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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.
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Related Experiment Video

Updated: Jun 11, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

PERMORY: an LD-exploiting permutation test algorithm for powerful genome-wide association testing.

Roman Pahl¹, Helmut Schäfer

¹Institut für Medizinische Biometrie und Epidemiologie, Philipps-Universität Marburg, Germany. rpahl@staff.uni-marburg.de

Bioinformatics (Oxford, England)

|July 8, 2010

Summary

This summary is machine-generated.

We developed a faster permutation test algorithm for genome-wide association studies (GWAS) that avoids approximations. This computationally efficient method provides unbiased results for large-scale genetic analyses.

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Last Updated: Jun 11, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

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Published on: August 21, 2016

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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

Area of Science:

Genetics
Statistical Genetics
Bioinformatics

Background:

Genome-wide association studies (GWAS) require statistical correction for multiple testing due to numerous genetic markers.
Permutation tests are the standard for correcting multiple testing in GWAS, offering unbiased error control and high power.
Traditional permutation tests are computationally intensive for large GWAS datasets, leading to the use of potentially biased approximations.

Purpose of the Study:

To address the computational burden of permutation testing in GWAS.
To develop a computationally efficient algorithm for genome-wide permutation testing.
To provide an unbiased alternative to approximate methods for multiple testing correction in GWAS.

Main Methods:

Development of a novel permutation test algorithm.
Implementation of the algorithm for large-scale GWAS data analysis.
Performance evaluation against existing permutation testing methods.

Main Results:

The new algorithm is one or more orders of magnitude faster than existing implementations.
The algorithm provides unbiased results, identical to standard permutation tests.
It shows particularly effective performance with high-density marker sets.

Conclusions:

The developed algorithm enables efficient and unbiased permutation testing on a genome-wide scale.
This advancement overcomes the computational limitations of traditional permutation tests in GWAS.
The method is freely available at http://www.permory.org.