不同环境条件下玉米穗部和籽粒性状的QTL定位及玉米穗行数主效QTL的验证
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摘要
玉米产量受各种生物与非生物胁迫的制约,并且与穗部和籽粒性状紧密相关。通过QTL定位和全基因组关联分析等方法,发掘玉米与产量性状相关的关键基因/QTL及其所在染色体区段,开发紧密连锁的分子标记和功能标记,对作物育种尤其是分子育种具有非常重要的实践意义。本研究的目的是,通过图谱整合和元分析发掘玉米产量性状QTL的热点区域：利用F2：3和F2：4群体对不同环境条件下的穗部和籽粒性状进行QTL定位,发掘穗行数的主效QTL位点,通过高代分离群体确认穗行数主效QTL的具体位置,为进一步对主效QTL进行精细定位打下基础；通过全基因组关联分析对不同环境条件下的穗行数性状进行关联分析,对相关联的遗传位点进行深入的挖掘。主要研究结果如下：
1、玉米穗部和籽粒性状一致性QTL的发掘：对来自不同玉米产量及其相关性状定位群体获得的946个QTL座位,通过QTLFinder软件的图谱映射和元分析功能构建了玉米产量性状QTL的一致性图谱。玉米产量性状QTL在10条染色体上分布不均匀,10号染色体上QTL最少,只有62个；1号染色体上QTL最多,有174个；控制轴粗和粒宽的QTL主要分布在1号和2号染色体上,控制穗长、百粒重、产量和行粒数的QTL主要分布在1号染色体上,穗粗QTL在2号和5号染色体上分布较多,而控制秃尖长等性状的QTL在各染色体上的分布差异不大。借助QTLFinder的元分析功能,共发掘出287个玉米产量性状的一致性QTL座位,一致性QTL存在成簇分布的现象。为玉米产量及其相关性状QTL的精细定位、基因克隆和分子标记辅助选择提供了参考依据。
2、玉米穗部和籽粒性状的QTL定位：以实验室自己选育的两个自交系Y1648和Y2348(穗部和籽粒性状存在较大差异)构建的F2：3和F2：4家系群体为实验材料,分别于2008年和2009年在不同环境条件下对玉米穗部和籽粒性状(穗长、穗粗、行粒数、穗重、轴重和粒重)进行统计分析,结果表明：除穗粗和穗长外,其它穗部和籽粒性状在同一环境条件下都呈显著或极显著相关；同一穗部和籽粒性状在不同环境条件下都是极显著相关。对不同环境条件下的玉米穗部和籽粒性状进行QTL分析,三种环境条件下一共检测到33个QTL位点,分布于10条染色体上；其中控制穗粗的QTL最多,有11个；控制穗重、穗长、轴重、行粒数和粒重的QTL分别有7、4、4、1个和6个,单个QTL能够解释表型变异的6.22-14.96%。通过发掘不同环境条件下可以稳定遗传的QTL位点,为玉米穗部和籽粒性状的分子改良以及精细定位提供有价值的参考。
3、玉米穗行数QTL定位及主效QTL验证：利用实验室内部选育的两个穗行数差异较大的自交系Y1648和Y2348构建的F23家系和F24家系群体,分别于2008年和2009年在不同环境条件下对控制玉米穗行数的QTL进行检测,三种环境条件下一共检测到13个QTL位点,分布于2号、3号、5号、8号和10号染色体上,单个QTL的贡献率在5.52%-12.95%之间。用来源于同一亲本材料的BC3F23家系群体对穗行数进行定位,在两种环境条件下共检测到4个QTL位点,位于5号和10号染色体上,单个QTL能够解释表型变异的9.19%-34.59%,基因作用方式以加性和部分显性为主。通过对BC5F23群体后代基因型和穗行数统计分析发现,在SSR1430和umc1077位置上具有Y2348带型的株系穗行数普遍比具有Y1648带型的株系穗行数多,杂合体的穗行数居于两者之间,且不同基因型间的穗行数差异极显著；按照穗行数进行分组,穗行数少的株系中主要为Y1648的基因型,穗行数多的株系中主要为Y2348的基因型。虽然不能在这3个群体中发现符合孟德尔分离规律的现象,但是可以证明目标QTL的存在,并且确认了目标QTL位于分子标记SSR1430和umc1077之间。这将为进一步精细定位和基因克隆该主效QTL提供依据和基础。
4、不同环境条件下玉米穗行数的全基因组关联分析：利用TASSEL3.0软件中的混合线性模型对不同环境条件下的玉米穗行数进行了关联分析。以P值小于0.0001为标准,对不同环境条件下的穗行数进行分析,一共检测到390个控制穗行数的SNP位点。其中有35个SNP位点在两种环境条件下检测到,有4个SNP位点在三种环境条件下检测到,有2个SNP位点可以在四种环境条件下检测到。在不同环境条件下稳定检测到的SNP位点为穗行数基因发掘提供了可能。
The maize production is affected by various biotic and abiotic stress, and is closely related with the ear and kernel traits. To identify the key QTL/gene for maize yield that could be used in the maize breeding by QTL mapping or GWAS is of great importance for maize yield improvement. In this study, in order to dissect the QTL controlling maize ear and kernel traits, we constructed an integration and consensus map for maize yield related traits. With the derivative F2:3and F2:4families to do QTL mapping for maize ear and kernel traits, and get the major QTL for maize kernel row number. Based on the primary mapping results, we foused on a major QTL on chromosome10for kernel row number and three segregation populations were constructed to confirm it. At the same time, we used genome-wide association mapping strategy to decipher the genetic basis of maize kernel row number in order to find more closely related SNPs. The main results are as follows:
