Quality control for Illumina 450K methylation data in the absence of iDat files using correlation structure in pedigrees and repeated measures

Marissa Leblanc,Christian M Page,Haakon E Nustad,Manuela Zucknick

doi:10.1186/s12863-018-0636-5

Abstract

BackgroundAn important feature in many genomic studies is quality control and normalization. This is particularly important when analyzing epigenetic data, where the process of obtaining measurements can be bias prone. The GAW20 data was from the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN), a study with multigeneration families, where DNA cytosine-phosphate-guanine (CpG) methylation was measured pre- and posttreatment with fenofibrate. We performed quality control assessment of the GAW20 DNA methylation data, including normalization, assessment of batch effects and detection of sample swaps.ResultsWe show that even after normalization, the GOLDN methylation data has systematic differences pre- and posttreatment. Through investigation of (a) CpGs sites containing a single nucleotide polymorphism, (b) the stability of breeding values for methylation across time points, and (c) autosomal gender-associated CpGs, 13 sample swaps were detected, 11 of which were posttreatment.ConclusionsThis paper demonstrates several ways to perform quality control of methylation data in the absence of raw data files and highlights the importance of normalization and quality control of the GAW20 methylation data from the GOLDN study.

Highlights

An important feature in many genomic studies is quality control and normalization
Comparing DNA methylation (DNAm) pre- and posttreatment, we identified approximately 300,000 significant CpGs after Bonferroni correction
While this aligned the distributions of the Type I and Type II probes, the principal component analysis (PCA) plot still shows systematic differences between time points (Fig. 1d)

Summary

Introduction

An important feature in many genomic studies is quality control and normalization. This is important when analyzing epigenetic data, where the process of obtaining measurements can be bias prone. The GAW20 data was from the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN), a study with multigeneration families, where DNA cytosine-phosphate-guanine (CpG) methylation was measured pre- and posttreatment with fenofibrate. We performed quality control assessment of the GAW20 DNA methylation data, including normalization, assessment of batch effects and detection of sample swaps. Genome-wide DNA methylation (DNAm) studies, those using chip-based technologies, are inherently more susceptible to biases than single nucleotide polymorphism (SNP) studies. Bisulfite conversion run in separate batches can lead to technical bias in DNAm studies. Most require raw data (iDat files) to run and these were not made available to GAW20 participants

Results

Discussion

Conclusion