Regions of common inter-individual DNA methylation differences in human monocytes: genetic basis and potential function

BackgroundThere is increasing evidence for inter-individual methylation differences at CpG dinucleotides in the human genome, but the regional extent and function of these differences have not yet been studied in detail. For identifying regions of common methylation differences, we used whole genome bisulfite sequencing data of monocytes from five donors and a novel bioinformatic strategy.ResultsWe identified 157 differentially methylated regions (DMRs) with four or more CpGs, almost none of which has been described before. The DMRs fall into different chromatin states, where methylation is inversely correlated with active, but not repressive histone marks. However, methylation is not correlated with the expression of associated genes. High-resolution single nucleotide polymorphism (SNP) genotyping of the five donors revealed evidence for a role of cis-acting genetic variation in establishing methylation patterns. To validate this finding in a larger cohort, we performed genome-wide association studies (GWAS) using SNP genotypes and 450k array methylation data from blood samples of 1128 individuals. Only 30/157 (19%) DMRs include at least one 450k CpG, which shows that these arrays miss a large proportion of DNA methylation variation. In most cases, the GWAS peak overlapped the CpG position, and these regions are enriched for CREB group, NF-1, Sp100 and CTCF binding motifs. In two cases, there was tentative evidence for a trans-effect by KRAB zinc finger proteins.ConclusionsAllele-specific DNA methylation occurs in discrete chromosomal regions and is driven by genetic variation in cis and trans, but in general has little effect on gene expression.

[1]  Elizabeth M. Smigielski,et al.  dbSNP: the NCBI database of genetic variation , 2001, Nucleic Acids Res..

[2]  D. Grönemeyer,et al.  Assessment of clinically silent atherosclerotic disease and established and novel risk factors for predicting myocardial infarction and cardiac death in healthy middle-aged subjects: rationale and design of the Heinz Nixdorf RECALL Study. Risk Factors, Evaluation of Coronary Calcium and Lifestyle. , 2002, American heart journal.

[3]  Long-Cheng Li,et al.  MethPrimer: designing primers for methylation PCRs , 2002, Bioinform..

[4]  István Simon,et al.  BiSearch: primer-design and search tool for PCR on bisulfite-treated genomes , 2005, Nucleic acids research.

[5]  Yuan Ji,et al.  Applications of beta-mixture models in bioinformatics , 2005, Bioinform..

[6]  István Simon,et al.  The BiSearch web server , 2006, BMC Bioinformatics.

[7]  Alexander E. Kel,et al.  TRANSFAC® and its module TRANSCompel®: transcriptional gene regulation in eukaryotes , 2005, Nucleic Acids Res..

[8]  Isomer specific effects of Conjugated Linoleic Acid on macrophage ABCG1 transcription by a SREBP-1c dependent mechanism. , 2007 .

[9]  Manuel A. R. Ferreira,et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses. , 2007, American journal of human genetics.

[10]  P. Leder,et al.  A maternal-zygotic effect gene, Zfp57, maintains both maternal and paternal imprints. , 2008, Developmental cell.

[11]  B. Tycko,et al.  Genomic surveys by methylation-sensitive SNP analysis identify sequence-dependent allele-specific DNA methylation , 2008, Nature Genetics.

[12]  P. Donnelly,et al.  A Flexible and Accurate Genotype Imputation Method for the Next Generation of Genome-Wide Association Studies , 2009, PLoS genetics.

[13]  H. Cedar,et al.  Linking DNA methylation and histone modification: patterns and paradigms , 2009, Nature Reviews Genetics.

[14]  A. Chess,et al.  Extensive sequence-influenced DNA methylation polymorphism in the human genome , 2010, Epigenetics & Chromatin.

[15]  R. Plomin,et al.  Allelic skewing of DNA methylation is widespread across the genome. , 2010, American journal of human genetics.

[16]  Cory Y. McLean,et al.  GREAT improves functional interpretation of cis-regulatory regions , 2010, Nature Biotechnology.

[17]  Klaus Mann,et al.  Coronary risk stratification, discrimination, and reclassification improvement based on quantification of subclinical coronary atherosclerosis: the Heinz Nixdorf Recall study. , 2010, Journal of the American College of Cardiology.

[18]  Robert S. Illingworth,et al.  Orphan CpG Islands Identify Numerous Conserved Promoters in the Mammalian Genome , 2010, PLoS genetics.

[19]  Luigi Ferrucci,et al.  Abundant Quantitative Trait Loci Exist for DNA Methylation and Gene Expression in Human Brain , 2010, PLoS genetics.

[20]  D. Trono,et al.  In Embryonic Stem Cells, ZFP57/KAP1 Recognize a Methylated Hexanucleotide to Affect Chromatin and DNA Methylation of Imprinting Control Regions , 2011, Molecular cell.

[21]  Gaël Varoquaux,et al.  The NumPy Array: A Structure for Efficient Numerical Computation , 2011, Computing in Science & Engineering.

[22]  D. Solter,et al.  Trim28 Is Required for Epigenetic Stability During Mouse Oocyte to Embryo Transition , 2012, Science.

