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Zhang W, Young JI, Gomez L, Schmidt MA, Lukacsovich D, Varma A, Chen XS, Kunkle B, Martin ER, Wang L. Critical evaluation of the reliability of DNA methylation probes on the Illumina MethylationEPIC v1.0 BeadChip microarrays. Epigenetics 2024; 19:2333660. [PMID: 38564759 PMCID: PMC10989698 DOI: 10.1080/15592294.2024.2333660] [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: 10/06/2023] [Accepted: 03/18/2024] [Indexed: 04/04/2024] Open
Abstract
DNA methylation (DNAm) plays a crucial role in a number of complex diseases. However, the reliability of DNAm levels measured using Illumina arrays varies across different probes. Previous research primarily assessed probe reliability by comparing duplicate samples between the 450k-450k or 450k-EPIC platforms, with limited investigations on Illumina EPIC v1.0 arrays. We conducted a comprehensive assessment of the EPIC v1.0 array probe reliability using 69 blood DNA samples, each measured twice, generated by the Alzheimer's Disease Neuroimaging Initiative study. We observed higher reliability in probes with average methylation beta values of 0.2 to 0.8, and lower reliability in type I probes or those within the promoter and CpG island regions. Importantly, we found that probe reliability has significant implications in the analyses of Epigenome-wide Association Studies (EWAS). Higher reliability is associated with more consistent effect sizes in different studies, the identification of differentially methylated regions (DMRs) and methylation quantitative trait locus (mQTLs), and significant correlations with downstream gene expression. Moreover, blood DNAm measurements obtained from probes with higher reliability are more likely to show concordance with brain DNAm measurements. Our findings, which provide crucial reliability information for probes on the EPIC v1.0 array, will serve as a valuable resource for future DNAm studies.
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Affiliation(s)
- Wei Zhang
- Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL, USA
| | - Juan I Young
- Dr. John T MacDonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine, Miami, FL, USA
- John P. Hussman Institute for Human Genomics, the University of Miami Miller School of Medicine, Miami, FL, USA
| | - Lissette Gomez
- John P. Hussman Institute for Human Genomics, the University of Miami Miller School of Medicine, Miami, FL, USA
| | - Michael A Schmidt
- John P. Hussman Institute for Human Genomics, the University of Miami Miller School of Medicine, Miami, FL, USA
| | - David Lukacsovich
- Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL, USA
| | - Achintya Varma
- John P. Hussman Institute for Human Genomics, the University of Miami Miller School of Medicine, Miami, FL, USA
| | - X Steven Chen
- Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miller School of Medicine, Miami, FL, USA
| | - Brian Kunkle
- Dr. John T MacDonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine, Miami, FL, USA
- John P. Hussman Institute for Human Genomics, the University of Miami Miller School of Medicine, Miami, FL, USA
| | - Eden R Martin
- Dr. John T MacDonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine, Miami, FL, USA
- John P. Hussman Institute for Human Genomics, the University of Miami Miller School of Medicine, Miami, FL, USA
| | - Lily Wang
- Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL, USA
- Dr. John T MacDonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine, Miami, FL, USA
- John P. Hussman Institute for Human Genomics, the University of Miami Miller School of Medicine, Miami, FL, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miller School of Medicine, Miami, FL, USA
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Wikström Shemer D, Mostafaei S, Tang B, Pedersen NL, Karlsson IK, Fall T, Hägg S. Associations between epigenetic aging and diabetes mellitus in a Swedish longitudinal study. GeroScience 2024:10.1007/s11357-024-01252-7. [PMID: 38937415 DOI: 10.1007/s11357-024-01252-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Accepted: 06/07/2024] [Indexed: 06/29/2024] Open
Abstract
Diabetes mellitus type 2 (T2D) is associated with accelerated biological aging and the increased risk of onset of other age-related diseases. Epigenetic changes in DNA methylation levels have been found to serve as reliable biomarkers for biological aging. This study explores the relationship between various epigenetic biomarkers of aging and diabetes risk using longitudinal data. Data from the Swedish Adoption/Twin Study of Aging (SATSA) was collected from 1984 to 2014 and included 536 individuals with at least one epigenetic measurement. The following epigenetic biomarkers of aging were employed: DNAm PAI-1, DNAmTL, DunedinPACE, PCHorvath1, PCHorvath2, PCHannum, PCPhenoAge, and PCGrimAge. Firstly, longitudinal analysis of biomarker trajectories was done. Secondly, linear correlations between the biomarkers and time to diabetes were studied within individuals developing diabetes. Thirdly, Cox proportional hazards (PH) models were used to assess the associations between these biomarkers and time of diabetes diagnosis, with adjustments for chronological age, sex, education, smoking, blood glucose, and BMI. The longitudinal trajectories of the biomarkers revealed differences between individuals with and without diabetes. Smoothened average curves for DunedinPACE and DNAm PAI-1 were higher for individuals with diabetes around the age 60-70, compared to controls. Likewise, DunedinPACE and DNAm PAI-1 were higher closer to diabetes onset. However, no significant associations were found between the epigenetic biomarkers of aging and risk of diabetes in Cox PH models. Our findings suggest the potential value of developing epigenetic biomarkers specifically tailored to T2D, should we wish to model and explore the potential for predicting the disease.
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Affiliation(s)
- Daniel Wikström Shemer
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 77, Stockholm, Sweden
- Molecular Epidemiology, Department of Medical Sciences, and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Shayan Mostafaei
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 77, Stockholm, Sweden
| | - Bowen Tang
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 77, Stockholm, Sweden
| | - Nancy L Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 77, Stockholm, Sweden
| | - Ida K Karlsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 77, Stockholm, Sweden
| | - Tove Fall
- Molecular Epidemiology, Department of Medical Sciences, and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Sara Hägg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 77, Stockholm, Sweden.
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Everson TM, Sehgal N, Barr DB, Panuwet P, Yakimavets V, Perez C, Shankar K, Eick SM, Pearson KJ, Andres A. Placental PFAS concentrations are associated with perturbations of placental DNA methylation at loci with important roles on cardiometabolic health. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.06.24306905. [PMID: 38766233 PMCID: PMC11100840 DOI: 10.1101/2024.05.06.24306905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
The placenta is crucial for fetal development, is affected by PFAS toxicity, and evidence is accumulating that gestational PFAS perturb the epigenetic activity of the placenta. Gestational PFAS exposure is can adversely affect offspring, yet individual and cumulative impacts of PFAS on the placental epigenome remain underexplored. Here, we conducted an epigenome-wide association study (EWAS) to examine the relationships between placental PFAS levels and DNA methylation in a cohort of mother-infant dyads in Arkansas. We measured 17 PFAS in human placental tissues and quantified placental DNA methylation levels via the Illumina EPIC Microarray. We tested for differential DNA methylation with individual PFAS, and with mixtures of multiple PFAS. Our results demonstrated that numerous epigenetic loci were perturbed by PFAS, with PFHxS exhibiting the most abundant effects. Mixture analyses suggested cumulative effects of PFOA and PFOS, while PFHxS may act more independently. We additionally explored whether sex-specific effects may be present and concluded that future large studies should explicitly test for sex-specific effects. The genes that are annotated to our PFAS-associated epigenetic loci are primarily involved in growth processes and cardiometabolic health, while some genes are involved in neurodevelopment. These findings shed light on how prenatal PFAS exposures affect birth outcomes and children's health, emphasizing the importance of understanding PFAS mechanisms in the in-utero environment.
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Affiliation(s)
- Todd M. Everson
- Gangarosa Department of Environmental Health, Emory University Rollins School of Public Health, Atlanta, GA, USA
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
| | - Neha Sehgal
- Gangarosa Department of Environmental Health, Emory University Rollins School of Public Health, Atlanta, GA, USA
| | - Dana Boyd Barr
- Gangarosa Department of Environmental Health, Emory University Rollins School of Public Health, Atlanta, GA, USA
| | - Parinya Panuwet
- Gangarosa Department of Environmental Health, Emory University Rollins School of Public Health, Atlanta, GA, USA
| | - Volha Yakimavets
- Gangarosa Department of Environmental Health, Emory University Rollins School of Public Health, Atlanta, GA, USA
| | - Cynthia Perez
- Gangarosa Department of Environmental Health, Emory University Rollins School of Public Health, Atlanta, GA, USA
| | - Kartik Shankar
- Department of Pediatrics, Section of Nutrition, University of Colorado School of Medicine, Aurora, CO
| | - Stephanie M. Eick
- Gangarosa Department of Environmental Health, Emory University Rollins School of Public Health, Atlanta, GA, USA
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
| | - Kevin J. Pearson
- Department of Pharmacology & Nutritional Sciences, University of Kentucky College of Medicine
| | - Aline Andres
- Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, AR
- Arkansas Children’s Nutrition Center, Little Rock, AR
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Broséus L, Guilbert A, Hough I, Kloog I, Chauvaud A, Seyve E, Vaiman D, Heude B, Chevrier C, Tost J, Slama R, Lepeule J. Placental DNA methylation signatures of prenatal air pollution exposure and potential effects on birth outcomes: an analysis of three prospective cohorts. Lancet Planet Health 2024; 8:e297-e308. [PMID: 38723642 DOI: 10.1016/s2542-5196(24)00045-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 02/16/2024] [Accepted: 03/20/2024] [Indexed: 05/12/2024]
Abstract
BACKGROUND Pregnancy air pollution exposure (PAPE) has been linked to a wide range of adverse birth and childhood outcomes, but there is a paucity of data on its influence on the placental epigenome, which can regulate the programming of physiological functions and affect child development. This study aimed to investigate the association between prenatal air pollutant exposure concentrations and changes in placental DNA methylation patterns, and to explore the potential windows of susceptibility and sex-specific alterations. METHODS This multi-site study used three prospective population-based mother-child cohorts: EDEN, PELAGIE, and SEPAGES, originating from four French geographical regions (Nancy, Poitiers, Brittany, and Grenoble). Pregnant women were included between 2003 and 2006 for EDEN and PELAGIE, and between 2014 and 2017 for SEPAGES. The main eligibility criteria were: being older than 18 years, having a singleton pregnancy, and living and planning to deliver in one of the maternity clinics in one of the study areas. A total of 1539 mother-child pairs were analysed, measuring placental DNA methylation using Illumina BeadChips. We used validated spatiotemporally resolved models to estimate PM2·5, PM10, and NO2 exposure over each trimester of pregnancy at the maternal residential address. We conducted a pooled adjusted epigenome-wide association study to identify differentially methylated 5'-C-phosphate-G-3' (CpG) sites and regions (assessed using the Infinium HumanMethylationEPIC BeadChip array, n=871), including sex-specific and sex-linked alterations, and independently validated our results (assessed using the Infinium HumanMethylation450 BeadChip array, n=668). FINDINGS We identified four CpGs and 28 regions associated with PAPE in the total population, 469 CpGs and 87 regions in male infants, and 150 CpGs and 66 regions in female infants. We validated 35% of the CpGs available. More than 30% of the identified CpGs were related to one (or more) birth outcome and most significant alterations were enriched for neural development, immunity, and metabolism related genes. The 28 regions identified for both sexes overlapped with imprinted genes (four genes), and were associated with neurodevelopment (nine genes), immune system (seven genes), and metabolism (five genes). Most associations were observed for the third trimester for female infants (134 of 150 CpGs), and throughout pregnancy (281 of 469 CpGs) and the first trimester (237 of 469 CpGs) for male infants. INTERPRETATION These findings highlight the molecular pathways through which PAPE might affect child health in a widespread and sex-specific manner, identifying the genes involved in the major physiological functions of a developing child. Further studies are needed to elucidate whether these epigenetic changes persist and affect health later in life. FUNDING French Agency for National Research, Fondation pour la Recherche Médicale, Fondation de France, and the Plan Cancer.
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Affiliation(s)
- Lucile Broséus
- Université Grenoble Alpes, Inserm, CNRS, Team of Environmental Epidemiology applied to Development and Respiratory Health, IAB, Grenoble, France.
| | - Ariane Guilbert
- Université Grenoble Alpes, Inserm, CNRS, Team of Environmental Epidemiology applied to Development and Respiratory Health, IAB, Grenoble, France
| | - Ian Hough
- Université Grenoble Alpes, Inserm, CNRS, Team of Environmental Epidemiology applied to Development and Respiratory Health, IAB, Grenoble, France; Institute of Environmental Geosciences, Université Grenoble Alpes, Grenoble, France; Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Itai Kloog
- Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Be'er Sheva, Israel; Department of Environmental Medicine and Climate Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Anath Chauvaud
- Université Grenoble Alpes, Inserm, CNRS, Team of Environmental Epidemiology applied to Development and Respiratory Health, IAB, Grenoble, France
| | - Emie Seyve
- Université Grenoble Alpes, Inserm, CNRS, Team of Environmental Epidemiology applied to Development and Respiratory Health, IAB, Grenoble, France
| | - Daniel Vaiman
- Institut Cochin, U1016 Inserm, Unité Mixte de Recherche 8104, CNRS, Paris-Descartes University, Paris, France
| | - Barbara Heude
- Université Paris Cité et Université Sorbonne Paris Nord, Inserm, INRAE, Centre de Recherche en Épidémiologie et Statistiques, Paris, France
| | - Cécile Chevrier
- University of Rennes, Inserm, Ecole des Hautes Etudes en Santé Publique, Institut de Recherche en Santé, Environnement et Travail, Unité Mixte de Recherche 1085, Rennes, France
| | - Jörg Tost
- Laboratory for Epigenetics and Environment, Centre National de Recherche en Génomique Humaine, Commissariat à l'Energie Atomique et aux Energies Alternatives, Institut de Biologie François Jacob, University Paris Saclay, Evry, France
| | - Rémy Slama
- Université Grenoble Alpes, Inserm, CNRS, Team of Environmental Epidemiology applied to Development and Respiratory Health, IAB, Grenoble, France
| | - Johanna Lepeule
- Université Grenoble Alpes, Inserm, CNRS, Team of Environmental Epidemiology applied to Development and Respiratory Health, IAB, Grenoble, France.
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Lester BM, Camerota M, Everson TM, Shuster CL, Marsit CJ. Toward a more holistic approach to the study of exposures and child outcomes. Epigenomics 2024; 16:635-651. [PMID: 38482639 PMCID: PMC11157992 DOI: 10.2217/epi-2023-0424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 02/27/2024] [Indexed: 06/09/2024] Open
Abstract
Aim: The current work was designed to demonstrate the application of the exposome framework in examining associations between exposures and children's long-term neurodevelopmental and behavioral outcomes. Methods: Longitudinal data were collected from birth through age 6 from 402 preterm infants. Three statistical methods were utilized to demonstrate the exposome framework: exposome-wide association study, cumulative exposure and machine learning models, with and without epigenetic data. Results: Each statistical approach answered a distinct research question regarding the impact of exposures on longitudinal child outcomes. Findings highlight associations between exposures, epigenetics and executive function. Conclusion: Findings demonstrate how an exposome-based approach can be utilized to understand relationships between internal (e.g., DNA methylation) and external (e.g., prenatal risk) exposures and long-term developmental outcomes in preterm children.
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Affiliation(s)
- Barry M Lester
- Department of Pediatrics, Brown Alpert Medical School & Women & Infants Hospital, Providence, RI 02905, USA
- Brown Center for the Study of Children at Risk, Brown Alpert Medical School & Women & Infants Hospital, Providence, RI 02905, USA
- Department of Psychiatry & Human Behavior, Brown Alpert Medical School, Providence, RI 02905, USA
| | - Marie Camerota
- Brown Center for the Study of Children at Risk, Brown Alpert Medical School & Women & Infants Hospital, Providence, RI 02905, USA
- Department of Psychiatry & Human Behavior, Brown Alpert Medical School, Providence, RI 02905, USA
| | - Todd M Everson
- Department of Environmental Health, Emory University Rollins School of Public Health, Atlanta, GA 30322, USA
| | - Coral L Shuster
- Department of Pediatrics, Brown Alpert Medical School & Women & Infants Hospital, Providence, RI 02905, USA
- Brown Center for the Study of Children at Risk, Brown Alpert Medical School & Women & Infants Hospital, Providence, RI 02905, USA
| | - Carmen J Marsit
- Department of Environmental Health, Emory University Rollins School of Public Health, Atlanta, GA 30322, USA
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Hodge KM, Zhabotynsky V, Burt AA, Carter BS, Fry RC, Helderman J, Hofheimer JA, McGowan EC, Neal CR, Pastyrnak SL, Smith LM, DellaGrotta SA, Dansereau LM, Lester BM, Marsit CJ, O'Shea TM, Everson TM. Epigenetic associations in HPA axis genes related to bronchopulmonary dysplasia and antenatal steroids. Pediatr Res 2024:10.1038/s41390-024-03116-4. [PMID: 38480856 DOI: 10.1038/s41390-024-03116-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 01/26/2024] [Accepted: 02/17/2024] [Indexed: 03/18/2024]
Abstract
BACKGROUND Bronchopulmonary dysplasia (BPD), a common morbidity among very preterm infants, is associated with chronic disease and neurodevelopmental impairments. A hypothesized mechanism for these outcomes lies in altered glucocorticoid (GC) activity. We hypothesized that BPD and its treatments may result in epigenetic differences in the hypothalamic-pituitary-adrenal (HPA) axis, which is modulated by GC, and could be ascertained using an established GC risk score and DNA methylation (DNAm) of HPA axis genes. METHODS DNAm was quantified from buccal tissue (ECHO-NOVI) and from neonatal blood spots (ELGAN ECHO) via the EPIC microarray. Prenatal maternal characteristics, pregnancy complication, and neonatal medical complication data were collected from medical record review and maternal interviews. RESULTS The GC score was not associated with steroid exposure or BPD. However, six HPA genes involved in stress response regulation demonstrated differential methylation with antenatal steroid exposure; two CpGs within FKBP5 and POMC were differentially methylated with BPD severity. These findings were sex-specific in both cohorts; males had greater magnitude of differential methylation within these genes. CONCLUSIONS These findings suggest that BPD severity and antenatal steroids are associated with DNAm at some HPA genes in very preterm infants and the effects appear to be sex-, tissue-, and age-specific. IMPACT This study addresses bronchopulmonary dysplasia (BPD), an important health outcome among preterm neonates, and interrogates a commonly studied pathway, the hypothalamic-pituitary-adrenal (HPA) axis. The combination of BPD, the HPA axis, and epigenetic markers has not been previously reported. In this study, we found that BPD itself was not associated with epigenetic responses in the HPA axis in infants born very preterm; however, antenatal treatment with steroids was associated with epigenetic responses.
