1
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Ciantar J, Marttila S, Rajić S, Kostiniuk D, Mishra PP, Lyytikäinen LP, Mononen N, Kleber ME, März W, Kähönen M, Raitakari O, Lehtimäki T, Raitoharju E. Identification and functional characterisation of DNA methylation differences between East- and West-originating Finns. Epigenetics 2024; 19:2397297. [PMID: 39217505 PMCID: PMC11382697 DOI: 10.1080/15592294.2024.2397297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 08/14/2024] [Accepted: 08/22/2024] [Indexed: 09/04/2024] Open
Abstract
Eastern and Western Finns show a striking difference in coronary heart disease-related mortality; genetics is a known contributor for this discrepancy. Here, we discuss the potential role of DNA methylation in mediating the discrepancy in cardiometabolic disease-risk phenotypes between the sub-populations. We used data from the Young Finns Study (n = 969) to compare the genome-wide DNA methylation levels of East- and West-originating Finns. We identified 21 differentially methylated loci (FDR < 0.05; Δβ >2.5%) and 7 regions (smoothed FDR < 0.05; CpGs ≥ 5). Methylation at all loci and regions associates with genetic variants (p < 5 × 10-8). Independently of genetics, methylation at 11 loci and 4 regions associates with transcript expression, including genes encoding zinc finger proteins. Similarly, methylation at 5 loci and 4 regions associates with cardiometabolic disease-risk phenotypes including triglycerides, glucose, cholesterol, as well as insulin treatment. This analysis was also performed in LURIC (n = 2371), a German cardiovascular patient cohort, and results replicated for the association of methylation at cg26740318 and DMR_11p15 with diabetes-related phenotypes and methylation at DMR_22q13 with triglyceride levels. Our results indicate that DNA methylation differences between East and West Finns may have a functional role in mediating the cardiometabolic disease discrepancy between the sub-populations.
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Affiliation(s)
- Joanna Ciantar
- Molecular Epidemiology (MOLE), Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Saara Marttila
- Molecular Epidemiology (MOLE), Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Gerontology Research Center, Tampere University, Tampere, Finland
- Tays Research Services, Wellbeing Services County of Pirkanmaa, Tampere University Hospital, Tampere, Finland
| | - Sonja Rajić
- Molecular Epidemiology (MOLE), Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Daria Kostiniuk
- Molecular Epidemiology (MOLE), Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Pashupati P Mishra
- Department of Clinical Chemistry, Tays Research Services, Fimlab Laboratories, and Finnish Cardiovascular Research Center, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Leo-Pekka Lyytikäinen
- Department of Clinical Chemistry, Tays Research Services, Fimlab Laboratories, and Finnish Cardiovascular Research Center, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Nina Mononen
- Department of Clinical Chemistry, Tays Research Services, Fimlab Laboratories, and Finnish Cardiovascular Research Center, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Marcus E Kleber
- Vth Department of Medicine (Nephrology, Hypertensiology, Endocrinology, Diabetology, Rheumatology), Medical Faculty of Mannheim, Heidelberg University, Mannheim, Germany
- SYNLAB MVZ Humangenetik Mannheim, Mannheim, Germany
| | - Winfried März
- Vth Department of Medicine (Nephrology, Hypertensiology, Endocrinology, Diabetology, Rheumatology), Medical Faculty of Mannheim, Heidelberg University, Mannheim, Germany
- Synlab Academy, SYNLAB Holding Deutschland GmbH, Mannheim, Germany
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital and Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Olli Raitakari
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Tays Research Services, Fimlab Laboratories, and Finnish Cardiovascular Research Center, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Emma Raitoharju
- Molecular Epidemiology (MOLE), Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Fimlab Laboratories, Tampere, Finland
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2
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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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3
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Hecker J, Weiss ST, Lasky-Su JA, DeMeo DL, Lange C. Letter to the editor: critical evaluation of the reliability of DNA methylation probes on the illumina MethylationEPIC v1.0 BeadChip microarrays. Epigenetics 2024; 19:2411470. [PMID: 39365898 PMCID: PMC11457593 DOI: 10.1080/15592294.2024.2411470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2024] [Revised: 09/19/2024] [Accepted: 09/25/2024] [Indexed: 10/06/2024] Open
Affiliation(s)
- Julian Hecker
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Scott T. Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Jessica A. Lasky-Su
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Dawn L. DeMeo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Christoph Lange
- Channing Division of Network Medicine, Department of 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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Warner B, Ratner E, Datta A, Lendasse A. A systematic review of phenotypic and epigenetic clocks used for aging and mortality quantification in humans. Aging (Albany NY) 2024; 16:12414-12427. [PMID: 39215995 PMCID: PMC11424583 DOI: 10.18632/aging.206098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 07/15/2024] [Indexed: 09/04/2024]
Abstract
Aging is the leading driver of disease in humans and has profound impacts on mortality. Biological clocks are used to measure the aging process in the hopes of identifying possible interventions. Biological clocks may be categorized as phenotypic or epigenetic, where phenotypic clocks use easily measurable clinical biomarkers and epigenetic clocks use cellular methylation data. In recent years, methylation clocks have attained phenomenal performance when predicting chronological age and have been linked to various age-related diseases. Additionally, phenotypic clocks have been proven to be able to predict mortality better than chronological age, providing intracellular insights into the aging process. This review aimed to systematically survey all proposed epigenetic and phenotypic clocks to date, excluding mitotic clocks (i.e., cancer risk clocks) and those that were modeled using non-human samples. We reported the predictive performance of 33 clocks and outlined the statistical or machine learning techniques used. We also reported the most influential clinical measurements used in the included phenotypic clocks. Our findings provide a systematic reporting of the last decade of biological clock research and indicate possible avenues for future research.
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Affiliation(s)
| | | | | | - Amaury Lendasse
- Department of IST, University of Houston, Houston, TX 77004, USA
- Department of Engineering Management and Systems Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA
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Grootswagers P, Bach D, Biemans Y, Behrouzi P, Horvath S, Kramer CS, Liu S, Manson JE, Shadyab AH, Stewart JD, Whitsel E, Yang B, de Groot L. Discovering the direct relations between nutrients and epigenetic ageing. J Nutr Health Aging 2024; 28:100324. [PMID: 39067141 DOI: 10.1016/j.jnha.2024.100324] [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: 06/14/2024] [Revised: 07/12/2024] [Accepted: 07/22/2024] [Indexed: 07/30/2024]
Abstract
BACKGROUND Along with the ageing of society, the absolute prevalence of age-related diseases is expected to rise, leading to a substantial burden on healthcare systems and society. Thus, there is an urgent need to promote healthy ageing. As opposed to chronological age, biological age was introduced to accurately represent the ageing process, as it considers physiological deterioration that is linked to morbidity and mortality risk. Furthermore, biological age responds to various factors, including nutritional factors, which have the potential to mitigate the risk of age-related diseases. As a result, a promising biomarker of biological age known as the epigenetic clock has emerged as a suitable measure to investigate the direct relations between nutritional factors and ageing, thereby identifying potential intervention targets to improve healthy ageing. METHODS In this study, we analysed data from 3,969 postmenopausal women from the Women's Health Initiative to identify nutrients that are associated with the rate of ageing by using an accurate measure of biological age called the PhenoAge epigenetic clock. We used Copula Graphical Models, a data-driven exploratory analysis tool, to identify direct relationships between nutrient intake and age-acceleration, while correcting for every variable in the dataset. RESULTS We revealed that increased dietary intakes of coumestrol, beta-carotene and arachidic acid were associated with decelerated epigenetic ageing. In contrast, increased intakes of added sugar, gondoic acid, behenic acid, arachidonic acid, vitamin A and ash were associated with accelerated epigenetic ageing in postmenopausal women. CONCLUSION Our study discovered direct relations between nutrients and epigenetic ageing, revealing promising areas for follow-up studies to determine the magnitude and causality of our estimated diet-epigenetic relationships.
