表型变异的7.0%～13.8%。穗总重检测到3个QTL,解释表型变异的8.5%～10.3%。穗长检测到3个QTL,解释表型变异的11.9%～22.0%。穗粗检测到6个QTL,解释表型变异的6.7%-26.4%。轴粗检测到3个位点,解释表型变异的6.9%～17.0%。秃长检测到4个位点,解释表型变异的6.5%～25.7%。穗粒数检测到3个位点,解释表型变异的9.9%～16.5%其中2个2环境共同检测到。株高检测到5个QTL,解释表型变异的6.4%～8.6%。穗位高检测到4个QTL,解释表型变异的6.7%～12.8%。粗缩病抗性检测到6个QTL,解释表型变异的6.9%～17.6%,其中有3个在两个环境检测到。在检测到位点的连锁群区段中,发现6个多效性区段。第23连锁群的g5M5708-n55区段同时控制穗粒重、穗总重、行粒数、穗粗和穗粒数性状。第12连锁群的g4M3707-g6M6811区段同时控制穗粒重、穗总重、行粒数、穗粒数和株高性状。第9连锁群的g5M5813-g6M6808区段同时控制穗行数、穗粒数和穗位高性状。第13连锁群的g7M778-g4M3801区段同时控制穗总重和穗粒重。第16连锁群的g8M8801-g8M8811区段同时控制百粒重和穗行数。第2连锁群的g5M5804-g5M5803区段同时控制百粒重和秃长。(2)用QTLNetwork软件中复合区间作图法,检测到9个主效QTL和7对非主效QTL间的互作。9个主效QTL分别是：1个控制穗粒重的位点YE5-12,解释表型变异7.4%；1个控制穗行数的位点RE9-15,解释表型变异的11.6%；2个控制穗总重的位点TWE5-12和TWE13-1,分别解释表型变异的7.3%和6.6%;2个控制轴粗的位点CD13-1和CD18-2,分别解释表型变异的7。7%和8.4%；1个控制穗粒数的位点(?)NE12-5,解释表型变异的7.2%；1个控制株高的位点PH5-10,解释表型变异的11.2%；1个控制粗缩病抗性的位点RDD2-22,解释表型变异的9.0%。7对非主效QTL间的互作发生在第1与3、5与18、3与5、5与19、7与12、1与2、5与13连锁群间,分别控制穗粒重、穗行数、百粒重、穗长、穗粗、秃尖长、粗缩病抗性7个性状,解释表型变异4.7%～11.9%。(3)运用多元回归模型和混合线性模型同时检测到的QTL有6个,分别是：控制穗粒重的YE5-12,控制穗粒数的KNE12-5,控制粗缩病抗性的RDD2-22,控制穗行数的RE9-15,控制株高的PH5-10和控制穗总重的TWE5-12。这6个QTL可靠性高,可用于进一步深入研究。
In China, the planting area for maize is24,634,000hm2, and the total yield of maize is121,310,000t with the unit area yield of4924.5kg/hm2. The planting area worldwide is137,500,000hm2, and total yield is602,200,0001with the unit area yield of4380kg/hm2. In USA unit area yield is10000kg/hm2. China's per unit area yield of maize is higher than world average level, but lag far behind that of the USA. Compared with USA there is great space for development of hybrid maize product ability. The increase of China's maize per unit yield has fluctuated since1990s, the reason for which is that the genetic basis of inbred lines is becoming narrower and narrower. Recently, new germplasm has been created and new inbred lines have been bred by introducing tropical-subtropical resources in maize in Institute of Agricultural Sciences of the Area Along Yangtse of Jiangsu. In order to evaluate the utilization value and genetic potential for further improvement of these inbred lines newly bred, we, firstly in this study, analysed general combining ability (GCA) and special combining ability (SCA) of11traits in9inbred lines (named as S1～S9) selected from the lines newly bred through seventy-two F1hybrids made by method3of Griffing diallel designs, and planted in Nantong and Nanjing, Jiangsu Province, respectively. Then, genetic analysis for9trait were conducted by using mixed major gene plus polygene inheritance models and joint segregation analytic method of P1, P2, F1, B1, B2and F2generations in two crosses made from the3elite inbred lines, S1, S3and S7. Lastly, QTL mapping was carried out for13traits by using a RIL population (109lines) made from a single cross hybrid of GY220/1145, using composite interval mapping method in both WinQTLCartographer2.5and QTLnetwork2.0softwares. Main research results obtained were as follows.
