Dissecting Complex Traits Using Omics Data: A Review on the Linear Mixed Models and Their Application in GWAS

Alamin, Md.1,2; Sultana, Most. Humaira1; Lou, Xiangyang3; Jin, Wenfei2; Xu, Haiming1

2022-12-01

10.3390/plants11233277

PLANTS-BASEL   影响因子和分区

2223-7747

Genome-wide association study (GWAS) is the most popular approach to dissecting complex traits in plants, humans, and animals. Numerous methods and tools have been proposed to discover the causal variants for GWAS data analysis. Among them, linear mixed models (LMMs) are widely used statistical methods for regulating confounding factors, including population structure, resulting in increased computational proficiency and statistical power in GWAS studies. Recently more attention has been paid to pleiotropy, multi-trait, gene-gene interaction, gene-environment interaction, and multi-locus methods with the growing availability of large-scale GWAS data and relevant phenotype samples. In this review, we have demonstrated all possible LMMs-based methods available in the literature for GWAS. We briefly discuss the different LMM methods, software packages, and available open-source applications in GWAS. Then, we include the advantages and weaknesses of the LMMs in GWAS. Finally, we discuss the future perspective and conclusion. The present review paper would be helpful to the researchers for selecting appropriate LMM models and methods quickly for GWAS data analysis and would benefit the scientific society.

linear mixed model (LMM) GWAS complex traits omics interaction effect

SCI

英语

通讯

Plant Sciences

Plant Sciences

WOS:000896118500001

MDPI

Web of Science

1.Zhejiang Univ, Inst Bioinformat, Hangzhou 310058, Peoples R China
2.Southern Univ Sci & Technol, Sch Life Sci, Dept Biol, Shenzhen 518055, Peoples R China
3.Univ Alabama Birmingham, Dept Biostat, Birmingham, AL 35294 USA

Alamin, Md.,Sultana, Most. Humaira,Lou, Xiangyang,et al. Dissecting Complex Traits Using Omics Data: A Review on the Linear Mixed Models and Their Application in GWAS[J]. PLANTS-BASEL,2022,11(23).

Alamin, Md.,Sultana, Most. Humaira,Lou, Xiangyang,Jin, Wenfei,&Xu, Haiming.(2022).Dissecting Complex Traits Using Omics Data: A Review on the Linear Mixed Models and Their Application in GWAS.PLANTS-BASEL,11(23).

Alamin, Md.,et al."Dissecting Complex Traits Using Omics Data: A Review on the Linear Mixed Models and Their Application in GWAS".PLANTS-BASEL 11.23(2022).