1
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Guanzon D, Ross JP, Ma C, Berry O, Liew YJ. Comparing methylation levels assayed in GC-rich regions with current and emerging methods. BMC Genomics 2024; 25:741. [PMID: 39080541 PMCID: PMC11289974 DOI: 10.1186/s12864-024-10605-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 07/08/2024] [Indexed: 08/02/2024] Open
Abstract
DNA methylation is an epigenetic mechanism that regulates gene expression, and for mammals typically occurs on cytosines within CpG dinucleotides. A significant challenge for methylation detection methods is accurately measuring methylation levels within GC-rich regions such as gene promoters, as inaccuracies compromise downstream biological interpretation of the data. To address this challenge, we compared methylation levels assayed using four different Methods Enzymatic Methyl-seq (EM-seq), whole genome bisulphite sequencing (WGBS), Infinium arrays (Illumina MethylationEPIC, "EPIC"), and Oxford Nanopore Technologies nanopore sequencing (ONT) applied to human DNA. Overall, all methods produced comparable and consistent methylation readouts across the human genome. The flexibility offered by current gold standard WGBS in interrogating genome-wide cytosines is surpassed technically by both EM-seq and ONT, as their coverages and methylation readouts are less prone to GC bias. These advantages are tempered by increased laboratory time (EM-seq) and higher complexity (ONT). We further assess the strengths and weaknesses of each method, and provide recommendations in choosing the most appropriate methylation method for specific scientific questions or translational needs.
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Affiliation(s)
- Dominic Guanzon
- CSIRO Health & Biosecurity, Westmead, NSW, Australia
- University of Queensland Centre for Clinical Research, Translational Extracellular Vesicles in Obstetrics and Gynae-Oncology Group, Faculty of Medicine, The University of Queensland, QLD, Australia
| | - Jason P Ross
- CSIRO Health & Biosecurity, Westmead, NSW, Australia
| | - Chenkai Ma
- CSIRO Health & Biosecurity, Westmead, NSW, Australia
| | - Oliver Berry
- Environomics Future Science Platform, CSIRO, Crawley, WA, Australia
| | - Yi Jin Liew
- CSIRO Health & Biosecurity, Westmead, NSW, Australia.
- Environomics Future Science Platform, CSIRO, Crawley, WA, Australia.
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2
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Hillary RF, McCartney DL, McRae AF, Campbell A, Walker RM, Hayward C, Horvath S, Porteous DJ, Evans KL, Marioni RE. Identification of influential probe types in epigenetic predictions of human traits: implications for microarray design. Clin Epigenetics 2022; 14:100. [PMID: 35948928 PMCID: PMC9367152 DOI: 10.1186/s13148-022-01320-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 07/29/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND CpG methylation levels can help to explain inter-individual differences in phenotypic traits. Few studies have explored whether identifying probe subsets based on their biological and statistical properties can maximise predictions whilst minimising array content. Variance component analyses and penalised regression (epigenetic predictors) were used to test the influence of (i) the number of probes considered, (ii) mean probe variability and (iii) methylation QTL status on the variance captured in eighteen traits by blood DNA methylation. Training and test samples comprised ≤ 4450 and ≤ 2578 unrelated individuals from Generation Scotland, respectively. RESULTS As the number of probes under consideration decreased, so too did the estimates from variance components and prediction analyses. Methylation QTL status and mean probe variability did not influence variance components. However, relative effect sizes were 15% larger for epigenetic predictors based on probes with known or reported methylation QTLs compared to probes without reported methylation QTLs. Relative effect sizes were 45% larger for predictors based on probes with mean Beta-values between 10 and 90% compared to those based on hypo- or hypermethylated probes (Beta-value ≤ 10% or ≥ 90%). CONCLUSIONS Arrays with fewer probes could reduce costs, leading to increased sample sizes for analyses. Our results show that reducing array content can restrict prediction metrics and careful attention must be given to the biological and distribution properties of CpG probes in array content selection.
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Affiliation(s)
- Robert F Hillary
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Crewe Road South, Edinburgh, EH4 2XU, UK.
| | - Daniel L McCartney
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Crewe Road South, Edinburgh, EH4 2XU, UK
| | - Allan F McRae
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, 4072, Australia
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Crewe Road South, Edinburgh, EH4 2XU, UK
| | - Rosie M Walker
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Crewe Road South, Edinburgh, EH4 2XU, UK.,Centre for Clinical Brain Sciences, University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Edinburgh, EH16 4SB, UK
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Steve Horvath
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90095-7088, USA.,Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, 90095-1772, USA
| | - David J Porteous
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Crewe Road South, Edinburgh, EH4 2XU, UK
| | - Kathryn L Evans
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Crewe Road South, Edinburgh, EH4 2XU, UK
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Crewe Road South, Edinburgh, EH4 2XU, UK
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3
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Lim IY, Lin X, Teh AL, Wu Y, Chen L, He M, Chan SY, MacIsaac JL, Chan JKY, Tan KH, Chong MFF, Kobor MS, Godfrey KM, Meaney MJ, Lee YS, Eriksson JG, Gluckman PD, Chong YS, Karnani N. Dichotomy in the Impact of Elevated Maternal Glucose Levels on Neonatal Epigenome. J Clin Endocrinol Metab 2022; 107:e1277-e1292. [PMID: 34633450 PMCID: PMC8852163 DOI: 10.1210/clinem/dgab710] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Indexed: 01/22/2023]
Abstract
CONTEXT Antenatal hyperglycemia is associated with increased risk of future adverse health outcomes in both mother and child. Variations in offspring's epigenome can reflect the impact and response to in utero glycemic exposure, and may have different consequences for the child. OBJECTIVE We examined possible differences in associations of basal glucose status and glucose handling during pregnancy with both clinical covariates and offspring cord tissue DNA methylation. RESEARCH DESIGN AND METHODS This study included 830 mother-offspring dyads from the Growing Up in Singapore Towards Healthy Outcomes cohort. The fetal epigenome of umbilical cord tissue was profiled using Illumina HumanMethylation450 arrays. Associations of maternal mid-pregnancy fasting (fasting plasma glucose [FPG]) and 2-hour plasma glucose (2hPG) after a 75-g oral glucose challenge with both maternal clinical phenotypes and offspring epigenome at delivery were investigated separately. RESULTS Maternal age, prepregnancy body mass index, and blood pressure measures were associated with both FPG and 2hPG, whereas Chinese ethnicity (P = 1.9 × 10-4), maternal height (P = 1.1 × 10-4), pregnancy weight gain (P = 2.2 × 10-3), prepregnancy alcohol consumption (P = 4.6 × 10-4), and tobacco exposure (P = 1.9 × 10-3) showed significantly opposite associations between the 2 glucose measures. Most importantly, we observed a dichotomy in the effects of these glycemic indices on the offspring epigenome. Offspring born to mothers with elevated 2hPG showed global hypomethylation. CpGs most associated with the 2 measures also reflected differences in gene ontologies and had different associations with offspring birthweight. CONCLUSIONS Our findings suggest that 2 traditionally used glycemic indices for diagnosing gestational diabetes may reflect distinctive pathophysiologies in pregnancy, and have differential impacts on the offspring's DNA methylome.
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Affiliation(s)
- Ives Yubin Lim
- Singapore Institute for Clinical Sciences (SICS), A*STAR, 117609, Singapore
- Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore (NUS), 119228, Singapore
- Bioinformatics Institute (BII), A*STAR, 138671, Singapore
| | - Xinyi Lin
- Singapore Institute for Clinical Sciences (SICS), A*STAR, 117609, Singapore
- Centre for Quantitative Medicine, Duke-National University of Singapore (NUS) Medical School, 169857, Singapore
- Singapore Clinical Research Institute, 138669, Singapore
| | - Ai Ling Teh
- Singapore Institute for Clinical Sciences (SICS), A*STAR, 117609, Singapore
| | - Yonghui Wu
- Singapore Institute for Clinical Sciences (SICS), A*STAR, 117609, Singapore
| | - Li Chen
- Singapore Institute for Clinical Sciences (SICS), A*STAR, 117609, Singapore
| | - Menglan He
- Duke-NUS Medical School, 169857, Singapore
| | - Shiao-Yng Chan
- Singapore Institute for Clinical Sciences (SICS), A*STAR, 117609, Singapore
- Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore (NUS), 119228, Singapore
| | - Julia L MacIsaac
- Centre for Molecular Medicine and Therapeutics, BC Children’s Hospital Research Institute, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, Vancouver, BC, V5Z 4H4, Canada
| | - Jerry K Y Chan
- KK Women’s and Children’s Hospital, 229899, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore (NUS), Singapore
| | - Kok Hian Tan
- KK Women’s and Children’s Hospital, 229899, Singapore
| | - Mary Foong Fong Chong
- Singapore Institute for Clinical Sciences (SICS), A*STAR, 117609, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore (NUS), Singapore
| | - Michael S Kobor
- Centre for Molecular Medicine and Therapeutics, BC Children’s Hospital Research Institute, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, Vancouver, BC, V5Z 4H4, Canada
| | - Keith M Godfrey
- MRC Lifecourse Epidemiology Unit and NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, SO16 6YD, UK
| | - Michael J Meaney
- Singapore Institute for Clinical Sciences (SICS), A*STAR, 117609, Singapore
- Douglas Mental Health University Institute, McGill University, Montréal, Canada
| | - Yung Seng Lee
- Singapore Institute for Clinical Sciences (SICS), A*STAR, 117609, Singapore
- Department of Paediatrics, Yong Loo Lin School of Medicine, NUS, 119228, Singapore
- Division of Paediatric Endocrinology and Diabetes, Khoo Teck Puat-National University Children’s Medical Institute, National University Hospital, Singapore
| | - Johan G Eriksson
- Singapore Institute for Clinical Sciences (SICS), A*STAR, 117609, Singapore
- Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore (NUS), 119228, Singapore
- Department of General Practice and Primary Health Care, University of Helsinki, Finland
- Folkhälsan Research Center, Helsinki, Finland
| | - Peter D Gluckman
- Singapore Institute for Clinical Sciences (SICS), A*STAR, 117609, Singapore
- Centre for Human Evolution, Adaptation and Disease, Liggins Institute, University of Auckland, Auckland, 1142, New Zealand
| | - Yap Seng Chong
- Singapore Institute for Clinical Sciences (SICS), A*STAR, 117609, Singapore
- Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore (NUS), 119228, Singapore
| | - Neerja Karnani
- Singapore Institute for Clinical Sciences (SICS), A*STAR, 117609, Singapore
- Bioinformatics Institute (BII), A*STAR, 138671, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, NUS, 117596, Singapore
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4
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Murray R, Kitaba N, Antoun E, Titcombe P, Barton S, Cooper C, Inskip HM, Burdge GC, Mahon PA, Deanfield J, Halcox JP, Ellins EA, Bryant J, Peebles C, Lillycrop K, Godfrey KM, Hanson MA. Influence of Maternal Lifestyle and Diet on Perinatal DNA Methylation Signatures Associated With Childhood Arterial Stiffness at 8 to 9 Years. Hypertension 2021; 78:787-800. [PMID: 34275334 PMCID: PMC8357051 DOI: 10.1161/hypertensionaha.121.17396] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Supplemental Digital Content is available in the text. Increases in aortic pulse wave velocity, a measure of arterial stiffness, can lead to elevated systolic blood pressure and increased cardiac afterload in adulthood. These changes are detectable in childhood and potentially originate in utero, where an adverse early life environment can alter DNA methylation patterns detectable at birth. Here, analysis of epigenome-wide methylation patterns using umbilical cord blood DNA from 470 participants in the Southampton’s Women’s Survey identified differential methylation patterns associated with systolic blood pressure, pulse pressure, arterial distensibility, and descending aorta pulse wave velocity measured by magnetic resonance imaging at 8 to 9 years. Perinatal methylation levels at 16 CpG loci were associated with descending aorta pulse wave velocity, with identified CpG sites enriched in pathways involved in DNA repair (P=9.03×10−11). The most significant association was with cg20793626 methylation (within protein phosphatase, Mg2+/Mn2+ dependent 1D; β=−0.05 m/s/1% methylation change, [95% CI, −0.09 to −0.02]). Genetic variation was also examined but had a minor influence on these observations. Eight pulse wave velocity-linked dmCpGs were associated with prenatal modifiable risk factors, with cg08509237 methylation (within palmitoyl-protein thioesterase-2) associated with maternal oily fish consumption in early and late pregnancy. Lower oily fish consumption in early pregnancy modified the relationship between methylation and pulse wave velocity, with lower consumption strengthening the association between cg08509237 methylation and increased pulse wave velocity. In conclusion, measurement of perinatal DNA methylation signatures has utility in identifying infants who might benefit from preventive interventions to reduce risk of later cardiovascular disease, and modifiable maternal factors can reduce this risk in the child.
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Affiliation(s)
- Robert Murray
- From the School of Human Development and Health, Institute of Developmental Sciences Building, Faculty of Medicine (R.M., N.K., E.A., G.C.B., K.M.G., M.A.H.), University of Southampton, United Kingdom
| | - Negusse Kitaba
- From the School of Human Development and Health, Institute of Developmental Sciences Building, Faculty of Medicine (R.M., N.K., E.A., G.C.B., K.M.G., M.A.H.), University of Southampton, United Kingdom
| | - Elie Antoun
- From the School of Human Development and Health, Institute of Developmental Sciences Building, Faculty of Medicine (R.M., N.K., E.A., G.C.B., K.M.G., M.A.H.), University of Southampton, United Kingdom.,Centre for Biological Sciences, Faculty of Natural and Environmental Sciences (E.A., K.L.), University of Southampton, United Kingdom
| | - Philip Titcombe
- MRC Lifecourse Epidemiology Unit (P.T., S.B., C.C., H.M.I., P.A.M., K.M.G.), University of Southampton, United Kingdom
| | - Sheila Barton
- MRC Lifecourse Epidemiology Unit (P.T., S.B., C.C., H.M.I., P.A.M., K.M.G.), University of Southampton, United Kingdom
| | - Cyrus Cooper
- MRC Lifecourse Epidemiology Unit (P.T., S.B., C.C., H.M.I., P.A.M., K.M.G.), University of Southampton, United Kingdom
| | - Hazel M Inskip
- MRC Lifecourse Epidemiology Unit (P.T., S.B., C.C., H.M.I., P.A.M., K.M.G.), University of Southampton, United Kingdom.,NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, United Kingdom (H.M.I., K.L., K.M.G., M.A.H.)
| | - Graham C Burdge
- From the School of Human Development and Health, Institute of Developmental Sciences Building, Faculty of Medicine (R.M., N.K., E.A., G.C.B., K.M.G., M.A.H.), University of Southampton, United Kingdom
| | - Pamela A Mahon
- MRC Lifecourse Epidemiology Unit (P.T., S.B., C.C., H.M.I., P.A.M., K.M.G.), University of Southampton, United Kingdom
| | - John Deanfield
- Institute of Cardiovascular Sciences, University College London, United Kingdom (J.D.)
| | - Julian P Halcox
- Swansea University Medical School, Swansea University, United Kingdom (J.P.H., E.A.E.)
| | - Elizabeth A Ellins
- Swansea University Medical School, Swansea University, United Kingdom (J.P.H., E.A.E.)
| | - Jennifer Bryant
- Department of Cardiac Magnetic Resonance Imaging, National Heart Centre Singapore (J.B.)
| | - Charles Peebles
- Wessex Cardiothoracic Centre, Southampton University Hospitals NHS Trust, United Kingdom (C.P.)
| | - Karen Lillycrop
- Centre for Biological Sciences, Faculty of Natural and Environmental Sciences (E.A., K.L.), University of Southampton, United Kingdom.,NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, United Kingdom (H.M.I., K.L., K.M.G., M.A.H.)
| | - Keith M Godfrey
- From the School of Human Development and Health, Institute of Developmental Sciences Building, Faculty of Medicine (R.M., N.K., E.A., G.C.B., K.M.G., M.A.H.), University of Southampton, United Kingdom.,MRC Lifecourse Epidemiology Unit (P.T., S.B., C.C., H.M.I., P.A.M., K.M.G.), University of Southampton, United Kingdom.,NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, United Kingdom (H.M.I., K.L., K.M.G., M.A.H.)
| | - Mark A Hanson
- From the School of Human Development and Health, Institute of Developmental Sciences Building, Faculty of Medicine (R.M., N.K., E.A., G.C.B., K.M.G., M.A.H.), University of Southampton, United Kingdom.,NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, United Kingdom (H.M.I., K.L., K.M.G., M.A.H.)
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5
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Cheung K, Burgers MJ, Young DA, Cockell S, Reynard LN. Correlation of Infinium HumanMethylation450K and MethylationEPIC BeadChip arrays in cartilage. Epigenetics 2019; 15:594-603. [PMID: 31833794 PMCID: PMC7574380 DOI: 10.1080/15592294.2019.1700003] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
DNA methylation of CpG sites is commonly measured using Illumina Infinium BeadChip platforms. The Infinium MethylationEPIC array has replaced the Infinium Methylation450K array. The two arrays use the same technology, with the EPIC array assaying almost double the number of sites than the 450K array. In this study, we compare DNA methylation values of shared CpGs of the same human cartilage samples assayed using both platforms. DNA methylation was measured in 21 human cartilage samples using the both 450K and EPIC arrays. Additional matched 450K and EPIC data in whole tumour and whole blood were downloaded from GEO GSE92580 and GSE86833, respectively. Data were processed using the Bioconductor package Minfi. DNA methylation of six CpG sites was validated for the same 21 cartilage samples by pyrosequencing. In cartilage samples, overall sample correlations of methylation values between arrays were high (Pearson’s r > 0.96). However, 50.5% of CpG sites showed poor correlation (r < 0.2) between arrays. Sites with limited variance and with either very high or very low methylation levels in cartilage exhibited lower correlation values, corroborating prior studies in whole blood. Bisulphite pyrosequencing did not highlight one array as generating more accurate methylation values. For a specific CpG site, the array methylation correlation coefficient differed between cartilage, tumour, and whole blood, reflecting the difference in methylation variance between cell types. Researchers should be cautious when analysing methylation of CpG sites that show low methylation variance within the cell type of interest, regardless of the method used to assay methylation.
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Affiliation(s)
- Kathleen Cheung
- Skeletal Research Group, Institute of Genetic Medicine, Newcastle University, Central Parkway , Newcastle upon Tyne, UK.,Bioinformatics Support Unit, Faculty of Medical Sciences, Newcastle University , Newcastle upon Tyne, UK
| | - Marjolein J Burgers
- Skeletal Research Group, Institute of Genetic Medicine, Newcastle University, Central Parkway , Newcastle upon Tyne, UK
| | - David A Young
- Skeletal Research Group, Institute of Genetic Medicine, Newcastle University, Central Parkway , Newcastle upon Tyne, UK
| | - Simon Cockell
- Bioinformatics Support Unit, Faculty of Medical Sciences, Newcastle University , Newcastle upon Tyne, UK
| | - Louise N Reynard
- Skeletal Research Group, Institute of Genetic Medicine, Newcastle University, Central Parkway , Newcastle upon Tyne, UK
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6
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Bahado-Singh RO, Vishweswaraiah S, Aydas B, Mishra NK, Yilmaz A, Guda C, Radhakrishna U. Artificial intelligence analysis of newborn leucocyte epigenomic markers for the prediction of autism. Brain Res 2019; 1724:146457. [DOI: 10.1016/j.brainres.2019.146457] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 09/10/2019] [Accepted: 09/11/2019] [Indexed: 01/05/2023]
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7
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Seow WJ, Ngo CS, Pan H, Barathi VA, Tompson SW, Whisenhunt KN, Vithana E, Chong YS, Juo SHH, Hysi P, Young TL, Karnani N, Saw SM. In-utero epigenetic factors are associated with early-onset myopia in young children. PLoS One 2019; 14:e0214791. [PMID: 31100065 PMCID: PMC6524791 DOI: 10.1371/journal.pone.0214791] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Accepted: 03/06/2019] [Indexed: 12/11/2022] Open
Abstract
Objectives To assess whether epigenetic mechanisms affecting gene expression may be involved in the pathogenesis of early-onset myopia, we performed genome-wide DNA methylation analyses of umbilical cord tissues, and assessed any associations between CpG site-specific methylation and the development of the disorder when the children were 3 years old. Methods Genome-wide DNA methylation profiling of umbilical cord samples from 519 Singaporean infants involved in a prospective birth cohort ‘Growing Up in Singapore Towards healthy Outcomes’ (GUSTO) was performed using the Illumina Infinium HumanMethylation450K chip microarray. Multivariable logistic regression models were used to assess any associations between site-specific CpG methylation of umbilical cord tissue at birth and myopia risk in 3 year old children, adjusting for potential confounders. Gene expression of genes located near CpG sites that demonstrated statistically significant associations were measured in relevant ocular tissues using human and mouse fetal and adult eye samples. Results We identified statistically significant associations between DNA methylation levels at five CpG sites and early-onset myopia risk after correcting for multiple comparisons using a false discovery rate of 5%. Two statistically significant CpG sites were identified in intergenic regions: 8p23(p = 1.70×10−7) and 12q23.2(p = 2.53×10−7). The remaining 3 statistically significant CpG sites were identified within the following genes: FGB (4q28, p = 3.60×10−7), PQLC1 (18q23, p = 8.9×10−7) and KRT12 (17q21.2, p = 1.2×10−6). Both PQLC1 and KRT12 were found to be significantly expressed in fetal and adult cornea and sclera tissues in both human and mouse. Conclusions We identified five CpG methylation sites that demonstrate a statistically significant association with increased risk of developing early-onset myopia. These findings suggest that variability in the neonatal cord epigenome may influence early-onset myopia risk in children. Further studies of the epigenetic influences on myopia risk in larger study populations, and the associations with adulthood myopia risk are warranted.