1. Among the F1hybrids, genetic variances of non-additive were larger than those of additive in the variations of grain yield per plot, kernel number per row, ear length and kernel weigth per plant, and genetic variances of additive were greater than those of non-additive in the variations of row number per ear,1000-kernel weight, ear diameter,100-kernel volume, stalk weight per plant and growth duration. Genetic variances of additive of plant height different between Nantong and Nanjing. Inbred lines S7and S3had excellent GCA for kernel yield per plot,1000-kernel weight,100-kernel volume and kernel weigth per plant. Inbred lines S6and S3showed elite GCA for rows per ear and ear diameter. Inbred lines S9and S2showed good GCA for kernel number per row and ear length. Inbred lines S7and S2showed good GCA for high plant height and Inbred lines S4and S1showed good GCA for low plant height. Summing up the evaluation from the11traits, the best line was S3, then S7, S2,S1and S5in order, and last is S4. GCA for kernel number per row was improved in S9into which tropic and subtropic germplasm was introduced. Reciprocal effects of the traits were not significant at5%probability level except kernel number per row in both sites and plant height in Nantong.
2. Kernel weight per ear and total weight per ear was controlled by two pairs of major gene with additive-dominance-epistatic effects plus polygenes with additive-dominance-epistatic effects, and the trait was mainly governed by major genes in both crosses.100-grain weight was controlled by two pairs of major gene with additive-dominance-epistatic effects plus polygenes with additive-dominance-epistatic effects in S1×S3, and by one pair of major gene with additive-dominance effects plus polygenes with additive-dominance effects in S3×S7, and was mainly governed by major genes in both crosses. Kernel row number was controlled by one additive-dominance major-gene and additive-dominance-epistasis polygenes, and was mainly governed by polygenes in cross S1×S3; whereas in cross S3×S7the trait was controlled by two additive-dominance-epistasis major-genes and additive-dominance-epistasis polygenes, and was mainly governed by major genes. Kernel number per row was controlled by one additive major-gene and additive-dominance polygenes, and was mainly governed by major gene in both crosses. Total effect of dominance was larger than that of additive in polygene loci. Ear length was controlled by additive-dominance-epistasis polygenes in cross S1×S3; whereas in cross S3×S7the trait was controlled by one additive-dominance major-gene and additive-dominance-epistasis polygenes. Ear diameter was controlled by one additive-dominance major-gene and additive-dominance-epistasis polygenes in cross S1×S3; whereas in cross S3×S7the trait was controlled by one wholly dominance major-gene and additive-dominance polygenes. Ear length and Ear diameter either was mainly governed by polygenes. Cob diameter was controlled by two additive-dominance-epistasis major-gene and additive-dominance polygenes in cross S1×S3; whereas in cross S3×S7the trait was controlled by two additive-dominance-epistasis major-gene and additive-dominance-epistasis polygenes, and was mainly governed by polygenes.100-kernel weight, kernel weight per ear, total weight per ear and kernel number per row was mainly governed by major genes.
