小麦遗传连锁图谱构建及主要农艺和品质性状QTL定位
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摘要
小麦(Triticum aestivum L.)是世界上重要的粮食作物之一。普通小麦是异源六倍体(2n=6x=42)，含有A、B和D三个染色体组，基因组巨大，而且其中80%以上的DNA为重复序列，导致遗传标记在不同材料间的多态性较差，小麦的遗传研究落后于玉米、水稻等作物。因此，构建高密度小麦分子标记遗传连锁图，对主要农艺和品质相关性状进行QTL定位，明确其在染色体上的位置和效应，对于主要农艺和品质性状的遗传改良具有重要意义。本研究利用两个品种间杂交获得的重组自交系（RIL）作图群体，进行了分子标记遗传连锁图谱的构建，并进行了主要农艺和品质性状的条件和非条件QTL分析，获得了以下主要结果：
（1）利用3123对不同来源的引物对亲本潍麦8号和洛旱2号进行多态性筛选，共检测出343对多态性引物。利用筛选的多态性引物对潍麦8号与洛旱2号杂交创制的重组自交系群体（RIL，F8:9）进行基因组扫描，共有246对引物在群体中扩增出条带清晰并有差异的位点，大多数位点在两个群体中的分布符合1:1的分离比例，且为纯合位点。表明该群体适于进行作图研究。
（2）运用作图软件MapMaker/EXP3.0和Joinmap v3.0，将348个位点分别定位在小麦的21条染色体上，构建出一张较高密度的遗传连锁图谱。该图谱全长3132.2cM，标记间的平均距离为9.0cM；其A、B和D三个基组的遗传长度分别是1086.1cM、1170.8cM和875.2cM；以7B染色体上标记最多，为47个，3D上最少，只有2个。三个基组中，B基组的标记密度最高，D组最低；在7个部分同源群中，第7部分同源群标记密度最高，第6部分同源群最低。标记在染色体上的位置和顺序与graingenes2.0（http://wheat.pw.usda.gov/GG2/index.shtml）的基本一致。
（3）利用完备区间作图软件IciMapping v3.0对小麦22个主要农艺性状（产量及其相关性状、株高、生育期、旗叶性状等）和8个主要品质性状（蛋白质含量、湿面筋含量、面团形成时间、面团稳定时间、容重、面粉白度、面粉吸水率和籽粒硬度）进行了2年3点的试验并进行QTL定位分析。共检测到30个性状的324个加性QTL位点，分布在所有的小麦染色体上，单个QTL可解释2.523.2%的性状表型变异；有54个QTL能解释>10%的性状表型变异，为主效QTL，其中12个QTL能在不同环境中被重复检测到。
（4）首次对小麦产量与产量构成因素进行条件QTL分析。分析结果表明，千粒重在单个QTL水平对产量的贡献最高，其次是1米行长穗数，但是二者的贡献大小相差不明显，再次是主茎穗粒数和穗粒重，平均穗粒数在单个QTL水平对1米行长产量的的贡献最低。通过对蛋白质含量与产量构成因素的条件分析表明，千粒重和穗粒数对蛋白质含量的影响较大，且二者对的蛋白含量的影响大小大体相当，但二者在QTL水平影响的位点不同；1米行长穗数在单个QTL水平对蛋白质含量的影响较小。通过对面团稳定时间和产量相关性状条件分析表明，穗粒数、千粒重和1米行长穗数对面团稳定时间的表型变异影响很小，表明产量因素对小麦的加工品质影响甚微，提高产量不会影响到面粉加工品质的改良。QDst-WL-2D、QGpc-WL-5B和QGpc-WL-7A在排除穗粒数、千粒重和1米行长穗数的影响后仍然能够被检测到，并且能够解释较高的表型变异率，说明这三个QTL受产量及相关因素影响很小，通过对这三个位点的标记选择可能实现产量和品质的同时提高。通过对株高与节间长的条件QTL分析结果表明，倒三节间长对株高在单个QTL水平贡献最高。
A associated RIL populations comprising302(Weimai8/Luohan2-derived, WL) lineswas used in the present study to construct a genetic linkage map using SSR, EST-SSR, RAPD,SRAP, STS and ISSR markers. Then the inclusive interval mapping method was utilized tomap QTL with additive effect by software Icimapping v3.0for agronomic traits, such asSpikelet number per spike(SPN), kernel number per spike(KNPS);1000-kernel weight(TKW)，Grain weight per meter row (GWPM)，Plant height (PH)，heading date (HD)，Blooming date (BD)，flag leaf length (FLL)，and quality traits, such as grain protein content(GPC), wet gluten content (WGC), flour whiteness (FW)，kernel hardness (KH)，waterabsorption (Abs), and dough stability time (DST). What’s more, based on the novel moleculargenetic map above, multivariate conditional QTL mapping analysis was conducted to specifythe genetic characteristics of yield and yield-related traits, and quality traits respect toyield-related traits at the QTL level. The results were as follows:
(1) Three thousand one handred and twenty three pairs of genomic primers were used togenotype the RIL populations and their parents, and in total246pairs of markers amplifiedclear and discrepant sites between Weimai8and Luohan2. The separation ratio of most sitesfit the1:1ratio, suggesting that the RIL population could be utilized in QTL mappinganalysis.
