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An R package to compute methylation quantitative trait loci (methQTL) from DNA methylation and genotyping data.

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MPIIComputationalEpigenetics/MAGAR

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MAGAR

Build Status License: GPL v3

Short description

MAGAR (Methylation-Aware Genotype Association in R) is an R package for computing DNA methylation quantitative trait loci (methQTL) from DNA methylation and matched genotyping data. The tool employs a two-stage approach to compute methQTL:

  • CpG correlation blocks are computed to mimic DNA methylation haplotypes. The correlation blocks are computed from CpGs that are highly correlated across the samples.
  • For each correlation block, a representative CpG (tag-CpG) is computed and used in a linear modeling strategy (or alternatively with fastQTL) to determine significant associations between genotype variation and changes in DNA methylation of particular CpGs.

The package furthermore uses established software tools for handling DNA methylation and genotyping data, including RnBeads, PLINK, and CRLMM. Thus, raw DNA methylation data (IDAT, BED files) and genotyping data (IDAT, PLINK files) can be used as input.

Installation

The package needs some Bioconductor dependencies, most notably RnBeads, which can be directly installed from the RnBeads website. Atfer instaling RnBeads, MAGAR can be installed in a R session using the devtools package

source("https://rnbeads.org/data/install.R")
if(!requireNamespace("devtools")) install.packages("devtools")
devtools::install_github("MPIIComputationalEpigenetics/MAGAR")

Documentation

You can find a detailed information for MAGAR here

Citation

Scherer, M., Gasparoni, G., Rahmouni, S. et al. Identification of tissue-specific and common methylation quantitative trait loci in healthy individuals using MAGAR. Epigenetics & Chromatin 14, 44 (2021). doi:10.1186/s13072-021-00415-6

Contact

You can contact Michael Scherer for reporting bugs, feature requests or questions.

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An R package to compute methylation quantitative trait loci (methQTL) from DNA methylation and genotyping data.

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