甘蓝型油菜遗传图谱的构建和品质相关性状的QTL分析
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摘要
油菜在植物分类上属于十字花科(Cruclferae)芸薹属(Brassica)，是世界广泛种植的油料作物，在世界油用作物中均占有重要的地位，在我国居五大油料作物(油菜、大豆、花生、芝麻、向日葵)之首，种植面积和总产均居世界首位，是重要的食用油源和蛋白质饲料来源，也是重要的工业原料。芸薹属(Brassica)包含三个基本种，即芸薹(Brassica campestris，AA 2n=20)、甘蓝(Brassica oleracea CC，2n=18)和黑芥(Brassica nigra，BB，2n=16)。另外，还包括三个复合种：即甘蓝型油菜(Brassica napus，AACC，2n-38)、芥菜型油菜(Brassica．juncea，AABB，2n-34)和埃塞俄比亚芥(Brassica carinata，BBCC，2n=36)。自从1975年刘后利在甘白杂交中在我国首次发现甘蓝型黄籽油菜以来，黄籽性状的研究已经成为油菜领域的研究热点。甘蓝型黄籽油菜具有种皮薄且纤维素含量少，饼粕中蛋白质含量高，多酚含量低，是优良的饲料原料；在相同的遗传背景下，黄籽的含油量比黑籽高3.0-5.0％，且毛油色浅，杂质少，清澈透明，加工成本低等诸多优点。但是，甘蓝型黄籽油菜的育种研究进展缓慢，效率低下，性状改良困难。现代DNA标记技术的发展，为育种者提供了一种快速、准确的选择方法。
本文主要利用SSR、RAPD、SRAP、TRAP、AFLP对RIL-1(GH 06×油研2号选系)和RIL-2 (GH 06×中油821选系)两个RIL群体进行了遗传连锁图谱的构建，并利用RIL-1和RIL-2两个群体进行了3个环境(2005年北碚和2006年北碚与万州)的品质相关性状QTL的定位分析，初步定位了控制种皮色泽的主效QTL的相关区域。其主要研究内容如下：作图亲本间标记多态性
RIL-1：64对AFLP引物组合在油研2号选系和GH 06作图亲本间扩增出4035条带，平均每个引物组合扩增63条带，获得170个多态位点，平均每个引物组合产生2.66个多态位点。777对SSR引物中153对引物在两亲本间具多态性，获得181个多态性位点，平均每对引物产生1.15个多态性位点。121个SRAP引物组合获得267个多态性位点，平均每个引物组合产生2.21个多态性位点。6对TRAP引物组合共获得9个多态性位点，平均每个引物组合产生1.83个多态性位点。
RIL-2：777对SSR引物中152对引物在两亲本间具多态性，获得172个多态性位点，平均每个引物产生1.13个多态性位点。260个SRAP引物组合共获得466个多态性位点，平均每个引物产生1.79个多态性位点。68条RAPD引物获得106个多态性位点，平均每个引物产生1.56个多态性位点。
遗传图谱的构建
RIL-1：对621个标记位点(261个SRAP，181个SSR，170 AFLP标记和9个TRAP标记)进行遗传连锁分析，构建的遗传连锁图包括502个标记位点，其中包括220个SRAP标记，155个SSR标记，123个AFLP标记和4个TRAP标记，27个连锁群，连锁群长度11.126cM，3-90个分子标记，标记间的平均距离为3.38cM，平均每个连锁群的遗传距离为62.89cM，覆盖1698 cM，约占甘蓝型油菜基因组67.92％。
RIL-2：对745个标记位点(466个SRAP，172个SSR，106个RAPD和2个形态标记)进行遗传连锁分析，构建的遗传连锁图包括570个标记位点，其中包括365个SRAP标记，134个SSR标记，71个RAPD标记，27个连锁群，连锁群长度27-163cM，3-92个分子标记，平均每个连锁群距离为78.48cM，标记间的平均距离为3.72cM，覆盖2119cM，约占甘蓝型油菜基因组84.76％。
作图亲本和株系性状统计分析
两个的群体的3个亲本在国内广泛的种植，GH 06具有完全显性的黄籽主效基因，黄籽度达到90％，黄籽性状表现稳定。中油821选系和油研2号选系为多代自交的黑籽亲本。
两个群体种植选择3个环境进行实验(2005年北碚和2006年北碚和万州)，不同的环境性状表现各异。性状表现呈现正态分布，可以用于QTL作图分析。方差分析表明品质性状之间存在基因和环境之间的互作，部分品质性状基因和环境之间互作效应不显著。
区间作图检测QTL的效应
RIL-1：区间作图检测到40个影响含油量的QTL，单个QTL解释含油量变异的5.39-15.66％；检测到28个影响蛋白质含量的QTL，单个QTL解释蛋白质变异的5.1-25.76％；检测到23个影响皮壳率的QTL，单个QTL解释皮壳率变异的5.26-11.59％：检测到影响千粒重的QTL14个，单个QTL解释千粒重变异的5.36-11.45％；检测到4个影响木质素含量的QTL，单个QTL解释木质素含量变异的5.51-10.18％；检测到7个影响单株产量的OTL，单个QTL解释单株产量变异的5.21-8.23％；检测到12个影响种皮色泽的QTL，单个QTL解释种皮色泽变异的5.39-59.61％；检测到6个影响花色素含量的QTL，单个QTL解释花色素含量变异的5.58-9.47％：检测到6个影响总酚含量的QTL，单个QTL解释总酚含量变异的5.65-13.03％；检测到6个影响黑色素含量的QTL，单个QTL解释黑色素含量变异的5.46-10.47％。
RIL-2：区间作图检测到34个影响含油量的QTL，单个QTL解释含油量变异的5.27-11.69％；检测到25个影响蛋白质含量的QTL，单个QTL解释蛋白质变异的5.19-10.4％；检测到18个影响皮壳率的QTL，单个QTL解释皮壳率变异的5.65-26.55％；检测到影响千粒重的QTL14个，单个QTL解释千粒重变异的5.08-11.51％；检测到6个影响木质素含量的QTL，单个QTL解释木质素含量变异的5.96-8.79％；检测到9个影响单株产量的QTL，单个QTL解释单株产量变异的5.03-10.59％；检测到27个影响种皮色泽的QTL，单个QTL解释种皮色泽变异的5.39-80.63％；检测到2个影响花色素含量的QTL，单个QTL解释花色素含量变异的分别为10.54％和9.86％；检测到10个影响总酚含量的OTL，单个QTL解释总酚含量变异的7.24-25.80％；检测到14个影响类黄酮含量的QTL，单个QTL解释类黄酮含量变异的6.03-28.92％；检测到13个影响黑色素含量的QTL，单个QTL解释黑色素含量变异的5.98-36.84％。
复合区间作图检测QTL的效应
RIL-1：复合区间作图检测到38个影响含油量的QTL，单个QTL解释含油量变异的2.92-12.93％；检测到18个影响蛋白质含量的QTL，单个QTL解释蛋白质变异的3.93-25.31％；检测到22个影响皮壳率的OTL，单个QTL解释皮壳率变异的3.83-13％；检测到影响千粒重的QTL16个，单个QTL解释千粒重变异的4.69-11.64％；检测到6个影响木质素含量的QTL，单个QTL解释木质素含量变异的4.12-11.77％；检测到13个影响单株产量的QTL，单个QTL解释单株产量变异的3.88-12.44％；检测到17个影响种皮色泽的QTL，单个QTL解释种皮色泽变异的4.01-13.43％；检测到7个影响花色素含量的QTL，单个QTL解释花色素含量变异的4.36-57.8％；检测到7个影响类黄酮含量的QTL，单个OTL解释类黄酮含量变异的7.13-62.35％；检测到11个影响总酚含量的QTL，单个QTL解释总酚含量变异的5.07-12.64％；检测到11个影响黑色素含量的QTL，单个QTL解释黑色素含量变异的5.26-16.91％。