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Affiliation(s)
- Kenyaita M Hodge
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Vasyl Zhabotynsky
- Institute for Environmental Health Solutions, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Amber A Burt
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Brian S Carter
- Department of Pediatrics-Neonatology, Children's Mercy Hospital, Kansas City, MO, USA
| | - Rebecca C Fry
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Pediatrics, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Jennifer Helderman
- Department of Pediatrics, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Julie A Hofheimer
- Department of Pediatrics, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Elisabeth C McGowan
- Department of Pediatrics, Warren Alpert Medical School of Brown University and Women and Infants Hospital, Providence, RI, USA
| | - Charles R Neal
- Department of Pediatrics, University of Hawaii John A. Burns School of Medicine, Honolulu, HI, USA
| | - Steven L Pastyrnak
- Department of Pediatrics, Spectrum Health-Helen Devos Hospital, Grand Rapids, MI, USA
| | - Lynne M Smith
- Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Sheri A DellaGrotta
- Brown Center for the Study of Children at Risk, Women and Infants Hospital, Providence, RI, USA
| | - Lynne M Dansereau
- Brown Center for the Study of Children at Risk, Women and Infants Hospital, Providence, RI, USA
| | - Barry M Lester
- Department of Pediatrics, Warren Alpert Medical School of Brown University and Women and Infants Hospital, Providence, RI, USA
- Brown Center for the Study of Children at Risk, Women and Infants Hospital, Providence, RI, USA
- Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Carmen J Marsit
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - T Michael O'Shea
- Department of Pediatrics, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Todd M Everson
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
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Tang Z, Gaskins AJ, Hood RB, Ford JB, Hauser R, Smith AK, Everson TM. Former smoking associated with epigenetic modifications in human granulosa cells among women undergoing assisted reproduction. Sci Rep 2024; 14:5009. [PMID: 38424222 PMCID: PMC10904848 DOI: 10.1038/s41598-024-54957-2] [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: 11/14/2023] [Accepted: 02/19/2024] [Indexed: 03/02/2024] Open
Abstract
Smoking exposure during adulthood can disrupt oocyte development in women, contributing to infertility and possibly adverse birth outcomes. Some of these effects may be reflected in epigenome profiles in granulosa cells (GCs) in human follicular fluid. We compared the epigenetic modifications throughout the genome in GCs from women who were former (N = 15) versus never smokers (N = 44) undergoing assisted reproductive technologies (ART). This study included 59 women undergoing ART. Smoking history including time since quitting was determined by questionnaire. GCs were collected during oocyte retrieval and DNA methylation (DNAm) levels were profiled using the Infinium MethylationEPIC BeadChip. We performed an epigenome-wide association study with robust linear models, regressing DNAm level at individual loci on smoking status, adjusting for age, ovarian stimulation protocol, and three surrogate variables. We performed differentially methylated regions (DMRs) analysis and over-representation analysis of the identified CpGs and corresponding gene set. 81 CpGs were differentially methylated among former smokers compared to never smokers (FDR < 0.05). We identified 2 significant DMRs (KCNQ1 and RHBDD2). The former smoking-associated genes were enriched in oxytocin signaling, adrenergic signaling in cardiomyocytes, platelet activation, axon guidance, and chemokine signaling pathway. These epigenetic variations have been associated with inflammatory responses, reproductive outcomes, cancer development, neurodevelopmental disorder, and cardiometabolic health. Secondarily, we examined the relationships between time since quitting and DNAm at significant CpGs. We observed three CpGs in negative associations with the length of quitting smoking (p < 0.05), which were cg04254052 (KCNIP1), cg22875371 (OGDHL), and cg27289628 (LOC148145), while one in positive association, which was cg13487862 (PLXNB1). As a pilot study, we demonstrated epigenetic modifications associated with former smoking in GCs. The study is informative to potential biological pathways underlying the documented association between smoking and female infertility and biomarker discovery for smoking-associated reproductive outcomes.
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Affiliation(s)
- Ziyin Tang
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Audrey J Gaskins
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Robert B Hood
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Jennifer B Ford
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Russ Hauser
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Alicia K Smith
- Department of Obstetrics and Gynecology, School of Medicine, Emory University, Atlanta, GA, USA
| | - Todd M Everson
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
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8
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Camerota M, Lester BM, Castellanos FX, Carter BS, Check J, Helderman J, Hofheimer JA, McGowan EC, Neal CR, Pastyrnak SL, Smith LM, O'Shea TM, Marsit CJ, Everson TM. Epigenome-wide association study identifies neonatal DNA methylation associated with two-year attention problems in children born very preterm. Transl Psychiatry 2024; 14:126. [PMID: 38418845 PMCID: PMC10902402 DOI: 10.1038/s41398-024-02841-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 02/07/2024] [Accepted: 02/16/2024] [Indexed: 03/02/2024] Open
Abstract
Prior research has identified epigenetic predictors of attention problems in school-aged children but has not yet investigated these in young children, or children at elevated risk of attention problems due to preterm birth. The current study evaluated epigenome-wide associations between neonatal DNA methylation and attention problems at age 2 years in children born very preterm. Participants included 441 children from the Neonatal Neurobehavior and Outcomes in Very Preterm Infants (NOVI) Study, a multi-site study of infants born < 30 weeks gestational age. DNA methylation was measured from buccal swabs collected at NICU discharge using the Illumina MethylationEPIC Bead Array. Attention problems were assessed at 2 years of adjusted age using the attention problems subscale of the Child Behavior Checklist (CBCL). After adjustment for multiple testing, DNA methylation at 33 CpG sites was associated with child attention problems. Differentially methylated CpG sites were located in genes previously linked to physical and mental health, including several genes associated with ADHD in prior epigenome-wide and genome-wide association studies. Several CpG sites were located in genes previously linked to exposure to prenatal risk factors in the NOVI sample. Neonatal epigenetics measured at NICU discharge could be useful in identifying preterm children at risk for long-term attention problems and related psychiatric disorders, who could benefit from early prevention and intervention efforts.
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Affiliation(s)
- Marie Camerota
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA.
- Brown Center for the Study of Children at Risk, Alpert Medical School of Brown University and Women and Infants Hospital, Providence, RI, USA.
| | - Barry M Lester
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA
- Brown Center for the Study of Children at Risk, Alpert Medical School of Brown University and Women and Infants Hospital, Providence, RI, USA
- Department of Pediatrics, Alpert Medical School of Brown University and Women and Infants Hospital, Providence, RI, USA
| | - Francisco Xavier Castellanos
- Department of Child and Adolescent Psychiatry, NYU Grossman School of Medicine, New York, NY, USA
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Brian S Carter
- Department of Pediatrics-Neonatology, Children's Mercy Hospital, Kansas City, MO, USA
| | - Jennifer Check
- Department of Pediatrics, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Jennifer Helderman
- Department of Pediatrics, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Julie A Hofheimer
- Department of Pediatrics, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Elisabeth C McGowan
- Department of Pediatrics, Alpert Medical School of Brown University and Women and Infants Hospital, Providence, RI, USA
| | - Charles R Neal
- Department of Pediatrics, University of Hawaii John A. Burns School of Medicine, Honolulu, HI, USA
| | - Steven L Pastyrnak
- Department of Pediatrics, Spectrum Health-Helen DeVos Hospital, Grand Rapids, MI, USA
| | - Lynne M Smith
- Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Thomas Michael O'Shea
- Department of Pediatrics, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Carmen J Marsit
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Todd M Everson
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
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9
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Apsley AT, Ye Q, Etzel L, Wolf S, Hastings WJ, Mattern BC, Siegel SR, Shalev I. Biological stability of DNA methylation measurements over varying intervals of time and in the presence of acute stress. Epigenetics 2023; 18:2230686. [PMID: 37393564 PMCID: PMC10316737 DOI: 10.1080/15592294.2023.2230686] [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: 03/23/2023] [Revised: 06/13/2023] [Accepted: 06/21/2023] [Indexed: 07/04/2023] Open
Abstract
Identifying factors that influence the stability of DNA methylation measurements across biological replicates is of critical importance in basic and clinical research. Using a within-person between-group experimental design (n = 31, number of observations = 192), we report the stability of biological replicates over a variety of unique temporal scenarios, both in the absence and presence of acute psychosocial stress, and between individuals who have experienced early life adversity (ELA) and non-exposed individuals. We found that varying time intervals, acute stress, and ELA exposure influenced the stability of repeated DNA methylation measurements. In the absence of acute stress, probes were less stable as time passed; however, stress exerted a stabilizing influence on probes over longer time intervals. Compared to non-exposed individuals, ELA-exposed individuals had significantly lower probe stability immediately following acute stress. Additionally, we found that across all scenarios, probes used in most epigenetic-based algorithms for estimating epigenetic age or immune cell proportions had average or below-average stability, except for the Principal Component and DunedinPACE epigenetic ageing clocks, which were enriched for more stable probes. Finally, using highly stable probes in the absence of stress, we identified multiple probes that were hypomethylated in the presence of acute stress, regardless of ELA status. Two hypomethylated probes are located near the transcription start site of the glutathione-disulfide reductase gene (GSR), which has previously been shown to be an integral part of the stress response to environmental toxins. We discuss implications for future studies concerning the reliability and reproducibility of DNA methylation measurements.Abbreviations: DNAm - DNA methylation, CpG - 5'-cytosine-phosphate-guanine-3,' ICC - Interclass correlation coefficient, ELA - Early-life adversity, PBMCs - Peripheral blood mononuclear cells, mQTL - Methylation quantitative trait loci, TSS - Transcription start site, GSR - Glutathione-disulfide reductase gene, TSST - Trier social stress test, PC - Principal component.
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Affiliation(s)
- Abner T. Apsley
- Department of Biobehavioral Health, The Pennsylvania State University, University Park, PA, USA
- Department of Molecular, Cellular and Integrative Biological Sciences, The Pennsylvania State University, University Park, PA, USA
| | - Qiaofeng Ye
- Department of Biobehavioral Health, The Pennsylvania State University, University Park, PA, USA
| | - Laura Etzel
- Department of Biobehavioral Health, The Pennsylvania State University, University Park, PA, USA
| | - Sarah Wolf
- Department of Biobehavioral Health, The Pennsylvania State University, University Park, PA, USA
| | - Waylon J. Hastings
- Department of Psychiatry, Tulane University School of Medicine, New Orleans, LA, USA
| | - Brooke C. Mattern
- Department of Biobehavioral Health, The Pennsylvania State University, University Park, PA, USA
| | - Sue Rutherford Siegel
- Department of Biobehavioral Health, The Pennsylvania State University, University Park, PA, USA
| | - Idan Shalev
- Department of Biobehavioral Health, The Pennsylvania State University, University Park, PA, USA
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10
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Hecker J, Lee S, Kachroo P, Prokopenko D, Maaser-Hecker A, Lutz SM, Hahn G, Irizarry R, Weiss ST, DeMeo DL, Lange C. A consistent pattern of slide effects in Illumina DNA methylation BeadChip array data. Epigenetics 2023; 18:2257437. [PMID: 37731367 PMCID: PMC11062373 DOI: 10.1080/15592294.2023.2257437] [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: 04/18/2023] [Accepted: 09/01/2023] [Indexed: 09/22/2023] Open
Abstract
Background: Recent studies have identified thousands of associations between DNA methylation CpGs and complex diseases/traits, emphasizing the critical role of epigenetics in understanding disease aetiology and identifying biomarkers. However, association analyses based on methylation array data are susceptible to batch/slide effects, which can lead to inflated false positive rates or reduced statistical powerResults: We use multiple DNA methylation datasets based on the popular Illumina Infinium MethylationEPIC BeadChip array to describe consistent patterns and the joint distribution of slide effects across CpGs, confirming and extending previous results. The susceptible CpGs overlap with the Illumina Infinium HumanMethylation450 BeadChip array content.Conclusions: Our findings reveal systematic patterns in slide effects. The observations provide further insights into the characteristics of these effects and can improve existing adjustment approaches.
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Affiliation(s)
- Julian Hecker
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Sanghun Lee
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Medical Consilience, Division of Medicine, Graduate School, Dankook University, Yongin-si, South Korea
| | - Priyadarshini Kachroo
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Dmitry Prokopenko
- Genetics and Aging Unit and McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Anna Maaser-Hecker
- Genetics and Aging Unit and McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Sharon M. Lutz
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Population Medicine, PRecisiOn Medicine Translational Research (PROMoTeR) Center, Harvard Pilgrim Health Care and Harvard Medical School, Boston, MA, USA
| | - Georg Hahn
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Rafael Irizarry
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Scott T. Weiss
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Dawn L. DeMeo
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Christoph Lange
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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11
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Bizzarri D, Reinders MJT, Beekman M, Slagboom PE, van den Akker EB. Technical Report: A Comprehensive Comparison between Different Quantification Versions of Nightingale Health's 1H-NMR Metabolomics Platform. Metabolites 2023; 13:1181. [PMID: 38132863 PMCID: PMC10745109 DOI: 10.3390/metabo13121181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/07/2023] [Accepted: 11/17/2023] [Indexed: 12/23/2023] Open
Abstract
1H-NMR metabolomics data is increasingly used to track health and disease. Nightingale Health, a major supplier of 1H-NMR metabolomics, has recently updated the quantification strategy to further align with clinical standards. Such updates, however, might influence backward replicability, particularly affecting studies with repeated measures. Using data from BBMRI-NL consortium (~28,000 samples from 28 cohorts), we compared Nightingale data, originally released in 2014 and 2016, with a re-quantified version released in 2020, of which both versions were based on the same NMR spectra. Apart from two discontinued and twenty-three new analytes, we generally observe a high concordance between quantification versions with 73 out of 222 (33%) analytes showing a mean ρ > 0.9 across all cohorts. Conversely, five analytes consistently showed lower Spearman's correlations (ρ < 0.7) between versions, namely acetoacetate, LDL-L, saturated fatty acids, S-HDL-C, and sphingomyelins. Furthermore, previously trained multi-analyte scores, such as MetaboAge or MetaboHealth, might be particularly sensitive to platform changes. Whereas MetaboHealth replicated well, the MetaboAge score had to be retrained due to use of discontinued analytes. Notably, both scores in the re-quantified data recapitulated mortality associations observed previously. Concluding, we urge caution in utilizing different platform versions to avoid mixing analytes, having different units, or simply being discontinued.
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Affiliation(s)
- Daniele Bizzarri
- Molecular Epidemiology, Department of Biomedical Data Science, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands
- Leiden Computational Biology Center, Department of Biomedical Data Science, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands
- Delft Bioinformatics Lab., Department of Intelligent Systems, TU Delft, 2628 XE Delft, The Netherlands
| | - Marcel J. T. Reinders
- Leiden Computational Biology Center, Department of Biomedical Data Science, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands
- Delft Bioinformatics Lab., Department of Intelligent Systems, TU Delft, 2628 XE Delft, The Netherlands
| | - Marian Beekman
- Molecular Epidemiology, Department of Biomedical Data Science, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands
| | - P. Eline Slagboom
- Molecular Epidemiology, Department of Biomedical Data Science, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands
- Max Planck Institute for the Biology of Ageing, 50931 Cologne, Germany
| | - Erik B. van den Akker
- Molecular Epidemiology, Department of Biomedical Data Science, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands
- Leiden Computational Biology Center, Department of Biomedical Data Science, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands
- Delft Bioinformatics Lab., Department of Intelligent Systems, TU Delft, 2628 XE Delft, The Netherlands
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12
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Gaare JJ, Brügger K, Nido GS, Tzoulis C. DNA Methylation Age Acceleration Is Not Associated with Age of Onset in Parkinson's Disease. Mov Disord 2023; 38:2064-2071. [PMID: 37551021 DOI: 10.1002/mds.29574] [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: 04/20/2023] [Revised: 07/04/2023] [Accepted: 07/24/2023] [Indexed: 08/09/2023] Open
Abstract
BACKGROUND Epigenetic clocks using DNA methylation (DNAm) to estimate biological age have become popular tools in the study of neurodegenerative diseases. Notably, several recent reports have shown a strikingly similar inverse relationship between accelerated biological aging, as measured by DNAm, and the age of onset of several neurodegenerative disorders, including Parkinson's disease (PD). Common to all of these studies is that they were performed without control subjects and using the exact same measure of accelerated aging: DNAm age minus chronological age. OBJECTIVE We aimed to assess the validity of these findings in PD, using the same dataset as in the original study, blood DNAm data from the Parkinson's Progression Markers Initiative cohort, but also including control samples in the analyses. METHODS We replicated the analyses and findings of the previous study and then reanalyzed the dataset incorporating control samples to account for underlying age-related biases. RESULTS Our reanalysis shows that there is no correlation between age of onset and DNAm age acceleration. Conversely, there is a pattern of overestimating DNAm age in younger and underestimating DNAm age in older individuals in the dataset that entirely explains the previously reported association. CONCLUSIONS Our findings refute the previously reported inverse relationship between DNAm age acceleration and age of onset in PD. We show that these findings are fully accounted for by an expected over/underestimation of DNAm age in younger/older individuals. Furthermore, this effect is likely to be responsible for nearly identical findings reported in other neurodegenerative diseases. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Johannes J Gaare
- Neuro-SysMed Center, Department of Neurology, Haukeland University Hospital, Bergen, Norway
- K.G. Jebsen Center for Translational Research in Parkinson's Disease, University of Bergen, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Kim Brügger
- Neuro-SysMed Center, Department of Neurology, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Gonzalo S Nido
- Neuro-SysMed Center, Department of Neurology, Haukeland University Hospital, Bergen, Norway
- K.G. Jebsen Center for Translational Research in Parkinson's Disease, University of Bergen, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Charalampos Tzoulis
- Neuro-SysMed Center, Department of Neurology, Haukeland University Hospital, Bergen, Norway
- K.G. Jebsen Center for Translational Research in Parkinson's Disease, University of Bergen, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
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13
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Zhang W, Young JI, Gomez L, Schmidt MA, Lukacsovich D, Varma A, Chen XS, Kunkle B, Martin ER, Wang L. Critical evaluation of the reliability of DNA methylation probes on the Illumina MethylationEPIC BeadChip microarrays. RESEARCH SQUARE 2023:rs.3.rs-3068938. [PMID: 37461726 PMCID: PMC10350239 DOI: 10.21203/rs.3.rs-3068938/v2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
DNA methylation (DNAm) plays a crucial role in a number of complex diseases. However, the reliability of DNAm levels measured using Illumina arrays varies across different probes. Previous research primarily assessed probe reliability by comparing duplicate samples between the 450k-450k or 450k-EPIC platforms, with limited investigations on Illumina EPIC arrays. We conducted a comprehensive assessment of the EPIC array probe reliability using 138 duplicated blood DNAm samples generated by the Alzheimer's Disease Neuroimaging Initiative study. We introduced a novel statistical measure, the modified intraclass correlation, to better account for the disagreement in duplicate measurements. We observed higher reliability in probes with average methylation beta values of 0.2 to 0.8, and lower reliability in type I probes or those within the promoter and CpG island regions. Importantly, we found that probe reliability has significant implications in the analyses of Epigenome-wide Association Studies (EWAS). Higher reliability is associated with more consistent effect sizes in different studies, the identification of differentially methylated regions (DMRs) and methylation quantitative trait locus (mQTLs), and significant correlations with downstream gene expression. Moreover, blood DNAm measurements obtained from probes with higher reliability are more likely to show concordance with brain DNAm measurements. Our findings, which provide crucial reliable information for probes on the EPIC array, will serve as a valuable resource for future DNAm studies.
