Universität zu Köln

Braegelmann, Johannes and Bermejo, Justo Lorenzo (2019). A comparative analysis of cell-type adjustment methods for epigenome-wide association studies based on simulated and real data sets. Brief. Bioinform., 20 (6). S. 2055 - 2066. OXFORD: OXFORD UNIV PRESS. ISSN 1477-4054

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Abstract

Technological advances and reduced costs of high-density methylation arrays have led to an increasing number of association studies on the possible relationship between human disease and epigenetic variability. DNA samples from peripheral blood or other tissue types are analyzed in epigenome-wide association studies (EWAS) to detect methylation differences related to a particular phenotype. Since information on the cell-type composition of the sample is generally not available and methylation profiles are cell-type specific, statistical methods have been developed for adjustment of cell-type heterogeneity in EWAS. In this study we systematically compared five popular adjustment methods: the factored spectrally transformed linear mixed model (FaST-LMM-EWASher), the sparse principal component analysis algorithm ReFACTor, surrogate variable analysis (SVA), independent SVA (ISVA) and an optimized version of SVA (SmartSVA). We used real data and applied a multilayered simulation framework to assess the type I error rate, the statistical power and the quality of estimated methylation differences according to major study characteristics. While all five adjustment methods improved false-positive rates compared with unadjusted analyses, FaST-LMM-EWASher resulted in the lowest type I error rate at the expense of low statistical power. SVA efficiently corrected for cell-type heterogeneity in EWAS up to 200 cases and 200 controls, but did not control type I error rates in larger studies. Results based on real data sets confirmed simulation findings with the strongest control of type I error rates by FaST-LMM-EWASher and SmartSVA. Overall, ReFACTor, ISVA and SmartSVA showed the best comparable statistical power, quality of estimated methylation differences and runtime.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Braegelmann, Johannes UNSPECIFIED UNSPECIFIED UNSPECIFIED Bermejo, Justo Lorenzo UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-128463
DOI:	10.1093/bib/bby068
Journal or Publication Title:	Brief. Bioinform.
Volume:	20
Number:	6
Page Range:	S. 2055 - 2066
Date:	2019
Publisher:	OXFORD UNIV PRESS
Place of Publication:	OXFORD
ISSN:	1477-4054
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language LINEAR MIXED MODELS; DNA METHYLATION; CANCER; GENOME; POPULATION; SMOKING; BLOOD; HETEROGENEITY; VALIDATION; MICROARRAY Multiple languages Biochemical Research Methods; Mathematical & Computational Biology Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/12846