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Affiliation(s)
- Wei Jie Seow
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore
| | - Cheryl S. Ngo
- Department of Ophthalmology, National University Health System, Singapore
| | - Hong Pan
- Singapore Institute for Clinical Sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore
| | - Veluchamy Amutha Barathi
- Singapore Eye Research Institute, Singapore
- The Ophthalmology and Visual Sciences Academic Clinical Program, DUKE-NUS Graduate Medical School, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Stuart W. Tompson
- Department of Ophthalmology and Visual Sciences, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
| | - Kristina N. Whisenhunt
- Department of Ophthalmology and Visual Sciences, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
| | | | - Yap-Seng Chong
- Department of Obstetrics and Gynaecology, National University of Singapore, Singapore, Singapore
| | - Suh-Hang H. Juo
- Institute of New Drug Development, Center for Myopia and Eye diseases, China Medical University and China Medical University Hospital, Taichung, Taiwan
| | - Pirro Hysi
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
- Johns Hopkins School of Public Health, Baltimore, Maryland, United States
| | - Terri L. Young
- Department of Ophthalmology and Visual Sciences, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
| | - Neerja Karnani
- Singapore Institute for Clinical Sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore
| | - Seang Mei Saw
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
- Singapore Eye Research Institute, Singapore
- The Ophthalmology and Visual Sciences Academic Clinical Program, DUKE-NUS Graduate Medical School, Singapore
- * E-mail:
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8
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Ong ML, Tuan TA, Poh J, Teh AL, Chen L, Pan H, MacIsaac JL, Kobor MS, Chong YS, Kwek K, Saw SM, Godfrey KM, Gluckman PD, Fortier MV, Karnani N, Meaney MJ, Qiu A, Holbrook JD. Neonatal amygdalae and hippocampi are influenced by genotype and prenatal environment, and reflected in the neonatal DNA methylome. GENES BRAIN AND BEHAVIOR 2019; 18:e12576. [PMID: 31020763 DOI: 10.1111/gbb.12576] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Revised: 03/01/2019] [Accepted: 04/13/2019] [Indexed: 12/28/2022]
Abstract
The amygdala and hippocampus undergo rapid development in early life. The relative contribution of genetic and environmental factors to the establishment of their developmental trajectories has yet to be examined. We performed imaging on neonates and examined how the observed variation in volume and microstructure of the amygdala and hippocampus varied by genotype, and compared with prenatal maternal mental health and socioeconomic status. Gene × Environment models outcompeted models containing genotype or environment only to best explain the majority of measures but some, especially of the amygdaloid microstructure, were best explained by genotype only. Models including DNA methylation measured in the neonate umbilical cords outcompeted the Gene and Gene × Environment models for the majority of amygdaloid measures and minority of hippocampal measures. This study identified brain region-specific gene networks associated with individual differences in fetal brain development. In particular, genetic and epigenetic variation within CUX1 was highlighted.
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Affiliation(s)
- Mei-Lyn Ong
- Singapore Institute of Clinical sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore
| | - Ta A Tuan
- Department of Biomedical Engineering, Clinical Imaging research Centre, National University of Singapore, Singapore
| | - Joann Poh
- Department of Biomedical Engineering, Clinical Imaging research Centre, National University of Singapore, Singapore
| | - Ai L Teh
- Singapore Institute of Clinical sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore
| | - Li Chen
- Singapore Institute of Clinical sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore
| | - Hong Pan
- Singapore Institute of Clinical sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore.,School of Computer Engineering, Nanyang Technological University (NTU), Singapore
| | - Julia L MacIsaac
- Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Michael S Kobor
- Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Yap S Chong
- Singapore Institute of Clinical sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore.,Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore
| | - Kenneth Kwek
- KK Women's and Children's Hospital, Duke National University of Singapore, Singapore
| | - Seang M Saw
- Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore
| | - Keith M Godfrey
- MRC Lifecourse Epidemiology Unit and NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Peter D Gluckman
- Singapore Institute of Clinical sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore.,Centre for Human Evolution, Adaptation and disease, Liggins Institute, University of Auckland, Auckland, New Zealand
| | - Marielle V Fortier
- KK Women's and Children's Hospital, Duke National University of Singapore, Singapore
| | - Neerja Karnani
- Singapore Institute of Clinical sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore
| | - Michael J Meaney
- Singapore Institute of Clinical sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore.,Ludmer Centre for Neuroinformatics and Mental Health, Sackler Program for Epigenetics & Psychobiology at McGill University, Douglas University Mental Health Institute, McGill University, Montreal, Canada
| | - Anqi Qiu
- Department of Biomedical Engineering, Clinical Imaging research Centre, National University of Singapore, Singapore.,Singapore Institute of Clinical sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore
| | - Joanna D Holbrook
- Singapore Institute of Clinical sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore
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9
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Epigenetically dysregulated genes and pathways implicated in the pathogenesis of non-syndromic high myopia. Sci Rep 2019; 9:4145. [PMID: 30858441 PMCID: PMC6411983 DOI: 10.1038/s41598-019-40299-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Accepted: 11/30/2018] [Indexed: 12/20/2022] Open
Abstract
Myopia, commonly referred to as nearsightedness, is one of the most common causes of visual disability throughout the world. It affects more people worldwide than any other chronic visual impairment condition. Although the prevalence varies among various ethnic groups, the incidence of myopia is increasing in all populations across globe. Thus, it is considered a pressing public health problem. Both genetics and environment play a role in development of myopia. To elucidate the epigenetic mechanism(s) underlying the pathophysiology of high-myopia, we conducted methylation profiling in 18 cases and 18 matched controls (aged 4–12 years), using Illumina MethylationEPIC BeadChips array. The degree of myopia was variable among subjects, ranging from −6 to −15D. We identified 1541 hypermethylated CpGs, representing 1745 genes (2.0-fold or higher) (false discovery rate (FDR) p ≤ 0.05), multiple CpGs were p < 5 × 10−8 with a receiver operating characteristic area under the curve (ROC-AUC) ≥ 0.75 in high-myopia subjects compared to controls. Among these, 48 CpGs had excellent correlation (AUC ≥ 0.90). Herein, we present the first genome-wide DNA methylation analysis in a unique high-myopia cohort, showing extensive and discrete methylation changes relative to controls. The genes we identified hold significant potential as targets for novel therapeutic intervention either alone, or in combination.
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10
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Lyu M, Zheng Y, Jia L, Zheng Y, Liu Y, Lin Y, Di P. Genome-wide DNA-methylation profiles in human bone marrow mesenchymal stem cells on titanium surfaces. Eur J Oral Sci 2019; 127:196-209. [PMID: 30791149 DOI: 10.1111/eos.12607] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/10/2018] [Indexed: 12/22/2022]
Abstract
The characteristics of titanium (Ti) have been shown to influence dental implant fixation. Treatment of surfaces using the sandblasted, large-grit, acid-etched (SLA) method is widely used to provide effective osseointegration. However, the DNA methylation-associated mechanism by which SLA surface treatment affects osseointegration of human bone marrow mesenchymal stem cells (hBMSCs) remains elusive. Genome-wide methylation profiling of hBMSCs on SLA-treated and machined smooth Ti was performed using Illumina Infinium Methylation EPIC BeadChip at day 7 of osteogenic induction. In total, 2,846 CpG sites were differentially methylated in the SLA group compared with the machined group. Of these sites, 1,651 (covering 1,066 genes) were significantly hypermethylated and 1,195 (covering 775 genes) were significantly hypomethylated. Thirty significant enrichment pathways were observed, with Wnt signaling being the most significant. mRNA expression was identified by microarray and combined with DNA-methylation profiles. Thirty-seven genes displayed negative association between mRNA expression and DNA-methylation level, with the osteogenesis-related genes insulin-like growth factor 2 (IGF2) and carboxypeptidase X, M14 Family Member 2 (CPXM2) showing significant up-regulation and down-regulation, respectively. In summary, our results demonstrate differences between SLA-treated and machined surfaces in their effects on genome-wide DNA methylation and enrichment of osteogenic pathways in hBMSCs. We provide novel insights into genes and pathways affected by SLA treatment in hBMSCs at the molecular level.
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Affiliation(s)
- Mingyue Lyu
- Department of Implantology, Peking University School and Hospital of Stomatology, Beijing, China
| | - Yunfei Zheng
- Department of Orthodontics, Peking University School and Hospital of Stomatology, Beijing, China
| | - Lingfei Jia
- Department of Central Laboratory, Peking University School and Hospital of Stomatology, Beijing, China
| | - Yan Zheng
- Department of Implantology, Peking University School and Hospital of Stomatology, Beijing, China
| | - Yanping Liu
- Department of Implantology, Peking University School and Hospital of Stomatology, Beijing, China
| | - Ye Lin
- Department of Implantology, Peking University School and Hospital of Stomatology, Beijing, China
| | - Ping Di
- Department of Implantology, Peking University School and Hospital of Stomatology, Beijing, China
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11
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Wu Y, Lin X, Lim IY, Chen L, Teh AL, MacIsaac JL, Tan KH, Kobor MS, Chong YS, Gluckman PD, Karnani N. Analysis of two birth tissues provides new insights into the epigenetic landscape of neonates born preterm. Clin Epigenetics 2019; 11:26. [PMID: 30744680 PMCID: PMC6371604 DOI: 10.1186/s13148-018-0599-4] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 12/17/2018] [Indexed: 01/04/2023] Open
Abstract
Background Preterm birth (PTB), defined as child birth before completion of 37 weeks of gestation, is a major challenge in perinatal health care and can bear long-term medical and financial burden. Over a million children die each year due to PTB complications, and those who survive can face developmental delays. Unfortunately, our understanding of the molecular pathways associated with PTB remains limited. There is a growing body of evidence suggesting the role of DNA methylation (DNAm) in mediating the effects of PTB on future health outcomes. Thus, epigenome-wide association studies (EWAS), where DNAm sites are examined for associations with PTB, can help shed light on the biological mechanisms linking the two. Results In an Asian cohort of 1019 infants (68 preterm, 951 full term), we examined and compared the associations between PTB and genome-wide DNAm profiles using both cord tissue (n = 1019) and cord blood (n = 332) samples on Infinium HumanMethylation450 arrays. PTB was significantly associated (P < 5.8e−7) with DNAm at 296 CpGs (209 genes) in the cord blood. Over 95% of these CpGs were replicated in other PTB/gestational age EWAS conducted in (cord) blood. This replication was apparent even across populations of different ethnic origin (Asians, Caucasians, and African Americans). More than a third of these 296 CpGs were replicated in at least 4 independent studies, thereby identifying a robust set of PTB-linked epigenetic signatures in cord blood. Interrogation of cord tissue in addition to cord blood provided novel insights into the epigenetic status of the neonates born preterm. Overall, 994 CpGs (608 genes, P < 3.7e−7) associated with PTB in cord tissue, of which only 10 of these CpGs were identified in the analysis using cord blood. Genes from cord tissue showed enrichment of molecular pathways related to fetal growth and development, while those from cord blood showed enrichment of immune response pathways. A substantial number of PTB-associated CpGs from both the birth tissues were also associated with gestational age. Conclusions Our findings provide insights into the epigenetic landscape of neonates born preterm, and that its status is captured more comprehensively by interrogation of more than one neonatal tissue in tandem. Both these neonatal tissues are clinically relevant in their unique ways and require careful consideration in identification of biomarkers related to PTB and gestational age. Trial registration This birth cohort is a prospective observational study designed to study the developmental origins of health and disease, and was retrospectively registered on 1 July 2010 under the identifier NCT01174875. Electronic supplementary material The online version of this article (10.1186/s13148-018-0599-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Yonghui Wu
- Singapore Institute for Clinical Sciences, A*STAR, 30 Medical Drive, Singapore, 117609, Singapore
| | - Xinyi Lin
- Singapore Institute for Clinical Sciences, A*STAR, 30 Medical Drive, Singapore, 117609, Singapore.,Duke-NUS Medical School, Singapore, Singapore
| | - Ives Yubin Lim
- Singapore Institute for Clinical Sciences, A*STAR, 30 Medical Drive, Singapore, 117609, Singapore
| | - Li Chen
- Singapore Institute for Clinical Sciences, A*STAR, 30 Medical Drive, Singapore, 117609, Singapore
| | - Ai Ling Teh
- Singapore Institute for Clinical Sciences, A*STAR, 30 Medical Drive, Singapore, 117609, Singapore
| | - Julia L MacIsaac
- Department of Medical Genetics, Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, University of British Columbia, Vancouver, Canada
| | - Kok Hian Tan
- KK Women's and Children's Hospital, Singapore, Singapore
| | - Michael S Kobor
- Department of Medical Genetics, Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, University of British Columbia, Vancouver, Canada
| | - Yap Seng Chong
- Singapore Institute for Clinical Sciences, A*STAR, 30 Medical Drive, Singapore, 117609, Singapore.,Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Peter D Gluckman
- Singapore Institute for Clinical Sciences, A*STAR, 30 Medical Drive, Singapore, 117609, Singapore.,Centre for Human Evolution, Adaptation and Disease, Liggins Institute, University of Auckland, Auckland, New Zealand
| | - Neerja Karnani
- Singapore Institute for Clinical Sciences, A*STAR, 30 Medical Drive, Singapore, 117609, Singapore. .,Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
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12
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Liang CL, Hsu PY, Ngo CS, Seow WJ, Karnani N, Pan H, Saw SM, Juo SHH. HOXA9 is a novel myopia risk gene. BMC Ophthalmol 2019; 19:28. [PMID: 30674274 PMCID: PMC6343304 DOI: 10.1186/s12886-019-1038-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Accepted: 01/15/2019] [Indexed: 11/10/2022] Open
Abstract
Purpose A recent meta-analysis revealed PAX6 as a risk gene for myopia. There is a link between PAX6 and HOXA9. Furthermore, HOXA9 has been reported to activate TGF-β that is a risk factor for myopia. We speculate HOXA9 may participate in myopia development. Methods The Singapore GUSTO birth cohort provides data on children’s cycloplegic refraction measured at age of 3 years and their methylation profile based on the umbilical cord DNA. The HOXA9 expression levels were measured in the eyes of mono-ocular form deprivation myopia in mice. The plasmid with the mouse HOXA9 cDNA was constructed and then transfected to mouse primary retinal pigment epithelial (RPE) cells. The expression levels of myopia-related genes and cell proliferation were measured in the HOXA9-overexpressed RPE cells. Results A total of 519 children had data on methylation profile and cycloplegic refraction. The mean spherical equivalent refraction (SE) was 0.90D. Among 8 SE outliers (worse than -2D), 7 children had HOXA9 hypomethylation. The HOXA9 levels in the retina of myopic eyes was 2.65-fold (p = 0.029; paired t-test) higher than the uncovered fellow eyes. When HOXA9 was over-expressed in the RPE cells, TGF-β, MMP2, FGF2 and IGF1R expression levels were dose-dependently increased by HOXA9. However, over-expression of HOXA9 had no significant influence on IGF1 or HGF expression. In addition, HOXA9 also increased RPE proliferation. Conclusion Based on the human, animal and cellular data, the transcription factor HOXA9 may promote the expression of pro-myopia genes and RPE proliferation, which eventually contribute to myopia development.
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Affiliation(s)
- Chung-Ling Liang
- Department of Ophthalmology, Asia University Hospital, Taichung, Taiwan.,Department of Optometry, College of Medical and Health Science, Asia University, Taichung, Taiwan.,Center for Myopia and Eye Disease, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan.,Bright-Eyes Clinic, Kaohsiung, Taiwan
| | - Po-Yuan Hsu
- Center for Myopia and Eye Disease, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
| | - Cheryl S Ngo
- Department of Ophthalmology, National University Hospital, Singapore, Singapore
| | - Wei Jie Seow
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Neerja Karnani
- Singapore Institute for Clinical Sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore, Singapore
| | - Hong Pan
- Singapore Institute for Clinical Sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore, Singapore
| | - Seang-Mei Saw
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore.,Singapore Eye Research Institute, Singapore, Singapore
| | - Suh-Hang H Juo
- Center for Myopia and Eye Disease, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan. .,The Ophthalmology & Visual Sciences Academic Clinical Program, DUKE-NUS Graduate Medical School, Singapore, Singapore. .,Graduate Institute of Biomedical Sciences, Singapore, Singapore. .,Institute of New Drug Development, Singapore, Singapore. .,Drug Development Center, China Medical University, Taichung, Taiwan.
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13
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Shade DC, Park HJ, Hausman DB, Hohos N, Meagher RB, Kauwell GPA, Kilaru V, Lewis RD, Smith AK, Bailey LB. DNA Methylation Changes in Whole Blood and CD16+ Neutrophils in Response to Chronic Folic Acid Supplementation in Women of Childbearing Age. INT J VITAM NUTR RES 2018; 87:271-278. [PMID: 30499755 DOI: 10.1024/0300-9831/a000491] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Folate, a water-soluble vitamin, is a key source of one-carbon groups for DNA methylation, but studies of the DNA methylation response to supplemental folic acid yield inconsistent results. These studies are commonly conducted using whole blood, which contains a mixed population of white blood cells that have been shown to confound results. The objective of this study was to determine if CD16+ neutrophils may provide more specific data than whole blood for identifying DNA methylation response to chronic folic acid supplementation. The study was performed in normal weight (BMI 18.5 - 24.9 kg/m2) women (18 - 35 y; n = 12), with blood samples taken before and after 8 weeks of folic acid supplementation at 800 μg/day. DNA methylation patterns from whole blood and isolated CD16+ neutrophils were measured across >485,000 CpG sites throughout the genome using the Infinium HumanMethylation450 BeadChip. Over the course of the 8-week supplementation, 6746 and 7513 CpG sites changed (p < 0.05) in whole blood and CD16+ neutrophils, respectively. DNA methylation decreased in 68.4% (whole blood) and 71.8% (CD16+ neutrophils) of these sites. There were only 182 CpG sites that changed in both the whole blood and CD16+ neutrophils, 139 of which changed in the same direction. These results suggest that the genome-wide DNA methylation response to chronic folic acid supplementation is different between whole blood and CD16+ neutrophils and that a single white blood cell type may function as a more specific epigenetic reporter of folate status than whole blood.
