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Multiple sclerosis genetics epidemiology

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References

Janson

Linton

Bergman

et al . Profiling of CD4+ T cells with epigenetic immune lineage analysis. J Immunol 2011; 186(1): 92–102.

Smith

Lai

et al . Genetic variants modify the effect of age on APOE methylation in the genetics of lipid lowering drugs and diet network study. Aging Cell 2015; 14: 49–59.

Huynh

Garg

Thin

et al . Epigenome-wide differences in pathology-free regions of multiple sclerosis-affected brains. Nat Neurosci 2014; 17(1): 121–130.

Bos

Page

Andreassen

et al . Genome-wide DNA methylation profiles indicate CD8+ T cell hypermethylation in multiple sclerosis. PLoS ONE 2015; 10(3): e0117403.

Houseman

Accomando

Koestler

et al . DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics 2012; 13: 86.

Houseman

Molitor

Marsit

CJ.

Reference-free cell mixture adjustments in analysis of DNA methylation data. Bioinformatics 2014; 30(10): 1431–1439.

Zou

Lippert

Heckerman

et al . Epigenome-wide association studies without the need for cell-type composition. Nat Methods 2014; 11(3): 309–311.

Leek

Storey

JD.

Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet 2007; 3(9): 1724–1735.

Teschendorff

Zhuang

Widschwendter

Independent surrogate variable analysis to deconvolve confounding factors in large-scale microarray profiling studies. Bioinformatics 2011; 27(11): 1496–1505.

10.

Repsilber

Kern

Telaar

et al . Biomarker discovery in heterogeneous tissue samples—Taking the in-silico deconfounding approach. BMC Bioinformatics 2010; 11: 27.

11.

Gagnon-Bartsch

Speed

TP.

Using control genes to correct for unwanted variation in microarray data. Biostatistics 2012; 13(3): 539–552.

12.

McGregor

Bernatsky

Colmegna

et al . An evaluation of methods correcting for cell-type heterogeneity in DNA methylation studies. Genome Biol 2016; 17: 84.

13.

Graves

Benton

Lea

et al . Methylation differences at the HLA-DRB1 locus in CD4+ T-Cells are associated with multiple sclerosis. Mult Scler 2014; 20(8): 1033–1041.

14.

Kumagai

Kalman

Middleton

et al . Increased promoter methylation of the immune regulatory gene SHP-1 in leukocytes of multiple sclerosis subjects. J Neuroimmunol 2012; 246(1–2): 51–57.

15.

Maltby

Graves

Lea

et al . Genome-wide DNA methylation profiling of CD8+ T cells shows a distinct epigenetic signature to CD4+ T cells in multiple sclerosis patients. Clin Epigenetics 2015; 7: 118.

16.

Calabrese

Zampieri

Mechelli

et al . Methylation-dependent PAD2 upregulation in multiple sclerosis peripheral blood. Mult Scler 2012; 18(3): 299–304.

17.

Ayuso

Aznar

Soriano

et al . Vitamin D receptor gene is epigenetically altered and transcriptionally up-regulated in multiple sclerosis. PLoS ONE 2017; 12(3): e0174726.

18.

Kulakova

Kabilov

Danilova

et al . Whole-genome DNA methylation analysis of peripheral blood mononuclear cells in multiple sclerosis patients with different disease courses. Acta Naturae 2016; 8(3): 103–110.

19.

Liggett

Melnikov

Tilwalli

et al . Methylation patterns of cell-free plasma DNA in relapsing-remitting multiple sclerosis. J Neurol Sci 2010; 290(1–2): 16–21.

20.

Neven

Piola

Angelici

et al . Repetitive element hypermethylation in multiple sclerosis patients. BMC Genet 2016; 17(1): 84.

21.

Ruhrmann

Ewing

Piket

et al . Hypermethylation of MIR21 in CD4+ T cells from patients with relapsing-remitting multiple sclerosis associates with lower miRNA-21 levels and concomitant up-regulation of its target genes. Mult Scler. Epub ahead of print 1 August 2017. DOI: 10.1177/1352458517721356.

22.

Faul

Erdfelder

Lang

et al . G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods 2007; 39(2): 175–91.

23.

