基于染色体片段导入系发掘抗玉米丝黑穗病主效QTL

选用抗玉米丝黑穗病自交系Mo17和SH15为供体,与受体感病自交系黄早四和昌7-2构建回交群体(BC3F1\BC4F2),通过田间人工接种玉米丝黑穗病原菌鉴定抗病性表现,评价群体抗病性。研究结果显示黄早四×(黄早四×Mo17)BC4F2群体发病率明显高于BC3F1群体;两个BC4F2黄早四×(黄早四×Mo17)和昌7-2×(昌7-2×SH15)群体的发病率差异较大。采用SSR标记分析抗病株的供体染色体导入片段,发现随着回交次数的增多,导入片段数量减少,但不同回交群体中供体导入片段数目明显不同。通过连锁不平衡分析,在染色体2.09和3.04区段发掘和验证2个抗玉米丝黑穗病主效QTL,连锁标记分别为umc2077和phio53或bnlg1965。本文研究结果为抗丝黑穗病基因精细定位和分子聚合育种提供了信息和材料。     

Identification of Major QTL for Waterlogging Tolerance Using Genome-Wide Association and Linkage Mapping of Maize Seedlings

To investigate the genetic basis of maize seedling response to waterlogging, we performed a genome-wide association study in 144 maize inbred lines, measuring length, fresh and dry weight of roots and shoots under normal and waterlogged conditions using 45,868 SNPs. This panel was divided into three subgroups based on the population structure results and the LD decay distance was 180 kb. A biparental advanced backcross (AB) population was also used to detect quantitative trait loci (QTL). In a comparison of 16 different models, principal components analysis (PCA/top PC3)?+?K was found to be best for reduction of false-positive associations for further analysis. A whole-genome scan detected four strong peak signals (P <?2.18?×?10^?5) significantly associated with the waterlogging response on chromosomes 5, 6 and 9. SNP4784, SNP200, SNP298, and SNP6314 showed significant association with corresponding traits under waterlogging and explained 14.99–19.36 %, 15.75–17.64 %, 16.08 % and 15.44 % of the phenotypic variation, respectively. The identified SNPs were located in GRMZM2G012046, GRMZM2G009808, GRMZM2G137108 and GRMZM2G369629 (AGPV1). SNP4784 (GRMZM2G012046) was colocalized with the major QTL that was identified with the same traits in the AB population. Forty-seven SNPs significantly associated (P?<?2.18?×?10^?4) with six traits in association mapping were identified and, among these, 33 SNPs were already reported in literature as waterlogging-related traits. These results will help elucidate the genetic basis of differential responses and tolerance to waterlogging stress among maize inbred lines, and provide novel loci for improvement of waterlogging tolerance of maize inbred lines using marker-assisted selection.     

玉米抗丝黑穗病基因RAPD分析

该文应用RAPD技术,以感病母本3045和抗病父本3024为试材,应用BSA法,对玉米抗丝黑穗病基因进行分析,建立并优化了玉米的RAPD反应体系,筛选了61条引物RAPD随机引物,18条引物为适宜引物,共扩增出89条DNA谱带,得到了4条具有稳定多态性标记的引物,分别为OPY-09、OPY-10、OPY-18、OPP-09。     

Association and Linkage Analysis of Aluminum Tolerance Genes in Maize

Background

Aluminum (Al) toxicity is a major worldwide constraint to crop productivity on acidic soils. Al becomes soluble at low pH, inhibiting root growth and severely reducing yields. Maize is an important staple food and commodity crop in acidic soil regions, especially in South America and Africa where these soils are very common. Al exclusion and intracellular tolerance have been suggested as two important mechanisms for Al tolerance in maize, but little is known about the underlying genetics.

Methodology

An association panel of 282 diverse maize inbred lines and three F₂ linkage populations with approximately 200 individuals each were used to study genetic variation in this complex trait. Al tolerance was measured as net root growth in nutrient solution under Al stress, which exhibited a wide range of variation between lines. Comparative and physiological genomics-based approaches were used to select 21 candidate genes for evaluation by association analysis.

