水稻不同遗传力的数量性状在中国和赞比亚4个环境下的遗传分析
发布时间：2020-10-08 15:56
尽管水稻在不同国家粮食作物中的重要性不同,但是它对全球粮食安全来说十分重要,这也使得提高水稻产量成为养活日益增长的全球人口的重要解决方案之一。水稻的产量和品质在稻米产业的可持续发展中起着同等重要的作用。在育种改良中,水稻产量和品质相关的数量性状是人们关注的重点,然而目前,对于遗传力较低的性状,如出米率等的还了解不够。为此,我们进行了一项实验,研究中使用的群体来自于籼稻明恢63和粳稻02428的亚种间杂交组合,主要目的是通过研究这些材料来加深对出米率等数量性状在不同环境下的相关遗传因素的理解。我们利用了3个群体对碾磨品质这一重要农艺性状的遗传背景效应进行了探讨,其中包括226份MH63_ILs、229份以02428为背景的导入系(02428_ILs)和261份重组自交系(RILs)。我们将供试群体分别种植在中国的北京(Env 1)和海南(Env 2)、赞比亚的Mongu Namushakende农业研究所(Env 3)和Mount Makulu研究站(Env 4)四种环境。在此基础上,我们在多种性状之间进行对数量性状位点的连锁分析。另外,我们也研究了出米率与高遗传力性状之间的相互关系,包括以品种50%抽穗为调查抽穗期的标准对其抽穗期和粒型性状的调查。QTL分析了粒型(粒长(GL)、粒宽(GW)、长宽比(LWR)、籽粒体积(GV))、碾磨质量(糙米率(BRR)、精米率(MRR)、整精米率(HRR))和包括抽穗期(DTH)、株高(PH)、粮食产量(GY)、千粒重(TGW)和籽粒灌浆速率(GFR)在内的农艺性状。我们在四个环境下一共检测到102个QTL,其中在3个碾磨品质中共定位到9个QTL,在4个粒型性状中定位到32个QTL,剩余其他5个被检测农艺性状中共定位到61个QTL。有22个QTL至少在两个环境中检测到,分别包括11个与粒型相关的QTL和11个与5个农艺性状相关的QTL。一共有27个粒型和农艺性状QTL不止一种环境中被检测到,其中包括qGL3c、qGL9a、qGL11a、qGW3a、qGW5a、qLWR3a、qLWR5a、qGV1a、qGV3a、qGV5a、qGV5b、qPH1e、qPH3c,qPH5c、qPH6a、qPH12a、qTGW3d、qTGW5a、qTGW5c、qTGW11a、qTGW11b、qGFR11a。此外,qDTH2a、qDTH5a、qPH12a、qTGW3d、qGY3a、qGY6a和qGFR3c不止在一个群体中被检测到。有3个QTL(qGY3a、qGY6a和qTGW3d)在多个环境和多个群体中检测到。我们的分析进一步揭示了17个与谷物品质性状相关的主效QTL的影响包括在8号染色体上的与水稻整精米率相关的QTL,qHR8a。遗传背景效应研究显示,在检测到的QTL中,大约有58.7%和28.6%的QTL分别在MH63_ILs和RILs中被检测到,有12.7%的QTL在MH63_ILs和RILs中被共同检测到。本研究中定位到17个控制粒型及其他农艺性状QTL所在区间与已克隆的基因或精细定位的QTL sd.1,sdg,GS3,GS5,Chalk5,GS7,qGL7,GIF1,qHD5,qSS7,SUS1,TGW6 and tgw11等一致。此外,有5个QTL可以分解为25个QTL簇,且至少控制一种碾磨品质。其中有两个QTL簇显示两种碾磨品质和抽穗期之间存在某种联系。1号染色体上位于M443和M450标记之间的QTL簇包含了qDTH1c和qMR1a,这两个QTL只相差2cM。3号染色体上包含qHR3a和qDTH3a的簇位于M989和M1028之间,说明在低遗传力的条件下,对抽穗期的选择是对加工品质的进行选择的主要目标。而且,结果还揭示了在不同环境下抽穗期对加控制工品质相关QTL表达变异的影响。值得提出的是,控制相关QTL的等位基因有73.5%来自于籼稻亲本明恢63。表型差异分析显示,与轮回亲本相比,导入系存在显著的超亲变异。其中,39.4%的导入系的整精米率对轮回亲本明恢63表现出正向超亲遗传。广义遗传率从低到高分别为碾磨品质(17 18%),农艺性状(24 60%)和粒型(69 83%)。通常遗传率由群体类型决定,环境因素同时也影响群体类型对遗传率的效应。本研究还表明,在特定的环境下,基于粒长、粒宽、千粒重和抽穗期与整精米率显著正相关,它们可以为整精米率提供有效的间接选择指标。本研究有助于拓宽我们对数量性状的认识,并为分子育种提供有用的信息。
【学位单位】：中国农业科学院
【学位级别】：博士
【学位年份】：2018
【中图分类】：S511
【文章目录】：
摘要
ABSTRACT
CHAPTER 1 INTRODUCTION
1.1 Background
1.2 The genus Oryza sativa L
1.3 Economic and nutritional importance
1.4 Rice production and milling quality
1.5 Rice milling quality
1.6 Rice grain appearance
1.7 Factors that affect milling quality of rice
1.8 Past achievements in rice breeding
1.9 Molecular breeding progress
1.10 Genetic mapping
1.10.1 Linkage analysis
1.10.2 Quantitative trait loci mapping for rice milling quality
1.10.3 Quantitative trait loci mapping for rice grain shape
1.11 Application of biotechnology in rice quality improvement
1.11.1 Marker assisted breeding for rice quality improvement
1.11.2 Genetic engineering
1.11.2.1 Genetic transformation
1.11.2.2 Genome editing
1.12 Future trends of rice quality improvement
1.13 Past research limitations
1.14 Aim of the study
1.15 Scope and outline of the thesis
CHAPTER 2 GENETIC VARIABILITY, HERITABILITY AND CORRELATION STUDIES OF MILLING QUALITY, GRAIN DIMENSION AND AGRONOMIC TRAITS IN INTROGRESSION LINES DERIVED FROM AN INTERSPECIFIC (INDICA X JAPONICA) CROSS OF RICE.
2.1 Introduction
2.2 Materials and methods
2.2.1 Materials
2.2.2 Methods
2.2.3 Trait measurements
2.2.4 Data analysis
2.3 Results
