全基因组选择的多种预测模型对中国冬小麦产量和品质性状的预测精度研究
发布时间：2022-02-20 04:44
传统植物育种依据各种目标性状的表型在大量的重组和分离后代中选择优良个体。对于遗传力较低、受多基因控制的性状,其选择效率较低,预测准确性不高。分子生物学和生物技术的进步,以及分子标记在复杂数量性状遗传研究中的广泛应用,为育种过程中开展基因型水平的选择提供了可能。一些基于分子标记的选择方法,如标记辅助轮回选择和全基因组选择已经被用来加速育种进程、提高选择效率。全基因组选择（GS）利用已知表型和基因型数据（称为训练群体）构建基因型到表型的模型,以预测新产生育种群体的表型。GS方法的选择效果受众多因素的影响,对这些因素进行田间评估是一项艰巨的任务,需要花费更多的时间、物力和劳力。计算机模拟一定程度上克服了这些限制,为在广泛的遗传模型（如加性、显性和上位性）下评价不同选择方法提供了便利。本研究的主要目的是利用一个冬小麦训练群体和模拟数据,评估不同遗传结构下各种选择方法的表现。1.中国冬小麦籽粒产量及产量相关性状的全基因组预测利用一个包括166个品种的小麦自然群体,评估不同SNP质量控制（QC）方案（即缺失率和低频等位基因频率）、缺失基因型填补和全基因组关联分析（GWAS）衍生标记对7个GS模型的...
【文章来源】：中国农业科学院北京市
【文章页数】：165 页
【学位级别】：博士
【文章目录】：
博士学位论文评阅人、答辩委员会签名表
摘要
abstract
List of abbreviations
Chapter1 Background
1.1 Conventional plant breeding
1.2 Modern plant breeding
1.2.1 Molecular markers for plant genetics and breeding
1.2.2 Marker-assisted recurrent selection
1.2.3 Genomic selection
1.2.4 Transgenic breeding
1.2.5 Genome editing
1.3 Genomic selection:a statistics-based selection method
1.3.1 Various prediction models in genomic selection
1.3.2 Factors affecting prediction accuracy of genomic selection models
1.4 Application of genomic selection in livestock and plant breeding
1.5 Computer simulation in plant breeding
1.6 Objectives of this study
Chapter2 Genomic prediction for grain yield and yield-related traits in Chinese winter wheat
2.1 Background
2.2 Materials and methods
2.2.1 DNA extraction,genotyping,and quality control
2.2.2 Phenotypic data analysis and analysis of variance(ANOVA)
2.2.3 Genotypic data analysis
2.2.4 GS Models and factors affecting prediction accuracy
2.2.5 Imputation for missing genotypes
2.2.6 GWAS-derived genomic selection
2.3 Results
2.3.1 Phenotypic evaluation
2.3.2 Marker coverage,genetic diversity,and linkage disequilibrium analysis
2.3.3 Prediction accuracy of different GS models under different missing rate and MAF levels
2.3.4 Effect of imputation for missing genotypes on GS
2.3.5 Effect of significant markers detected by GWAS
2.4 Discussion
2.4.1 Marker quality control,density,and linkage disequilibrium
2.4.2 Effect of missing rate and MAF quality control on prediction accuracy
2.4.3 Effect of GS models on prediction accuracy
2.4.4 Effect of imputation and GWAS on prediction accuracy
Chapter3 Assessing prediction accuracy of flour-color related traits in wheat
3.1 Background
3.2 Materials and methods
3.2.1 Plant materials and phenotypic evaluations
3.2.2 Phenotypic analysis of flour-color and related traits
3.2.3 Genotypic data analysis
3.2.4 Effect of marker subsetting scenarios on prediction accuracy
3.3 Results
3.3.1 Phenotypic variation and heritability
3.3.2 Genome-wide markers subset for genomic prediction scenarios
3.3.3 Effect of all marker subset on prediction accuracy
3.3.4 Effect of trait correlation type-based marker subset and GWAS-derived markers associated with traits on prediction accuracy
3.4 Discussion
Chapter4 Modeling and simulation of recurrent phenotypic and genomic selections in plant breeding under the presence of linkage phases and epistasis networks
4.1 Background
4.2 Materials and methods
4.2.1 Quantitative genetics and breeding simulation platform of QU-GENE
4.2.2 The Qu MARS application module
4.2.3 Genetic models used in the simulation
4.2.4 Simulation of base or training populations
4.2.5 Genotype-to-phenotype prediction models implemented in Qu MARS
4.2.6 Design and outcomes of the simulation experiment
4.3 Results
4.3.1 Selection responses from PS,MARS,and GS under the additive model
4.3.2 Selection responses from PS,MARS,and GS under the coupling QTL linkage(CL)model
4.3.3 Selection responses from PS,MARS,and GS under the repulsion QTL linkage(RL)model
4.3.4 Selection responses from PS,MARS,and GS under epistasis models
4.4 Discussion
4.4.1 Factors affecting gains in selection
4.4.2 Change in total genetic and additive variances after selection
4.4.3 Comparison of PS with other selection methods
4.4.4 Potential applications in plant breeding
Chapter5 Factors affecting the prediction accuracy in simulated populations
5.1 Background and objectives
5.2 Materials and methods
5.2.1 QU-GENE:a simulation platform for quantitative analysis of genetic models
5.2.2 Qu Line application module
5.2.3 The genetic model used in the simulation experiment
5.2.4 Development of a base population using Qu Line