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Affiliation(s)
- Deanna C Shade
- a Co-first authors; these authors contributed equally.,1 Department of Foods and Nutrition, University of Georgia, Athens, GA, USA
| | - Hea Jin Park
- a Co-first authors; these authors contributed equally.,1 Department of Foods and Nutrition, University of Georgia, Athens, GA, USA
| | - Dorothy B Hausman
- 1 Department of Foods and Nutrition, University of Georgia, Athens, GA, USA
| | - Natalie Hohos
- 1 Department of Foods and Nutrition, University of Georgia, Athens, GA, USA
| | | | - Gail P A Kauwell
- 3 Food Science and Human Nutrition Department, University of Florida, Gainesville, FL, USA
| | - Varun Kilaru
- 4 Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Richard D Lewis
- 1 Department of Foods and Nutrition, University of Georgia, Athens, GA, USA
| | - Alicia K Smith
- 4 Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Lynn B Bailey
- 1 Department of Foods and Nutrition, University of Georgia, Athens, GA, USA
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14
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Abstract
Prenatal adversity shapes child neurodevelopment and risk for later mental health problems. The quality of the early care environment can buffer some of the negative effects of prenatal adversity on child development. Retrospective studies, in adult samples, highlight epigenetic modifications as sentinel markers of the quality of the early care environment; however, comparable data from pediatric cohorts are lacking. Participants were drawn from the Maternal Adversity Vulnerability and Neurodevelopment (MAVAN) study, a longitudinal cohort with measures of infant attachment, infant development, and child mental health. Children provided buccal epithelial samples (mean age = 6.99, SD = 1.33 years, n = 226), which were used for analyses of genome-wide DNA methylation and genetic variation. We used a series of linear models to describe the association between infant attachment and (a) measures of child outcome and (b) DNA methylation across the genome. Paired genetic data was used to determine the genetic contribution to DNA methylation at attachment-associated sites. Infant attachment style was associated with infant cognitive development (Mental Development Index) and behavior (Behavior Rating Scale) assessed with the Bayley Scales of Infant Development at 36 months. Infant attachment style moderated the effects of prenatal adversity on Behavior Rating Scale scores at 36 months. Infant attachment was also significantly associated with a principal component that accounted for 11.9% of the variation in genome-wide DNA methylation. These effects were most apparent when comparing children with a secure versus a disorganized attachment style and most pronounced in females. The availability of paired genetic data revealed that DNA methylation at approximately half of all infant attachment-associated sites was best explained by considering both infant attachment and child genetic variation. This study provides further evidence that infant attachment can buffer some of the negative effects of early adversity on measures of infant behavior. We also highlight the interplay between infant attachment and child genotype in shaping variation in DNA methylation. Such findings provide preliminary evidence for a molecular signature of infant attachment and may help inform attachment-focused early intervention programs.
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15
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Lee JR, Ryu DS, Park SJ, Choe SH, Cho HM, Lee SR, Kim SU, Kim YH, Huh JW. Successful application of human-based methyl capture sequencing for methylome analysis in non-human primate models. BMC Genomics 2018; 19:267. [PMID: 29669513 PMCID: PMC5907189 DOI: 10.1186/s12864-018-4666-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Accepted: 04/12/2018] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND The characterization of genomic or epigenomic variation in human and animal models could provide important insight into pathophysiological mechanisms of various diseases, and lead to new developments in disease diagnosis and clinical intervention. The African green monkey (AGM; Chlorocebus aethiops) and cynomolgus monkey (CM; Macaca fascicularis) have long been considered important animal models in biomedical research. However, non-human primate-specific methods applicable to epigenomic analyses in AGM and CM are lacking. The recent development of methyl-capture sequencing (MC-seq) has an unprecedented advantage of cost-effectiveness, and further allows for extending the methylome coverage compared to conventional sequencing approaches. RESULTS Here, we used a human probe-designed MC-seq method to assay DNA methylation in DNA obtained from 13 CM and three AGM blood samples. To effectively adapt the human probe-designed target region for methylome analysis in non-human primates, we redefined the target regions, focusing on regulatory regions and intragenic regions with consideration of interspecific sequence homology and promoter region variation. Methyl-capture efficiency was controlled by the sequence identity between the captured probes based on the human reference genome and the AGM and CM genome sequences, respectively. Using reasonable guidelines, 56 and 62% of the human-based capture probes could be effectively mapped for DNA methylome profiling in the AGM and CM genome, respectively, according to numeric global statistics. In particular, our method could cover up to 89 and 87% of the regulatory regions of the AGM and CM genome, respectively. CONCLUSIONS Use of human-based MC-seq methods provides an attractive, cost-effective approach for the methylome profiling of non-human primates at the single-base resolution level.
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Affiliation(s)
- Ja-Rang Lee
- Primate Resource Center, Korea Research Institute of Bioscience and Biotechnology, Jeongeup, 56216, Republic of Korea
| | - Dong-Sung Ryu
- Theragen Etex Bio Institute, Suwon, Republic of Korea
| | - Sang-Je Park
- National Primate Research Center, Korea Research Institute of Bioscience and Biotechnology, Cheongju, 28116, Republic of Korea
| | - Se-Hee Choe
- National Primate Research Center, Korea Research Institute of Bioscience and Biotechnology, Cheongju, 28116, Republic of Korea.,Department of Functional Genomics, KRIBB School of Bioscience, Korea University of Science and Technology (UST), Daejeon, 34113, Republic of Korea
| | - Hyeon-Mu Cho
- National Primate Research Center, Korea Research Institute of Bioscience and Biotechnology, Cheongju, 28116, Republic of Korea.,Department of Functional Genomics, KRIBB School of Bioscience, Korea University of Science and Technology (UST), Daejeon, 34113, Republic of Korea
| | - Sang-Rae Lee
- National Primate Research Center, Korea Research Institute of Bioscience and Biotechnology, Cheongju, 28116, Republic of Korea.,Department of Functional Genomics, KRIBB School of Bioscience, Korea University of Science and Technology (UST), Daejeon, 34113, Republic of Korea
| | - Sun-Uk Kim
- Futuristic Animal Resource and Research Center, Korea Research Institute of Bioscience and Biotechnology, Cheongju, 28116, Republic of Korea.,Department of Functional Genomics, KRIBB School of Bioscience, Korea University of Science and Technology (UST), Daejeon, 34113, Republic of Korea
| | - Young-Hyun Kim
- National Primate Research Center, Korea Research Institute of Bioscience and Biotechnology, Cheongju, 28116, Republic of Korea. .,Department of Functional Genomics, KRIBB School of Bioscience, Korea University of Science and Technology (UST), Daejeon, 34113, Republic of Korea.
| | - Jae-Won Huh
- National Primate Research Center, Korea Research Institute of Bioscience and Biotechnology, Cheongju, 28116, Republic of Korea. .,Department of Functional Genomics, KRIBB School of Bioscience, Korea University of Science and Technology (UST), Daejeon, 34113, Republic of Korea.
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16
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Guillaume B, Wang C, Poh J, Shen MJ, Ong ML, Tan PF, Karnani N, Meaney M, Qiu A. Improving mass-univariate analysis of neuroimaging data by modelling important unknown covariates: Application to Epigenome-Wide Association Studies. Neuroimage 2018; 173:57-71. [PMID: 29448075 DOI: 10.1016/j.neuroimage.2018.01.073] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Revised: 01/03/2018] [Accepted: 01/28/2018] [Indexed: 10/18/2022] Open
Abstract
Statistical inference on neuroimaging data is often conducted using a mass-univariate model, equivalent to fitting a linear model at every voxel with a known set of covariates. Due to the large number of linear models, it is challenging to check if the selection of covariates is appropriate and to modify this selection adequately. The use of standard diagnostics, such as residual plotting, is clearly not practical for neuroimaging data. However, the selection of covariates is crucial for linear regression to ensure valid statistical inference. In particular, the mean model of regression needs to be reasonably well specified. Unfortunately, this issue is often overlooked in the field of neuroimaging. This study aims to adopt the existing Confounder Adjusted Testing and Estimation (CATE) approach and to extend it for use with neuroimaging data. We propose a modification of CATE that can yield valid statistical inferences using Principal Component Analysis (PCA) estimators instead of Maximum Likelihood (ML) estimators. We then propose a non-parametric hypothesis testing procedure that can improve upon parametric testing. Monte Carlo simulations show that the modification of CATE allows for more accurate modelling of neuroimaging data and can in turn yield a better control of False Positive Rate (FPR) and Family-Wise Error Rate (FWER). We demonstrate its application to an Epigenome-Wide Association Study (EWAS) on neonatal brain imaging and umbilical cord DNA methylation data obtained as part of a longitudinal cohort study. Software for this CATE study is freely available at http://www.bioeng.nus.edu.sg/cfa/Imaging_Genetics2.html.
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Affiliation(s)
- Bryan Guillaume
- Department of Biomedical Engineering, National University of Singapore, Singapore
| | - Changqing Wang
- Department of Biomedical Engineering, National University of Singapore, Singapore
| | - Joann Poh
- Department of Biomedical Engineering, National University of Singapore, Singapore; Singapore Institute for Clinical Sciences, Agency for Science, Technology, and Research, Singapore
| | - Mo Jun Shen
- Department of Biomedical Engineering, National University of Singapore, Singapore; Singapore Institute for Clinical Sciences, Agency for Science, Technology, and Research, Singapore
| | - Mei Lyn Ong
- Singapore Institute for Clinical Sciences, Agency for Science, Technology, and Research, Singapore
| | - Pei Fang Tan
- Singapore Institute for Clinical Sciences, Agency for Science, Technology, and Research, Singapore
| | - Neerja Karnani
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 119228, Singapore; Singapore Institute for Clinical Sciences, Agency for Science, Technology, and Research, Singapore
| | - Michael Meaney
- Ludmer Centre for Neuroinformatics and Mental Health, Douglas Mental Health University Institute, McGill University, Canada; Sackler Program for Epigenetics and Psychobiology at McGill University, Canada; Singapore Institute for Clinical Sciences, Agency for Science, Technology, and Research, Singapore
| | - Anqi Qiu
- Department of Biomedical Engineering, National University of Singapore, Singapore; Clinical Imaging Research Centre, National University of Singapore, Singapore; Singapore Institute for Clinical Sciences, Agency for Science, Technology, and Research, Singapore.
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17
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Lin X, Teh AL, Chen L, Lim IY, Tan PF, MacIsaac JL, Morin AM, Yap F, Tan KH, Saw SM, Lee YS, Holbrook JD, Godfrey KM, Meaney MJ, Kobor MS, Chong YS, Gluckman PD, Karnani N. Choice of surrogate tissue influences neonatal EWAS findings. BMC Med 2017; 15:211. [PMID: 29202839 PMCID: PMC5715509 DOI: 10.1186/s12916-017-0970-x] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2017] [Accepted: 10/31/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Epigenomes are tissue specific and thus the choice of surrogate tissue can play a critical role in interpreting neonatal epigenome-wide association studies (EWAS) and in their extrapolation to target tissue. To develop a better understanding of the link between tissue specificity and neonatal EWAS, and the contributions of genotype and prenatal factors, we compared genome-wide DNA methylation of cord tissue and cord blood, two of the most accessible surrogate tissues at birth. METHODS In 295 neonates, DNA methylation was profiled using Infinium HumanMethylation450 beadchip arrays. Sites of inter-individual variability in DNA methylation were mapped and compared across the two surrogate tissues at birth, i.e., cord tissue and cord blood. To ascertain the similarity to target tissues, DNA methylation profiles of surrogate tissues were compared to 25 primary tissues/cell types mapped under the Epigenome Roadmap project. Tissue-specific influences of genotype on the variable CpGs were also analyzed. Finally, to interrogate the impact of the in utero environment, EWAS on 45 prenatal factors were performed and compared across the surrogate tissues. RESULTS Neonatal EWAS results were tissue specific. In comparison to cord blood, cord tissue showed higher inter-individual variability in the epigenome, with a lower proportion of CpGs influenced by genotype. Both neonatal tissues were good surrogates for target tissues of mesodermal origin. They also showed distinct phenotypic associations, with effect sizes of the overlapping CpGs being in the same order of magnitude. CONCLUSIONS The inter-relationship between genetics, prenatal factors and epigenetics is tissue specific, and requires careful consideration in designing and interpreting future neonatal EWAS. TRIAL REGISTRATION This birth cohort is a prospective observational study, designed to study the developmental origins of health and disease, and was retrospectively registered on 1 July 2010 under the identifier NCT01174875 .
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Affiliation(s)
- Xinyi Lin
- Singapore Institute for Clinical Sciences, A*STAR, Singapore, 117609, Singapore.,Duke NUS Medical School, Singapore, 169857, Singapore
| | - Ai Ling Teh
- Singapore Institute for Clinical Sciences, A*STAR, Singapore, 117609, Singapore
| | - Li Chen
- Singapore Institute for Clinical Sciences, A*STAR, Singapore, 117609, Singapore
| | - Ives Yubin Lim
- Singapore Institute for Clinical Sciences, A*STAR, Singapore, 117609, Singapore
| | - Pei Fang Tan
- Singapore Institute for Clinical Sciences, A*STAR, Singapore, 117609, Singapore
| | - Julia L MacIsaac
- Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, Vancouver, BC, V5Z 4H4, Canada
| | - Alexander M Morin
- Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, Vancouver, BC, V5Z 4H4, Canada
| | - Fabian Yap
- KK Women's and Children's Hospital, Singapore, 229899, Singapore
| | - Kok Hian Tan
- KK Women's and Children's Hospital, Singapore, 229899, Singapore
| | - Seang Mei Saw
- Duke NUS Medical School, Singapore, 169857, Singapore.,Saw Swee Hock School of Public Health, National University of Singapore, Singapore, 117597, Singapore.,Singapore Eye Research Institute, Singapore, 169856, Singapore
| | - Yung Seng Lee
- Singapore Institute for Clinical Sciences, A*STAR, Singapore, 117609, Singapore.,Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore.,Division of Paediatric Endocrinology and Diabetes, Khoo Teck Puat-National University Children's Medical Institute, National University Health System, Singapore, 119228, Singapore
| | - Joanna D Holbrook
- Singapore Institute for Clinical Sciences, A*STAR, Singapore, 117609, Singapore.,NIHR Biomedical Research Centre, University of Southampton, Southampton, SO16 6YD, UK
| | - Keith M Godfrey
- MRC Lifecourse Epidemiology Unit and NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, SO16 6YD, UK
| | - Michael J Meaney
- Singapore Institute for Clinical Sciences, A*STAR, Singapore, 117609, Singapore.,Ludmer Centre for Neuroinformatics and Mental Health, Douglas University Mental Health Institute, McGill University, Montreal, Quebec, H4H 1R3, Canada
| | - Michael S Kobor
- Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, Vancouver, BC, V5Z 4H4, Canada
| | - Yap Seng Chong
- Singapore Institute for Clinical Sciences, A*STAR, Singapore, 117609, Singapore.,Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore
| | - Peter D Gluckman
- Singapore Institute for Clinical Sciences, A*STAR, Singapore, 117609, Singapore.,Centre for Human Evolution, Adaptation and Disease, Liggins Institute, University of Auckland, Auckland, 1142, New Zealand
| | - Neerja Karnani
- Singapore Institute for Clinical Sciences, A*STAR, Singapore, 117609, Singapore. .,Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore.
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18
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Tilley SK, Kim WY, Fry RC. Analysis of bladder cancer tumor CpG methylation and gene expression within The Cancer Genome Atlas identifies GRIA1 as a prognostic biomarker for basal-like bladder cancer. Am J Cancer Res 2017; 7:1850-1862. [PMID: 28979808 PMCID: PMC5622220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2017] [Accepted: 03/06/2017] [Indexed: 06/07/2023] Open
Abstract
Increased methylation levels at cytosines proximal to guanines (CpG) in the promoter regions of tumor suppressor genes have been reported to play an important role in the development and progression of bladder cancer. In this study, we conducted a genome-wide analysis using data from The Cancer Genome Atlas to better characterize CpG methylation and mRNA expression patterns in urothelial carcinomas and to identify new epigenetic biomarkers of survival. Across 408 tumors, we identified 223 genes that displayed significant relationships between CpG methylation and mRNA expression levels. Hypermethylation within 200 base pairs upstream of the transcription start site and hypomethylation within the 3' untranslated region and body region were associated with gene silencing. These 223 genes were functionally enriched for their role in glutamate receptor signaling and among them was a novel, tumor-stage-independent epigenetic biomarker of overall mortality, GRIA1. GRIA1 hypermethylation and elevated mRNA expression levels were associated with significantly worse survival outcomes in patients with basal-like urothelial carcinomas. Furthermore, 70 genes associated with glutamate receptor signaling were differentially expressed between basal (n = 203 tumors) and luminal (n = 205 tumors) subtypes of bladder cancer, including genes involved in glutamate receptor-mediated activation of the calmodulin, PI3K/Akt, and EGFR signaling pathways. The majority of genes displayed increased expression levels in basal-like subtypes. This research highlights glutamate receptors as targets for investigation in the development and pharmacological treatment of urothelial cancer.
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Affiliation(s)
- Sloane K Tilley
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North CarolinaChapel Hill, NC, 27599, USA
| | - William Y Kim
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel HillChapel Hill, North Carolina 27514, USA
| | - Rebecca C Fry
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North CarolinaChapel Hill, NC, 27599, USA
- Curriculum in Toxicology, The University of North CarolinaChapel Hill, NC, 27599, USA
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19
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Korkmaz FT, Kerr DE. Genome-wide methylation analysis reveals differentially methylated loci that are associated with an age-dependent increase in bovine fibroblast response to LPS. BMC Genomics 2017; 18:405. [PMID: 28545453 PMCID: PMC5445414 DOI: 10.1186/s12864-017-3796-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2016] [Accepted: 05/16/2017] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Differences in DNA methylation are known to contribute to the development of immune-related disorders in humans but relatively little is known about how methylation regulates immune function in cattle. Utilizing whole-transcriptome analyses of bovine dermal fibroblasts, we have previously identified an age and breed-dependent up-regulation of genes within the toll-like receptor 4 (TLR4) pathway that correlates with enhanced fibroblast production of IL-8 in response to lipopolysaccharide (LPS). Age-dependent differences in IL-8 production are abolished by treatment with 5-aza-2-deoxycytidine and Trichostatin A (AZA-TSA), suggesting epigenetic regulation of the innate response to LPS. In the current study, we performed reduced representation bisulfite sequencing (RRBS) on fibroblast cultures isolated from the same animals at 5- and 16-months of age to identify genes that exhibit variable methylation with age. To validate the role of methylation in gene expression, six innate response genes that were hyper-methylated in young animals were assessed by RT-qPCR in fibroblasts from animals at different ages and from different breeds. RESULTS We identified 14,094 differentially methylated CpGs (DMCs) that differed between fibroblast cultures at 5- versus 16-months of age. Of the 5065 DMCs that fell within gene regions, 1117 were located within promoters, 1057 were within gene exons and 2891 were within gene introns and 67% were more methylated in young cultures. Transcription factor enrichment of the promoter regions hyper-methylated in young cultures revealed significant regulation by the key pro-inflammatory regulator, NF-κB. Additionally, five out of six chosen genes (PIK3R1, FES, NFATC1, TNFSF13 and RORA) that were more methylated in young cultures showed a significant reduction in expression post-LPS treatment in comparison with older cultures. Two of these genes, FES and NFATC1, were similarly down-regulated in Angus cultures that also exhibit a low LPS response phenotype. CONCLUSIONS Our study has identified immune-related loci regulated by DNA methylation in cattle that may contribute to differential cellular response to LPS, two of which exhibit an identical expression profile in both low-responding age and breed phenotypes. Methylation biomarkers of differential immunity may prove useful in developing selection strategies for replacement cows that are less susceptible to severe infections, such as coliform mastitis.
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Affiliation(s)
- Filiz T Korkmaz
- Cellular, Molecular and Biomedical Sciences Program, University of Vermont, 89 Beaumont Avenue, C141C Given, Burlington, VT, 05405, USA. .,Department of Animal and Veterinary Sciences, University of Vermont, 570 Main Street, 213 Terrill Hall, Burlington, VT, 05405, USA.
| | - David E Kerr
- Cellular, Molecular and Biomedical Sciences Program, University of Vermont, 89 Beaumont Avenue, C141C Given, Burlington, VT, 05405, USA.,Department of Animal and Veterinary Sciences, University of Vermont, 570 Main Street, 213 Terrill Hall, Burlington, VT, 05405, USA
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20
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Han X, Wang J, Sun Y. Circulating Tumor DNA as Biomarkers for Cancer Detection. GENOMICS, PROTEOMICS & BIOINFORMATICS 2017; 15:59-72. [PMID: 28392479 PMCID: PMC5414889 DOI: 10.1016/j.gpb.2016.12.004] [Citation(s) in RCA: 153] [Impact Index Per Article: 19.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2016] [Revised: 12/13/2016] [Accepted: 12/20/2016] [Indexed: 12/23/2022]
Abstract
Detection of circulating tumor DNAs (ctDNAs) in cancer patients is an important component of cancer precision medicine ctDNAs. Compared to the traditional physical and biochemical methods, blood-based ctDNA detection offers a non-invasive and easily accessible way for cancer diagnosis, prognostic determination, and guidance for treatment. While studies on this topic are currently underway, clinical translation of ctDNA detection in various types of cancers has been attracting much attention, due to the great potential of ctDNA as blood-based biomarkers for early diagnosis and treatment of cancers. ctDNAs are detected and tracked primarily based on tumor-related genetic and epigenetic alterations. In this article, we reviewed the available studies on ctDNA detection and described the representative methods. We also discussed the current understanding of ctDNAs in cancer patients and their availability as potential biomarkers for clinical purposes. Considering the progress made and challenges involved in accurate detection of specific cell-free nucleic acids, ctDNAs hold promise to serve as biomarkers for cancer patients, and further validation is needed prior to their broad clinical use.