(2) The genetic linage map, with23linkage groups, was constructed using the softwareMAPMAKER/EXP3.0and Joinmap v3.0, which was comprising348sites, with the wholegenome length of3132.20cM and an average distance of9.00cM between markers, and thenumber of markers being2on chromosomes3D to47on7B, showing that the mappingpopulation was suitable for QTL mapping.
(3) Based on the genetic linkage map, the software IciMapping v3.0(with inclusiveinterval mapping method) was used to conduct QTL mapping of the agrononmic and qualitytraits, in three environments. Totally274QTL with additive effect for the twenty twoagronomic traits were mapped on all wheat chromosomes, with the explanation of phenotypic variation (PVE) from2.54%to40.64%of a single QTL. Of these,30of which accounted forat least10%of the phenotypic variation,14QTL showed significance in at least two trials ofE1, E1, E3and P, being major stable QTL. In total, up to50QTL with additive effect foreight traits were mapped, with the explanation of phenotypic variation (PVE) from3.60%to16.15%of a single QTL. Of these,7QTL showed significance in at least two trials of E1, E1,E3and P,10of which can explain at least10%of the phenotypic variation in one or twoenvironments, being major QTL.
(4) For the first time, conditional QTL mapping was conducted for grain yield and yiledcomponents in wheat. The results showed that all of the yield related components, at the QTLlevel, thousand-grain-weight (TKW) contributed to yield (GWPM) the most, followed byspike number per meter row (SNPM), but the contribution of the two sizes is pretty much thesame, and next to the kernel number per spike of main stem (KNPS) and kernel weight per ear(KWPS), and the everage kernel number per spike (EKNPS) has the lest contribution to it.When protein content (GPC) conditional on yield related traits, the conditional QTL mappingshowed that thousand-kernel-weight (TKW) and kernel number per spike (KNPS) has equalcontribution to protein content, although they did not affected all the same QTL sites, bycontrast, the spike number per meter row (SNPR) has a little influence on it. When doughtstable time (DST) conditional on yield related traits, the conditional mapping analysis showedthat kernel number per spike (KNPS), thousand-kernel-weight (TKW) and spike number permeter row (SMPM) has little effect on the phenotypic variation of dought stable time (DST),which means that the grain yiled componens have little effects on quality trais of wheat, andthat makes the yield and flour processing quality improving simultaneously possible. Thepossible genetic relationships anasysis between plant height (PH) and its components showed,that spike length (SL) contributed the least to PH, the third internode length from the top(TITL) had the strongest influence on PH. Conditional and unconditional QTL mappingshowed that when the effects of kernel numbper per spike (KNPS), thousand-kernel-weight(TKW) and spike number per meter row (SNPR) were excluded, the QTL QDst-WL-2D、QGpc-WL-5B and QGpc-WL-7A can still be detected, which means that those three QTL wereaffected very little by the three yiled components, therefore, by Marker-asisted selection tothese three sites, it may makes the improving of the yield and quality at the same time.
