Literature DB >> 23959097

Considerations for normalization of DNA methylation data by Illumina 450K BeadChip assay in population studies.

Paul Yousefi1, Karen Huen1, Raul Aguilar Schall1, Anna Decker1, Emon Elboudwarej1, Hong Quach1, Lisa Barcellos1, Nina Holland1.   

Abstract

Analysis of epigenetic mechanisms, particularly DNA methylation, is of increasing interest for epidemiologic studies examining disease etiology and impacts of environmental exposures. The Infinium HumanMethylation450 BeadChip(®) (450K), which interrogates over 480,000 CpG sites and is relatively cost effective, has become a popular tool to characterize the DNA methylome. For large-scale studies, minimizing technical variability and potential bias is paramount. The goal of this paper was to evaluate the performance of several existing and novel color channel normalizations designed to reduce technical variability and batch effects in 450K analysis from a large population study. Comparative assessment of 10 normalization procedures included the GenomeStudio(®) Illumina procedure, the lumi smooth quantile approach, and the newly proposed All Sample Mean Normalization (ASMN). We also examined the performance of normalizations in combination with correction for the two types of Infinium chemistry utilized on the 450K array. We observed that the performance of the GenomeStudio(®) normalization procedure was highly variable and dependent on the quality of the first sample analyzed in an experiment, which is used as a reference in this procedure. While the lumi normalization was able to decrease batch variability, it increased variation among technical replicates, potentially reducing biologically meaningful findings. The proposed ASMN procedure performed consistently well, both at reducing batch effects and improving replicate comparability. In summary, the ASMN procedure can improve existing color channel normalization, especially for large epidemiologic studies, and can be successfully implemented to enhance a 450K DNA methylation data pipeline.

Entities:  

Keywords:  ASMN; DNA methylome; bias correction; epigenetics; microarray; pipeline; technical variability

Mesh:

Year:  2013        PMID: 23959097      PMCID: PMC6242262          DOI: 10.4161/epi.26037

Source DB:  PubMed          Journal:  Epigenetics        ISSN: 1559-2294            Impact factor:   4.528


  14 in total

1.  Evaluation of the Infinium Methylation 450K technology.

Authors:  Sarah Dedeurwaerder; Matthieu Defrance; Emilie Calonne; Hélène Denis; Christos Sotiriou; François Fuks
Journal:  Epigenomics       Date:  2011-12       Impact factor: 4.778

2.  Complete pipeline for Infinium(®) Human Methylation 450K BeadChip data processing using subset quantile normalization for accurate DNA methylation estimation.

Authors:  Nizar Touleimat; Jörg Tost
Journal:  Epigenomics       Date:  2012-06       Impact factor: 4.778

Review 3.  Techniques used in studies of epigenome dysregulation due to aberrant DNA methylation: an emphasis on fetal-based adult diseases.

Authors:  Shuk-mei Ho; Wan-yee Tang
Journal:  Reprod Toxicol       Date:  2007-01-19       Impact factor: 3.143

Review 4.  Prospects for epigenetic epidemiology.

Authors:  Debra L Foley; Jeffrey M Craig; Ruth Morley; Craig A Olsson; Craig J Olsson; Terence Dwyer; Katherine Smith; Richard Saffery
Journal:  Am J Epidemiol       Date:  2009-01-12       Impact factor: 4.897

5.  lumi: a pipeline for processing Illumina microarray.

Authors:  Pan Du; Warren A Kibbe; Simon M Lin
Journal:  Bioinformatics       Date:  2008-05-08       Impact factor: 6.937

Review 6.  Principles and challenges of genomewide DNA methylation analysis.

Authors:  Peter W Laird
Journal:  Nat Rev Genet       Date:  2010-03       Impact factor: 53.242

7.  High density DNA methylation array with single CpG site resolution.

Authors:  Marina Bibikova; Bret Barnes; Chan Tsan; Vincent Ho; Brandy Klotzle; Jennie M Le; David Delano; Lu Zhang; Gary P Schroth; Kevin L Gunderson; Jian-Bing Fan; Richard Shen
Journal:  Genomics       Date:  2011-08-02       Impact factor: 5.736

8.  Validation of a DNA methylation microarray for 450,000 CpG sites in the human genome.

Authors:  Juan Sandoval; Holger Heyn; Sebastian Moran; Jordi Serra-Musach; Miguel A Pujana; Marina Bibikova; Manel Esteller
Journal:  Epigenetics       Date:  2011-06-01       Impact factor: 4.528