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Affiliation(s)
- Xiao Han
- CAS Key Laboratory of Genomic and Precision Medicine, China Gastrointestinal Cancer Research Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Junyun Wang
- CAS Key Laboratory of Genomic and Precision Medicine, China Gastrointestinal Cancer Research Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Yingli Sun
- CAS Key Laboratory of Genomic and Precision Medicine, China Gastrointestinal Cancer Research Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.
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21
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Sheppard A, Ngo S, Li X, Boyne M, Thompson D, Pleasants A, Gluckman P, Forrester T. Molecular Evidence for Differential Long-term Outcomes of Early Life Severe Acute Malnutrition. EBioMedicine 2017; 18:274-280. [PMID: 28330812 PMCID: PMC5405153 DOI: 10.1016/j.ebiom.2017.03.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Revised: 02/14/2017] [Accepted: 03/01/2017] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Severe acute malnutrition (SAM) in infants may present as one of two distinct syndromic forms: non-edematous (marasmus), with severe wasting and no nutritional edema; or edematous (kwashiorkor) with moderately severe wasting. These differences may be related to developmental changes prior to the exposure to SAM and phenotypic changes appear to persist into adulthood with differences between the two groups. We examined whether the different response to SAM and subsequent trajectories may be explained by developmentally-induced epigenetic differences. METHODS We extracted genomic DNA from muscle biopsies obtained from adult survivors of kwashiorkor (n=21) or marasmus (n=23) and compared epigenetic profiles (CpG methylation) between the two groups using the Infinium® 450K BeadChip array. FINDINGS We found significant differences in methylation of CpG sites from 63 genes in skeletal muscle DNA. Gene ontology studies showed significant differential methylation of genes in immune, body composition, metabolic, musculoskeletal growth, neuronal function and cardiovascular pathways, pathways compatible with the differences in the pathophysiology of adult survivors of SAM. INTERPRETATION These findings suggest persistent developmental influences on adult physiology in survivors of SAM. Since children who develop marasmus have lower birth weights and after rehabilitation have different intermediary metabolism, these studies provide further support for persistent developmentally-induced phenomena mediated by epigenetic processes affecting both the infant response to acute malnutrition and later life consequences. FUNDING Supported by a Grant from the Bill and Melinda Gates Foundation (Global Health OPP1066846), Grand Challenge "Discover New Ways to Achieve Healthy Growth." EVIDENCE BEFORE THIS STUDY Previous research has shown that infants who develop either kwashiorkor or marasmus in response to SAM differ in birth weight and subsequently have different metabolic patterns in both infancy and adulthood. ADDED VALUE OF THIS STUDY This study demonstrates epigenetic differences in the skeletal muscle of adult survivors of marasmus versus kwashiorkor and these differences are in genes that may underlie the longer-term consequences. IMPLICATIONS OF ALL THE AVAILABLE EVIDENCE These data are compatible with the different clinical responses to SAM arising from developmentally-induced epigenetic changes laid down largely before birth and provide evidence for the predictive adaptive response model operating in human development.
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Affiliation(s)
- Allan Sheppard
- The Liggins Institute, University of Auckland, 85 Park Road, Grafton, Auckland, New Zealand
| | - Sherry Ngo
- The Liggins Institute, University of Auckland, 85 Park Road, Grafton, Auckland, New Zealand
| | - Xiaoling Li
- The Liggins Institute, University of Auckland, 85 Park Road, Grafton, Auckland, New Zealand
| | - Michael Boyne
- Tropical Medicine Research Institute, The University of the West Indies, Mona, Kingston 7, Jamaica
| | - Debbie Thompson
- Tropical Medicine Research Institute, The University of the West Indies, Mona, Kingston 7, Jamaica
| | - Anthony Pleasants
- The Liggins Institute, University of Auckland, 85 Park Road, Grafton, Auckland, New Zealand
| | - Peter Gluckman
- The Liggins Institute, University of Auckland, 85 Park Road, Grafton, Auckland, New Zealand
| | - Terrence Forrester
- The Liggins Institute, University of Auckland, 85 Park Road, Grafton, Auckland, New Zealand; UWI Solutions for Developing Countries, The University of the West Indies, Mona, Kingston 7, Jamaica.
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22
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Lin X, Lim IY, Wu Y, Teh AL, Chen L, Aris IM, Soh SE, Tint MT, MacIsaac JL, Morin AM, Yap F, Tan KH, Saw SM, Kobor MS, Meaney MJ, Godfrey KM, Chong YS, Holbrook JD, Lee YS, Gluckman PD, Karnani N. Developmental pathways to adiposity begin before birth and are influenced by genotype, prenatal environment and epigenome. BMC Med 2017; 15:50. [PMID: 28264723 PMCID: PMC5340003 DOI: 10.1186/s12916-017-0800-1] [Citation(s) in RCA: 76] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2016] [Accepted: 01/21/2017] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Obesity is an escalating health problem worldwide, and hence the causes underlying its development are of primary importance to public health. There is growing evidence that suboptimal intrauterine environment can perturb the metabolic programing of the growing fetus, thereby increasing the risk of developing obesity in later life. However, the link between early exposures in the womb, genetic susceptibility, and perturbed epigenome on metabolic health is not well understood. In this study, we shed more light on this aspect by performing a comprehensive analysis on the effects of variation in prenatal environment, neonatal methylome, and genotype on birth weight and adiposity in early childhood. METHODS In a prospective mother-offspring cohort (N = 987), we interrogated the effects of 30 variables that influence the prenatal environment, umbilical cord DNA methylation, and genotype on offspring weight and adiposity, over the period from birth to 48 months. This is an interim analysis on an ongoing cohort study. RESULTS Eleven of 30 prenatal environments, including maternal adiposity, smoking, blood glucose and plasma unsaturated fatty acid levels, were associated with birth weight. Polygenic risk scores derived from genetic association studies on adult adiposity were also associated with birth weight and child adiposity, indicating an overlap between the genetic pathways influencing metabolic health in early and later life. Neonatal methylation markers from seven gene loci (ANK3, CDKN2B, CACNA1G, IGDCC4, P4HA3, ZNF423 and MIRLET7BHG) were significantly associated with birth weight, with a subset of these in genes previously implicated in metabolic pathways in humans and in animal models. Methylation levels at three of seven birth weight-linked loci showed significant association with prenatal environment, but none were affected by polygenic risk score. Six of these birth weight-linked loci continued to show a longitudinal association with offspring size and/or adiposity in early childhood. CONCLUSIONS This study provides further evidence that developmental pathways to adiposity begin before birth and are influenced by environmental, genetic and epigenetic factors. These pathways can have a lasting effect on offspring size, adiposity and future metabolic outcomes, and offer new opportunities for risk stratification and prevention of obesity. CLINICAL TRIAL REGISTRATION This birth cohort is a prospective observational study, designed to study the developmental origins of health and disease, and was retrospectively registered on 1 July 2010 under the identifier NCT01174875 .
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Affiliation(s)
- Xinyi Lin
- Singapore Institute for Clinical Sciences, A*STAR, 30 Medical Drive, Singapore, 117609, Singapore
| | - Ives Yubin Lim
- Singapore Institute for Clinical Sciences, A*STAR, 30 Medical Drive, Singapore, 117609, Singapore.,Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore
| | - Yonghui Wu
- Singapore Institute for Clinical Sciences, A*STAR, 30 Medical Drive, Singapore, 117609, Singapore
| | - Ai Ling Teh
- Singapore Institute for Clinical Sciences, A*STAR, 30 Medical Drive, Singapore, 117609, Singapore
| | - Li Chen
- Singapore Institute for Clinical Sciences, A*STAR, 30 Medical Drive, Singapore, 117609, Singapore
| | - Izzuddin M Aris
- Singapore Institute for Clinical Sciences, A*STAR, 30 Medical Drive, Singapore, 117609, Singapore
| | - Shu E Soh
- Singapore Institute for Clinical Sciences, A*STAR, 30 Medical Drive, Singapore, 117609, Singapore.,Department of Pediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore
| | - Mya Thway Tint
- Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore.,Department of Pediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore
| | - Julia L MacIsaac
- Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, Vancouver, BC, V5Z 4H4, Canada
| | - Alexander M Morin
- Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, Vancouver, BC, V5Z 4H4, Canada
| | - Fabian Yap
- KK Women's and Children's Hospital, Singapore, 229899, Singapore
| | - Kok Hian Tan
- KK Women's and Children's Hospital, Singapore, 229899, Singapore
| | - Seang Mei Saw
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, 117597, Singapore.,Singapore Eye Research Institute, Singapore, 169856, Singapore.,Duke NUS Medical School, Singapore, 169857, Singapore
| | - Michael S Kobor
- Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, Vancouver, BC, V5Z 4H4, Canada
| | - Michael J Meaney
- Singapore Institute for Clinical Sciences, A*STAR, 30 Medical Drive, Singapore, 117609, Singapore.,Ludmer Centre for Neuroinformatics and Mental Health, Douglas University Mental Health Institute, McGill University, Montreal, Quebec, H4H 1R3, Canada
| | - Keith M Godfrey
- MRC Lifecourse Epidemiology Unit and NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, SO16 6YD, UK
| | - Yap Seng Chong
- Singapore Institute for Clinical Sciences, A*STAR, 30 Medical Drive, Singapore, 117609, Singapore.,Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore
| | - Joanna D Holbrook
- Singapore Institute for Clinical Sciences, A*STAR, 30 Medical Drive, Singapore, 117609, Singapore
| | - Yung Seng Lee
- Singapore Institute for Clinical Sciences, A*STAR, 30 Medical Drive, Singapore, 117609, Singapore.,Department of Pediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore.,Division of Paediatric Endocrinology and Diabetes, Khoo Teck Puat-National University Children's Medical Institute, National University Health System, Singapore, 119228, Singapore
| | - Peter D Gluckman
- Singapore Institute for Clinical Sciences, A*STAR, 30 Medical Drive, Singapore, 117609, Singapore.,Centre for Human Evolution, Adaptation and Disease, Liggins Institute, University of Auckland, Auckland, 1142, New Zealand
| | - Neerja Karnani
- Singapore Institute for Clinical Sciences, A*STAR, 30 Medical Drive, Singapore, 117609, Singapore. .,Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore.
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23
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Wang J, Han X, Sun Y. DNA methylation signatures in circulating cell-free DNA as biomarkers for the early detection of cancer. SCIENCE CHINA-LIFE SCIENCES 2017; 60:356-362. [DOI: 10.1007/s11427-016-0253-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2016] [Accepted: 11/16/2016] [Indexed: 02/06/2023]
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24
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Begue G, Raue U, Jemiolo B, Trappe S. DNA methylation assessment from human slow- and fast-twitch skeletal muscle fibers. J Appl Physiol (1985) 2017; 122:952-967. [PMID: 28057818 DOI: 10.1152/japplphysiol.00867.2016] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Revised: 12/07/2016] [Accepted: 12/30/2016] [Indexed: 11/22/2022] Open
Abstract
A new application of the reduced representation bisulfite sequencing method was developed using low-DNA input to investigate the epigenetic profile of human slow- and fast-twitch skeletal muscle fibers. Successful library construction was completed with as little as 15 ng of DNA, and high-quality sequencing data were obtained with 32 ng of DNA. Analysis identified 143,160 differentially methylated CpG sites across 14,046 genes. In both fiber types, selected genes predominantly expressed in slow or fast fibers were hypomethylated, which was supported by the RNA-sequencing analysis. These are the first fiber type-specific methylation data from human skeletal muscle and provide a unique platform for future research.NEW & NOTEWORTHY This study validates a low-DNA input reduced representation bisulfite sequencing method for human muscle biopsy samples to investigate the methylation patterns at a fiber type-specific level. These are the first fiber type-specific methylation data reported from human skeletal muscle and thus provide initial insight into basal state differences in myosin heavy chain I and IIa muscle fibers among young, healthy men.
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Affiliation(s)
- Gwénaëlle Begue
- Human Performance Laboratory, Ball State University, Muncie, Indiana
| | - Ulrika Raue
- Human Performance Laboratory, Ball State University, Muncie, Indiana
| | - Bozena Jemiolo
- Human Performance Laboratory, Ball State University, Muncie, Indiana
| | - Scott Trappe
- Human Performance Laboratory, Ball State University, Muncie, Indiana
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25
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Lin X, Barton S, Holbrook JD. How to make DNA methylome wide association studies more powerful. Epigenomics 2016; 8:1117-29. [PMID: 27052998 PMCID: PMC5066141 DOI: 10.2217/epi-2016-0017] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2016] [Accepted: 03/23/2016] [Indexed: 02/06/2023] Open
Abstract
Genome-wide association studies had a troublesome adolescence, while researchers increased statistical power, in part by increasing subject numbers. Interrogating the interaction of genetic and environmental influences raised new challenges of statistical power, which were not easily bested by the addition of subjects. Screening the DNA methylome offers an attractive alternative as methylation can be thought of as a proxy for the combined influences of genetics and environment. There are statistical challenges unique to DNA methylome data and also multiple features, which can be exploited to increase power. We anticipate the development of DNA methylome association study designs and new analytical methods, together with integration of data from other molecular species and other studies, which will boost statistical power and tackle causality. In this way, the molecular trajectories that underlie disease development will be uncovered.
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Affiliation(s)
- Xinyi Lin
- Singapore Institute for Clinical Sciences (SICS), Agency for Science & Technology Research (A*STAR), Brenner Centre for Molecular Medicine, 30 Medical Drive, 117609, Singapore
| | - Sheila Barton
- MRC Lifecourse Epidemiology Unit, Faculty of Medicine, University of Southampton, Southampton, SO16 6YD, UK
| | - Joanna D Holbrook
- Singapore Institute for Clinical Sciences (SICS), Agency for Science & Technology Research (A*STAR), Brenner Centre for Molecular Medicine, 30 Medical Drive, 117609, Singapore
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26
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Jeyapalan JN, Doctor GT, Jones TA, Alberman SN, Tep A, Haria CM, Schwalbe EC, Morley ICF, Hill AA, LeCain M, Ottaviani D, Clifford SC, Qaddoumi I, Tatevossian RG, Ellison DW, Sheer D. DNA methylation analysis of paediatric low-grade astrocytomas identifies a tumour-specific hypomethylation signature in pilocytic astrocytomas. Acta Neuropathol Commun 2016; 4:54. [PMID: 27229157 PMCID: PMC4882864 DOI: 10.1186/s40478-016-0323-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2016] [Accepted: 05/04/2016] [Indexed: 12/30/2022] Open
Abstract
Low-grade gliomas (LGGs) account for about a third of all brain tumours in children. We conducted a detailed study of DNA methylation and gene expression to improve our understanding of the biology of pilocytic and diffuse astrocytomas. Pilocytic astrocytomas were found to have a distinctive signature at 315 CpG sites, of which 312 were hypomethylated and 3 were hypermethylated. Genomic analysis revealed that 182 of these sites are within annotated enhancers. The signature was not present in diffuse astrocytomas, or in published profiles of other brain tumours and normal brain tissue. The AP-1 transcription factor was predicted to bind within 200 bp of a subset of the 315 differentially methylated CpG sites; the AP-1 factors, FOS and FOSL1 were found to be up-regulated in pilocytic astrocytomas. We also analysed splice variants of the AP-1 target gene, CCND1, which encodes cell cycle regulator cyclin D1. CCND1a was found to be highly expressed in both pilocytic and diffuse astrocytomas, but diffuse astrocytomas have far higher expression of the oncogenic variant, CCND1b. These findings highlight novel genetic and epigenetic differences between pilocytic and diffuse astrocytoma, in addition to well-described alterations involving BRAF, MYB and FGFR1.
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Affiliation(s)
- Jennie N Jeyapalan
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, 4 Newark Street, London, E1 2AT, UK
| | - Gabriel T Doctor
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, 4 Newark Street, London, E1 2AT, UK
| | - Tania A Jones
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, 4 Newark Street, London, E1 2AT, UK
| | - Samuel N Alberman
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, 4 Newark Street, London, E1 2AT, UK
| | - Alexander Tep
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, 4 Newark Street, London, E1 2AT, UK
| | - Chirag M Haria
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, 4 Newark Street, London, E1 2AT, UK
| | - Edward C Schwalbe
- Northern Institute for Cancer Research, Newcastle University, Newcastle upon Tyne, UK
- Department of Applied Sciences, Northumbria University, Newcastle upon Tyne, UK
| | - Isabel C F Morley
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, 4 Newark Street, London, E1 2AT, UK
| | - Alfred A Hill
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, 4 Newark Street, London, E1 2AT, UK
| | - Magdalena LeCain
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, 4 Newark Street, London, E1 2AT, UK
| | - Diego Ottaviani
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, 4 Newark Street, London, E1 2AT, UK
| | - Steven C Clifford
- Northern Institute for Cancer Research, Newcastle University, Newcastle upon Tyne, UK
| | - Ibrahim Qaddoumi
- Department of Oncology, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Ruth G Tatevossian
- Department of Pathology, St Jude Children's Research Hospital, Memphis, TN, 38105-3678, USA
| | - David W Ellison
- Department of Pathology, St Jude Children's Research Hospital, Memphis, TN, 38105-3678, USA.
| | - Denise Sheer
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, 4 Newark Street, London, E1 2AT, UK.
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27
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Genome-Wide DNA Methylation Analysis and Epigenetic Variations Associated with Congenital Aortic Valve Stenosis (AVS). PLoS One 2016; 11:e0154010. [PMID: 27152866 PMCID: PMC4859473 DOI: 10.1371/journal.pone.0154010] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2015] [Accepted: 04/07/2016] [Indexed: 11/19/2022] Open
Abstract
Congenital heart defect (CHD) is the most common cause of death from congenital anomaly. Among several candidate epigenetic mechanisms, DNA methylation may play an important role in the etiology of CHDs. We conducted a genome-wide DNA methylation analysis using an Illumina Infinium 450k human methylation assay in a cohort of 24 newborns who had aortic valve stenosis (AVS), with gestational-age matched controls. The study identified significantly-altered CpG methylation at 59 sites in 52 genes in AVS subjects as compared to controls (either hypermethylated or demethylated). Gene Ontology analysis identified biological processes and functions for these genes including positive regulation of receptor-mediated endocytosis. Consistent with prior clinical data, the molecular function categories as determined using DAVID identified low-density lipoprotein receptor binding, lipoprotein receptor binding and identical protein binding to be over-represented in the AVS group. A significant epigenetic change in the APOA5 and PCSK9 genes known to be involved in AVS was also observed. A large number CpG methylation sites individually demonstrated good to excellent diagnostic accuracy for the prediction of AVS status, thus raising possibility of molecular screening markers for this disorder. Using epigenetic analysis we were able to identify genes significantly involved in the pathogenesis of AVS.
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How to interpret epigenetic association studies: a guide for clinicians. BONEKEY REPORTS 2016; 5:797. [PMID: 27195108 DOI: 10.1038/bonekey.2016.24] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2015] [Accepted: 03/15/2016] [Indexed: 01/23/2023]
Abstract
Epigenetic mechanisms are able to alter gene expression, without altering DNA sequence, in a stable manner through cell divisions. They include, among others, the methylation of DNA cytosines and microRNAs and allow the cells to adapt to changing environmental conditions. In recent years, epigenetic association studies are providing new insights into the pathogenesis of complex disorders including prevalent skeletal disorders. Unlike the genome, the epigenome is cell and tissue specific and may change with age and a number of acquired factors. This poses particular difficulties for the design and interpretation of epigenetic studies, particularly those exploring the association of genome-wide epigenetic marks with disease phenotypes. In this report, we propose a framework to help in the critical appraisal of epigenetic association studies. In line with previous suggestions, we focus on the questions critical to appraise the validity of the study, to interpret the results and to assess the generalizability and relevance of the information.
