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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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Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Published on: September 18, 2020

A novel method for detecting association between DNA methylation and diseases using spatial information.

Wai-Ki Yip¹, Heide Fier, Dawn L DeMeo

¹Harvard University, Boston, Massachusetts, United States of America.

Genetic Epidemiology

|September 25, 2014

Summary

We developed a Spatial Clustering Method (SCM) to find DNA methylation differences in complex diseases. This method effectively identifies differentially methylated regions (DMRs) associated with disease states, aiding in understanding genetic contributions.

Keywords:

DNA methylation Genome-Wide Association Test (GWAS)differentially methylated regions (DMRs)genetic analysis spatial analysis

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Area of Science:

Genetics
Epigenetics
Bioinformatics

Background:

DNA methylation is implicated in complex trait genetics but lacks advanced statistical analysis methods.
Methylated sites exhibit clustering, offering a novel approach for detection.

Purpose of the Study:

To introduce a Spatial Clustering Method (SCM) for identifying differentially methylated regions (DMRs).
To leverage spatial information of DNA methylation marks for enhanced detection in case-control studies.

Main Methods:

SCM compares DNA methylation mark distances between cases and controls.
A statistic is computed based on distance distributions, with significance evaluated via permutation testing.
Type I error rates are maintained for robust statistical inference.

Main Results:

Simulation studies demonstrate SCM's good power in detecting disease-associated DMRs.
Application to colorectal cancer data identified statistically significant regions on chromosome 14.
The method proves effective in exploratory epigenetic analyses.

Conclusions:

SCM provides a reliable statistical approach for identifying DMRs in disease states.
This method enhances understanding of methylated sites' contribution to complex diseases.
SCM is valuable for exploratory epigenetic research and identifying disease-associated genomic regions.