RIL-2：复合区间作图检测到14个影响含油量的OTL，单个QTL解释含油量变异的4.47-12.21％；检测到13个影响蛋白质含量的QTL，单个QTL解释蛋白质变异的4.01-9.40％；检测到16个影响皮壳率的OTL，单个QTL解释皮壳率变异的3.36-11.86％；检测到影响15个千粒重的QTL，单个QTL解释千粒重变异的4.03-11.03％；检测到7个影响木质素含量的QTL，单个QTL解释木质素含量变异的4.71-7.64％；检测到4个影响单株产量的QTL，单个QTL解释单株产量变异的4.65-6.42％；检测到28个影响种皮色泽的QTL，单个QTL解释种皮色泽变异的2.46-24.52％；检测到4个影响花色素含量的OTL，单个QTL解释花色素含量变异的5.25-9.94％；检测到12个影响总酚含量的QTL，单个QTL解释总酚含量变异的3.72-16.23％；检测到7个影响类黄酮含量的OTL，单个QTL解释类黄酮含量变异的3.38-14.31％；检测到12个影响黑色素含量的QTL，单个QTL解释黑色素含量变异的3.72-16.23％。
不同环境检测到的QTL比较
应用区间作图检测RIL-1和RIL2两个群体在2005年北碚和2006年北碚和万州3个环境OTL，多数OTL不能在不同环境中检测到，只有少数QTL能够在两个或者三个环境在相近区域检测到，分别为RIL-1群体共有12个QTLs(占全部QTL的8.28％)和RIL-2群体共有35个QTLs(占全部QTL的18.13％)。
应用复合区间作图检测RIL-1和RIL-2两个群体在3个环境的QTL，RIL-1群体共有14个QTLs(占全部QTL的8.43％)和RIL-2群体共有21个QTLs(占全部QTL的15.22％)能够在相近区域重复检测到。
不同作图方法检测QTL比较
RIL-1群体区间作图共检测到了145个品质性状QTL，复合区间作图共检测到了166个QTL，复合区间作图分析方法比单区间作图分析方法多21个QTL。RIL-2群体区间作图共检测到了193个品质性状QTL，复合区间作图共检测到了138个QTL复合区间作图分析方法比单区间作图分析方法少了55个QTL。区间作图和复合区间作图差别比较大，但是两种方法均能检测到解释表型变异值大于10％的QTL。
QTL在连锁群上的分布
含油量与含油量相关性状(皮壳率、千粒重和蛋白质含量)和种皮色泽与色泽相关性状(花色素含量、类黄酮含量、总酚含量和黑色素含量)的QTL成簇的分布在某几个连锁群上。在RIL-1中主要分布LG12和LG17，RIL-2主要分布在LG1和LG18。各个性状的OTL在A、C两个基因组间都有分布。
种皮色泽QTL的初步定位
种皮色泽和四种种皮提取色素存在极显著的负相关，而四种种皮提取色素之间存在极显著的正相关。
应用Win QTL2.5的复合区间作图分析方法(CIM，composite interval mapping；LOD≥2.0)，2005年在北碚检测到11个与色泽相关的QTL，分别分布在第2、9、14和18四个连锁群。2006年在万州检测到9个与色泽相关的QTL，分别分布在第2、9、18和22连锁群。2006年在北碚检测到8个与色泽相关的QTL，分别分布在第2、11、12和18四个连锁群。其中第2连锁群和第18连锁群的QTL在三个环境都可以检测到。
复合区间作图分析，在2006北碚，检测到5个影响种皮类黄酮含量的QTL 8个影响种皮总酚含量的QTL，6个影响种皮黑色素含量的QTL,没有检测到影响种皮花色素含量的QTL。在2006万州检测到4个影响种皮花色素含量的QTL，2个影响种皮类黄酮含量的QTL，4个影响种皮总酚含量的QTL，4个影响种皮黑色素含量的QTL。
这些在三个环境下都能检测到的种皮色泽和与种皮色泽相关的提取色素QTL分别与sNRD03／120，EM40ME13／260，S455／500，EM13ME22／270，EM13ME22／270标记靠近，位于第18连锁群的90-144cM这个区间。克隆这个区间的SRAP引物标记序列，共得到了9个片段序列，片段大小从115bp-491bp，其中7个片段都和拟南芥的第5染色体具有较高的同源性。这些片同源区域分布在拟南芥第5染色体11个透明种皮的基因之间。与某个或者几个透明种皮基因同源的某个基因或几个基因在控制或者调控种皮色泽基因的表达。
Oilseed is Brassica of Cruclferae, is one of main oil crops in the world and holds an important status in the oil crops. In our country, it is the most important of the five oil crops (oilseed, soybean, peanut, gingili, and sunflower), the largest planting area and the most products. It is an important source to the vegetable oil and meal protein and industry material. Brassica include three elementary species (Brassica campestris, AA 2n=20; Brassica oleracea CC, 2n=18 and Brassica nigra, BB, 2n=16) and three aggregate species (Brassica napus, AACC, 2n=38; Brassica. Juncea, AABB, 2n=34 and Brassica carinata, BBCC, 2n=36). The researchers have focused on yellow seed coat of B. napus since Liu firstly found in recombination between B. napus and B.rapa in 1975. Yellow seed of B. napus is an excellent fodder material with lower hull proportion and fibrin content, higher protein content in meal and less polyphenol than brown or black one. In the same genetic, yellow seed has higher 3.0-5.0% oil content and more transparent oil and lower producing cost than brown or black one. However, it is very slow advance, very low efficiency and very difficult improvement to the selection breeding of B. napus. Recent advances in DNA markers offer plant breeders a rapid and precise alternative approach to conventional selection schemes to improve quantitative traits.