1. In this research,946QTL for grain yield and its related traits in maize were collected from36experiments and were used to construct the integration and consensus map. QTL-Finder was used for QTL integration and meta-analysis with IBM22008Neighbors as a reference map. As shown in the result, the QTL for maize yield and related traits were unevenly distributed on all10chromosomes, with most of174QTL on chromosome1and least of62QTL on chromosome10. QTL for ear length,100kernel weight, kernel number per row and grain yield were mainly distributed on chromosome1, while QTL for cob diameter and kernel width on chromosome1and2, QTL for ear diameter are mainly on chromosome1and5. QTL for other traits are evenly distributed on the10chromosomes. Two-hundreds and eighty-seven consensus QTL for grain yield and its related traits were identified by the meta-analysis function of QTLFinder, some consensus QTL for different traits located on the same or near chromosome regions. These results provide a good basis for studying genetic mechanism and molecular marker assisted selection for maize yield improvement.
2. In this research, six ear and kernel related traits including ear weight, ear length, ear diameter, kernel number per row, cob weight and kernel weight, were investigated using F2:3and F2:4populations, which were derived from two inbred lines of Y1648and Y2348cultivated by our lab. Respectively, the two populations were evaluated under different regions in2008and2009. The results showed that the maize ear and kernel traits under the same environment are significantly correlated, except for ear diameter and ear length, the same traits under different environments are significantly correlated. A total of33QTLs for maize ear and kernel traits were identified under different environments, including12QTLs for maize ear diameter,7QTLs for ear weight,4QTLs for ear length,4,1,6for cob weight, kernel number per row and kernel weight respectively. The phenotypic variation explained by each QTL is from6.22-14.96%, some QTLs can be detected under more than one environments. These results may help to further elucidate the genetic basis of maize yield.
3. In this paper, with the F2:3and F2:4population development from two inbred lines of Y1648and Y2348cultivated by our lab,13QTLs for maize kernel row number are identified under different environments, and these QTL are distributed on chromosome2、3、5、8and10, for each single QTL can explain5.52%to12.95%of the kernel row number variation. Meantime, with a BC3F2:3population developed from the same two parental lines,4QTLs located on chromosome5and chromosome10for maize kernel row number are identified under two environments, the contribution for each single QTL is between9.19%and34.59%, the QTL on chromosome10between ssr1430and umc1077is in good accordance with the QTL identified in the F2:3and F2:4population, and can explain more phenotype variation. Three BC5F2:3populations which segregated at qKRN10, the major QTL for maize kernel row number on chromosome10were used to reconfirm the target QTL. The lines that could amplify the same size with Y2348at the marker loci umc2348and umc1077generally have more maize kernel row number than lines that amplify the same product with Y1648, while the heterozygous lines are between the two homozygous types, the maize kernel row number between different genotypes are significantly varied. Though we could not mendelize of this QTL, we confirmed this locus in the marker region from ssr1430to umc1077. This result further confirm the major QTL for maize kernel row number on chromosome10and imply the possibility for fine mapping and map-base cloning of this major QTL.
