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Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

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: Jun 5, 2026

Infinium Assay for Large-scale SNP Genotyping Applications

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Quality control procedures for genome-wide association studies.

Stephen Turner¹, Loren L Armstrong, Yuki Bradford

¹Center for Human Genetics Research, Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, Tennessee, USA.

Current Protocols in Human Genetics

|January 15, 2011

Summary

This summary is machine-generated.

Quality control (QC) for genome-wide association studies (GWAS) is crucial for reliable complex disease research. The eMERGE network implements robust QC methods to ensure high-quality GWAS data, minimizing bias and errors.

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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

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Last Updated: Jun 5, 2026

Infinium Assay for Large-scale SNP Genotyping Applications

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Area of Science:

Genomics
Population Genetics
Biostatistics

Background:

Genome-wide association studies (GWAS) are vital for understanding complex disease pathophysiology.
The accuracy of GWAS findings heavily relies on the quality of the underlying genetic data.
Current quality control (QC) procedures for GWAS are complex and continually evolving.

Purpose of the Study:

To outline the challenges encountered during GWAS data QC.
To describe the quality assurance strategies employed by the electronic MEdical Records and Genomics (eMERGE) network for GWAS data.
To minimize bias and errors in GWAS results through rigorous QC.

Main Methods:

Systematic enumeration of common GWAS data QC challenges.
Description of eMERGE network's quality assurance approaches for GWAS data.
Discussion of issues including data formats, software, sex chromosome anomalies, sample identity, relatedness, population substructure, batch effects, and marker quality.

Main Results:

Identification of critical QC challenges in GWAS data processing.
Implementation of standardized QC protocols within the eMERGE network.
Demonstration of methods to enhance the reliability of GWAS results.

Conclusions:

Effective QC is paramount for the utility of GWAS findings in complex disease research.
The eMERGE network's QC strategies provide a framework for robust GWAS data quality assurance.
Ongoing research and best practices are essential for advancing GWAS QC methodologies.