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Affiliation(s)
- Pol Grootswagers
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, Netherlands.
| | - Daimy Bach
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, Netherlands
| | - Ynte Biemans
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, Netherlands
| | - Pariya Behrouzi
- Biometris, Mathematical and Statistical Methods, Wageningen University and Research, Wageningen, Netherlands
| | - Steve Horvath
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, USA; Altos Labs, San Diego Institute of Science, San Diego, CA, USA; Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, USA
| | - Charlotte S Kramer
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, Netherlands
| | - Simin Liu
- Department of Epidemiology and Center for Global Cardiometabolic Health, School of Public Health, Departments of Medicine and Surgery, Alpert School of Medicine, Brown University, Providence, RI, USA
| | - JoAnn E Manson
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Aladdin H Shadyab
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, USA
| | - James D Stewart
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Eric Whitsel
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - Bo Yang
- Department of Epidemiology and Center for Global Cardiometabolic Health, School of Public Health, Brown University, Providence, RI, USA
| | - Lisette de Groot
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, Netherlands
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6
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Markov Y, Levine M, Higgins-Chen AT. Reliable detection of stochastic epigenetic mutations and associations with cardiovascular aging. GeroScience 2024:10.1007/s11357-024-01191-3. [PMID: 38736015 DOI: 10.1007/s11357-024-01191-3] [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: 02/29/2024] [Accepted: 05/01/2024] [Indexed: 05/14/2024] Open
Abstract
Stochastic epigenetic mutations (SEMs) have been proposed as novel aging biomarkers to capture heterogeneity in age-related DNA methylation changes. SEMs are defined as outlier methylation patterns at cytosine-guanine dinucleotide sites, categorized as hypermethylated (hyperSEM) or hypomethylated (hypoSEM) relative to a reference. Because SEMs are defined by their outlier status, it is critical to differentiate extreme values due to technical noise or data artifacts from those due to real biology. Using technical replicate data, we found SEM detection is not reliable: across 3 datasets, 24 to 39% of hypoSEM and 46 to 67% of hyperSEM are not shared between replicates. We identified factors influencing SEM reliability-including blood cell type composition, probe beta-value statistics, genomic location, and presence of SNPs. We used these factors in a training dataset to build a machine learning-based filter that removes unreliable SEMs, and found this filter enhances reliability in two independent validation datasets. We assessed associations between SEM loads and aging phenotypes in the Framingham Heart Study and discovered that associations with aging outcomes were in large part driven by hypoSEMs at baseline methylated probes and hyperSEMs at baseline unmethylated probes, which are the same subsets that demonstrate highest technical reliability. These aging associations were preserved after filtering out unreliable SEMs and were enhanced after adjusting for blood cell composition. Finally, we utilized these insights to formulate best practices for SEM detection and introduce a novel R package, SEMdetectR, which uses parallel programming for efficient SEM detection with comprehensive options for detection, filtering, and analysis.
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Affiliation(s)
- Yaroslav Markov
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - Morgan Levine
- Altos Labs, San Diego Institute of Sciences, San Diego, CA, USA
| | - Albert T Higgins-Chen
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA.
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7
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Corley MJ, Pang APS, Shikuma CM, Ndhlovu LC. Cell-type specific impact of metformin on monocyte epigenetic age reversal in virally suppressed older people living with HIV. Aging Cell 2024; 23:e13926. [PMID: 37675817 PMCID: PMC10776116 DOI: 10.1111/acel.13926] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 06/22/2023] [Accepted: 06/28/2023] [Indexed: 09/08/2023] Open
Abstract
The anti-diabetic drug metformin may promote healthy aging. However, few clinical trials of metformin assessing biomarkers of aging have been completed. In this communication, we retrospectively examined the effect of metformin on epigenetic age using principal component (PC)-based epigenetic clocks, mitotic clocks, and pace of aging in peripheral monocytes and CD8+ T cells from participants in two clinical trials of virologically-suppressed people living with HIV (PLWH) with normal glucose receiving metformin. In a small 24-week clinical trial that randomized participants to receive either adjunctive metformin or observation, we observed significantly decreased PCPhenoAge and PCGrimAge estimates of monocytes from only participants in the metformin arm by a mean decrease of 3.53 and 1.84 years from baseline to Week 24. In contrast, we observed no significant differences in all PC clocks for participants in the observation arm over 24 weeks. Notably, our analysis of epigenetic mitotic clocks revealed significant increases for monocytes in the metformin arm when comparing baseline to Week 24, suggesting an impact of metformin on myeloid cell kinetics. Analysis of a single-arm clinical trial of adjunctive metformin in eight PLWH revealed no significant differences across all epigenetic clocks assessed in CD8+ T cells at 4- and 8-week time points. Our results suggest cell-type-specific myeloid effects of metformin captured by PC-based epigenetic clock biomarkers. Larger clinical studies of metformin are needed to validate these observations and this report highlights the need for further inclusion of PLWH in geroscience trials evaluating the effect of metformin on increasing healthspan and lifespan.
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Affiliation(s)
- Michael J. Corley
- Department of Medicine, Division of Infectious DiseasesWeill Cornell MedicineNew York CityNew YorkUSA
| | - Alina P. S. Pang
- Department of Medicine, Division of Infectious DiseasesWeill Cornell MedicineNew York CityNew YorkUSA
| | - Cecilia M. Shikuma
- Hawaii Center for AIDS, John A. Burns School of MedicineUniversity of HawaiiHonoluluHawaiiUSA
| | - Lishomwa C. Ndhlovu
- Department of Medicine, Division of Infectious DiseasesWeill Cornell MedicineNew York CityNew YorkUSA
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8
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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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9
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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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10
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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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11
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Waziry R, Gu Y, Williams O, Hägg S. Connections between cross-tissue and intra-tissue biomarkers of aging biology in older adults. EPIGENETICS COMMUNICATIONS 2023; 3:7. [PMID: 38037563 PMCID: PMC10688599 DOI: 10.1186/s43682-023-00022-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 09/28/2023] [Indexed: 12/02/2023]
Abstract
Background Saliva measures are generally more accessible than blood, especially in vulnerable populations. However, connections between aging biology biomarkers in different body tissues remain unknown. Methods The present study included individuals (N = 2406) who consented for saliva and blood draw in the Health and Retirement Telomere length study in 2008 and the Venous blood study in 2016 who had complete data for both tissues. We assessed biological aging based on telomere length in saliva and DNA methylation and physiology measures in blood. DNA methylation clocks combine information from CpGs to produce the aging measures representative of epigenetic aging in humans. We analyzed DNA methylation clocks proposed by Horvath (353 CpG sites), Hannum (71 CpG sites), Levine or PhenoAge, (513 CpG sites), GrimAge, (epigenetic surrogate markers for select plasma proteins), Horvath skin and blood (391 CpG sites), Lin (99 CpG sites), Weidner (3 CpG sites), and VidalBralo (8 CpG sites). Physiology measures (referred to as phenotypic age) included albumin, creatinine, glucose, [log] C-reactive protein, lymphocyte percent, mean cell volume, red blood cell distribution width, alkaline phosphatase, and white blood cell count. The phenotypic age algorithm is based on parametrization of Gompertz proportional hazard models. Average telomere length was assayed using quantitative PCR (qPCR) by comparing the telomere sequence copy number in each patient's sample (T) to a single-copy gene copy number (S). The resulting T/S ratio was proportional to telomere length, mean. Within individual, relationships between aging biology measures in blood and saliva and variations according to sex were assessed. Results Saliva-based telomere length showed inverse associations with both physiology-based and DNA methylation-based aging biology biomarkers in blood. Longer saliva-based telomere length was associated with 1 to 4 years slower biological aging based on blood-based biomarkers with the highest magnitude being Weidner (β = - 3.97, P = 0.005), GrimAge (β = - 3.33, P < 0.001), and Lin (β = - 3.45, P = 0.008) biomarkers of DNA methylation. Conclusions There are strong connections between aging biology biomarkers in saliva and blood in older adults. Changes in telomere length vary with changes in DNA methylation and physiology biomarkers of aging biology. We observed variations in the relationship between each body system represented by physiology biomarkers and biological aging, particularly at the DNA methylation level. These observations provide novel opportunities for integration of both blood-based and saliva-based biomarkers in clinical care of vulnerable and clinically difficult to reach populations where either or both tissues would be accessible for clinical monitoring purposes.
