摘要
高油玉米具有全面的优良籽粒品质和较高利用价值,是优质玉米领域发展较快的一种类型。前人主要针对IHO、IHP和BHO种质背景,利用单一遗传背景和测交群体对玉米籽粒品质性状开展了QTL定位研究。本研究选用与IHO和BHO具有不同种质背景的高油玉米自交系GY220,同时与两个普通玉米自交系8984和8622杂交构建的两组284个(P1)和265个(P2)F2和F2:3家系群体,利用SSR分子标记构建高密度遗传图谱,通过同时进行两种不同环境条件下的田间试验,采用复合区间作图法定位籽粒油分、蛋白质、淀粉含量3个籽粒品质性状QTL,采用多区间作图法分析定位QTL间的上位效应,并采用条件QTL分析和多性状联合分析的复合区间作图方法探讨各籽粒品质性状间及其与主要产量性状间的分子遗传关系,筛选具有不同遗传背景和环境稳定表达的有效分子标记,为玉米籽粒品质性状的分子遗传剖析、分子标记辅助育种、QTL精细定位及克隆奠定基础。主要试验和结论如下:
1.用665对SSR引物分别对两个组合普通玉米自交系8984×GY220 (P1)和普通玉米自交系8622×GY220 (P2)的亲本自交系进行多态性检测,分别筛选出212对和205对多态性标记。两组亲本间多态性标记存在较大差异,仅有104对相同,占49.1%和50.7%。去除严重偏分离的标记,用Mapmaker3.0b作图软件构建两张分别包含185个和173个多态性标记的玉米遗传连锁图谱,图谱总长度分别为2111.7 cM和2298.5 cM,相邻标记间平均距离为11.41 cM和13.29 cM。
2.两组F2和F2:3家系群体在两种环境条件下共检测到53个籽粒品质性状相关QTL,P1F2和P1F2:3家系群体检测到10个和22个QTL,P2F2和P2F2:3家系群体检测到6个和19个QTL,两组群体没有定位到位于相同标记区间的QTL。
3.籽粒油分含量共检测到22个QTL,18个与以往利用不同材料的相关研究具有相同或相似的染色体bin位点,2个籽粒油分含量QTL(qzOIL1-10-1、qzOIL1-4-1)与胚乳突变体基因accB(乙酰CoA)、bt2(脆质胚乳2)具有相同的染色体bin位点。其中P1F2和P1F2:3家系群体检测到5个和7相关QTL,P2F2和P2F2:3家系群体检测到3个和9个相关QTL,位于第1、3、4、5、6、7、8和10染色体上。其中2个QTL在来自同一组合的F2和F2:3家系群体均检测到,F2:3家系群体在两种环境条件下未检测到共同QTL,5个QTL在F2:3家系群体一个环境和两环境平均都检测到。单个QTL贡献率为5.0%～26.5%,12个QTL的贡献率大于10%,其中有11个QTL在以往研究中均被检测到,同时也具有较大的贡献率。具有环境稳定性和不同研究一致性的QTL qlOIL2-8-1和qzOIL2-6-1适宜作为进一步研究和实施MAS的主要目标。油分含量QTL位点的增效基因除一个来自普通玉米亲本自交系8622外,其余21个均来自高油亲本GY220,充分显示针对籽粒油分含量长期选择对相关基因的高度聚集效果。
4.籽粒蛋白质含量共检测到12个QTL,9个与以往利用不同材料的相关研究具有相同或相似的染色体bin位点,qpPRO1-3-1与胚乳突变体基因sh2(皱缩2)具有相同的染色体bin位点。其中P1F2和P1F2:3家系群体检测到3个和6个相关QTL,P2F2和P2F2:3家系群体检测到0个和3个相关QTL,位于第1、3、5、6、8、9和10染色体上。F2和F2:3家系群体及F2:3家系群体在两种环境条件下均未检测到共同QTL,2个QTL在F2:3家系群体一个环境和两环境平均都被检测到。单个QTL贡献率为3.9%～17.5%,其中6个QTL的贡献率大于10%,有4个QTL在以往研究中均被检测到,同时也具有较大贡献率。qxPRO1-8-1和qxPRO1-8-2具有环境稳定性和不同研究一致性,适宜作为进一步研究和实施MAS的主要目标QTL。5个蛋白质含量的增效基因来自于高油亲本GY220,5个和2个分别来自普通亲本8984和8622。
5.籽粒淀粉含量共检测到19个QTL,14个与以往利用不同材料的相关研究具有相同或相似的染色体bin位点,qpSTA1-5-2与胚乳突变体基因ae1(直链淀粉扩充者)具有相同的染色体bin位点。其中P1F2和P1F2:3家系群体检测到2个和9相关QTL,P2F2和P2F2:3家系群体检测到3个和7个相关QTL,位于第2、4、5、6、8和10染色体上。其中2个QTL在来自同一组合的F2和F2:3家系群体均检测到,1个QTL在F2:3家系群体两种环境条件下均检测到,2个QTL在F2:3家系群体一个环境和两环境平均都检测到。单个QTL贡献率为2.9%～14.0%,其中6个QTL的贡献率大于10%,有5个QTL在以往研究中均被检测到,同时也具有较大的贡献率。qzSTA2-6-1具有环境稳定性和不同研究一致性,适宜作为进一步研究和实施MAS的主要目标QTL。分别有10个和8个淀粉含量QTL的增效基因来自普通亲本8984和8622,仅一个增效基因来自高油亲本GY220。
6.籽粒油分、蛋白质和淀粉含量表现为加性、部分显性、完全显性和超显性的QTL数目为12个、25个、7个和9个,部分显性和加性对玉米籽粒品质性状的遗传起着主要作用。定位QTL间的互作效应较小。
7.条件QTL分析表明,百粒重、穗粒重对籽粒蛋白质含量QTL的表达影响最大,对籽粒油分、淀粉含量QTL的表达也有较大影响,籽粒蛋白质和淀粉含量对油分含量QTL的表达均有较大的影响。与籽粒油分、蛋白质和淀粉含量相关的3个、0个和2个QTL基本不受其它性状的影响,可以实现单个QTL的分子聚合。多性状联合QTL分析可以提高检测功效,第1、2、4、5、6、7、8和10染色体上控制不同籽粒品质性状及第1、2、4、5、6、7、8、9和10染色体上控制产量性状和籽粒品质性状的QTL存在一因多效或紧密连锁。