Conclusions

Six candidate genes had significant results from association analysis, but only four were confirmed by linkage analysis as putatively contributing to Al tolerance: Zea mays Alt_SB like (ZmASL), Zea mays aluminum-activated malate transporter2 (ALMT2), S-adenosyl-L-homocysteinase (SAHH), and Malic Enzyme (ME). These four candidate genes are high priority subjects for follow-up biochemical and physiological studies on the mechanisms of Al tolerance in maize. Immediately, elite haplotype-specific molecular markers can be developed for these four genes and used for efficient marker-assisted selection of superior alleles in Al tolerance maize breeding programs.     

玉米5个农艺性状的QTL定位

利用“豫玉22”构建的266个玉米F2：3家系为材料,通过一年两点的随机区组田间试验和分子标记分析,研究了玉米穗位高、雄穗分支数、茎粗、抽雄期、吐丝期5个重要农艺性状。相关分析表明,穗位高、雄穗分支数、茎粗与单株产量显著正相关,抽雄期与吐丝期高度正相关,雄穗分支数与茎粗显著正相关。采用复合区间作图法,通过500次排列测验分别确定各性状的LOD阈值,在武汉和襄樊两地共定位了7个穗位高QTL、9个雄穗分支数QTL、8个茎粗QTL、9个抽雄期QTL和7个吐丝期QTL;这些QTL在染色体上分布不均匀,具有集中分布的特点。研究表明,数量性状间的表型相关可能源于控制数量性状的QTL位点间的相关。     

引进美国GEM材料的抗玉米青枯病和丝黑穗病种质资源筛选鉴定

近年来,青枯病、丝黑穗病上升为我国玉米生产中的主要病害,挖掘新的抗病资源,进行抗病育种是解决病害威胁的根本所在。本研究对引进的美国GEM材料进行了接种条件下的抗病性鉴定,并结合抗病材料的多年重复验证,筛选出一批新的抗病种质资源。这些材料的深入研究和广泛应用,对拓宽我国玉米青枯病、丝黑穗病抗性遗传基础,增加种质资源遗传多样性具有重要利用价值。     

玉米RFLP连锁图谱构建及大斑病QTL定位

玉米大斑病菌存在有生理小种分化的现象,目前5个已定名的生理小种在我国均已发现,还有一些尚未定位名的新类群也出出现,提高玉米对大斑病的抗性,只有提高数量抗性才能达到目的,为了弄清楚玉米对大斑病数量抗性的基因数目及效效应,利用抗病自交5系P138和感病自交系缩3为亲本构建了F2：3家系群体,采用RFLP标记构建了包含了124个标记的玉米RFLP连锁图,覆盖玉米基因组1999．8cM,标记间平均距离为16．5cM,定位了玉米大斑病的病斑长,病斑宽和病斑面积的QTL分别为3、3、2个,其联合贡献率分别为58．1％、71．5％和27．5％,没有检测到病斑数／叶的QTL,其表现为单基因或者寡基因控制的性状,研究结果增加了对玉米大斑病的认识,对玉米抗大斑病育种具有重要的指导意义。     

玉米雄穗颜色QTL分析

雄穗是玉米的重要生殖器官,不同品种间玉米的雄穗外观差异明显。对玉米雄穗的颜色进行遗传分析和QTL定位,筛选与雄穗颜色紧密连锁的分子标记,可以作为玉米的品种保护和品种鉴别的有用工具。同时,紫色雄穗中花色苷类色素含量较高,与玉米雄穗的抗虫性密切相关。本研究利用一个黑玉米自交系SDM为共同父本,分别与白玉米自交系木6和黄玉米自交系Mo17杂交,构建2个相关F2∶3群体,分别命名为MuS(木6×SDM)和MoS(Mo17×SDM),在云南和重庆两个不同的环境中种植,对玉米花药颜色(COAn)和花药护颖颜色(COCa)2个性状进行QTL定位。结果表明:玉米花药和花药护颖的颜色均为数量性状,受主效基因和微效基因共同控制。2个群体在2个环境中共检测到7个与花药颜色相关的QTL,位于第2、3、6和10染色体上,其中位于第10染色体标记区间umc1196a-IDP8526内的QTL在重庆和云南同时表达,对表型的贡献率分别为23.17%和19.98%;2个群体在2个环境中共检测到9个与花药护颖颜色相关的QTL,位于第3、6、9和10染色体上,其中3个QTL为环境钝感QTL(在2个环境中均表达,且至少在1个环境中贡献率大于10%),分别位于第6染色体标记区间umc1979-umc1796、mmc0523-umc2006内和第10染色体标记区间umc1196a-umc2043内,对表型的贡献率为10.69%～59.30%。2个群体检测到的主效QTL的位置和效应高度一致,且控制花药颜色和花药护颖颜色2个性状的主效QTL有连锁分布的现象,主要表现在bins 6.04处的标记mmc0523和bins 10.04处的标记IDP8526附近。位于第6和第10染色体上的在不同环境和遗传背景下稳定的QTL可以作为进一步精细定位的靶位点,也可以为玉米雄穗颜色的分子标记辅助选择提供有价值的参考。     

