Adjustment of Cell-Type Composition Minimizes Systematic Bias in Blood DNA Methylation Profiles Derived by DNA Collection ProtocolsReportar como inadecuado




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Differences in DNA collection protocols may be a potential confounder in epigenome-wide association studies EWAS using a large number of blood specimens from multiple biobanks and-or cohorts. Here we show that pre-analytical procedures involved in DNA collection can induce systematic bias in the DNA methylation profiles of blood cells that can be adjusted by cell-type composition variables. In Experiment 1, whole blood from 16 volunteers was collected to examine the effect of a 24 h storage period at 4°C on DNA methylation profiles as measured using the Infinium HumanMethylation450 BeadChip array. Our statistical analysis showed that the P-value distribution of more than 450,000 CpG sites was similar to the theoretical distribution in quantile-quantile plot, λ = 1.03 when comparing two control replicates, which was remarkably deviated from the theoretical distribution λ = 1.50 when comparing control and storage conditions. We then considered cell-type composition as a possible cause of the observed bias in DNA methylation profiles and found that the bias associated with the cold storage condition was largely decreased λadjusted = 1.14 by taking into account a cell-type composition variable. As such, we compared four respective sample collection protocols used in large-scale Japanese biobanks or cohorts as well as two control replicates. Systematic biases in DNA methylation profiles were observed between control and three of four protocols without adjustment of cell-type composition λ = 1.12–1.45 and no remarkable biases were seen after adjusting for cell-type composition in all four protocols λadjusted = 1.00–1.17. These results revealed important implications for comparing DNA methylation profiles between blood specimens from different sources and may lead to discovery of disease-associated DNA methylation markers and the development of DNA methylation profile-based predictive risk models.



Autor: Yuh Shiwa, Tsuyoshi Hachiya, Ryohei Furukawa, Hideki Ohmomo, Kanako Ono, Hisaaki Kudo, Jun Hata, Atsushi Hozawa, Motoki Iwasaki,

Fuente: http://plos.srce.hr/



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