What is it about?
The paper is a review of linear mixed models (LMMs) and their application in genome-wide association studies (GWAS). LMMs are widely used statistical methods for regulating confounding factors, including population structure, resulting in increased computational proficiency and statistical power in GWAS studies. The review discusses different LMM methods, software packages, and available open-source applications in GWAS. It also includes the advantages and weaknesses of LMMs in GWAS.
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Why is it important?
This review paper is important because it provides a comprehensive overview of linear mixed models (LMMs) and their application in genome-wide association studies (GWAS). LMMs are widely used statistical methods for regulating confounding factors, including population structure, resulting in increased computational proficiency and statistical power in GWAS studies. By discussing different LMM methods, software packages, and available open-source applications in GWAS, as well as the advantages and weaknesses of LMMs in GWAS, this review can help researchers quickly select appropriate LMM models and methods for GWAS data analysis.
Perspectives
The future directions of this research could involve further development and refinement of LMMs for use in GWAS data analysis, as well as the application of these methods to new datasets to improve our understanding of complex traits.
Dr. Md. Alamin
Southern University of Science and Technology
Read the Original
This page is a summary of: Dissecting Complex Traits Using Omics Data: A Review on the Linear Mixed Models and Their Application in GWAS, Plants, November 2022, MDPI AG,
DOI: 10.3390/plants11233277.
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