Rxivist logo

Meffil: efficient normalisation and analysis of very large DNA methylation samples

By Josine Min, Gibran Hemani, George Davey Smith, Caroline L. Relton, Matthew Suderman

Posted 28 Apr 2017
bioRxiv DOI: 10.1101/125963 (published DOI: 10.1093/bioinformatics/bty476)

Background. Technological advances in high throughput DNA methylation microarrays have allowed dramatic growth of a new branch of epigenetic epidemiology. DNA methylation datasets are growing ever larger in terms of the number of samples profiled, the extent of genome coverage, and the number of studies being meta-analysed. Novel computational solutions are required to efficiently handle these data. Methods. We have developed meffil, an R package designed to quality control, normalize and perform epigenome-wide association studies (EWAS) efficiently on large samples of Illumina Infinium HumanMethylation450 and MethylationEPIC BeadChip microarrays. We tested meffil by applying it to 6000 450k microarrays generated from blood collected for two different datasets. Results. A complete reimplementation of functional normalization minimizes computational memory requirements to 5% of that required by other R packages, without increasing running time. Incorporating fixed and random fixed effects alongside functional normalization, and automated estimation of functional normalisation parameters reduces technical variation in DNA methylation levels, thus reducing false positive associations and improving power. We also demonstrate that the ability to normalize datasets distributed across physically different locations without sharing any biologically-based individual-level data may reduce heterogeneity in meta-analyses of epigenome-wide association studies. However, when batch is perfectly confounded with cases and controls functional normalization is unable to prevent spurious associations. Conclusions. meffil is available online (https://github.com/perishky/meffil/) along with tutorials covering typical use cases.

Download data

  • Downloaded 1,240 times
  • Download rankings, all-time:
    • Site-wide: 9,439 out of 101,121
    • In bioinformatics: 1,539 out of 9,284
  • Year to date:
    • Site-wide: 45,552 out of 101,121
  • Since beginning of last month:
    • Site-wide: 31,779 out of 101,121

Altmetric data


Downloads over time

Distribution of downloads per paper, site-wide


PanLingua

Sign up for the Rxivist weekly newsletter! (Click here for more details.)


News

  • 20 Oct 2020: Support for sorting preprints using Twitter activity has been removed, at least temporarily, until a new source of social media activity data becomes available.
  • 18 Dec 2019: We're pleased to announce PanLingua, a new tool that enables you to search for machine-translated bioRxiv preprints using more than 100 different languages.
  • 21 May 2019: PLOS Biology has published a community page about Rxivist.org and its design.
  • 10 May 2019: The paper analyzing the Rxivist dataset has been published at eLife.
  • 1 Mar 2019: We now have summary statistics about bioRxiv downloads and submissions.
  • 8 Feb 2019: Data from Altmetric is now available on the Rxivist details page for every preprint. Look for the "donut" under the download metrics.
  • 30 Jan 2019: preLights has featured the Rxivist preprint and written about our findings.
  • 22 Jan 2019: Nature just published an article about Rxivist and our data.
  • 13 Jan 2019: The Rxivist preprint is live!