3.(1) Sixty-three loci were detected for the13traits by the CIM method. For trait of yield per ear,4QTLs, explaining7.8%～25.8%of phenotypic variation, were detected and2of the4were detected in both environments. For trait of rows per ear,4QTLs, explaining6.4%～11.7%of phenotypic variation, were detected and1of the4was detected in both environments. For trait of kernel number per row,4QTLs, explaining8.4%～17.2%of phenotypic variation, were detected and1of the4was detected in both environments. For trait of100-kernel weight,6QTLs, explaining7.0%～13.8%of phenotypic variation, were detected. For trait of total weight per ear,3QTLs, explaining8.5%～10.3%of phenotypic variation, were detected. For trait of ear length,3QTLs, explaining11.9%～22.0%of phenotypic variation, were detected. For trait of ear diameter,6QTLs, explaining6.7%～26.4%of phenotypic variation, were detected. For trait of cob diameter,3QTLs, explaining6.9%～17.0%of phenotypic variation, were detected. For trait of tip barren length,4QTLs, explaining6.5%～25.7%of phenotypic variation, were detected. For trait of kernel number per ear,3QTLs, explaining9.9%～16.5%of phenotypic variation, were detected and2of the3were detected in both environments. For trait of plant height,5QTLs, explaining6.4%～8.6%of phenotypic variation, were detected. For trait of ear height,4QTLs, explaining6.7%～12.8%of phenotypic variation, were detected. For trait of rough dwarf disease,6QTLs, explaining6.9%～17.6%of phenotypic variation, were detected and3of the6were detected in both environments. Six intervals with multiple effects were found among the intervals harboring loci. Locus between g5M5708-n55on23linkage conditioned yield per ear, total weight per ear, kernel number per row, ear diameter and kernel number per ear simultaneously. Locus between g4M3707-g6M6811on12linkage conditioned yield per ear, total weight per ear, kernel number per row, kernel number per ear and plant height simultaneously. Locus between g5M5813-g6M6808on9linkage conditioned row per ear, kernel number per ear and ear height simultaneously. Locus between g7M778-g4M3801on13linkage conditioned total weight per ear and yield per ear simultaneously. Locus between g8M8801-g8M8811on16linkage conditioned100-kernel weight and row per ear simultaneously. Locus between g5M5804-g5M5803on2linkage conditioned100-kernel weight and tip barren length simultaneously.(2) Nine main effect QTLs and7pairs of interaction between non main effect QTLs were detected by the QTLNetwork method. The9main effect QTLs were as follows. One was YE5-12, controlling yield per ear, explained7.4%of phenotypic variation. One was RE9-15, controlling row per ear, explained11.6%of phenotypic variation. Two were TWE5-12and TWE13-1, controlling total weight per ear, explained7.3%and6.6%of phenotypic variation respectively. Two were CD13-1and CD18-2, controlling cob diameter, explained7.7%and8.4%of phenotypic variation respectively. One was KNE12-5, controlling kernel number per ear, explained7.2%of phenotypic variation. One was PH5-10, controlling plant height, explained11.2%of phenotypic variation. One was KNE12-5, controlling kernel number per ear, explained7.2%of phenotypic variation. One was RDD2-22, controlling rough dwarf disease, explained9.0%of phenotypic variation. Seven pairs of interaction between non main effect QTLs were occurred linkages between1and3,5and18,3and5,5and19,7and12,1and2, and5and13, controlling yield per ear, row per ear,100-kernel weight, ear length, ear diameter, tip barren length and rough dwarf disease respectively, and explained4.7%-11.9%of phenotypic variation.(3) Six QTLs were detected simultaneously by the two genetic models, i.e. multiple regression model and mixed linear model. The6QTLs were YE5-12, controlling yield per ear, KNE12-5, controlling kernel number per ear, RDD2-22, controlling rough dwarf disease, RE9-15, controlling row per ear, PH5-10, controlling plant height, and TWE5-12, controlling total weight per ear. It is worth to study the6QTLs further since they are reliable QTLs.