2.3.1 Phenotypic variability assessment
2.3.2 Phenotypic variance components
2.3.3 Trait mean performance of introgression lines and parents
2.3.4 Heritability
2.3.5 Pearson’s correlation analysis
2.3.6 Path analysis for head rice recovery
2.3.7 Path analysis for grain yield
2.4 Discussion
2.4.1 High genetic variability among genotypes
2.4.2 Genotype x Environment Interactions
2.4.3 Inferences from trait mean performance of introgression lines and parents
2.4.4 Inferences from variation of heritability among traits
2.4.5 Inferences from trait correlations
2.4.6 Effect of various traits on head rice recovery
2.4.7 Effect of various traits on grain yield
2.5 Conclusion
CHAPTER 3 MAPPING QUANTITATIVE TRAIT LOCI FOR MILLING QUALITY AND GRAIN DIMENSION TRAITS OF A SET OF INTROGRESSION LINES DERIVED FROM AN INTERSPECIFIC CROSS (indica x japonica) OF RICE
3.1 Introduction
3.2 Materials and methods
3.2.1 Materials
3.2.2 Methods
3.2.3 Genotyping
3.2.4 QTL analysis
3.3 Results
3.3.1 QTL detection for milling quality traits
3.3.2 QTL detection for grain dimension traits
3.3.3 Stably expressed QTL for quality related traits
3.3.4 QTL clusters for quality related traits
3.4 Discussion
3.4.1 Contributions of QTL main effects of milling quality traits
3.4.2 Contributions of QTL main effects of grain dimension traits
3.4.3 Stably expressed QTL for quality related traits
3.4.4 Genetic interactions among quality traits
3.5 Conclusion
CHAPTER 4 GENETIC BACKGROUND EFFECTS ON QUANTITATIVE TRAIT LOCI MAPPING OF AGRONOMIC TRAITS USING MULTIPLE POPULATIONS IN FOUR ENVIRONMENTS
4.1 Introduction
4.2 Materials and methods
4.2.1 Materials
4.2.2 Experimental sites
4.2.3 Methods
4.2.4 Data analysis
4.2.5 QTL analysis
4.3 Results
4.3.1 Trait variability assessment
4.3.2 Trait mean performance and heritability
4.3.3 Pearson’s correlation and path analysis
4.3.4 QTL mapping of agronomic traits across populations
4.3.5 QTL clusters
4.4 Discussion
4.4.1 High genetic variability among genotypes across populations
4.4.2 Heritability inferences from various populations
4.4.3 Inferences from trait correlations
4.4.4 Contributions of QTL main effect of agronomic traits
4.4.5 Genetic interactions between DTH and milling quality traits
4.4.6 Genetic interactions between milling quality and other traits
4.4.7 Genetic association between traitsprobably caused by QTL clusters
4.4.8 Overall novel QTL of the study
4.5 Conclusion
CHAPTER 5
5.1 SUMMARY AND CONCLUSIONS
REFERENCES
APPENDICES
ACKNOWLEDGEMENT
RESUME
本文编号：2832438
【学位单位】：中国农业科学院
【学位级别】：博士
【学位年份】：2018
【中图分类】：S511
【文章目录】：
摘要
ABSTRACT
CHAPTER 1 INTRODUCTION
1.1 Background
1.2 The genus Oryza sativa L
1.3 Economic and nutritional importance
1.4 Rice production and milling quality
1.5 Rice milling quality
1.6 Rice grain appearance
1.7 Factors that affect milling quality of rice
1.8 Past achievements in rice breeding
1.9 Molecular breeding progress
1.10 Genetic mapping
1.10.1 Linkage analysis
1.10.2 Quantitative trait loci mapping for rice milling quality
1.10.3 Quantitative trait loci mapping for rice grain shape
1.11 Application of biotechnology in rice quality improvement
1.11.1 Marker assisted breeding for rice quality improvement