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Affiliation(s)
- R. Waziry
- Department of Neurology, Columbia University Irving Medical Center, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
| | - Y. Gu
- Department of Neurology, Columbia University Irving Medical Center, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
- The Taub Institute for Research in Alzheimer’s Disease and the Aging Brain, Columbia University, New York, NY, USA
- G.H. Sergievsky Center, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York, USA
- The Department of Epidemiology, Joseph P. Mailman School of Public Health, Columbia University, New York, NY, USA
| | - O. Williams
- Department of Neurology, Columbia University Irving Medical Center, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
| | - S. Hägg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden
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12
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Zhao N, Teles F, Lu J, Koestler DC, Beck J, Boerwinkle E, Bressler J, Kelsey KT, Platz EA, Michaud DS. Epigenome-wide association study using peripheral blood leukocytes identifies genomic regions associated with periodontal disease and edentulism in the Atherosclerosis Risk in Communities study. J Clin Periodontol 2023; 50:1140-1153. [PMID: 37464577 PMCID: PMC10528731 DOI: 10.1111/jcpe.13852] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 06/28/2023] [Accepted: 06/30/2023] [Indexed: 07/20/2023]
Abstract
AIM To investigate individual susceptibility to periodontitis by conducting an epigenome-wide association study using peripheral blood. MATERIALS AND METHODS We included 1077 African American and 457 European American participants of the Atherosclerosis Risk in Communities (ARIC) study who had completed a dental examination or reported being edentulous at Visit 4 and had available data on DNA methylation from Visit 2 or 3. DNA methylation levels were compared by periodontal disease severity and edentulism through discovery analyses and subsequent testing of individual CpGs. RESULTS Our discovery analysis replicated findings from a previous study reporting a region in gene ZFP57 (6p22.1) that was significantly hypomethylated in severe periodontal disease compared with no/mild periodontal disease in European American participants. Higher methylation levels in a separate region in an unknown gene (located in Chr10: 743,992-744,958) was associated with significantly higher odds of edentulism compared with no/mild periodontal disease in African American participants. In subsequent CpG testing, four CpGs in a region previously associated with periodontitis located within HOXA4 were significantly hypermethylated in severe periodontal disease compared with no/mild periodontal disease in African American participants (odds ratio per 1 SD increase in methylation level: cg11015251: 1.28 (1.02, 1.61); cg14359292: 1.24 (1.01, 1.54); cg07317062: 1.30 (1.05, 1.61); cg08657492: 1.25 (1.01, 1.55)). CONCLUSIONS Our study highlights epigenetic variations in ZPF57 and HOXA4 that are significantly and reproducibly associated with periodontitis. Future studies should evaluate gene regulatory mechanisms in the candidate regions of these loci.
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Affiliation(s)
- Naisi Zhao
- Department of Public Health & Community Medicine, Tufts University School of Medicine, Tufts University, Boston, MA
| | - Flavia Teles
- Department of Basic & Translational Sciences, University of Pennsylvania, Philadelphia, PA
| | - Jiayun Lu
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Devin C. Koestler
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS
- University of Kansas Cancer Center, Kansas City, KS
| | - James Beck
- Division of Comprehensive Oral Health/Periodontology, Adams School of Dentistry, University of North Carolina, Chapel Hill, NC
| | - Eric Boerwinkle
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX
| | - Jan Bressler
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX
| | - Karl T. Kelsey
- Department of Epidemiology, Brown University, Providence, RI
- Department of Pathology and Laboratory Medicine, Brown University, Providence, RI
| | - Elizabeth A. Platz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
- The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD
| | - Dominique S. Michaud
- Department of Public Health & Community Medicine, Tufts University School of Medicine, Tufts University, Boston, MA
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13
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Bafei SEC, Shen C. Biomarkers selection and mathematical modeling in biological age estimation. NPJ AGING 2023; 9:13. [PMID: 37393295 DOI: 10.1038/s41514-023-00110-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 05/08/2023] [Indexed: 07/03/2023]
Abstract
Biological age (BA) is important for clinical monitoring and preventing aging-related disorders and disabilities. Clinical and/or cellular biomarkers are measured and integrated in years using mathematical models to display an individual's BA. To date, there is not yet a single or set of biomarker(s) and technique(s) that is validated as providing the BA that reflects the best real aging status of individuals. Herein, a comprehensive overview of aging biomarkers is provided and the potential of genetic variations as proxy indicators of the aging state is highlighted. A comprehensive overview of BA estimation methods is also provided as well as a discussion of their performances, advantages, limitations, and potential approaches to overcome these limitations.
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Affiliation(s)
- Solim Essomandan Clémence Bafei
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, Jiangsu, China
| | - Chong Shen
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, Jiangsu, China.
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14
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El Khoury LY, Pan X, Hlady RA, Wagner RT, Shaikh S, Wang L, Humphreys MR, Castle EP, Stanton ML, Ho TH, Robertson KD. Extensive intratumor regional epigenetic heterogeneity in clear cell renal cell carcinoma targets kidney enhancers and is associated with poor outcome. Clin Epigenetics 2023; 15:71. [PMID: 37120552 PMCID: PMC10149001 DOI: 10.1186/s13148-023-01471-3] [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: 12/09/2022] [Accepted: 03/21/2023] [Indexed: 05/01/2023] Open
Abstract
BACKGROUND Clear cell renal cell cancer (ccRCC), the 8th leading cause of cancer-related death in the US, is challenging to treat due to high level intratumoral heterogeneity (ITH) and the paucity of druggable driver mutations. CcRCC is unusual for its high frequency of epigenetic regulator mutations, such as the SETD2 histone H3 lysine 36 trimethylase (H3K36me3), and low frequency of traditional cancer driver mutations. In this work, we examined epigenetic level ITH and defined its relationships with pathologic features, aspects of tumor biology, and SETD2 mutations. RESULTS A multi-region sampling approach coupled with EPIC DNA methylation arrays was conducted on a cohort of normal kidney and ccRCC. ITH was assessed using DNA methylation (5mC) and CNV-based entropy and Euclidian distances. We found elevated 5mC heterogeneity and entropy in ccRCC relative to normal kidney. Variable CpGs are highly enriched in enhancer regions. Using intra-class correlation coefficient analysis, we identified CpGs that segregate tumor regions according to clinical phenotypes related to tumor aggressiveness. SETD2 wild-type tumors overall possess greater 5mC and copy number ITH than SETD2 mutant tumor regions, suggesting SETD2 loss contributes to a distinct epigenome. Finally, coupling our regional data with TCGA, we identified a 5mC signature that links regions within a primary tumor with metastatic potential. CONCLUSION Taken together, our results reveal marked levels of epigenetic ITH in ccRCC that are linked to clinically relevant tumor phenotypes and could translate into novel epigenetic biomarkers.