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Affiliation(s)
- Wei Zhang
- Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL 33136, USA
| | - Juan I. Young
- Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine, Miami, FL 33136, USA
- John P. Hussman Institute for Human Genomics, the University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Lissette Gomez
- John P. Hussman Institute for Human Genomics, the University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Michael A. Schmidt
- John P. Hussman Institute for Human Genomics, the University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - David Lukacsovich
- Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL 33136, USA
| | - Achintya Varma
- John P. Hussman Institute for Human Genomics, the University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - X. Steven Chen
- Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL 33136, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miller School of Medicine, Miami, FL 33136, USA
| | - Brian Kunkle
- Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine, Miami, FL 33136, USA
- John P. Hussman Institute for Human Genomics, the University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Eden R. Martin
- Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine, Miami, FL 33136, USA
- John P. Hussman Institute for Human Genomics, the University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Lily Wang
- Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL 33136, USA
- Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine, Miami, FL 33136, USA
- John P. Hussman Institute for Human Genomics, the University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miller School of Medicine, Miami, FL 33136, USA
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14
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Zhang W, Young JI, Gomez L, Schmidt MA, Lukacsovich D, Varma A, Chen XS, Kunkle B, Martin ER, Wang L. Critical evaluation of the reliability of DNA methylation probes on the Illumina MethylationEPIC BeadChip microarrays. RESEARCH SQUARE 2023:rs.3.rs-3068938. [PMID: 37461726 PMCID: PMC10350239 DOI: 10.21203/rs.3.rs-3068938/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/24/2023]
Abstract
DNA methylation (DNAm) plays a crucial role in a number of complex diseases. However, the reliability of DNAm levels measured using Illumina arrays varies across different probes. Previous research primarily assessed probe reliability by comparing duplicate samples between the 450k-450k or 450k-EPIC platforms, with limited investigations on Illumina EPIC arrays. We conducted a comprehensive assessment of the EPIC array probe reliability using 138 duplicated blood DNAm samples generated by the Alzheimer's Disease Neuroimaging Initiative study. We introduced a novel statistical measure, the modified intraclass correlation, to better account for the disagreement in duplicate measurements. We observed higher reliability in probes with average methylation beta values of 0.2 to 0.8, and lower reliability in type I probes or those within the promoter and CpG island regions. Importantly, we found that probe reliability has significant implications in the analyses of Epigenome-wide Association Studies (EWAS). Higher reliability is associated with more consistent effect sizes in different studies, the identification of differentially methylated regions (DMRs) and methylation quantitative trait locus (mQTLs), and significant correlations with downstream gene expression. Moreover, blood DNAm measurements obtained from probes with higher reliability are more likely to show concordance with brain DNAm measurements. Our findings, which provide crucial reliable information for probes on the EPIC array, will serve as a valuable resource for future DNAm studies.
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Affiliation(s)
- Wei Zhang
- Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL 33136, USA
| | - Juan I. Young
- Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine, Miami, FL 33136, USA
- John P. Hussman Institute for Human Genomics, the University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Lissette Gomez
- John P. Hussman Institute for Human Genomics, the University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Michael A. Schmidt
- John P. Hussman Institute for Human Genomics, the University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - David Lukacsovich
- Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL 33136, USA
| | - Achintya Varma
- John P. Hussman Institute for Human Genomics, the University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - X. Steven Chen
- Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL 33136, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miller School of Medicine, Miami, FL 33136, USA
| | - Brian Kunkle
- Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine, Miami, FL 33136, USA
- John P. Hussman Institute for Human Genomics, the University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Eden R. Martin
- Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine, Miami, FL 33136, USA
- John P. Hussman Institute for Human Genomics, the University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Lily Wang
- Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL 33136, USA
- Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine, Miami, FL 33136, USA
- John P. Hussman Institute for Human Genomics, the University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miller School of Medicine, Miami, FL 33136, USA
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15
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Van Asselt AJ, Beck JJ, Finnicum CT, Johnson BN, Kallsen N, Hottenga JJ, de Geus EJC, Boomsma DI, Ehli EA, van Dongen J. Genome-Wide DNA Methylation Profiles in Whole-Blood and Buccal Samples-Cross-Sectional, Longitudinal, and across Platforms. Int J Mol Sci 2023; 24:14640. [PMID: 37834090 PMCID: PMC10572275 DOI: 10.3390/ijms241914640] [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: 08/21/2023] [Revised: 09/22/2023] [Accepted: 09/24/2023] [Indexed: 10/15/2023] Open
Abstract
The field of DNA methylation research is rapidly evolving, focusing on disease and phenotype changes over time using methylation measurements from diverse tissue sources and multiple array platforms. Consequently, identifying the extent of longitudinal, inter-tissue, and inter-platform variation in DNA methylation is crucial for future advancement. DNA methylation was measured in 375 individuals, with 197 of those having 2 blood sample measurements ~10 years apart. Whole-blood samples were measured on Illumina Infinium 450K and EPIC methylation arrays, and buccal samples from a subset of 58 participants were measured on EPIC array. The data were analyzed with the aims to examine the correlation between methylation levels in longitudinal blood samples in 197 individuals, examine the correlation between methylation levels in the blood and buccal samples in 58 individuals, and examine the correlation between blood methylation profiles assessed on the EPIC and 450K arrays in 83 individuals. We identified 136,833, 7674, and 96,891 CpGs significantly and strongly correlated (>0.50) longitudinally, across blood and buccal samples as well as array platforms, respectively. A total of 3674 of these CpGs were shared across all three sets. Analysis of these shared CpGs identified previously found associations with aging, ancestry, and 7016 mQTLs as well.
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Affiliation(s)
- Austin J. Van Asselt
- Avera McKennan Hospital, University Health Center, Sioux Falls, SD 57105, USA; (A.J.V.A.)
- Department of Biological Psychology, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands
| | - Jeffrey J. Beck
- Avera McKennan Hospital, University Health Center, Sioux Falls, SD 57105, USA; (A.J.V.A.)
| | - Casey T. Finnicum
- Avera McKennan Hospital, University Health Center, Sioux Falls, SD 57105, USA; (A.J.V.A.)
| | - Brandon N. Johnson
- Avera McKennan Hospital, University Health Center, Sioux Falls, SD 57105, USA; (A.J.V.A.)
| | - Noah Kallsen
- Avera McKennan Hospital, University Health Center, Sioux Falls, SD 57105, USA; (A.J.V.A.)
| | - Jouke Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands
| | - Eco J. C. de Geus
- Department of Biological Psychology, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands
| | | | - Dorret I. Boomsma
- Department of Biological Psychology, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands
| | - Erik A. Ehli
- Avera McKennan Hospital, University Health Center, Sioux Falls, SD 57105, USA; (A.J.V.A.)
| | - Jenny van Dongen
- Department of Biological Psychology, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands
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16
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Aurich S, Müller L, Kovacs P, Keller M. Implication of DNA methylation during lifestyle mediated weight loss. Front Endocrinol (Lausanne) 2023; 14:1181002. [PMID: 37614712 PMCID: PMC10442821 DOI: 10.3389/fendo.2023.1181002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 07/18/2023] [Indexed: 08/25/2023] Open
Abstract
Over the past 50 years, the number of overweight/obese people increased significantly, making obesity a global public health challenge. Apart from rare monogenic forms, obesity is a multifactorial disease, most likely resulting from a concerted interaction of genetic, epigenetic and environmental factors. Although recent studies opened new avenues in elucidating the complex genetics behind obesity, the biological mechanisms contributing to individual's risk to become obese are not yet fully understood. Non-genetic factors such as eating behaviour or physical activity are strong contributing factors for the onset of obesity. These factors may interact with genetic predispositions most likely via epigenetic mechanisms. Epigenome-wide association studies or methylome-wide association studies are measuring DNA methylation at single CpGs across thousands of genes and capture associations to obesity phenotypes such as BMI. However, they only represent a snapshot in the complex biological network and cannot distinguish between causes and consequences. Intervention studies are therefore a suitable method to control for confounding factors and to avoid possible sources of bias. In particular, intervention studies documenting changes in obesity-associated epigenetic markers during lifestyle driven weight loss, make an important contribution to a better understanding of epigenetic reprogramming in obesity. To investigate the impact of lifestyle in obesity state specific DNA methylation, especially concerning the development of new strategies for prevention and individual therapy, we reviewed 19 most recent human intervention studies. In summary, this review highlights the huge potential of targeted interventions to alter disease-associated epigenetic patterns. However, there is an urgent need for further robust and larger studies to identify the specific DNA methylation biomarkers which influence obesity.
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Affiliation(s)
- Samantha Aurich
- Medical Department III - Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
- Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Center Munich at the University of Leipzig and University Hospital Leipzig, Leipzig, Germany
| | - Luise Müller
- Medical Department III - Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
| | - Peter Kovacs
- Medical Department III - Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
- Deutsches Zentrum für Diabetesforschung e.V., Neuherberg, Germany
| | - Maria Keller
- Medical Department III - Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
- Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Center Munich at the University of Leipzig and University Hospital Leipzig, Leipzig, Germany
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17
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Wang SS, Lewis MJ, Pitzalis C. DNA Methylation Signatures of Response to Conventional Synthetic and Biologic Disease-Modifying Antirheumatic Drugs (DMARDs) in Rheumatoid Arthritis. Biomedicines 2023; 11:1987. [PMID: 37509625 PMCID: PMC10377185 DOI: 10.3390/biomedicines11071987] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 07/03/2023] [Accepted: 07/11/2023] [Indexed: 07/30/2023] Open
Abstract
Rheumatoid arthritis (RA) is a complex condition that displays heterogeneity in disease severity and response to standard treatments between patients. Failure rates for conventional, target synthetic, and biologic disease-modifying rheumatic drugs (DMARDs) are significant. Although there are models for predicting patient response, they have limited accuracy, require replication/validation, or for samples to be obtained through a synovial biopsy. Thus, currently, there are no prediction methods approved for routine clinical use. Previous research has shown that genetics and environmental factors alone cannot explain the differences in response between patients. Recent studies have demonstrated that deoxyribonucleic acid (DNA) methylation plays an important role in the pathogenesis and disease progression of RA. Importantly, specific DNA methylation profiles associated with response to conventional, target synthetic, and biologic DMARDs have been found in the blood of RA patients and could potentially function as predictive biomarkers. This review will summarize and evaluate the evidence for DNA methylation signatures in treatment response mainly in blood but also learn from the progress made in the diseased tissue in cancer in comparison to RA and autoimmune diseases. We will discuss the benefits and challenges of using DNA methylation signatures as predictive markers and the potential for future progress in this area.
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Affiliation(s)
- Susan Siyu Wang
- Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Queen Mary University of London and Barts Health NIHR BRC & NHS Trust, London EC1M 6BQ, UK
| | - Myles J Lewis
- Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Queen Mary University of London and Barts Health NIHR BRC & NHS Trust, London EC1M 6BQ, UK
| | - Costantino Pitzalis
- Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Queen Mary University of London and Barts Health NIHR BRC & NHS Trust, London EC1M 6BQ, UK
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18
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Adams C, Nair N, Plant D, Verstappen SMM, Quach HL, Quach DL, Carvidi A, Nititham J, Nakamura M, Graf J, Barton A, Criswell LA, Barcellos LF. Identification of Cell-Specific Differential DNA Methylation Associated With Methotrexate Treatment Response in Rheumatoid Arthritis. Arthritis Rheumatol 2023; 75:1088-1097. [PMID: 36716083 PMCID: PMC10313739 DOI: 10.1002/art.42464] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 01/13/2023] [Accepted: 01/26/2023] [Indexed: 01/31/2023]
Abstract
OBJECTIVE We undertook this study to estimate changes in cell-specific DNA methylation (DNAm) associated with methotrexate (MTX) response using whole blood samples collected from rheumatoid arthritis (RA) patients before and after initiation of MTX treatment. METHODS Patients included in this study were from the Rheumatoid Arthritis Medication Study (n = 66) and the University of California San Francisco Rheumatoid Arthritis study (n = 11). All patients met the American College of Rheumatology RA classification criteria. Blood samples were collected at baseline and following treatment. Disease Activity Scores in 28 joints using the C-reactive protein level were collected at baseline and after 3-6 months of treatment with MTX. Methylation profiles were generated using the Illumina Infinium HumanMethylation450 and MethylationEPIC v1.0 BeadChip arrays using DNA from whole blood. MTX response was defined using the EULAR response criteria (responders showed good/moderate response; nonresponders showed no response). Differentially methylated positions were identified using the Limma software package and Tensor Composition Analysis, which is a method for identifying cell-specific differential DNAm at the CpG level from tissue-level ("bulk") data. Differentially methylated regions were identified using Comb-p software. RESULTS We found evidence of differential global methylation between treatment response groups. Further, we found patterns of cell-specific differential global methylation associated with MTX response. After correction for multiple testing, 1 differentially methylated position was associated with differential DNAm between responders and nonresponders at baseline in CD4+ T cells, CD8+ T cells, and natural killer cells. Thirty-nine cell-specific differentially methylated regions associated with MTX treatment response were identified. There were no significant findings in analyses of whole blood samples. CONCLUSION We identified cell-specific changes in DNAm that were associated with MTX treatment response in RA patients. Future studies of DNAm and MTX treatment response should include measurements of DNAm from sorted cells.
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Affiliation(s)
- Cameron Adams
- School of Public Health, University of CaliforniaBerkeley
| | - Nisha Nair
- Centre of Genetics and Genomics Versus Arthritis, Manchester Academic Health Sciences Centre, The University of ManchesterManchesterUK
| | - Darren Plant
- Centre of Genetics and Genomics Versus Arthritis, Manchester Academic Health Sciences Centre, NIHR Manchester BRC, Manchester University Foundation Trust, The University of ManchesterManchesterUK
| | - Suzanne M. M. Verstappen
- NIHR Manchester BRC, Manchester University Foundation Trust, and Centre for Epidemiology Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, Manchester Academic Health Sciences Centre, The University of Manchester, Manchester, UK, Institute of Cellular Medicine, Newcastle University, and NIHR Newcastle BRC, Newcastle upon Tyne Hospitals NHS Foundation TrustNewcastle upon TyneUK
| | - Hong L. Quach
- School of Public Health, University of CaliforniaBerkeley
| | - Diana L. Quach
- School of Public Health, University of CaliforniaBerkeley
| | | | - Joanne Nititham
- National Human Genome Research Institute, NIHBethesdaMaryland
| | - Mary Nakamura
- University of California and San Francisco Veterans Administration Health SystemSan FranciscoCalifornia
| | | | - Anne Barton
- Centre of Genetics and Genomics Versus Arthritis, Manchester Academic Health Sciences Centre, NIHR Manchester BRC, Manchester University Foundation Trust, The University of ManchesterManchesterUK
| | | | - Lisa F. Barcellos
- School of Public Health, University of California, Berkeley, and National Human Genome Research Institute, NIHBethesdaMaryland
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Hellbach F, Sinke L, Costeira R, Baumeister SE, Beekman M, Louca P, Leeming ER, Mompeo O, Berry S, Wilson R, Wawro N, Freuer D, Hauner H, Peters A, Winkelmann J, Koenig W, Meisinger C, Waldenberger M, Heijmans BT, Slagboom PE, Bell JT, Linseisen J. Pooled analysis of epigenome-wide association studies of food consumption in KORA, TwinsUK and LLS. Eur J Nutr 2023; 62:1357-1375. [PMID: 36571600 PMCID: PMC10030421 DOI: 10.1007/s00394-022-03074-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 12/12/2022] [Indexed: 12/27/2022]
Abstract
PURPOSE Examining epigenetic patterns is a crucial step in identifying molecular changes of disease pathophysiology, with DNA methylation as the most accessible epigenetic measure. Diet is suggested to affect metabolism and health via epigenetic modifications. Thus, our aim was to explore the association between food consumption and DNA methylation. METHODS Epigenome-wide association studies were conducted in three cohorts: KORA FF4, TwinsUK, and Leiden Longevity Study, and 37 dietary exposures were evaluated. Food group definition was harmonized across the three cohorts. DNA methylation was measured using Infinium MethylationEPIC BeadChip in KORA and Infinium HumanMethylation450 BeadChip in the Leiden study and the TwinsUK study. Overall, data from 2293 middle-aged men and women were included. A fixed-effects meta-analysis pooled study-specific estimates. The significance threshold was set at 0.05 for false-discovery rate-adjusted p values per food group. RESULTS We identified significant associations between the methylation level of CpG sites and the consumption of onions and garlic (2), nuts and seeds (18), milk (1), cream (11), plant oils (4), butter (13), and alcoholic beverages (27). The signals targeted genes of metabolic health relevance, for example, GLI1, RPTOR, and DIO1, among others. CONCLUSION This EWAS is unique with its focus on food groups that are part of a Western diet. Significant findings were mostly related to food groups with a high-fat content.
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Affiliation(s)
- Fabian Hellbach
- Institute for Medical Information Processing, Biometry, and Epidemiology, Medical Faculty, Ludwig-Maximilian University Munich, Marchioninistr. 15, 81377, Munich, Germany.
- Epidemiology, Faculty of Medicine, University of Augsburg, University Hospital Augsburg, Stenglinstraße 2, 86156, Augsburg, Germany.
| | - Lucy Sinke
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Einthovenweg 20, 2333 ZC, Leiden, The Netherlands
| | - Ricardo Costeira
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, England, UK
| | - Sebastian-Edgar Baumeister
- Institute of Health Services Research in Dentistry, Medical Faculty, University of Münster, Albert-Schweitzer-Campus 1, 48149, Münster, Germany
| | - Marian Beekman
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Einthovenweg 20, 2333 ZC, Leiden, The Netherlands
| | - Panayiotis Louca
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, England, UK
| | - Emily R Leeming
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, England, UK
| | - Olatz Mompeo
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, England, UK
| | - Sarah Berry
- Department of Nutritional Sciences, King's College London, London, UK
| | - Rory Wilson
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
| | - Nina Wawro
- Institute for Medical Information Processing, Biometry, and Epidemiology, Medical Faculty, Ludwig-Maximilian University Munich, Marchioninistr. 15, 81377, Munich, Germany
- Epidemiology, Faculty of Medicine, University of Augsburg, University Hospital Augsburg, Stenglinstraße 2, 86156, Augsburg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
| | - Dennis Freuer
- Epidemiology, Faculty of Medicine, University of Augsburg, University Hospital Augsburg, Stenglinstraße 2, 86156, Augsburg, Germany
| | - Hans Hauner
- Else Kröner-Fresenius-Center for Nutritional Medicine, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany
- Institute of Nutritional Medicine, School of Medicine, Technical University of Munich, Georg-Brauchle-Ring 62, 80992, Munich, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
- German Center for Diabetes Research (DZD E.V.), Ingolstädter Landstr. 1, 85764, Munich-Neuherberg, Germany
| | - Juliane Winkelmann
- Institute of Neurogenomics, Helmholtz Zentrum München, German Research Center for Environmental Health (HmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
| | - Wolfgang Koenig
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Pettenkoferstr. 8A & 9, 80336, Munich, Germany
- German Heart Centre Munich, Technical University Munich, Lazarettstr. 36, 80636, Munich, Germany
- Institute of Epidemiology and Medical Biometry, University of Ulm, Helmholtzstr. 22, 89081, Ulm, Germany
| | - Christa Meisinger
- Epidemiology, Faculty of Medicine, University of Augsburg, University Hospital Augsburg, Stenglinstraße 2, 86156, Augsburg, Germany
| | - Melanie Waldenberger
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
- German Center for Diabetes Research (DZD E.V.), Ingolstädter Landstr. 1, 85764, Munich-Neuherberg, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Pettenkoferstr. 8A & 9, 80336, Munich, Germany
| | - Bastiaan T Heijmans
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Einthovenweg 20, 2333 ZC, Leiden, The Netherlands
| | - P Eline Slagboom
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Einthovenweg 20, 2333 ZC, Leiden, The Netherlands
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, England, UK
| | - Jakob Linseisen
- Institute for Medical Information Processing, Biometry, and Epidemiology, Medical Faculty, Ludwig-Maximilian University Munich, Marchioninistr. 15, 81377, Munich, Germany
- Epidemiology, Faculty of Medicine, University of Augsburg, University Hospital Augsburg, Stenglinstraße 2, 86156, Augsburg, Germany
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20
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Avalos D, Rey G, Ribeiro DM, Ramisch A, Dermitzakis ET, Delaneau O. Genetic variation in cis-regulatory domains suggests cell type-specific regulatory mechanisms in immunity. Commun Biol 2023; 6:335. [PMID: 36977773 PMCID: PMC10050075 DOI: 10.1038/s42003-023-04688-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 03/09/2023] [Indexed: 03/30/2023] Open
Abstract
Studying the interplay between genetic variation, epigenetic changes, and regulation of gene expression is crucial to understand the modification of cellular states in various conditions, including immune diseases. In this study, we characterize the cell-specificity in three key cells of the human immune system by building cis maps of regulatory regions with coordinated activity (CRDs) from ChIP-seq peaks and methylation data. We find that only 33% of CRD-gene associations are shared between cell types, revealing how similarly located regulatory regions provide cell-specific modulation of gene activity. We emphasize important biological mechanisms, as most of our associations are enriched in cell-specific transcription factor binding sites, blood-traits, and immune disease-associated loci. Notably, we show that CRD-QTLs aid in interpreting GWAS findings and help prioritize variants for testing functional hypotheses within human complex diseases. Additionally, we map trans CRD regulatory associations, and among 207 trans-eQTLs discovered, 46 overlap with the QTLGen Consortium meta-analysis in whole blood, showing that mapping functional regulatory units using population genomics allows discovering important mechanisms in the regulation of gene expression in immune cells. Finally, we constitute a comprehensive resource describing multi-omics changes to gain a greater understanding of cell-type specific regulatory mechanisms of immunity.