4. By the MLM model of TASSEL3.0,390SNP significant loci that associate with the kernel row number were identified. And35SNP could be detected under2environments,4SNP under3different environments,2SNP for kernel row number under4conditions, these SNP supply a good basis for the gene discovery for maize kernel row number.
1、玉米穗部和籽粒性状一致性QTL的发掘：对来自不同玉米产量及其相关性状定位群体获得的946个QTL座位,通过QTLFinder软件的图谱映射和元分析功能构建了玉米产量性状QTL的一致性图谱。玉米产量性状QTL在10条染色体上分布不均匀,10号染色体上QTL最少,只有62个；1号染色体上QTL最多,有174个；控制轴粗和粒宽的QTL主要分布在1号和2号染色体上,控制穗长、百粒重、产量和行粒数的QTL主要分布在1号染色体上,穗粗QTL在2号和5号染色体上分布较多,而控制秃尖长等性状的QTL在各染色体上的分布差异不大。借助QTLFinder的元分析功能,共发掘出287个玉米产量性状的一致性QTL座位,一致性QTL存在成簇分布的现象。为玉米产量及其相关性状QTL的精细定位、基因克隆和分子标记辅助选择提供了参考依据。
2、玉米穗部和籽粒性状的QTL定位：以实验室自己选育的两个自交系Y1648和Y2348(穗部和籽粒性状存在较大差异)构建的F2：3和F2：4家系群体为实验材料,分别于2008年和2009年在不同环境条件下对玉米穗部和籽粒性状(穗长、穗粗、行粒数、穗重、轴重和粒重)进行统计分析,结果表明：除穗粗和穗长外,其它穗部和籽粒性状在同一环境条件下都呈显著或极显著相关；同一穗部和籽粒性状在不同环境条件下都是极显著相关。对不同环境条件下的玉米穗部和籽粒性状进行QTL分析,三种环境条件下一共检测到33个QTL位点,分布于10条染色体上；其中控制穗粗的QTL最多,有11个；控制穗重、穗长、轴重、行粒数和粒重的QTL分别有7、4、4、1个和6个,单个QTL能够解释表型变异的6.22-14.96%。通过发掘不同环境条件下可以稳定遗传的QTL位点,为玉米穗部和籽粒性状的分子改良以及精细定位提供有价值的参考。
3、玉米穗行数QTL定位及主效QTL验证：利用实验室内部选育的两个穗行数差异较大的自交系Y1648和Y2348构建的F23家系和F24家系群体,分别于2008年和2009年在不同环境条件下对控制玉米穗行数的QTL进行检测,三种环境条件下一共检测到13个QTL位点,分布于2号、3号、5号、8号和10号染色体上,单个QTL的贡献率在5.52%-12.95%之间。用来源于同一亲本材料的BC3F23家系群体对穗行数进行定位,在两种环境条件下共检测到4个QTL位点,位于5号和10号染色体上,单个QTL能够解释表型变异的9.19%-34.59%,基因作用方式以加性和部分显性为主。通过对BC5F23群体后代基因型和穗行数统计分析发现,在SSR1430和umc1077位置上具有Y2348带型的株系穗行数普遍比具有Y1648带型的株系穗行数多,杂合体的穗行数居于两者之间,且不同基因型间的穗行数差异极显著；按照穗行数进行分组,穗行数少的株系中主要为Y1648的基因型,穗行数多的株系中主要为Y2348的基因型。虽然不能在这3个群体中发现符合孟德尔分离规律的现象,但是可以证明目标QTL的存在,并且确认了目标QTL位于分子标记SSR1430和umc1077之间。这将为进一步精细定位和基因克隆该主效QTL提供依据和基础。
4、不同环境条件下玉米穗行数的全基因组关联分析：利用TASSEL3.0软件中的混合线性模型对不同环境条件下的玉米穗行数进行了关联分析。以P值小于0.0001为标准,对不同环境条件下的穗行数进行分析,一共检测到390个控制穗行数的SNP位点。其中有35个SNP位点在两种环境条件下检测到,有4个SNP位点在三种环境条件下检测到,有2个SNP位点可以在四种环境条件下检测到。在不同环境条件下稳定检测到的SNP位点为穗行数基因发掘提供了可能。
The maize production is affected by various biotic and abiotic stress, and is closely related with the ear and kernel traits. To identify the key QTL/gene for maize yield that could be used in the maize breeding by QTL mapping or GWAS is of great importance for maize yield improvement. In this study, in order to dissect the QTL controlling maize ear and kernel traits, we constructed an integration and consensus map for maize yield related traits. With the derivative F2:3and F2:4families to do QTL mapping for maize ear and kernel traits, and get the major QTL for maize kernel row number. Based on the primary mapping results, we foused on a major QTL on chromosome10for kernel row number and three segregation populations were constructed to confirm it. At the same time, we used genome-wide association mapping strategy to decipher the genetic basis of maize kernel row number in order to find more closely related SNPs. The main results are as follows:
1. In this research,946QTL for grain yield and its related traits in maize were collected from36experiments and were used to construct the integration and consensus map. QTL-Finder was used for QTL integration and meta-analysis with IBM22008Neighbors as a reference map. As shown in the result, the QTL for maize yield and related traits were unevenly distributed on all10chromosomes, with most of174QTL on chromosome1and least of62QTL on chromosome10. QTL for ear length,100kernel weight, kernel number per row and grain yield were mainly distributed on chromosome1, while QTL for cob diameter and kernel width on chromosome1and2, QTL for ear diameter are mainly on chromosome1and5. QTL for other traits are evenly distributed on the10chromosomes. Two-hundreds and eighty-seven consensus QTL for grain yield and its related traits were identified by the meta-analysis function of QTLFinder, some consensus QTL for different traits located on the same or near chromosome regions. These results provide a good basis for studying genetic mechanism and molecular marker assisted selection for maize yield improvement.
