大豆种子蛋白质含量和油分含量重演性QTL的发掘
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摘要
大豆(soybean)是我国重要的农作物之一,是植物蛋白质和油分的主要来源。发掘品质性状基因/QTL并利用分子标记辅助选择对于培育优质大豆品种具有深远的意义。
近些年来,国内外学者经过多年研究,对控制大豆重要性状的QTL进行定位。因此对已发表的QTL定位结果进行整合与总结,从中发现重演性好的位点,对于理解性状形成的分子机制、提高分子标记辅助选择效果具有较好的理论和应用价值。本研究的目的是利用大豆QTL定位数据库(soybase)和大豆QTL定位有关的文献资料信息,利用物理整合、元分析、overview等三种方法发掘被多次定位到相同或相近区间(20cM以内)的蛋白含量和油分含量QTL位点,为分子标记辅助育种提供参考,主要结果如下：
1、鉴定出大豆蛋白质含量的重演性QTL,发掘出候选基因,在I、E、B1、C1、C2、K、H上整合出7个重演性较好的大豆蛋白含量QTL,相关区间侧翼标记为Sat_219-Satt496、Satt268-Satt045、A118_1-A520_1、Satt338-Satt682、Satt281-Satt520、Satt349-Satt240、Satt192-BLT046_1;I连锁群连锁群上的Satt127具有较高的遗传贡献率,且在元分析中最小置信区间,overview方法也优化到该标记附近。选择蛋白含量QTL重复次数较高的最小置信区间(Sat_219-Satt496)预测基因,对25份大豆材料重测序并结合表型性状鉴定1个蛋白含量相关的候选基因Glyma20g17550.1。
2、鉴定出与大豆油分含量的重演性QTL并发掘出候选基因。在C2、A2、I、Al、L连锁群上整合了5个重演性较好的与大豆油分含量相关的QTL,相关区间侧翼标记为Satt277-Satt557、L199_1-SAC7_2、A407_1-A515_1、Sat_344-Satt625、Satt388-Satt462,其中稳定性最高的是位于C2连锁群上的QTL,其overview分析结果仅与元分析定位的区间位置相距2.3cM,故其标记Satt277和Satt557可选作标记辅助鉴定；脂肪酸组分重演性较好的区间分别为-亚麻酸含量相关区间：Satt555-Sct 196；亚油酸含量相关区间：OP_M12b-A636_1、AW620774-A741_1;油酸相关区间：L026_1-Satt615、Satt163-Satt038、Satt325-Satt130、K493_1-T005_2。选择油分含量QTL重复次数较高(大于5次)的最小置信区间(Satt426-Satt511)并分析基因序列,筛选出油分含量候选基因Glyma11g08230.1(B1连锁群)；对25份大豆材料测序并结合表型性状预测到油分含量候选基因Glyma11g08110.2、Glyma11g08210.1.
3、鉴定出重演性好且与多个性状相关的标记或区间,其中T155 1、SOYGPATR、Satt442-Sat_401处的QTL,既与蛋白含量相关,也与油分含量有关联。而通过对产量性状QTL进行物理整合发现Satt277-Satt557处的QTL与油分含量和产量性状均显著相关,这些标记对于解释“一因多效”具有重要意义。
Soybean is a kind of important crops which supply plenty of fat and protein for our daily life. It is useful for us to find QTL or gene for high quality soybean and to use markers to do good for marker-assisted selection.
Recently investigators launched their research through investigations of important traits in distinct years and locations, Thus generalizing and summarizing the QTL results already published to find locus whose conformity is an evident phenomon can show great value in understanding the molecular merchanism of traits and increasing the effects of molecular marker-assited analysis. The main purpose of this research is to combine the data of soybase and the previous papers about the QTL loci or soybean important agronomic traits with three ways (physical integration meta-analysis、overview analysis) and to find the QTL (in the interval of 20cMs) related with important traits (protein contents、oil contents and weights of 100 soybean seeds).Further more, the research can supply consultation for marker-assisted breeding. The main results are:
1. Identify 7 most stable soybean protein content QTLs and find genes for soybean protein content.7 most stable soybean protein content QTLs are in linkage I, E, B1, C1, C2, K, H. Their related markers are Sat_219-Satt496、Satt268-Satt045、A118 1-A520 1、Satt338-Satt682、Satt281-Satt520、Satt349-Satt240、Satt192-BLT046_1, which could be used in marker-assisted selection in soybean breeding create new markers to help breeding. The linkage F contained one marker-Sattl27, whose related QTL effect explained by it is the highest, further more,it appeared near/in the smallest confidence interval supplied by biomercator and also are qualified by overview method. Select the smallest confidence interval (Sat_219-Satt496) related with protein contents whose repeated times are high to some extent.Use 25 materials to find the gene sequences and combine this data with protein phenotype traits, finally predict one gene-Glyma20g17550.1.
