甘蓝型油菜产量和品质相关性状关联分析
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摘要
油菜是世界范围内广泛种植的油料作物,油菜产量和种子中含油量、脂肪酸成份等品质性状都越来越被人们重视和关注。前人已经有大量关于油菜产量和品质相关性状的QTL定位和生化代谢途径的研究,为其遗传基础的解析提供了理论依据。关联分析是一种高效,分辨率较高的QTL定位方法,已经在人类复杂遗传疾病和作物重要农艺性状研究中得到广泛应用。本研究通过构建一个来自于世界主要油菜种植国家的192份甘蓝型油菜品种和自交系的自然群体,利用451个SSR和740个AFLP标记对株高、分枝高度、主花序长度、角果长、每角果粒数和千粒重等6个产量相关性状和含油量、蛋白质、芥酸、硫苷、油酸、亚油酸、亚麻酸、二十碳烯酸、棕榈酸和硬脂酸等10个品质相关性状进行了初步全基因组关联分析。主要结果如下：
1.甘蓝型油菜产量和品质相关性状的表型变异：在关联群体中,16个产量和品质相关性状都存在广泛的表型变异,表现出典型的数量性状,且大部分性状之间存在显著的遗传相关和表型相关。群体内所有性状的广义遗传力都在80%以上,表明这些性状的表型变异主要由基因型控制,遗传较稳定。这些结果意味着群体内的表型数据可以用于后续分析。
2.群体结构对性状表型变异的效应和6种统计模型的比较分析：群体结构对表型的效应分析发现,群体结构对大部分性状表型变异的影响都达到了显著水平(P<0.01),且亚群之间表型均值的方差分析也发现,大部分性状在P1亚群和P2亚群之间的表型值也达到显著水平(P<0.01),证明群体内群体结构对表型变异具有显著的影响。因此我们利用6种统计模型进行比较分析,发现同时控制群体结构和亲缘关系的Q+K模型表现最佳,能有效控制不同性状关联分析的假阳性,以用于后续的关联检测。
3.群体内各个产量相关性状的关联分析：在Q+K模型下,利用1109个SSR和AFLP标记对甘蓝型油菜6个产量相关性状三年的平均值进行全基因组关联扫描,共检测到37个显著关联的标记位点(P<0.001),对表型解释的效应值在3.51%-9.03%之间。其中检测到2个标记与主花序长度显著关联,14个标记与株高显著关联。有12个关联标记与前人定位的QTLs共定位。其中,分枝高度和株高检测到5个共同的显著关联标记(EA02MC04_7、EA14MC08_1、EA09MC11_5、EA14MC02_7和EA03MC04_10);角果长和千粒重也检测到1个共同的关联标记(EA05MC08_1)。
4.群体内各个品质相关性状的关联分析：在Q+K模型下,利用1109个SSR和AFLP标记对甘蓝型油菜10个品质相关性状进行全基因组关联扫描,三年中共检测到177个显著关联的标记位点,对表型解释的效应值在0.11%-16.56%之间。其中有79个标记在两年或三年中被同时检测到(P<0.001)。前面的相关分析已经证实大部分性状之间存在显著相关,我们也发现有61个标记同时与两个或两个以上性状关联。含油量和蛋白质在2009和2011年分别检测到一个共同的关联标记(EA06MG06-8和EA09MC08-7)。二十碳烯酸与油酸检测到的共同标记数是最多的,三年中分别检测到32、30和31个标记位点。在三年10个表型性状中,我们发现有的标记与多年多个性状同时检测到关联。如标记EA06MG08_8,在三年中被检测到与芥酸、硫苷、二十碳烯酸、油酸和棕榈酸这5个性状显著关联；在2009和2010年被检测到与硬脂酸显著关联。
5.品质相关性状主成分的关联分析：针对关联群体大部分品质性状之间存在显著相关,我们将10个品质相关性状通过主成分分析合成的前三个主成分轴(PC),在三年中分别总的解释了表型变异的86.47%、87.51%和87.53%。其中PC1主要与脂肪酸成份,芥酸,硫苷等性状相关；PC2主要与含油量和蛋白质相关；PC3主要与亚麻酸相关。并且我们对这三个主成分轴进行关联检测,发现剔除含油量,蛋白质和亚麻酸这三个性状,在7个性状中我们发现共有57个标记同时与两个或两个以上性状关联,PC1在至少两年中均检测到的30个关联标记和只在一年中检测到6个关联标记,均包含在这57个标记里面。PC2没有检测到与含油量和蛋白质共同的关联标记。PC3三年共检测到13个关联标记,其中有3个与控制亚麻酸性状的关联标记相同(BnEMS620、EA04MG04_3和EA09MC14_11)。
6.共同关联标记的等位基因和单倍型效应分析：关联位点等位基因效应分析发现,针对在多个性状中同时检测到的关联标记,对所有正相关的表型性状来说,携带某一等位基因的材料在含量上都显著高于携带另一等位基因的材料；而与这些表型性状呈负相关的性状,则表现出相反的效应趋势。如标记EA06MG08_8携带等位基因Ao的材料在芥酸和二十碳烯酸含量上都显著高于携带等位基因A1的材料,但在油酸含量上都显著低于携带等位基因A1的材料。同时关联标记之间不同等位基因组合的单倍型效应分析发现,单倍型对表型解释的联合效应明显高于单个标记对表型解释的效应。对株高而言,标记EA02MC04_7和BrGMS2998不同等位基因的材料之间芥酸含量的最大平均差值分别为10.88和23.68cm,等位基因解释的表型变异分别为6.67%和7.33%。而这两个标记不同等位基因组成的单倍型的材料之间的最大平均差值达到35.20cm,单倍型解释的表型变异达到22.48%。同时我们在这192份甘蓝型油菜种质中也检测到很多对重要产量和品质性状具有正向贡献的优异等位基因和单倍型。这些结果显示出甘蓝型油菜重要性状微效优异等位基因的挖掘和聚合是一种改良油菜产量和品质的有效手段,为分子育种提供思路和前景。
