Prediction of smoking by multiplex bisulfite PCR with long amplicons considering allele-specific effects on DNA methylation 06 Biological Sciences 0604 Genetics

Abstract

Background: Methylation of DNA is associated with a variety of biological processes. With whole-genome studies of DNA methylation, it became possible to determine a set of genomic sites where DNA methylation is associated with a specific phenotype. A method is needed that allows detailed follow-up studies of the sites, including taking into account genetic information. Bisulfite PCR is a natural choice for this kind of task, but multiplexing is one of the most important problems impeding its implementation. To address this task, we took advantage of a recently published method based on Pacbio sequencing of long bisulfite PCR products (single-molecule real-time bisulfite sequencing, SMRT-BS) and tested the validity of the improved methodology with a smoking phenotype. Results: Herein, we describe the "panhandle" modification of the method, which permits a more robust PCR with multiple targets. We applied this technique to determine smoking by DNA methylation in 71 healthy people and 83 schizophrenia patients (n = 50 smokers and n = 104 non-smokers, Russians of the Moscow region). We used five targets known to be influenced by smoking (regions of genes AHRR, ALPPL2, IER3, GNG12, and GFI1). We discovered significant allele-specific methylation effects in the AHRR and IER3 regions and assessed how this information could be exploited to improve the prediction of smoking based on the collected DNA methylation data. We found no significant difference in the methylation profiles of selected targets in relation to schizophrenia suggesting that smoking affects methylation at the studied genomic sites in healthy people and schizophrenia patients in a similar way. Conclusions: We determined that SMRT-BS with "panhandle" modification performs well in the described setting. Additional information regarding methylation and allele-specific effects could improve the predictive accuracy of DNA methylation-based models, which could be valuable for both basic research and clinical applications.