2. Identify 5 most stable soybean oil content QTLs and find genes for soybean oil content.The QTLs of soybean oil are intergrated in linkage C2、A2、I、A1、L. Their related markers are are Satt277-Satt557、L199_1-SAC7_2、A407_1-A515_1、Satt62 5、Satt388-Satt462, the left and right marker of the most stable oil QTL are in C2 linkage group. The position of this QTL detected by overview method is only 2.3cM apart from the position of the interval by meta-analysis. So the conclusion is that markers Satt277 and Satt557 can be used for marker-assisted identifications. The following are the QTLs related with the composition of oil:Satt555-Sct_196 related with linolenic acid, OP_M12b-A636_1, AW620774-A741_1 related with linoleic acid and L026_1-Satt615, Satt163-Satt038, Satt235-Satt130, K493_1-T005_2 related with oleic acid. Select the smallest confidence interval (Satt426-Satt511) related with oil contents whose repeated times are high to some extent (more than 5) and analyze the sequence. Through this procedure, the predicted gene Glyma11g08230.1 (B1 linkage) jumped into our sight. This can provide consultation for gene cloning. Use 25 materials to find the gene sequences and combine this data with oil phenotype traits, finally predict another two genes-Glyma11g08110.2 and Glyma11g08210.1.
3.Through the combination of statistic method and software, we found many maker and intervals about many traits and repeated for many times. T155_1、SOYGPATR、Satt442-Sat_401 are related with both protein contents and oil contents.Through the physical integration of QTL related to soybean yield, we found QTL near Satt277-Satt557 were related to both oil content and yield. These markers may be the main reason of "pleiotropism" and can be available to the following breeding work of breeders.
近些年来,国内外学者经过多年研究,对控制大豆重要性状的QTL进行定位。因此对已发表的QTL定位结果进行整合与总结,从中发现重演性好的位点,对于理解性状形成的分子机制、提高分子标记辅助选择效果具有较好的理论和应用价值。本研究的目的是利用大豆QTL定位数据库(soybase)和大豆QTL定位有关的文献资料信息,利用物理整合、元分析、overview等三种方法发掘被多次定位到相同或相近区间(20cM以内)的蛋白含量和油分含量QTL位点,为分子标记辅助育种提供参考,主要结果如下：
1、鉴定出大豆蛋白质含量的重演性QTL,发掘出候选基因,在I、E、B1、C1、C2、K、H上整合出7个重演性较好的大豆蛋白含量QTL,相关区间侧翼标记为Sat_219-Satt496、Satt268-Satt045、A118_1-A520_1、Satt338-Satt682、Satt281-Satt520、Satt349-Satt240、Satt192-BLT046_1;I连锁群连锁群上的Satt127具有较高的遗传贡献率,且在元分析中最小置信区间,overview方法也优化到该标记附近。选择蛋白含量QTL重复次数较高的最小置信区间(Sat_219-Satt496)预测基因,对25份大豆材料重测序并结合表型性状鉴定1个蛋白含量相关的候选基因Glyma20g17550.1。
2、鉴定出与大豆油分含量的重演性QTL并发掘出候选基因。在C2、A2、I、Al、L连锁群上整合了5个重演性较好的与大豆油分含量相关的QTL,相关区间侧翼标记为Satt277-Satt557、L199_1-SAC7_2、A407_1-A515_1、Sat_344-Satt625、Satt388-Satt462,其中稳定性最高的是位于C2连锁群上的QTL,其overview分析结果仅与元分析定位的区间位置相距2.3cM,故其标记Satt277和Satt557可选作标记辅助鉴定；脂肪酸组分重演性较好的区间分别为-亚麻酸含量相关区间：Satt555-Sct 196；亚油酸含量相关区间：OP_M12b-A636_1、AW620774-A741_1;油酸相关区间：L026_1-Satt615、Satt163-Satt038、Satt325-Satt130、K493_1-T005_2。选择油分含量QTL重复次数较高(大于5次)的最小置信区间(Satt426-Satt511)并分析基因序列,筛选出油分含量候选基因Glyma11g08230.1(B1连锁群)；对25份大豆材料测序并结合表型性状预测到油分含量候选基因Glyma11g08110.2、Glyma11g08210.1.