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Affiliation(s)
- Diana Avalos
- Department of Genetic Medicine and Development, University of Geneva, Geneva, Switzerland
- Swiss Institute of Bioinformatics (SIB), University of Geneva, Geneva, Switzerland
- Institute of Genetics and Genomics in Geneva, University of Geneva, Geneva, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Guillaume Rey
- Department of Genetic Medicine and Development, University of Geneva, Geneva, Switzerland
- Swiss Institute of Bioinformatics (SIB), University of Geneva, Geneva, Switzerland
- Institute of Genetics and Genomics in Geneva, University of Geneva, Geneva, Switzerland
| | - Diogo M Ribeiro
- Swiss Institute of Bioinformatics (SIB), University of Geneva, Geneva, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Anna Ramisch
- Department of Genetic Medicine and Development, University of Geneva, Geneva, Switzerland
- Swiss Institute of Bioinformatics (SIB), University of Geneva, Geneva, Switzerland
- Institute of Genetics and Genomics in Geneva, University of Geneva, Geneva, Switzerland
| | - Emmanouil T Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva, Geneva, Switzerland
- Swiss Institute of Bioinformatics (SIB), University of Geneva, Geneva, Switzerland
- Institute of Genetics and Genomics in Geneva, University of Geneva, Geneva, Switzerland
| | - Olivier Delaneau
- Swiss Institute of Bioinformatics (SIB), University of Geneva, Geneva, Switzerland.
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
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21
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Welsh H, Batalha CMPF, Li W, Mpye KL, Souza-Pinto NC, Naslavsky MS, Parra EJ. A systematic evaluation of normalization methods and probe replicability using infinium EPIC methylation data. Clin Epigenetics 2023; 15:41. [PMID: 36906598 PMCID: PMC10008016 DOI: 10.1186/s13148-023-01459-z] [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: 10/10/2022] [Accepted: 02/24/2023] [Indexed: 03/13/2023] Open
Abstract
BACKGROUND The Infinium EPIC array measures the methylation status of > 850,000 CpG sites. The EPIC BeadChip uses a two-array design: Infinium Type I and Type II probes. These probe types exhibit different technical characteristics which may confound analyses. Numerous normalization and pre-processing methods have been developed to reduce probe type bias as well as other issues such as background and dye bias. METHODS This study evaluates the performance of various normalization methods using 16 replicated samples and three metrics: absolute beta-value difference, overlap of non-replicated CpGs between replicate pairs, and effect on beta-value distributions. Additionally, we carried out Pearson's correlation and intraclass correlation coefficient (ICC) analyses using both raw and SeSAMe 2 normalized data. RESULTS The method we define as SeSAMe 2, which consists of the application of the regular SeSAMe pipeline with an additional round of QC, pOOBAH masking, was found to be the best performing normalization method, while quantile-based methods were found to be the worst performing methods. Whole-array Pearson's correlations were found to be high. However, in agreement with previous studies, a substantial proportion of the probes on the EPIC array showed poor reproducibility (ICC < 0.50). The majority of poor performing probes have beta values close to either 0 or 1, and relatively low standard deviations. These results suggest that probe reliability is largely the result of limited biological variation rather than technical measurement variation. Importantly, normalizing the data with SeSAMe 2 dramatically improved ICC estimates, with the proportion of probes with ICC values > 0.50 increasing from 45.18% (raw data) to 61.35% (SeSAMe 2).
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Affiliation(s)
- H Welsh
- Department of Anthropology, University of Toronto at Mississauga, Mississauga, Canada.
| | - C M P F Batalha
- Department of Biochemistry, University of São Paulo, São Paulo, Brazil
| | - W Li
- The Centre for Applied Genomics, Hospital for Sick Children, Toronto, Canada
| | - K L Mpye
- Department of Anthropology, University of Toronto at Mississauga, Mississauga, Canada
| | - N C Souza-Pinto
- Department of Biochemistry, University of São Paulo, São Paulo, Brazil
| | - M S Naslavsky
- Department of Genetics and Evolutionary Biology, University of São Paulo, São Paulo, Brazil
| | - E J Parra
- Department of Anthropology, University of Toronto at Mississauga, Mississauga, Canada
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22
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Carry PM, Vigers T, Vanderlinden LA, Keeter C, Dong F, Buckner T, Litkowski E, Yang I, Norris JM, Kechris K. Propensity scores as a novel method to guide sample allocation and minimize batch effects during the design of high throughput experiments. BMC Bioinformatics 2023; 24:86. [PMID: 36882691 PMCID: PMC9990331 DOI: 10.1186/s12859-023-05202-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 02/22/2023] [Indexed: 03/09/2023] Open
Abstract
BACKGROUND We developed a novel approach to minimize batch effects when assigning samples to batches. Our algorithm selects a batch allocation, among all possible ways of assigning samples to batches, that minimizes differences in average propensity score between batches. This strategy was compared to randomization and stratified randomization in a case-control study (30 per group) with a covariate (case vs control, represented as β1, set to be null) and two biologically relevant confounding variables (age, represented as β2, and hemoglobin A1c (HbA1c), represented as β3). Gene expression values were obtained from a publicly available dataset of expression data obtained from pancreas islet cells. Batch effects were simulated as twice the median biological variation across the gene expression dataset and were added to the publicly available dataset to simulate a batch effect condition. Bias was calculated as the absolute difference between observed betas under the batch allocation strategies and the true beta (no batch effects). Bias was also evaluated after adjustment for batch effects using ComBat as well as a linear regression model. In order to understand performance of our optimal allocation strategy under the alternative hypothesis, we also evaluated bias at a single gene associated with both age and HbA1c levels in the 'true' dataset (CAPN13 gene). RESULTS Pre-batch correction, under the null hypothesis (β1), maximum absolute bias and root mean square (RMS) of maximum absolute bias, were minimized using the optimal allocation strategy. Under the alternative hypothesis (β2 and β3 for the CAPN13 gene), maximum absolute bias and RMS of maximum absolute bias were also consistently lower using the optimal allocation strategy. ComBat and the regression batch adjustment methods performed well as the bias estimates moved towards the true values in all conditions under both the null and alternative hypotheses. Although the differences between methods were less pronounced following batch correction, estimates of bias (average and RMS) were consistently lower using the optimal allocation strategy under both the null and alternative hypotheses. CONCLUSIONS Our algorithm provides an extremely flexible and effective method for assigning samples to batches by exploiting knowledge of covariates prior to sample allocation.
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Affiliation(s)
- Patrick M Carry
- Colorado Program for Musculoskeletal Research, Department of Orthopedics, University of Colorado Anschutz Medical Campus, 12631 E. 17Th Ave, Room 4602, Mail Stop B202, Aurora, CO, 80045, USA. .,Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA.
| | - Tim Vigers
- Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, USA.,Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Lauren A Vanderlinden
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA.,Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, USA
| | - Carson Keeter
- Colorado Program for Musculoskeletal Research, Department of Orthopedics, University of Colorado Anschutz Medical Campus, 12631 E. 17Th Ave, Room 4602, Mail Stop B202, Aurora, CO, 80045, USA
| | - Fran Dong
- Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Teresa Buckner
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA
| | - Elizabeth Litkowski
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA
| | - Ivana Yang
- Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Jill M Norris
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA
| | - Katerina Kechris
- Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, USA
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23
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Faul JD, Kim JK, Levine ME, Thyagarajan B, Weir DR, Crimmins EM. Epigenetic-based age acceleration in a representative sample of older Americans: Associations with aging-related morbidity and mortality. Proc Natl Acad Sci U S A 2023; 120:e2215840120. [PMID: 36802439 PMCID: PMC9992763 DOI: 10.1073/pnas.2215840120] [Citation(s) in RCA: 45] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 01/12/2023] [Indexed: 02/23/2023] Open
Abstract
Biomarkers developed from DNA methylation (DNAm) data are of growing interest as predictors of health outcomes and mortality in older populations. However, it is unknown how epigenetic aging fits within the context of known socioeconomic and behavioral associations with aging-related health outcomes in a large, population-based, and diverse sample. This study uses data from a representative, panel study of US older adults to examine the relationship between DNAm-based age acceleration measures in the prediction of cross-sectional and longitudinal health outcomes and mortality. We examine whether recent improvements to these scores, using principal component (PC)-based measures designed to remove some of the technical noise and unreliability in measurement, improve the predictive capability of these measures. We also examine how well DNAm-based measures perform against well-known predictors of health outcomes such as demographics, SES, and health behaviors. In our sample, age acceleration calculated using "second and third generation clocks," PhenoAge, GrimAge, and DunedinPACE, is consistently a significant predictor of health outcomes including cross-sectional cognitive dysfunction, functional limitations and chronic conditions assessed 2 y after DNAm measurement, and 4-y mortality. PC-based epigenetic age acceleration measures do not significantly change the relationship of DNAm-based age acceleration measures to health outcomes or mortality compared to earlier versions of these measures. While the usefulness of DNAm-based age acceleration as a predictor of later life health outcomes is quite clear, other factors such as demographics, SES, mental health, and health behaviors remain equally, if not more robust, predictors of later life outcomes.
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Affiliation(s)
- Jessica D. Faul
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI48104
| | - Jung Ki Kim
- Davis School of Gerontology, University of Southern California, Los Angeles, CA90089
| | - Morgan E. Levine
- Department of Pathology, Yale School of Medicine, New Haven, CT06510
| | - Bharat Thyagarajan
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN55455
| | - David R. Weir
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI48104
| | - Eileen M. Crimmins
- Davis School of Gerontology, University of Southern California, Los Angeles, CA90089
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24
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Bernabeu E, McCartney DL, Gadd DA, Hillary RF, Lu AT, Murphy L, Wrobel N, Campbell A, Harris SE, Liewald D, Hayward C, Sudlow C, Cox SR, Evans KL, Horvath S, McIntosh AM, Robinson MR, Vallejos CA, Marioni RE. Refining epigenetic prediction of chronological and biological age. Genome Med 2023; 15:12. [PMID: 36855161 PMCID: PMC9976489 DOI: 10.1186/s13073-023-01161-y] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 02/06/2023] [Indexed: 03/02/2023] Open
Abstract
BACKGROUND Epigenetic clocks can track both chronological age (cAge) and biological age (bAge). The latter is typically defined by physiological biomarkers and risk of adverse health outcomes, including all-cause mortality. As cohort sample sizes increase, estimates of cAge and bAge become more precise. Here, we aim to develop accurate epigenetic predictors of cAge and bAge, whilst improving our understanding of their epigenomic architecture. METHODS First, we perform large-scale (N = 18,413) epigenome-wide association studies (EWAS) of chronological age and all-cause mortality. Next, to create a cAge predictor, we use methylation data from 24,674 participants from the Generation Scotland study, the Lothian Birth Cohorts (LBC) of 1921 and 1936, and 8 other cohorts with publicly available data. In addition, we train a predictor of time to all-cause mortality as a proxy for bAge using the Generation Scotland cohort (1214 observed deaths). For this purpose, we use epigenetic surrogates (EpiScores) for 109 plasma proteins and the 8 component parts of GrimAge, one of the current best epigenetic predictors of survival. We test this bAge predictor in four external cohorts (LBC1921, LBC1936, the Framingham Heart Study and the Women's Health Initiative study). RESULTS Through the inclusion of linear and non-linear age-CpG associations from the EWAS, feature pre-selection in advance of elastic net regression, and a leave-one-cohort-out (LOCO) cross-validation framework, we obtain cAge prediction with a median absolute error equal to 2.3 years. Our bAge predictor was found to slightly outperform GrimAge in terms of the strength of its association to survival (HRGrimAge = 1.47 [1.40, 1.54] with p = 1.08 × 10-52, and HRbAge = 1.52 [1.44, 1.59] with p = 2.20 × 10-60). Finally, we introduce MethylBrowsR, an online tool to visualise epigenome-wide CpG-age associations. CONCLUSIONS The integration of multiple large datasets, EpiScores, non-linear DNAm effects, and new approaches to feature selection has facilitated improvements to the blood-based epigenetic prediction of biological and chronological age.
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Affiliation(s)
- Elena Bernabeu
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Daniel L McCartney
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Danni A Gadd
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Robert F Hillary
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Ake T Lu
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Altos Labs, San Diego, USA
| | - Lee Murphy
- Edinburgh Clinical Research Facility, University of Edinburgh, Edinburgh, UK
| | - Nicola Wrobel
- Edinburgh Clinical Research Facility, University of Edinburgh, Edinburgh, UK
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Sarah E Harris
- Department of Psychology, Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK
| | - David Liewald
- Department of Psychology, Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK
| | - Caroline Hayward
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Cathie Sudlow
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- BHF Data Science Centre, Health Data Research UK, London, UK
- Edinburgh Medical School, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Simon R Cox
- Department of Psychology, Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK
| | - Kathryn L Evans
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Steve Horvath
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Altos Labs, San Diego, USA
| | - Andrew M McIntosh
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | | | - Catalina A Vallejos
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- The Alan Turing Institute, London, UK
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
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25
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Liao X, Kennel PJ, Liu B, Nash TR, Zhuang RZ, Godier-Furnemont AF, Xue C, Lu R, Colombo PC, Uriel N, Reilly MP, Marx SO, Vunjak-Novakovic G, Topkara VK. Effect of mechanical unloading on genome-wide DNA methylation profile of the failing human heart. JCI Insight 2023; 8:161788. [PMID: 36656640 PMCID: PMC9977498 DOI: 10.1172/jci.insight.161788] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 01/11/2023] [Indexed: 01/20/2023] Open
Abstract
Heart failure (HF) is characterized by global alterations in myocardial DNA methylation, yet little is known about the epigenetic regulation of the noncoding genome and potential reversibility of DNA methylation with left ventricular assist device (LVAD) therapy. Genome-wide mapping of myocardial DNA methylation in 36 patients with HF at LVAD implantation, 8 patients at LVAD explantation, and 7 nonfailing (NF) donors using a high-density bead array platform identified 2,079 differentially methylated positions (DMPs) in ischemic cardiomyopathy (ICM) and 261 DMPs in nonischemic cardiomyopathy (NICM). LVAD support resulted in normalization of 3.2% of HF-associated DMPs. Methylation-expression correlation analysis yielded several protein-coding genes that are hypomethylated and upregulated (HTRA1, FBXO16, EFCAB13, and AKAP13) or hypermethylated and downregulated (TBX3) in HF. A potentially novel cardiac-specific super-enhancer long noncoding RNA (lncRNA) (LINC00881) is hypermethylated and downregulated in human HF. LINC00881 is an upstream regulator of sarcomere and calcium channel gene expression including MYH6, CACNA1C, and RYR2. LINC00881 knockdown reduces peak calcium amplitude in the beating human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). These data suggest that HF-associated changes in myocardial DNA methylation within coding and noncoding genomes are minimally reversible with mechanical unloading. Epigenetic reprogramming strategies may be necessary to achieve sustained clinical recovery from heart failure.
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Affiliation(s)
- Xianghai Liao
- Division of Cardiology, Columbia University Irving Medical Center - New York Presbyterian, New York, New York, USA
| | - Peter J Kennel
- Division of Cardiology, Columbia University Irving Medical Center - New York Presbyterian, New York, New York, USA
| | - Bohao Liu
- Department of Biomedical Engineering, Columbia University, New York, New York, USA
| | - Trevor R Nash
- Department of Biomedical Engineering, Columbia University, New York, New York, USA
| | - Richard Z Zhuang
- Department of Biomedical Engineering, Columbia University, New York, New York, USA
| | | | - Chenyi Xue
- Division of Cardiology, Columbia University Irving Medical Center - New York Presbyterian, New York, New York, USA
| | - Rong Lu
- Division of Cardiology, Columbia University Irving Medical Center - New York Presbyterian, New York, New York, USA
| | - Paolo C Colombo
- Division of Cardiology, Columbia University Irving Medical Center - New York Presbyterian, New York, New York, USA
| | - Nir Uriel
- Division of Cardiology, Columbia University Irving Medical Center - New York Presbyterian, New York, New York, USA
| | - Muredach P Reilly
- Division of Cardiology, Columbia University Irving Medical Center - New York Presbyterian, New York, New York, USA
| | - Steven O Marx
- Division of Cardiology, Columbia University Irving Medical Center - New York Presbyterian, New York, New York, USA
| | | | - Veli K Topkara
- Division of Cardiology, Columbia University Irving Medical Center - New York Presbyterian, New York, New York, USA
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26
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Yang MN, Huang R, Zheng T, Dong Y, Wang WJ, Xu YJ, Mehra V, Zhou GD, Liu X, He H, Fang F, Li F, Fan JG, Zhang J, Ouyang F, Briollais L, Li J, Luo ZC. Genome-wide placental DNA methylations in fetal overgrowth and associations with leptin, adiponectin and fetal growth factors. Clin Epigenetics 2022; 14:192. [PMID: 36585686 PMCID: PMC9801645 DOI: 10.1186/s13148-022-01412-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 12/15/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Fetal overgrowth "programs" an elevated risk of type 2 diabetes in adulthood. Epigenetic alterations may be a mechanism in programming the vulnerability. We sought to characterize genome-wide alterations in placental gene methylations in fetal overgrowth and the associations with metabolic health biomarkers including leptin, adiponectin and fetal growth factors. RESULTS Comparing genome-wide placental gene DNA methylations in large-for-gestational-age (LGA, an indicator of fetal overgrowth, n = 30) versus optimal-for-gestational-age (OGA, control, n = 30) infants using the Illumina Infinium Human Methylation-EPIC BeadChip, we identified 543 differential methylation positions (DMPs; 397 hypermethylated, 146 hypomethylated) at false discovery rate < 5% and absolute methylation difference > 0.05 after adjusting for placental cell-type heterogeneity, maternal age, pre-pregnancy BMI and HbA1c levels during pregnancy. Twenty-five DMPs annotated to 20 genes (QSOX1, FCHSD2, LOC101928162, ADGRB3, GCNT1, TAP1, MYO16, NAV1, ATP8A2, LBXCOR1, EN2, INCA1, CAMTA2, SORCS2, SLC4A4, RPA3, UMAD1,USP53, OR2L13 and NR3C2) could explain 80% of the birth weight variations. Pathway analyses did not detect any statistically significant pathways after correcting for multiple tests. We validated a newly discovered differentially (hyper-)methylated gene-visual system homeobox 1 (VSX1) in an independent pyrosequencing study sample (LGA 47, OGA 47). Our data confirmed a hypermethylated gene-cadherin 13 (CDH13) reported in a previous epigenome-wide association study. Adiponectin in cord blood was correlated with its gene methylation in the placenta, while leptin and fetal growth factors (insulin, IGF-1, IGF-2) were not. CONCLUSIONS Fetal overgrowth may be associated with a large number of altered placental gene methylations. Placental VSX1 and CDH13 genes are hypermethylated in fetal overgrowth. Placental ADIPOQ gene methylations and fetal circulating adiponectin levels were correlated, suggesting the contribution of placenta-originated adiponectin to cord blood adiponectin.