In the present study, the high-density genetic linkage maps have been constructed using SSR marker, RAPD markers, SRAP marker, TRAP marker and AFLP marker to RIL-1 population (GH06×Youyan 2) and RIL-2 population (GH 06×Zhongyou 821). WinQTL 2.5 has detected QTLs of quality about two populations in three environments (in Beibei in 2005 and in both Beibei and Wanzhou in 2006) with LOD value 2.0. The major QTL potential regions of the seed coat color have been primarily located. The mainly results were as following:
DNA polymorphism among three parents
RIL-1: The 64 AFLP primer combinations yielded 4035 bands between Youyan 2 and GH 06 with 63 bands each primer. Among of the bands yielded 170 polymorphic bands with an average 2.66 polymorphic bands (between 1 and 12) informative AFLP per primer combination. 153 polymorphism primer pairs found among 777 SSR primer pairs and yielded 181 polymorphic bands with an average 1.15 polymorphic bands per primer. 121 SRAP primer combinations yielded 267 polymorphic bands with an average 2.21 polymorphic bands per primer combination. 6 TRAP primer combinations yielded 9 polymorphic bands with an average 1.83 polymorphic bands per primer combination.
RIL-2: 152 polymorphism primer pairs found among 777 SSR primer pairs and yielded 172 polymorphic bands with an average 1.15 polymorphic bands per primer. 260 primer combinations yielded 466 polymorphic bands with an average 1.79 polymorphic bands per primer combination. 68 polymorphic primers yielded 106 polymorphic bands with an average 1.56 polymorphic bands per primer.
Construction of the genetic linkage map
RIL-1: 621 polymorphic loci, including 261 SRAP markers, 181 SSR markers and 9 TRAP markers, were employed to perform linkage analysis using the RIL-1 population of B. napus. 502 markers (220 SRAP markers, 155 SSR markers, 123 AFLP markers and 9 TRAP markers) were mapped to 27 linkage groups ranging from 11 to 121 cM with an average length of 62.89 cM containing 3 to 90 markers in each linkage group. The map covered a total of 1698 cM with a coverage percentage of about 67.92% (Lombard and Delourme, 2001), and the average distance between two adjacent markers was 3.38 cM.
RIL-2: 745 polymorphic loci, including 466 SRAP markers, 172 SSR markers, 106 RAPD markers and 2 morphological loci were employed to perform linkage analysis using the RIL-2 population of B. napus. 570 markers (365 SRAP markers, 134 SSR markers and 71 RAPD markers) were mapped to 27 linkage groups ranging from 27 to 163 cM with an average length of 74.48 cM containing 3 to 92 markers in each linkage group. The map covered a total of 2119 cM with a coverage percentage of about 84.76% (Lombard and Delourme, 2001), and the average distance between two adjacent markers was 3.72 cM.
Analysis of traits of mapping parents and lines
Three parents of two populations are broadly plant in China. GH 06 has a complete dominant main gene of yellow seed coat with 90 % degree of yellow seed and the trait of yellow seed shows very steady. Zhongyou 821 and Youyan 2 are two black-seeded parents by self-inbreded.
Two populations were plant in different three environments (in Beibei in 2005 and in Beibei and Wanzhou in 2006). The traits had different phenotype in different environments. The phenotype of the traits showed the normal distribution and could be used to analysis of QTL. The variance analysis of the trait indicated that the some qualities of B. napus interacted between gene and environment, and other qualities had not significant interaction effect between gene and environment.
Analysis of QTL effects based on interval mapping
RIL-1: In Beibei in 2005, 4 QTLs of effecting lignin content were identified by interval mapping (IM) with one QTL explaining 5.51-10.18% of the lignin content variance and 7 QTLs of effecting single-plant weight were detected with explaining 5.21-8.23% of single-plant weight variation. 40 QTLs of effecting oil content were detected by IM with explaining 5.39-15.66% of oil content variance in different environments (in Beibei in 2005 and in both Beibei and Wanzhou in 2006). 28 QTLs of effecting protein content were detected with one QTL explaining 5.1-25.76% of protein content variance. 23 QTLs of effecting hull proportion were detected with one QTL explaining 5.26-11.59% of hull proportion variance. 14 QTLs of effecting kilo-seeds weight were detected with one QTL explaining 5.36-11.45% of kilo-seeds weight variance. 12 QTLs of effecting seed coat color were detected with one QTL explaining 5.39-59.61% of seed coat color variance. 6 QTLs of effecting anthocyanidin content were detected in both Beibei and Wanzhou in 2006 with one QTL explaining 5.39-59.61% of anthocyanidin content variance. 6 QTLs of effecting total phenol content were detected with one QTL explaining 5.65-13.03% of total phenol content variance. 6 QTLs of effecting melanin content were detected with one QTL explaining 5.46-10.47% of melanin content variance.