3、鉴定出重演性好且与多个性状相关的标记或区间,其中T155 1、SOYGPATR、Satt442-Sat_401处的QTL,既与蛋白含量相关,也与油分含量有关联。而通过对产量性状QTL进行物理整合发现Satt277-Satt557处的QTL与油分含量和产量性状均显著相关,这些标记对于解释“一因多效”具有重要意义。
Soybean is a kind of important crops which supply plenty of fat and protein for our daily life. It is useful for us to find QTL or gene for high quality soybean and to use markers to do good for marker-assisted selection.
Recently investigators launched their research through investigations of important traits in distinct years and locations, Thus generalizing and summarizing the QTL results already published to find locus whose conformity is an evident phenomon can show great value in understanding the molecular merchanism of traits and increasing the effects of molecular marker-assited analysis. The main purpose of this research is to combine the data of soybase and the previous papers about the QTL loci or soybean important agronomic traits with three ways (physical integration meta-analysis、overview analysis) and to find the QTL (in the interval of 20cMs) related with important traits (protein contents、oil contents and weights of 100 soybean seeds).Further more, the research can supply consultation for marker-assisted breeding. The main results are:
1. Identify 7 most stable soybean protein content QTLs and find genes for soybean protein content.7 most stable soybean protein content QTLs are in linkage I, E, B1, C1, C2, K, H. Their related markers are Sat_219-Satt496、Satt268-Satt045、A118 1-A520 1、Satt338-Satt682、Satt281-Satt520、Satt349-Satt240、Satt192-BLT046_1, which could be used in marker-assisted selection in soybean breeding create new markers to help breeding. The linkage F contained one marker-Sattl27, whose related QTL effect explained by it is the highest, further more,it appeared near/in the smallest confidence interval supplied by biomercator and also are qualified by overview method. Select the smallest confidence interval (Sat_219-Satt496) related with protein contents whose repeated times are high to some extent.Use 25 materials to find the gene sequences and combine this data with protein phenotype traits, finally predict one gene-Glyma20g17550.1.
2. Identify 5 most stable soybean oil content QTLs and find genes for soybean oil content.The QTLs of soybean oil are intergrated in linkage C2、A2、I、A1、L. Their related markers are are Satt277-Satt557、L199_1-SAC7_2、A407_1-A515_1、Satt62 5、Satt388-Satt462, the left and right marker of the most stable oil QTL are in C2 linkage group. The position of this QTL detected by overview method is only 2.3cM apart from the position of the interval by meta-analysis. So the conclusion is that markers Satt277 and Satt557 can be used for marker-assisted identifications. The following are the QTLs related with the composition of oil:Satt555-Sct_196 related with linolenic acid, OP_M12b-A636_1, AW620774-A741_1 related with linoleic acid and L026_1-Satt615, Satt163-Satt038, Satt235-Satt130, K493_1-T005_2 related with oleic acid. Select the smallest confidence interval (Satt426-Satt511) related with oil contents whose repeated times are high to some extent (more than 5) and analyze the sequence. Through this procedure, the predicted gene Glyma11g08230.1 (B1 linkage) jumped into our sight. This can provide consultation for gene cloning. Use 25 materials to find the gene sequences and combine this data with oil phenotype traits, finally predict another two genes-Glyma11g08110.2 and Glyma11g08210.1.
3.Through the combination of statistic method and software, we found many maker and intervals about many traits and repeated for many times. T155_1、SOYGPATR、Satt442-Sat_401 are related with both protein contents and oil contents.Through the physical integration of QTL related to soybean yield, we found QTL near Satt277-Satt557 were related to both oil content and yield. These markers may be the main reason of "pleiotropism" and can be available to the following breeding work of breeders.
引文
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