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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: Dec 31, 2025

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

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Region-based interaction detection in genome-wide case-control studies.

Sen Zhang¹, Wei Jiang², Ronald Cw Ma³

¹Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology,, Kowloon, Hong Kong, China.

BMC Medical Genomics

|January 1, 2020

Summary

This study introduces RRIntCC, a novel region-region interaction detection method for genome-wide association studies. RRIntCC outperforms traditional SNP-SNP interaction methods, improving the detection of complex genetic associations.

Keywords:

GWAS LD contrast test Region-based method Statistical interaction detection

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Last Updated: Dec 31, 2025

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

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

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Published on: January 9, 2020

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 and Genomics
Bioinformatics
Statistical Genetics

Background:

Genome-wide association studies (GWAS) traditionally focus on single nucleotide polymorphism (SNP)-SNP interactions.
SNPs may not represent the smallest functional units for complex phenotypes.
Region-based strategies show promise for detecting marginal genetic effects.

Purpose of the Study:

To develop a novel region-region interaction detection method for case-control studies.
To address limitations of SNP-based interaction detection in GWAS.
To improve the power and accuracy of detecting genetic interactions.

Main Methods:

Introduction of RRIntCC (region-region interaction detection for case-control studies).
Utilizes correlations between SNP-SNP interactions based on linkage disequilibrium (LD) contrast test.
A novel region-based approach for interaction analysis.

Main Results:

Simulation experiments demonstrate higher power compared to conventional SNP-based methods.
Maintained similar type-I-error rates.
RRIntCC identified significant regions in two real datasets where BOOST found none.

Conclusions:

A new region-based interaction detection method, RRIntCC, has been proposed.
RRIntCC exhibits superior performance over SNP-based interaction detection methods.
The method offers improved capabilities for identifying complex genetic interactions in GWAS.