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Unlocking Rice Germplasm Genetic Potential Using Genotypic Value to Develop Quality Core Collections

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Acknowledgements

1 Introduction

2 Literature review

2.1Germplasm resources

2.2What is a core collection?

2.2. 1 Methods for creating core collections

2.2.2 Challenges facing core collections

2.2.3 Promise

2.2.4 Core collection sample size

2.2.5 Core development strategies

2.3Recent advances and plant breeding applications of core collections

2.3.1 The link between Core collections and genebank research

2.3.2 Study of plant diversity

2.3.3 Core concept with user's interest

2.3.4 Synteny among plant genomes

3 Materials and methods

3.1Rice sample preparation

3.2Spectroscopic analysis

3.2.1 NIR Spectroscopy

3.2.2 Development of sample calibration

3.2.3 Data preprocessing

3.2.4 Derivation of calibration equations

3.2.5 NIRS rice sample trait analysis

3.3Statistical models

3.3.1 Mahalanobis distance

3.3.2 Cluster analysis

3.4Sampling strategies

3.5Evaluation of core collection

4 Results

4.1Phenotypic values for 26 rice quality traits

4.2Predicted genotypic values for 26 rice quality traits

4.3Trend of core collection at increasing sample proportion

4.4Development of 24 core collections

4.4.1 Core development procedure

4.4.2 Dendrograms for 24-core collections based on hierarchical cluster methods and sampling strategies(1)

4.4.2 Dendrograms for 24-core collections based on hierarchical cluster methods and sampling strategies(2)

4.5Comparison of core collections at different sampling proportion

4.5.1 Comparison of core collection at 10%, 15% and 20%

4.5.2 Analysis of the different cluster methods

4.5.3 Comparison of core collection with initial collection for traits

5 Discussion

5.1Gemplasm genetic resource

5.2Core collections approaches and sampling strategies

5.3Sample size and cluster methods

5.4NIRS quality trait analysis

6 References

7 Appendix

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摘要

本研究应用近红外反射光谱技术(NIRS)分析了990个水稻品种(系)的26个稻米品质性状,采用混合线性模型无偏预测这些品质性状的基因型值,进而计算马氏距离分析品种的遗传相似性。用最短距离法、最长距离法、中间距离法、重心法、不加权类平均法、离差平方和法、可变法和加权配对算术甲均法等8种系统聚类方法,结合随机取样法、优先取样法和变异度取样法等3种取样方法,发展了24个水稻核心种质库。用于比较和评价核心种质的4个统计参数分别为:均值差异百分率,方差差异百分率,极差符合率,变异系数变化率。

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