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Teh AL, Pan H, Lin X, Lim YI, Patro CPK, Cheong CY, Gong M, MacIsaac JL, Kwoh CK, Meaney MJ, Kobor MS, Chong YS, Gluckman PD, Holbrook JD, Karnani N. Comparison of Methyl-capture Sequencing vs. Infinium 450K methylation array for methylome analysis in clinical samples. Epigenetics 2016; 11:36-48. [PMID: 26786415 PMCID: PMC4846133 DOI: 10.1080/15592294.2015.1132136] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
Interindividual variability in the epigenome has gained tremendous attention for its potential in pathophysiological investigation, disease diagnosis, and evaluation of clinical intervention. DNA methylation is the most studied epigenetic mark in epigenome-wide association studies (EWAS) as it can be detected from limited starting material. Infinium 450K methylation array is the most popular platform for high-throughput profiling of this mark in clinical samples, as it is cost-effective and requires small amounts of DNA. However, this method suffers from low genome coverage and errors introduced by probe cross-hybridization. Whole-genome bisulfite sequencing can overcome these limitations but elevates the costs tremendously. Methyl-Capture Sequencing (MC Seq) is an attractive intermediate solution to increase the methylome coverage in large sample sets. Here we first demonstrate that MC Seq can be employed using DNA amounts comparable to the amounts used for Infinium 450K. Second, to provide guidance when choosing between the 2 platforms for EWAS, we evaluate and compare MC Seq and Infinium 450K in terms of coverage, technical variation, and concordance of methylation calls in clinical samples. Last, since the focus in EWAS is to study interindividual variation, we demonstrate the utility of MC Seq in studying interindividual variation in subjects from different ethnicities.
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Affiliation(s)
- Ai Ling Teh
- a Singapore Institute for Clinical Sciences, A*STAR , Singapore
| | - Hong Pan
- a Singapore Institute for Clinical Sciences, A*STAR , Singapore.,b School of Computer Engineering, Nanyang Technological University , Singapore
| | - Xinyi Lin
- a Singapore Institute for Clinical Sciences, A*STAR , Singapore
| | - Yubin Ives Lim
- a Singapore Institute for Clinical Sciences, A*STAR , Singapore.,c Yong Loo Lin School of Medicine, National University of Singapore , Singapore
| | | | | | - Min Gong
- a Singapore Institute for Clinical Sciences, A*STAR , Singapore
| | - Julia L MacIsaac
- e Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute , Department of Medical Genetics , University of British Columbia , Vancouver , BC , Canada
| | - Chee-Keong Kwoh
- b School of Computer Engineering, Nanyang Technological University , Singapore
| | - Michael J Meaney
- a Singapore Institute for Clinical Sciences, A*STAR , Singapore.,d Ludmer Center for Neuroinformatics & Mental Health, Douglas University Mental Health Institute, McGill University , Montreal , Quebec Canada
| | - Michael S Kobor
- e Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute , Department of Medical Genetics , University of British Columbia , Vancouver , BC , Canada
| | - Yap-Seng Chong
- a Singapore Institute for Clinical Sciences, A*STAR , Singapore.,c Yong Loo Lin School of Medicine, National University of Singapore , Singapore
| | - Peter D Gluckman
- a Singapore Institute for Clinical Sciences, A*STAR , Singapore.,f Centre for Human Evolution, Adaptation and Disease, Liggins Institute, University of Auckland , Auckland , New Zealand
| | | | - Neerja Karnani
- a Singapore Institute for Clinical Sciences, A*STAR , Singapore.,c Yong Loo Lin School of Medicine, National University of Singapore , Singapore
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Abstract
Next-generation sequencing (NGS) approaches are highly applicable to clinical studies. We review recent advances in sequencing technologies, as well as their benefits and tradeoffs, to provide an overview of clinical genomics from study design to computational analysis. Sequencing technologies enable genomic, transcriptomic, and epigenomic evaluations. Studies that use a combination of whole genome, exome, mRNA, and bisulfite sequencing are now feasible due to decreasing sequencing costs. Single-molecule sequencing increases read length, with the MinIONTM nanopore sequencer, which offers a uniquely portable option at a lower cost. Many of the published comparisons we review here address the challenges associated with different sequencing methods. Overall, NGS techniques, coupled with continually improving analysis algorithms, are useful for clinical studies in many realms, including cancer, chronic illness, and neurobiology. We, and others in the field, anticipate the clinical use of NGS approaches will continue to grow, especially as we shift into an era of precision medicine.
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Affiliation(s)
- Priyanka Vijay
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, New York. Tri-Institutional Training Program in Computational Biology and Medicine, Weill Cornell Medical College, New York, New York
| | - Alexa B.R. McIntyre
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, New York. Tri-Institutional Training Program in Computational Biology and Medicine, Weill Cornell Medical College, New York, New York
| | - Christopher E. Mason
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, New York. Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, New York. Feil Family Brain and Mind Research Institute, New York, New York
| | - Jeffrey P. Greenfield
- Department of Neurological Surgery, New York-Presbyterian Hospital, Weill Cornell Medical College, New York, New York
| | - Sheng Li
- Department of Neurological Surgery, New York-Presbyterian Hospital, Weill Cornell Medical College, New York, New York
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Riancho JA. Epigenetics of Osteoporosis: Critical Analysis of Epigenetic Epidemiology Studies. Curr Genomics 2015; 16:405-10. [PMID: 27019615 PMCID: PMC4765527 DOI: 10.2174/1389202916666150817213250] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2015] [Revised: 06/15/2015] [Accepted: 06/15/2015] [Indexed: 11/22/2022] Open
Abstract
Osteoarthritis (OA) is an age-related disease with poorly understood pathogenesis. Recent studies have demonstrated that miRNA might play a key role in OA initiation and development. We reviewed recent publications and elucidated the connection between miRNA and OA cartilage anabolic and catabolic signals, including four signaling pathways: TGF-β/Smads and BMPs signaling, associated with cartilage anabolism; and MAPK and NF-KB signaling, associated with cartilage catabolism. We also explored the relationships with MMP, ADAMTS and NOS (NitricOxide Synthases) families, as well as with the catabolic cytokines IL-1 and TNF-α. The potential role of miRNAs in biological processes such as cartilage degeneration, chondrocyte proliferation, and differentiation is discussed. Collective evidence indicates that miRNAs play a critical role in cartilage degeneration. These findings will aid in understanding the molecular network that governs articular cartilage homeostasis and in to elucidate the role of miRNA in the pathogenesis of OA.
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Affiliation(s)
- José A. Riancho
- Service of Internal Medicine, Hospital U.M. Valdecilla, and Department of Medicine, University of Cantabria. IDIVAL, RETICEF. Santander, Spain
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32
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Kato N, Loh M, Takeuchi F, Verweij N, Wang X, Zhang W, Kelly TN, Saleheen D, Lehne B, Leach IM, Drong AW, Abbott J, Wahl S, Tan ST, Scott WR, Campanella G, Chadeau-Hyam M, Afzal U, Ahluwalia TS, Bonder MJ, Chen P, Dehghan A, Edwards TL, Esko T, Go MJ, Harris SE, Hartiala J, Kasela S, Kasturiratne A, Khor CC, Kleber ME, Li H, Yu Mok Z, Nakatochi M, Sapari NS, Saxena R, Stewart AFR, Stolk L, Tabara Y, Teh AL, Wu Y, Wu JY, Zhang Y, Aits I, Da Silva Couto Alves A, Das S, Dorajoo R, Hopewell JC, Kim YK, Koivula RW, Luan J, Lyytikäinen LP, Nguyen QN, Pereira MA, Postmus I, Raitakari OT, Bryan MS, Scott RA, Sorice R, Tragante V, Traglia M, White J, Yamamoto K, Zhang Y, Adair LS, Ahmed A, Akiyama K, Asif R, Aung T, Barroso I, Bjonnes A, Braun TR, Cai H, Chang LC, Chen CH, Cheng CY, Chong YS, Collins R, Courtney R, Davies G, Delgado G, Do LD, Doevendans PA, Gansevoort RT, Gao YT, Grammer TB, Grarup N, Grewal J, Gu D, Wander GS, Hartikainen AL, Hazen SL, He J, Heng CK, Hixson JE, Hofman A, Hsu C, Huang W, Husemoen LLN, Hwang JY, et alKato N, Loh M, Takeuchi F, Verweij N, Wang X, Zhang W, Kelly TN, Saleheen D, Lehne B, Leach IM, Drong AW, Abbott J, Wahl S, Tan ST, Scott WR, Campanella G, Chadeau-Hyam M, Afzal U, Ahluwalia TS, Bonder MJ, Chen P, Dehghan A, Edwards TL, Esko T, Go MJ, Harris SE, Hartiala J, Kasela S, Kasturiratne A, Khor CC, Kleber ME, Li H, Yu Mok Z, Nakatochi M, Sapari NS, Saxena R, Stewart AFR, Stolk L, Tabara Y, Teh AL, Wu Y, Wu JY, Zhang Y, Aits I, Da Silva Couto Alves A, Das S, Dorajoo R, Hopewell JC, Kim YK, Koivula RW, Luan J, Lyytikäinen LP, Nguyen QN, Pereira MA, Postmus I, Raitakari OT, Bryan MS, Scott RA, Sorice R, Tragante V, Traglia M, White J, Yamamoto K, Zhang Y, Adair LS, Ahmed A, Akiyama K, Asif R, Aung T, Barroso I, Bjonnes A, Braun TR, Cai H, Chang LC, Chen CH, Cheng CY, Chong YS, Collins R, Courtney R, Davies G, Delgado G, Do LD, Doevendans PA, Gansevoort RT, Gao YT, Grammer TB, Grarup N, Grewal J, Gu D, Wander GS, Hartikainen AL, Hazen SL, He J, Heng CK, Hixson JE, Hofman A, Hsu C, Huang W, Husemoen LLN, Hwang JY, Ichihara S, Igase M, Isono M, Justesen JM, Katsuya T, Kibriya MG, Kim YJ, Kishimoto M, Koh WP, Kohara K, Kumari M, Kwek K, Lee NR, Lee J, Liao J, Lieb W, Liewald DCM, Matsubara T, Matsushita Y, Meitinger T, Mihailov E, Milani L, Mills R, Mononen N, Müller-Nurasyid M, Nabika T, Nakashima E, Ng HK, Nikus K, Nutile T, Ohkubo T, Ohnaka K, Parish S, Paternoster L, Peng H, Peters A, Pham ST, Pinidiyapathirage MJ, Rahman M, Rakugi H, Rolandsson O, Ann Rozario M, Ruggiero D, Sala CF, Sarju R, Shimokawa K, Snieder H, Sparsø T, Spiering W, Starr JM, Stott DJ, Stram DO, Sugiyama T, Szymczak S, Tang WHW, Tong L, Trompet S, Turjanmaa V, Ueshima H, Uitterlinden AG, Umemura S, Vaarasmaki M, van Dam RM, van Gilst WH, van Veldhuisen DJ, Viikari JS, Waldenberger M, Wang Y, Wang A, Wilson R, Wong TY, Xiang YB, Yamaguchi S, Ye X, Young RD, Young TL, Yuan JM, Zhou X, Asselbergs FW, Ciullo M, Clarke R, Deloukas P, Franke A, Franks PW, Franks S, Friedlander Y, Gross MD, Guo Z, Hansen T, Jarvelin MR, Jørgensen T, Jukema JW, kähönen M, Kajio H, Kivimaki M, Lee JY, Lehtimäki T, Linneberg A, Miki T, Pedersen O, Samani NJ, Sørensen TIA, Takayanagi R, Toniolo D, Ahsan H, Allayee H, Chen YT, Danesh J, Deary IJ, Franco OH, Franke L, Heijman BT, Holbrook JD, Isaacs A, Kim BJ, Lin X, Liu J, März W, Metspalu A, Mohlke KL, Sanghera DK, Shu XO, van Meurs JBJ, Vithana E, Wickremasinghe AR, Wijmenga C, Wolffenbuttel BHW, Yokota M, Zheng W, Zhu D, Vineis P, Kyrtopoulos SA, Kleinjans JCS, McCarthy MI, Soong R, Gieger C, Scott J, Teo YY, He J, Elliott P, Tai ES, van der Harst P, Kooner JS, Chambers JC. Trans-ancestry genome-wide association study identifies 12 genetic loci influencing blood pressure and implicates a role for DNA methylation. Nat Genet 2015; 47:1282-1293. [PMID: 26390057 PMCID: PMC4719169 DOI: 10.1038/ng.3405] [Show More Authors] [Citation(s) in RCA: 255] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2015] [Accepted: 08/21/2015] [Indexed: 12/17/2022]
Abstract
We carried out a trans-ancestry genome-wide association and replication study of blood pressure phenotypes among up to 320,251 individuals of East Asian, European and South Asian ancestry. We find genetic variants at 12 new loci to be associated with blood pressure (P = 3.9 × 10(-11) to 5.0 × 10(-21)). The sentinel blood pressure SNPs are enriched for association with DNA methylation at multiple nearby CpG sites, suggesting that, at some of the loci identified, DNA methylation may lie on the regulatory pathway linking sequence variation to blood pressure. The sentinel SNPs at the 12 new loci point to genes involved in vascular smooth muscle (IGFBP3, KCNK3, PDE3A and PRDM6) and renal (ARHGAP24, OSR1, SLC22A7 and TBX2) function. The new and known genetic variants predict increased left ventricular mass, circulating levels of NT-proBNP, and cardiovascular and all-cause mortality (P = 0.04 to 8.6 × 10(-6)). Our results provide new evidence for the role of DNA methylation in blood pressure regulation.
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Affiliation(s)
- Norihiro Kato
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Marie Loh
- Institute of Health Sciences, University of Oulu, Oulu, Finland
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Translational Laboratory in Genetic Medicine, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Fumihiko Takeuchi
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Niek Verweij
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Xu Wang
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Ealing Hospital National Health Service (NHS) Trust, Middlesex, UK
| | - Tanika N Kelly
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA
| | - Danish Saleheen
- Center for Non-Communicable Diseases, Karachi, Pakistan
- Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
- Cardiovascular Institute, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Benjamin Lehne
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - Irene Mateo Leach
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Alexander W Drong
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - James Abbott
- Bioinformatics Support Service, Imperial College London, London, UK
| | - Simone Wahl
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Sian-Tsung Tan
- Ealing Hospital National Health Service (NHS) Trust, Middlesex, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - William R Scott
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Gianluca Campanella
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - Marc Chadeau-Hyam
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - Uzma Afzal
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Ealing Hospital National Health Service (NHS) Trust, Middlesex, UK
| | - Tarunveer S Ahluwalia
- Novo Nordisk Foundation Centre for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Copenhagen Prospective Studies on Asthma in Childhood (COSPAC), Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
- Steno Diabetes Center, Gentofte, Denmark
| | - Marc Jan Bonder
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Peng Chen
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
| | - Abbas Dehghan
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Todd L Edwards
- Vanderbilt Epidemiology Center, Center for Human Genetics Research, Division of Epidemiology, Department of Medicine, Vanderbilt University, Nashville, Tennessee, USA
| | - Tõnu Esko
- Estonian Genome Center, University of Tartu, Tartu, Estonia
- Division of Endocrinology, Children’s Hospital Boston, Boston, Massachusetts, USA
- Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Min Jin Go
- Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do, Republic of Korea
| | - Sarah E Harris
- Medical Genetics Section, University of Edinburgh Centre for Genomic and Experimental Medicine and Medical Research Council (MRC) Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, UK
- Centre for Cognitive Aging and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Jaana Hartiala
- Department of Preventive Medicine, University of Southern California Keck School of Medicine, Los Angeles, California, USA
- Institute for Genetic Medicine, University of Southern California Keck School of Medicine, Los Angeles, California, USA
| | - Silva Kasela
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | | | - Chiea-Chuen Khor
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
- Genome Institute of Singapore, A*STAR, Singapore
- Department of Ophthalmology, National University of Singapore, Singapore
- Department of Paediatrics, National University of Singapore, Singapore
| | - Marcus E Kleber
- Medical Clinic V, Mannheim Medical Faculty, University of Heidelberg, Mannheim, Germany
| | - Huaixing Li
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Zuan Yu Mok
- Cancer Science Institute of Singapore, National University of Singapore, Singapore
| | - Masahiro Nakatochi
- Center for Advanced Medicine and Clinical Research, Nagoya University Hospital, Nagoya, Japan
| | - Nur Sabrina Sapari
- Cancer Science Institute of Singapore, National University of Singapore, Singapore
| | - Richa Saxena
- Center for Human Genetic Research, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Alexandre F R Stewart
- University of Ottawa Heart Institute, Cardiovascular Research Methods Centre, Ottawa, Ontario, Canada
- Ruddy Canadian Cardiovascular Genetics Centre, Ottawa, Ontario, Canada
| | - Lisette Stolk
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Yasuharu Tabara
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Ai Ling Teh
- Singapore Institute for Clinical Sciences (SICS), A*STAR, Singapore
| | - Ying Wu
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Jer-Yuarn Wu
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
- School of Chinese Medicine, China Medical University, Taichung, Taiwan
| | - Yi Zhang
- State Key Laboratory of Medical Genetics, Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Institute of Hypertension, Shanghai, China
| | - Imke Aits
- Institute of Epidemiology and Biobank popgen, Christian Albrechts University of Kiel, Kiel, Germany
| | - Alexessander Da Silva Couto Alves
- Department of Epidemiology and Biostatistics, MRC Health Protection Agency (PHE) Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Shikta Das
- Department of Epidemiology and Biostatistics, MRC Health Protection Agency (PHE) Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | | | - Jemma C Hopewell
- Clinical Trials Support Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yun Kyoung Kim
- Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do, Republic of Korea
| | - Robert W Koivula
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Skåne University Hospital Malmö, Malmö, Sweden
| | - Jian’an Luan
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Leo-Pekka Lyytikäinen
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Department of Clinical Chemistry, University of Tampere School of Medicine, Tampere, Finland
| | - Quang N Nguyen
- Vietnam National Heart Institute, Bach Mai Hospital, Hanoi, Vietnam
| | - Mark A Pereira
- School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA
| | - Iris Postmus
- Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
- Netherlands Consortium for Healthy Ageing, Leiden, the Netherlands
| | - Olli T Raitakari
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
| | - Molly Scannell Bryan
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois, USA
| | - Robert A Scott
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Rossella Sorice
- Institute of Genetics and Biophysics A Buzzati-Traverso, CNR, Naples, Italy
| | - Vinicius Tragante
- Division of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Michela Traglia
- Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milan, Italy
- Institute for Maternal and Child Health, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) ‘Burlo Garofolo’, Trieste, Italy
| | - Jon White
- University College London Genetics Institute, Department of Genetics, Environment and Evolution, University College London, London, UK
| | - Ken Yamamoto
- Division of Genomics, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | - Yonghong Zhang
- Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, China
| | - Linda S Adair
- Department of Nutrition, University of North Carolina, Chapel Hill, North Carolina, USA
| | | | - Koichi Akiyama
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Rasheed Asif
- Center for Non-Communicable Diseases, Karachi, Pakistan
| | - Tin Aung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Inês Barroso
- Metabolic Disease Group, Wellcome Trust Sanger Institute, Cambridge, UK
- National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, UK
- University of Cambridge Metabolic Research Laboratories, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, UK
| | - Andrew Bjonnes
- Center for Human Genetic Research, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Timothy R Braun
- Department of Pediatrics, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA
| | - Hui Cai
- Vanderbilt Epidemiology Center, Division of Epidemiology, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt-Ingram Cancer Center, Division of Epidemiology, Department of Medicine, Vanderbilt University, Nashville, Tennessee, USA
| | - Li-Ching Chang
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Chien-Hsiun Chen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
- School of Chinese Medicine, China Medical University, Taichung, Taiwan
| | - Ching-Yu Cheng
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
- Department of Ophthalmology, National University of Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Centre for Quantitative Medicine, Office of Clinical Sciences, Duke–National University of Singapore Graduate Medical School, Singapore
| | - Yap-Seng Chong
- Singapore Institute for Clinical Sciences (SICS), A*STAR, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Rory Collins
- Clinical Trials Support Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Regina Courtney
- Vanderbilt Epidemiology Center, Division of Epidemiology, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt-Ingram Cancer Center, Division of Epidemiology, Department of Medicine, Vanderbilt University, Nashville, Tennessee, USA
| | - Gail Davies
- Centre for Cognitive Aging and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Graciela Delgado
- Medical Clinic V, Mannheim Medical Faculty, University of Heidelberg, Mannheim, Germany
| | - Loi D Do
- Vietnam National Heart Institute, Bach Mai Hospital, Hanoi, Vietnam
| | - Pieter A Doevendans
- Division of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Ron T Gansevoort
- Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Yu-Tang Gao
- Department of Epidemiology, Shanghai Cancer Institute, Shanghai, China
| | - Tanja B Grammer
- Medical Clinic V, Mannheim Medical Faculty, University of Heidelberg, Mannheim, Germany
| | - Niels Grarup
- Novo Nordisk Foundation Centre for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jagvir Grewal
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Ealing Hospital National Health Service (NHS) Trust, Middlesex, UK
| | - Dongfeng Gu
