A data-driven approach to preprocessing Illumina 450K methylation array data
Ruth Pidsley,Chloe C. Y. Wong,Manuela Volta,Katie Lunnon,Jonathan Mill,Jonathan Mill,Leonard C. Schalkwyk +6 more
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TLDR
It is demonstrated that quantile normalization methods produce marked improvement, even in highly consistent data, by all three metrics, and that careful selection of preprocessing steps can minimize variance and thus improve statistical power, especially for the detection of the small absolute DNA methylation changes likely associated with complex disease phenotypes.Abstract:
As the most stable and experimentally accessible epigenetic mark, DNA methylation is of great interest to the research community. The landscape of DNA methylation across tissues, through development and in disease pathogenesis is not yet well characterized. Thus there is a need for rapid and cost effective methods for assessing genome-wide levels of DNA methylation. The Illumina Infinium HumanMethylation450 (450K) BeadChip is a very useful addition to the available methods for DNA methylation analysis but its complex design, incorporating two different assay methods, requires careful consideration. Accordingly, several normalization schemes have been published. We have taken advantage of known DNA methylation patterns associated with genomic imprinting and X-chromosome inactivation (XCI), in addition to the performance of SNP genotyping assays present on the array, to derive three independent metrics which we use to test alternative schemes of correction and normalization. These metrics also have potential utility as quality scores for datasets. The standard index of DNA methylation at any specific CpG site is β = M/(M + U + 100) where M and U are methylated and unmethylated signal intensities, respectively. Betas (βs) calculated from raw signal intensities (the default GenomeStudio behavior) perform well, but using 11 methylomic datasets we demonstrate that quantile normalization methods produce marked improvement, even in highly consistent data, by all three metrics. The commonly used procedure of normalizing betas is inferior to the separate normalization of M and U, and it is also advantageous to normalize Type I and Type II assays separately. More elaborate manipulation of quantiles proves to be counterproductive. Careful selection of preprocessing steps can minimize variance and thus improve statistical power, especially for the detection of the small absolute DNA methylation changes likely associated with complex disease phenotypes. For the convenience of the research community we have created a user-friendly R software package called wateRmelon, downloadable from bioConductor, compatible with the existing methylumi, minfi and IMA packages, that allows others to utilize the same normalization methods and data quality tests on 450K data.read more
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DNA methylation mediates the effect of maternal smoking during pregnancy on birthweight of the offspring
Leanne K. Küpers,Xiaojing Xu,Soesma A Jankipersadsing,Ahmad Vaez,Sacha la Bastide-van Gemert,Salome Scholtens,Ilja M. Nolte,Rebecca C Richmond,Caroline L Relton,Janine F. Felix,Liesbeth Duijts,Joyce B. J. van Meurs,Henning Tiemeier,Vincent W. V. Jaddoe,Xiaoling Wang,Eva Corpeleijn,Harold Snieder +16 more
TL;DR: Maternal smoking during pregnancy was associated with cord blood methylation differences andFunctional network analysis suggested a role in activating the immune system and functional enrichment analysis pointed towards activation of cell-mediated immunity.
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Epigenome‐wide study identifies novel methylation loci associated with body mass index and waist circumference
Stella Aslibekyan,Ellen W. Demerath,Michael M. Mendelson,Degui Zhi,Weihua Guan,Liming Liang,Jin Sha,James S. Pankow,Chunyu Liu,Marguerite R. Irvin,Myriam Fornage,Bertha Hidalgo,Li-An Lin,Krista S. Thibeault,Jan Bressler,Michael Y. Tsai,Megan L. Grove,Paul N. Hopkins,Eric Boerwinkle,Ingrid B. Borecki,Jose M. Ordovas,Daniel Levy,Hemant K. Tiwari,Devin Absher,Donna K. Arnett +24 more
TL;DR: To conduct an epigenome‐wide analysis of DNA methylation and obesity traits, the objective was to establish a baseline level for methylation in the genome and establish an upper bound on the total number of methyl groups in the DNA.
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Life course socioeconomic status and DNA methylation in genes related to stress reactivity and inflammation: The multi-ethnic study of atherosclerosis
Belinda L. Needham,Jennifer A. Smith,Wei Zhao,Xu Wang,Bhramar Mukherjee,Sharon L.R. Kardia,Carol A. Shively,Teresa E. Seeman,Yongmei Liu,Ava V. Diez Roux +9 more
TL;DR: Examination of associations between life course measures of socioeconomic status (SES) and DNA methylation (DNAm) in 18 genes related to stress reactivity and inflammation using a multi-level modeling approach showed that low SES was associated with increased DNAm and gene expression data was available for 7 genes that showed a significant relationship between SES and DNAm.
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MST4 Phosphorylation of ATG4B Regulates Autophagic Activity, Tumorigenicity, and Radioresistance in Glioblastoma
Tianzhi Huang,Chung Kwon Kim,Chung Kwon Kim,Angel Alvarez,Rajendra P. Pangeni,Rajendra P. Pangeni,Xuechao Wan,Xiao Song,Taiping Shi,Yongyong Yang,Namratha Sastry,Craig Horbinski,Songjian Lu,Roger Stupp,John A. Kessler,Ryo Nishikawa,Ichiro Nakano,Erik P. Sulman,Xinghua Lu,Charles David James,Xiao Ming Yin,Bo Hu,Shi Yuan Cheng +22 more
TL;DR: In this paper, the authors identify ATG4B as a substrate of mammalian sterile20-like kinase (STK) 26/MST4, which stimulates autophagocyte activity and increases autophagic flux.
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The Proteogenomic Landscape of Curable Prostate Cancer.
Ankit Sinha,Vincent Huang,Julie Livingstone,Jenny Wang,Jenny Wang,Natalie S. Fox,Natalie S. Fox,Natalie Kurganovs,Natalie Kurganovs,Vladimir Ignatchenko,Katharina Fritsch,Katharina Fritsch,Nilgun Donmez,Lawrence E. Heisler,Yu Jia Shiah,Cindy Q. Yao,Javier A. Alfaro,Javier A. Alfaro,Stas Volik,Anna Lapuk,Michael Fraser,Ken Kron,Alex Murison,Mathieu Lupien,Mathieu Lupien,Mathieu Lupien,Cenk Sahinalp,Colin Collins,Colin Collins,Bernard Têtu,Mehdi Masoomian,David M. Berman,Theodorus van der Kwast,Theodorus van der Kwast,Robert G. Bristow,Robert G. Bristow,Thomas Kislinger,Thomas Kislinger,Paul C. Boutros +38 more
TL;DR: It is discovered that the genomic subtypes of prostate cancer converge on five proteomic subtypes, with distinct clinical trajectories, and prognostic biomarkers combining genomic or epigenomic features with proteomic ones significantly outperform biomarkers comprised of a single data type.
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