Baranzini

Mudge

van Velkinburgh

et al . Genome, epigenome and RNA sequences of monozygotic twins discordant for multiple sclerosis. Nature 2010; 464(7293): 1351–1356.

24.

Mastronardi

Noor

Wood

et al . Peptidyl argininedeiminase 2 CpG island in multiple sclerosis white matter is hypomethylated. J Neurosci Res 2007; 85(9): 2006–2016.

25.

Ramagopalan

Dyment

Morrison

et al . Methylation of class II transactivator gene promoter IV is not associated with susceptibility to multiple sclerosis. BMC Med Genet 2008; 9: 63.

26.

Handel

De Luca

Morahan

et al . No evidence for an effect of DNA methylation on multiple sclerosis severity at HLA-DRB1*15 or HLA-DRB5. J Neuroimmunol 2010; 223(1–2): 120–123.

27.

Calabrese

Valentini

Ciccarone

et al . TET2 gene expression and 5-hydroxymethylcytosine level in multiple sclerosis peripheral blood cells. Biochim Biophys Acta 2014; 1842(7): 1130–1136.

28.

Hauser

Reinherz

Hoban

et al . Immunoregulatory T-cells and lymphocytotoxic antibodies in active multiple sclerosis: Weekly analysis over a six-month period. Ann Neurol 1983; 13(4): 418–425.

29.

Bhasin

Zhang

Reinherz

et al . Prediction of methylated CpGs in DNA sequences using a support vector machine. FEBS Lett 2005; 579(20): 4302–4308.

30.

Bock

Paulsen

Tierling

et al . CpG island methylation in human lymphocytes is highly correlated with DNA sequence, repeats, and predicted DNA structure. PLoS Genet 2006; 2(3): e26.

31.

Das

Dimitrova

Xuan

et al . Computational prediction of methylation status in human genomic sequences. Proc Natl Acad Sci U S A 2006; 103(28): 10713–10716.

32.

Wrzodek

Buchel

Hinselmann

et al . Linking the epigenome to the genome: Correlation of different features to DNA methylation of CpG islands. PLoS ONE 2012; 7(4): e35327.

33.

Walker

Willis-Own

SAG

et al . Predicting DNA methylation level across human tissues. Nucleic Acids Res 2014; 42: 3515–3528.

34.

Yan

Zhang

Liu

et al . Chromatin modifications and genomic contexts linked to dynamic DNA methylation patterns across human cell types. Sci Rep 2015; 5: 8410.

35.

Zhang

Spector

Deloukas

et al . Predicting genome-wide DNA methylation using methylation marks, genomic position, and DNA regulatory elements. Genome Biol 2015; 16: 14.

36.

Fan

Huang

et al . Predicting CpG methylation levels by integrating Infinium HumanMethylation450 BeadChip array data. Genomics 2016; 107(4): 132–137.

37.

Wang

Liu

et al . Predicting DNA methylation state of CpG dinucleotide using genome topological features and deep networks. Sci Rep 2016; 6: 19598.

38.

Pavlovic

Ray

Pavlovic

et al . DIRECTION: a machine learning framework for predicting and characterizing DNA methylation and hydroxymethylation in mammalian genomes. Bioinformatics 2017; 33: 2986–2994.

39.

Yuan

GC.

Targeted recruitment of histone modifications in humans predicted by genomic sequences. J Comput Biol 2009; 16(2): 341–355.

40.

Ernst

Kellis

ChromHMM: Automating chromatin-state discovery and characterization. Nat Methods 2012; 9(3): 215–216.

41.

Ernst

Kellis

Large-scale imputation of epigenomic datasets for systematic annotation of diverse human tissues. Nat Biotechnol 2015; 33(4): 364–376.

42.

Whitaker

Chen

Wang

Predicting the human epigenome from DNA motifs. Nat Methods 2015; 12(3): 265–727.

43.

De Jager

Srivastava

Lunnon

et al . Alzheimer’s disease: Early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci. Nat Neurosci 2014; 17(9): 1156–1163.

44.

Lunnon

Smith

Hannon

et al . Methylomic profiling implicates cortical deregulation of ANK1 in Alzheimer’s disease. Nat Neurosci 2014; 17(9): 1164–1170.

Designing an epigenomic study

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Keywords

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