High-oil corn is fastly developing in quality maize breeding with its comprehensive kernel nutritional quality traits and higher commercial value. Previous researches about QTL mapping for kernel nutritional quality traits have been conducted with IHO, IHP and BHO high-oil corn germplasms and using single genetic background and testcross populations. In this study, 284 and 265 F2 populations and their F2:3 family lines were respectively developed from two crosses between the same high-oil corn inbred GY220 and two normal corn inbreds, 8984 and 8622. The germplasm background of GY220 was different from IHO and BHO. SSR markers were used to construct high-density genetic maps. QTL associated with three kernel nutritional quality characters were detected according to the LOD thresholds after 1000 permutations. Using composite interval mapping (CIM) method, QTL mapping of kernel oil, protein and starch concentration was done for the two environments individually and together. 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 for two or three different characters were done using CIM method of multiple traits analysis among three kernel nutritional quality and two main yield component characters. Our objectives were to reveal the molecular genetic mechanism of these characters and the genetic correlations among different kernel nutritional quality characters, and between these characters and yield components, and to select effective molecular markers with stable expression across different populations and environments. These results will do great help in fine mapping QTL associated with kernel nutritional quality characters 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 parents of two crosses, 8984×GY220 (P1) and 8622×GY220 (P2). 212 and 205 polymorphism markers were selected, respectively. Great differences in polymorphism markers were found between the two sets of parents. Only 104 pairs of markers were in common, which accounted for 49.1% and 50.7%, respectively. 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. 53 QTL were detected for three kernel nutritional quality characters using both F2 and F2:3 families of the two crosses under two environments. 10 and 22 QTL were detected in P1F2 population and P1F2:3 families, repectively. In P2F2 population and P2F2:3 families 6 and 19 QTL were detected. No common QTL were detected in both populations.
