通过将顺序表型转化为连续表型,提高全基因组关联研究的效力
Ming Yang1, Yangjun Wen2, Jinchang Zheng1
1Key Laboratory of Biology and Genetics Improvement of Soybean, Ministry of Agriculture/Zhongshan Biological Breeding Laboratory (ZSBBL)/National Innovation Platform for Soybean Breeding and Industry-Education Integration/State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization/College of Agriculture, Nanjing Agricultural University, Nanjing, China.
Frontiers in plant science
|November 29, 2023
概括
一种新的方法,多阶段性质序列到连续性 (MTOTC),通过将层次数据转换为连续的表型数据,增强了序列性质的全基因组关联研究 (GWAS). 这种方法提高了作物中的定量特征核酸检测能力.
科学领域:
- 农业科学 农业科学
- 遗传学 是一个遗传学.
- 生物信息学是一种生物信息学.
背景情况:
- 顺序性质在作物遗传学中至关重要,但对传统的全基因组关联研究 (GWAS) 具有挑战性.
- 目前用于分析GWAS中的序列特征的方法,包括连续定量特征GWAS (C-GWAS) 和单位序列特征分析,其检测能力较低.
- 这种限制阻碍了作物中复杂的顺序性特征的有效基因挖掘.
研究的目的:
- 开发一种新的方法,即多级特征序列到连续 (MTOTC),以改进序列特征的GWAS.
- 提高量化特征核酸 (QTN) 识别在序列特征中的检测能力和准确性.
- 提供一种更有效的工具,用于在作物种群中进行基因挖掘,这些作物种群具有序列性质.
主要方法:
- 提出了MTOTC方法,该方法将层次序列特征数据转换为连续的表型数据 (CPData).
- 应用C-GWAS方法,特别是FASTmrMLM,在生成的CPD数据上执行GWAS.
- 使用模拟研究和真实大豆数据,与六种不同的C-GWAS方法 (FASTmrMLM,FASTmrEMMA,mrMLM,ISIS EM-BLASSO,pLARmEB,pKWmEB) 结合评估了MTOTC.
主要成果:
- 模拟研究表明,MTOTC与FASTmrMLM相结合,在四个或更少的等级级别的顺序特征上,优于经典方法.
- 当与各种C-GWAS方法集成时,MTOTC在QTN检测中始终显示出高功率和低假阳性率.
- 对大豆盐-耐受性数据的应用显示,MTOTC在所有测试的C-GWAS组合中显著增加了检测到的显著QTN的数量.
结论:
- MTOTC方法为分析GWAS中的顺序特征提供了显著的进步.
- 这种方法扩大了GWAS的工具包,使作物中顺序性状的基因发现更有效.
- 在不同的作物资源种群中,MTOTC促进了控制复杂顺序特征的基因的挖掘.
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