An Overview of Genome-wide Association Studies in Plants

doi:10.11983/CBB20091

Abstract

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

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

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URL: https://www.chinbullbotany.com/EN/10.11983/CBB20091

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

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References 114

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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