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Affiliation(s)
- Meng-Nan Yang
- grid.16821.3c0000 0004 0368 8293Ministry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, 200092 China ,grid.17063.330000 0001 2157 2938Lunenfeld-Tanenbaum Research Institute, Prosserman Centre for Population Health Research, Department of Obstetrics and Gynecology, Mount Sinai Hospital, Faculty of Medicine, University of Toronto, L5-240, Murray Street 60, Toronto, ON M5G 1X5 Canada
| | - Rong Huang
- grid.17063.330000 0001 2157 2938Lunenfeld-Tanenbaum Research Institute, Prosserman Centre for Population Health Research, Department of Obstetrics and Gynecology, Mount Sinai Hospital, Faculty of Medicine, University of Toronto, L5-240, Murray Street 60, Toronto, ON M5G 1X5 Canada
| | - Tao Zheng
- grid.16821.3c0000 0004 0368 8293Department of Obstetrics and Gynecology, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, 200092 China
| | - Yu Dong
- grid.16821.3c0000 0004 0368 8293Ministry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, 200092 China
| | - Wen-Juan Wang
- grid.16821.3c0000 0004 0368 8293Ministry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, 200092 China
| | - Ya-Jie Xu
- grid.16821.3c0000 0004 0368 8293Ministry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, 200092 China
| | - Vrati Mehra
- grid.17063.330000 0001 2157 2938Lunenfeld-Tanenbaum Research Institute, Prosserman Centre for Population Health Research, Department of Obstetrics and Gynecology, Mount Sinai Hospital, Faculty of Medicine, University of Toronto, L5-240, Murray Street 60, Toronto, ON M5G 1X5 Canada
| | - Guang-Di Zhou
- grid.16821.3c0000 0004 0368 8293Ministry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, 200092 China
| | - Xin Liu
- grid.16821.3c0000 0004 0368 8293Ministry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, 200092 China
| | - Hua He
- grid.16821.3c0000 0004 0368 8293Ministry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, 200092 China
| | - Fang Fang
- grid.16821.3c0000 0004 0368 8293Ministry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, 200092 China
| | - Fei Li
- grid.16821.3c0000 0004 0368 8293Ministry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, 200092 China
| | - Jian-Gao Fan
- grid.16821.3c0000 0004 0368 8293Center for Fatty Liver, Shanghai Key Lab of Pediatric Gastroenterology and Nutrition, Department of Gastroenterology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092 China
| | - Jun Zhang
- grid.16821.3c0000 0004 0368 8293Ministry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, 200092 China
| | - Fengxiu Ouyang
- grid.16821.3c0000 0004 0368 8293Ministry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, 200092 China
| | - Laurent Briollais
- grid.17063.330000 0001 2157 2938Lunenfeld-Tanenbaum Research Institute, Prosserman Centre for Population Health Research, Department of Obstetrics and Gynecology, Mount Sinai Hospital, Faculty of Medicine, University of Toronto, L5-240, Murray Street 60, Toronto, ON M5G 1X5 Canada
| | - Jiong Li
- grid.16821.3c0000 0004 0368 8293Ministry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, 200092 China ,grid.7048.b0000 0001 1956 2722Department of Clinical Medicine-Department of Clinical Epidemiology, Aarhus University, Olof Palmes Allé 43-45, 8200 Aathus, Denmark
| | - Zhong-Cheng Luo
- grid.16821.3c0000 0004 0368 8293Ministry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, 200092 China ,grid.17063.330000 0001 2157 2938Lunenfeld-Tanenbaum Research Institute, Prosserman Centre for Population Health Research, Department of Obstetrics and Gynecology, Mount Sinai Hospital, Faculty of Medicine, University of Toronto, L5-240, Murray Street 60, Toronto, ON M5G 1X5 Canada
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27
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Abstract
Age is the key risk factor for diseases and disabilities of the elderly. Efforts to tackle age-related diseases and increase healthspan have suggested targeting the ageing process itself to 'rejuvenate' physiological functioning. However, achieving this aim requires measures of biological age and rates of ageing at the molecular level. Spurred by recent advances in high-throughput omics technologies, a new generation of tools to measure biological ageing now enables the quantitative characterization of ageing at molecular resolution. Epigenomic, transcriptomic, proteomic and metabolomic data can be harnessed with machine learning to build 'ageing clocks' with demonstrated capacity to identify new biomarkers of biological ageing.
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Affiliation(s)
- Jarod Rutledge
- Department of Genetics, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Paul F. Glenn Center for the Biology of Ageing, Stanford University School of Medicine, Stanford, CA, USA
| | - Hamilton Oh
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Paul F. Glenn Center for the Biology of Ageing, Stanford University School of Medicine, Stanford, CA, USA
- Graduate Program in Stem Cell and Regenerative Medicine, Stanford University, Stanford, CA, USA
| | - Tony Wyss-Coray
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.
- Paul F. Glenn Center for the Biology of Ageing, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.
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28
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Zheng Y, Lunetta KL, Liu C, Katrinli S, Smith AK, Miller MW, Logue MW. An evaluation of the genome-wide false positive rates of common methods for identifying differentially methylated regions using illumina methylation arrays. Epigenetics 2022; 17:2241-2258. [PMID: 36047742 PMCID: PMC9665129 DOI: 10.1080/15592294.2022.2115600] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 07/28/2022] [Accepted: 08/17/2022] [Indexed: 11/03/2022] Open
Abstract
Differentially methylated regions (DMRs) are genomic regions with specific methylation patterns across multiple loci that are associated with a phenotype. We examined the genome-wide false positive (GFP) rates of five widely used DMR methods: comb-p, Bumphunter, DMRcate, mCSEA and coMethDMR using both Illumina HumanMethylation450 (450 K) and MethylationEPIC (EPIC) data and simulated continuous and dichotomous null phenotypes (i.e., generated independently of methylation data). coMethDMR provided well-controlled GFP rates (~5%) except when analysing skewed continuous phenotypes. DMRcate generally had well-controlled GFP rates when applied to 450 K data except for the skewed continuous phenotype and EPIC data only for the normally distributed continuous phenotype. GFP rates for mCSEA were at least 0.096 and comb-p yielded GFP rates above 0.34. Bumphunter had high GFP rates of at least 0.35 across conditions, reaching as high as 0.95. Analysis of the performance of these methods in specific regions of the genome found that regions with higher correlation across loci had higher regional false positive rates on average across methods. Based on the false positive rates, coMethDMR is the most recommended analysis method, and DMRcate had acceptable performance when analysing 450 K data. However, as both could display higher levels of FPs for skewed continuous distributions, a normalizing transformation of skewed continuous phenotypes is suggested. This study highlights the importance of genome-wide simulations when evaluating the performance of DMR-analysis methods.
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Affiliation(s)
- Yuanchao Zheng
- National Center for PTSD, VA Boston Healthcare System, Boston, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Kathryn L. Lunetta
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Chunyu Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Seyma Katrinli
- Department of Gynecology and Obstetrics, Emory University, Atlanta, GA, USA
| | - Alicia K. Smith
- Department of Gynecology and Obstetrics, Emory University, Atlanta, GA, USA
- Emory University School of Medicine, Department of Psychiatry and Behavioral Sciences, Atlanta, GA, USA
| | - Mark W. Miller
- National Center for PTSD, VA Boston Healthcare System, Boston, MA, USA
- Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
| | - Mark W. Logue
- National Center for PTSD, VA Boston Healthcare System, Boston, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
- Biomedical Genetics, Boston University School of Medicine, Boston, MA, USA
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29
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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: 1.0] [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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30
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Higgins-Chen AT, Thrush KL, Wang Y, Minteer CJ, Kuo PL, Wang M, Niimi P, Sturm G, Lin J, Moore AZ, Bandinelli S, Vinkers CH, Vermetten E, Rutten BPF, Geuze E, Okhuijsen-Pfeifer C, van der Horst MZ, Schreiter S, Gutwinski S, Luykx JJ, Picard M, Ferrucci L, Crimmins EM, Boks MP, Hägg S, Hu-Seliger TT, Levine ME. A computational solution for bolstering reliability of epigenetic clocks: Implications for clinical trials and longitudinal tracking. NATURE AGING 2022; 2:644-661. [PMID: 36277076 PMCID: PMC9586209 DOI: 10.1038/s43587-022-00248-2] [Citation(s) in RCA: 97] [Impact Index Per Article: 48.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 06/08/2022] [Indexed: 01/09/2023]
Abstract
Epigenetic clocks are widely used aging biomarkers calculated from DNA methylation data, but this data can be surprisingly unreliable. Here we show technical noise produces deviations up to 9 years between replicates for six prominent epigenetic clocks, limiting their utility. We present a computational solution to bolster reliability, calculating principal components from CpG-level data as input for biological age prediction. Our retrained principal-component versions of six clocks show agreement between most replicates within 1.5 years, improved detection of clock associations and intervention effects, and reliable longitudinal trajectories in vivo and in vitro. This method entails only one additional step compared to traditional clocks, requires no replicates or prior knowledge of CpG reliabilities for training, and can be applied to any existing or future epigenetic biomarker. The high reliability of principal component-based clocks is critical for applications to personalized medicine, longitudinal tracking, in vitro studies, and clinical trials of aging interventions.
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Affiliation(s)
- Albert T Higgins-Chen
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- VA Connecticut Healthcare System, West Haven, CT, USA
| | - Kyra L Thrush
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - Yunzhang Wang
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | | | - Pei-Lun Kuo
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA
| | - Meng Wang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - Peter Niimi
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - Gabriel Sturm
- Departments of Psychiatry and Neurology, Division of Behavioral Medicine, Columbia University Irving Medical Center, New York, NY, United States
- New York State Psychiatric Institute, New York, NY United States
| | - Jue Lin
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA, United States
| | - Ann Zenobia Moore
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA
| | | | - Christiaan H Vinkers
- Department of Psychiatry, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Eric Vermetten
- Department Psychiatry, Leiden University Medical Center, Leiden, The Netherlands
| | - Bart P F Rutten
- School for Mental Health and Neuroscience, Department of Psychiatry and Neuropsychology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Elbert Geuze
- Department of Psychiatry, Brain Center University Medical Center Utrecht, Utrecht University, The Netherlands
- Brain Research & Innovation Centre, Ministry of Defence, Utrecht, the Netherlands
| | - Cynthia Okhuijsen-Pfeifer
- Department of Psychiatry, Brain Center University Medical Center Utrecht, Utrecht University, The Netherlands
| | - Marte Z van der Horst
- Department of Psychiatry, Brain Center University Medical Center Utrecht, Utrecht University, The Netherlands
- Second Opinion Outpatient Clinic, GGNet Mental Health, Warnsveld, The Netherlands
| | - Stefanie Schreiter
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Stefan Gutwinski
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Jurjen J Luykx
- Department of Psychiatry, Brain Center University Medical Center Utrecht, Utrecht University, The Netherlands
- Second Opinion Outpatient Clinic, GGNet Mental Health, Warnsveld, The Netherlands
| | - Martin Picard
- Departments of Psychiatry and Neurology, Division of Behavioral Medicine, Columbia University Irving Medical Center, New York, NY, United States
- New York State Psychiatric Institute, New York, NY United States
| | - Luigi Ferrucci
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA
| | - Eileen M Crimmins
- Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Marco P Boks
- Department of Psychiatry, Brain Center University Medical Center Utrecht, Utrecht University, The Netherlands
| | - Sara Hägg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | | | - Morgan E Levine
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
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31
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Olstad EW, Nordeng HME, Sandve GK, Lyle R, Gervin K. Low reliability of DNA methylation across Illumina Infinium platforms in cord blood: implications for replication studies and meta-analyses of prenatal exposures. Clin Epigenetics 2022; 14:80. [PMID: 35765087 PMCID: PMC9238140 DOI: 10.1186/s13148-022-01299-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Accepted: 06/12/2022] [Indexed: 11/20/2022] Open
Abstract
Background There is an increasing interest in the role of epigenetics in epidemiology, but the emerging research field faces several critical biological and technical challenges. In particular, recent studies have shown poor correlation of measured DNA methylation (DNAm) levels within and across Illumina Infinium platforms in various tissues. In this study, we have investigated concordance between 450 k and EPIC Infinium platforms in cord blood. We could not replicate our previous findings on the association of prenatal paracetamol exposure with cord blood DNAm, which prompted an investigation of cross-platform DNAm differences. Results This study is based on two DNAm data sets from cord blood samples selected from the Norwegian Mother, Father and Child Cohort Study (MoBa). DNAm of one data set was measured using the 450 k platform and the other data set was measured using the EPIC platform. Initial analyses of the EPIC data could not replicate any of our previous significant findings in the 450 k data on associations between prenatal paracetamol exposure and cord blood DNAm. A subset of the samples (n = 17) was included in both data sets, which enabled analyses of technical sources potentially contributing to the negative replication. Analyses of these 17 samples with repeated measurements revealed high per-sample correlations (\documentclass[12pt]{minimal}
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\begin{document}$$\stackrel{\mathrm{-}}{\text{R}}$$\end{document}R- ≈ 0.24) between the platforms. 1.7% of the CpGs exhibited a mean DNAm difference across platforms > 0.1. Furthermore, only 26.7% of the CpGs exhibited a moderate or better cross-platform reliability (intra-class correlation coefficient ≥ 0.5). Conclusion The observations of low cross-platform probe correlation and reliability corroborate previous reports in other tissues. Our study cannot determine the origin of the differences between platforms. Nevertheless, it emulates the setting in studies using data from multiple Infinium platforms, often analysed several years apart. Therefore, the findings may have important implications for future epigenome-wide association studies (EWASs), in replication, meta-analyses and longitudinal studies. Cognisance and transparency of the challenges related to cross-platform studies may enhance the interpretation, replicability and validity of EWAS results both in cord blood and other tissues, ultimately improving the clinical relevance of epigenetic epidemiology. Supplementary Information The online version contains supplementary material available at 10.1186/s13148-022-01299-3.
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Pang APS, Higgins-Chen AT, Comite F, Raica I, Arboleda C, Went H, Mendez T, Schotsaert M, Dwaraka V, Smith R, Levine ME, Ndhlovu LC, Corley MJ. Longitudinal Study of DNA Methylation and Epigenetic Clocks Prior to and Following Test-Confirmed COVID-19 and mRNA Vaccination. Front Genet 2022; 13:819749. [PMID: 35719387 PMCID: PMC9203887 DOI: 10.3389/fgene.2022.819749] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 04/25/2022] [Indexed: 01/01/2023] Open
Abstract
The host epigenetic landscape rapidly changes during SARS-CoV-2 infection, and evidence suggest that severe COVID-19 is associated with durable scars to the epigenome. Specifically, aberrant DNA methylation changes in immune cells and alterations to epigenetic clocks in blood relate to severe COVID-19. However, a longitudinal assessment of DNA methylation states and epigenetic clocks in blood from healthy individuals prior to and following test-confirmed non-hospitalized COVID-19 has not been performed. Moreover, the impact of mRNA COVID-19 vaccines upon the host epigenome remains understudied. Here, we first examined DNA methylation states in the blood of 21 participants prior to and following test-confirmed COVID-19 diagnosis at a median time frame of 8.35 weeks; 756 CpGs were identified as differentially methylated following COVID-19 diagnosis in blood at an FDR adjusted p-value < 0.05. These CpGs were enriched in the gene body, and the northern and southern shelf regions of genes involved in metabolic pathways. Integrative analysis revealed overlap among genes identified in transcriptional SARS-CoV-2 infection datasets. Principal component-based epigenetic clock estimates of PhenoAge and GrimAge significantly increased in people over 50 following infection by an average of 2.1 and 0.84 years. In contrast, PCPhenoAge significantly decreased in people fewer than 50 following infection by an average of 2.06 years. This observed divergence in epigenetic clocks following COVID-19 was related to age and immune cell-type compositional changes in CD4+ T cells, B cells, granulocytes, plasmablasts, exhausted T cells, and naïve T cells. Complementary longitudinal epigenetic clock analyses of 36 participants prior to and following Pfizer and Moderna mRNA-based COVID-19 vaccination revealed that vaccination significantly reduced principal component-based Horvath epigenetic clock estimates in people over 50 by an average of 3.91 years for those who received Moderna. This reduction in epigenetic clock estimates was significantly related to chronological age and immune cell-type compositional changes in B cells and plasmablasts pre- and post-vaccination. These findings suggest the potential utility of epigenetic clocks as a biomarker of COVID-19 vaccine responses. Future research will need to unravel the significance and durability of short-term changes in epigenetic age related to COVID-19 exposure and mRNA vaccination.