Dynamic QTL Analysis and Candidate Gene Mapping for Waterlogging Tolerance at Maize Seedling Stage

Soil waterlogging is one of the major abiotic stresses adversely affecting maize growth and yield. To identify dynamic expression of genes or quantitative trait loci (QTL), QTL associated with plant height, root length, root dry weight, shoot dry weight and total dry weight were identified via conditional analysis in a mixed linear model and inclusive composite interval mapping method at three respective periods under waterlogging and control conditions. A total of 13, 19 and 23 QTL were detected at stages 3D|0D (the period during 0–3 d of waterlogging), 6D|3D and 9D|6D, respectively. The effects of each QTL were moderate and distributed over nine chromosomes, singly explaining 4.14–18.88% of the phenotypic variation. Six QTL (ph6-1, rl1-2, sdw4-1, sdw7-1, tdw4-1 and tdw7-1) were identified at two consistent stages of seedling development, which could reflect a continuous expression of genes; the remaining QTL were detected at only one stage. Thus, expression of most QTL was influenced by the developmental status. In order to provide additional evidence regarding the role of corresponding genes in waterlogging tolerance, mapping of Expressed Sequence Tags markers and microRNAs were conducted. Seven candidate genes were observed to co-localize with the identified QTL on chromosomes 1, 4, 6, 7 and 9, and may be important candidate genes for waterlogging tolerance. These results are a good starting point for understanding the genetic basis for selectively expressing of QTL in different stress periods and the common genetic control mechanism of the co-localized traits.     

Linkage Disequilibrium with Linkage Analysis of Multiline Crosses Reveals Different Multiallelic QTL for Hybrid Performance in the Flint and Dent Heterotic Groups of Maize

Multiparental designs combined with dense genotyping of parents have been proposed as a way to increase the diversity and resolution of quantitative trait loci (QTL) mapping studies, using methods combining linkage disequilibrium information with linkage analysis (LDLA). Two new nested association mapping designs adapted to European conditions were derived from the complementary dent and flint heterotic groups of maize (Zea mays L.). Ten biparental dent families (N = 841) and 11 biparental flint families (N = 811) were genotyped with 56,110 single nucleotide polymorphism markers and evaluated as test crosses with the central line of the reciprocal design for biomass yield, plant height, and precocity. Alleles at candidate QTL were defined as (i) parental alleles, (ii) haplotypic identity by descent, and (iii) single-marker groupings. Between five and 16 QTL were detected depending on the model, trait, and genetic group considered. In the flint design, a major QTL (R² = 27%) with pleiotropic effects was detected on chromosome 10, whereas other QTL displayed milder effects (R² < 10%). On average, the LDLA models detected more QTL but generally explained lower percentages of variance, consistent with the fact that most QTL display complex allelic series. Only 15% of the QTL were common to the two designs. A joint analysis of the two designs detected between 15 and 21 QTL for the five traits. Of these, between 27 for silking date and 41% for tasseling date were significant in both groups. Favorable allelic effects detected in both groups open perspectives for improving biomass production.     

基于元分析的抗玉米丝黑穗病QTL比较定位

以玉米遗传连锁图谱IBM2 2005 Neighbors为参考图谱,通过映射整合不同试验中的抗玉米丝黑穗病QTL,构建QTL综合图谱。在国内外种质中,共发现22个抗病QTL,分布在除第7染色体外的9条玉米染色体上。采用元分析技术,获得2个“一致性”抗病QTL,图距分别为8.79 cM和18.92cM。从MaizeGDB网站下载“一致性”QTL区间内基因和标记的原始序列;采用NCBI网站在线软件BLASTx通过同源比对在2个“一致性”QTL区间内初步获得4个抗病位置候选基因。借助比较基因电子定位策略,将69个水稻和玉米抗性基因定位于玉米IBM2图谱上,在2个“一致性”QTL区间内分别发现1个水稻抗性基因,初步推断为抗病位置候选基因。本文结果为抗玉米丝黑穗病QTL精细定位和分子育种提供了基础。     