- Fu Wai Hospital and Cardiovascular Institute, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Gurpreet S Wander
- Dayanand Medical College and Hospital Unit, Hero DMC Heart Institute, Ludhiana, India
| | - Anna-Liisa Hartikainen
- Department of Obstetrics and Gynecology, Oulu University Hospital, Oulu, Finland
- Medical Research Center, University of Oulu, Oulu, Finland
- Unit of Primary Care, Oulu University Hospital, Oulu, Finland
| | - Stanley L Hazen
- Department of Cardiovascular Medicine, Heart and Vascular Institute, Cleveland Clinic, Cleveland, Ohio, USA
- Department of Cell Biology, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Jing He
- Department of Biostatistics, Vanderbilt University, Nashville, Tennessee, USA
| | - Chew-Kiat Heng
- Department of Paediatrics, Yong Loo Lin School of Medicine, Singapore
| | - James E Hixson
- Human Genetics Center, University of Texas School of Public Health at Houston, Houston, Texas, USA
| | - Albert Hofman
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Chris Hsu
- University of Southern California Keck School of Medicine, Los Angeles, California, USA
| | - Wei Huang
- Department of Genetics, Chinese National Human Genomic Center, Shanghai, China
| | - Lise L N Husemoen
- Research Centre for Prevention and Health, Glostrup University Hospital, Glostrup, Denmark
| | - Joo-Yeon Hwang
- Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do, Republic of Korea
| | - Sahoko Ichihara
- Graduate School of Regional Innovation Studies, Mie University, Tsu, Japan
| | - Michiya Igase
- Department of Geriatric Medicine, Ehime University Graduate School of Medicine, Toon, Ehime, Japan
| | - Masato Isono
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Johanne M Justesen
- Novo Nordisk Foundation Centre for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Tomohiro Katsuya
- Department of Clinical Gene Therapy, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Geriatric Medicine and Nephrology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Muhammad G Kibriya
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois, USA
| | - Young Jin Kim
- Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do, Republic of Korea
| | | | - Woon-Puay Koh
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
- Duke–National University of Singapore Graduate Medical School, Singapore
| | - Katsuhiko Kohara
- Department of Geriatric Medicine, Ehime University Graduate School of Medicine, Toon, Ehime, Japan
| | - Meena Kumari
- Department of Epidemiology and Public Health, University College London, London, UK
| | | | - Nanette R Lee
- University of San Carlos Office of Population Studies Foundation, University of San Carlos, Cebu City, Philippines
- Department of Anthropology, Sociology and History, University of San Carlos, Cebu City, Philippines
| | - Jeannette Lee
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
| | - Jiemin Liao
- Department of Ophthalmology, National University of Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Wolfgang Lieb
- Institute of Epidemiology and Biobank popgen, Christian Albrechts University of Kiel, Kiel, Germany
| | - David C M Liewald
- Centre for Cognitive Aging and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Tatsuaki Matsubara
- Department of Internal Medicine, Aichi-Gakuin University School of Dentistry, Nagoya, Japan
| | - Yumi Matsushita
- National Center for Global Health and Medicine, Toyama, Japan
| | - Thomas Meitinger
- Institute of Human Genetics, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, Technische Universität München, Munich, Germany
| | | | - Lili Milani
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Rebecca Mills
- Ealing Hospital National Health Service (NHS) Trust, Middlesex, UK
| | - Nina Mononen
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Department of Clinical Chemistry, University of Tampere School of Medicine, Tampere, Finland
| | - Martina Müller-Nurasyid
- Institute of Genetic Epidemiology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
- Department of Medicine I, Ludwig Maximilians University Munich, Munich, Germany
- German Center for Cardiovascular Research (DZHK), partner site Munich Heart Alliance, Munich, Germany
| | - Toru Nabika
- Department of Functional Pathology, Shimane University Faculty of Medicine, Izumo, Japan
| | - Eitaro Nakashima
- Division of Endocrinology and Diabetes, Department of Internal Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan
- Department of Diabetes and Endocrinology, Chubu Rosai Hospital, Nagoya, Japan
| | - Hong Kiat Ng
- Cancer Science Institute of Singapore, National University of Singapore, Singapore
| | - Kjell Nikus
- Heart Centre, Department of Cardiology, Tampere University Hospital and University of Tampere School of Medicine, Tampere, Finland
| | - Teresa Nutile
- Institute of Genetics and Biophysics A Buzzati-Traverso, CNR, Naples, Italy
| | - Takayoshi Ohkubo
- Department of Hygiene and Public Health, Teikyo University School of Medicine, Tokyo, Japan
| | - Keizo Ohnaka
- Department of Geriatric Medicine, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Sarah Parish
- Clinical Trials Support Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | - Hao Peng
- Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, China
| | - Annette Peters
- Institute of Epidemiology II, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Cardiovascular Research (DZHK), partner site Munich Heart Alliance, Munich, Germany
| | - Son T Pham
- Vietnam National Heart Institute, Bach Mai Hospital, Hanoi, Vietnam
| | | | - Mahfuzar Rahman
- UChicago Research Bangladesh, Uttara, Dhaka, Bangladesh
- Research and Evaluation Division, Bangladesh Rehabilitation Assistance Committee (BRAC), Dhaka, Bangladesh
| | - Hiromi Rakugi
- Department of Geriatric Medicine and Nephrology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Olov Rolandsson
- Department of Public Health and Clinical Medicine, Section for Family Medicine, Umeå Universitet, Umeå, Sweden
| | - Michelle Ann Rozario
- Cancer Science Institute of Singapore, National University of Singapore, Singapore
| | - Daniela Ruggiero
- Institute of Genetics and Biophysics A Buzzati-Traverso, CNR, Naples, Italy
| | - Cinzia F Sala
- Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milan, Italy
| | - Ralhan Sarju
- Dayanand Medical College and Hospital Unit, Hero DMC Heart Institute, Ludhiana, India
| | - Kazuro Shimokawa
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Thomas Sparsø
- Novo Nordisk Foundation Centre for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Wilko Spiering
- Department of Vascular Medicine, University Medical Center Utrecht, Utrecht, the Netherlands
| | - John M Starr
- Centre for Cognitive Aging and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, UK
| | - David J Stott
- Academic Section of Geriatric Medicine, Institute of Cardiovascular and Medical Sciences, Faculty of Medicine, University of Glasgow, Glasgow, UK
| | - Daniel O Stram
- University of Southern California Keck School of Medicine, Los Angeles, California, USA
| | - Takao Sugiyama
- Institute for Adult Diseases, Asahi Life Foundation, Tokyo, Japan
| | - Silke Szymczak
- Institute of Clinical Molecular Biology, Christian Albrechts University of Kiel, Kiel, Germany
| | - W H Wilson Tang
- Department of Cardiovascular Medicine, Heart and Vascular Institute, Cleveland Clinic, Cleveland, Ohio, USA
- Department of Cellular and Molecular Medicine, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Lin Tong
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois, USA
| | - Stella Trompet
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Väinö Turjanmaa
- Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland
- Department of Clinical Physiology, University of Tampere School of Medicine, Tampere, Finland
| | - Hirotsugu Ueshima
- Department of Health Science, Shiga University of Medical Science, Otsu, Japan
- Center for Epidemiologic Research in Asia, Shiga University of Medical Science, Otsu, Japan
| | - André G Uitterlinden
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Satoshi Umemura
- Department of Medical Science and Cardiorenal Medicine, Yokohama City University Graduate School of Medicine, Yokohama, Japan
| | - Marja Vaarasmaki
- Department of Obstetrics and Gynecology, Oulu University Hospital, Oulu, Finland
- Medical Research Center, University of Oulu, Oulu, Finland
- Unit of Primary Care, Oulu University Hospital, Oulu, Finland
| | - Rob M van Dam
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
| | - Wiek H van Gilst
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Dirk J van Veldhuisen
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Jorma S Viikari
- Division of Medicine, Turku University Hospital, Turku, Finland
- Department of Medicine, University of Turku, Turku, Finland
| | - Melanie Waldenberger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology II, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
| | - Yiqin Wang
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Aili Wang
- Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, China
| | - Rory Wilson
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology II, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
| | - Tien-Yin Wong
- Department of Ophthalmology, National University of Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Yong-Bing Xiang
- Department of Epidemiology, Shanghai Cancer Institute, Shanghai, China
| | - Shuhei Yamaguchi
- Third Department of Internal Medicine, Shimane University Faculty of Medicine, Izumo, Japan
| | - Xingwang Ye
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Robin D Young
- Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
| | - Terri L Young
- Neuroscience and Behavioural Disorders (NBD) Program, Duke–National University of Singapore Graduate Medical School, Singapore
- Duke Eye Center, Duke University Medical Center, Durham, North Carolina, USA
| | - Jian-Min Yuan
- Cancer Control and Population Sciences, University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania, USA
| | - Xueya Zhou
- Bioinformatics Division, Tsinghua National Laboratory for Informatics Science and Technology (TNLIST), Ministry of Education Key Laboratory of Bioinformatics, Department of Automation, Tsinghua University, Beijing, China
- Center for Synthetic and Systems Biology, TNLIST, Ministry of Education Key Laboratory of Bioinformatics, Department of Automation, Tsinghua University, Beijing, China
- Department of Psychiatry, University of Hong Kong, Hong Kong
| | - Folkert W Asselbergs
- Division of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht, the Netherlands
- Durrer Center for Cardiogenetic Research, Interuniversity Cardiology Institute of the Netherlands (ICIN)–Netherlands Heart Institute, Utrecht, the Netherlands
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK
| | - Marina Ciullo
- Institute of Genetics and Biophysics A Buzzati-Traverso, CNR, Naples, Italy
| | - Robert Clarke
- Clinical Trials Support Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Panos Deloukas
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- King Abdulaziz University, Jeddah, Saudi Arabia
| | - Andre Franke
- Institute of Clinical Molecular Biology, Christian Albrechts University of Kiel, Kiel, Germany
| | - Paul W Franks
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Skåne University Hospital Malmö, Malmö, Sweden
- Department of Public Health and Clinical Medicine, Section for Family Medicine, Umeå Universitet, Umeå, Sweden
- Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Steve Franks
- Institute of Reproductive and Developmental Biology, Imperial College London, Hammersmith Hospital, London, UK
| | | | - Myron D Gross
- School of Medicine, University of Minnesota, Minneapolis, Minnesota, USA
| | - Zhirong Guo
- Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, China
| | - Torben Hansen
- Novo Nordisk Foundation Centre for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Marjo-Riitta Jarvelin
- Institute of Health Sciences, University of Oulu, Oulu, Finland
- Department of Epidemiology and Biostatistics, MRC Health Protection Agency (PHE) Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Unit of Primary Care, Oulu University Hospital, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- Center for Life Course Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Torben Jørgensen
- Research Centre for Prevention and Health, Glostrup University Hospital, Glostrup, Denmark
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
- Durrer Center for Cardiogenetic Research, Interuniversity Cardiology Institute of the Netherlands (ICIN)–Netherlands Heart Institute, Utrecht, the Netherlands
- ICIN, Utrecht, the Netherlands
| | - Mika kähönen
- Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland
- Department of Clinical Physiology, University of Tampere School of Medicine, Tampere, Finland
| | - Hiroshi Kajio
- National Center for Global Health and Medicine, Toyama, Japan
| | - Mika Kivimaki
- Department of Epidemiology and Public Health, University College London, London, UK
| | - Jong-Young Lee
- Ministry of Health and Welfare, Seoul, Republic of Korea
- THERAGEN ETEX Bio Institute, Suwon, Republic of Korea
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Department of Clinical Chemistry, University of Tampere School of Medicine, Tampere, Finland
| | - Allan Linneberg
- Research Centre for Prevention and Health, Glostrup University Hospital, Glostrup, Denmark
- Department of Clinical Experimental Research, Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Tetsuro Miki
- Department of Geriatric Medicine, Ehime University Graduate School of Medicine, Toon, Ehime, Japan
| | - Oluf Pedersen
- Novo Nordisk Foundation Centre for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Nilesh J Samani
- Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, Leicester, UK
- NIHR Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester, UK
| | - Thorkild I A Sørensen
- Novo Nordisk Foundation Centre for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Institute of Preventive Medicine, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
| | - Ryoichi Takayanagi
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Kyushu, Japan
| | - Daniela Toniolo
- Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milan, Italy
- Institute of Molecular Genetics, National Research Council (CNR), Pavia, Italy
| | | | | | | | | | - Habibul Ahsan
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois, USA
| | - Hooman Allayee
- Department of Preventive Medicine, University of Southern California Keck School of Medicine, Los Angeles, California, USA
- Institute for Genetic Medicine, University of Southern California Keck School of Medicine, Los Angeles, California, USA
| | - Yuan-Tsong Chen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - John Danesh
- Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
- Wellcome Trust Sanger Institute, Hinxton, UK
| | - Ian J Deary
- Centre for Cognitive Aging and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Oscar H Franco
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Lude Franke
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Bastiaan T Heijman
- Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | | | - Aaron Isaacs
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Bong-Jo Kim
- Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do, Republic of Korea
| | - Xu Lin
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Jianjun Liu
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
- Genome Institute of Singapore, A*STAR, Singapore
| | - Winfried März
- Medical Clinic V, Mannheim Medical Faculty, University of Heidelberg, Mannheim, Germany
- Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria
- Synlab Academy, Synlab Services, Mannheim, Germany
| | | | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Dharambir K Sanghera
- Department of Pediatrics, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA
| | - Xiao-Ou Shu
- Vanderbilt Epidemiology Center, Division of Epidemiology, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt-Ingram Cancer Center, Division of Epidemiology, Department of Medicine, Vanderbilt University, Nashville, Tennessee, USA
| | - Joyce B J van Meurs
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Eranga Vithana
- Department of Ophthalmology, National University of Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Neuroscience and Behavioural Disorders (NBD) Program, Duke–National University of Singapore Graduate Medical School, Singapore
| | | | - Cisca Wijmenga
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Bruce H W Wolffenbuttel
- Department of Endocrinology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Mitsuhiro Yokota
- Department of Genome Science, Aichi-Gakuin University School of Dentistry, Nagoya, Japan
| | - Wei Zheng
- Vanderbilt Epidemiology Center, Division of Epidemiology, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt-Ingram Cancer Center, Division of Epidemiology, Department of Medicine, Vanderbilt University, Nashville, Tennessee, USA
| | - Dingliang Zhu
- State Key Laboratory of Medical Genetics, Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Institute of Hypertension, Shanghai, China
| | - Paolo Vineis
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - Soterios A Kyrtopoulos
- National Hellenic Research Foundation, Institute of Biology, Medicinal Chemistry and Biotechnology, Athens, Greece
| | - Jos C S Kleinjans
- Department of Toxicogenomics, Maastricht University, Maastricht, the Netherlands
| | - Mark I McCarthy
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Richie Soong
- Cancer Science Institute of Singapore, National University of Singapore, Singapore
- Department of Pathology, National University of Singapore, Singapore
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology II, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
| | - James Scott
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Yik-Ying Teo
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
- Genome Institute of Singapore, A*STAR, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- National University of Singapore Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore
- Life Sciences Institute, National University of Singapore, Singapore
- Department of Statistics and Applied Probability, National University of Singapore, Singapore
| | - Jiang He
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - E Shyong Tai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
- Department of Clinical Gene Therapy, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Pim van der Harst
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Durrer Center for Cardiogenetic Research, Interuniversity Cardiology Institute of the Netherlands (ICIN)–Netherlands Heart Institute, Utrecht, the Netherlands
| | - Jaspal S Kooner
- Ealing Hospital National Health Service (NHS) Trust, Middlesex, UK
- National Heart and Lung Institute, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, UK
| | - John C Chambers
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Ealing Hospital National Health Service (NHS) Trust, Middlesex, UK
- Imperial College Healthcare NHS Trust, London, UK
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Genome-wide DNA methylation detection by MethylCap-seq and Infinium HumanMethylation450 BeadChips: an independent large-scale comparison. Sci Rep 2015; 5:15375. [PMID: 26482909 PMCID: PMC4612737 DOI: 10.1038/srep15375] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2014] [Accepted: 09/24/2015] [Indexed: 01/10/2023] Open
Abstract
Two cost-efficient genome-scale methodologies to assess DNA-methylation are MethylCap-seq and Illumina’s Infinium HumanMethylation450 BeadChips (HM450). Objective information regarding the best-suited methodology for a specific research question is scant. Therefore, we performed a large-scale evaluation on a set of 70 brain tissue samples, i.e. 65 glioblastoma and 5 non-tumoral tissues. As MethylCap-seq coverages were limited, we focused on the inherent capacity of the methodology to detect methylated loci rather than a quantitative analysis. MethylCap-seq and HM450 data were dichotomized and performances were compared using a gold standard free Bayesian modelling procedure. While conditional specificity was adequate for both approaches, conditional sensitivity was systematically higher for HM450. In addition, genome-wide characteristics were compared, revealing that HM450 probes identified substantially fewer regions compared to MethylCap-seq. Although results indicated that the latter method can detect more potentially relevant DNA-methylation, this did not translate into the discovery of more differentially methylated loci between tumours and controls compared to HM450. Our results therefore indicate that both methodologies are complementary, with a higher sensitivity for HM450 and a far larger genome-wide coverage for MethylCap-seq, but also that a more comprehensive character does not automatically imply more significant results in biomarker studies.
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Bahado-Singh RO, Zaffra R, Albayarak S, Chelliah A, Bolinjkar R, Turkoglu O, Radhakrishna U. Epigenetic markers for newborn congenital heart defect (CHD). J Matern Fetal Neonatal Med 2015; 29:1881-7. [PMID: 26429603 DOI: 10.3109/14767058.2015.1069811] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
OBJECTIVE Our objective was to determine whether there were significant differences in genome-wide DNA methylation in newborns with major congenital heart defect (CHD) compared to controls. We also evaluated methylation of cytosines in CpG motifs for the detection of these CHDs. METHODS Genome-wide DNA methylation analysis was performed on DNA from 60 newborns with various CHDs, including hypoplastic left heart syndrome, ventricular septal deficit, atrial septal defect, pulmonary stenosis, coarctation of the aorta and Tetralogy of Fallot, and 32 controls. RESULTS Highly significant differences in cytosine methylation were seen in a large number of genes throughout the genome for all CHD categories. Gene ontology analysis of CHD overall indicated over-represented biological processes involving cell development and differentiation, and anatomical structure morphogenesis. Methylation of individual cytosines in CpG motifs had high diagnostic accuracy for the detection of CHD. For example, for coarctation one predictive model based on levels of particular cytosine nucleotides achieved a sensitivity of 100% and specificity of 93.8% (AUC = 0.974, p < 0.00001). CONCLUSION Profound differences in cytosine methylation were observed in hundreds of genes in newborns with different types of CHD. There appears to be the potential for development of accurate genetic biomarkers for CHD detection in newborns.