2. In this research, six ear and kernel related traits including ear weight, ear length, ear diameter, kernel number per row, cob weight and kernel weight, were investigated using F2:3and F2:4populations, which were derived from two inbred lines of Y1648and Y2348cultivated by our lab. Respectively, the two populations were evaluated under different regions in2008and2009. The results showed that the maize ear and kernel traits under the same environment are significantly correlated, except for ear diameter and ear length, the same traits under different environments are significantly correlated. A total of33QTLs for maize ear and kernel traits were identified under different environments, including12QTLs for maize ear diameter,7QTLs for ear weight,4QTLs for ear length,4,1,6for cob weight, kernel number per row and kernel weight respectively. The phenotypic variation explained by each QTL is from6.22-14.96%, some QTLs can be detected under more than one environments. These results may help to further elucidate the genetic basis of maize yield.
3. In this paper, with the F2:3and F2:4population development from two inbred lines of Y1648and Y2348cultivated by our lab,13QTLs for maize kernel row number are identified under different environments, and these QTL are distributed on chromosome2、3、5、8and10, for each single QTL can explain5.52%to12.95%of the kernel row number variation. Meantime, with a BC3F2:3population developed from the same two parental lines,4QTLs located on chromosome5and chromosome10for maize kernel row number are identified under two environments, the contribution for each single QTL is between9.19%and34.59%, the QTL on chromosome10between ssr1430and umc1077is in good accordance with the QTL identified in the F2:3and F2:4population, and can explain more phenotype variation. Three BC5F2:3populations which segregated at qKRN10, the major QTL for maize kernel row number on chromosome10were used to reconfirm the target QTL. The lines that could amplify the same size with Y2348at the marker loci umc2348and umc1077generally have more maize kernel row number than lines that amplify the same product with Y1648, while the heterozygous lines are between the two homozygous types, the maize kernel row number between different genotypes are significantly varied. Though we could not mendelize of this QTL, we confirmed this locus in the marker region from ssr1430to umc1077. This result further confirm the major QTL for maize kernel row number on chromosome10and imply the possibility for fine mapping and map-base cloning of this major QTL.
4. By the MLM model of TASSEL3.0,390SNP significant loci that associate with the kernel row number were identified. And35SNP could be detected under2environments,4SNP under3different environments,2SNP for kernel row number under4conditions, these SNP supply a good basis for the gene discovery for maize kernel row number.
引文
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