1.9个新选自交系间杂种F1小区产量、行粒数、穗长、单株粒重的变异中,非加性遗传方差大于加性遗传方差；穗行数、千粒重、穗粗、百粒体积、单株杆重、生育期的变异中,加性遗传方差大于非加性遗传方差。株高的加性遗传方差占总遗传方差的比重在2试点间不一致。小区产量、千粒重、百粒体积、单株粒重性状一般配合力好的自交系是S7和S3。穗行数、穗粗一般配合力较好的自交系是S6和S3。行粒数、穗长一般配合力较好的自交系是S9和S2。株高高杆一般配合力好的是S7和S2,矮杆一般配合力好的是S4和S1。早熟性一般配合力最好的是S3。综合11个性状评价,s3最好,其次为s7。导入了热带亚热带种质的s9和s2等行粒数一般配合力有所提高。除行粒数和南通点株高外,其它性状的反交效应均不显著。
2.玉米穗粒重、穗总重性状在s1×s3和s3×s72个组合中均表现为2对加性-显性-上位性主基因+加性-显性-上位性多基因遗传,以主基因遗传为主。行粒数性状在2个组合中均表现为1对加性主基因+加性-显性多基因混合遗传,主基因遗传为主；多基因位点显性总效应大于加性总效应。百粒重性状在s1×S3组合中表现为2对加性-显性-上位性主基因+加性-显性-上位性多基因混合遗传,在S3×S7组合中表现为1对加性-显性主基因+加性-显性多基因混合遗传,均以主基因遗传为主。玉米穗行数性状在S1×S3组合中表现为1对加性-显性主基因+加性-显性-上位性多基因遗传,以多基因遗传为主；在S3×S7组合中表现为2对加性-显性-上位性主基因+加性-显性-上位性多基因混合遗传,以主基因遗传为主。轴粗性状在组合S1×s3中表现为2对加性-显性-上位性主基因+加性-显性多基因混合遗传,主基因遗传为主；在S3×S7组合中表现为2对加性-显性-上位性主基因+加性-显性-上位性多基因混合遗传,多基因遗传为主。穗长性状在组合s1×s3中表现为加性-显性-上位性多基因遗传；在s3xS7组合中表现为1对加性-显性主基因+加性-显性-上位性多基因混合遗传。穗粗性状在组合S1×S3中表现为1对加性-显性主基因+加性-显性-上位性多基因遗传；在S3xS7组合中表现为1对完全显性主基因+加性-显性多基因混合遗传。穗长、穗粗性状均表现为多基因遗传为主。百粒重、穗粒重、穗总重、行粒数性状以主基因遗传为主。
3.(1)运用WinQTLCartographer软件中复合区间作图法,13个性状共检测到63个位点。穗粒重共检测到4个QTL,解释表型变异的7.8%～25.8%,其中有2个在两个环境中被共同检测到,增效等位基因来自1145。穗行数检测到4个QTL,解释表型变异的6.4%～11.7%,其中1个2环境都存在。行粒数检测到4个位点,解释表型变异的8.4%-17.2%,其中1个2环境共同检测到。百粒重检测到6个QTL,解释表型变异的7.0%～13.8%。穗总重检测到3个QTL,解释表型变异的8.5%～10.3%。穗长检测到3个QTL,解释表型变异的11.9%～22.0%。穗粗检测到6个QTL,解释表型变异的6.7%-26.4%。轴粗检测到3个位点,解释表型变异的6.9%～17.0%。秃长检测到4个位点,解释表型变异的6.5%～25.7%。穗粒数检测到3个位点,解释表型变异的9.9%～16.5%其中2个2环境共同检测到。株高检测到5个QTL,解释表型变异的6.4%～8.6%。穗位高检测到4个QTL,解释表型变异的6.7%～12.8%。粗缩病抗性检测到6个QTL,解释表型变异的6.9%～17.6%,其中有3个在两个环境检测到。在检测到位点的连锁群区段中,发现6个多效性区段。第23连锁群的g5M5708-n55区段同时控制穗粒重、穗总重、行粒数、穗粗和穗粒数性状。第12连锁群的g4M3707-g6M6811区段同时控制穗粒重、穗总重、行粒数、穗粒数和株高性状。第9连锁群的g5M5813-g6M6808区段同时控制穗行数、穗粒数和穗位高性状。第13连锁群的g7M778-g4M3801区段同时控制穗总重和穗粒重。第16连锁群的g8M8801-g8M8811区段同时控制百粒重和穗行数。第2连锁群的g5M5804-g5M5803区段同时控制百粒重和秃长。(2)用QTLNetwork软件中复合区间作图法,检测到9个主效QTL和7对非主效QTL间的互作。9个主效QTL分别是：1个控制穗粒重的位点YE5-12,解释表型变异7.4%；1个控制穗行数的位点RE9-15,解释表型变异的11.6%；2个控制穗总重的位点TWE5-12和TWE13-1,分别解释表型变异的7.3%和6.6%;2个控制轴粗的位点CD13-1和CD18-2,分别解释表型变异的7。7%和8.4%；1个控制穗粒数的位点(?)NE12-5,解释表型变异的7.2%；1个控制株高的位点PH5-10,解释表型变异的11.2%；1个控制粗缩病抗性的位点RDD2-22,解释表型变异的9.0%。7对非主效QTL间的互作发生在第1与3、5与18、3与5、5与19、7与12、1与2、5与13连锁群间,分别控制穗粒重、穗行数、百粒重、穗长、穗粗、秃尖长、粗缩病抗性7个性状,解释表型变异4.7%～11.9%。(3)运用多元回归模型和混合线性模型同时检测到的QTL有6个,分别是：控制穗粒重的YE5-12,控制穗粒数的KNE12-5,控制粗缩病抗性的RDD2-22,控制穗行数的RE9-15,控制株高的PH5-10和控制穗总重的TWE5-12。这6个QTL可靠性高,可用于进一步深入研究。
In China, the planting area for maize is24,634,000hm2, and the total yield of maize is121,310,000t with the unit area yield of4924.5kg/hm2. The planting area worldwide is137,500,000hm2, and total yield is602,200,0001with the unit area yield of4380kg/hm2. In USA unit area yield is10000kg/hm2. China's per unit area yield of maize is higher than world average level, but lag far behind that of the USA. Compared with USA there is great space for development of hybrid maize product ability. The increase of China's maize per unit yield has fluctuated since1990s, the reason for which is that the genetic basis of inbred lines is becoming narrower and narrower. Recently, new germplasm has been created and new inbred lines have been bred by introducing tropical-subtropical resources in maize in Institute of Agricultural Sciences of the Area Along Yangtse of Jiangsu. In order to evaluate the utilization value and genetic potential for further improvement of these inbred lines newly bred, we, firstly in this study, analysed general combining ability (GCA) and special combining ability (SCA) of11traits in9inbred lines (named as S1～S9) selected from the lines newly bred through seventy-two F1hybrids made by method3of Griffing diallel designs, and planted in Nantong and Nanjing, Jiangsu Province, respectively. Then, genetic analysis for9trait were conducted by using mixed major gene plus polygene inheritance models and joint segregation analytic method of P1, P2, F1, B1, B2and F2generations in two crosses made from the3elite inbred lines, S1, S3and S7. Lastly, QTL mapping was carried out for13traits by using a RIL population (109lines) made from a single cross hybrid of GY220/1145, using composite interval mapping method in both WinQTLCartographer2.5and QTLnetwork2.0softwares. Main research results obtained were as follows.