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Affiliation(s)
- Alina P. S. Pang
- Division of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, New York, NY, United States
| | - Albert T. Higgins-Chen
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
- VA Connecticut Healthcare System, West Haven, CT, United States
| | - Florence Comite
- Comite Center for Precision Medicine & Health, New York, NY, United States
- Lenox Hill Hospital/Northwell, New York, NY, United States
| | - Ioana Raica
- Comite Center for Precision Medicine & Health, New York, NY, United States
| | | | | | | | - Michael Schotsaert
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | | | - Ryan Smith
- TruDiagnostic, Lexington, KY, United States
| | - Morgan E. Levine
- Department of Pathology, Yale University School of Medicine, New Haven, CT, United States
| | - Lishomwa C. Ndhlovu
- Division of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, New York, NY, United States
| | - Michael J. Corley
- Division of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, New York, NY, United States
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Petrovic D, Bodinier B, Dagnino S, Whitaker M, Karimi M, Campanella G, Haugdahl Nøst T, Polidoro S, Palli D, Krogh V, Tumino R, Sacerdote C, Panico S, Lund E, Dugué PA, Giles GG, Severi G, Southey M, Vineis P, Stringhini S, Bochud M, Sandanger TM, Vermeulen RCH, Guida F, Chadeau-Hyam M. Epigenetic mechanisms of lung carcinogenesis involve differentially methylated CpG sites beyond those associated with smoking. Eur J Epidemiol 2022; 37:629-640. [PMID: 35595947 PMCID: PMC9288379 DOI: 10.1007/s10654-022-00877-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 04/25/2022] [Indexed: 12/24/2022]
Abstract
Smoking-related epigenetic changes have been linked to lung cancer, but the contribution of epigenetic alterations unrelated to smoking remains unclear. We sought for a sparse set of CpG sites predicting lung cancer and explored the role of smoking in these associations. We analysed CpGs in relation to lung cancer in participants from two nested case-control studies, using (LASSO)-penalised regression. We accounted for the effects of smoking using known smoking-related CpGs, and through conditional-independence network. We identified 29 CpGs (8 smoking-related, 21 smoking-unrelated) associated with lung cancer. Models additionally adjusted for Comprehensive Smoking Index-(CSI) selected 1 smoking-related and 49 smoking-unrelated CpGs. Selected CpGs yielded excellent discriminatory performances, outperforming information provided by CSI only. Of the 8 selected smoking-related CpGs, two captured lung cancer-relevant effects of smoking that were missed by CSI. Further, the 50 CpGs identified in the CSI-adjusted model complementarily explained lung cancer risk. These markers may provide further insight into lung cancer carcinogenesis and help improving early identification of high-risk patients.
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Affiliation(s)
- Dusan Petrovic
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
- Department of Epidemiology and Health Systems (DESS), University Centre for General Medicine and Public Health (UNISANTE), Lausanne, Switzerland
- Department and Division of Primary Care Medicine, University Hospital of Geneva, Geneva, Switzerland
| | - Barbara Bodinier
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
| | - Sonia Dagnino
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
| | - Matthew Whitaker
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
| | - Maryam Karimi
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
- Bureau de Biostatistique et d'Épidémiologie, Institut Gustave Roussy, Université Paris-Saclay, Villejuif, France
- Oncostat U1018, Inserm, Équipe Labellisée Ligue Contre Le Cancer, Université Paris-Saclay, Villejuif, France
| | - Gianluca Campanella
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
| | - Therese Haugdahl Nøst
- Department of Community Medicine, UiT The Arctic University of Norway, Tromsø, Norway
| | | | - Domenico Palli
- Molecular and Nutritional Epidemiology Unit, Cancer Research and Prevention Institute-ISPO, Florence, Italy
| | - Vittorio Krogh
- Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Rosario Tumino
- Hyblean Association for Epidemiological Research, AIRE- ONLUS, Ragusa, Italy
| | - Carlotta Sacerdote
- Unit of Cancer Epidemiology Città Della Salute e della Scienza University-Hospital, Via Santena 7, 10126, Turin, Italy
| | - Salvatore Panico
- Department of Clinical Medicine and Surgery, Federico II University, Naples, Italy
| | - Eiliv Lund
- Department of Community Medicine, UiT The Arctic University of Norway, Tromsø, Norway
- The Norwegian Cancer Registry, Oslo, Norway
| | - Pierre-Antoine Dugué
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Australia
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Australia
| | - Gianluca Severi
- Centre for Research in Epidemiology and Population Health, Inserm (Institut National de La Sante Et de a Recherche Medicale), Villejuif, France
| | - Melissa Southey
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Australia
- Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, Australia
| | - Paolo Vineis
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
| | - Silvia Stringhini
- Department of Epidemiology and Health Systems (DESS), University Centre for General Medicine and Public Health (UNISANTE), Lausanne, Switzerland
- Department and Division of Primary Care Medicine, University Hospital of Geneva, Geneva, Switzerland
| | - Murielle Bochud
- Department of Epidemiology and Health Systems (DESS), University Centre for General Medicine and Public Health (UNISANTE), Lausanne, Switzerland
| | - Torkjel M Sandanger
- Department of Community Medicine, UiT The Arctic University of Norway, Tromsø, Norway
| | - Roel C H Vermeulen
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands
- Julius Centre for Health Sciences and Primary Care, University Medical Centre, Utrecht, Utrecht, The Netherlands
| | - Florence Guida
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
- Group of Genetic Epidemiology, International Agency for Research on Cancer (IARC) - World Health Organization (WHO), Lyon, France
| | - Marc Chadeau-Hyam
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK.
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands.
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Methylation at CpG sites related to growth differentiation factor-15 was not prospectively associated with cardiovascular death in discordant monozygotic twins. Sci Rep 2022; 12:4410. [PMID: 35292700 PMCID: PMC8924170 DOI: 10.1038/s41598-022-08369-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 03/07/2022] [Indexed: 11/29/2022] Open
Abstract
Myocardial infarction patients had decreased methylation at four growth differentiating factor-15 (GDF-15) related CpG sites (cg13033858, cg16936953, cg17150809, and cg18608055). These sites had not been studied for their association with cardiovascular disease (CVD) deaths. Thus, we aimed to assess the associations independent of genes, shared environment, and traditional CVD risk factors. Nineteen white, male, monozygotic twin pairs discordant for CVD deaths were included from the National Heart, Lung and Blood Institute Twin Study (NHLBI) initiated in 1969. Data on vital status was collected through December 31, 2014. Methylation of buffy coat DNA at exam 3 (1986–87) was measured using the Illumina HumanMethylation450 BeadChip. Principal component analysis was used to generate a score representing blood leukocyte composition and baseline CVD risk factors and predominated with natural killer cells, CD4+ T cells, and Framingham risk score. Conditional logistic regression demonstrated that methylation at the four CpG sites was not associated with CVD deaths before (all p > 0.05, bootstrapped p > 0.05) and after adjustment for the score (all p > 0.05). Joint influences of cg16936953 and the score were statistically significant (p < 0.05). In conclusion, joint influences of methylation at the site cg16936953 and the score are prospectively associated with CVD deaths independent of germline and common environment. ClinicalTrials.gov Identifier for NHLBI Twin Study: NCT00005124.
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Silver MJ, Saffari A, Kessler NJ, Chandak GR, Fall CHD, Issarapu P, Dedaniya A, Betts M, Moore SE, Routledge MN, Herceg Z, Cuenin C, Derakhshan M, James PT, Monk D, Prentice AM. Environmentally sensitive hotspots in the methylome of the early human embryo. eLife 2022; 11:e72031. [PMID: 35188105 PMCID: PMC8912923 DOI: 10.7554/elife.72031] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 02/18/2022] [Indexed: 11/26/2022] Open
Abstract
In humans, DNA methylation marks inherited from gametes are largely erased following fertilisation, prior to construction of the embryonic methylome. Exploiting a natural experiment of seasonal variation including changes in diet and nutritional status in rural Gambia, we analysed three datasets covering two independent child cohorts and identified 259 CpGs showing consistent associations between season of conception (SoC) and DNA methylation. SoC effects were most apparent in early infancy, with evidence of attenuation by mid-childhood. SoC-associated CpGs were enriched for metastable epialleles, parent-of-origin-specific methylation and germline differentially methylated regions, supporting a periconceptional environmental influence. Many SoC-associated CpGs overlapped enhancers or sites of active transcription in H1 embryonic stem cells and fetal tissues. Half were influenced but not determined by measured genetic variants that were independent of SoC. Environmental 'hotspots' providing a record of environmental influence at periconception constitute a valuable resource for investigating epigenetic mechanisms linking early exposures to lifelong health and disease.
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Affiliation(s)
- Matt J Silver
- Medical Research Council Unit The Gambia at the London School of Hygiene and Tropical MedicineGambiaUnited Kingdom
| | - Ayden Saffari
- Medical Research Council Unit The Gambia at the London School of Hygiene and Tropical MedicineGambiaUnited Kingdom
| | - Noah J Kessler
- Department of Genetics, University of CambridgeCambridgeUnited Kingdom
| | - Gririraj R Chandak
- Genomic Research on Complex Diseases, CSIR-Centre for Cellular and Molecular BiologyHyderabadIndia
| | - Caroline HD Fall
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton General HospitalSouthamptonUnited Kingdom
| | - Prachand Issarapu
- Genomic Research on Complex Diseases, CSIR-Centre for Cellular and Molecular BiologyHyderabadIndia
| | - Akshay Dedaniya
- Genomic Research on Complex Diseases, CSIR-Centre for Cellular and Molecular BiologyHyderabadIndia
| | - Modupeh Betts
- Medical Research Council Unit The Gambia at the London School of Hygiene and Tropical MedicineGambiaUnited Kingdom
| | - Sophie E Moore
- Medical Research Council Unit The Gambia at the London School of Hygiene and Tropical MedicineGambiaUnited Kingdom
- Department of Women and Children's Health, King's College LondonLondonUnited Kingdom
| | - Michael N Routledge
- School of Medicine, University of LeedsLeedsUnited Kingdom
- School of Food and Biological Engineering, Jiangsu UniversityZhenjiangChina
| | - Zdenko Herceg
- Epigenomics and Mechanisms Branch, International Agency For Research On CancerLyonFrance
| | - Cyrille Cuenin
- Epigenomics and Mechanisms Branch, International Agency For Research On CancerLyonFrance
| | - Maria Derakhshan
- Medical Research Council Unit The Gambia at the London School of Hygiene and Tropical MedicineGambiaUnited Kingdom
| | - Philip T James
- Medical Research Council Unit The Gambia at the London School of Hygiene and Tropical MedicineGambiaUnited Kingdom
| | - David Monk
- Biomedical Research Centre, University of East AngliaNorwichUnited Kingdom
- Bellvitge Institute for Biomedical ResearchBarcelonaSpain
| | - Andrew M Prentice
- Medical Research Council Unit The Gambia at the London School of Hygiene and Tropical MedicineGambiaUnited Kingdom
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Raffington L, Belsky DW. Integrating DNA Methylation Measures of Biological Aging into Social Determinants of Health Research. Curr Environ Health Rep 2022; 9:196-210. [PMID: 35181865 DOI: 10.1007/s40572-022-00338-8] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/10/2022] [Indexed: 12/13/2022]
Abstract
PURPOSE OF REVIEW Acceleration of biological processes of aging is hypothesized to drive excess morbidity and mortality in socially disadvantaged populations. DNA methylation measures of biological aging provide tools for testing this hypothesis. RECENT FINDINGS Next-generation DNA methylation measures of biological aging developed to predict mortality risk and physiological decline are more predictive of morbidity and mortality than the original epigenetic clocks developed to predict chronological age. These new measures show consistent evidence of more advanced and faster biological aging in people exposed to socioeconomic disadvantage and may be able to record the emergence of socially determined health inequalities as early as childhood. Next-generation DNA methylation measures of biological aging also indicate race/ethnic disparities in biological aging. More research is needed on these measures in samples of non-Western and non-White populations. New DNA methylation measures of biological aging open opportunities for refining inference about the causes of social disparities in health and devising policies to eliminate them. Further refining measures of biological aging by including more diversity in samples used for measurement development is a critical priority for the field.
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Affiliation(s)
- Laurel Raffington
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
- Population Research Center, The University of Texas at Austin, Austin, TX, USA
| | - Daniel W Belsky
- Department of Epidemiology, Columbia University Mailman School of Public Health, 722 W 168th St. Rm 413, New York, NY, 10032, USA.
- Robert N Butler Columbia Aging Center, Columbia University Mailman School of Public Health, New York, NY, USA.
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Wang WJ, Huang R, Zheng T, Du Q, Yang MN, Xu YJ, Liu X, Tao MY, He H, Fang F, Li F, Fan JG, Zhang J, Briollais L, Ouyang F, Luo ZC. Genome-Wide Placental Gene Methylations in Gestational Diabetes Mellitus, Fetal Growth and Metabolic Health Biomarkers in Cord Blood. Front Endocrinol (Lausanne) 2022; 13:875180. [PMID: 35721735 PMCID: PMC9204344 DOI: 10.3389/fendo.2022.875180] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 04/21/2022] [Indexed: 12/03/2022] Open
Abstract
Gestational diabetes mellitus (GDM) "program" an elevated risk of metabolic syndrome in the offspring. Epigenetic alterations are a suspected mechanism. GDM has been associated with placental DNA methylation changes in some epigenome-wide association studies. It remains unclear which genes or pathways are affected, and whether any placental differential gene methylations are correlated to fetal growth or circulating metabolic health biomarkers. In an epigenome-wide association study using the Infinium MethylationEPIC Beadchip, we sought to identify genome-wide placental differentially methylated genes and enriched pathways in GDM, and to assess the correlations with fetal growth and metabolic health biomarkers in cord blood. The study samples were 30 pairs of term placentas in GDM vs. euglycemic pregnancies (controls) matched by infant sex and gestational age at delivery in the Shanghai Birth Cohort. Cord blood metabolic health biomarkers included insulin, C-peptide, proinsulin, IGF-I, IGF-II, leptin and adiponectin. Adjusting for maternal age, pre-pregnancy BMI, parity, mode of delivery and placental cell type heterogeneity, 256 differentially methylated positions (DMPs,130 hypermethylated and 126 hypomethylated) were detected between GDM and control groups accounting for multiple tests with false discovery rate <0.05 and beta-value difference >0.05. WSCD2 was identified as a differentially methylated gene in both site- and region-level analyses. We validated 7 hypermethylated (CYP1A2, GFRA1, HDAC4, LIMS2, NAV3, PAX6, UPK1B) and 10 hypomethylated (DPP10, CPLX1, CSMD2, GPR133, NRXN1, PCSK9, PENK, PRDM16, PTPRN2, TNXB) genes reported in previous epigenome-wide association studies. We did not find any enriched pathway accounting for multiple tests. DMPs in 11 genes (CYP2D7P1, PCDHB15, ERG, SIRPB1, DKK2, RAPGEF5, CACNA2D4, PCSK9, TSNARE1, CADM2, KCNAB2) were correlated with birth weight (z score) accounting for multiple tests. There were no significant correlations between placental gene methylations and cord blood biomarkers. In conclusions, GDM was associated with DNA methylation changes in a number of placental genes, but these placental gene methylations were uncorrelated to the observed metabolic health biomarkers (fetal growth factors, leptin and adiponectin) in cord blood. We validated 17 differentially methylated placental genes in GDM, and identified 11 differentially methylated genes relevant to fetal growth.
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Affiliation(s)
- Wen-Juan Wang
- Ministry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, and Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
- Lunenfeld-Tanenbaum Research Institute, Prosserman Centre for Population Health Research, Department of Obstetrics and Gynecology, Mount Sinai Hospital, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Clinical Skills Center, School of Clinical Medicine, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China
| | - Rong Huang
- Lunenfeld-Tanenbaum Research Institute, Prosserman Centre for Population Health Research, Department of Obstetrics and Gynecology, Mount Sinai Hospital, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Tao Zheng
- Department of Obstetrics and Gynecology, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
| | - Qinwen Du
- Ministry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, and Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
- Department of Obstetrics and Gynecology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Meng-Nan Yang
- Ministry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, and Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
| | - Ya-Jie Xu
- Ministry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, and Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
| | - Xin Liu
- Ministry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, and Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
| | - Min-Yi Tao
- Ministry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, and Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
| | - Hua He
- Ministry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, and Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
| | - Fang Fang
- Ministry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, and Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
| | - Fei Li
- Ministry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, and Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
| | - Jian-Gao Fan
- Center for Fatty Liver, Shanghai Key Lab of Pediatric Gastroenterology and Nutrition, Department of Gastroenterology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jun Zhang
- Ministry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, and Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
| | - Laurent Briollais
- Lunenfeld-Tanenbaum Research Institute, Prosserman Centre for Population Health Research, Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Fengxiu Ouyang
- Ministry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, and Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
- *Correspondence: Zhong-Cheng Luo, ; Fengxiu Ouyang,
| | - Zhong-Cheng Luo
- Ministry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Early Life Health Institute, and Department of Pediatrics, Xinhua Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
- Lunenfeld-Tanenbaum Research Institute, Prosserman Centre for Population Health Research, Department of Obstetrics and Gynecology, Mount Sinai Hospital, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- *Correspondence: Zhong-Cheng Luo, ; Fengxiu Ouyang,
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Foox J, Nordlund J, Lalancette C, Gong T, Lacey M, Lent S, Langhorst BW, Ponnaluri VKC, Williams L, Padmanabhan KR, Cavalcante R, Lundmark A, Butler D, Mozsary C, Gurvitch J, Greally JM, Suzuki M, Menor M, Nasu M, Alonso A, Sheridan C, Scherer A, Bruinsma S, Golda G, Muszynska A, Łabaj PP, Campbell MA, Wos F, Raine A, Liljedahl U, Axelsson T, Wang C, Chen Z, Yang Z, Li J, Yang X, Wang H, Melnick A, Guo S, Blume A, Franke V, Ibanez de Caceres I, Rodriguez-Antolin C, Rosas R, Davis JW, Ishii J, Megherbi DB, Xiao W, Liao W, Xu J, Hong H, Ning B, Tong W, Akalin A, Wang Y, Deng Y, Mason CE. The SEQC2 epigenomics quality control (EpiQC) study. Genome Biol 2021; 22:332. [PMID: 34872606 PMCID: PMC8650396 DOI: 10.1186/s13059-021-02529-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 10/28/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Cytosine modifications in DNA such as 5-methylcytosine (5mC) underlie a broad range of developmental processes, maintain cellular lineage specification, and can define or stratify types of cancer and other diseases. However, the wide variety of approaches available to interrogate these modifications has created a need for harmonized materials, methods, and rigorous benchmarking to improve genome-wide methylome sequencing applications in clinical and basic research. Here, we present a multi-platform assessment and cross-validated resource for epigenetics research from the FDA's Epigenomics Quality Control Group. RESULTS Each sample is processed in multiple replicates by three whole-genome bisulfite sequencing (WGBS) protocols (TruSeq DNA methylation, Accel-NGS MethylSeq, and SPLAT), oxidative bisulfite sequencing (TrueMethyl), enzymatic deamination method (EMSeq), targeted methylation sequencing (Illumina Methyl Capture EPIC), single-molecule long-read nanopore sequencing from Oxford Nanopore Technologies, and 850k Illumina methylation arrays. After rigorous quality assessment and comparison to Illumina EPIC methylation microarrays and testing on a range of algorithms (Bismark, BitmapperBS, bwa-meth, and BitMapperBS), we find overall high concordance between assays, but also differences in efficiency of read mapping, CpG capture, coverage, and platform performance, and variable performance across 26 microarray normalization algorithms. CONCLUSIONS The data provided herein can guide the use of these DNA reference materials in epigenomics research, as well as provide best practices for experimental design in future studies. By leveraging seven human cell lines that are designated as publicly available reference materials, these data can be used as a baseline to advance epigenomics research.