玉米子粒中锌及铁铜锰含量的遗传及QLT分析

分别利用三交组合DH8×登海40和DH86×沈137创建F1代DH群体A和群体B,比较2个不同遗传背景下DH群体子粒中锌及铁铜锰含量的变化,并对2个供试群体2年间的试验结果进行了QTL分析。发现玉米子粒中锌及铁铜锰的含量在不同个体间、不同年份间受环境影响比较大,且表现不稳定;群体内呈现连续性数量性状变化,服从于正态分布。对群体A2年子粒中锌及铁铜锰含量进行QTL定位,结果2007年检测到了5个与这些性状有关的QTL,可解释的遗传变异范围为9.41%~43.67%;2008年检测到9个QTL,可解释的遗传变异范围为11.21%~42.96%。2年间末检测到相同的QTL位点。对群体B进行QTL定位,2年间检测到18个QTL位点,分布于除第5染色体以外的其余9条染色体上。其中,2007年检测到12个QTL,2008年获得6个QTL,相同的QTL为2个,控制锌含量的1个位点位于第3染色体的p-umc1399-p-bnlg1605区段,控制铜含量的1个位点位于第2染色体的bnlg1746区段。     

Identification, Mapping and Linkage Analysis of Randomly Amplified DNA Polymorphisms in Tetrahymena Thermophila

Using the random amplified polymorphic DNA (RAPD) technique and exploiting the unique genetics of Tetrahymena thermophila, we have identified and characterized 40 DNA polymorphisms occurring between two inbred strains (B and C3) of this ciliated protozoan. These RAPD markers permit the PCR amplification of a DNA species using template DNA from SB1969 (B strain) but fail to do so using DNA from C3-368-5 (C3 strain). Polymorphisms were mapped to chromosomes using a panel of monosomic strains constructed by crossing B strain-derived nullisomic strains to inbred strain C3. They map to all five chromosomes and appear to be evenly distributed throughout the genome. Chromosomal groups were then analyzed for linkage using meiotic segregants; four linkage groups were identified in chromosomes 1R, 2L, 3 and 5. The RAPD method appears useful for the construction of a genetic map of the Tetrahymena genome based on DNA polymorphisms.     

基于玉米导入系群体的3个农艺性状QTL分析

通过回交育种程序结合SSR标记构建以玉米自交系农系531为受体亲本、10个具有不同农艺性状的自交系为供体的染色体片段导入系群体,在该群体BC3F3世代,利用GGT32图示基因型软件和Windows QTL IciMapping v1.0对导入片段进行检测、并结合田间调查对控制玉米穗位高、穗上叶夹角和株高的QTL进行分析.研究表明,导入系群体的创建在遗传结构上改良了农系531穗位偏高、穗上叶夹角偏大的不足,并得到基本符合育种目标的改良株系.通过QTL分析,在具有相同性状改良的单株上,分别检测到4个包含穗位高、4个包含穗上叶夹角和6个包含株高QTL的共同的导入染色体片段.     

BC群体不同连锁模式分子区间标记QTL作图相关方法的精确度分析

该文分析了BC群体不同连锁模式分子区间标记QTL作图相关方法的精确度,提出了相应参数的适用范匿,连锁顺序的检测方法,分析步骤,为QTL作图提供了理论基础。     

Genome-Wide Association of Carbon and Nitrogen Metabolism in the Maize Nested Association Mapping Population