RIL-2: In Beibei in 2005, 6 QTLs of efffecting lignin content were detected by IM with one QTL explaining 5.96-8.79% of lignin content variance and 9 QTLS of effecting single-plant weight were detected with explaining 5.03-10.59% of single-plant weight variance. 34 QTLs of effecting oil content were detected by IM with explaining 5.27-11.69% of oil content variance in different environments (in Beibei in 2005 and in both Beibei and Wanzhou in 2006). 25 QTLs of effecting protein content were detected with one QTL explaining 5.19-10.4% of protein content variance. 18 QTLs of effecting hull proportion were detected with one QTL explaining 5.65-26.55% of hull proportion variance. 14 QTLs of effecting kilo-seeds weight were detected with one QTL explaining 5.08-11.51% of kilo-seeds weight variance. 27 QTLs of effecting seed coat color were detected with one QTL explaining 5.39-80.63% of seed coat color variance. 2 QTLs of effecting anthocyanidin content were detected by IM in both Beibei and Wanzhou in 2006 with one QTL explaining 10.54 percent and 9.86% of anthocyanidin content variance. 10 QTLs of effecting total phenol content were detected with one QTL explaining 7.24-25.80% of total phenol content variance. 14 QTLs of effecting flavonoid content were detected with one QTL explaining 6.03-28.92% of flavonoid content variance. 13 QTLs of effecting melanin content were detected with one QTL explaining 5.98-36.84% of melanin content variance.
Analysis of QTL effects based on composite interval mapping
RIL-1: In Beibei in 2005, 6 QTLs of effecting lignin content were detected by composite intrval mapping (CIM) with one QTL explaining 4.12-11.77% of variance and 13 QTLS of effecting single-plant weight were detected with explaining 3.88-12.44% of single-plant weight variance. 38 QTLs of effecting oil content were detected by CIM with explaining 2.92-12.93% of oil content variance in different environments (in Beibei in 2005 and in both Beibei and Wanzhou in 2006). 18 QTLs of effecting protein content were detected with one QTL explaining 3.93-25.31% of protein content variance. 22 QTLs of effecting hull proportion were detected with one QTL explaining 3.83-13% of hull proportion variance. 16 QTLs of effecting kilo-seeds weight were detected with one QTL explaining 4.69-11.64% of kilo-seeds weight variance. 17 QTLs of effecting seed coat color were detected with one QTL explaining 4.01-13.43% of seed coat color variance. 7 QTLs of efecting anthocyanidin content were detected by CIM in both Beibei and Wanzhou in 2006 with one QTL explaining 4.36-57.8% of anthocyanidin content variance. 7 QTLs of flavonoid content were detected by CIM with one QTL explaining 7.13-62.35% of flavonoid content variance. 11 QTLs of effecting total phenol content were detected with one QTL explaining 5.07-12.64% of total phenol content variance. 11 QTLs of effecting melanin content were detected with one QTL explaining 5.26-16.91% of melanin content variance.
RIL-2: In Beibei in 2005, 7 QTLs of effecting lignin content were detected by CIM with one QTL explaining 4.71-7.64% of lignin content variance and 4 QTLS of effecting single-plant weight were detected with explaining 4.65-6.42% of single-plant weight variance. 14 QTLs of effecting oil content were detected by CIM with explaining 4.47-12.21% of oil content variance in different environments (in Beibei in 2005 and in both Beibei and Wanzhou in 2006). 13 QTLs of effecting protein content were detected with one QTL explaining 4.01-9.40% of protein content variance. 16 QTLs of effecting hull proportion were detected with one QTL explaining 3.36-11.86% of hull proportion variance. 15 QTLs of effecting kilo-seeds weight were detected with one QTL explaining 4.03-11.03% of kilo-seeds weight variance. 28 QTLs of effecting seed coat color were detected with one QTL explaining 2.46-24.52% of seed coat color variance. 4 QTLs of effecting anthocyanidin content were detected by IM in both Beibei and Wanzhou in 2006 with one QTL explaining 5.25-9.94% of anthocyanidin content variance. 12 QTLs of effecting total phenol content were detected with one QTL explaining 3.72-16.23% of total phenol content variance. 7 QTLs of effecting flavonoid content were detected with one QTL explaining 3.38-14.31% of flavonoid content variance. 12 QTLs of effecting melanin content were detected with one QTL explaining 3.72-16.23% of melanin content variance.
Comparing to QTL effect in different environments
Based on IM: The most QTLs of RIL-1 and RIL2 in three different environments (in Beibei in 2005 and in Beibei and Wanzhou in 2006) could not be repeatedly identified in three environments. Some QTLs could be repeatedly detected in near region of the same linkage group in three environments, which were 12 QTLs (8.28% of all QTLs) of RIL-1 and 35 QTLs (18.13%) of RIL-2.
Based on CIM: The QTLs of RIL-1 and RIL2 that were identified in three different environments were repeated in these environments could be repeatedly detected in near region of the same linkage group in three environments, which were 14 QTLs (8.43% of all QTLs) of RIL-1 and 21 QTLs (15.22%) of RIL-2.
Comparing to QTL effect in different mapping methods
145 quality QTLs of RIL-1 were identified by IM and 166 QTLs were identified by CIM, the QTL numbers by CIM were more 21 than the numbers by IM. 193 quality QTLs of RIL-2 were identified by IM and 138 QTLs were identified by CIM, the QTL number by IM were more 55 than the numbers by CIM. It is very different to between IM and CIM, but they could detect the QTL with explaining =10% of phenotypic variance.
QTL distribution
The QTLs of oil content and oil content related traits(hull proportion, kilo-seed weight and protein content) clustered in some linkage groups as well as the QTLs of seed coat color and seed coat color related to traits(anthocyanidin content, flavonoid content, total phenol content and melanin content). Such as, in the linkage groups of RIL-1 they mainly distributed in LG 12 and LG 17 and mainly distributed in LG 1 and LG18 in ones of RIL-2. Added to this, QTLs of all quality distributed both A genome and C genome.
Primary location QTL of seed coat color
Seed coat color is very significant negative correlation to four kinds of pigments (anthocyanidin, flavonoid, total phenol and melanin), but four kinds of pigments is very significant positive correlation to each other.
11 significant QTLs of seed coat color were identified in four linkage groups (LG 2, 9, 14 and 18) with a LOD value threshold of 2.0 by CIM in Beibei in 2005. In Wanzhou 9 significant QTLs were identified in four linkage groups (LG 2, 9, 18 and 22) in 2006 and 8 significant QTLs were identified in four linkage groups (LG 2,11,12 and 18) in Beibei in 2006. The QTL in the LG 2 and LG18 could be detected in three environments.
5 significant QTLs of flavonoid content, 8 significant QTLs of total phenol content and 6 significant QTLs of melanin content were detected in Beibei in 2006 and QTL of anthocyanidin content was not detected. In Wanzhou in 2006, 4 significant QTLs of anthocyanidin content, 2 significant QTLs of flavonoid content, 4 significant QTLs of total phenol content and 4 significant QTLs of melanin content were detected.