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Affiliation(s)
- Louis Y El Khoury
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - Xiaoyu Pan
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - Ryan A Hlady
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - Ryan T Wagner
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - Shafiq Shaikh
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, USA
| | - Liguo Wang
- Division of Biomedical Statistics and Informatics, Department of Health Science Research, Mayo Clinic, Rochester, MN, USA
| | | | - Erik P Castle
- Department of Urology, Tulane University School of Medicine, New Orleans, LA, USA
| | - Melissa L Stanton
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Phoenix, AZ, USA
| | - Thai H Ho
- Division of Hematology and Medical Oncology, Mayo Clinic Arizona, Scottsdale, AZ, USA.
| | - Keith D Robertson
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA.
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15
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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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16
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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: 53] [Impact Index Per Article: 53.0] [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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17
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Gunasekara CJ, MacKay H, Scott CA, Li S, Laritsky E, Baker MS, Grimm SL, Jun G, Li Y, Chen R, Wiemels JL, Coarfa C, Waterland RA. Systemic interindividual epigenetic variation in humans is associated with transposable elements and under strong genetic control. Genome Biol 2023; 24:2. [PMID: 36631879 PMCID: PMC9835319 DOI: 10.1186/s13059-022-02827-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 12/01/2022] [Indexed: 01/13/2023] Open
Abstract
BACKGROUND Genetic variants can modulate phenotypic outcomes via epigenetic intermediates, for example at methylation quantitative trait loci (mQTL). We present the first large-scale assessment of mQTL at human genomic regions selected for interindividual variation in CpG methylation, which we call correlated regions of systemic interindividual variation (CoRSIVs). These can be assayed in blood DNA and do not reflect interindividual variation in cellular composition. RESULTS We use target-capture bisulfite sequencing to assess DNA methylation at 4086 CoRSIVs in multiple tissues from each of 188 donors in the NIH Gene-Tissue Expression (GTEx) program. At CoRSIVs, DNA methylation in peripheral blood correlates with methylation and gene expression in internal organs. We also discover unprecedented mQTL at these regions. Genetic influences on CoRSIV methylation are extremely strong (median R2=0.76), cumulatively comprising over 70-fold more human mQTL than detected in the most powerful previous study. Moreover, mQTL beta coefficients at CoRSIVs are highly skewed (i.e., the major allele predicts higher methylation). Both surprising findings are independently validated in a cohort of 47 non-GTEx individuals. Genomic regions flanking CoRSIVs show long-range enrichments for LINE-1 and LTR transposable elements; the skewed beta coefficients may therefore reflect evolutionary selection of genetic variants that promote their methylation and silencing. Analyses of GWAS summary statistics show that mQTL polymorphisms at CoRSIVs are associated with metabolic and other classes of disease. CONCLUSIONS A focus on systemic interindividual epigenetic variants, clearly enhanced in mQTL content, should likewise benefit studies attempting to link human epigenetic variation to the risk of disease.
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Affiliation(s)
- Chathura J. Gunasekara
- grid.508989.50000 0004 6410 7501USDA/ARS Children’s Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX USA
| | - Harry MacKay
- grid.508989.50000 0004 6410 7501USDA/ARS Children’s Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX USA
| | - C. Anthony Scott
- grid.508989.50000 0004 6410 7501USDA/ARS Children’s Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX USA
| | - Shaobo Li
- grid.42505.360000 0001 2156 6853Keck School of Medicine, University of Southern California, Los Angeles, CA USA
| | - Eleonora Laritsky
- grid.508989.50000 0004 6410 7501USDA/ARS Children’s Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX USA
| | - Maria S. Baker
- grid.508989.50000 0004 6410 7501USDA/ARS Children’s Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX USA
| | - Sandra L. Grimm
- grid.39382.330000 0001 2160 926XDepartment of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX USA
| | - Goo Jun
- grid.267308.80000 0000 9206 2401Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX USA
| | - Yumei Li
- grid.39382.330000 0001 2160 926XDepartment of Molecular & Human Genetics, Baylor College of Medicine, Houston, TX USA
| | - Rui Chen
- grid.39382.330000 0001 2160 926XDepartment of Molecular & Human Genetics, Baylor College of Medicine, Houston, TX USA
| | - Joseph L. Wiemels
- grid.42505.360000 0001 2156 6853Keck School of Medicine, University of Southern California, Los Angeles, CA USA
| | - Cristian Coarfa
- grid.39382.330000 0001 2160 926XDepartment of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX USA ,grid.39382.330000 0001 2160 926XDan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX USA
| | - Robert A. Waterland
- grid.508989.50000 0004 6410 7501USDA/ARS Children’s Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX USA ,grid.39382.330000 0001 2160 926XDepartment of Molecular & Human Genetics, Baylor College of Medicine, Houston, TX USA
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18
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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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19
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Derakhshan M, Kessler NJ, Ishida M, Demetriou C, Brucato N, Moore G, Fall CHD, Chandak GR, Ricaut FX, Prentice A, Hellenthal G, Silver M. Tissue- and ethnicity-independent hypervariable DNA methylation states show evidence of establishment in the early human embryo. Nucleic Acids Res 2022; 50:6735-6752. [PMID: 35713545 PMCID: PMC9749461 DOI: 10.1093/nar/gkac503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 05/06/2022] [Accepted: 05/27/2022] [Indexed: 12/24/2022] Open
Abstract
We analysed DNA methylation data from 30 datasets comprising 3474 individuals, 19 tissues and 8 ethnicities at CpGs covered by the Illumina450K array. We identified 4143 hypervariable CpGs ('hvCpGs') with methylation in the top 5% most variable sites across multiple tissues and ethnicities. hvCpG methylation was influenced but not determined by genetic variation, and was not linked to probe reliability, epigenetic drift, age, sex or cell heterogeneity effects. hvCpG methylation tended to covary across tissues derived from different germ-layers and hvCpGs were enriched for proximity to ERV1 and ERVK retrovirus elements. hvCpGs were also enriched for loci previously associated with periconceptional environment, parent-of-origin-specific methylation, and distinctive methylation signatures in monozygotic twins. Together, these properties position hvCpGs as strong candidates for studying how stochastic and/or environmentally influenced DNA methylation states which are established in the early embryo and maintained stably thereafter can influence life-long health and disease.