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Affiliation(s)
- Jonathan Foox
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, New York, USA
| | - Jessica Nordlund
- Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
- EATRIS ERIC- European Infrastructure for Translational Medicine, De Boelelaan 1118, 1081, HZ, Amsterdam, The Netherlands
| | - Claudia Lalancette
- BRCF Epigenomics Core, University of Michigan Medicine, Ann Arbor, MI, 48109, USA
| | - Ting Gong
- Department of Quantitative Health Sciences, University of Hawaii John A. Burns School of Medicine, Honolulu, HI, 96813, USA
| | | | - Samantha Lent
- AbbVie Genomics Research Center, 1 N. Waukegan Rd, North Chicago, IL, 60036, USA
| | | | | | | | | | - Raymond Cavalcante
- BRCF Epigenomics Core, University of Michigan Medicine, Ann Arbor, MI, 48109, USA
| | - Anders Lundmark
- Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
- EATRIS ERIC- European Infrastructure for Translational Medicine, De Boelelaan 1118, 1081, HZ, Amsterdam, The Netherlands
| | - Daniel Butler
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York, USA
| | - Christopher Mozsary
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York, USA
| | - Justin Gurvitch
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York, USA
| | - John M Greally
- Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Masako Suzuki
- Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Mark Menor
- Department of Quantitative Health Sciences, University of Hawaii John A. Burns School of Medicine, Honolulu, HI, 96813, USA
| | - Masaki Nasu
- Department of Quantitative Health Sciences, University of Hawaii John A. Burns School of Medicine, Honolulu, HI, 96813, USA
| | - Alicia Alonso
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York, USA
| | - Caroline Sheridan
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York, USA
- Division of Hematology/Oncology, Department of Medicine, Epigenomics Core Facility, Weill Cornell Medicine, New York, NY, USA
| | - Andreas Scherer
- EATRIS ERIC- European Infrastructure for Translational Medicine, De Boelelaan 1118, 1081, HZ, Amsterdam, The Netherlands
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | | | - Gosia Golda
- Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, Krakow, Poland
| | - Agata Muszynska
- Małopolska Centre of Biotechnology, Jagiellonian University, Krakow, Poland
| | - Paweł P Łabaj
- Małopolska Centre of Biotechnology, Jagiellonian University, Krakow, Poland
| | | | - Frank Wos
- New York Genome Center, New York, NY, 10013, USA
| | - Amanda Raine
- Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
- EATRIS ERIC- European Infrastructure for Translational Medicine, De Boelelaan 1118, 1081, HZ, Amsterdam, The Netherlands
| | - Ulrika Liljedahl
- Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
- EATRIS ERIC- European Infrastructure for Translational Medicine, De Boelelaan 1118, 1081, HZ, Amsterdam, The Netherlands
| | - Tomas Axelsson
- Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
- EATRIS ERIC- European Infrastructure for Translational Medicine, De Boelelaan 1118, 1081, HZ, Amsterdam, The Netherlands
| | - Charles Wang
- Center for Genomics, School of Medicine, Loma Linda University, Loma Linda, CA, 92350, USA
| | - Zhong Chen
- Center for Genomics, School of Medicine, Loma Linda University, Loma Linda, CA, 92350, USA
| | - Zhaowei Yang
- Center for Genomics, School of Medicine, Loma Linda University, Loma Linda, CA, 92350, USA
- Department of Allergy and Clinical Immunology, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Jing Li
- Center for Genomics, School of Medicine, Loma Linda University, Loma Linda, CA, 92350, USA
- Department of Allergy and Clinical Immunology, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Xiaopeng Yang
- Department of Neurology, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, 450014, China
| | - Hongwei Wang
- Development of Medicine, the University of Chicago, Chicago, IL, 60637, USA
| | - Ari Melnick
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York, USA
| | - Shang Guo
- Department of Neurology, the Second Affiliated Hospital of Zhengzhou University, Zhengzhou, 450014, China
| | - Alexander Blume
- Bioinformatics and Omics Data Science Platform, Berlin Institute for Medical Systems Biology, Max Delbrueck Center for Molecular Medicine, Berlin, Germany
| | - Vedran Franke
- Bioinformatics and Omics Data Science Platform, Berlin Institute for Medical Systems Biology, Max Delbrueck Center for Molecular Medicine, Berlin, Germany
| | - Inmaculada Ibanez de Caceres
- EATRIS ERIC- European Infrastructure for Translational Medicine, De Boelelaan 1118, 1081, HZ, Amsterdam, The Netherlands
- Cancer Epigenetics Laboratory, INGEMM, IdiPAZ, Madrid, Spain
| | - Carlos Rodriguez-Antolin
- EATRIS ERIC- European Infrastructure for Translational Medicine, De Boelelaan 1118, 1081, HZ, Amsterdam, The Netherlands
- Cancer Epigenetics Laboratory, INGEMM, IdiPAZ, Madrid, Spain
| | - Rocio Rosas
- EATRIS ERIC- European Infrastructure for Translational Medicine, De Boelelaan 1118, 1081, HZ, Amsterdam, The Netherlands
- Cancer Epigenetics Laboratory, INGEMM, IdiPAZ, Madrid, Spain
| | - Justin Wade Davis
- AbbVie Genomics Research Center, 1 N. Waukegan Rd, North Chicago, IL, 60036, USA
| | | | - Dalila B Megherbi
- CMINDS Research Center, Francis College of Engineering, University of Massachusetts Lowell, Lowell, MA, 01854, USA
| | - Wenming Xiao
- Center for Devices and Radiological Health, Food and Drug Administration, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA
| | - Will Liao
- New York Genome Center, New York, NY, 10013, USA
| | - Joshua Xu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, Food and Drug Administration, 3900 NCTR Road, Jefferson, AR, 72079, USA
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, Food and Drug Administration, 3900 NCTR Road, Jefferson, AR, 72079, USA
| | - Baitang Ning
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, Food and Drug Administration, 3900 NCTR Road, Jefferson, AR, 72079, USA
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, Food and Drug Administration, 3900 NCTR Road, Jefferson, AR, 72079, USA
| | - Altuna Akalin
- Bioinformatics and Omics Data Science Platform, Berlin Institute for Medical Systems Biology, Max Delbrueck Center for Molecular Medicine, Berlin, Germany
| | - Yunliang Wang
- Department of Neurology, the Second Affiliated Hospital of Zhengzhou University, Zhengzhou, 450014, China.
| | - Youping Deng
- Department of Quantitative Health Sciences, University of Hawaii John A. Burns School of Medicine, Honolulu, HI, 96813, USA.
| | - Christopher E Mason
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York, USA.
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, New York, USA.
- The Feil Family Brain and Mind Research Institute, New York, New York, USA.
- The WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY, USA.
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Vanderlinden LA, Johnson RK, Carry PM, Dong F, DeMeo DL, Yang IV, Norris JM, Kechris K. An effective processing pipeline for harmonizing DNA methylation data from Illumina's 450K and EPIC platforms for epidemiological studies. BMC Res Notes 2021; 14:352. [PMID: 34496950 PMCID: PMC8424820 DOI: 10.1186/s13104-021-05741-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 08/16/2021] [Indexed: 02/06/2023] Open
Abstract
OBJECTIVE Illumina BeadChip arrays are commonly used to generate DNA methylation data for large epidemiological studies. Updates in technology over time create challenges for data harmonization within and between studies, many of which obtained data from the older 450K and newer EPIC platforms. The pre-processing pipeline for DNA methylation is not trivial, and influences the downstream analyses. Incorporating different platforms adds a new level of technical variability that has not yet been taken into account by recommended pipelines. Our study evaluated the performance of various tools on different versions of platform data harmonization at each step of pre-processing pipeline, including quality control (QC), normalization, batch effect adjustment, and genomic inflation. We illustrate our novel approach using 450K and EPIC data from the Diabetes Autoimmunity Study in the Young (DAISY) prospective cohort. RESULTS We found normalization and probe filtering had the biggest effect on data harmonization. Employing a meta-analysis was an effective and easily executable method for accounting for platform variability. Correcting for genomic inflation also helped with harmonization. We present guidelines for studies seeking to harmonize data from the 450K and EPIC platforms, which includes the use of technical replicates for evaluating numerous pre-processing steps, and employing a meta-analysis.
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Affiliation(s)
- Lauren A Vanderlinden
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Randi K Johnson
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Patrick M Carry
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Fran Dong
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Dawn L DeMeo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Ivana V Yang
- School of Medicine, University of Colorado, Anschutz Medical Campus, Aurora, CO, USA
| | - Jill M Norris
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Katerina Kechris
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
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Díez-Villanueva A, Jordà M, Carreras-Torres R, Alonso H, Cordero D, Guinó E, Sanjuan X, Santos C, Salazar R, Sanz-Pamplona R, Moreno V. Identifying causal models between genetically regulated methylation patterns and gene expression in healthy colon tissue. Clin Epigenetics 2021; 13:162. [PMID: 34419169 PMCID: PMC8380335 DOI: 10.1186/s13148-021-01148-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 08/07/2021] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND DNA methylation is involved in the regulation of gene expression and phenotypic variation, but the inter-relationship between genetic variation, DNA methylation and gene expression remains poorly understood. Here we combine the analysis of genetic variants related to methylation markers (methylation quantitative trait loci: mQTLs) and gene expression (expression quantitative trait loci: eQTLs) with methylation markers related to gene expression (expression quantitative trait methylation: eQTMs), to provide novel insights into the genetic/epigenetic architecture of colocalizing molecular markers. RESULTS Normal mucosa from 100 patients with colon cancer and 50 healthy donors included in the Colonomics project have been analyzed. Linear models have been used to find mQTLs and eQTMs within 1 Mb of the target gene. From 32,446 eQTLs previously detected, we found a total of 6850 SNPs, 114 CpGs and 52 genes interrelated, generating 13,987 significant combinations of co-occurring associations (meQTLs) after Bonferromi correction. Non-redundant meQTLs were 54, enriched in genes involved in metabolism of glucose and xenobiotics and immune system. SNPs in meQTLs were enriched in regulatory elements (enhancers and promoters) compared to random SNPs within 1 Mb of genes. Three colorectal cancer GWAS SNPs were related to methylation changes, and four SNPs were related to chemerin levels. Bayesian networks have been used to identify putative causal relationships among associated SNPs, CpG and gene expression triads. We identified that most of these combinations showed the canonical pathway of methylation markers causes gene expression variation (60.1%) or non-causal relationship between methylation and gene expression (33.9%); however, in up to 6% of these combinations, gene expression was causing variation in methylation markers. CONCLUSIONS In this study we provided a characterization of the regulation between genetic variants and inter-dependent methylation markers and gene expression in a set of 150 healthy colon tissue samples. This is an important finding for the understanding of molecular susceptibility on colon-related complex diseases.
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Affiliation(s)
- Anna Díez-Villanueva
- Unit of Biomarkers and Susceptibility, Cancer Prevention and Control Program, Catalan Institute of Oncology (ICO), Av Gran Via 199-203, 08907, L'Hospitalet de Llobregat, Barcelona, Spain
- Colorectal Cancer Group, Bellvitge Biomedical Research Institute (IDIBELL), Hospitalet de Llobregat, Barcelona, Spain
- Biomedical Research Centre Network for Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Mireia Jordà
- Program of Predictive and Personalized Medicine of Cancer (PMPPC), Germans Trias i Pujol Research Institute (IGTP), Badalona, Barcelona, Spain
| | - Robert Carreras-Torres
- Unit of Biomarkers and Susceptibility, Cancer Prevention and Control Program, Catalan Institute of Oncology (ICO), Av Gran Via 199-203, 08907, L'Hospitalet de Llobregat, Barcelona, Spain
- Colorectal Cancer Group, Bellvitge Biomedical Research Institute (IDIBELL), Hospitalet de Llobregat, Barcelona, Spain
- Biomedical Research Centre Network for Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Henar Alonso
- Unit of Biomarkers and Susceptibility, Cancer Prevention and Control Program, Catalan Institute of Oncology (ICO), Av Gran Via 199-203, 08907, L'Hospitalet de Llobregat, Barcelona, Spain
- Colorectal Cancer Group, Bellvitge Biomedical Research Institute (IDIBELL), Hospitalet de Llobregat, Barcelona, Spain
- Biomedical Research Centre Network for Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
| | - David Cordero
- Unit of Biomarkers and Susceptibility, Cancer Prevention and Control Program, Catalan Institute of Oncology (ICO), Av Gran Via 199-203, 08907, L'Hospitalet de Llobregat, Barcelona, Spain
- Colorectal Cancer Group, Bellvitge Biomedical Research Institute (IDIBELL), Hospitalet de Llobregat, Barcelona, Spain
- Biomedical Research Centre Network for Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Elisabet Guinó
- Unit of Biomarkers and Susceptibility, Cancer Prevention and Control Program, Catalan Institute of Oncology (ICO), Av Gran Via 199-203, 08907, L'Hospitalet de Llobregat, Barcelona, Spain
- Colorectal Cancer Group, Bellvitge Biomedical Research Institute (IDIBELL), Hospitalet de Llobregat, Barcelona, Spain
- Biomedical Research Centre Network for Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Xavier Sanjuan
- Colorectal Cancer Group, Bellvitge Biomedical Research Institute (IDIBELL), Hospitalet de Llobregat, Barcelona, Spain
- Pathology Department, University Hospital Bellvitge (HUB), L'Hospitalet de Llobregat, Barcelona, Spain
| | - Cristina Santos
- Colorectal Cancer Group, Bellvitge Biomedical Research Institute (IDIBELL), Hospitalet de Llobregat, Barcelona, Spain
- Medical Oncology Service, Catalan Institute of Oncology (ICO), Barcelona, Spain
- Biomedical Research Centre Network for Oncology (CIBERONC), Madrid, Spain
| | - Ramón Salazar
- Colorectal Cancer Group, Bellvitge Biomedical Research Institute (IDIBELL), Hospitalet de Llobregat, Barcelona, Spain
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
- Medical Oncology Service, Catalan Institute of Oncology (ICO), Barcelona, Spain
- Biomedical Research Centre Network for Oncology (CIBERONC), Madrid, Spain
| | - Rebeca Sanz-Pamplona
- Unit of Biomarkers and Susceptibility, Cancer Prevention and Control Program, Catalan Institute of Oncology (ICO), Av Gran Via 199-203, 08907, L'Hospitalet de Llobregat, Barcelona, Spain.
- Colorectal Cancer Group, Bellvitge Biomedical Research Institute (IDIBELL), Hospitalet de Llobregat, Barcelona, Spain.
- Biomedical Research Centre Network for Epidemiology and Public Health (CIBERESP), Madrid, Spain.
| | - Victor Moreno
- Unit of Biomarkers and Susceptibility, Cancer Prevention and Control Program, Catalan Institute of Oncology (ICO), Av Gran Via 199-203, 08907, L'Hospitalet de Llobregat, Barcelona, Spain.
- Colorectal Cancer Group, Bellvitge Biomedical Research Institute (IDIBELL), Hospitalet de Llobregat, Barcelona, Spain.
- Biomedical Research Centre Network for Epidemiology and Public Health (CIBERESP), Madrid, Spain.
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain.
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Risky family climates presage increased cellular aging in young adulthood. Psychoneuroendocrinology 2021; 130:105256. [PMID: 34058561 PMCID: PMC8217285 DOI: 10.1016/j.psyneuen.2021.105256] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 04/30/2021] [Accepted: 05/02/2021] [Indexed: 12/16/2022]
Abstract
A scientific consensus is emerging that children reared in risky family climates are prone to chronic diseases and premature death later in life. Few prospective data, however, are available to inform the mechanisms of these relationships. In a prospective study involving 323 Black families, we sought to determine whether, and how, childhood risky family climates are linked to a potential risk factor for later-life disease: increases in cellular aging (indexed by epigenetic aging). As hypothesized, risky family climates were associated with greater outflows of the stress hormones epinephrine and norepinephrine at ages 19 and 20 years; this, in turn, led to increases in cellular aging across ages 20-27 years. If sustained, these tendencies may place children from risky family climates on a trajectory toward the chronic diseases of aging.
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Li G, Raffield L, Logue M, Miller MW, Santos HP, O’Shea TM, Fry RC, Li Y. CUE: CpG impUtation ensemble for DNA methylation levels across the human methylation450 (HM450) and EPIC (HM850) BeadChip platforms. Epigenetics 2021; 16:851-861. [PMID: 33016200 PMCID: PMC8330997 DOI: 10.1080/15592294.2020.1827716] [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: 05/20/2020] [Revised: 08/08/2020] [Accepted: 09/09/2020] [Indexed: 10/23/2022] Open
Abstract
DNA methylation at CpG dinucleotides is one of the most extensively studied epigenetic marks. With technological advancements, geneticists can profile DNA methylation with multiple reliable approaches. However, profiling platforms can differ substantially in the CpGs they assess, consequently hindering integrated analysis across platforms. Here, we present CpG impUtation Ensemble (CUE), which leverages multiple classical statistical and modern machine learning methods, to impute from the Illumina HumanMethylation450 (HM450) BeadChip to the Illumina HumanMethylationEPIC (HM850) BeadChip. Data were analysed from two population cohorts with methylation measured both by HM450 and HM850: the Extremely Low Gestational Age Newborns (ELGAN) study (n = 127, placenta) and the VA Boston Posttraumatic Stress Disorder (PTSD) genetics repository (n = 144, whole blood). Cross-validation results show that CUE achieves the lowest predicted root-mean-square error (RMSE) (0.026 in PTSD) and the highest accuracy (99.97% in PTSD) compared with five individual methods tested, including k-nearest-neighbours, logistic regression, penalized functional regression, random forest, and XGBoost. Finally, among all 339,033 HM850-only CpG sites shared between ELGAN and PTSD, CUE successfully imputed 289,604 (85.4%) sites, where success was defined as RMSE < 0.05 and accuracy >95% in PTSD. In summary, CUE is a valuable tool for imputing CpG methylation from the HM450 to HM850 platform.
