Stratified randomization controls better for batch effects in 450K methylation analysis: a cautionary tale.

Area of Science:

• Genomics
• Epigenetics
• Bioinformatics

Background:

• Batch effects in DNA methylation microarray experiments can compromise data integrity.
• Uncontrolled batch effects can lead to spurious associations between DNA methylation and phenotypes.

Purpose of the Study:

• To investigate chip- and row-specific batch effects in DNA methylation patterns associated with obesity in Samoan men.
• To evaluate the impact of study design on the identification of differentially methylated positions (DMPs).

Main Methods:

• Two pilot studies utilized Illumina's Infinium HumanMethylation450 BeadChip for DNA methylation analysis.
• Data were analyzed using R packages (methylumi, watermelon, limma) and ComBat for batch effect correction.
• Principal component analysis and linear regression identified batch associations; moderated t-tests identified DMPs.

Main Results:

• Chip effects were removed in Sample Two (balanced design) but not Sample One (unbalanced design).
• ComBat correction resulted in 94,191 DMPs in Sample One versus zero in Sample Two.
• Confounding of obesity status with batch effects in Sample One likely caused disparate results.

Conclusions:

• Statistical adjustments for batch effects may not fully eliminate them.
• Robust study design is essential to prevent spurious findings in DNA methylation research.
• Careful sample plating and balancing are critical for reliable genomic association studies.