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

Area of Science:

• Genetics and Genomics
• Statistical Genetics
• Bioinformatics

Background:

• Genome-wide association studies (GWAS) are crucial for dissecting complex traits in diverse organisms.
• Linear mixed models (LMMs) are widely adopted for their ability to control confounding factors like population structure, enhancing statistical power and computational efficiency in GWAS.
• Emerging research focuses on pleiotropy, multi-trait analyses, and gene-environment interactions, driven by large-scale GWAS data.

Purpose of the Study:

• To comprehensively review existing LMM-based methods for GWAS data analysis.
• To provide researchers with a guide for selecting appropriate LMM models and tools.
• To discuss the advantages, limitations, and future directions of LMM applications in GWAS.

Main Methods:

• Systematic literature review of LMM methodologies applied to GWAS.
• Discussion of various LMM approaches, including those addressing pleiotropy and interactions.
• Overview of available software packages and open-source applications for LMM-based GWAS.

Main Results:

• Identification and categorization of diverse LMMs applicable to GWAS.
• Analysis of the pros and cons associated with different LMM techniques.
• Summary of current software solutions for implementing LMMs in GWAS.

Conclusions:

• LMMs offer robust statistical frameworks for analyzing complex traits in GWAS.
• This review serves as a valuable resource for researchers navigating LMM selection for GWAS.
• Continued development of LMMs will further advance the discovery of causal variants and genetic architectures.