Rapeseed(Brassica napus L.) is a widely cultivated oil crop in the world. Yield and quality-related traits in rapeseed are increasingly taken great importance. QTL mapping and biological metabolic pathway analysis were done to dissect the genetic bases of yield and quality-related traits in Brassica napus. Association mapping (AM) with its high resolution is a powerful approach in quantitative trait loci mapping, and has been widely adopted in complex genetic diseases in human and important agronomic traits in crops. In the study, a panel of192inbred lines of Brassica napus from all over the world was genotyped using451single-locus microsatellite markers and740amplified fragment length polymorphism (AFLP) markers. Genome-wide association studies were conducted with six yield-related traits and ten quality-related traits in our association mapping, main results are as follows:
1. Phenotypic analysis of yield and quality-related traits in Brassica napus:In our association mapping, extensive phenotypic variations were observed for all traits, and all traits had an H2higher than80%, suggesting that all these traits are stably inherited. Highly genetic and phenotypic correlations were observed between almost all the traits. The phenotypic analysis suggesting that the phenotypic traits can be applied to the following association mapping.
2. Model comparison on controlling false associations:ANOVA results indicated that most of all measured traits showed a strong influence of population structure (P<0.01), and most of all traits were significantly different between subgroups (P<0.01). These results indicating that population structure had significant impacts on phenotypic variation for almost all traits. The comparision with six statistical models in association analyses indicating that the Q+K model could effectively control false positive associations and was chosen for association analyses between the SSR and AFLP markers and yield and quality-related traits.
3. Association mapping of six yield-related traits:Using the optimal Q+K model, association analyses were carried out for the six yield-related traits using the means across three years in Brassica napus. A total of37associated loci were detected, with two to fourteen markers associated with individual traits. The effects of phenotypic variance explained by associated markers were relatively small, ranged from3.51%to9.03%. And 12markers were located within or close to QTLs identified in previous studies. Among these, five markers (EA02MC04_7, EA14MC08J, EA09MC11)5, EA14MC02_7and EA03MC04_10) were repeatedly associated with first branch height and plant height, and one common marker was detected association with silique length and seed weight.