（1）利用3123对不同来源的引物对亲本潍麦8号和洛旱2号进行多态性筛选，共检测出343对多态性引物。利用筛选的多态性引物对潍麦8号与洛旱2号杂交创制的重组自交系群体（RIL，F8:9）进行基因组扫描，共有246对引物在群体中扩增出条带清晰并有差异的位点，大多数位点在两个群体中的分布符合1:1的分离比例，且为纯合位点。表明该群体适于进行作图研究。
（2）运用作图软件MapMaker/EXP3.0和Joinmap v3.0，将348个位点分别定位在小麦的21条染色体上，构建出一张较高密度的遗传连锁图谱。该图谱全长3132.2cM，标记间的平均距离为9.0cM；其A、B和D三个基组的遗传长度分别是1086.1cM、1170.8cM和875.2cM；以7B染色体上标记最多，为47个，3D上最少，只有2个。三个基组中，B基组的标记密度最高，D组最低；在7个部分同源群中，第7部分同源群标记密度最高，第6部分同源群最低。标记在染色体上的位置和顺序与graingenes2.0（http://wheat.pw.usda.gov/GG2/index.shtml）的基本一致。
（3）利用完备区间作图软件IciMapping v3.0对小麦22个主要农艺性状（产量及其相关性状、株高、生育期、旗叶性状等）和8个主要品质性状（蛋白质含量、湿面筋含量、面团形成时间、面团稳定时间、容重、面粉白度、面粉吸水率和籽粒硬度）进行了2年3点的试验并进行QTL定位分析。共检测到30个性状的324个加性QTL位点，分布在所有的小麦染色体上，单个QTL可解释2.523.2%的性状表型变异；有54个QTL能解释>10%的性状表型变异，为主效QTL，其中12个QTL能在不同环境中被重复检测到。
（4）首次对小麦产量与产量构成因素进行条件QTL分析。分析结果表明，千粒重在单个QTL水平对产量的贡献最高，其次是1米行长穗数，但是二者的贡献大小相差不明显，再次是主茎穗粒数和穗粒重，平均穗粒数在单个QTL水平对1米行长产量的的贡献最低。通过对蛋白质含量与产量构成因素的条件分析表明，千粒重和穗粒数对蛋白质含量的影响较大，且二者对的蛋白含量的影响大小大体相当，但二者在QTL水平影响的位点不同；1米行长穗数在单个QTL水平对蛋白质含量的影响较小。通过对面团稳定时间和产量相关性状条件分析表明，穗粒数、千粒重和1米行长穗数对面团稳定时间的表型变异影响很小，表明产量因素对小麦的加工品质影响甚微，提高产量不会影响到面粉加工品质的改良。QDst-WL-2D、QGpc-WL-5B和QGpc-WL-7A在排除穗粒数、千粒重和1米行长穗数的影响后仍然能够被检测到，并且能够解释较高的表型变异率，说明这三个QTL受产量及相关因素影响很小，通过对这三个位点的标记选择可能实现产量和品质的同时提高。通过对株高与节间长的条件QTL分析结果表明，倒三节间长对株高在单个QTL水平贡献最高。
A associated RIL populations comprising302(Weimai8/Luohan2-derived, WL) lineswas used in the present study to construct a genetic linkage map using SSR, EST-SSR, RAPD,SRAP, STS and ISSR markers. Then the inclusive interval mapping method was utilized tomap QTL with additive effect by software Icimapping v3.0for agronomic traits, such asSpikelet number per spike(SPN), kernel number per spike(KNPS);1000-kernel weight(TKW)，Grain weight per meter row (GWPM)，Plant height (PH)，heading date (HD)，Blooming date (BD)，flag leaf length (FLL)，and quality traits, such as grain protein content(GPC), wet gluten content (WGC), flour whiteness (FW)，kernel hardness (KH)，waterabsorption (Abs), and dough stability time (DST). What’s more, based on the novel moleculargenetic map above, multivariate conditional QTL mapping analysis was conducted to specifythe genetic characteristics of yield and yield-related traits, and quality traits respect toyield-related traits at the QTL level. The results were as follows:
(1) Three thousand one handred and twenty three pairs of genomic primers were used togenotype the RIL populations and their parents, and in total246pairs of markers amplifiedclear and discrepant sites between Weimai8and Luohan2. The separation ratio of most sitesfit the1:1ratio, suggesting that the RIL population could be utilized in QTL mappinganalysis.
