Meta-Analysis for Epigenome-Wide Association Studies

Nan Wang; Shuilin Jin

doi:10.1007/978-1-0716-1994-0_8

Meta-Analysis for Epigenome-Wide Association Studies

Methods Mol Biol. 2022:2432:101-111. doi: 10.1007/978-1-0716-1994-0_8.

Authors

Nan Wang¹, Shuilin Jin²

Affiliations

¹ School of Mathematics, Harbin Institute of Technology, Harbin, Heilongjiang, China. nanwang688@gmail.com.
² School of Mathematics, Harbin Institute of Technology, Harbin, Heilongjiang, China. jinsl@hit.edu.cn.

PMID: 35505210
DOI: 10.1007/978-1-0716-1994-0_8

Abstract

With the rapid development of methylation profiling technology, many datasets are generated to quantify genome-wide methylation patterns. Given the heavy burden of multiple testing of hundreds of thousands of DNA methylation markers, individual studies often have limited sample sizes and power. The EWAS meta-analysis is an approach that combines results from multiple studies on the same scientific question. It helps to improve statistical power by combining information from individual studies and reduce the chances of false positives. This chapter introduces commonly used meta-analysis methods and analytical tools with application to EWAS data.

Keywords: Analytical tools; Meta-analysis; Methylation data.

Publication types

Meta-Analysis

MeSH terms

DNA Methylation
Epigenesis, Genetic*
Epigenome*
Genome-Wide Association Study / methods
Sample Size