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Affiliation(s)
| | - Noah J Kessler
- Department of Genetics, University of Cambridge,
Cambridge CB2 3EH, UK
| | - Miho Ishida
- UCL Great Ormond Street Institute of Child Health, UK
| | | | - Nicolas Brucato
- Laboratoire Évolution and Diversité Biologique (EDB UMR 5174), Université
de Toulouse Midi-Pyrénées, CNRS, IRD, UPS,Toulouse, France
| | | | - Caroline H D Fall
- MRC Lifecourse Epidemiology Unit, University of Southampton,
Southampton, UK
| | - Giriraj R Chandak
- Genomic Research on Complex Diseases (GRC Group), CSIR-Centre for Cellular
and Molecular Biology,Hyderabad, India
| | - Francois-Xavier Ricaut
- Laboratoire Évolution and Diversité Biologique (EDB UMR 5174), Université
de Toulouse Midi-Pyrénées, CNRS, IRD, UPS,Toulouse, France
| | - Andrew M Prentice
- Medical Research Council Unit The Gambia at the London School of Hygiene
and Tropical Medicine, The Gambia
| | - Garrett Hellenthal
- UCL Genetics Institute, University College London,
Gower Street, London WC1E 6BT, UK
| | - Matt J Silver
- London School of Hygiene and Tropical Medicine, UK
- Medical Research Council Unit The Gambia at the London School of Hygiene
and Tropical Medicine, The Gambia
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20
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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: 106] [Impact Index Per Article: 53.0] [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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21
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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: 18] [Impact Index Per Article: 9.0] [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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22
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Ross JP, van Dijk S, Phang M, Skilton MR, Molloy PL, Oytam Y. Batch-effect detection, correction and characterisation in Illumina HumanMethylation450 and MethylationEPIC BeadChip array data. Clin Epigenetics 2022; 14:58. [PMID: 35488315 PMCID: PMC9055778 DOI: 10.1186/s13148-022-01277-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 04/10/2022] [Indexed: 11/20/2022] Open
Abstract
Background Genomic technologies can be subject to significant batch-effects which are known to reduce experimental power and to potentially create false positive results. The Illumina Infinium Methylation BeadChip is a popular technology choice for epigenome-wide association studies (EWAS), but presently, little is known about the nature of batch-effects on these designs. Given the subtlety of biological phenotypes in many EWAS, control for batch-effects should be a consideration.
Results Using the batch-effect removal approaches in the ComBat and Harman software, we examined two in-house datasets and compared results with three large publicly available datasets, (1214 HumanMethylation450 and 1094 MethylationEPIC BeadChips in total), and find that despite various forms of preprocessing, some batch-effects persist. This residual batch-effect is associated with the day of processing, the individual glass slide and the position of the array on the slide. Consistently across all datasets, 4649 probes required high amounts of correction. To understand the impact of this set to EWAS studies, we explored the literature and found three instances where persistently batch-effect prone probes have been reported in abstracts as key sites of differential methylation. As well as batch-effect susceptible probes, we also discover a set of probes which are erroneously corrected. We provide batch-effect workflows for Infinium Methylation data and provide reference matrices of batch-effect prone and erroneously corrected features across the five datasets spanning regionally diverse populations and three commonly collected biosamples (blood, buccal and saliva). Conclusions Batch-effects are ever present, even in high-quality data, and a strategy to deal with them should be part of experimental design, particularly for EWAS. Batch-effect removal tools are useful to reduce technical variance in Infinium Methylation data, but they need to be applied with care and make use of post hoc diagnostic measures. Supplementary Information The online version contains supplementary material available at 10.1186/s13148-022-01277-9.
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Affiliation(s)
- Jason P Ross
- Human Health Program, Health and Biosecurity, CSIRO, Sydney, Australia.
| | - Susan van Dijk
- Human Health Program, Health and Biosecurity, CSIRO, Sydney, Australia
| | - Melinda Phang
- Charles Perkins Centre, The University of Sydney, Sydney, Australia
| | - Michael R Skilton
- Charles Perkins Centre, The University of Sydney, Sydney, Australia.,Sydney Medical School, The University of Sydney, Sydney, Australia.,Sydney Institute for Women, Children and Their Families, Sydney Local Health District, Sydney, Australia
| | - Peter L Molloy
- Human Health Program, Health and Biosecurity, CSIRO, Sydney, Australia
| | - Yalchin Oytam
- Clinical Insights and Analytics Unit, South Eastern Sydney Local Health District, Sydney, Australia
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23
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Roberts ML, Kotchen TA, Pan X, Li Y, Yang C, Liu P, Wang T, Laud PW, Chelius TH, Munyura Y, Mattson DL, Liu Y, Cowley AW, Kidambi S, Liang M. Unique Associations of DNA Methylation Regions With 24-Hour Blood Pressure Phenotypes in Blacks. Hypertension 2022; 79:761-772. [PMID: 34994206 PMCID: PMC8917053 DOI: 10.1161/hypertensionaha.121.18584] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Epigenetic marks (eg, DNA methylation) may capture the effect of gene-environment interactions. DNA methylation is involved in blood pressure (BP) regulation and hypertension development; however, no studies have evaluated its relationship with 24-hour BP phenotypes (daytime, nighttime, and 24-hour average BPs). METHODS We examined the association of whole blood DNA methylation with 24-hour BP phenotypes and clinic BPs in a discovery cohort of 281 Blacks using reduced representation bisulfite sequencing. We developed a deep and region-specific methylation sequencing method, Bisulfite ULtrapLEx Targeted Sequencing and utilized it to validate our findings in a separate validation cohort (n=117). RESULTS Analysis of 38 215 DNA methylation regions (MRs), derived from 1 549 368 CpG sites across the genome, identified up to 72 regions that were significantly associated with 24-hour BP phenotypes. No MR was significantly associated with clinic BP. Two to 3 MRs were significantly associated with various 24-hour BP phenotypes after adjustment for age, sex, and body mass index. Together, these MRs explained up to 16.5% of the variance of 24-hour average BP, while age, sex, and BMI explained up to 11.0% of the variance. Analysis of one of the MRs in an independent cohort using Bisulfite ULtrapLEx Targeted Sequencing confirmed its association with 24-hour average BP phenotype. CONCLUSIONS We identified several MRs that explain a substantial portion of variances in 24-hour BP phenotypes, which might be excellent markers of cumulative effect of factors influencing 24-hour BP levels. The Bisulfite ULtrapLEx Targeted Sequencing workflow has potential to be suitable for clinical testing and population screenings on a large scale.
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Affiliation(s)
- Michelle L Roberts
- Center of Systems Molecular Medicine, Department of Physiology, Medical College of Wisconsin, Milwaukee. (M.L.R., X.P., Y.L., C.Y., P.L., F.L.M., A.W.C., M.L.)
| | - Theodore A Kotchen
- Department of Medicine, Medical College of Wisconsin, Milwaukee. (T.A.K., Y.M., S.K.)
| | - Xiaoqing Pan
- Center of Systems Molecular Medicine, Department of Physiology, Medical College of Wisconsin, Milwaukee. (M.L.R., X.P., Y.L., C.Y., P.L., F.L.M., A.W.C., M.L.).,Department of Mathematics, Shanghai Normal University, China (X.P.)
| | - Yingchuan Li
- Center of Systems Molecular Medicine, Department of Physiology, Medical College of Wisconsin, Milwaukee. (M.L.R., X.P., Y.L., C.Y., P.L., F.L.M., A.W.C., M.L.).,Department of Critical Care Medicine, Shanghai JiaoTong University affiliated the Sixth People's Hospital, China (Y.L.)
| | - Chun Yang
- Center of Systems Molecular Medicine, Department of Physiology, Medical College of Wisconsin, Milwaukee. (M.L.R., X.P., Y.L., C.Y., P.L., F.L.M., A.W.C., M.L.)
| | - Pengyuan Liu
- Center of Systems Molecular Medicine, Department of Physiology, Medical College of Wisconsin, Milwaukee. (M.L.R., X.P., Y.L., C.Y., P.L., F.L.M., A.W.C., M.L.).,The Sir Run Run Shaw Hospital, Institute of Translational Medicine, Zhejiang University, China (P.L.)
| | | | - Purushottam W Laud
- Division of Biostatistics, Institute for Health and Equity, Medical College of Wisconsin, Milwaukee. (P.W.L.)
| | - Thomas H Chelius
- Division of Epidemiology, Institute for Health and Equity, Medical College of Wisconsin, Milwaukee. (T.H.C.)
| | - Yannick Munyura
- Department of Medicine, Medical College of Wisconsin, Milwaukee. (T.A.K., Y.M., S.K.)
| | - David L Mattson
- Department of Physiology, Medical College of Georgia, Augusta (D.L.M.)
| | | | - Allen W Cowley
- Center of Systems Molecular Medicine, Department of Physiology, Medical College of Wisconsin, Milwaukee. (M.L.R., X.P., Y.L., C.Y., P.L., F.L.M., A.W.C., M.L.)
| | - Srividya Kidambi
- Department of Medicine, Medical College of Wisconsin, Milwaukee. (T.A.K., Y.M., S.K.)
| | - Mingyu Liang
- Center of Systems Molecular Medicine, Department of Physiology, Medical College of Wisconsin, Milwaukee. (M.L.R., X.P., Y.L., C.Y., P.L., F.L.M., A.W.C., M.L.)