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Affiliation(s)
- Ray O Bahado-Singh
- a Department of Obstetrics and Gynecology , William Beaumont School of Medicine, Oakland University , Royal Oak , MI , USA and
| | - Rita Zaffra
- b Department of Obstetrics and Gynecology , Wayne State University School of Medicine , Detroit , MI , USA
| | - Samet Albayarak
- b Department of Obstetrics and Gynecology , Wayne State University School of Medicine , Detroit , MI , USA
| | - Anushka Chelliah
- b Department of Obstetrics and Gynecology , Wayne State University School of Medicine , Detroit , MI , USA
| | - Rashmi Bolinjkar
- b Department of Obstetrics and Gynecology , Wayne State University School of Medicine , Detroit , MI , USA
| | - Onur Turkoglu
- a Department of Obstetrics and Gynecology , William Beaumont School of Medicine, Oakland University , Royal Oak , MI , USA and
| | - Uppala Radhakrishna
- a Department of Obstetrics and Gynecology , William Beaumont School of Medicine, Oakland University , Royal Oak , MI , USA and
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Choi M, Lee J, Le MT, Nguyen DT, Park S, Soundrarajan N, Schachtschneider KM, Kim J, Park JK, Kim JH, Park C. Genome-wide analysis of DNA methylation in pigs using reduced representation bisulfite sequencing. DNA Res 2015; 22:343-55. [PMID: 26358297 PMCID: PMC4596400 DOI: 10.1093/dnares/dsv017] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2015] [Accepted: 07/31/2015] [Indexed: 01/15/2023] Open
Abstract
DNA methylation plays a major role in the epigenetic regulation of gene expression. Although a few DNA methylation profiling studies of porcine genome which is one of the important biomedical models for human diseases have been reported, the available data are still limited. We tried to study methylation patterns of diverse pig tissues as a study of the International Swine Methylome Consortium to generate the swine reference methylome map to extensively evaluate the methylation profile of the pig genome at a single base resolution. We generated and analysed the DNA methylome profiles of five different tissues and a cell line originated from pig. On average, 39.85 and 62.1% of cytosine and guanine dinucleotides (CpGs) of CpG islands and 2 kb upstream of transcription start sites were covered, respectively. We detected a low rate (an average of 1.67%) of non-CpG methylation in the six samples except for the neocortex (2.3%). The observed global CpG methylation patterns of pigs indicated high similarity to other mammals including humans. The percentage of CpG methylation associated with gene features was similar among the tissues but not for a 3D4/2 cell line. Our results provide essential information for future studies of the porcine epigenome.
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Affiliation(s)
- Minkyeung Choi
- Department of Animal Biotechnology, Konkuk University, Kwangjin-gu, Seoul 143-701, Korea
| | - Jongin Lee
- Department of Animal Biotechnology, Konkuk University, Kwangjin-gu, Seoul 143-701, Korea
| | - Min Thong Le
- Department of Animal Biotechnology, Konkuk University, Kwangjin-gu, Seoul 143-701, Korea
| | - Dinh Truong Nguyen
- Department of Animal Biotechnology, Konkuk University, Kwangjin-gu, Seoul 143-701, Korea
| | - Suhyun Park
- Department of Animal Biotechnology, Konkuk University, Kwangjin-gu, Seoul 143-701, Korea
| | | | - Kyle M Schachtschneider
- Department of Animal Sciences, University of Illinois, Urbana, IL, USA Animal Breeding and Genomics Center, Wageningen University, Wageningen, The Netherlands
| | - Jaebum Kim
- Department of Animal Biotechnology, Konkuk University, Kwangjin-gu, Seoul 143-701, Korea
| | - Jin-Ki Park
- Animal Biotechnology Division, National Institute of Animal Science, Suwon, Korea
| | - Jin-Hoi Kim
- Department of Animal Biotechnology, Konkuk University, Kwangjin-gu, Seoul 143-701, Korea
| | - Chankyu Park
- Department of Animal Biotechnology, Konkuk University, Kwangjin-gu, Seoul 143-701, Korea
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36
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Walker DL, Bhagwate AV, Baheti S, Smalley RL, Hilker CA, Sun Z, Cunningham JM. DNA methylation profiling: comparison of genome-wide sequencing methods and the Infinium Human Methylation 450 Bead Chip. Epigenomics 2015; 7:1287-302. [PMID: 26192535 DOI: 10.2217/epi.15.64] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
AIMS To compare the performance of four sequence-based and one microarray methods for DNA methylation profiling. METHODS DNA from two cell lines were profiled by reduced representation bisulfite sequencing, methyl capture sequencing (SS-Meth Seq), NimbleGen SeqCapEpi CpGiant(Nimblegen MethSeq), methylated DNA immunoprecipitation (MeDIP) and the Human Methylation 450 Bead Chip (Meth450K). RESULTS & CONCLUSION Despite differences in genome-wide coverage, high correlation and concordance were observed between different methods. Significant overlap of differentially methylated regions was identified between sequenced-based platforms. MeDIP provided the best coverage for the whole genome and gene body regions, while RRBS and Nimblegen MethSeq were superior for CpGs in CpG islands and promoters. Methylation analyses can be achieved by any of the five methods but understanding their differences may better address the research question being posed.
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Affiliation(s)
- Denise L Walker
- Medical Genome Facility, Mayo Clinic, 200, 1st St, SW, Rochester, MN 55905, USA
| | | | - Saurabh Baheti
- Division of Biomedical Statistics & Informatics, Mayo Clinic, Rochester, MN, USA
| | - Regenia L Smalley
- Medical Genome Facility, Mayo Clinic, 200, 1st St, SW, Rochester, MN 55905, USA
| | | | - Zhifu Sun
- Division of Biomedical Statistics & Informatics, Mayo Clinic, Rochester, MN, USA
| | - Julie M Cunningham
- Medical Genome Facility, Mayo Clinic, 200, 1st St, SW, Rochester, MN 55905, USA.,Division of Experimental Pathology, Department of Laboratory Medicine & Pathology, Mayo Clinic, Rochester, MN, USA
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37
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Pan H, Lin X, Wu Y, Chen L, Teh AL, Soh SE, Lee YS, Tint MT, MacIsaac JL, Morin AM, Tan KH, Yap F, Saw SM, Kobor MS, Meaney MJ, Godfrey KM, Chong YS, Gluckman PD, Karnani N, Holbrook JD. HIF3A association with adiposity: the story begins before birth. Epigenomics 2015; 7:937-50. [PMID: 26011824 PMCID: PMC4863876 DOI: 10.2217/epi.15.45] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Aim: Determine if the association of HIF3A DNA methylation with weight and adiposity is detectable early in life. Material & methods: We determined HIF3A genotype and DNA methylation patterns (on hybridization arrays) in DNA extracted from umbilical cords of 991 infants. Methylation levels at three CpGs in the HIF3A first intron were related to neonatal and infant anthropometry and to genotype at nearby polymorphic sites. Results & conclusion: Higher methylation levels at three previously described HIF3A CpGs were associated with greater infant weight and adiposity. The effect sizes were slightly smaller than those reported for adult BMI. There was also an interaction within cis-genotype. The association between higher DNA methylation at HIF3A and increased adiposity is present in neonates. In this study, no particular prenatal factor strongly influenced HIF3A hypermethylation. Our data nonetheless suggest shared prenatal influences on HIF3A methylation and adiposity.
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Affiliation(s)
- Hong Pan
- Singapore Institute for Clinical Sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, 117609, Singapore.,School of Computer Engineering, Nanyang Technological University (NTU), 639798, Singapore
| | - Xinyi Lin
- Singapore Institute for Clinical Sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, 117609, Singapore
| | - Yonghui Wu
- Singapore Institute for Clinical Sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, 117609, Singapore
| | - Li Chen
- Singapore Institute for Clinical Sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, 117609, Singapore
| | - Ai Ling Teh
- Singapore Institute for Clinical Sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, 117609, Singapore
| | - Shu E Soh
- Saw Swee Hock School of Public Health, National University of Singapore (NUS), 117597, Singapore.,Yong Loo Lin School of Medicine, National University of Singapore (NUS), 119228, Singapore
| | - Yung Seng Lee
- Singapore Institute for Clinical Sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, 117609, Singapore.,Yong Loo Lin School of Medicine, National University of Singapore (NUS), 119228, Singapore.,Division of Paediatric Endocrinology & Diabetes, Khoo Teck Puat-National University Children's Medical Institute, National University Health System, 119228, Singapore
| | - Mya Thway Tint
- Yong Loo Lin School of Medicine, National University of Singapore (NUS), 119228, Singapore
| | - Julia L MacIsaac
- Department of Medical Genetics, Centre for Molecular Medicine & Therapeutics, Child & Family Research Institute, University of British Columbia, Vancouver, BC, V5Z 4H4 Canada
| | - Alexander M Morin
- Department of Medical Genetics, Centre for Molecular Medicine & Therapeutics, Child & Family Research Institute, University of British Columbia, Vancouver, BC, V5Z 4H4 Canada
| | - Kok Hian Tan
- KK Women's and Children's Hospital, 229899, Singapore
| | - Fabian Yap
- KK Women's and Children's Hospital, 229899, Singapore
| | - Seang Mei Saw
- Saw Swee Hock School of Public Health, National University of Singapore (NUS), 117597, Singapore
| | - Michael S Kobor
- Department of Medical Genetics, Centre for Molecular Medicine & Therapeutics, Child & Family Research Institute, University of British Columbia, Vancouver, BC, V5Z 4H4 Canada
| | - Michael J Meaney
- Singapore Institute for Clinical Sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, 117609, Singapore.,Ludmer Centre for Neuroinformatics & Mental Health, Douglas University Mental Health Institute, McGill University, Montreal, (Quebec) H4H 1R3, Canada
| | - Keith M Godfrey
- MRC Lifecourse Epidemiology Unit & NIHR Southampton Biomedical Research Centre, University of Southampton & University Hospital Southampton NHS Foundation Trust, Southampton, SO16 6YD, UK
| | - Yap-Seng Chong
- Singapore Institute for Clinical Sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, 117609, Singapore.,Yong Loo Lin School of Medicine, National University of Singapore (NUS), 119228, Singapore
| | - Peter D Gluckman
- Singapore Institute for Clinical Sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, 117609, Singapore.,Centre for Human Evolution, Adaptation & Disease, Liggins Institute, University of Auckland, Auckland, 1142, New Zealand
| | - Neerja Karnani
- Singapore Institute for Clinical Sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, 117609, Singapore.,Yong Loo Lin School of Medicine, National University of Singapore (NUS), 119228, Singapore
| | - Joanna D Holbrook
- Singapore Institute for Clinical Sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, 117609, Singapore
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38
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Warton K, Samimi G. Methylation of cell-free circulating DNA in the diagnosis of cancer. Front Mol Biosci 2015; 2:13. [PMID: 25988180 PMCID: PMC4428375 DOI: 10.3389/fmolb.2015.00013] [Citation(s) in RCA: 152] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2015] [Accepted: 04/07/2015] [Indexed: 01/04/2023] Open
Abstract
A range of molecular alterations found in tumor cells, such as DNA mutations and DNA methylation, is reflected in cell-free circulating DNA (circDNA) released from the tumor into the blood, thereby making circDNA an ideal candidate for the basis of a blood-based cancer diagnosis test. In many cancer types, mutations driving tumor development and progression are present in a wide range of oncogenes and tumor suppressor genes. However, even when a gene is consistently mutated in a particular cancer, the mutations can be spread over very large regions of its sequence, making evaluation difficult. This diversity of sequence changes in tumor DNA presents a challenge for the development of blood tests based on DNA mutations for cancer diagnosis. Unlike mutations, DNA methylation that can be consistently measured, as it tends to occur in specific regions of the DNA called CpG islands. Since DNA methylation is reflected within circDNA, detection of tumor-specific DNA methylation in patient plasma is a feasible approach for the development of a blood-based test. Aberrant circDNA methylation has been described in most cancer types and is actively being investigated for clinical applications. A commercial blood test for colorectal cancer based on the methylation of the SEPT9 promoter region in circDNA is under review for approval by the Federal Drug Administration (FDA) for clinical use. In this paper, we review the state of research in circDNA methylation as an application for blood-based diagnostic tests in colorectal, breast, lung, pancreatic and ovarian cancers, and we consider some of the future directions and challenges in this field. There are a number of potential circDNA biomarkers currently under investigation, and experience with SEPT9 shows that the time to clinical translation can be relatively rapid, supporting the promise of circDNA as a biomarker.
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Affiliation(s)
- Kristina Warton
- Garvan Institute of Medical Research, The Kinghorn Cancer Centre and St Vincent's Clinical School, University of New South Wales Sydney, NSW, Australia
| | - Goli Samimi
- Garvan Institute of Medical Research, The Kinghorn Cancer Centre and St Vincent's Clinical School, University of New South Wales Sydney, NSW, Australia
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39
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Zheleznyakova GY, Nilsson EK, Kiselev AV, Maretina MA, Tishchenko LI, Fredriksson R, Baranov VS, Schiöth HB. Methylation levels of SLC23A2 and NCOR2 genes correlate with spinal muscular atrophy severity. PLoS One 2015; 10:e0121964. [PMID: 25821969 PMCID: PMC4378931 DOI: 10.1371/journal.pone.0121964] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2014] [Accepted: 02/09/2015] [Indexed: 11/19/2022] Open
Abstract
Spinal muscular atrophy (SMA) is a monogenic neurodegenerative disorder subdivided into four different types. Whole genome methylation analysis revealed 40 CpG sites associated with genes that are significantly differentially methylated between SMA patients and healthy individuals of the same age. To investigate the contribution of methylation changes to SMA severity, we compared the methylation level of found CpG sites, designed as "targets", as well as the nearest CpG sites in regulatory regions of ARHGAP22, CDK2AP1, CHML, NCOR2, SLC23A2 and RPL9 in three groups of SMA patients. Of notable interest, compared to type I SMA male patients, the methylation level of a target CpG site and one nearby CpG site belonging to the 5'UTR of SLC23A2 were significantly hypomethylated 19-22% in type III-IV patients. In contrast to type I SMA male patients, type III-IV patients demonstrated a 16% decrease in the methylation levels of a target CpG site, belonging to the 5'UTR of NCOR2. To conclude, this study validates the data of our previous study and confirms significant methylation changes in the SLC23A2 and NCOR2 regulatory regions correlates with SMA severity.
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Affiliation(s)
- Galina Yu. Zheleznyakova
- Department of Neuroscience, Uppsala University, Uppsala, Sweden
- Faculty of Biology, Saint-Petersburg State University, Saint-Petersburg, Russia
- Laboratory for Prenatal Diagnostics of Inherited Diseases, D.O. Ott Research Institute of Obstetrics and Gynecology RAMS, Saint-Petersburg, Russia
- * E-mail:
| | - Emil K. Nilsson
- Department of Neuroscience, Uppsala University, Uppsala, Sweden
| | - Anton V. Kiselev
- Laboratory for Prenatal Diagnostics of Inherited Diseases, D.O. Ott Research Institute of Obstetrics and Gynecology RAMS, Saint-Petersburg, Russia
| | - Marianna A. Maretina
- Faculty of Biology, Saint-Petersburg State University, Saint-Petersburg, Russia
- Laboratory for Prenatal Diagnostics of Inherited Diseases, D.O. Ott Research Institute of Obstetrics and Gynecology RAMS, Saint-Petersburg, Russia
| | | | | | - Vladislav S. Baranov
- Faculty of Biology, Saint-Petersburg State University, Saint-Petersburg, Russia
- Laboratory for Prenatal Diagnostics of Inherited Diseases, D.O. Ott Research Institute of Obstetrics and Gynecology RAMS, Saint-Petersburg, Russia
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40
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Field SF, Beraldi D, Bachman M, Stewart SK, Beck S, Balasubramanian S. Accurate measurement of 5-methylcytosine and 5-hydroxymethylcytosine in human cerebellum DNA by oxidative bisulfite on an array (OxBS-array). PLoS One 2015; 10:e0118202. [PMID: 25706862 PMCID: PMC4338296 DOI: 10.1371/journal.pone.0118202] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2014] [Accepted: 01/08/2015] [Indexed: 01/27/2023] Open
Abstract
The Infinium 450K Methylation array is an established tool for measuring methylation. However, the bisulfite (BS) reaction commonly used with the 450K array cannot distinguish between 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC). The oxidative-bisulfite assay disambiguates 5mC and 5hmC. We describe the use of oxBS in conjunction with the 450K array (oxBS-array) to analyse 5hmC/5mC in cerebellum DNA. The “methylation” level derived by the BS reaction is the combined level of 5mC and 5hmC at a given base, while the oxBS reaction gives the level of 5mC alone. The level of 5hmC is derived by subtracting the oxBS level from the BS level. Here we present an analysis method that distinguishes genuine positive levels of 5hmC at levels as low as 3%. We performed four replicates of the same sample of cerebellum and found a high level of reproducibility (average r for BS = 98.3, and average r for oxBS = 96.8). In total, 114,734 probes showed a significant positive measurement for 5hmC. The range at which we were able to distinguish 5hmC occupancy was between 3% and 42%. In order to investigate the effects of multiple replicates on 5hmC detection we also simulated fewer replicates and found that decreasing the number of replicates to two reduced the number of positive probes identified by > 50%. We validated our results using qPCR in conjunction with glucosylation of 5hmC sites followed by MspI digestion and we found good concordance with the array estimates (r = 0.94). This experiment provides a map of 5hmC in the cerebellum and a robust dataset for use as a standard in future 5hmC analyses. We also provide a novel method for validating the presence of 5hmC at low levels, and highlight some of the pitfalls associated with measuring 5hmC and 5mC.
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Affiliation(s)
- Sarah F. Field
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, United Kingdom
| | - Dario Beraldi
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, United Kingdom
| | - Martin Bachman
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, United Kingdom
| | - Sabrina K. Stewart
- Department of Cancer Biology, UCL Cancer Institute, University College London, London WC1E 6BT, United Kingdom
| | - Stephan Beck
- Department of Cancer Biology, UCL Cancer Institute, University College London, London WC1E 6BT, United Kingdom
| | - Shankar Balasubramanian
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, United Kingdom
- Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, United Kingdom
- * E-mail:
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41
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Brain-derived neurotrophic factor (BDNF) Val66Met polymorphism influences the association of the methylome with maternal anxiety and neonatal brain volumes. Dev Psychopathol 2015; 27:137-50. [DOI: 10.1017/s0954579414001357] [Citation(s) in RCA: 56] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
AbstractEarly life environments interact with genotype to determine stable phenotypic outcomes. Here we examined the influence of a variant in the brain-derived neurotropic factor (BDNF) gene (Val66Met), which underlies synaptic plasticity throughout the central nervous system, on the degree to which antenatal maternal anxiety associated with neonatal DNA methylation. We also examined the association between neonatal DNA methylation and brain substructure volume, as a function of BDNF genotype. Infant, but not maternal, BDNF genotype dramatically influences the association of antenatal anxiety on the epigenome at birth as well as that between the epigenome and neonatal brain structure. There was a greater impact of antenatal maternal anxiety on the DNA methylation of infants with the methionine (Met)/Met compared to both Met/valine (Val) and Val/Val genotypes. There were significantly more cytosine–phosphate–guanine sites where methylation levels covaried with right amygdala volume among Met/Met compared with both Met/Val and Val/Val carriers. In contrast, more cytosine–phosphate–guanine sites covaried with left hippocampus volume in Val/Val infants compared with infants of the Met/Val or Met/Met genotype. Thus, antenatal Maternal Anxiety × BDNF Val66Met Polymorphism interactions at the level of the epigenome are reflected differently in the structure of the amygdala and the hippocampus. These findings suggest that BDNF genotype regulates the sensitivity of the methylome to early environment and that differential susceptibility to specific environmental conditions may be both tissue and function specific.
