Meffil: efficient normalization and analysis of very large DNA methylation datasets

Josine Min; Gibran Hemani; George Davey Smith; Caroline Relton; Matthew Suderman

doi:10.1093/bioinformatics/bty476

Meffil: efficient normalization and analysis of very large DNA methylation datasets

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Abstract

Motivation: DNA methylation datasets are growing ever larger both in sample size and genome coverage. Novel computational solutions are required to efficiently handle these data.

Results: We have developed meffil, an R package designed for efficient quality control, normalization and epigenome-wide association studies of large samples of Illumina Methylation BeadChip microarrays. A complete reimplementation of functional normalization minimizes computational memory without increasing running time. Incorporating fixed and random effects within functional normalization, and automated estimation of functional normalization parameters reduces technical variation in DNA methylation levels, thus reducing false positive rates and improving power. Support for normalization of datasets distributed across physically different locations without needing to share biologically-based individual-level data means that meffil can be used to reduce heterogeneity in meta-analyses of epigenome-wide association studies.

Availability and implementation
https://github.com/perishky/meffil/

Original language	English
Article number	bty476
Number of pages	5
Journal	Bioinformatics
Early online date	21 Jun 2018
DOIs	https://doi.org/10.1093/bioinformatics/bty476
Publication status	E-pub ahead of print - 21 Jun 2018

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Final published version, 422 KBLicence: CC BY

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IEU 2 Relton Programme - Epigenetic Epidemiology
Relton, C. L. (Principal Investigator)
1/04/18 → 31/03/23
Project: Research
IEU: MRC Integrative Epidemiology Unit Quinquennial renewal
Gaunt, L. F. (Principal Investigator) & Davey Smith, G. (Principal Investigator)
1/04/18 → 31/03/23
Project: Research
(IEU) Epigenetics: Environment, Embodiment & Equality (E4)
Relton, C. L. (Principal Investigator)
1/01/16 → 31/12/19
Project: Research

Professor Gibran Hemani
- g.hemanibristol.acuk
- Epidemiology and Health Data Science - Professor in Statistical Genetics
- Bristol Population Health Science Institute
- MRC Integrative Epidemiology Unit
Person: Academic , Member

Cite this

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title = "Meffil: efficient normalization and analysis of very large DNA methylation datasets",

abstract = "Motivation: DNA methylation datasets are growing ever larger both in sample size and genome coverage. Novel computational solutions are required to efficiently handle these data. Results: We have developed meffil, an R package designed for efficient quality control, normalization and epigenome-wide association studies of large samples of Illumina Methylation BeadChip microarrays. A complete reimplementation of functional normalization minimizes computational memory without increasing running time. Incorporating fixed and random effects within functional normalization, and automated estimation of functional normalization parameters reduces technical variation in DNA methylation levels, thus reducing false positive rates and improving power. Support for normalization of datasets distributed across physically different locations without needing to share biologically-based individual-level data means that meffil can be used to reduce heterogeneity in meta-analyses of epigenome-wide association studies. Availability and implementation https://github.com/perishky/meffil/",

author = "Josine Min and Gibran Hemani and \{Davey Smith\}, George and Caroline Relton and Matthew Suderman",

year = "2018",

month = jun,

day = "21",

doi = "10.1093/bioinformatics/bty476",

language = "English",

journal = "Bioinformatics",

issn = "1367-4811",

publisher = "Oxford University Press",

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T2 - efficient normalization and analysis of very large DNA methylation datasets

AU - Min, Josine

AU - Hemani, Gibran

AU - Davey Smith, George

AU - Relton, Caroline

AU - Suderman, Matthew

PY - 2018/6/21

Y1 - 2018/6/21

N2 - Motivation: DNA methylation datasets are growing ever larger both in sample size and genome coverage. Novel computational solutions are required to efficiently handle these data. Results: We have developed meffil, an R package designed for efficient quality control, normalization and epigenome-wide association studies of large samples of Illumina Methylation BeadChip microarrays. A complete reimplementation of functional normalization minimizes computational memory without increasing running time. Incorporating fixed and random effects within functional normalization, and automated estimation of functional normalization parameters reduces technical variation in DNA methylation levels, thus reducing false positive rates and improving power. Support for normalization of datasets distributed across physically different locations without needing to share biologically-based individual-level data means that meffil can be used to reduce heterogeneity in meta-analyses of epigenome-wide association studies. Availability and implementation https://github.com/perishky/meffil/

AB - Motivation: DNA methylation datasets are growing ever larger both in sample size and genome coverage. Novel computational solutions are required to efficiently handle these data. Results: We have developed meffil, an R package designed for efficient quality control, normalization and epigenome-wide association studies of large samples of Illumina Methylation BeadChip microarrays. A complete reimplementation of functional normalization minimizes computational memory without increasing running time. Incorporating fixed and random effects within functional normalization, and automated estimation of functional normalization parameters reduces technical variation in DNA methylation levels, thus reducing false positive rates and improving power. Support for normalization of datasets distributed across physically different locations without needing to share biologically-based individual-level data means that meffil can be used to reduce heterogeneity in meta-analyses of epigenome-wide association studies. Availability and implementation https://github.com/perishky/meffil/

U2 - 10.1093/bioinformatics/bty476

DO - 10.1093/bioinformatics/bty476

M3 - Article (Academic Journal)

C2 - 29931280

SN - 1367-4811

JO - Bioinformatics

JF - Bioinformatics

M1 - bty476

ER -

Meffil: efficient normalization and analysis of very large DNA methylation datasets

Abstract

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Projects

IEU 2 Relton Programme - Epigenetic Epidemiology

IEU: MRC Integrative Epidemiology Unit Quinquennial renewal

(IEU) Epigenetics: Environment, Embodiment & Equality (E4)

Profiles

Professor Gibran Hemani

Cite this