These QTLs of seed coat color and pigment that could be detected in three environments is near to some marker, such as sNRD03/120, EM40ME13/260, S455/500, EM13ME22/270, EM13ME22/270, and located 90-144 cM in the 18~(th) linkage group. Cloned SRAP marker sequence in the region, and gained 9 sequence with length ranging from 115bp to 491, 7 sequence among of these sequences have homology with 5~(th) chromosome of Arabidopsis thaliana. These homologous areas are scattered among the 11 transparent testa (77) genes in 5~(th) chromosome of A. thaliana. So one gene or some genes, which are homologous with one TT gene or some TT gene, regulate and control the expression of the seed coat color gene.
本文主要利用SSR、RAPD、SRAP、TRAP、AFLP对RIL-1(GH 06×油研2号选系)和RIL-2 (GH 06×中油821选系)两个RIL群体进行了遗传连锁图谱的构建，并利用RIL-1和RIL-2两个群体进行了3个环境(2005年北碚和2006年北碚与万州)的品质相关性状QTL的定位分析，初步定位了控制种皮色泽的主效QTL的相关区域。其主要研究内容如下：作图亲本间标记多态性
RIL-1：64对AFLP引物组合在油研2号选系和GH 06作图亲本间扩增出4035条带，平均每个引物组合扩增63条带，获得170个多态位点，平均每个引物组合产生2.66个多态位点。777对SSR引物中153对引物在两亲本间具多态性，获得181个多态性位点，平均每对引物产生1.15个多态性位点。121个SRAP引物组合获得267个多态性位点，平均每个引物组合产生2.21个多态性位点。6对TRAP引物组合共获得9个多态性位点，平均每个引物组合产生1.83个多态性位点。
RIL-2：777对SSR引物中152对引物在两亲本间具多态性，获得172个多态性位点，平均每个引物产生1.13个多态性位点。260个SRAP引物组合共获得466个多态性位点，平均每个引物产生1.79个多态性位点。68条RAPD引物获得106个多态性位点，平均每个引物产生1.56个多态性位点。
遗传图谱的构建
RIL-1：对621个标记位点(261个SRAP，181个SSR，170 AFLP标记和9个TRAP标记)进行遗传连锁分析，构建的遗传连锁图包括502个标记位点，其中包括220个SRAP标记，155个SSR标记，123个AFLP标记和4个TRAP标记，27个连锁群，连锁群长度11.126cM，3-90个分子标记，标记间的平均距离为3.38cM，平均每个连锁群的遗传距离为62.89cM，覆盖1698 cM，约占甘蓝型油菜基因组67.92％。
RIL-2：对745个标记位点(466个SRAP，172个SSR，106个RAPD和2个形态标记)进行遗传连锁分析，构建的遗传连锁图包括570个标记位点，其中包括365个SRAP标记，134个SSR标记，71个RAPD标记，27个连锁群，连锁群长度27-163cM，3-92个分子标记，平均每个连锁群距离为78.48cM，标记间的平均距离为3.72cM，覆盖2119cM，约占甘蓝型油菜基因组84.76％。
作图亲本和株系性状统计分析
两个的群体的3个亲本在国内广泛的种植，GH 06具有完全显性的黄籽主效基因，黄籽度达到90％，黄籽性状表现稳定。中油821选系和油研2号选系为多代自交的黑籽亲本。
两个群体种植选择3个环境进行实验(2005年北碚和2006年北碚和万州)，不同的环境性状表现各异。性状表现呈现正态分布，可以用于QTL作图分析。方差分析表明品质性状之间存在基因和环境之间的互作，部分品质性状基因和环境之间互作效应不显著。
区间作图检测QTL的效应
RIL-1：区间作图检测到40个影响含油量的QTL，单个QTL解释含油量变异的5.39-15.66％；检测到28个影响蛋白质含量的QTL，单个QTL解释蛋白质变异的5.1-25.76％；检测到23个影响皮壳率的QTL，单个QTL解释皮壳率变异的5.26-11.59％：检测到影响千粒重的QTL14个，单个QTL解释千粒重变异的5.36-11.45％；检测到4个影响木质素含量的QTL，单个QTL解释木质素含量变异的5.51-10.18％；检测到7个影响单株产量的OTL，单个QTL解释单株产量变异的5.21-8.23％；检测到12个影响种皮色泽的QTL，单个QTL解释种皮色泽变异的5.39-59.61％；检测到6个影响花色素含量的QTL，单个QTL解释花色素含量变异的5.58-9.47％：检测到6个影响总酚含量的QTL，单个QTL解释总酚含量变异的5.65-13.03％；检测到6个影响黑色素含量的QTL，单个QTL解释黑色素含量变异的5.46-10.47％。
RIL-2：区间作图检测到34个影响含油量的QTL，单个QTL解释含油量变异的5.27-11.69％；检测到25个影响蛋白质含量的QTL，单个QTL解释蛋白质变异的5.19-10.4％；检测到18个影响皮壳率的QTL，单个QTL解释皮壳率变异的5.65-26.55％；检测到影响千粒重的QTL14个，单个QTL解释千粒重变异的5.08-11.51％；检测到6个影响木质素含量的QTL，单个QTL解释木质素含量变异的5.96-8.79％；检测到9个影响单株产量的QTL，单个QTL解释单株产量变异的5.03-10.59％；检测到27个影响种皮色泽的QTL，单个QTL解释种皮色泽变异的5.39-80.63％；检测到2个影响花色素含量的QTL，单个QTL解释花色素含量变异的分别为10.54％和9.86％；检测到10个影响总酚含量的OTL，单个QTL解释总酚含量变异的7.24-25.80％；检测到14个影响类黄酮含量的QTL，单个QTL解释类黄酮含量变异的6.03-28.92％；检测到13个影响黑色素含量的QTL，单个QTL解释黑色素含量变异的5.98-36.84％。
复合区间作图检测QTL的效应
RIL-1：复合区间作图检测到38个影响含油量的QTL，单个QTL解释含油量变异的2.92-12.93％；检测到18个影响蛋白质含量的QTL，单个QTL解释蛋白质变异的3.93-25.31％；检测到22个影响皮壳率的OTL，单个QTL解释皮壳率变异的3.83-13％；检测到影响千粒重的QTL16个，单个QTL解释千粒重变异的4.69-11.64％；检测到6个影响木质素含量的QTL，单个QTL解释木质素含量变异的4.12-11.77％；检测到13个影响单株产量的QTL，单个QTL解释单株产量变异的3.88-12.44％；检测到17个影响种皮色泽的QTL，单个QTL解释种皮色泽变异的4.01-13.43％；检测到7个影响花色素含量的QTL，单个QTL解释花色素含量变异的4.36-57.8％；检测到7个影响类黄酮含量的QTL，单个OTL解释类黄酮含量变异的7.13-62.35％；检测到11个影响总酚含量的QTL，单个QTL解释总酚含量变异的5.07-12.64％；检测到11个影响黑色素含量的QTL，单个QTL解释黑色素含量变异的5.26-16.91％。