4. Association mapping of ten quality-related traits:Using the optimal Q+K model, association analyses were carried out with the369SSR and740AFLP markers for ten quality-related traits in Brassica napus. A total of177associated loci were detected in three years (P<0.001), the effects of phenotypic variance explained by associated markers were ranged from0.11%to16.56%. Of these,79markers were repeatedly detected in two or three years. The correlation analysis demonstrated that highly genetic and phenotypic correlations were observed between almost all the traits. We found that61common markers were associated with correlated traits in our association mapping. Among these, two markers (EA06MG06-8and EA09MC08-7) were repeatedly associated with both oil content and protein content.32,30,31common markers were detected association with eicosenoic acid and oleic acid in three years, respectively, which was the most common markers associated with correlated traits. For ten quality-related traits in three years, some markers were repeatedly detected association with multiple traits in multiple years. For the marker EA06MG08_8, was significantly associated with erucic acid, seed glucosinolate, eicosenoic acid, oleic acid and palmitic acid in all three years, and was significantly associated with stearic acid in2009,2010.
5. Association mapping of the principal components axis for ten quality-related traits: Using the PCA analysis, ten quality-related traits were synthesized into the first three principal components axes which totally accounting for86.47%,87.51%,87.53%of phenotypic variance in three years, respectively. The first PC axis accounted for erucic acid, seed glucosinolate and fatty acid composition; the second PC axis accounted for oil content and protein content; the third PC axis accounted for linolenic acids. And we conducted association analysis with these three PC axes.30associated markers detected with the first PC axis in two or three years and6associated markers detected in one year, were included in57common associated markers which detected with more than two traits in ten quality-related traits but oil content, protein content and linolenic acids. No common associated marker was detected with the second PC axis and oil content, protein content.3common associated markers detected with the third PC axis and linolenic acids (BnEMS620, EA04MG04_3and EA09MC14_11).
6. Allele effects of common associated markers on yield and quality-related traits: We found that for each associated marker detected with correlated traits, the lines contained one allele was preferentially shared higher content than that contained the other allele for positively correlated traits, and was shared lower content than that contained the other allele for the traits, which were nagetively correlated with positively correlated traits. For the marker EA06MG088, the lines contained the allele A0were preferentially shared higher content than that contained the allele A1with erucic acid and eicosenoic acid, and were shared lower content than that contained the allele A1with oleic acid. And the combination effects of haplotypes of associated markers were preferentially higher than the effects explained by the individual markers. EA02MC04_7and BrGMS2998explained6.67%and7.33%of phenotypic variance of plant height, respectively, while they jointly explained22.48%of phenotypic variance. These results suggested that the pyramiding of favorable alleles with minor effects is an effective way to enhance trait performance of rapeseed variety for yield and quality-related traits, which depict us the perspective of application in molecular breeding.