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Affiliation(s)
- Gang Li
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Laura Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Mark Logue
- National Center for PTSD: Behavioral Sciences Division at VA Boston Healthcare System, Boston, MA, USA
- Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
- Biomedical Genetics, Boston University School of Medicine, Boston, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Mark W. Miller
- National Center for PTSD: Behavioral Sciences Division at VA Boston Healthcare System, Boston, MA, USA
- Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
| | - Hudson P. Santos
- School of Nursing, University of North Carolina, Chapel Hill, NC, USA
- Institute for Environmental Health Solutions, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - T. Michael O’Shea
- Department of Pediatrics, School of Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - Rebecca C. Fry
- Institute for Environmental Health Solutions, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
- Curriculum in Toxicology and Environmental Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - Yun Li
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA
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43
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Severity of Idiopathic Scoliosis Is Associated with Differential Methylation: An Epigenome-Wide Association Study of Monozygotic Twins with Idiopathic Scoliosis. Genes (Basel) 2021; 12:genes12081191. [PMID: 34440365 PMCID: PMC8391702 DOI: 10.3390/genes12081191] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 07/29/2021] [Accepted: 07/29/2021] [Indexed: 12/27/2022] Open
Abstract
Epigenetic mechanisms may contribute to idiopathic scoliosis (IS). We identified 8 monozygotic twin pairs with IS, 6 discordant (Cobb angle difference > 10°) and 2 concordant (Cobb angle difference ≤ 2°). Genome-wide methylation in blood was measured with the Infinium HumanMethylation EPIC Beadchip. We tested for differences in methylation and methylation variability between discordant twins and tested the association between methylation and curve severity in all twins. Differentially methylated region (DMR) analyses identified gene promoter regions. Methylation at cg12959265 (chr. 7 DPY19L1) was less variable in cases (false discovery rate (FDR) = 0.0791). We identified four probes (false discovery rate, FDR < 0.10); cg02477677 (chr. 17, RARA gene), cg12922161 (chr. 2 LOC150622 gene), cg08826461 (chr. 2), and cg16382077 (chr. 7) associated with curve severity. We identified 57 DMRs where hyper- or hypo-methylation was consistent across the region and 28 DMRs with a consistent association with curve severity. Among DMRs, 21 were correlated with bone methylation. Prioritization of regions based on methylation concordance in bone identified promoter regions for WNT10A (WNT signaling), NPY (regulator of bone and energy homeostasis), and others predicted to be relevant for bone formation/remodeling. These regions may aid in understanding the complex interplay between genetics, environment, and IS.
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44
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Xu K, Li S, Whitehead TP, Pandey P, Kang AY, Morimoto LM, Kogan SC, Metayer C, Wiemels JL, de Smith AJ. Epigenetic Biomarkers of Prenatal Tobacco Smoke Exposure Are Associated with Gene Deletions in Childhood Acute Lymphoblastic Leukemia. Cancer Epidemiol Biomarkers Prev 2021; 30:1517-1525. [PMID: 34020997 DOI: 10.1158/1055-9965.epi-21-0009] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Revised: 03/17/2021] [Accepted: 05/14/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Parental smoking is implicated in the etiology of acute lymphoblastic leukemia (ALL), the most common childhood cancer. We recently reported an association between an epigenetic biomarker of early-life tobacco smoke exposure at the AHRR gene and increased frequency of somatic gene deletions among ALL cases. METHODS Here, we further assess this association using two epigenetic biomarkers for maternal smoking during pregnancy-DNA methylation at AHRR CpG cg05575921 and a recently established polyepigenetic smoking score-in an expanded set of 482 B-cell ALL (B-ALL) cases in the California Childhood Leukemia Study with available Illumina 450K or MethylationEPIC array data. Multivariable Poisson regression models were used to test the associations between the epigenetic biomarkers and gene deletion numbers. RESULTS We found an association between DNA methylation at AHRR CpG cg05575921 and deletion number among 284 childhood B-ALL cases with MethylationEPIC array data, with a ratio of means (RM) of 1.31 [95% confidence interval (CI), 1.02-1.69] for each 0.1 β value reduction in DNA methylation, an effect size similar to our previous report in an independent set of 198 B-ALL cases with 450K array data [meta-analysis summary RM (sRM) = 1.32; 95% CI, 1.10-1.57]. The polyepigenetic smoking score was positively associated with gene deletion frequency among all 482 B-ALL cases (sRM = 1.31 for each 4-unit increase in score; 95% CI, 1.09-1.57). CONCLUSIONS We provide further evidence that prenatal tobacco-smoke exposure may influence the generation of somatic copy-number deletions in childhood B-ALL. IMPACT Analyses of deletion breakpoint sequences are required to further understand the mutagenic effects of tobacco smoke in childhood ALL.
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Affiliation(s)
- Keren Xu
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California.,Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Shaobo Li
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California.,Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Todd P Whitehead
- School of Public Health, University of California Berkeley, Berkeley, California
| | - Priyatama Pandey
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California.,Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Alice Y Kang
- School of Public Health, University of California Berkeley, Berkeley, California
| | - Libby M Morimoto
- School of Public Health, University of California Berkeley, Berkeley, California
| | - Scott C Kogan
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, California
| | - Catherine Metayer
- School of Public Health, University of California Berkeley, Berkeley, California
| | - Joseph L Wiemels
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California.,Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Adam J de Smith
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California. .,Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, California
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45
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Söderhäll C, Reinius LE, Salmenperä P, Gentile M, Acevedo N, Konradsen JR, Nordlund B, Hedlin G, Scheynius A, Myllykangas S, Kere J. High-resolution targeted bisulfite sequencing reveals blood cell type-specific DNA methylation patterns in IL13 and ORMDL3. Clin Epigenetics 2021; 13:106. [PMID: 33971943 PMCID: PMC8111952 DOI: 10.1186/s13148-021-01093-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 04/26/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Methylation of DNA at CpG sites is an epigenetic modification and a potential modifier of disease risk, possibly mediating environmental effects. Currently, DNA methylation is commonly assessed using specific microarrays that sample methylation at a few % of all methylated sites. METHODS To understand if significant information on methylation can be added by a more comprehensive analysis of methylation, we set up a quantitative method, bisulfite oligonucleotide-selective sequencing (Bs-OS-seq), and compared the data with microarray-derived methylation data. We assessed methylation at two asthma-associated genes, IL13 and ORMDL3, in blood samples collected from children with and without asthma and fractionated white blood cell types from healthy adult controls. RESULTS Our results show that Bs-OS-seq can uncover vast amounts of methylation variation not detected by commonly used array methods. We found that high-density methylation information from even one gene can delineate the main white blood cell lineages. CONCLUSIONS We conclude that high-resolution methylation studies can yield clinically important information at selected specific loci missed by array-based methods, with potential implications for future studies of methylation-disease associations.
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Affiliation(s)
- Cilla Söderhäll
- Department of Biosciences and Nutrition, Karolinska Institutet, Stockholm, Sweden. .,Department of Women's and Children's Health, Karolinska Institutet, Bioclinicum J9:30, Visionsgatan 4, 171 64, Stockholm, Sweden. .,Department of Pediatric Allergy and Pulmonology, Astrid Lindgren Children's Hospital, Karolinska University Hospital, Stockholm, Sweden.
| | - Lovisa E Reinius
- Department of Biosciences and Nutrition, Karolinska Institutet, Stockholm, Sweden
| | | | | | - Nathalie Acevedo
- Department of Clinical Science and Education, Karolinska Institutet, and Sachs' Children and Youth Hospital, Södersjukhuset, 118 83, Stockholm, Sweden.,Institute for Immunological Research, University of Cartagena, Cartagena, Colombia
| | - Jon R Konradsen
- Department of Women's and Children's Health, Karolinska Institutet, Bioclinicum J9:30, Visionsgatan 4, 171 64, Stockholm, Sweden.,Department of Pediatric Allergy and Pulmonology, Astrid Lindgren Children's Hospital, Karolinska University Hospital, Stockholm, Sweden
| | - Björn Nordlund
- Department of Women's and Children's Health, Karolinska Institutet, Bioclinicum J9:30, Visionsgatan 4, 171 64, Stockholm, Sweden.,Department of Pediatric Allergy and Pulmonology, Astrid Lindgren Children's Hospital, Karolinska University Hospital, Stockholm, Sweden
| | - Gunilla Hedlin
- Department of Women's and Children's Health, Karolinska Institutet, Bioclinicum J9:30, Visionsgatan 4, 171 64, Stockholm, Sweden.,Department of Pediatric Allergy and Pulmonology, Astrid Lindgren Children's Hospital, Karolinska University Hospital, Stockholm, Sweden
| | - Annika Scheynius
- Department of Clinical Science and Education, Karolinska Institutet, and Sachs' Children and Youth Hospital, Södersjukhuset, 118 83, Stockholm, Sweden.,Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden
| | | | - Juha Kere
- Department of Biosciences and Nutrition, Karolinska Institutet, Stockholm, Sweden.,Folkhälsan Research Center, Helsinki, Finland.,Stem Cells and Metabolism Research Program, University of Helsinki, Helsinki, Finland
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46
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Epigenetic prediction of 17β-estradiol and relationship to trauma-related outcomes in women. COMPREHENSIVE PSYCHONEUROENDOCRINOLOGY 2021; 6:100045. [PMID: 35757356 PMCID: PMC9216622 DOI: 10.1016/j.cpnec.2021.100045] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 03/03/2021] [Indexed: 11/30/2022] Open
Abstract
17β-estradiol (E2) levels in women correlate with multiple neuropsychiatric symptoms, including those that are stress-related. Furthermore, prior work from our group has demonstrated that E2 status influences DNA methylation (DNAm) across the genome. We developed and validated a DNAm-based predictor of E2 (one of four naturally occurring estrogens) using a training set of 183 females and a test set of 79 females from the same traumatized cohort. We showed that predicted E2 levels were highly correlated with measured E2 concentrations in our testing set (r = 0.75, p = 1.8e-15). We further demonstrated that predicted E2 concentrations, in combination with measured values, negatively correlated with current post-traumatic stress disorder (PTSD) (β = −0.38, p = 0.01) and major depressive disorder (MDD) diagnoses (β = −0.45, p = 0.02), as well as a continuous measure of PTSD symptom severity (β = −2.3, p = 0.007) in females. Finally, we tested our predictor in an independent data set (n = 85) also comprised of recently traumatized female subjects to determine if the predictor would generalize to a different population than the one on which it was developed. We found that the correlation between predicted and actual E2 concentrations in the external validation data set was also high (r = 0.48, p = 3.0e-6). While further validation is warranted, a DNAm predictor of E2 concentrations will advance our understanding of hormone-epigenetic interactions. Furthermore, such a DNAm predictor may serve as an epigenetic proxy for E2 concentrations and thus provide an important biomarker to better evaluate the contribution of E2 to current and potentially future psychiatric symptoms in samples for which E2 is not measured. Neuropsychiatric symptoms correlate with estradiol (E2) levels in females. We developed a DNA methylation-based E2 predictor using machine learning approach. Our predictor performed well in an external validation cohort of traumatized women. Predicted E2 concentrations correlated with stress-related phenotypes. Our model may serve as an epigenetic biomarker of E2 status in women.
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47
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Villicaña S, Bell JT. Genetic impacts on DNA methylation: research findings and future perspectives. Genome Biol 2021; 22:127. [PMID: 33931130 PMCID: PMC8086086 DOI: 10.1186/s13059-021-02347-6] [Citation(s) in RCA: 87] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 04/09/2021] [Indexed: 12/17/2022] Open
Abstract
Multiple recent studies highlight that genetic variants can have strong impacts on a significant proportion of the human DNA methylome. Methylation quantitative trait loci, or meQTLs, allow for the exploration of biological mechanisms that underlie complex human phenotypes, with potential insights for human disease onset and progression. In this review, we summarize recent milestones in characterizing the human genetic basis of DNA methylation variation over the last decade, including heritability findings and genome-wide identification of meQTLs. We also discuss challenges in this field and future areas of research geared to generate insights into molecular processes underlying human complex traits.
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Affiliation(s)
- Sergio Villicaña
- Department of Twin Research and Genetic Epidemiology, St. Thomas’ Hospital, King’s College London, 3rd Floor, South Wing, Block D, London, SE1 7EH UK
| | - Jordana T. Bell
- Department of Twin Research and Genetic Epidemiology, St. Thomas’ Hospital, King’s College London, 3rd Floor, South Wing, Block D, London, SE1 7EH UK
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48
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Gadd DA, Stevenson AJ, Hillary RF, McCartney DL, Wrobel N, McCafferty S, Murphy L, Russ TC, Harris SE, Redmond P, Taylor AM, Smith C, Rose J, Millar T, Spires-Jones TL, Cox SR, Marioni RE. Epigenetic predictors of lifestyle traits applied to the blood and brain. Brain Commun 2021; 3:fcab082. [PMID: 34041477 PMCID: PMC8134833 DOI: 10.1093/braincomms/fcab082] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 02/02/2021] [Accepted: 03/04/2021] [Indexed: 11/14/2022] Open
Abstract
Modifiable lifestyle factors influence the risk of developing many neurological diseases. These factors have been extensively linked with blood-based genome-wide DNA methylation, but it is unclear if the signatures from blood translate to the target tissue of interest-the brain. To investigate this, we apply blood-derived epigenetic predictors of four lifestyle traits to genome-wide DNA methylation from five post-mortem brain regions and the last blood sample prior to death in 14 individuals in the Lothian Birth Cohort 1936. Using these matched samples, we found that correlations between blood and brain DNA methylation scores for smoking, high-density lipoprotein cholesterol, alcohol and body mass index were highly variable across brain regions. Smoking scores in the dorsolateral prefrontal cortex had the strongest correlations with smoking scores in blood (r = 0.5, n = 14, P = 0.07) and smoking behaviour (r = 0.56, n = 9, P = 0.12). This was also the brain region which exhibited the largest correlations for DNA methylation at site cg05575921 - the single strongest correlate of smoking in blood-in relation to blood (r = 0.61, n = 14, P = 0.02) and smoking behaviour (r = -0.65, n = 9, P = 0.06). This suggested a particular vulnerability to smoking-related differential methylation in this region. Our work contributes to understanding how lifestyle factors affect the brain and suggest that lifestyle-related DNA methylation is likely to be both brain region dependent and in many cases poorly proxied for by blood. Though these pilot data provide a rarely-available opportunity for the comparison of methylation patterns across multiple brain regions and the blood, due to the limited sample size available our results must be considered as preliminary and should therefore be used as a basis for further investigation.
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Affiliation(s)
- Danni A Gadd
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh 2XU, UK
| | - Anna J Stevenson
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh 2XU, UK
| | - Robert F Hillary
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh 2XU, UK
| | - Daniel L McCartney
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh 2XU, UK
| | - Nicola Wrobel
- Edinburgh Clinical Research Facility, Western General Hospital, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Sarah McCafferty
- Edinburgh Clinical Research Facility, Western General Hospital, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Lee Murphy
- Edinburgh Clinical Research Facility, Western General Hospital, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Tom C Russ
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh EH8 9JZ, UK
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, UK
| | - Sarah E Harris
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, UK
| | - Paul Redmond
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, UK
| | - Adele M Taylor
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, UK
| | - Colin Smith
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH8 9JZ, UK
| | - Jamie Rose
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh EH8 9JZ, UK
| | - Tracey Millar
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH8 9JZ, UK
| | - Tara L Spires-Jones
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh EH8 9JZ, UK
| | - Simon R Cox
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, UK
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh 2XU, UK
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49
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Epigenome wide association study of response to methotrexate in early rheumatoid arthritis patients. PLoS One 2021; 16:e0247709. [PMID: 33690661 PMCID: PMC7946177 DOI: 10.1371/journal.pone.0247709] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 02/11/2021] [Indexed: 11/21/2022] Open
Abstract
Aim To identify differentially methylated positions (DMPs) and regions (DMRs) that predict response to Methotrexate (MTX) in early rheumatoid arthritis (RA) patients. Materials and methods DNA from baseline peripheral blood mononuclear cells was extracted from 72 RA patients. DNA methylation, quantified using the Infinium MethylationEPIC, was assessed in relation to response to MTX (combination) therapy over the first 3 months. Results Baseline DMPs associated with response were identified; including hits previously described in RA. Additionally, 1309 DMR regions were observed. However, none of these findings were genome-wide significant. Likewise, no specific pathways were related to response, nor could we replicate associations with previously identified DMPs. Conclusion No baseline genome-wide significant differences were identified as biomarker for MTX (combination) therapy response; hence meta-analyses are required.
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50
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Wolf EJ, Chen CD, Zhao X, Zhou Z, Morrison FG, Daskalakis NP, Stone A, Schichman S, Grenier JG, Fein-Schaffer D, Huber BR, Abraham CR, Miller MW, Logue MW. Klotho, PTSD, and advanced epigenetic age in cortical tissue. Neuropsychopharmacology 2021; 46:721-730. [PMID: 33096543 PMCID: PMC8027437 DOI: 10.1038/s41386-020-00884-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 09/12/2020] [Accepted: 09/29/2020] [Indexed: 01/04/2023]
Abstract
This study examined the klotho (KL) longevity gene polymorphism rs9315202 and psychopathology, including posttraumatic stress disorder (PTSD), depression, and alcohol-use disorders, in association with advanced epigenetic age in three postmortem cortical tissue regions: dorsolateral and ventromedial prefrontal cortices and motor cortex. Using data from the VA National PTSD Brain Bank (n = 117), we found that rs9315202 interacted with PTSD to predict advanced epigenetic age in motor cortex among the subset of relatively older (>=45 years), white non-Hispanic decedents (corrected p = 0.014, n = 42). An evaluation of 211 additional common KL variants revealed that only variants in linkage disequilibrium with rs9315202 showed similarly high levels of significance. Alcohol abuse was nominally associated with advanced epigenetic age in motor cortex (p = 0.039, n = 114). The rs9315202 SNP interacted with PTSD to predict decreased KL expression via DNAm age residuals in motor cortex among older white non-Hispanics decedents (indirect β = -0.198, p = 0.027). Finally, in dual-luciferase enhancer reporter system experiments, we found that inserting the minor allele of rs9315202 in a human kidney cell line HK-2 genomic DNA resulted in a change in KL transcriptional activities, likely operating via long noncoding RNA in this region. This was the first study to examine multiple forms of psychopathology in association with advanced DNA methylation age across several brain regions, to extend work concerning the association between rs9315202 and advanced epigenetic to brain tissue, and to identify the effects of rs9315202 on KL gene expression. KL augmentation holds promise as a therapeutic intervention to slow the pace of cellular aging, disease onset, and neuropathology, particularly in older, stressed populations.
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Affiliation(s)
- Erika J Wolf
- National Center for PTSD at VA Boston Healthcare System, Boston, MA, USA.
- Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA.
| | - Ci-Di Chen
- Department of Biochemistry, Boston University School of Medicine, Boston, MA, USA
| | - Xiang Zhao
- National Center for PTSD at VA Boston Healthcare System, Boston, MA, USA
- Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
| | - Zhenwei Zhou
- National Center for PTSD at VA Boston Healthcare System, Boston, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Filomene G Morrison
- National Center for PTSD at VA Boston Healthcare System, Boston, MA, USA
- Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
| | | | - Annjanette Stone
- Pharmacogenomics Analysis Laboratory, Research Service, Central Arkansas Veterans Healthcare System, Little Rock, AR, USA
| | - Steven Schichman
- Pharmacogenomics Analysis Laboratory, Research Service, Central Arkansas Veterans Healthcare System, Little Rock, AR, USA
| | - Jaclyn Garza Grenier
- Brigham and Women's Hospital, Channing Division of Network Medicine, Boston, MA, USA
| | - Dana Fein-Schaffer
- National Center for PTSD at VA Boston Healthcare System, Boston, MA, USA
| | - Bertrand R Huber
- Pathology and Laboratory Medicine, VA Boston Healthcare System, Boston, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Carmela R Abraham
- Department of Biochemistry, Boston University School of Medicine, Boston, MA, USA
- Department of Pharmacology and Experimental Therapeutics, Boston University School of Medicine, Boston, MA, USA
| | - Mark W Miller
- National Center for PTSD at VA Boston Healthcare System, Boston, MA, USA
- Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
| | - Mark W Logue
- National Center for PTSD at VA Boston Healthcare System, Boston, MA, USA
- Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Biomedical Genetics, Boston University School of Medicine, Boston, MA, USA
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