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24
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Cao R, Guan W. Evaluating Reliability of DNA Methylation Measurement. Methods Mol Biol 2022; 2432:15-24. [PMID: 35505204 DOI: 10.1007/978-1-0716-1994-0_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
DNA methylation is a widely studied epigenetic phenomenon. Alterations in methylation patterns influence human phenotypes and risk of disease. The Illumina Infinium HumanMethylation450 (HM450) and MethylationEPIC (EPIC) BeadChip are widely used microarray-based platforms for epigenome-wide association studies (EWASs). In this chapter, we will discuss the use of intraclass correlation coefficient (ICC) for assessing technical variations induced by methylation arrays at single-CpG level. ICC compares variation of methylation levels within- and between-replicate measurements, ranging between 0 and 1. We further characterize the distribution of ICCs using a mixture of truncated normal and normal distributions, and cluster CpG sites on the arrays into low- and high-reliability groups. In practice, we recommend that extra caution needs to be taken for associations at the CpG sites with low ICC values.
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Affiliation(s)
- Rui Cao
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Weihua Guan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA.
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25
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Cui B, Cui S, Huang J, Chen J. Increase the Power of Epigenome-Wide Association Testing Using ICC-Based Hypothesis Weighting. Methods Mol Biol 2022; 2432:113-122. [PMID: 35505211 DOI: 10.1007/978-1-0716-1994-0_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
For large-scale hypothesis testing such as epigenome-wide association testing, adaptively focusing power on the more promising hypotheses can lead to a much more powerful multiple testing procedure. In this chapter, we introduce a multiple testing procedure that weights each hypothesis based on the intraclass correlation coefficient (ICC), a measure of "noisiness" of CpG methylation measurement, to increase the power of epigenome-wide association testing. Compared to the traditional multiple testing procedure on a filtered CpG set, the proposed procedure circumvents the difficulty to determine the optimal ICC cutoff value and is overall more powerful. We illustrate the procedure and compare the power to classical multiple testing procedures using an example data.
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Affiliation(s)
- Bowen Cui
- State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, Rui Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shuya Cui
- Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jinyan Huang
- State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, Rui Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jun Chen
- Department of Quantitative Health Sciences and Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA.
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26
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Gallego-Paüls M, Hernández-Ferrer C, Bustamante M, Basagaña X, Barrera-Gómez J, Lau CHE, Siskos AP, Vives-Usano M, Ruiz-Arenas C, Wright J, Slama R, Heude B, Casas M, Grazuleviciene R, Chatzi L, Borràs E, Sabidó E, Carracedo Á, Estivill X, Urquiza J, Coen M, Keun HC, González JR, Vrijheid M, Maitre L. Variability of multi-omics profiles in a population-based child cohort. BMC Med 2021; 19:166. [PMID: 34289836 PMCID: PMC8296694 DOI: 10.1186/s12916-021-02027-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 06/08/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Multiple omics technologies are increasingly applied to detect early, subtle molecular responses to environmental stressors for future disease risk prevention. However, there is an urgent need for further evaluation of stability and variability of omics profiles in healthy individuals, especially during childhood. METHODS We aimed to estimate intra-, inter-individual and cohort variability of multi-omics profiles (blood DNA methylation, gene expression, miRNA, proteins and serum and urine metabolites) measured 6 months apart in 156 healthy children from five European countries. We further performed a multi-omics network analysis to establish clusters of co-varying omics features and assessed the contribution of key variables (including biological traits and sample collection parameters) to omics variability. RESULTS All omics displayed a large range of intra- and inter-individual variability depending on each omics feature, although all presented a highest median intra-individual variability. DNA methylation was the most stable profile (median 37.6% inter-individual variability) while gene expression was the least stable (6.6%). Among the least stable features, we identified 1% cross-omics co-variation between CpGs and metabolites (e.g. glucose and CpGs related to obesity and type 2 diabetes). Explanatory variables, including age and body mass index (BMI), explained up to 9% of serum metabolite variability. CONCLUSIONS Methylation and targeted serum metabolomics are the most reliable omics to implement in single time-point measurements in large cross-sectional studies. In the case of metabolomics, sample collection and individual traits (e.g. BMI) are important parameters to control for improved comparability, at the study design or analysis stage. This study will be valuable for the design and interpretation of epidemiological studies that aim to link omics signatures to disease, environmental exposures, or both.
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Affiliation(s)
- Marta Gallego-Paüls
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Consorcio de Investigacion Biomedica en Red de Epidemiologia y Salud Publica (CIBERESP), Madrid, Spain
| | - Carles Hernández-Ferrer
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Consorcio de Investigacion Biomedica en Red de Epidemiologia y Salud Publica (CIBERESP), Madrid, Spain
| | - Mariona Bustamante
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Consorcio de Investigacion Biomedica en Red de Epidemiologia y Salud Publica (CIBERESP), Madrid, Spain
- Center for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Xavier Basagaña
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Consorcio de Investigacion Biomedica en Red de Epidemiologia y Salud Publica (CIBERESP), Madrid, Spain
| | - Jose Barrera-Gómez
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Consorcio de Investigacion Biomedica en Red de Epidemiologia y Salud Publica (CIBERESP), Madrid, Spain
| | - Chung-Ho E Lau
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, South Kensington, London, UK
| | - Alexandros P Siskos
- Cancer Metabolism & Systems Toxicology Group, Division of Cancer, Department of Surgery & Cancer and Division of Systems Medicine, Department of Metabolism, Digestion & Reproduction, Imperial College London, London, UK
| | - Marta Vives-Usano
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Consorcio de Investigacion Biomedica en Red de Epidemiologia y Salud Publica (CIBERESP), Madrid, Spain
- Center for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Carlos Ruiz-Arenas
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Consorcio de Investigacion Biomedica en Red de Epidemiologia y Salud Publica (CIBERESP), Madrid, Spain
| | - John Wright
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Remy Slama
- Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Institute for Advanced Biosciences (IAB), Inserm, CNRS, Université Grenoble Alpes, Grenoble, France
| | - Barbara Heude
- Université de Paris, Centre for Research in Epidemiology and Statistics (CRESS), INSERM, INRAE, F-75004, Paris, France
| | - Maribel Casas
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Consorcio de Investigacion Biomedica en Red de Epidemiologia y Salud Publica (CIBERESP), Madrid, Spain
| | | | - Leda Chatzi
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Eva Borràs
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Center for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Eduard Sabidó
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Center for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Ángel Carracedo
- Medicine Genomics Group, Centro de Investigación Biomédica en Red Enfermedades Raras (CIBERER), University of Santiago de Compostela, CEGEN-PRB3, Santiago de Compostela, Spain
- Galician Foundation of Genomic Medicine, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Servicio Gallego de Salud (SERGAS), Santiago de Compostela, Galicia, Spain
| | - Xavier Estivill
- Center for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Jose Urquiza
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Consorcio de Investigacion Biomedica en Red de Epidemiologia y Salud Publica (CIBERESP), Madrid, Spain
| | - Muireann Coen
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, South Kensington, London, UK
- Oncology Safety, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Hector C Keun
- Cancer Metabolism & Systems Toxicology Group, Division of Cancer, Department of Surgery & Cancer and Division of Systems Medicine, Department of Metabolism, Digestion & Reproduction, Imperial College London, London, UK
| | - Juan R González
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Consorcio de Investigacion Biomedica en Red de Epidemiologia y Salud Publica (CIBERESP), Madrid, Spain
| | - Martine Vrijheid
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Consorcio de Investigacion Biomedica en Red de Epidemiologia y Salud Publica (CIBERESP), Madrid, Spain
| | - Léa Maitre
- ISGlobal, Barcelona, Spain.
