Measuring epigenetics as the mediator of gene/environment interactions in DOHaD

Journal of Developmental Origins of Health and Disease

Volumn 6, Issue 1, 2014, Pages 10-16

  Ong, M L ^a   Lin, X ^a   Holbrook, J D ^a  

^a AGENCY FOR SCIENCE   (Singapore)

Author keywords

DNA methylation; epigenomics; gene environment interactions

Indexed keywords

CYTOSINE; HYDROGEN SULFITE; PYRIMIDINE NUCLEOTIDE; SULFITE;

DNA METHYLATION; ECONOMICS; GENETIC ASSOCIATION; GENETIC EPIGENESIS; GENETICS; GENOTYPE ENVIRONMENT INTERACTION; HUMAN; PHENOTYPE; PROCEDURES; SINGLE NUCLEOTIDE POLYMORPHISM; STATISTICAL ANALYSIS;

CYTOSINE; CYTOSINE NUCLEOTIDES; DATA INTERPRETATION, STATISTICAL; DNA METHYLATION; EPIGENESIS, GENETIC; GENE-ENVIRONMENT INTERACTION; GENOME-WIDE ASSOCIATION STUDY; HUMANS; PHENOTYPE; POLYMORPHISM, SINGLE NUCLEOTIDE; SULFITES;

EID: 84921715705     PISSN: 20401744     EISSN: 20401752     Source Type: Journal    
DOI: 10.1017/S2040174414000506     Document Type: Article

References (66)