Carbon (C) and nitrogen (N) metabolism are critical to plant growth and development and are at the basis of crop yield and adaptation. We performed high-throughput metabolite analyses on over 12,000 samples from the nested association mapping population to identify genetic variation in C and N metabolism in maize (Zea mays ssp. mays). All samples were grown in the same field and used to identify natural variation controlling the levels of 12 key C and N metabolites, namely chlorophyll a, chlorophyll b, fructose, fumarate, glucose, glutamate, malate, nitrate, starch, sucrose, total amino acids, and total protein, along with the first two principal components derived from them. Our genome-wide association results frequently identified hits with single-gene resolution. In addition to expected genes such as invertases, natural variation was identified in key C₄ metabolism genes, including carbonic anhydrases and a malate transporter. Unlike several prior maize studies, extensive pleiotropy was found for C and N metabolites. This integration of field-derived metabolite data with powerful mapping and genomics resources allows for the dissection of key metabolic pathways, providing avenues for future genetic improvement.Carbon (C) and nitrogen (N) metabolism are the basis for life on Earth. The production, balance, and tradeoffs of C and N metabolism are critical to all plant growth, yield, and local adaptation (Coruzzi and Bush, 2001; Coruzzi et al., 2007). In plants, there is a critical balance between the tissues that are producing energy (sources) and those using it (sinks), as the identities and locations of these vary through time and developmental stage (Smith et al., 2004). While a great deal of research has focused on the key genes and proteins involved in these processes (Wang et al., 1993; Kim et al., 2000; Takahashi et al., 2009), relatively little is known about the natural variation within a species that fine-tunes these processes in individual plants.In addition, a key aspect of core C metabolism involves the nature of plant photosynthesis. While the majority of plants use standard C₃ photosynthetic pathways, some, including maize (Zea mays) and many other grasses, use C₄ photosynthesis to concentrate CO₂ in bundle sheath cells to avoid wasteful photorespiration (Sage, 2004). Under some conditions (such as drought or high temperatures), C₄ photosynthesis is much more efficient than C₃ photosynthesis. Since these conditions are expected to become more prevalent in the near future due to climate change, various research groups are working to convert C₃ crop species to C₄ metabolism in order to boost crop production and food security (Sage and Zhu, 2011). Beyond this, better understanding of both C₃ and C₄ metabolic pathways will aid efforts to breed crops for superior yield, N-use efficiency, and other traits important for global food production.In the last two decades, quantitative trait locus (QTL) mapping, first with linkage analysis and later with association mapping, has been used to dissect C and N metabolism in several species, including Arabidopsis (Arabidopsis thaliana; Mitchell-Olds and Pedersen, 1998; Keurentjes et al., 2008; Lisec et al., 2008; Sulpice et al., 2009), tomato (Solanum lycopersicum; Schauer et al., 2006), and maize (Hirel et al., 2001; Limami et al., 2002; Zhang et al., 2006, 2010a, 2010b). These studies identified key genetic regions underlying variation in core C and N metabolism, many of which include candidate genes known to be involved in these processes.Previous studies of genetic variation for C and N metabolism are limited by the fact that they identified trait loci only through linkage mapping in artificial families or through association mapping across populations of unrelated individuals. Linkage mapping benefits from high statistical power due to many individuals sharing the same genotype at any given location, but it suffers from low resolution due to the limited number of generations (and hence recombination events) since the initial founders. Association mapping, in turn, enjoys high resolution due to the long recombination histories of natural populations but suffers from low power, since most genotypes occur in only a few individuals. In addition, many of these studies focused on C and N in artificial settings (e.g. greenhouses or growth chambers) instead of field conditions, running the risk that important genetic loci could be missed if the conditions do not include important (and potentially unknown) natural environmental variables.To address these issues and improve our understanding of C and N metabolism in maize, we used a massive and diverse germplasm resource, the maize nested association mapping (NAM) population (Buckler et al., 2009; McMullen et al., 2009), to evaluate genetic variation underlying the accumulation of 12 targeted metabolites in maize leaf tissue under field conditions. This population was formed by mating 25 diverse maize lines to the reference line, B73, and creating a 200-member biparental family from each of these crosses. The entire 5,000-member NAM population thus combines the strengths of both linkage and association mapping (McMullen et al., 2009), and it has been used to identify QTLs for important traits such as flowering time (Buckler et al., 2009), disease resistance (Kump et al., 2011; Poland et al., 2011), and plant architecture (Tian et al., 2011; Peiffer et al., 2013). Most importantly, this combination of power and resolution frequently resolves associations down to the single-gene level, even when using field-based data.The metabolites we profiled are key indicators of photosynthesis, respiration, glycolysis, and protein and sugar metabolism in the plant (Sulpice et al., 2009). By taking advantage of a robotized metabolic phenotyping platform (Gibon et al., 2004), we performed more than 100,000 assays across 12,000 samples, with two independent samples per experimental plot. Raw data and the best linear unbiased predictors (BLUPs) of these data were included as part of a study of general functional variation in maize (Wallace et al., 2014), but, to our knowledge, this is the first in-depth analysis of these metabolic data. We find strong correlations among several of the metabolites, and we also find extensive pleiotropy among the different traits. Many of the top QTLs are also near or within candidate genes relating to C and N metabolism, thus identifying targets for future breeding and selection. These results provide a powerful resource for those working with core C and N metabolism in plants and for improving maize performance in particular.     

