DNA methylation levels are highly correlated between pooled samples and averaged values when analysed using the Infinium HumanMethylation450 BeadChip array.

Gallego-Fabrega, C, Carrera, C, Muiño, E, Montaner, J, Krupinski, J and Fernandez-Cadenas, I (2015) DNA methylation levels are highly correlated between pooled samples and averaged values when analysed using the Infinium HumanMethylation450 BeadChip array. Clinical Epigenetics, 7. ISSN 1868-7075

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Abstract

BACKGROUND: DNA methylation is a heritable and stable epigenetic mark implicated in complex human traits. Epigenome-wide association studies (EWAS) using array-based technology are becoming widely used to identify differentially methylated sites associated with complex diseases. EWAS studies require large sample sizes to detect small effects, which increases project costs. In the present study we propose to pool DNA samples in methylation array studies as an affordable and accurate alternative to individual samples studies, in order to reduce economic costs or when low amounts of DNA are available. For this study, 20 individual DNA samples and 4 pooled DNA samples were analysed using the Illumina Infinium HumanMethylation450 BeadChip array to evaluate the efficiency of the pooling approach in EWAS studies. Statistical power calculations were also performed to discover the minimum sample size needed for the pooling strategy in EWAS. RESULTS: A total of 485,577 CpG sites across the whole genome were assessed. Comparison of methylation levels of all CpG sites between individual samples and their related pooled samples revealed highly significant correlations (rho > 0.99, p-val < 10(-16)). These results remained similar when assessing the 101 most differentially methylated CpG sites (rho > 0.98, p-val < 10(-16)). Also, it was calculated that n = 43 is the minimum sample size required to achieve a 95 % statistical power and a 10(-06) significance level in EWAS, when using a DNA pool strategy. CONCLUSIONS: DNA pooling strategies seems to accurately provide estimations of averaged DNA methylation state using array based EWAS studies. This type of approach can be applied to the assessment of disease phenotypes, reducing the amount of DNA required and the cost of large-scale epigenetic analyses.