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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: May 22, 2026

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

Detecting sample misidentifications in genetic association studies.

Claus T Ekstrøm¹, Bjarke Feenstra

¹University of Southern Denmark, Biostatistics, Faculty of Health Sciences, Denmark.

Statistical Applications in Genetics and Molecular Biology

|May 23, 2012

Summary

This summary is machine-generated.

Sample misidentification in genetic studies can be detected by checking genotype-phenotype consistency. Combining multiple known associations significantly improves the power to identify these errors in genome-wide association studies (GWAS).

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A Strategy to Identify de Novo Mutations in Common Disorders such as Autism and Schizophrenia

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Last Updated: May 22, 2026

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

A Strategy to Identify de Novo Mutations in Common Disorders such as Autism and Schizophrenia

A Strategy to Identify de Novo Mutations in Common Disorders such as Autism and Schizophrenia

Published on: June 15, 2011

Area of Science:

Genetics
Bioinformatics
Population Genetics

Background:

Accurate linkage of genotype and phenotype data is crucial for genetic association studies.
Sample misidentification errors can compromise study power and lead to incorrect conclusions.
Existing methods for detecting sample misidentification are limited, especially in genome-wide association studies (GWAS) using unrelated individuals.

Purpose of the Study:

To develop and validate a novel method for identifying potential sample misidentifications in genetic association studies, including GWAS.
To enhance the reliability of genotype-phenotype data linkage by leveraging multiple known associations.
To improve the power and accuracy of error detection compared to existing ad-hoc methods.

Main Methods:

Generalizing the X-linked genotype and sex check to incorporate multiple known genotype-phenotype associations.
Developing an analytical and simulation framework to assess the power of the proposed method.
Evaluating the method's sensitivity and specificity based on the number and informativeness of genotype-phenotype associations.

Main Results:

Combining several known genotype-phenotype associations substantially increases the power to detect sample misidentifications.
The proposed method demonstrates good sensitivity and specificity with as few as ten moderately informative associations.
The method was successfully applied to GWAS data from the Danish National Birth Cohort, identifying potential sample errors.

Conclusions:

The developed method provides a robust approach for identifying sample misidentifications in genetic association studies.
Leveraging multiple genotype-phenotype associations is an effective strategy to improve error detection accuracy.
This method can enhance the integrity of genetic association studies, particularly large-scale GWAS.