(2) The genetic linage map, with23linkage groups, was constructed using the softwareMAPMAKER/EXP3.0and Joinmap v3.0, which was comprising348sites, with the wholegenome length of3132.20cM and an average distance of9.00cM between markers, and thenumber of markers being2on chromosomes3D to47on7B, showing that the mappingpopulation was suitable for QTL mapping.
(3) Based on the genetic linkage map, the software IciMapping v3.0(with inclusiveinterval mapping method) was used to conduct QTL mapping of the agrononmic and qualitytraits, in three environments. Totally274QTL with additive effect for the twenty twoagronomic traits were mapped on all wheat chromosomes, with the explanation of phenotypic variation (PVE) from2.54%to40.64%of a single QTL. Of these,30of which accounted forat least10%of the phenotypic variation,14QTL showed significance in at least two trials ofE1, E1, E3and P, being major stable QTL. In total, up to50QTL with additive effect foreight traits were mapped, with the explanation of phenotypic variation (PVE) from3.60%to16.15%of a single QTL. Of these,7QTL showed significance in at least two trials of E1, E1,E3and P,10of which can explain at least10%of the phenotypic variation in one or twoenvironments, being major QTL.
(4) For the first time, conditional QTL mapping was conducted for grain yield and yiledcomponents in wheat. The results showed that all of the yield related components, at the QTLlevel, thousand-grain-weight (TKW) contributed to yield (GWPM) the most, followed byspike number per meter row (SNPM), but the contribution of the two sizes is pretty much thesame, and next to the kernel number per spike of main stem (KNPS) and kernel weight per ear(KWPS), and the everage kernel number per spike (EKNPS) has the lest contribution to it.When protein content (GPC) conditional on yield related traits, the conditional QTL mappingshowed that thousand-kernel-weight (TKW) and kernel number per spike (KNPS) has equalcontribution to protein content, although they did not affected all the same QTL sites, bycontrast, the spike number per meter row (SNPR) has a little influence on it. When doughtstable time (DST) conditional on yield related traits, the conditional mapping analysis showedthat kernel number per spike (KNPS), thousand-kernel-weight (TKW) and spike number permeter row (SMPM) has little effect on the phenotypic variation of dought stable time (DST),which means that the grain yiled componens have little effects on quality trais of wheat, andthat makes the yield and flour processing quality improving simultaneously possible. Thepossible genetic relationships anasysis between plant height (PH) and its components showed,that spike length (SL) contributed the least to PH, the third internode length from the top(TITL) had the strongest influence on PH. Conditional and unconditional QTL mappingshowed that when the effects of kernel numbper per spike (KNPS), thousand-kernel-weight(TKW) and spike number per meter row (SNPR) were excluded, the QTL QDst-WL-2D、QGpc-WL-5B and QGpc-WL-7A can still be detected, which means that those three QTL wereaffected very little by the three yiled components, therefore, by Marker-asisted selection tothese three sites, it may makes the improving of the yield and quality at the same time.
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