利用非条件和条件QTL解析油菜产量相关性状的遗传关系
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摘要
基于前期研究中构建的Sollux/Gaoyou DH群体在9个环境中的表型数据和新版遗传图谱,对油菜角果长度进行QTL定位,估测QTL的加性、上位性和环境互作效应。并通过条件QTL方法,解析角果长度与角果粒数和粒重之间的遗传关系,以期利用标记辅助,探讨通过选择角果长度基因型以增加角果粒数、提高千粒重,最终达到增加产量的可能性。结果共检测到在3个环境以上稳定表达的控制角果长度QTL 8个,加性效应值在0.09~0.26 cm之间,效应总和解释群体遗传总变异的60%。8对上位性QTL效应值在0.035~0.075 cm之间,效应总和为加性总效应的38%。QTL与环境互作效应只在少数位点和个别环境中显著。条件QTL研究表明,q SLA2、q SLC1-2和q SLC8-1位点,角果长度的变化对角果粒数影响较大;而通过选择q SLA7、q SLC1-2、q SLC8-1和q SLC8-2长角果标记基因型,可望同时提高角果粒数和千粒重。6个主效QTL 11个连锁标记基因型和表现型的关联分析,验证了条件QTL分析结果,表明通过对q SLA2、q SLA7、q SLC8-1和q SLC8-2位点6个连锁标记(ZAAS423、SUC1-3、ZAAS12a、ZAASA7-28、ZAAS433和ZAAS437)长角果基因型的聚合,可增长角果约2 cm,间接增加角果粒数2粒,同时提高千粒重0.4 g,从而可望实质性地提高油菜产量水平。
Quantitative Trait Loci(QTLs) for silique length(SL) were mapped in the updated SG map using the phenotypic data from nine environments. QTLs with additive and epistatic effects and their interactions with environments were estimated. At QTL level, conditional QTL analysis was performed to dissect the genetic relationships between silique length and seed number per silique(SS), and between silique length and 1000-seed weight(SW). Our goal was to identify QTLs that are important for silique length, as indexed by their positive correlations with either seeds per silique or 1000-seed weight, or both of the traits. Markers linked to the target QTL can be developed for indirect selection of SS and SW. As shown by the results, we detected eight QTLs with additive effects, which together accounted for around 60% of the phenotypic variations. While the total effects of eight pairs of epistatic loci(additive × additive) ranged from 0.035 to 0.075 cm and their summation was 38% of the total additive effects. QTL by environmental interactions were significant only in few environments with small amount of genetic effects. The conditional QTL analysis revealed large impact of silique length on seed number per silique in three QTLs(q SLA2, q SLC1-2, and q SLC8-1). Allelic selection for long silique length in q SLA7, q SLC1-2, q SLC8-1, and q SLC8-2 loci could potentially increase the seed number per silique(SS) and 1000-seed weight(SW). Association analysis between genotypes linking to six related QTLs and the corresponding phenotypes of yield related traits indicated that the combination of long silique alleles from four QTLs(q SLA2, q SLA7, q SLC8-1, and q SLC8-2) by marker assistant selection of ZAAS423, SUC1-3, ZAAS12 a, ZAASA7-28, ZAAS433, and ZAAS437 significantly increased about two cm in silique length. Meanwhile, two additional seeds per silique were increased, and the 1000-seed weight was enhanced by 0.4 g. Taken together, we suggest the importance of these QTLs and markers for yield breeding purpose in Brassica napus.
Quantitative Trait Loci(QTLs) for silique length(SL) were mapped in the updated SG map using the phenotypic data from nine environments. QTLs with additive and epistatic effects and their interactions with environments were estimated. At QTL level, conditional QTL analysis was performed to dissect the genetic relationships between silique length and seed number per silique(SS), and between silique length and 1000-seed weight(SW). Our goal was to identify QTLs that are important for silique length, as indexed by their positive correlations with either seeds per silique or 1000-seed weight, or both of the traits. Markers linked to the target QTL can be developed for indirect selection of SS and SW. As shown by the results, we detected eight QTLs with additive effects, which together accounted for around 60% of the phenotypic variations. While the total effects of eight pairs of epistatic loci(additive × additive) ranged from 0.035 to 0.075 cm and their summation was 38% of the total additive effects. QTL by environmental interactions were significant only in few environments with small amount of genetic effects. The conditional QTL analysis revealed large impact of silique length on seed number per silique in three QTLs(q SLA2, q SLC1-2, and q SLC8-1). Allelic selection for long silique length in q SLA7, q SLC1-2, q SLC8-1, and q SLC8-2 loci could potentially increase the seed number per silique(SS) and 1000-seed weight(SW). Association analysis between genotypes linking to six related QTLs and the corresponding phenotypes of yield related traits indicated that the combination of long silique alleles from four QTLs(q SLA2, q SLA7, q SLC8-1, and q SLC8-2) by marker assistant selection of ZAAS423, SUC1-3, ZAAS12 a, ZAASA7-28, ZAAS433, and ZAAS437 significantly increased about two cm in silique length. Meanwhile, two additional seeds per silique were increased, and the 1000-seed weight was enhanced by 0.4 g. Taken together, we suggest the importance of these QTLs and markers for yield breeding purpose in Brassica napus.