[23]  Cole Trapnell,et al.  TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions , 2013, Genome Biology.

[24]  Steven L Salzberg,et al.  Fast gapped-read alignment with Bowtie 2 , 2012, Nature Methods.

[25]  B. Langmead,et al.  BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions , 2012, Genome Biology.

[26]  J. Kere,et al.  Differential DNA Methylation in Purified Human Blood Cells: Implications for Cell Lineage and Studies on Disease Susceptibility , 2012, PloS one.

[27]  Manolis Kellis,et al.  ChromHMM: automating chromatin-state discovery and characterization , 2012, Nature Methods.

[28]  N. Schork,et al.  Comparative Anatomy of Chromosomal Domains with Imprinted and Non-Imprinted Allele-Specific DNA Methylation , 2013, PLoS genetics.

[29]  R. Siebert,et al.  Correction: Deep Bisulfite Sequencing of Aberrantly Methylated Loci in a Patient with Multiple Methylation Defects , 2013, PLoS ONE.

[30]  Lennart Martens,et al.  LNCipedia: a database for annotated human lncRNA transcript sequences and structures , 2012, Nucleic Acids Res..

[31]  Bernhard Horsthemke,et al.  Amplikyzer: Automated methylation analysis of amplicons from bisulfite flowgram sequencing , 2013 .

[32]  Thomas Lengauer,et al.  Comprehensive Analysis of DNA Methylation Data with RnBeads , 2014, Nature Methods.

[33]  L. Klein-Hitpass,et al.  Evolutionary Origin and Methylation Status of Human Intronic CpG Islands that Are Not Present in Mouse , 2014, Genome biology and evolution.

[34]  T. Bestor,et al.  Notes on the role of dynamic DNA methylation in mammalian development , 2014, Proceedings of the National Academy of Sciences.

[35]  Fidel Ramírez,et al.  deepTools: a flexible platform for exploring deep-sequencing data , 2014, Nucleic Acids Res..

[36]  R. Klose,et al.  Understanding the relationship between DNA methylation and histone lysine methylation , 2014, Biochimica et biophysica acta.

[37]  Robert Andrews,et al.  Inter-individual variability contrasts with regional homogeneity in the human brain DNA methylome , 2015, Nucleic acids research.

[38]  Gabor T. Marth,et al.  A global reference for human genetic variation , 2015, Nature.

[39]  Irene M. Kaplow,et al.  A pooling-based approach to mapping genetic variants associated with DNA methylation , 2015, bioRxiv.

[40]  Patrick F. Sullivan,et al.  High density methylation QTL analysis in human blood via next-generation sequencing of the methylated genomic DNA fraction , 2015, Genome Biology.

[41]  Lennart Martens,et al.  An update on LNCipedia: a database for annotated human lncRNA sequences , 2014, Nucleic acids research.

[42]  G. Ast,et al.  The alternative role of DNA methylation in splicing regulation. , 2015, Trends in genetics : TIG.

[43]  S. Salzberg,et al.  StringTie enables improved reconstruction of a transcriptome from RNA-seq reads , 2015, Nature Biotechnology.

[44]  Michael Q. Zhang,et al.  Integrative analysis of 111 reference human epigenomes , 2015, Nature.

[45]  Pierre-Étienne Jacques,et al.  The International Human Epigenome Consortium Data Portal. , 2016, Cell systems.

[46]  Michael J. Ziller,et al.  Information recovery from low coverage whole-genome bisulfite sequencing , 2016, Nature Communications.

[47]  Manolis Kellis,et al.  HaploReg v4: systematic mining of putative causal variants, cell types, regulators and target genes for human complex traits and disease , 2015, Nucleic Acids Res..

[48]  B. Tycko,et al.  Mechanisms and Disease Associations of Haplotype-Dependent Allele-Specific DNA Methylation. , 2016, American journal of human genetics.

[49]  Jeffrey T Leek,et al.  Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown , 2016, Nature Protocols.

[50]  Lior Pachter,et al.  Near-optimal probabilistic RNA-seq quantification , 2016, Nature Biotechnology.

[51]  E. Nora,et al.  CTCF and Cohesin in Genome Folding and Transcriptional Gene Regulation. , 2016, Annual review of genomics and human genetics.

[52]  Stefan Wallner,et al.  Epigenetic dynamics of monocyte-to-macrophage differentiation , 2016, Epigenetics & Chromatin.

[53]  B. Deplancke,et al.  The Genetics of Transcription Factor DNA Binding Variation , 2016, Cell.

[54]  A hybrid parameter estimation algorithm for beta mixtures and applications to methylation state classification , 2017, Algorithms for Molecular Biology.

[55]  B. Tycko,et al.  Genetic–epigenetic interactions in cis: a major focus in the post-GWAS era , 2017, Genome Biology.

[56]  Cheung Warren,et al.  Additional file 5: Figure S1. of Functional variation in allelic methylomes underscores a strong genetic contribution and reveals novel epigenetic alterations in the human epigenome , 2017 .