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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
Published on: July 27, 2021
Computational tools for discovery and interpretation of expression quantitative trait loci.
Fred A Wright1, Andrey A Shabalin, Ivan Rusyn
1Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA.
Pharmacogenomics
|February 7, 2012
Summary
Expression quantitative trait locus (eQTL) analysis is crucial for understanding gene regulation and disease. This review guides researchers to suitable eQTL analysis tools, simplifying complex genomic data interpretation.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Expression quantitative trait locus (eQTL) analysis is increasingly vital in genomics, linking genetic variations to gene expression.
- Existing computational tools for eQTL analysis are often not specialized, requiring researchers to develop custom code.
- Navigating available resources for eQTL analysis and interpretation, especially for genome-wide association studies (GWAS), can be challenging for investigators.
Purpose of the Study:
- To provide a comprehensive guide to computational tools for expression quantitative trait locus (eQTL) analysis.
- To assist researchers in selecting appropriate software for eQTL discovery and interpretation.
- To facilitate the integration of eQTL analysis with genome-wide association studies (GWAS) and other genomic investigations.
Main Methods:
- Literature review of existing computational tools for eQTL analysis.
- Categorization and comparison of available software based on functionality and application.
- Discussion of the relevance of eQTL analysis in the context of human disease, pharmacogenomics, and toxicogenomics.
Main Results:
- Identification of a range of computational tools suitable for various eQTL analysis needs.
- Highlighting the importance of user-friendly and specialized tools for efficient eQTL studies.
- Demonstrating the utility of eQTL analysis in interpreting GWAS results and advancing biological discovery.
Conclusions:
- Accessible and appropriate computational tools are essential for advancing eQTL analysis.
- This review serves as a valuable resource for researchers seeking to implement eQTL studies.
- Effective use of eQTL analysis tools can enhance our understanding of genetic contributions to complex traits and diseases.
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