引文
[1]Shi J Q,Li R Y,Qiu D,Jiang C C,Long Y,Morgan C,Bancroft I,Zhao J Y,Meng J L.Unraveling the complex trait of crop yield with quantitative trait loci mapping in Brassica napus.Genetics,2009,182:851–861
[2]Fan C C,Cai G Q,Qin J,Li Q Y,Wu J Z,Fu T D,Liu K D,Zhou Y M.Mapping of quantitative trait loci and development of allele-specific markers for seed weight in Brassica napus.Thero Appl Genet,2001,121:1289–1301
[3]Chay P,Thurling N.Identification of genes controlling pod length in spring rapeseed,Brassica napus L.,and their utilization for yield improvement.Plant Breed,1989,103:54–62
[4]刘定富,蔡怀武.甘蓝型油菜特长荚突变体的发现和鉴定.湖北农学院学报,1994,4(2):1–4Liu D F,Cai H W.Detection and identification of specially-long pod mutant in Brassica napus L.J Hubei Agric Coll,1994,4(2):1–4(in Chinese with English abstract)
[5]Cai D F,Xiao Y J,Yang W,Ye W,Wang B.Association mapping of six yield related traits in rapeseed Brassica napus L.Theor Appl Genet,2014,127:85–96
[6]Zhang L W,Yang G S,Liu P W,Hong D F,Li S P,He Q B.Genetic and correlation analysis of silique-traits in Brassica napus L.by quantitative trait locus mapping.Theor Appl Genet,2011,122:21–31
[7]Yang P,Shu C,Chen L,Xu J S,Wu J,Liu K D.Identification of a major QTL for silique length and seed weight in oilseed rape(Brassica napus L.).Theor Appl Genet,2012,125:285–296
[8]Li N,Shi J Q,Wang X F,Liu G H.A combined linkage and regional association mapping validation and fine mapping of two major pleiotropic QTLs for seed weight and silique length in rapeseed(Brassica napus L.).BMC Plant Biol,2014,14:114
[9]张书芬,宋文光,任乐见.甘蓝型双低油菜数量性状的遗传力及基因效应.中国油料,1996,18(3):1–3Zhang S F,Song W G,Ren L J.Heritability and genetic effects of quantitative characters in double-low rapeseed(Brassica napus L.).Oil Crops China,1996,18(3):1–3(in Chinese with English abstract)
[10]Udall J A,Qui J D,Lambert B,Osborn T C.Quantitative trait analysis of seed yield and other complex traits in hybrid spring rapeseed(Brassica napus L.):2.Identification of alleles from unadapted germplasm.Theor Appl Genet,2006,113:597–609
[11]Chen W,Zhang Y,Liu X P,Chen B Y,Tu J X,Fu T D.Detection of QTL for six yield-related traits in oilseed rape(Brassica napus)using DH and immortalized F2 population.Theor Appl Genet,2007,115:849–858
[12]Li X N,Ramchiary N,Dhandapani V,Choi S R,Hur Y,Nou I S,Yoon M K,Lim Y P.Quantitative trait loci mapping in Brassica rapa revealed the structural and functional conservation of genetic loci governing morphological and yield component traits in the A,B,and C subgenomes of Brassica species.DNA Res,2013,20:1–16
[13]Zhao J Y,Becker H C,Zhang D Q,Zhang Y F,Ecke W G.Oil content in a European×Chinese rapeseed population:QTL with additive and epistatic effects and their genotype-environment interactions.Crop Sci,2005,45:51–59
[14]Zhao J Y,Huang J X,Chen F,Ni X Y,Xu F,Wang Y L,Jiang C C,Wang H,Xu A X,Huang R Z,Li D R,Meng J L.Molecular mapping of Arabidopsis thaliana lipid-related orthologous genes in Brassica napus.Theor Appl Genet,2012,124:407–421
[15]Zhu J.Analysis of conditional genetic effects and variance components in developmental genetics.Genetics,1995,141:1633–1639
[16]Wang S C,Bastern C J,Zeng Z B.Window QTL Cartographer2.5.Department of Statistics,North Carolina State University,Raleigh,USA,2006
[17]Yang J,Zhu J,Williams R W.Mapping the genetic architecture of complex traits in experimental populations.Bioinformatics,2007,23:1527–1536
[18]Wang D L,Zhu J,Li Z K,Paterson A H.Mapping QTLs with epistatic effects and QTL×environment interactions by mixed linear model approaches.Theor Appl Genet,1999,99:1255–1264
[19]Qi L P,Mao L,Sun C M.Interpreting the genetic basis of silique traits in Brassica napus using a joint QTL network.Plant Breed,2014,133:52–60