1. Among the F1hybrids, genetic variances of non-additive were larger than those of additive in the variations of grain yield per plot, kernel number per row, ear length and kernel weigth per plant, and genetic variances of additive were greater than those of non-additive in the variations of row number per ear,1000-kernel weight, ear diameter,100-kernel volume, stalk weight per plant and growth duration. Genetic variances of additive of plant height different between Nantong and Nanjing. Inbred lines S7and S3had excellent GCA for kernel yield per plot,1000-kernel weight,100-kernel volume and kernel weigth per plant. Inbred lines S6and S3showed elite GCA for rows per ear and ear diameter. Inbred lines S9and S2showed good GCA for kernel number per row and ear length. Inbred lines S7and S2showed good GCA for high plant height and Inbred lines S4and S1showed good GCA for low plant height. Summing up the evaluation from the11traits, the best line was S3, then S7, S2,S1and S5in order, and last is S4. GCA for kernel number per row was improved in S9into which tropic and subtropic germplasm was introduced. Reciprocal effects of the traits were not significant at5%probability level except kernel number per row in both sites and plant height in Nantong.
2. Kernel weight per ear and total weight per ear was controlled by two pairs of major gene with additive-dominance-epistatic effects plus polygenes with additive-dominance-epistatic effects, and the trait was mainly governed by major genes in both crosses.100-grain weight was controlled by two pairs of major gene with additive-dominance-epistatic effects plus polygenes with additive-dominance-epistatic effects in S1×S3, and by one pair of major gene with additive-dominance effects plus polygenes with additive-dominance effects in S3×S7, and was mainly governed by major genes in both crosses. Kernel row number was controlled by one additive-dominance major-gene and additive-dominance-epistasis polygenes, and was mainly governed by polygenes in cross S1×S3; whereas in cross S3×S7the trait was controlled by two additive-dominance-epistasis major-genes and additive-dominance-epistasis polygenes, and was mainly governed by major genes. Kernel number per row was controlled by one additive major-gene and additive-dominance polygenes, and was mainly governed by major gene in both crosses. Total effect of dominance was larger than that of additive in polygene loci. Ear length was controlled by additive-dominance-epistasis polygenes in cross S1×S3; whereas in cross S3×S7the trait was controlled by one additive-dominance major-gene and additive-dominance-epistasis polygenes. Ear diameter was controlled by one additive-dominance major-gene and additive-dominance-epistasis polygenes in cross S1×S3; whereas in cross S3×S7the trait was controlled by one wholly dominance major-gene and additive-dominance polygenes. Ear length and Ear diameter either was mainly governed by polygenes. Cob diameter was controlled by two additive-dominance-epistasis major-gene and additive-dominance polygenes in cross S1×S3; whereas in cross S3×S7the trait was controlled by two additive-dominance-epistasis major-gene and additive-dominance-epistasis polygenes, and was mainly governed by polygenes.100-kernel weight, kernel weight per ear, total weight per ear and kernel number per row was mainly governed by major genes.