1.甘蓝型油菜产量和品质相关性状的表型变异：在关联群体中,16个产量和品质相关性状都存在广泛的表型变异,表现出典型的数量性状,且大部分性状之间存在显著的遗传相关和表型相关。群体内所有性状的广义遗传力都在80%以上,表明这些性状的表型变异主要由基因型控制,遗传较稳定。这些结果意味着群体内的表型数据可以用于后续分析。
2.群体结构对性状表型变异的效应和6种统计模型的比较分析：群体结构对表型的效应分析发现,群体结构对大部分性状表型变异的影响都达到了显著水平(P<0.01),且亚群之间表型均值的方差分析也发现,大部分性状在P1亚群和P2亚群之间的表型值也达到显著水平(P<0.01),证明群体内群体结构对表型变异具有显著的影响。因此我们利用6种统计模型进行比较分析,发现同时控制群体结构和亲缘关系的Q+K模型表现最佳,能有效控制不同性状关联分析的假阳性,以用于后续的关联检测。
3.群体内各个产量相关性状的关联分析：在Q+K模型下,利用1109个SSR和AFLP标记对甘蓝型油菜6个产量相关性状三年的平均值进行全基因组关联扫描,共检测到37个显著关联的标记位点(P<0.001),对表型解释的效应值在3.51%-9.03%之间。其中检测到2个标记与主花序长度显著关联,14个标记与株高显著关联。有12个关联标记与前人定位的QTLs共定位。其中,分枝高度和株高检测到5个共同的显著关联标记(EA02MC04_7、EA14MC08_1、EA09MC11_5、EA14MC02_7和EA03MC04_10);角果长和千粒重也检测到1个共同的关联标记(EA05MC08_1)。
4.群体内各个品质相关性状的关联分析：在Q+K模型下,利用1109个SSR和AFLP标记对甘蓝型油菜10个品质相关性状进行全基因组关联扫描,三年中共检测到177个显著关联的标记位点,对表型解释的效应值在0.11%-16.56%之间。其中有79个标记在两年或三年中被同时检测到(P<0.001)。前面的相关分析已经证实大部分性状之间存在显著相关,我们也发现有61个标记同时与两个或两个以上性状关联。含油量和蛋白质在2009和2011年分别检测到一个共同的关联标记(EA06MG06-8和EA09MC08-7)。二十碳烯酸与油酸检测到的共同标记数是最多的,三年中分别检测到32、30和31个标记位点。在三年10个表型性状中,我们发现有的标记与多年多个性状同时检测到关联。如标记EA06MG08_8,在三年中被检测到与芥酸、硫苷、二十碳烯酸、油酸和棕榈酸这5个性状显著关联；在2009和2010年被检测到与硬脂酸显著关联。
5.品质相关性状主成分的关联分析：针对关联群体大部分品质性状之间存在显著相关,我们将10个品质相关性状通过主成分分析合成的前三个主成分轴(PC),在三年中分别总的解释了表型变异的86.47%、87.51%和87.53%。其中PC1主要与脂肪酸成份,芥酸,硫苷等性状相关；PC2主要与含油量和蛋白质相关；PC3主要与亚麻酸相关。并且我们对这三个主成分轴进行关联检测,发现剔除含油量,蛋白质和亚麻酸这三个性状,在7个性状中我们发现共有57个标记同时与两个或两个以上性状关联,PC1在至少两年中均检测到的30个关联标记和只在一年中检测到6个关联标记,均包含在这57个标记里面。PC2没有检测到与含油量和蛋白质共同的关联标记。PC3三年共检测到13个关联标记,其中有3个与控制亚麻酸性状的关联标记相同(BnEMS620、EA04MG04_3和EA09MC14_11)。
6.共同关联标记的等位基因和单倍型效应分析：关联位点等位基因效应分析发现,针对在多个性状中同时检测到的关联标记,对所有正相关的表型性状来说,携带某一等位基因的材料在含量上都显著高于携带另一等位基因的材料；而与这些表型性状呈负相关的性状,则表现出相反的效应趋势。如标记EA06MG08_8携带等位基因Ao的材料在芥酸和二十碳烯酸含量上都显著高于携带等位基因A1的材料,但在油酸含量上都显著低于携带等位基因A1的材料。同时关联标记之间不同等位基因组合的单倍型效应分析发现,单倍型对表型解释的联合效应明显高于单个标记对表型解释的效应。对株高而言,标记EA02MC04_7和BrGMS2998不同等位基因的材料之间芥酸含量的最大平均差值分别为10.88和23.68cm,等位基因解释的表型变异分别为6.67%和7.33%。而这两个标记不同等位基因组成的单倍型的材料之间的最大平均差值达到35.20cm,单倍型解释的表型变异达到22.48%。同时我们在这192份甘蓝型油菜种质中也检测到很多对重要产量和品质性状具有正向贡献的优异等位基因和单倍型。这些结果显示出甘蓝型油菜重要性状微效优异等位基因的挖掘和聚合是一种改良油菜产量和品质的有效手段,为分子育种提供思路和前景。
Rapeseed(Brassica napus L.) is a widely cultivated oil crop in the world. Yield and quality-related traits in rapeseed are increasingly taken great importance. QTL mapping and biological metabolic pathway analysis were done to dissect the genetic bases of yield and quality-related traits in Brassica napus. Association mapping (AM) with its high resolution is a powerful approach in quantitative trait loci mapping, and has been widely adopted in complex genetic diseases in human and important agronomic traits in crops. In the study, a panel of192inbred lines of Brassica napus from all over the world was genotyped using451single-locus microsatellite markers and740amplified fragment length polymorphism (AFLP) markers. Genome-wide association studies were conducted with six yield-related traits and ten quality-related traits in our association mapping, main results are as follows:
1. Phenotypic analysis of yield and quality-related traits in Brassica napus:In our association mapping, extensive phenotypic variations were observed for all traits, and all traits had an H2higher than80%, suggesting that all these traits are stably inherited. Highly genetic and phenotypic correlations were observed between almost all the traits. The phenotypic analysis suggesting that the phenotypic traits can be applied to the following association mapping.