QTL Mapping of Low-Temperature Germination Ability in the Maize IBM Syn4 RIL Population

Low temperature is the primary factor to affect maize sowing in early spring. It is, therefore, vital for maize breeding programs to improve tolerance to low temperatures at seed germination stage. However, little is known about maize QTL involved in low-temperature germination ability. 243 lines of the intermated B73×Mo17 (IBM) Syn4 recombinant inbred line (RIL) population was used for QTL analysis of low-temperature germination ability. There were significant differences in germination-related traits under both conditions of low temperature (12°C/16h, 18°C/8h) and optimum temperature (28°C/24h) between the parental lines. Only three QTL were identified for controlling optimum-temperature germination rate. Six QTL controlling low-temperature germination rate were detected on chromosome 4, 5, 6, 7 and 9, and contribution rate of single QTL explained between 3.39%~11.29%. In addition, six QTL controlling low-temperature primary root length were detected in chromosome 4, 5, 6, and 9, and the contribution rate of single QTL explained between 3.96%~8.41%. Four pairs of QTL were located at the same chromosome position and together controlled germination rate and primary root length under low temperature condition. The nearest markers apart from the corresponding QTL (only 0.01 cM) were umc1303 (265.1 cM) on chromosome 4, umc1 (246.4 cM) on chromosome 5, umc62 (459.1 cM) on chromosome 6, bnl14.28a (477.4 cM) on chromosome 9, respectively. A total of 3155 candidate genes were extracted from nine separate intervals based on the Maize Genetics and Genomics Database (http://www.maizegdb.org). Five candidate genes were selected for analysis as candidates putatively affecting seed germination and seedling growth at low temperature. The results provided a basis for further fine mapping, molecular marker assisted breeding and functional study of cold-tolerance at the stage of seed germination in maize.     

Mapping QTL for Grain Yield and Plant Traits in a Tropical Maize Population

The vast majority of reported QTL mapping for maize (Zea mays L.) traits are from temperate germplasm and, also, QTL by environment interaction (QTL × E) has not been thoroughly evaluated and analyzed in most of these papers. The maize growing areas in tropical regions are more prone to environmental variability than in temperate areas, and, therefore, genotype by environment interaction is of great concern for maize breeders. The objectives of this study were to map QTL and to test their interaction with environments for several traits in a tropical maize population. Two-hundred and fifty-six F_2:3 families evaluated in five environments, a genetic map with 139 microsatellites markers, and the multiple-environment joint analysis (mCIM) were used to map QTL and to test QTL × E interaction. Sixteen, eight, six, six, nine, and two QTL were mapped for grain yield, ears per plant, plant lodging, plant height, ear height, and number of leaves, respectively. Most of these QTL interacted significantly with environments, most of them displayed overdominance for all traits, and genetic correlated traits had a low number of QTL mapped in the same genomic regions. Few of the QTL mapped had already been reported in both temperate and tropical germplasm. The low number of stable QTL across environments imposes additional challenges to design marker-assisted selection in tropical areas, unless the breeding programs could be directed towards specific target areas.     

玉米开花期相关性状的QTL分析

利用玉米强优势组合(Mo17×黄早四)自交衍生的191个F_2单株构建了由SSR和AFLP标记组成的分子连锁图谱,用F2进一步自交产生的184个F_(2:3)家系调查散粉期、吐丝期和开花-吐丝间隔期(ASI)的表型值,采用基于混合线性模型的复合区间作图法和相应的作图软件QTLmapper/V2.0,在两个生长环境下定位了与散粉期、吐丝期和ASI相关的QTL数目分别为13、7和5个,检测到3对控制散粉期、17对控制吐丝期和5对控制ASI的上位性效应位点;同时发现了与环境存在显著互作的3个散粉期、3个吐丝期和2个ASI单位点标记区域以及1对散粉期、3对吐丝期和2对ASI上位性效应区域.对玉米散粉期、吐丝期和ASI遗传基础中遗传因素相对作用大小分析表明,加性效应、部分显性效应和上位性效应是玉米开花期相关性状的重要遗传基础.