中文版 | English
Title

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

Author
Corresponding AuthorJin, Wenfei; Xu, Haiming
Publication Years
2022-12-01
DOI
Source Title
EISSN
2223-7747
Volume11Issue:23
Abstract
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.
Keywords
URL[Source Record]
Indexed By
Language
English
SUSTech Authorship
Corresponding
WOS Research Area
Plant Sciences
WOS Subject
Plant Sciences
WOS Accession No
WOS:000896118500001
Publisher
Data Source
Web of Science
Citation statistics
Cited Times [WOS]:1
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/417100
DepartmentDepartment of Biology
生命科学学院
Affiliation
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
First Author AffilicationDepartment of Biology;  School of Life Sciences
Corresponding Author AffilicationDepartment of Biology;  School of Life Sciences
Recommended Citation
GB/T 7714
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).
APA
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).
MLA
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).
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