3. 22 QTL for kernel oil concentration were detected. Of these, 18 QTL were on identical or similar chromosomal bin locations with previous studies. qzOIL1-10-1 and qzOIL1-4-1 had the same chromosome regions as endosperm mutant genes accB and bt2, respectively. 5 and 7 QTL were detected in P1F2 population and P1F2:3 families, and 3 and 9 QTL for oil in P2F2 population and P2F2:3 families. These QTL were located on chromosome 1, 3, 4, 5, 6, 7, 8 and 10. Two identical QTL were detected in both F2 population and F2:3 families from the same cross. No identical QTL was detected in both F2:3 families. 5 QTL were simultaneously detected in F2:3 families under one environment and the average of two enviroments. The contribution of single QTL to phenotypic variation varied from 5.0% to 26.5%. Of 12 QTL with contributions higher than 10%, 11 QTL were detected by previous researchers. qlOIL2-8-1 and qzOIL2-6-1 had stability between different environments and consistency in different studies. They could be used as the main objevtive QTL in further studies and in MAS. Except one, all the favorable alleles of 21 QTL were from the high-oil corn parent GY220, which clearly showed high degree of kernel oil gene pyramiding through long-term selection.
4. Of 12 QTL for kernel protein concentration, 9 QTL were on identical or similar chromosomal bin locations with previous studies. qpPRO1-3-1 had the same chromosome regions as endosperm mutant gene sh2. 3 and 6 protein QTL were detected in P1F2 population and P1F2:3 families. No protein QTL was detected in P2F2 population. 3 QTL were detected in P2F2:3 families. These QTL were located on chromosome 1, 3, 5, 6, 8, 9 and 10. No identical QTL was detected in both F2 population and F2:3 families, and in F2:3 families under two environments. 2 QTL were simultaneously detected in F2:3 families under one environment and the average of two enviroments. The contribution of single QTL to phenotypic variation varied from 3.9% to 17.5%. Of 6 QTL with contributions more than 10%, 4 QTL were detected by previous researchers. qxPRO1-8-1 and qzPRO1-1-1 could be used as the main objective QTL in further studies and in MAS with their stability between different environments and consistency between different studies. The favorable alleles of 5 QTL were contributed by GY220, and 5 and 2 QTL were from 8984 and 8622, repectively.
5. 19 QTL for kernel starch concentration were detected. 9 QTL were on identical or similar chromosomal bin locations with those previous studies. qpSTA1-5-2 had the same chromosome regions to endosperm mutant gene ae1. 2 and 9 starch QTL were detected in P1F2 population and P1F2:3 families, and 3 and 7 QTL in P2F2 population and P2F2:3 families. These QTL were located on chromosome 2, 4, 5, 6, 8 and 10. Three identical QTL were detected in both F2 population and F2:3 families from the same cross. One identical QTL was detected in F2:3 families under two environments. Two QTL were simultaneously detected in F2:3 families under one environment and the average of two enviroments. The contribution of single QTL to phenotypic variation varied from 2.9% to14.0%. Of 6 QTL with contributions higher than 10%, 5 QTL were detected by previous researchers. qzSTA2-8-1 and qzSTA2-6-1 had stability between different environments and consistency between different studies, which could be used as the main objective QTL in further studies and in MAS. In all favorable QTL for starch concentration, only one was from GY220, and 10 and 8 QTL were from 8984 and 8622, repectively.
6. The numbers of QTL expressing additive, partially dominance, dominance and over-dominance effects were 12, 25, 7, 9. Partially dominance and additive effects all played important role in the heredity of kernel nutritional quality characters. Very low interaction effects between detected QTL were estimated.
7. The results of conditional QTL mapping showed that 100-kernel weight and ear-kernel weight had the greatest effect on kernel protein concentration. Their effects on kernel oil and starch concentration were also significant. Kernel oil concentration was also affected greatly by kernel protein and starch concentration. 3 and 2 QTL associated with oil and starch concentration were not affected by other characters, which could be pyramided in single QTL level. The QTL controlling different kernel nutritional quality characters mapped on chromosome 1, 2, 4, 5, 6, 7, 8 and 10, and QTL for them and yield components on chromosome 1, 2, 4, 5, 6, 7, 8,9 and 10, showed pleiotropy or tight linkage.
引文
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