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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: Sep 1, 2025

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

Demystifying non-coding GWAS variants: an overview of computational tools and methods.

Marijn Schipper¹, Danielle Posthuma¹

¹Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University Amsterdam, De Boelelaan 1105, Amsterdam 1081HV, The Netherlands.

Human Molecular Genetics

|August 16, 2022

Summary

Genome-wide association studies (GWAS) identify many non-coding variants linked to disease. This review offers tools and a workflow to analyze these variants, aiding in understanding disease mechanisms and prioritizing research targets.

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Related Experiment Videos

Last Updated: Sep 1, 2025

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

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

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

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
Bioinformatics
Genomic Regulation

Background:

Genome-wide association studies (GWAS) frequently identify disease-associated variants within non-coding DNA regions.
Significant progress has been made in mapping non-coding regulatory elements.
Developing computational tools is crucial for interpreting these non-coding variants.

Conclusions:

Computational approaches are essential for dissecting the functional impact of non-coding GWAS variants.
The proposed workflow can guide researchers in prioritizing non-coding variants for further investigation.
There is a clear need for the development of more robust tools and comprehensive benchmarks for non-coding variant analysis.