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Clinical Epigenetics
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Clinical Epigenetics
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Improved filtering of DNA methylation microarray data by detection p values and its impact on downstream analyses

Authors: Heiss, Jonathan A.; Just, Allan C.;

Improved filtering of DNA methylation microarray data by detection p values and its impact on downstream analyses

Abstract

Background: DNA methylation microarrays are popular for epigenome-wide association studies (EWAS), but spurious values complicate downstream analysis and threaten replication. Conventional cut-offs for detection p values for filtering out undetected probes were demonstrated in a single previous study as insufficient leading to many apparent methylation calls in samples from females in probes targeting the Y-chromosome. We present an alternative approach to calculate more accurate detection p values utilizing non-specific background fluorescence. We evaluate and compare our proposed approach of filtering observations with conventional ones by assessing the detection of Y-chromosome probes among males and females in 2755 samples from 17 studies on the 450K microarray and masking of large outliers between technical replicates and their impact downstream via an EWAS reanalysis. Results: In contrast to conventional approaches, ours marks most Y-chromosome probes in females as undetected while removing a median of only 0.14% of the data per sample, catches more (30% vs. 6%) of large outliers (more than 20 percentage point difference between technical replicates), and helps to identify strong associations previously obfuscated by outliers between whole blood DNA methylation and chronological age in a well-powered EWAS (n = 729). Conclusions: We provide guidance for filtering both 450K and EPIC microarrays as an essential preprocessing step to reduce spurious values. An implementation (including a function compatible with objects from the popular minfi package) was added to ewastools, an R package for comprehensive quality control of DNA methylation microarrays.

Unzip and rerun analysis by executing the Makefile. Requirements: Unix System; ~100GB of RAM and ~50GB of disk space; GNU make; R 3.5;

Keywords

Epigenomics, Male, Chromosomes, Human, Y, DNA methylation, R, Methodology, Microarray analysis, Sequence Analysis, DNA, QH426-470, DNA Methylation, Epigenesis, Genetic, Epigeome-wide association study, Data cleaning, Genetics, Outlier detection, Medicine, Humans, CpG Islands, Female, Illumina 450K, Oligonucleotide Array Sequence Analysis, EWAS

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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