5.2.5 Simulation outputs
5.2.6 Genomic prediction analysis
5.3 Results
5.3.1 Prediction accuracy from different genetic architectures
5.4 Discussion
Conclusions
References
Appendix
Acknowledgements
Curriculum Vitae
本文编号：3634339
【文章来源】：中国农业科学院北京市
【文章页数】：165 页
【学位级别】：博士
【文章目录】：
博士学位论文评阅人、答辩委员会签名表
摘要
abstract
List of abbreviations
Chapter1 Background
1.1 Conventional plant breeding
1.2 Modern plant breeding
1.2.1 Molecular markers for plant genetics and breeding
1.2.2 Marker-assisted recurrent selection
1.2.3 Genomic selection
1.2.4 Transgenic breeding
1.2.5 Genome editing
1.3 Genomic selection:a statistics-based selection method
1.3.1 Various prediction models in genomic selection
1.3.2 Factors affecting prediction accuracy of genomic selection models
1.4 Application of genomic selection in livestock and plant breeding
1.5 Computer simulation in plant breeding
1.6 Objectives of this study
Chapter2 Genomic prediction for grain yield and yield-related traits in Chinese winter wheat
2.1 Background
2.2 Materials and methods
2.2.1 DNA extraction,genotyping,and quality control
2.2.2 Phenotypic data analysis and analysis of variance(ANOVA)
2.2.3 Genotypic data analysis
2.2.4 GS Models and factors affecting prediction accuracy
2.2.5 Imputation for missing genotypes
2.2.6 GWAS-derived genomic selection
2.3 Results
2.3.1 Phenotypic evaluation
2.3.2 Marker coverage,genetic diversity,and linkage disequilibrium analysis
2.3.3 Prediction accuracy of different GS models under different missing rate and MAF levels
2.3.4 Effect of imputation for missing genotypes on GS
2.3.5 Effect of significant markers detected by GWAS
2.4 Discussion
2.4.1 Marker quality control,density,and linkage disequilibrium
2.4.2 Effect of missing rate and MAF quality control on prediction accuracy
2.4.3 Effect of GS models on prediction accuracy
2.4.4 Effect of imputation and GWAS on prediction accuracy
Chapter3 Assessing prediction accuracy of flour-color related traits in wheat
3.1 Background
3.2 Materials and methods
3.2.1 Plant materials and phenotypic evaluations
3.2.2 Phenotypic analysis of flour-color and related traits
3.2.3 Genotypic data analysis
3.2.4 Effect of marker subsetting scenarios on prediction accuracy
3.3 Results
3.3.1 Phenotypic variation and heritability
3.3.2 Genome-wide markers subset for genomic prediction scenarios
3.3.3 Effect of all marker subset on prediction accuracy
3.3.4 Effect of trait correlation type-based marker subset and GWAS-derived markers associated with traits on prediction accuracy
3.4 Discussion
Chapter4 Modeling and simulation of recurrent phenotypic and genomic selections in plant breeding under the presence of linkage phases and epistasis networks
4.1 Background
4.2 Materials and methods
4.2.1 Quantitative genetics and breeding simulation platform of QU-GENE
4.2.2 The Qu MARS application module
4.2.3 Genetic models used in the simulation
4.2.4 Simulation of base or training populations
4.2.5 Genotype-to-phenotype prediction models implemented in Qu MARS
4.2.6 Design and outcomes of the simulation experiment
4.3 Results
4.3.1 Selection responses from PS,MARS,and GS under the additive model
4.3.2 Selection responses from PS,MARS,and GS under the coupling QTL linkage(CL)model
4.3.3 Selection responses from PS,MARS,and GS under the repulsion QTL linkage(RL)model
4.3.4 Selection responses from PS,MARS,and GS under epistasis models
4.4 Discussion
4.4.1 Factors affecting gains in selection
4.4.2 Change in total genetic and additive variances after selection
4.4.3 Comparison of PS with other selection methods
4.4.4 Potential applications in plant breeding
Chapter5 Factors affecting the prediction accuracy in simulated populations
5.1 Background and objectives
5.2 Materials and methods
5.2.1 QU-GENE:a simulation platform for quantitative analysis of genetic models
5.2.2 Qu Line application module
5.2.3 The genetic model used in the simulation experiment
5.2.4 Development of a base population using Qu Line
5.2.5 Simulation outputs
5.2.6 Genomic prediction analysis
5.3 Results
5.3.1 Prediction accuracy from different genetic architectures
5.4 Discussion
Conclusions
References
Appendix
Acknowledgements
Curriculum Vitae
本文编号：3634339
本文链接：https://www.wllwen.com/nykjlw/nzwlw/3634339.html
最近更新
教材专著
