傳統(tǒng)植物育種依據(jù)各種目標(biāo)性狀的表型在大量的重組和分離后代中選擇優(yōu)良個(gè)體。對(duì)于遺傳力較低、受多基因控制的性狀,其選擇效率較低,預(yù)測(cè)準(zhǔn)確性不高。分子生物學(xué)和生物技術(shù)的進(jìn)步,以及分子標(biāo)記在復(fù)雜數(shù)量性狀遺傳研究中的廣泛應(yīng)用,為育種過(guò)程中開展基因型水平的選擇提供了可能。一些基于分子標(biāo)記的選擇方法,如標(biāo)記輔助輪回選擇和全基因組選擇已經(jīng)被用來(lái)加速育種進(jìn)程、提高選擇效率。全基因組選擇（GS）利用已知表型和基因型數(shù)據(jù)（稱為訓(xùn)練群體）構(gòu)建基因型到表型的模型,以預(yù)測(cè)新產(chǎn)生育種群體的表型。GS方法的選擇效果受眾多因素的影響,對(duì)這些因素進(jìn)行田間評(píng)估是一項(xiàng)艱巨的任務(wù),需要花費(fèi)更多的時(shí)間、物力和勞力。計(jì)算機(jī)模擬一定程度上克服了這些限制,為在廣泛的遺傳模型（如加性、顯性和上位性）下評(píng)價(jià)不同選擇方法提供了便利。本研究的主要目的是利用一個(gè)冬小麥訓(xùn)練群體和模擬數(shù)據(jù),評(píng)估不同遺傳結(jié)構(gòu)下各種選擇方法的表現(xiàn)。1.中國(guó)冬小麥籽粒產(chǎn)量及產(chǎn)量相關(guān)性狀的全基因組預(yù)測(cè)利用一個(gè)包括166個(gè)品種的小麥自然群體,評(píng)估不同SNP質(zhì)量控制（QC）方案（即缺失率和低頻等位基因頻率）、缺失基因型填補(bǔ)和全基因組關(guān)聯(lián)分析（GWAS）衍生標(biāo)記對(duì)7個(gè)GS模型的... 

【文章來(lái)源】：中國(guó)農(nóng)業(yè)科學(xué)院北京市

【文章頁(yè)數(shù)】：165 頁(yè)

【學(xué)位級(jí)別】：博士

【文章目錄】：
博士學(xué)位論文評(píng)閱人、答辯委員會(huì)簽名表
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



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