RIL-2：复合区间作图检测到14个影响含油量的OTL，单个QTL解释含油量变异的4.47-12.21％；检测到13个影响蛋白质含量的QTL，单个QTL解释蛋白质变异的4.01-9.40％；检测到16个影响皮壳率的OTL，单个QTL解释皮壳率变异的3.36-11.86％；检测到影响15个千粒重的QTL，单个QTL解释千粒重变异的4.03-11.03％；检测到7个影响木质素含量的QTL，单个QTL解释木质素含量变异的4.71-7.64％；检测到4个影响单株产量的QTL，单个QTL解释单株产量变异的4.65-6.42％；检测到28个影响种皮色泽的QTL，单个QTL解释种皮色泽变异的2.46-24.52％；检测到4个影响花色素含量的OTL，单个QTL解释花色素含量变异的5.25-9.94％；检测到12个影响总酚含量的QTL，单个QTL解释总酚含量变异的3.72-16.23％；检测到7个影响类黄酮含量的OTL，单个QTL解释类黄酮含量变异的3.38-14.31％；检测到12个影响黑色素含量的QTL，单个QTL解释黑色素含量变异的3.72-16.23％。
不同环境检测到的QTL比较
应用区间作图检测RIL-1和RIL2两个群体在2005年北碚和2006年北碚和万州3个环境OTL，多数OTL不能在不同环境中检测到，只有少数QTL能够在两个或者三个环境在相近区域检测到，分别为RIL-1群体共有12个QTLs(占全部QTL的8.28％)和RIL-2群体共有35个QTLs(占全部QTL的18.13％)。
应用复合区间作图检测RIL-1和RIL-2两个群体在3个环境的QTL，RIL-1群体共有14个QTLs(占全部QTL的8.43％)和RIL-2群体共有21个QTLs(占全部QTL的15.22％)能够在相近区域重复检测到。
不同作图方法检测QTL比较
RIL-1群体区间作图共检测到了145个品质性状QTL，复合区间作图共检测到了166个QTL，复合区间作图分析方法比单区间作图分析方法多21个QTL。RIL-2群体区间作图共检测到了193个品质性状QTL，复合区间作图共检测到了138个QTL复合区间作图分析方法比单区间作图分析方法少了55个QTL。区间作图和复合区间作图差别比较大，但是两种方法均能检测到解释表型变异值大于10％的QTL。
QTL在连锁群上的分布
含油量与含油量相关性状(皮壳率、千粒重和蛋白质含量)和种皮色泽与色泽相关性状(花色素含量、类黄酮含量、总酚含量和黑色素含量)的QTL成簇的分布在某几个连锁群上。在RIL-1中主要分布LG12和LG17，RIL-2主要分布在LG1和LG18。各个性状的OTL在A、C两个基因组间都有分布。
种皮色泽QTL的初步定位
种皮色泽和四种种皮提取色素存在极显著的负相关，而四种种皮提取色素之间存在极显著的正相关。
应用Win QTL2.5的复合区间作图分析方法(CIM，composite interval mapping；LOD≥2.0)，2005年在北碚检测到11个与色泽相关的QTL，分别分布在第2、9、14和18四个连锁群。2006年在万州检测到9个与色泽相关的QTL，分别分布在第2、9、18和22连锁群。2006年在北碚检测到8个与色泽相关的QTL，分别分布在第2、11、12和18四个连锁群。其中第2连锁群和第18连锁群的QTL在三个环境都可以检测到。
复合区间作图分析，在2006北碚，检测到5个影响种皮类黄酮含量的QTL 8个影响种皮总酚含量的QTL，6个影响种皮黑色素含量的QTL,没有检测到影响种皮花色素含量的QTL。在2006万州检测到4个影响种皮花色素含量的QTL，2个影响种皮类黄酮含量的QTL，4个影响种皮总酚含量的QTL，4个影响种皮黑色素含量的QTL。
这些在三个环境下都能检测到的种皮色泽和与种皮色泽相关的提取色素QTL分别与sNRD03／120，EM40ME13／260，S455／500，EM13ME22／270，EM13ME22／270标记靠近，位于第18连锁群的90-144cM这个区间。克隆这个区间的SRAP引物标记序列，共得到了9个片段序列，片段大小从115bp-491bp，其中7个片段都和拟南芥的第5染色体具有较高的同源性。这些片同源区域分布在拟南芥第5染色体11个透明种皮的基因之间。与某个或者几个透明种皮基因同源的某个基因或几个基因在控制或者调控种皮色泽基因的表达。
Oilseed is Brassica of Cruclferae, is one of main oil crops in the world and holds an important status in the oil crops. In our country, it is the most important of the five oil crops (oilseed, soybean, peanut, gingili, and sunflower), the largest planting area and the most products. It is an important source to the vegetable oil and meal protein and industry material. Brassica include three elementary species (Brassica campestris, AA 2n=20; Brassica oleracea CC, 2n=18 and Brassica nigra, BB, 2n=16) and three aggregate species (Brassica napus, AACC, 2n=38; Brassica. Juncea, AABB, 2n=34 and Brassica carinata, BBCC, 2n=36). The researchers have focused on yellow seed coat of B. napus since Liu firstly found in recombination between B. napus and B.rapa in 1975. Yellow seed of B. napus is an excellent fodder material with lower hull proportion and fibrin content, higher protein content in meal and less polyphenol than brown or black one. In the same genetic, yellow seed has higher 3.0-5.0% oil content and more transparent oil and lower producing cost than brown or black one. However, it is very slow advance, very low efficiency and very difficult improvement to the selection breeding of B. napus. Recent advances in DNA markers offer plant breeders a rapid and precise alternative approach to conventional selection schemes to improve quantitative traits.
