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Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

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MethylToSNP: identifying SNPs in Illumina DNA methylation array data.

Brenna A LaBarre^1,2, Alexander Goncearenco², Hanna M Petrykowska²

¹Graduate Program in Bioinformatics, Boston University, Boston, MA, USA.

Epigenetics & Chromatin

|December 22, 2019

Summary

This study introduces MethylToSNP, an R package that identifies DNA methylation sites confounded by genetic polymorphisms, minimizing data loss and enabling new variant discovery. It accurately predicts single nucleotide polymorphisms (SNPs) in methylation data, improving genomic analysis.

Keywords:

Bisulfite sequencing CTCF sites Data analysis Enhancers Illumina methylation array Methylation probes Polymorphisms Single nucleotide polymorphisms (SNPs)

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Area of Science:

Genomics
Epigenetics
Bioinformatics

Background:

Array-based DNA methylation measurement relies on bisulfite conversion.
Polymorphisms at probe sites create ambiguity in DNA methylation data interpretation.
Current methods often exclude a significant portion of probes, leading to data loss.

Purpose of the Study:

To develop an algorithm to detect and infer DNA methylation sites confounded by single nucleotide polymorphisms (SNPs).
To provide a tool that minimizes data loss in methylation array analysis.
To enable the identification of new polymorphisms and their functional impact.

Main Methods:

An algorithm implemented in an R Bioconductor package, MethylToSNP.
Detection of characteristic data patterns to infer polymorphism-confounded sites.
Calibration of parameters and thresholds using simulated and real methylation data.
Validation through SNP databases and bisulfite/genomic sequencing.

Main Results:

MethylToSNP accurately detects sites likely confounded by polymorphisms.
A reliability score is provided for thresholding SNP predictions.
Predictions were validated across diverse populations (YRI, CEPH, KhoeSan) and array platforms (Illumina 450K, 850K EPIC, 27K).

Conclusions:

MethylToSNP prevents extensive data loss by analyzing individual sample SNPs.
The tool can identify novel polymorphisms in understudied genetic landscapes.
It identifies variants in functional genomic regions, potentially impacting regulatory activities.