1. Gibbs JR, van Der Brug MP, Hernandez DG, et al. Abundant quantitative trait loci exist for DNA methylation and gene expression in human brain. PLoS Genet. 2010; 6, e1000952
2. Zhang D, Cheng L, Badner JA, et al. Genetic control of individual differences in gene-specific methylation in human brain. Am J Hum Genet. 2010; 86, 411-419
3. Fraser HB, Lam LL, Neumann SM, Kobor MS. Populationspecificity of human DNAmethylation. Genome Biol. 2012; 13, R8
4. Lam LL, Emberly E, Fraser HB, et al. Factors underlying variable DNA methylation in a human community cohort. Proc Natl Acad Sci USA. 2012; 109 (Suppl 2), 17253-17260
5. McGowan PO, Sasaki A, D'Alessio AC, et al. Epigenetic regulation of the glucocorticoid receptor in human brain associates with childhood abuse. Nat Neurosci. 2009; 12, 342-348
6. Bock C. Analysing and interpreting DNA methylation data. Nat Rev Genetics. 2012; 13, 705-719
7. Rakyan VK, Down TA, Balding DJ, Beck S. Epigenome-wide association studies for common human diseases. Nat Rev Genet. 2011; 12, 529-541
8. Heijmans BT, Mill J. Commentary: the seven plagues of epigenetic epidemiology. Int J Epidemiol. 2012; 41, 74-78
9. Harris RA, Wang T, Coarfa C, et al. Comparison of sequencingbased methods to profile DNA methylation and identification of monoallelic epigenetic modifications. Nat Biotechnol. 2010; 28, 1097-1105
10. Dedeurwaerder S, Defrance M, Calonne E, et al. Evaluation of the infinium methylation 450K technology. Epigenomics. 2011; 3, 771-784
11. Ziller MJ, Gu H, Muller F, et al. Charting a dynamic DNA methylation landscape of the human genome. Nature. 2013; 500, 477-481
12. Bock C, Tomazou EM, Brinkman AB, et al. Quantitative comparison of genome-wide DNA methylation mapping technologies. Nat Biotechnol. 2010; 28, 1106-1114
13. Bibikova M, Barnes B, Tsan C, et al. High density DNA methylation array with single CpG site resolution. Genomics. 2011; 98, 288-295
14. Pan H, Chen L, Dogra S, et al. Measuring the methylome in clinical samples: improved processing of the Infinium Human Methylation450 BeadChip Array. Epigenetics. 2012; 7, 1173-1187
15. Rakyan VK, Down TA, Balding DJ, Beck S. Epigenome-wide association studies for common human diseases. Nat Rev Genet. 2011; 12, 529-541
16. Michels KB, Binder AM, Dedeurwaerder S, et al. Recommendations for the design and analysis of epigenome-wide association studies. Nat Methods. 2013; 10, 949-955
17. Mill J, Heijmans BT. From promises to practical strategies in epigenetic epidemiology. Nat Rev Genet. 2013; 14, 585-594
18. Meaney MJ, Ferguson-Smith AC. Epigenetic regulation of the neural transcriptome: the meaning of the marks. Nat Neurosci. 2010; 13, 1313-1318
19. Hernando-Herraez I, Prado-Martinez J, Garg P, et al. Dynamics of DNA methylation in recent human and great ape evolution. PLoS Genet. 2013; 9, e1003763
20. Ong ML, Tan PY, MacIsaac JL, et al. Infinium monkeys: Infinium 450K array for the Cynomolgus macaque (Macaca fascicularis). G3. 2014; 4, 1227-1234
21. Wong NC, Ng J, Hall NE, et al. Exploring the utility of human DNA methylation arrays for profiling mouse genomic DNA. Genomics. 2013; 102, 38-46
22. Du P, Zhang X, Huang CC, et al. Comparison of beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics. 2010; 11, 587
23. Touleimat N, Tost J. Complete pipeline for Infinium® Human Methylation 450K BeadChip data processing using subset quantile normalization for accurate DNA methylation estimation. Epigenomics. 2012; 4, 325-341
24. Teschendorff AE, Marabita F, Lechner M, et al. A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data. Bioinformatics. 2013; 29, 189-196
25. Maksimovic J, Gordon L, Oshlack A. SWAN: Subset-quantile within array normalization for illumina infinium HumanMethylation450 BeadChips. Genome Biol. 2012; 13, R44
26. Wu MC, Joubert BR, Kuan PF, et al. A systematic assessment of normalization approaches for the Infinium 450K methylation platform. Epigenetics. 2013; 9, 318-329
27. Sun Z, Chai HS, Wu Y, et al. Batch effect correction for genome-wide methylation data with Illumina Infinium platform. BMC Med Genom. 2011; 4, 84
28. Leek JT, Scharpf RB, Bravo HC, et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet. 2010; 11, 733-739
29. Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007; 8, 118-127
30. Jaffe AE, Irizarry RA. Accounting for cellular heterogeneity is critical in epigenome-wide association studies. Genome Biol. 2014; 15, R31
31. Reinius LE, Acevedo N, Joerink M, et al. Differential DNA methylation in purified human blood cells: implications for cell lineage and studies on disease susceptibility. PLoS One. 2012; 7, e41361
32. Houseman EA, Accomando WP, Koestler DC, et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics. 2012; 13, 86
33. Houseman EA, Molitor J, Marsit CJ. Reference-free cell mixture adjustments in analysis of DNA methylation data. BMC Bioinformatics. 2014; 30, 1431
34. Zou J, Lippert C, Heckerman D, Aryee M, Listgarten J. Epigenome-wide association studies without the need for cell-type composition. Nat Methods. 2014
35. Liu Y, Aryee MJ, Padyukov L, et al. Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis. Nat Biotechnol. 2013; 31, 142-147