In the present study, the high-density genetic linkage maps have been constructed using SSR marker, RAPD markers, SRAP marker, TRAP marker and AFLP marker to RIL-1 population (GH06×Youyan 2) and RIL-2 population (GH 06×Zhongyou 821). WinQTL 2.5 has detected QTLs of quality about two populations in three environments (in Beibei in 2005 and in both Beibei and Wanzhou in 2006) with LOD value 2.0. The major QTL potential regions of the seed coat color have been primarily located. The mainly results were as following:
DNA polymorphism among three parents
RIL-1: The 64 AFLP primer combinations yielded 4035 bands between Youyan 2 and GH 06 with 63 bands each primer. Among of the bands yielded 170 polymorphic bands with an average 2.66 polymorphic bands (between 1 and 12) informative AFLP per primer combination. 153 polymorphism primer pairs found among 777 SSR primer pairs and yielded 181 polymorphic bands with an average 1.15 polymorphic bands per primer. 121 SRAP primer combinations yielded 267 polymorphic bands with an average 2.21 polymorphic bands per primer combination. 6 TRAP primer combinations yielded 9 polymorphic bands with an average 1.83 polymorphic bands per primer combination.
RIL-2: 152 polymorphism primer pairs found among 777 SSR primer pairs and yielded 172 polymorphic bands with an average 1.15 polymorphic bands per primer. 260 primer combinations yielded 466 polymorphic bands with an average 1.79 polymorphic bands per primer combination. 68 polymorphic primers yielded 106 polymorphic bands with an average 1.56 polymorphic bands per primer.
Construction of the genetic linkage map
RIL-1: 621 polymorphic loci, including 261 SRAP markers, 181 SSR markers and 9 TRAP markers, were employed to perform linkage analysis using the RIL-1 population of B. napus. 502 markers (220 SRAP markers, 155 SSR markers, 123 AFLP markers and 9 TRAP markers) were mapped to 27 linkage groups ranging from 11 to 121 cM with an average length of 62.89 cM containing 3 to 90 markers in each linkage group. The map covered a total of 1698 cM with a coverage percentage of about 67.92% (Lombard and Delourme, 2001), and the average distance between two adjacent markers was 3.38 cM.
RIL-2: 745 polymorphic loci, including 466 SRAP markers, 172 SSR markers, 106 RAPD markers and 2 morphological loci were employed to perform linkage analysis using the RIL-2 population of B. napus. 570 markers (365 SRAP markers, 134 SSR markers and 71 RAPD markers) were mapped to 27 linkage groups ranging from 27 to 163 cM with an average length of 74.48 cM containing 3 to 92 markers in each linkage group. The map covered a total of 2119 cM with a coverage percentage of about 84.76% (Lombard and Delourme, 2001), and the average distance between two adjacent markers was 3.72 cM.
Analysis of traits of mapping parents and lines
Three parents of two populations are broadly plant in China. GH 06 has a complete dominant main gene of yellow seed coat with 90 % degree of yellow seed and the trait of yellow seed shows very steady. Zhongyou 821 and Youyan 2 are two black-seeded parents by self-inbreded.
Two populations were plant in different three environments (in Beibei in 2005 and in Beibei and Wanzhou in 2006). The traits had different phenotype in different environments. The phenotype of the traits showed the normal distribution and could be used to analysis of QTL. The variance analysis of the trait indicated that the some qualities of B. napus interacted between gene and environment, and other qualities had not significant interaction effect between gene and environment.
Analysis of QTL effects based on interval mapping
RIL-1: In Beibei in 2005, 4 QTLs of effecting lignin content were identified by interval mapping (IM) with one QTL explaining 5.51-10.18% of the lignin content variance and 7 QTLs of effecting single-plant weight were detected with explaining 5.21-8.23% of single-plant weight variation. 40 QTLs of effecting oil content were detected by IM with explaining 5.39-15.66% of oil content variance in different environments (in Beibei in 2005 and in both Beibei and Wanzhou in 2006). 28 QTLs of effecting protein content were detected with one QTL explaining 5.1-25.76% of protein content variance. 23 QTLs of effecting hull proportion were detected with one QTL explaining 5.26-11.59% of hull proportion variance. 14 QTLs of effecting kilo-seeds weight were detected with one QTL explaining 5.36-11.45% of kilo-seeds weight variance. 12 QTLs of effecting seed coat color were detected with one QTL explaining 5.39-59.61% of seed coat color variance. 6 QTLs of effecting anthocyanidin content were detected in both Beibei and Wanzhou in 2006 with one QTL explaining 5.39-59.61% of anthocyanidin content variance. 6 QTLs of effecting total phenol content were detected with one QTL explaining 5.65-13.03% of total phenol content variance. 6 QTLs of effecting melanin content were detected with one QTL explaining 5.46-10.47% of melanin content variance.
RIL-2: In Beibei in 2005, 6 QTLs of efffecting lignin content were detected by IM with one QTL explaining 5.96-8.79% of lignin content variance and 9 QTLS of effecting single-plant weight were detected with explaining 5.03-10.59% of single-plant weight variance. 34 QTLs of effecting oil content were detected by IM with explaining 5.27-11.69% of oil content variance in different environments (in Beibei in 2005 and in both Beibei and Wanzhou in 2006). 25 QTLs of effecting protein content were detected with one QTL explaining 5.19-10.4% of protein content variance. 18 QTLs of effecting hull proportion were detected with one QTL explaining 5.65-26.55% of hull proportion variance. 14 QTLs of effecting kilo-seeds weight were detected with one QTL explaining 5.08-11.51% of kilo-seeds weight variance. 27 QTLs of effecting seed coat color were detected with one QTL explaining 5.39-80.63% of seed coat color variance. 2 QTLs of effecting anthocyanidin content were detected by IM in both Beibei and Wanzhou in 2006 with one QTL explaining 10.54 percent and 9.86% of anthocyanidin content variance. 10 QTLs of effecting total phenol content were detected with one QTL explaining 7.24-25.80% of total phenol content variance. 14 QTLs of effecting flavonoid content were detected with one QTL explaining 6.03-28.92% of flavonoid content variance. 13 QTLs of effecting melanin content were detected with one QTL explaining 5.98-36.84% of melanin content variance.