- Universitat Pompeu Fabra (UPF), Barcelona, Spain.
- Consorcio de Investigacion Biomedica en Red de Epidemiologia y Salud Publica (CIBERESP), Madrid, Spain.
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Gondalia R, Baldassari A, Holliday KM, Justice AE, Stewart JD, Liao D, Yanosky JD, Engel SM, Sheps D, Jordahl KM, Bhatti P, Horvath S, Assimes TL, Demerath EW, Guan W, Fornage M, Bressler J, North KE, Conneely KN, Li Y, Hou L, Baccarelli AA, Whitsel EA. Epigenetically mediated electrocardiographic manifestations of sub-chronic exposures to ambient particulate matter air pollution in the Women's Health Initiative and Atherosclerosis Risk in Communities Study. ENVIRONMENTAL RESEARCH 2021; 198:111211. [PMID: 33895111 PMCID: PMC8179344 DOI: 10.1016/j.envres.2021.111211] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 03/10/2021] [Accepted: 04/19/2021] [Indexed: 06/03/2023]
Abstract
BACKGROUND Short-duration exposure to ambient particulate matter (PM) air pollution is associated with cardiac autonomic dysfunction and prolonged ventricular repolarization. However, associations with sub-chronic exposures to coarser particulates are relatively poorly characterized as are molecular mechanisms underlying their potential relationships with cardiovascular disease. MATERIALS AND METHODS We estimated associations between monthly mean concentrations of PM < 10 μm and 2.5-10 μm in diameter (PM10; PM2.5-10) with time-domain measures of heart rate variability (HRV) and QT interval duration (QT) among U.S. women and men in the Women's Health Initiative and Atherosclerosis Risk in Communities Study (nHRV = 82,107; nQT = 76,711). Then we examined mediation of the PM-HRV and PM-QT associations by DNA methylation (DNAm) at three Cytosine-phosphate-Guanine (CpG) sites (cg19004594, cg24102420, cg12124767) with known sensitivity to monthly mean PM concentrations in a subset of the participants (nHRV = 7,169; nQT = 6,895). After multiply imputing missing PM, electrocardiographic and covariable data, we estimated associations using attrition-weighted, linear, mixed, longitudinal models adjusting for sociodemographic, behavioral, meteorological, and clinical characteristics. We assessed mediation by estimating the proportions of PM-HRV and PM-QT associations mediated by DNAm. RESULTS We found little evidence of PM-HRV association, PM-QT association, or mediation by DNAm. CONCLUSIONS The findings suggest that among racially/ethnically and environmentally diverse U.S. populations, sub-chronic exposures to coarser particulates may not exert appreciable, epigenetically mediated effects on cardiac autonomic function or ventricular repolarization. Further investigation in better-powered studies is warranted, with additional focus on shorter duration exposures to finer particulates and non-electrocardiographic outcomes among relatively susceptible populations.
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Affiliation(s)
- Rahul Gondalia
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.
| | - Antoine Baldassari
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Katelyn M Holliday
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA; Department of Community and Family Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Anne E Justice
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA; Geisinger Health System, Danville, PA, USA
| | - James D Stewart
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Duanping Liao
- Division of Epidemiology, Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Jeff D Yanosky
- Division of Epidemiology, Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Stephanie M Engel
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - David Sheps
- Department of Epidemiology, University of Florida, Gainesville, FL, USA
| | - Kristina M Jordahl
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
| | - Parveen Bhatti
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
| | - Steve Horvath
- Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA; Biostatistics, School of Public Health, University of California Los Angeles, Los Angeles, USA
| | | | - Ellen W Demerath
- Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA
| | - Weihua Guan
- Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA
| | - Myriam Fornage
- Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Jan Bressler
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Kari E North
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA; Carolina Center for Genome Sciences, University of North Carolina, Chapel Hill, NC, USA
| | - Karen N Conneely
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 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
| | - Lifang Hou
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University Chicago, Evanston, IL, USA; Center for Population Epigenetics, Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Andrea A Baccarelli
- Laboratory of Environmental Epigenetics, Departments of Environmental Health Sciences and Epidemiology, Columbia University Mailman School of Public Health, New York, NY, USA
| | - Eric A Whitsel
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA; Department of Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC, USA
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Wright JD, Folsom AR, Coresh J, Sharrett AR, Couper D, Wagenknecht LE, Mosley TH, Ballantyne CM, Boerwinkle EA, Rosamond WD, Heiss G. The ARIC (Atherosclerosis Risk In Communities) Study: JACC Focus Seminar 3/8. J Am Coll Cardiol 2021; 77:2939-2959. [PMID: 34112321 PMCID: PMC8667593 DOI: 10.1016/j.jacc.2021.04.035] [Citation(s) in RCA: 250] [Impact Index Per Article: 83.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 04/13/2021] [Indexed: 02/08/2023]
Abstract
ARIC (Atherosclerosis Risk In Communities) initiated community-based surveillance in 1987 for myocardial infarction and coronary heart disease (CHD) incidence and mortality and created a prospective cohort of 15,792 Black and White adults ages 45 to 64 years. The primary aims were to improve understanding of the decline in CHD mortality and identify determinants of subclinical atherosclerosis and CHD in Black and White middle-age adults. ARIC has examined areas including health disparities, genomics, heart failure, and prevention, producing more than 2,300 publications. Results have had strong clinical impact and demonstrate the importance of population-based research in the spectrum of biomedical research to improve health.