3.(1) Sixty-three loci were detected for the13traits by the CIM method. For trait of yield per ear,4QTLs, explaining7.8%～25.8%of phenotypic variation, were detected and2of the4were detected in both environments. For trait of rows per ear,4QTLs, explaining6.4%～11.7%of phenotypic variation, were detected and1of the4was detected in both environments. For trait of kernel number per row,4QTLs, explaining8.4%～17.2%of phenotypic variation, were detected and1of the4was detected in both environments. For trait of100-kernel weight,6QTLs, explaining7.0%～13.8%of phenotypic variation, were detected. For trait of total weight per ear,3QTLs, explaining8.5%～10.3%of phenotypic variation, were detected. For trait of ear length,3QTLs, explaining11.9%～22.0%of phenotypic variation, were detected. For trait of ear diameter,6QTLs, explaining6.7%～26.4%of phenotypic variation, were detected. For trait of cob diameter,3QTLs, explaining6.9%～17.0%of phenotypic variation, were detected. For trait of tip barren length,4QTLs, explaining6.5%～25.7%of phenotypic variation, were detected. For trait of kernel number per ear,3QTLs, explaining9.9%～16.5%of phenotypic variation, were detected and2of the3were detected in both environments. For trait of plant height,5QTLs, explaining6.4%～8.6%of phenotypic variation, were detected. For trait of ear height,4QTLs, explaining6.7%～12.8%of phenotypic variation, were detected. For trait of rough dwarf disease,6QTLs, explaining6.9%～17.6%of phenotypic variation, were detected and3of the6were detected in both environments. Six intervals with multiple effects were found among the intervals harboring loci. Locus between g5M5708-n55on23linkage conditioned yield per ear, total weight per ear, kernel number per row, ear diameter and kernel number per ear simultaneously. Locus between g4M3707-g6M6811on12linkage conditioned yield per ear, total weight per ear, kernel number per row, kernel number per ear and plant height simultaneously. Locus between g5M5813-g6M6808on9linkage conditioned row per ear, kernel number per ear and ear height simultaneously. Locus between g7M778-g4M3801on13linkage conditioned total weight per ear and yield per ear simultaneously. Locus between g8M8801-g8M8811on16linkage conditioned100-kernel weight and row per ear simultaneously. Locus between g5M5804-g5M5803on2linkage conditioned100-kernel weight and tip barren length simultaneously.(2) Nine main effect QTLs and7pairs of interaction between non main effect QTLs were detected by the QTLNetwork method. The9main effect QTLs were as follows. One was YE5-12, controlling yield per ear, explained7.4%of phenotypic variation. One was RE9-15, controlling row per ear, explained11.6%of phenotypic variation. Two were TWE5-12and TWE13-1, controlling total weight per ear, explained7.3%and6.6%of phenotypic variation respectively. Two were CD13-1and CD18-2, controlling cob diameter, explained7.7%and8.4%of phenotypic variation respectively. One was KNE12-5, controlling kernel number per ear, explained7.2%of phenotypic variation. One was PH5-10, controlling plant height, explained11.2%of phenotypic variation. One was KNE12-5, controlling kernel number per ear, explained7.2%of phenotypic variation. One was RDD2-22, controlling rough dwarf disease, explained9.0%of phenotypic variation. Seven pairs of interaction between non main effect QTLs were occurred linkages between1and3,5and18,3and5,5and19,7and12,1and2, and5and13, controlling yield per ear, row per ear,100-kernel weight, ear length, ear diameter, tip barren length and rough dwarf disease respectively, and explained4.7%-11.9%of phenotypic variation.(3) Six QTLs were detected simultaneously by the two genetic models, i.e. multiple regression model and mixed linear model. The6QTLs were YE5-12, controlling yield per ear, KNE12-5, controlling kernel number per ear, RDD2-22, controlling rough dwarf disease, RE9-15, controlling row per ear, PH5-10, controlling plant height, and TWE5-12, controlling total weight per ear. It is worth to study the6QTLs further since they are reliable QTLs.
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