[20]Zhou Q H,Fu D H.In silico integration of quantitative trait loci for seed yield and yield-related traits in Brassica napus.Mol Breed,2014,33:881–894
[2]Fan C C,Cai G Q,Qin J,Li Q Y,Wu J Z,Fu T D,Liu K D,Zhou Y M.Mapping of quantitative trait loci and development of allele-specific markers for seed weight in Brassica napus.Thero Appl Genet,2001,121:1289–1301
[3]Chay P,Thurling N.Identification of genes controlling pod length in spring rapeseed,Brassica napus L.,and their utilization for yield improvement.Plant Breed,1989,103:54–62
[4]刘定富,蔡怀武.甘蓝型油菜特长荚突变体的发现和鉴定.湖北农学院学报,1994,4(2):1–4Liu D F,Cai H W.Detection and identification of specially-long pod mutant in Brassica napus L.J Hubei Agric Coll,1994,4(2):1–4(in Chinese with English abstract)
[5]Cai D F,Xiao Y J,Yang W,Ye W,Wang B.Association mapping of six yield related traits in rapeseed Brassica napus L.Theor Appl Genet,2014,127:85–96
[6]Zhang L W,Yang G S,Liu P W,Hong D F,Li S P,He Q B.Genetic and correlation analysis of silique-traits in Brassica napus L.by quantitative trait locus mapping.Theor Appl Genet,2011,122:21–31
[7]Yang P,Shu C,Chen L,Xu J S,Wu J,Liu K D.Identification of a major QTL for silique length and seed weight in oilseed rape(Brassica napus L.).Theor Appl Genet,2012,125:285–296
[8]Li N,Shi J Q,Wang X F,Liu G H.A combined linkage and regional association mapping validation and fine mapping of two major pleiotropic QTLs for seed weight and silique length in rapeseed(Brassica napus L.).BMC Plant Biol,2014,14:114
[9]张书芬,宋文光,任乐见.甘蓝型双低油菜数量性状的遗传力及基因效应.中国油料,1996,18(3):1–3Zhang S F,Song W G,Ren L J.Heritability and genetic effects of quantitative characters in double-low rapeseed(Brassica napus L.).Oil Crops China,1996,18(3):1–3(in Chinese with English abstract)
[10]Udall J A,Qui J D,Lambert B,Osborn T C.Quantitative trait analysis of seed yield and other complex traits in hybrid spring rapeseed(Brassica napus L.):2.Identification of alleles from unadapted germplasm.Theor Appl Genet,2006,113:597–609
[11]Chen W,Zhang Y,Liu X P,Chen B Y,Tu J X,Fu T D.Detection of QTL for six yield-related traits in oilseed rape(Brassica napus)using DH and immortalized F2 population.Theor Appl Genet,2007,115:849–858
[12]Li X N,Ramchiary N,Dhandapani V,Choi S R,Hur Y,Nou I S,Yoon M K,Lim Y P.Quantitative trait loci mapping in Brassica rapa revealed the structural and functional conservation of genetic loci governing morphological and yield component traits in the A,B,and C subgenomes of Brassica species.DNA Res,2013,20:1–16
[13]Zhao J Y,Becker H C,Zhang D Q,Zhang Y F,Ecke W G.Oil content in a European×Chinese rapeseed population:QTL with additive and epistatic effects and their genotype-environment interactions.Crop Sci,2005,45:51–59
[14]Zhao J Y,Huang J X,Chen F,Ni X Y,Xu F,Wang Y L,Jiang C C,Wang H,Xu A X,Huang R Z,Li D R,Meng J L.Molecular mapping of Arabidopsis thaliana lipid-related orthologous genes in Brassica napus.Theor Appl Genet,2012,124:407–421
[15]Zhu J.Analysis of conditional genetic effects and variance components in developmental genetics.Genetics,1995,141:1633–1639
[16]Wang S C,Bastern C J,Zeng Z B.Window QTL Cartographer2.5.Department of Statistics,North Carolina State University,Raleigh,USA,2006
[17]Yang J,Zhu J,Williams R W.Mapping the genetic architecture of complex traits in experimental populations.Bioinformatics,2007,23:1527–1536
[18]Wang D L,Zhu J,Li Z K,Paterson A H.Mapping QTLs with epistatic effects and QTL×environment interactions by mixed linear model approaches.Theor Appl Genet,1999,99:1255–1264
[19]Qi L P,Mao L,Sun C M.Interpreting the genetic basis of silique traits in Brassica napus using a joint QTL network.Plant Breed,2014,133:52–60
[20]Zhou Q H,Fu D H.In silico integration of quantitative trait loci for seed yield and yield-related traits in Brassica napus.Mol Breed,2014,33:881–894