使用历史不平衡产量数据进行全基因组关联研究和开发小麦育种基因组预测模型的新策略
Chenggen Chu1,2, Shichen Wang3, Jackie C Rudd1
1Texas A&M AgriLife Research Center, Amarillo, TX 79106 USA.
Molecular breeding : new strategies in plant improvement
|June 13, 2023
概括
这项研究开发了一个基因组预测模型,使用不平衡的小麦育种历史产量数据. 该模型准确地确定了谷物产量和昆虫耐药性的基因组区域,改善了作物选择.
科学领域:
- 植物育种 植物育种
- 基因组学就是基因组学.
- 农业科学 农业科学
背景情况:
- 根据不平衡的历史产量数据预测作物表现对育种者来说是一个挑战.
- 全基因组关联研究 (GWAS) 和高通量基因型定型提高了预测的准确性.
研究的目的:
- 利用不平衡的历史数据开发一种可靠的基因组预测模型,用于谷物产量选择.
- 确定与小麦的谷物产量和昆虫耐药性相关的基因组区域.
主要方法:
- 使用227行德克萨斯精英 (TXE) 小麦面板用于GWAS和模型开发.
- 在87个不平衡收益率数据环境中应用了基于相关性的分组.
- 使用最佳线性公正估计 (蓝色) 对产量和昆虫耐药性数据进行了GWAS.
主要成果:
- 确定了74个与谷物产量相关的基因组区域,数据组之间有两个共同区域.
- 发现了绿虫耐药性 (3DL,6DS) 的新型基因组区域,并证实了Gb3 (7DL).
- 黑森的耐药性与1AS上的区域有关;G2模型显示出更高的预测可靠性.
结论:
- 从不平衡的历史育种数据开发基因组预测模型的强大方法被建立起来.
- 确定了在小麦育种计划中提高谷物产量和昆虫抵抗力的关键基因组区域.
更多相关视频
08:36Development of Targeting Induced Local Lesions IN Genomes TILLING Populations in Small Grain Crops by Ethyl Methanesulfonate Mutagenesis
Published on: July 16, 2019
11.7K
08:27Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
Published on: July 27, 2021
3.7K
相关概念视频
Genome-wide Association Studies-GWAS
13.6K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
GWAS does not require the identification of the target gene involved in...
13.6K
Plant Breeding and Biotechnology
19.5K
Crop cultivation has a long history in human civilization, with records showing the cultivation of cereal plants beginning at around 8000 BC. This early plant breeding was developed primarily to provide a steady supply of food.
19.5K
