EWAS

EWAS performs epigenome-wide association analyses of DNA methylation data to identify epigenetic variants associated with diseases and phenotypes within a population epigenetic framework.

Key Features:

• Epigenome-Wide Single Marker Association Analysis: Identifies associations between individual DNA methylation markers and specific diseases or phenotypes.
• Methylation Haplotype (Meplotype) Association Analysis: Analyzes methylation haplotypes (meplotypes) to evaluate combined effects of multiple methylation sites.
• Epigenome-Wide Meta-Analysis: Integrates and analyzes epigenetic association data across multiple studies to increase statistical power.
• Statistical Analysis Framework: Supports chi-square tests, t-tests, linear regression, logistic regression, and Pearson's correlation coefficient for association testing.
• Methylation Disequilibrium Analysis: Identifies epi-allele associations, calculates methylation disequilibrium (MD) blocks and MD coefficients, and estimates meplotype frequencies.

Scientific Applications:

• Epigenome-Wide Association Studies: Detects DNA methylation markers associated with complex diseases and phenotypes.
• Population Epigenetics Research: Analyzes epigenetic variation and methylation haplotypes across populations.
• Epigenetic Variant Discovery: Identifies epi-alleles and methylation disequilibrium patterns linked to biological traits.

Methodology:

EWAS analyzes DNA methylation data using single-marker association tests, methylation haplotype (meplotype) association analysis, and meta-analysis, applying statistical methods including chi-square tests, t-tests, linear regression, logistic regression, and Pearson's correlation coefficients, while calculating methylation disequilibrium blocks, MD coefficients, and meplotype frequencies.