9.  SWAN: Subset-quantile within array normalization for illumina infinium HumanMethylation450 BeadChips.

Authors:  Jovana Maksimovic; Lavinia Gordon; Alicia Oshlack
Journal:  Genome Biol       Date:  2012-06-15       Impact factor: 13.583

10.  Quantitative cross-validation and content analysis of the 450k DNA methylation array from Illumina, Inc.

Authors:  Jessica Roessler; Ole Ammerpohl; Jana Gutwein; Britta Hasemeier; Sumadi Lukman Anwar; Hans Kreipe; Ulrich Lehmann
Journal:  BMC Res Notes       Date:  2012-04-30
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  34 in total

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Authors:  Emon Elboudwarej; Michael Cole; Farren B S Briggs; Alexandra Fouts; Pamela R Fain; Hong Quach; Diana Quach; Elizabeth Sinclair; Lindsey A Criswell; Julie A Lane; Andrea K Steck; Lisa F Barcellos; Janelle A Noble
Journal:  J Autoimmun       Date:  2016-01-09       Impact factor: 7.094

2.  DNA methylation of imprinted genes in Mexican-American newborn children with prenatal phthalate exposure.

Authors:  Gwen Tindula; Susan K Murphy; Carole Grenier; Zhiqing Huang; Karen Huen; Maria Escudero-Fung; Asa Bradman; Brenda Eskenazi; Cathrine Hoyo; Nina Holland
Journal:  Epigenomics       Date:  2018-06-29       Impact factor: 4.778

3.  Obesity accelerates epigenetic aging of human liver.

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Journal:  Proc Natl Acad Sci U S A       Date:  2014-10-13       Impact factor: 11.205

4.  Comparison of Illumina 450K and EPIC arrays in placental DNA methylation.

Authors:  Nora Fernandez-Jimenez; Catherine Allard; Luigi Bouchard; Patrice Perron; Mariona Bustamante; Jose Ramon Bilbao; Marie-France Hivert
Journal:  Epigenetics       Date:  2019-06-28       Impact factor: 4.528

5.  Multiple correlation analyses revealed complex relationship between DNA methylation and mRNA expression in human peripheral blood mononuclear cells.

Authors:  Fang-Fei Xie; Fei-Yan Deng; Long-Fei Wu; Xing-Bo Mo; Hong Zhu; Jian Wu; Yu-Fan Guo; Ke-Qin Zeng; Ming-Jun Wang; Xiao-Wei Zhu; Wei Xia; Lan Wang; Pei He; Peng-Fei Bing; Xin Lu; Yong-Hong Zhang; Shu-Feng Lei
Journal:  Funct Integr Genomics       Date:  2017-07-22       Impact factor: 3.410

6.  Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays.

Authors:  Martin J Aryee; Andrew E Jaffe; Hector Corrada-Bravo; Christine Ladd-Acosta; Andrew P Feinberg; Kasper D Hansen; Rafael A Irizarry
Journal:  Bioinformatics       Date:  2014-01-28       Impact factor: 6.937

7.  Relationship between expression and methylation of obesity-related genes in children.

Authors:  Veronica Davé; Paul Yousefi; Karen Huen; Vitaly Volberg; Nina Holland
Journal:  Mutagenesis       Date:  2015-01-14       Impact factor: 3.000

8.  PON1 DNA methylation and neurobehavior in Mexican-American children with prenatal organophosphate exposure.

Authors:  Karen Huen; Olivia Solomon; Katherine Kogut; Brenda Eskenazi; Nina Holland
Journal:  Environ Int       Date:  2018-08-30       Impact factor: 9.621

9.  Identification and validation of seven new loci showing differential DNA methylation related to serum lipid profile: an epigenome-wide approach. The REGICOR study.

Authors:  S Sayols-Baixeras; I Subirana; C Lluis-Ganella; F Civeira; J Roquer; A N Do; D Absher; A Cenarro; D Muñoz; C Soriano-Tárraga; J Jiménez-Conde; J M Ordovas; M Senti; S Aslibekyan; J Marrugat; D K Arnett; R Elosua
Journal:  Hum Mol Genet       Date:  2016-10-15       Impact factor: 6.150

10.  Epigenomics of Total Acute Sleep Deprivation in Relation to Genome-Wide DNA Methylation Profiles and RNA Expression.

Authors:  Emil K Nilsson; Adrian E Boström; Jessica Mwinyi; Helgi B Schiöth
Journal:  OMICS       Date:  2016-06
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