Analysis of QTL effects based on composite interval mapping
RIL-1: In Beibei in 2005, 6 QTLs of effecting lignin content were detected by composite intrval mapping (CIM) with one QTL explaining 4.12-11.77% of variance and 13 QTLS of effecting single-plant weight were detected with explaining 3.88-12.44% of single-plant weight variance. 38 QTLs of effecting oil content were detected by CIM with explaining 2.92-12.93% of oil content variance in different environments (in Beibei in 2005 and in both Beibei and Wanzhou in 2006). 18 QTLs of effecting protein content were detected with one QTL explaining 3.93-25.31% of protein content variance. 22 QTLs of effecting hull proportion were detected with one QTL explaining 3.83-13% of hull proportion variance. 16 QTLs of effecting kilo-seeds weight were detected with one QTL explaining 4.69-11.64% of kilo-seeds weight variance. 17 QTLs of effecting seed coat color were detected with one QTL explaining 4.01-13.43% of seed coat color variance. 7 QTLs of efecting anthocyanidin content were detected by CIM in both Beibei and Wanzhou in 2006 with one QTL explaining 4.36-57.8% of anthocyanidin content variance. 7 QTLs of flavonoid content were detected by CIM with one QTL explaining 7.13-62.35% of flavonoid content variance. 11 QTLs of effecting total phenol content were detected with one QTL explaining 5.07-12.64% of total phenol content variance. 11 QTLs of effecting melanin content were detected with one QTL explaining 5.26-16.91% of melanin content variance.
RIL-2: In Beibei in 2005, 7 QTLs of effecting lignin content were detected by CIM with one QTL explaining 4.71-7.64% of lignin content variance and 4 QTLS of effecting single-plant weight were detected with explaining 4.65-6.42% of single-plant weight variance. 14 QTLs of effecting oil content were detected by CIM with explaining 4.47-12.21% of oil content variance in different environments (in Beibei in 2005 and in both Beibei and Wanzhou in 2006). 13 QTLs of effecting protein content were detected with one QTL explaining 4.01-9.40% of protein content variance. 16 QTLs of effecting hull proportion were detected with one QTL explaining 3.36-11.86% of hull proportion variance. 15 QTLs of effecting kilo-seeds weight were detected with one QTL explaining 4.03-11.03% of kilo-seeds weight variance. 28 QTLs of effecting seed coat color were detected with one QTL explaining 2.46-24.52% of seed coat color variance. 4 QTLs of effecting anthocyanidin content were detected by IM in both Beibei and Wanzhou in 2006 with one QTL explaining 5.25-9.94% of anthocyanidin content variance. 12 QTLs of effecting total phenol content were detected with one QTL explaining 3.72-16.23% of total phenol content variance. 7 QTLs of effecting flavonoid content were detected with one QTL explaining 3.38-14.31% of flavonoid content variance. 12 QTLs of effecting melanin content were detected with one QTL explaining 3.72-16.23% of melanin content variance.
Comparing to QTL effect in different environments
Based on IM: The most QTLs of RIL-1 and RIL2 in three different environments (in Beibei in 2005 and in Beibei and Wanzhou in 2006) could not be repeatedly identified in three environments. Some QTLs could be repeatedly detected in near region of the same linkage group in three environments, which were 12 QTLs (8.28% of all QTLs) of RIL-1 and 35 QTLs (18.13%) of RIL-2.
Based on CIM: The QTLs of RIL-1 and RIL2 that were identified in three different environments were repeated in these environments could be repeatedly detected in near region of the same linkage group in three environments, which were 14 QTLs (8.43% of all QTLs) of RIL-1 and 21 QTLs (15.22%) of RIL-2.
Comparing to QTL effect in different mapping methods
145 quality QTLs of RIL-1 were identified by IM and 166 QTLs were identified by CIM, the QTL numbers by CIM were more 21 than the numbers by IM. 193 quality QTLs of RIL-2 were identified by IM and 138 QTLs were identified by CIM, the QTL number by IM were more 55 than the numbers by CIM. It is very different to between IM and CIM, but they could detect the QTL with explaining =10% of phenotypic variance.
QTL distribution
The QTLs of oil content and oil content related traits(hull proportion, kilo-seed weight and protein content) clustered in some linkage groups as well as the QTLs of seed coat color and seed coat color related to traits(anthocyanidin content, flavonoid content, total phenol content and melanin content). Such as, in the linkage groups of RIL-1 they mainly distributed in LG 12 and LG 17 and mainly distributed in LG 1 and LG18 in ones of RIL-2. Added to this, QTLs of all quality distributed both A genome and C genome.
Primary location QTL of seed coat color
Seed coat color is very significant negative correlation to four kinds of pigments (anthocyanidin, flavonoid, total phenol and melanin), but four kinds of pigments is very significant positive correlation to each other.
11 significant QTLs of seed coat color were identified in four linkage groups (LG 2, 9, 14 and 18) with a LOD value threshold of 2.0 by CIM in Beibei in 2005. In Wanzhou 9 significant QTLs were identified in four linkage groups (LG 2, 9, 18 and 22) in 2006 and 8 significant QTLs were identified in four linkage groups (LG 2,11,12 and 18) in Beibei in 2006. The QTL in the LG 2 and LG18 could be detected in three environments.
5 significant QTLs of flavonoid content, 8 significant QTLs of total phenol content and 6 significant QTLs of melanin content were detected in Beibei in 2006 and QTL of anthocyanidin content was not detected. In Wanzhou in 2006, 4 significant QTLs of anthocyanidin content, 2 significant QTLs of flavonoid content, 4 significant QTLs of total phenol content and 4 significant QTLs of melanin content were detected.
These QTLs of seed coat color and pigment that could be detected in three environments is near to some marker, such as sNRD03/120, EM40ME13/260, S455/500, EM13ME22/270, EM13ME22/270, and located 90-144 cM in the 18~(th) linkage group. Cloned SRAP marker sequence in the region, and gained 9 sequence with length ranging from 115bp to 491, 7 sequence among of these sequences have homology with 5~(th) chromosome of Arabidopsis thaliana. These homologous areas are scattered among the 11 transparent testa (77) genes in 5~(th) chromosome of A. thaliana. So one gene or some genes, which are homologous with one TT gene or some TT gene, regulate and control the expression of the seed coat color gene.
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