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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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Related Experiment Video

Updated: Oct 19, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Eagle for better genome-wide association mapping.

Andrew W George1, Arunas Verbyla2, Joshua Bowden3

  • 1Data61, Commonwealth Scientific and Industrial Research Organisation, Brisbane 4102, Australia.

G3 (Bethesda, Md.)
|September 20, 2021
PubMed
Summary
This summary is machine-generated.

Eagle is a new R package for genome-wide association studies, improving multi-locus analysis over single-locus methods. It offers greater power to detect SNP-trait associations using advanced statistical approaches.

Keywords:
GWASgenetic discoverymodel selectionmulti-locusquantitative trait

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Genome-wide association studies (GWAS) traditionally rely on single-locus analysis.
  • Single-locus methods may lack the power to detect complex genetic associations.
  • There is a need for more powerful and user-friendly tools for multi-locus association mapping in GWAS.

Purpose of the Study:

  • Introduce Eagle, an R package designed for efficient multi-locus association mapping.
  • Demonstrate Eagle's advantages over single-locus methods in detecting SNP-trait associations.
  • Provide a user-friendly tool for both R users and non-users.

Main Methods:

  • Eagle employs model selection and linear mixed models.
  • It utilizes a novel approach to incorporate random effects for identifying SNP-trait associations.
  • The package offers both command-line and graphical user interface options.

Main Results:

  • Eagle significantly enhances the power to detect single nucleotide polymorphism (SNP)-trait associations compared to single-locus methods.
  • Analysis of real mouse data with Eagle provides clearer insights than traditional single-locus findings.
  • The package is user-friendly and well-documented with a dedicated website.

Conclusions:

  • Eagle represents a significant advancement in multi-locus association mapping for GWAS.
  • The package offers greater power and clarity in identifying genetic associations.
  • Eagle is recommended to complement and potentially replace single-locus analyses in GWAS.