Literature DB >> 35505210

Meta-Analysis for Epigenome-Wide Association Studies.

Nan Wang1, Shuilin Jin2.   

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.
© 2022. Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Analytical tools; Meta-analysis; Methylation data

Mesh:

Year:  2022        PMID: 35505210     DOI: 10.1007/978-1-0716-1994-0_8

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  21 in total

Review 1.  [Introduction to traditional meta-analysis].

Authors:  Rafael Bolaños Díaz; María Calderón Cahua
Journal:  Rev Gastroenterol Peru       Date:  2014 Jan-Mar

Review 2.  Epigenetic epidemiology: promises for public health research.

Authors:  Kelly M Bakulski; M Daniele Fallin
Journal:  Environ Mol Mutagen       Date:  2014-01-21       Impact factor: 3.216

3.  A new era for epigenetic epidemiology.

Authors:  Chathura J Gunasekara; Robert A Waterland
Journal:  Epigenomics       Date:  2019-11-15       Impact factor: 4.778

Review 4.  The Cancer Genome Atlas of renal cell carcinoma: findings and clinical implications.

Authors:  W Marston Linehan; Christopher J Ricketts
Journal:  Nat Rev Urol       Date:  2019-07-05       Impact factor: 14.432

Review 5.  The power of meta-analysis in genome-wide association studies.

Authors:  Orestis A Panagiotou; Cristen J Willer; Joel N Hirschhorn; John P A Ioannidis
Journal:  Annu Rev Genomics Hum Genet       Date:  2013-05-24       Impact factor: 8.929

6.  MetaOmics: analysis pipeline and browser-based software suite for transcriptomic meta-analysis.

Authors:  Tianzhou Ma; Zhiguang Huo; Anche Kuo; Li Zhu; Zhou Fang; Xiangrui Zeng; Chien-Wei Lin; Silvia Liu; Lin Wang; Peng Liu; Tanbin Rahman; Lun-Ching Chang; Sunghwan Kim; Jia Li; Yongseok Park; Chi Song; Steffi Oesterreich; Etienne Sibille; George C Tseng
Journal:  Bioinformatics       Date:  2019-05-01       Impact factor: 6.937

Review 7.  Comprehensive literature review and statistical considerations for microarray meta-analysis.

Authors:  George C Tseng; Debashis Ghosh; Eleanor Feingold
Journal:  Nucleic Acids Res       Date:  2012-01-19       Impact factor: 16.971

8.  DiseaseMeth: a human disease methylation database.

Authors:  Jie Lv; Hongbo Liu; Jianzhong Su; Xueting Wu; Hui Liu; Boyan Li; Xue Xiao; Fang Wang; Qiong Wu; Yan Zhang
Journal:  Nucleic Acids Res       Date:  2011-12-01       Impact factor: 16.971

9.  Meta-analysis: a tool for clinical and experimental research in psychiatry.

Authors:  Thelma Beatriz González-Castro; Carlos Alfonso Tovilla-Zárate
Journal:  Nord J Psychiatry       Date:  2013-09-17       Impact factor: 2.202

10.  Increasing the power of meta-analysis of genome-wide association studies to detect heterogeneous effects.

Authors:  C H Lee; E Eskin; B Han
Journal:  Bioinformatics       Date:  2017-07-15       Impact factor: 6.937

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