Evaluation of commonly used analysis strategies for epigenome- and transcriptome-wide association studies through replication of large-scale population studies | Genome Biology | Full Text

Evaluation of commonly used analysis strategies for epigenome- and transcriptome-wide association studies through replication of large-scale population studies | Genome Biology | Full Text

Evaluation of commonly used analysis strategies for epigenome- and transcriptome-wide association studies through replication of large-scale population studies

• Jeroen van Rooij, 
• Pooja R. Mandaviya, 
• Annique Claringbould, 
• Janine F. Felix, 
• Jenny van Dongen, 
• Rick Jansen, 
• Lude Franke, 
• BIOS consortium, 
• Peter A. C. ’t Hoen, 
• Bas Heijmans & 
• Joyce B. J. van Meurs 

Genome Biology volume 20, Article number: 235 (2019) Cite this article

• 1555 Accesses
• 30 Altmetric
• Metricsdetails

Abstract

Background

A large number of analysis strategies are available for DNA methylation (DNAm) array and RNA-seq datasets, but it is unclear which strategies are best to use. We compare commonly used strategies and report how they influence results in large cohort studies.

Results

We tested the associations of DNAm and RNA expression with age, BMI, and smoking in four different cohorts (n = ~ 2900). By comparing strategies against the base model on the number and percentage of replicated CpGs for DNAm analyses or genes for RNA-seq analyses in a leave-one-out cohort replication approach, we find the choice of the normalization method and statistical test does not strongly influence the results for DNAm array data. However, adjusting for cell counts or hidden confounders substantially decreases the number of replicated CpGs for age and increases the number of replicated CpGs for BMI and smoking. For RNA-seq data, the choice of the normalization method, gene expression inclusion threshold, and statistical test does not strongly influence the results. Including five principal components or excluding correction of technical covariates or cell counts decreases the number of replicated genes.

Conclusions

Results were not influenced by the normalization method or statistical test. However, the correction method for cell counts, technical covariates, principal components, and/or hidden confounders does influence the results.