生物信息学分析方法I: 全基因组关联分析概述

doi:10.11983/CBB20091

摘要/Abstract

摘要：

全基因组关联分析(GWAS)是动植物复杂性状相关基因定位的常用手段。高通量基因分型技术的应用极大地推动了GWAS的发展。在植物中, 利用GWAS不仅能够以较高的分辨率在全基因组水平鉴定出各种自然群体特定性状相关的基因或区间, 而且可揭示表型变异的遗传架构全景图。目前, 人们利用GWAS分析方法已在拟南芥(Arabidopsis thaliana)、水稻(Oryza sativa)、小麦(Triticum aestivum)、玉米(Zea mays)和大豆(Glycine max)等模式植物和重要农作物品系中发掘出与各种性状显著相关的数量性状座位(QTL)及其候选基因位点, 阐明了这些性状的遗传基础, 并为揭示这些性状背后的分子机理提供候选基因, 也为作物高产优质品种的选育提供了理论依据。该文对GWAS的方法、影响因素及数据分析流程进行了详细描述, 以期为相关研究提供参考。

关键词: 混合线性模型, 全基因组关联分析(GWAS), 生物信息学

Abstract:

Genome-wide association study (GWAS) is a general approach for unraveling genetic variations associated with complex traits in both animals and plants. The development of high-throughput genotyping has greatly boosted the development and application of GWAS. GWAS is not only used to identify genes/loci contributing to specific traits from diversenatural populations with high-resolution genome-wide markers, it also systematically reveals the genetic architecture underlying complex traits. During recent years, GWAS has successfully detected a large number of QTLs and candidate genes associated with various traits in plants including Arabidopsis, rice, wheat, soybean and maize. All these findings provided candidate genes controlling the traits and theoretical basis for breeding of high-yield and high-quality varieties. Here we review the methods, the factors affecting the power, and a data analysis pipeline of GWAS to provide reference for relevant research.

Key words: mixed linear model, genome-wide association study (GWAS), bioinformatics

赵宇慧, 李秀秀, 陈倬, 鲁宏伟, 刘羽诚, 张志方, 梁承志. 生物信息学分析方法I: 全基因组关联分析概述. 植物学报, 2020, 55(6): 715-732.

Yuhui Zhao, Xiuxiu Li, Zhuo Chen, Hongwei Lu, Yucheng Liu, Zhifang Zhang, Chengzhi Liang. An Overview of Genome-wide Association Studies in Plants. Chinese Bulletin of Botany, 2020, 55(6): 715-732.

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链接本文: https://www.chinbullbotany.com/CN/10.11983/CBB20091

https://www.chinbullbotany.com/CN/Y2020/V55/I6/715

图/表 5

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Method	Population structure	Kinship	Precision	Characteristic	Computational speed	Statistical power	Application
Standard MLM	P	All markers			Low	High	>100 papers
GRAMMAR	P		Approximate method		Very fast	Intermediate	Barley (200)
EMMA	P		Exact method		Intermediate	Similar to Standard MLM	>100 papers
EMMAX	P	All markers	Approximate method	High marker densities	Fast	Similar to Standard MLM	>100 papers
CMLM	P			Large sample sizes		Better than Standard MLM	>100 papers
FaST-LMM	P	A subset of genetic markers	Exact method	Large sample sizes	Fast	Similar to Standard MLM	Rice (200?1500)
GEMMA	P		Exact method		Fast	Similar to Standard MLM	Arabidopsis thaliana (190-500)
ECMLM	P				Intermediate	Better than Standard MLM	Sorghum (250-350), soybean (200-400), wheat (250-300)
GRAMMAR- Gamma	P		Approximate method	High marker densities	Fast	Similar to Standard MLM	Oilseed rape (200)
SUPER	P	Trait-associated markers		Large sample size & high marker density	Fast	Better than Standard MLM	Wheat (300-400)
Farm-CPU	P	A subset of genetic markers	Approximate method	Large sample size & high marker density	Fast	Better than Standard MLM	Wheat (100-1200), maize (100-5000)
BLINK	P	A subset of genetic markers	Approximate method	Large sample size & high marker density	Faster than FarmCPU	Better than FarmCPU

Method	Population structure	Kinship	Precision	Characteristic	Computational speed	Statistical power	Application
Standard MLM	P	All markers			Low	High	>100 papers
GRAMMAR	P		Approximate method		Very fast	Intermediate	Barley (200)
EMMA	P		Exact method		Intermediate	Similar to Standard MLM	>100 papers
EMMAX	P	All markers	Approximate method	High marker densities	Fast	Similar to Standard MLM	>100 papers
CMLM	P			Large sample sizes		Better than Standard MLM	>100 papers
FaST-LMM	P	A subset of genetic markers	Exact method	Large sample sizes	Fast	Similar to Standard MLM	Rice (200?1500)
GEMMA	P		Exact method		Fast	Similar to Standard MLM	Arabidopsis thaliana (190-500)
ECMLM	P				Intermediate	Better than Standard MLM	Sorghum (250-350), soybean (200-400), wheat (250-300)
GRAMMAR- Gamma	P		Approximate method	High marker densities	Fast	Similar to Standard MLM	Oilseed rape (200)
SUPER	P	Trait-associated markers		Large sample size & high marker density	Fast	Better than Standard MLM	Wheat (300-400)
Farm-CPU	P	A subset of genetic markers	Approximate method	Large sample size & high marker density	Fast	Better than Standard MLM	Wheat (100-1200), maize (100-5000)
BLINK	P	A subset of genetic markers	Approximate method	Large sample size & high marker density	Faster than FarmCPU	Better than FarmCPU