1.11.2 Genetic engineering
1.11.2.1 Genetic transformation
1.11.2.2 Genome editing
1.12 Future trends of rice quality improvement
1.13 Past research limitations
1.14 Aim of the study
1.15 Scope and outline of the thesis
CHAPTER 2 GENETIC VARIABILITY, HERITABILITY AND CORRELATION STUDIES OF MILLING QUALITY, GRAIN DIMENSION AND AGRONOMIC TRAITS IN INTROGRESSION LINES DERIVED FROM AN INTERSPECIFIC (INDICA X JAPONICA) CROSS OF RICE.
2.1 Introduction
2.2 Materials and methods
2.2.1 Materials
2.2.2 Methods
2.2.3 Trait measurements
2.2.4 Data analysis
2.3 Results
2.3.1 Phenotypic variability assessment
2.3.2 Phenotypic variance components
2.3.3 Trait mean performance of introgression lines and parents
2.3.4 Heritability
2.3.5 Pearson’s correlation analysis
2.3.6 Path analysis for head rice recovery
2.3.7 Path analysis for grain yield
2.4 Discussion
2.4.1 High genetic variability among genotypes
2.4.2 Genotype x Environment Interactions
2.4.3 Inferences from trait mean performance of introgression lines and parents
2.4.4 Inferences from variation of heritability among traits
2.4.5 Inferences from trait correlations
2.4.6 Effect of various traits on head rice recovery
2.4.7 Effect of various traits on grain yield
2.5 Conclusion
CHAPTER 3 MAPPING QUANTITATIVE TRAIT LOCI FOR MILLING QUALITY AND GRAIN DIMENSION TRAITS OF A SET OF INTROGRESSION LINES DERIVED FROM AN INTERSPECIFIC CROSS (indica x japonica) OF RICE
3.1 Introduction
3.2 Materials and methods
3.2.1 Materials
3.2.2 Methods
3.2.3 Genotyping
3.2.4 QTL analysis
3.3 Results
3.3.1 QTL detection for milling quality traits
3.3.2 QTL detection for grain dimension traits
3.3.3 Stably expressed QTL for quality related traits
3.3.4 QTL clusters for quality related traits
3.4 Discussion
3.4.1 Contributions of QTL main effects of milling quality traits
3.4.2 Contributions of QTL main effects of grain dimension traits
3.4.3 Stably expressed QTL for quality related traits
3.4.4 Genetic interactions among quality traits
3.5 Conclusion
CHAPTER 4 GENETIC BACKGROUND EFFECTS ON QUANTITATIVE TRAIT LOCI MAPPING OF AGRONOMIC TRAITS USING MULTIPLE POPULATIONS IN FOUR ENVIRONMENTS
4.1 Introduction
4.2 Materials and methods
4.2.1 Materials
4.2.2 Experimental sites
4.2.3 Methods
4.2.4 Data analysis
4.2.5 QTL analysis
4.3 Results
4.3.1 Trait variability assessment
4.3.2 Trait mean performance and heritability
4.3.3 Pearson’s correlation and path analysis
4.3.4 QTL mapping of agronomic traits across populations
4.3.5 QTL clusters
4.4 Discussion
4.4.1 High genetic variability among genotypes across populations
4.4.2 Heritability inferences from various populations
4.4.3 Inferences from trait correlations
4.4.4 Contributions of QTL main effect of agronomic traits
4.4.5 Genetic interactions between DTH and milling quality traits
4.4.6 Genetic interactions between milling quality and other traits
4.4.7 Genetic association between traitsprobably caused by QTL clusters
4.4.8 Overall novel QTL of the study
4.5 Conclusion
CHAPTER 5
5.1 SUMMARY AND CONCLUSIONS
REFERENCES
APPENDICES
ACKNOWLEDGEMENT
RESUME
本文编号：2832438
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