2. Model comparison on controlling false associations:ANOVA results indicated that most of all measured traits showed a strong influence of population structure (P<0.01), and most of all traits were significantly different between subgroups (P<0.01). These results indicating that population structure had significant impacts on phenotypic variation for almost all traits. The comparision with six statistical models in association analyses indicating that the Q+K model could effectively control false positive associations and was chosen for association analyses between the SSR and AFLP markers and yield and quality-related traits.
3. Association mapping of six yield-related traits:Using the optimal Q+K model, association analyses were carried out for the six yield-related traits using the means across three years in Brassica napus. A total of37associated loci were detected, with two to fourteen markers associated with individual traits. The effects of phenotypic variance explained by associated markers were relatively small, ranged from3.51%to9.03%. And 12markers were located within or close to QTLs identified in previous studies. Among these, five markers (EA02MC04_7, EA14MC08J, EA09MC11)5, EA14MC02_7and EA03MC04_10) were repeatedly associated with first branch height and plant height, and one common marker was detected association with silique length and seed weight.
4. Association mapping of ten quality-related traits:Using the optimal Q+K model, association analyses were carried out with the369SSR and740AFLP markers for ten quality-related traits in Brassica napus. A total of177associated loci were detected in three years (P<0.001), the effects of phenotypic variance explained by associated markers were ranged from0.11%to16.56%. Of these,79markers were repeatedly detected in two or three years. The correlation analysis demonstrated that highly genetic and phenotypic correlations were observed between almost all the traits. We found that61common markers were associated with correlated traits in our association mapping. Among these, two markers (EA06MG06-8and EA09MC08-7) were repeatedly associated with both oil content and protein content.32,30,31common markers were detected association with eicosenoic acid and oleic acid in three years, respectively, which was the most common markers associated with correlated traits. For ten quality-related traits in three years, some markers were repeatedly detected association with multiple traits in multiple years. For the marker EA06MG08_8, was significantly associated with erucic acid, seed glucosinolate, eicosenoic acid, oleic acid and palmitic acid in all three years, and was significantly associated with stearic acid in2009,2010.
5. Association mapping of the principal components axis for ten quality-related traits: Using the PCA analysis, ten quality-related traits were synthesized into the first three principal components axes which totally accounting for86.47%,87.51%,87.53%of phenotypic variance in three years, respectively. The first PC axis accounted for erucic acid, seed glucosinolate and fatty acid composition; the second PC axis accounted for oil content and protein content; the third PC axis accounted for linolenic acids. And we conducted association analysis with these three PC axes.30associated markers detected with the first PC axis in two or three years and6associated markers detected in one year, were included in57common associated markers which detected with more than two traits in ten quality-related traits but oil content, protein content and linolenic acids. No common associated marker was detected with the second PC axis and oil content, protein content.3common associated markers detected with the third PC axis and linolenic acids (BnEMS620, EA04MG04_3and EA09MC14_11).
6. Allele effects of common associated markers on yield and quality-related traits: We found that for each associated marker detected with correlated traits, the lines contained one allele was preferentially shared higher content than that contained the other allele for positively correlated traits, and was shared lower content than that contained the other allele for the traits, which were nagetively correlated with positively correlated traits. For the marker EA06MG088, the lines contained the allele A0were preferentially shared higher content than that contained the allele A1with erucic acid and eicosenoic acid, and were shared lower content than that contained the allele A1with oleic acid. And the combination effects of haplotypes of associated markers were preferentially higher than the effects explained by the individual markers. EA02MC04_7and BrGMS2998explained6.67%and7.33%of phenotypic variance of plant height, respectively, while they jointly explained22.48%of phenotypic variance. These results suggested that the pyramiding of favorable alleles with minor effects is an effective way to enhance trait performance of rapeseed variety for yield and quality-related traits, which depict us the perspective of application in molecular breeding.
引文
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