单特征,多地点和多特征GWAS使用四种不同的模型来确定面包小麦的产量特征
Parveen Malik1, Jitendra Kumar1,2, Sahadev Singh1
1Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut 250004, India.
Molecular breeding : new strategies in plant improvement
|June 13, 2023
概括
这项全基因组关联研究确定了10种春小麦特征的新标记. 这些发现有助于通过标记辅助选择 (MAS) 开发改进的小麦品种.
科学领域:
- 植物遗传学 植物遗传学
- 农业科学 农业科学
- 定量遗传学 是一种定量遗传学.
背景情况:
- 了解春小麦产量和产量组件的遗传基础对于作物改善至关重要.
- 全基因组关联研究 (GWAS) 是解剖复杂特征的强大工具.
研究的目的:
- 在225种不同的春小麦基因型中对10种产量和产量成分特征进行GWAS.
- 识别标记特征关联 (MTA) 和改善农学特征的候选基因.
主要方法:
- 基因型定制 225种春小麦基因型,共计10904个SNP.
- 在三年内,在三个环境中进行表型评估.
- 使用四种统计模型 (CMLM,FarmCPU,SUP,mvLMM) 和BLUP进行MTA分析.
- 识别表皮性相互作用和候选基因.
主要成果:
- 在特征中观察到高遗传率 (29.21-97.69%).
- 使用不同的模型确定了重要的MTA,FarmCPU和mvLMM在Bonferroni校正后产生了最重要的关联.
- 发现了164个与特征相关的假定候选基因 (CGs).
- 在10个特征中的7个特征中发现了表皮性相互作用.
结论:
- 这项研究为小麦产量和成分特征的遗传学提供了宝贵的见解.
- 确定了新型标记物,促进了标记物辅助选择 (MAS) 以开发改进的小麦品种.
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