A coherent approach for analysis of the Illumina HumanMethylation450 BeadChip improves data quality and performance in epigenome-wide association studies

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• 作者：Benjamin Lehne (1)
  Alexander W Drong (2)
  Marie Loh (1) (3)
  Weihua Zhang (1) (4)
  William R Scott (1) (5)
  Sian-Tsung Tan (4) (5)
  Uzma Afzal (1) (4)
  James Scott (5)
  Marjo-Riitta Jarvelin (1) (3) (6) (7) (8) (9)
  Paul Elliott (1) (9)
  Mark I McCarthy (10) (2)
  Jaspal S Kooner (11) (4) (5)
  John C Chambers (1) (11) (4)
  
  1. Department of Epidemiology and Biostatistics ; Imperial College London ; London ; W2 1PG ; UK
  2. Wellcome Trust Centre for Human Genetics ; University of Oxford ; Oxford ; UK
  3. Institute of Health Sciences ; University of Oulu ; P.O. Box 5000 ; Oulu ; FI-90014 ; Finland
  4. Ealing Hospital NHS Trust ; Middlesex ; UB1 3HW ; UK
  5. National Heart and Lung Institute ; Imperial College London ; London ; W12 0NN ; UK
  6. Biocenter Oulu ; University of Oulu ; P.O. Box 5000 ; Aapistie 5A ; Oulu ; FI-90014 ; Finland
  7. Unit of Primary Care ; Oulu University Hospital ; Kajaanintie 50 ; P.O. Box 20 ; FI-90220 ; Oulu ; 90029 OYS ; Finland
  8. Department of Children and Young People and Families ; National Institute for Health and Welfare ; Aapistie 1 ; Box 310 ; Oulu ; FI-90101 ; Finland
  9. MRC-PHE Centre for Environment and Health ; School of Public Health ; Imperial College London ; London ; W2 1PG ; UK
  10. Oxford Centre for Diabetes Endocrinology and Metabolism ; University of Oxford ; Oxford ; UK
  11. Imperial College Healthcare NHS Trust ; London ; W12 0HS ; UK
  
• 刊名：Genome Biology
• 出版年：2015
• 出版时间：December 2015
• 年：2015
• 卷：16
• 期：1
• 全文大小：1,506 KB
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• 刊物主题：Animal Genetics and Genomics; Human Genetics; Plant Genetics & Genomics; Microbial Genetics and Genomics; Fungus Genetics; Bioinformatics;
• 出版者：BioMed Central
• ISSN：1465-6906

文摘

DNA methylation plays a fundamental role in the regulation of the genome, but the optimal strategy for analysis of genome-wide DNA methylation data remains to be determined. We developed a comprehensive analysis pipeline for epigenome-wide association studies (EWAS) using the Illumina Infinium HumanMethylation450 BeadChip, based on 2,687 individuals, with 36 samples measured in duplicate. We propose new approaches to quality control, data normalisation and batch correction through control-probe adjustment and establish a null hypothesis for EWAS using permutation testing. Our analysis pipeline outperforms existing approaches, enabling accurate identification of methylation quantitative trait loci for hypothesis driven follow-up experiments.