36. Joubert BR, Håberg SE, Nilsen RM, et al. 450K epigenome-wide scan identifies differential DNA methylation in newborns related to maternal smoking during pregnancy. Environ Health Perspect. 2012; 120, 1425
37. Dick KJ, Nelson CP, Tsaprouni L, et al. DNA methylation and body-mass index: a genome-wide analysis. Lancet. 2014; 383, 1990-1998
38. Engel SM, Joubert BR, Wu MC, et al. Neonatal genome-wide methylation patterns in relation to birth weight in the Norwegian mother and child cohort. Am J Epidemiol. 2014; 179, 834-842
39. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B. 1995; 57, 289-300
40. Wan ES, Qiu W, Baccarelli A, et al. Cigarette smoking behaviors and time since quitting are associated with differential DNA methylation across the human genome. Hum Mol Genet. 2012; 21, 3073-3082
41. Zhang Y, Yang R, Burwinkel B, et al. F2RL3 methylation in blood DNA is a strong predictor of mortality. Int J Epidemiol. 2014; 43, 1215-1225
42. Sun YV, Smith AK, Conneely KN, et al. Epigenomic association analysis identifies smoking-related DNA methylation sites in African Americans. Hum Genet. 2013; 132, 1027-1037
43. Monick MM, Beach SR, Plume J, et al. Coordinated changes in AHRR methylation in lymphoblasts and pulmonary macrophages from smokers. Am J Med Genet B Neuropsychiatr Genet. 2012; 159B, 141-151
44. Shenker NS, Ueland PM, Polidoro S, et al. DNA methylation as a long-term biomarker of exposure to tobacco smoke. Epidemiology. 2013; 24, 712-716
45. Jaffe AE, Murakami P, Lee H, et al. Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies. Int J Epidemiol. 2012; 41, 200-209
46. Ong ML, Holbrook JD. Novel region discovery method for Infinium 450K DNA methylation data reveals changes associated with aging in muscle and neuronal pathways. Aging Cell. 2014; 13, 142-155
47. Jaffe AE, Feinberg AP, Irizarry RA, Leek JT. Significance analysis and statistical dissection of variably methylated regions. Biostatistics. 2012; 13, 166-178
48. Morley M, Molony CM, Weber TM, et al. Genetic analysis of genome-wide variation in human gene expression. Nature. 2004; 430, 743-747
49. Gamazon E, Badner J, Cheng L, et al. Enrichment of cis-regulatory gene expression SNPs and methylation quantitative trait loci among bipolar disorder susceptibility variants. Mol Psychiatry. 2013; 18, 340-346
50. Huang Y-T, VanderWeele TJ, Lin X. Joint analysis of SNP and gene expression data in genetic association studies of complex diseases. Ann Appl Statist. 2014; 8, 352
51. Huang Y-T. Integrative modeling of multiple genomic data from different types of genetic association studies. Biostatistics. 2014; 15, 587-602
52. Petersen A-K, Zeilinger S, Kastenmüller G, et al. Epigenetics meets metabolomics: an epigenome-wide association study with blood serum metabolic traits. HumMol Genet. 2014; 23, 534-545
53. Gieger C, Geistlinger L, Altmaier E, et al. Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum. PLoS Genet. 2008; 4, e1000282
54. Gutierrez-Arcelus M, Lappalainen T, Montgomery SB, et al. Passive and active DNA methylation and the interplay with genetic variation in gene regulation. Elife. 2013; 2, e00523
55. Sachs K, Perez O, Pe'er D, et al. Causal protein-signaling networks derived from multiparameter single-cell data. Science. 2005; 308, 523
56. Schadt EE, Lamb J, Yang X, et al. An integrative genomics approach to infer causal associations between gene expression and disease. Nat Genet. 2005; 37, 710
57. Liu Y, Li X, Aryee MJ, et al. GeMes, clusters of DNA methylation under genetic control, can inform genetic and epigenetic analysis of disease. Am J Hum Genet. 2014; 94, 485-495
58. Millstein J, Zhang B, Zhu J, et al. Disentangling molecular relationships with a causal inference test. BMC Genet. 2009; 10
59. Relton CL, Smith GD. Two-step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease. Int J Epidemiol. 2012; 41, 161-176
60. Sheehan NA, Didelez V, Burton PR, Tobin MD. Mendelian randomisation and causal inference in observational epidemiology. PLoS Med. 2008; 5, e177
61. Klengel T, Mehta D, Anacker C, et al. Allele-specific FKBP5 DNA demethylation mediates gene-childhood trauma interactions. Nat Neurosci. 2013; 16, 33-41
62. Yousefi M, Karmaus W, Zhang H, et al. The methylation of the LEPR/LEPROT genotype at the promoter and body regions influence concentrations of leptin in girls and BMI at age 18 years if their mother smoked during pregnancy. Int J Mol Epidemiol Genet. 2013; 4, 86
63. Teh AL, Pan H, Chen L, et al. The effect of genotype and in utero environment on inter-individual variation in neonate DNA methylomes. Genome Res. 2014; 24, 1064-1074
64. Chen L, Pan H, Tuan TA, et al. Infant BDNF Val66Met influences the association of the DNA methylome with maternal anxiety and neonatal brain volumes. Development and Psycopathology. 2014 (in press
65. Suter M, Ma J, Harris A, et al. Maternal tobacco use modestly alters correlated epigenome-wide placental DNA methylation and gene expression. Epigenetics. 2011; 6, 1284
66. Soto-Ramírez N, Arshad SH, Holloway JW, et al. The interaction of genetic variants and DNA methylation of the interleukin-4 receptor gene increase the risk of asthma at age 18 years. Clin Epigenetics. 2013; 5, 1