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42
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Finer S, Mathews C, Lowe R, Smart M, Hillman S, Foo L, Sinha A, Williams D, Rakyan VK, Hitman GA. Maternal gestational diabetes is associated with genome-wide DNA methylation variation in placenta and cord blood of exposed offspring. Hum Mol Genet 2015; 24:3021-9. [DOI: 10.1093/hmg/ddv013] [Citation(s) in RCA: 151] [Impact Index Per Article: 15.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2014] [Accepted: 01/16/2015] [Indexed: 12/21/2022] Open
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43
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Huang RC, Garratt ES, Pan H, Wu Y, Davis EA, Barton SJ, Burdge GC, Godfrey KM, Holbrook JD, Lillycrop KA. Genome-wide methylation analysis identifies differentially methylated CpG loci associated with severe obesity in childhood. Epigenetics 2015; 10:995-1005. [PMID: 26646899 PMCID: PMC4844195 DOI: 10.1080/15592294.2015.1080411] [Citation(s) in RCA: 65] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2014] [Revised: 07/27/2015] [Accepted: 08/01/2015] [Indexed: 12/24/2022] Open
Abstract
Childhood obesity is a major public health issue. Here we investigated whether differential DNA methylation was associated with childhood obesity. We studied DNA methylation profiles in whole blood from 78 obese children (mean BMI Z-score: 2.6) and 71 age- and sex-matched controls (mean BMI Z-score: 0.1). DNA samples from obese and control groups were pooled and analyzed using the Infinium HumanMethylation450 BeadChip array. Comparison of the methylation profiles between obese and control subjects revealed 129 differentially methylated CpG (DMCpG) loci associated with 80 unique genes that had a greater than 10% difference in methylation (P-value < 0.05). The top pathways enriched among the DMCpGs included developmental processes, immune system regulation, regulation of cell signaling, and small GTPase-mediated signal transduction. The associations between the methylation of selected DMCpGs with childhood obesity were validated using sodium bisulfite pyrosequencing across loci within the FYN, PIWIL4, and TAOK3 genes in individual subjects. Three CpG loci within FYN were hypermethylated in obese individuals (all P < 0.01), while obesity was associated with lower methylation of CpG loci within PIWIL4 (P = 0.003) and TAOK3 (P = 0.001). After building logistic regression models, we determined that a 1% increase in methylation in TAOK3, multiplicatively decreased the odds of being obese by 0.91 (95% CI: 0.86 - 0.97), and an increase of 1% methylation in FYN CpG3, multiplicatively increased the odds of being obese by 1.03 (95% CI: 0.99 - 1.07). In conclusion, these findings provide evidence that childhood obesity is associated with specific DNA methylation changes in whole blood, which may have utility as biomarkers of obesity risk.
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Affiliation(s)
- R C Huang
- Telethon Institute for Child Health Research; University of Western Australia; Perth, Australia
| | - E S Garratt
- Academic Unit of Human Development and Health; Faculty of Medicine; University of Southampton; Southampton, UK
| | - H Pan
- Singapore Institute for Clinical Sciences (SICS); A*STAR; Brenner Center for Molecular Medicine; Singapore
- School of Computer Engineering; Nanyang Technological University (NTU); Singapore
| | - Y Wu
- Singapore Institute for Clinical Sciences (SICS); A*STAR; Brenner Center for Molecular Medicine; Singapore
| | - E A Davis
- Telethon Institute for Child Health Research; University of Western Australia; Perth, Australia
| | - S J Barton
- MRC Lifecourse Epidemiology Unit; University of Southampton; Southampton, UK
| | - G C Burdge
- Academic Unit of Human Development and Health; Faculty of Medicine; University of Southampton; Southampton, UK
| | - K M Godfrey
- MRC Lifecourse Epidemiology Unit; University of Southampton; Southampton, UK
- NIHR Southampton Biomedical Research Center; University of Southampton and University Hospital Southampton NHS Foundation Trust; Southampton, UK
| | - J D Holbrook
- Singapore Institute for Clinical Sciences (SICS); A*STAR; Brenner Center for Molecular Medicine; Singapore
- Yong Loo Lin School of Medicine; National University of Singapore (NUS); Singapore
| | - K A Lillycrop
- Academic Unit of Human Development and Health; Faculty of Medicine; University of Southampton; Southampton, UK
- Faculty of Natural and Environmental Sciences; University of Southampton; Southampton, UK
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44
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Measuring epigenetics as the mediator of gene/environment interactions in DOHaD. J Dev Orig Health Dis 2014; 6:10-6. [PMID: 25315715 DOI: 10.1017/s2040174414000506] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Analysis of DNA methylation data in epigenome-wide association studies provides many bioinformatics and statistical challenges. Not least of these, are the non-independence of individual DNA methylation marks from each other, from genotype and from technical sources of variation. In this review we discuss DNA methylation data from the Infinium450K array and processing methodologies to reduce technical variation. We describe recent approaches to harness the concordance of neighbouring DNA methylation values to improve power in association studies. We also describe how the non-independence of genotype and DNA methylation has been used to infer causality (in the case of Mendelian randomization approaches); suggest the mediating effect of DNA methylation in linking intergenic single nucleotide polymorphisms, identified in genome-wide association studies, to phenotype; and to uncover the widespread influence of gene and environment interactions on methylation levels.
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45
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Soh SE, Chong YS, Kwek K, Saw SM, Meaney MJ, Gluckman PD, Holbrook JD, Godfrey KM. Insights from the Growing Up in Singapore Towards Healthy Outcomes (GUSTO) cohort study. ANNALS OF NUTRITION AND METABOLISM 2014; 64:218-25. [PMID: 25300263 DOI: 10.1159/000365023] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
BACKGROUND The dramatic emergence of noncommunicable diseases (NCD) in Asia, albeit with ethnic variation, has coincided with the rapid socioeconomic and nutritional transition taking place in the region, with the prevalence of diabetes rising 5-fold in Singapore in less than 4 decades. The Growing Up in Singapore Towards healthy Outcomes (GUSTO) cohort study recruited 1,247 expectant mothers of Chinese, Malay, or Indian ethnicity in their first trimester, with detailed longitudinal tracking--through the antenatal period, birth, and the child's first 4 years of life--to examine the potential roles of fetal, developmental, and epigenetic factors in early pathways to metabolic and neurodevelopmental outcomes. KEY MESSAGES A number of findings with a translational and clinical focus have already emerged. In the mothers, we found that changes and differences in food consumption varied across ethnic groups, with persistence of traditional beliefs, during pregnancy and the postpartum period. During pregnancy, higher maternal glucose levels, even in the absence of gestational diabetes mellitus, had graded relations with infant adiposity. Relations between maternal emotional health and birth outcomes and neurodevelopment have been identified. Genotype (25%) and in particular gene × environment interactions (75%) shape interindividual variations in the DNA methylome at birth. The complex effects of fixed genetic variations and different in utero environments can influence the epigenetic status at birth and the later-life phenotype. CONCLUSIONS The richness of the clinical data in 3 ethnicities, the extent of the biospecimen collection, and the extensive infancy and preschool follow-up have allowed us to study the biological pathways that link fetal development to health outcomes. In the coming years, more sophisticated analyses of epigenotype-phenotype relationships will become possible as the children grow and develop. Our studies will lead to the development of clinical and population-based interventions to reduce the burden of NCD.
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Affiliation(s)
- Shu-E Soh
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
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Harvey N, Dennison E, Cooper C. Osteoporosis: a lifecourse approach. J Bone Miner Res 2014; 29:1917-25. [PMID: 24861883 DOI: 10.1002/jbmr.2286] [Citation(s) in RCA: 105] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2014] [Revised: 04/25/2014] [Accepted: 05/16/2014] [Indexed: 01/20/2023]
Abstract
It is becoming increasingly apparent that the risk of developing osteoporosis is accrued throughout the entire lifecourse, even from as early as conception. Thus early growth is associated with bone mass at peak and in older age, and risk of hip fracture. Novel findings from mother-offspring cohorts have yielded greater understanding of relationships between patterns of intrauterine and postnatal growth in the context of later bone development. Study of biological samples from these populations has helped characterize potential mechanistic underpinnings, such as epigenetic processes. Global policy has recognized the importance of early growth and nutrition to the risk of developing adult chronic noncommunicable diseases such as osteoporosis; testing of pregnancy interventions aimed at optimizing offspring bone health is now underway. It is hoped that through such programs, novel public health strategies may be established with the ultimate goal of reducing the burden of osteoporotic fracture in older age.
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Affiliation(s)
- Nicholas Harvey
- Medical Research Council (MRC) Lifecourse Epidemiology Unit, University of Southampton, Southampton General Hospital, Southampton, UK; National Institute for Health Research (NIHR) Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK
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47
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Glossop JR, Emes RD, Nixon NB, Haworth KE, Packham JC, Dawes PT, Fryer AA, Mattey DL, Farrell WE. Genome-wide DNA methylation profiling in rheumatoid arthritis identifies disease-associated methylation changes that are distinct to individual T- and B-lymphocyte populations. Epigenetics 2014; 9:1228-37. [PMID: 25147922 DOI: 10.4161/epi.29718] [Citation(s) in RCA: 70] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
Changes to the DNA methylome have been described in patients with rheumatoid arthritis (RA). In previous work, we reported genome-wide methylation differences in T-lymphocyte and B-lymphocyte populations from healthy individuals. Now, using HumanMethylation450 BeadChips to interrogate genome-wide DNA methylation, we have determined disease-associated methylation changes in blood-derived T- and B-lymphocyte populations from 12 female patients with seropositive established RA, relative to 12 matched healthy individuals. Array data were analyzed using NIMBL software and bisulfite pyrosequencing was used to validate array candidates. Genome-wide DNA methylation, determined by analysis of LINE-1 sequences, revealed higher methylation in B-lymphocytes compared with T-lymphocytes (P ≤ 0.01), which is consistent with our findings in healthy individuals. Moreover, loci-specific methylation differences that distinguished T-lymphocytes from B-lymphocytes in healthy individuals were also apparent in RA patients. However, disease-associated methylation differences were also identified in RA. In these cases, we identified 509 and 252 CpGs in RA-derived T- and B-lymphocytes, respectively, that showed significant changes in methylation compared with their cognate healthy counterparts. Moreover, this included a restricted set of 32 CpGs in T-lymphocytes and 20 CpGs in B-lymphocytes (representing 15 and 10 genes, respectively, and including two, MGMT and CCS, that were common to both cell types) that displayed more substantial changes in methylation. These changes, apparent as hyper- or hypo-methylation, were independently confirmed by pyrosequencing analysis. Validation by pyrosequencing also revealed additional sites in some candidate genes that also displayed altered methylation in RA. In this first study of genome-wide DNA methylation in individual T- and B-lymphocyte populations in RA patients, we report disease-associated methylation changes that are distinct to each cell type and which support a role for discrete epigenetic regulation in this disease.
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Affiliation(s)
- John R Glossop
- Institute for Science and Technology in Medicine; Keele University; Guy Hilton Research Centre; Stoke-on-Trent, Staffordshire UK; Haywood Rheumatology Centre; Haywood Hospital; Stoke-on-Trent, Staffordshire UK
| | - Richard D Emes
- School of Veterinary Medicine and Science; University of Nottingham; Sutton Bonington, Leicestershire UK; Advanced Data Analysis Centre; University of Nottingham; Sutton Bonington, Leicestershire UK
| | - Nicola B Nixon
- Haywood Rheumatology Centre; Haywood Hospital; Stoke-on-Trent, Staffordshire UK
| | - Kim E Haworth
- Institute for Science and Technology in Medicine; Keele University; Guy Hilton Research Centre; Stoke-on-Trent, Staffordshire UK
| | - Jon C Packham
- Haywood Rheumatology Centre; Haywood Hospital; Stoke-on-Trent, Staffordshire UK
| | - Peter T Dawes
- Haywood Rheumatology Centre; Haywood Hospital; Stoke-on-Trent, Staffordshire UK
| | - Anthony A Fryer
- Institute for Science and Technology in Medicine; Keele University; Guy Hilton Research Centre; Stoke-on-Trent, Staffordshire UK
| | - Derek L Mattey
- Institute for Science and Technology in Medicine; Keele University; Guy Hilton Research Centre; Stoke-on-Trent, Staffordshire UK; Haywood Rheumatology Centre; Haywood Hospital; Stoke-on-Trent, Staffordshire UK
| | - William E Farrell
- Institute for Science and Technology in Medicine; Keele University; Guy Hilton Research Centre; Stoke-on-Trent, Staffordshire UK
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48
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Infinium monkeys: Infinium 450K array for the Cynomolgus macaque (Macaca fascicularis). G3-GENES GENOMES GENETICS 2014; 4:1227-34. [PMID: 24815017 PMCID: PMC4455772 DOI: 10.1534/g3.114.010967] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
The Infinium Human Methylation450 BeadChip Array (Infinium 450K) is a robust and cost-efficient survey of genome-wide DNA methylation patterns. Macaca fascicularis (Cynomolgus macaque) is an important disease model; however, its genome sequence is only recently published, and few tools exist to interrogate the molecular state of Cynomolgus macaque tissues. Although the Infinium 450K is a hybridization array designed to the human genome, the relative conservation between the macaque and human genomes makes its use in macaques feasible. Here, we used the Infinium 450K array to assay DNA methylation in 11 macaque muscle biopsies. We showed that probe hybridization efficiency was related to the degree of sequence identity between the human probes and the macaque genome sequence. Approximately 61% of the Human Infinium 450K probes could be reliably mapped to the Cynomolgus macaque genome and contain a CpG site of interest. We also compared the Infinium 450K data to reduced representation bisulfite sequencing data generated on the same samples and found a high level of concordance between the two independent methodologies, which can be further improved by filtering for probe sequence identity and mismatch location. We conclude that the Infinium 450K array can be used to measure the DNA methylome of Cynomolgus macaque tissues using the provided filters. We also provide a pipeline for validation of the array in other species using a simple BLAST-based sequence identify filter.
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van Dongen J, Ehli EA, Slieker RC, Bartels M, Weber ZM, Davies GE, Slagboom PE, Heijmans BT, Boomsma DI. Epigenetic variation in monozygotic twins: a genome-wide analysis of DNA methylation in buccal cells. Genes (Basel) 2014; 5:347-65. [PMID: 24802513 PMCID: PMC4094937 DOI: 10.3390/genes5020347] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2014] [Revised: 03/31/2014] [Accepted: 04/16/2014] [Indexed: 12/19/2022] Open
Abstract
DNA methylation is one of the most extensively studied epigenetic marks in humans. Yet, it is largely unknown what causes variation in DNA methylation between individuals. The comparison of DNA methylation profiles of monozygotic (MZ) twins offers a unique experimental design to examine the extent to which such variation is related to individual-specific environmental influences and stochastic events or to familial factors (DNA sequence and shared environment). We measured genome-wide DNA methylation in buccal samples from ten MZ pairs (age 8–19) using the Illumina 450k array and examined twin correlations for methylation level at 420,921 CpGs after QC. After selecting CpGs showing the most variation in the methylation level between subjects, the mean genome-wide correlation (rho) was 0.54. The correlation was higher, on average, for CpGs within CpG islands (CGIs), compared to CGI shores, shelves and non-CGI regions, particularly at hypomethylated CpGs. This finding suggests that individual-specific environmental and stochastic influences account for more variation in DNA methylation in CpG-poor regions. Our findings also indicate that it is worthwhile to examine heritable and shared environmental influences on buccal DNA methylation in larger studies that also include dizygotic twins.
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Affiliation(s)
- Jenny van Dongen
- Department of Biological Psychology, VU University Amsterdam, Van der Boechorststraat 1, 1081 BT Amsterdam, The Netherlands.
| | - Erik A Ehli
- Avera Institute for Human Genetics, 3720 W. 69th Street, Sioux Falls, SD 57108, USA.
| | - Roderick C Slieker
- Department of Molecular Epidemiology, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands.
| | - Meike Bartels
- Department of Biological Psychology, VU University Amsterdam, Van der Boechorststraat 1, 1081 BT Amsterdam, The Netherlands.
| | - Zachary M Weber
- Avera Institute for Human Genetics, 3720 W. 69th Street, Sioux Falls, SD 57108, USA.
| | - Gareth E Davies
- Avera Institute for Human Genetics, 3720 W. 69th Street, Sioux Falls, SD 57108, USA.
| | - P Eline Slagboom
- Department of Molecular Epidemiology, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands.
| | - Bastiaan T Heijmans
- Department of Molecular Epidemiology, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands.
| | - Dorret I Boomsma
- Department of Biological Psychology, VU University Amsterdam, Van der Boechorststraat 1, 1081 BT Amsterdam, The Netherlands.
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50
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Teh AL, Pan H, Chen L, Ong ML, Dogra S, Wong J, MacIsaac JL, Mah SM, McEwen LM, Saw SM, Godfrey KM, Chong YS, Kwek K, Kwoh CK, Soh SE, Chong MFF, Barton S, Karnani N, Cheong CY, Buschdorf JP, Stünkel W, Kobor MS, Meaney MJ, Gluckman PD, Holbrook JD. The effect of genotype and in utero environment on interindividual variation in neonate DNA methylomes. Genome Res 2014; 24:1064-74. [PMID: 24709820 PMCID: PMC4079963 DOI: 10.1101/gr.171439.113] [Citation(s) in RCA: 226] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Integrating the genotype with epigenetic marks holds the promise of better understanding the biology that underlies the complex interactions of inherited and environmental components that define the developmental origins of a range of disorders. The quality of the in utero environment significantly influences health over the lifecourse. Epigenetics, and in particular DNA methylation marks, have been postulated as a mechanism for the enduring effects of the prenatal environment. Accordingly, neonate methylomes contain molecular memory of the individual in utero experience. However, interindividual variation in methylation can also be a consequence of DNA sequence polymorphisms that result in methylation quantitative trait loci (methQTLs) and, potentially, the interaction between fixed genetic variation and environmental influences. We surveyed the genotypes and DNA methylomes of 237 neonates and found 1423 punctuate regions of the methylome that were highly variable across individuals, termed variably methylated regions (VMRs), against a backdrop of homogeneity. MethQTLs were readily detected in neonatal methylomes, and genotype alone best explained ∼25% of the VMRs. We found that the best explanation for 75% of VMRs was the interaction of genotype with different in utero environments, including maternal smoking, maternal depression, maternal BMI, infant birth weight, gestational age, and birth order. Our study sheds new light on the complex relationship between biological inheritance as represented by genotype and individual prenatal experience and suggests the importance of considering both fixed genetic variation and environmental factors in interpreting epigenetic variation.
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Affiliation(s)
- Ai Ling Teh
- Singapore Institute of Clinical Sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore 117609
| | - Hong Pan
- Singapore Institute of Clinical Sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore 117609; School of Computer Engineering, Nanyang Technological University (NTU), Singapore 639798
| | - Li Chen
- Singapore Institute of Clinical Sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore 117609
| | - Mei-Lyn Ong
- Singapore Institute of Clinical Sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore 117609
| | - Shaillay Dogra
- Singapore Institute of Clinical Sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore 117609
| | - Johnny Wong
- Singapore Institute of Clinical Sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore 117609
| | - Julia L MacIsaac
- Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, Vancouver, BC V5Z 4H4 Canada
| | - Sarah M Mah
- Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, Vancouver, BC V5Z 4H4 Canada
| | - Lisa M McEwen
- Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, Vancouver, BC V5Z 4H4 Canada
| | - Seang-Mei Saw
- Saw Swee Hock School of Public Health, NUS, Singapore 117597
| | - Keith M Godfrey
- MRC Lifecourse Epidemiology Unit and NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, SO16 6YD, United Kingdom
| | - Yap-Seng Chong
- Singapore Institute of Clinical Sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore 117609; Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore 119228
| | - Kenneth Kwek
- KK Women's and Children's Hospital, Singapore 229899
| | - Chee-Keong Kwoh
- School of Computer Engineering, Nanyang Technological University (NTU), Singapore 639798
| | - Shu-E Soh
- Saw Swee Hock School of Public Health, NUS, Singapore 117597; Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore 119228
| | - Mary F F Chong
- Singapore Institute of Clinical Sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore 117609; Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore 119228
| | - Sheila Barton
- MRC Lifecourse Epidemiology Unit and NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, SO16 6YD, United Kingdom
| | - Neerja Karnani
- Singapore Institute of Clinical Sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore 117609
| | - Clara Y Cheong
- Singapore Institute of Clinical Sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore 117609
| | - Jan Paul Buschdorf
- Singapore Institute of Clinical Sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore 117609
| | - Walter Stünkel
- Singapore Institute of Clinical Sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore 117609
| | - Michael S Kobor
- Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, Vancouver, BC V5Z 4H4 Canada
| | - Michael J Meaney
- Singapore Institute of Clinical Sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore 117609; Ludmer Centre for Neuroinformatics and Mental Health, Douglas University Mental Health Institute, McGill University, Montreal, (Quebec) H4H 1R3 Canada
| | - Peter D Gluckman
- Singapore Institute of Clinical Sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore 117609; Centre for Human Evolution, Adaptation and Disease, Liggins Institute, University of Auckland, Auckland 1142, New Zealand
| | - Joanna D Holbrook
- Singapore Institute of Clinical Sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, Singapore 117609
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