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Affiliation(s)
- Jacqueline D Wright
- Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, Bethesda, Maryland, USA.
| | - Aaron R Folsom
- Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, Minnesota, USA
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, USA
| | - A Richey Sharrett
- Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, USA
| | - David Couper
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Lynne E Wagenknecht
- Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Thomas H Mosley
- Memory Impairment and Neurodegenerative Dementia Center, University of Mississippi Medical Center, Jackson, Mississippi, USA
| | | | - Eric A Boerwinkle
- School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Wayne D Rosamond
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Gerardo Heiss
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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29
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Xu Z, Taylor JA. Reliability of DNA methylation measures using Illumina methylation BeadChip. Epigenetics 2021; 16:495-502. [PMID: 32749174 PMCID: PMC8078668 DOI: 10.1080/15592294.2020.1805692] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 07/07/2020] [Accepted: 07/24/2020] [Indexed: 12/22/2022] Open
Abstract
Illumina BeadChips are widely utilized in epigenome-wide association studies (EWAS). Several studies have reported that many probes on these arrays have poor reliability. Here, we compare different pre-processing methods to improve intra-class correlation coefficients (ICC). We describe the characteristics of ICC across the genome, within and between studies, and across different array platforms. Using technical duplicates from 128 subjects, we find that with raw data only 22.5% of the CpGs on 450 K array have 'acceptable' ICCs (>0.5). Data preprocessing steps, such as background correction and dye bias correction, can reduce technical noise and improve the percentage to 38.5%. Similar to previous studies, we found that ICC is associated with CpG methylation level such that 83% of CpGs with intermediate methylation (0.1< beta-value <0.9) have acceptable ICCs, whereas only 21% of CpGs with low or high methylation (beta-value <0.1 or >0.9) have acceptable ICCs. ICC is also correlated with CpG methylation variance; after mutual adjustment for beta-value and variance, only variance remains correlated. Many CpGs with poor ICCs (<0.5) are located in biologically important regulatory regions, including gene promoters and CpG islands. Poor ICC at these sites appears to be a consequence of low biologic variation among individuals rather than increased technical measurement variation. ICCs quality classifications are highly concordant across different array platforms and across different studies. We find that ICC can be reliably estimated with 30 pairs of duplicate samples. CpGs with acceptable ICC have higher study power and are more commonly reported in published epigenome-wide studies.
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Affiliation(s)
- Zongli Xu
- Epidemiology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA
| | - Jack A. Taylor
- Epidemiology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA
- Epigenetics & Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA
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30
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Kuk M, Ward NC, Dwivedi G. Extrinsic and Intrinsic Responses in the Development and Progression of Atherosclerosis. Heart Lung Circ 2021; 30:807-816. [PMID: 33468387 DOI: 10.1016/j.hlc.2020.12.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2020] [Revised: 11/10/2020] [Accepted: 12/02/2020] [Indexed: 11/25/2022]
Abstract
Atherosclerosis is a multifactorial disease that is thought to be primarily inflammatory in origin. Given the contribution of inflammation to the development and progression of atherosclerosis, other conditions that are characterised by a dysregulated inflammatory response have also been proposed to play a role. The purpose of this review is to organise and present the various inflammatory processes that can affect atherosclerosis into two broad categories: extrinsic or host-independent and intrinsic or host-dependent. Within these two categories, we will discuss various processes that may contribute to the development and progression of atherosclerosis and the clinical studies describing these associations. Although the clinical trials investigating anti-inflammatory therapies have to date provided mixed results, further studies, particularly in conjunction with lipid-lowering and blood pressure lowering therapies should be considered.
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Affiliation(s)
- Mariya Kuk
- Division of Cardiology, Department of Medicine, University of Ottawa Heart Institute, Ottawa, Canada; McGill University Health Centre, McGill University, Montreal, Canada
| | - Natalie C Ward
- School of Public Health, Curtin University, Perth, WA, Australia; Medical School, University of Western Australia, Perth, WA, Australia
| | - Girish Dwivedi
- Medical School, University of Western Australia, Perth, WA, Australia; Harry Perkins Institute for Medical Research, Fiona Stanley Hospital, Perth, WA, Australia.
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31
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Planterose Jiménez B, Liu F, Caliebe A, Montiel González D, Bell JT, Kayser M, Vidaki A. Equivalent DNA methylation variation between monozygotic co-twins and unrelated individuals reveals universal epigenetic inter-individual dissimilarity. Genome Biol 2021; 22:18. [PMID: 33402197 PMCID: PMC7786996 DOI: 10.1186/s13059-020-02223-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 12/07/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Although the genomes of monozygotic twins are practically identical, their methylomes may evolve divergently throughout their lifetime as a consequence of factors such as the environment or aging. Particularly for young and healthy monozygotic twins, DNA methylation divergence, if any, may be restricted to stochastic processes occurring post-twinning during embryonic development and early life. However, to what extent such stochastic mechanisms can systematically provide a stable source of inter-individual epigenetic variation remains uncertain until now. RESULTS We enriched for inter-individual stochastic variation by using an equivalence testing-based statistical approach on whole blood methylation microarray data from healthy adolescent monozygotic twins. As a result, we identified 333 CpGs displaying similarly large methylation variation between monozygotic co-twins and unrelated individuals. Although their methylation variation surpasses measurement error and is stable in a short timescale, susceptibility to aging is apparent in the long term. Additionally, 46% of these CpGs were replicated in adipose tissue. The identified sites are significantly enriched at the clustered protocadherin loci, known for stochastic methylation in developing neurons. We also confirmed an enrichment in monozygotic twin DNA methylation discordance at these loci in whole genome bisulfite sequencing data from blood and adipose tissue. CONCLUSIONS We have isolated a component of stochastic methylation variation, distinct from genetic influence, measurement error, and epigenetic drift. Biomarkers enriched in this component may serve in the future as the basis for universal epigenetic fingerprinting, relevant for instance in the discrimination of monozygotic twin individuals in forensic applications, currently impossible with standard DNA profiling.
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Affiliation(s)
- Benjamin Planterose Jiménez
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Fan Liu
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
- Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, People’s Republic of China
- University of Chinese Academy of Sciences, Beijing, People’s Republic of China
| | - Amke Caliebe
- Institute of Medical Informatics and Statistics, Kiel University, Kiel, Germany
- University Medical Centre Schleswig-Holstein, Kiel, Germany
| | - Diego Montiel González
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Jordana T. Bell
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK
| | - Manfred Kayser
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Athina Vidaki
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
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32
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Epigenetic biotypes of post-traumatic stress disorder in war-zone exposed veteran and active duty males. Mol Psychiatry 2021; 26:4300-4314. [PMID: 33339956 PMCID: PMC8550967 DOI: 10.1038/s41380-020-00966-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2019] [Revised: 02/10/2020] [Accepted: 11/18/2020] [Indexed: 12/31/2022]
Abstract
Post-traumatic stress disorder (PTSD) is a heterogeneous condition evidenced by the absence of objective physiological measurements applicable to all who meet the criteria for the disorder as well as divergent responses to treatments. This study capitalized on biological diversity observed within the PTSD group observed following epigenome-wide analysis of a well-characterized Discovery cohort (N = 166) consisting of 83 male combat exposed veterans with PTSD, and 83 combat veterans without PTSD in order to identify patterns that might distinguish subtypes. Computational analysis of DNA methylation (DNAm) profiles identified two PTSD biotypes within the PTSD+ group, G1 and G2, associated with 34 clinical features that are associated with PTSD and PTSD comorbidities. The G2 biotype was associated with an increased PTSD risk and had higher polygenic risk scores and a greater methylation compared to the G1 biotype and healthy controls. The findings were validated at a 3-year follow-up (N = 59) of the same individuals as well as in two independent, veteran cohorts (N = 54 and N = 38), and an active duty cohort (N = 133). In some cases, for example Dopamine-PKA-CREB and GABA-PKC-CREB signaling pathways, the biotypes were oppositely dysregulated, suggesting that the biotypes were not simply a function of a dimensional relationship with symptom severity, but may represent distinct biological risk profiles underpinning PTSD. The identification of two novel distinct epigenetic biotypes for PTSD may have future utility in understanding biological and clinical heterogeneity in PTSD and potential applications in risk assessment for active duty military personnel under non-clinician-administered settings, and improvement of PTSD diagnostic markers.
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33
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Hop PJ, Zwamborn RAJ, Hannon EJ, Dekker AM, van Eijk K, Walker E, Iacoangeli A, Jones A, Shatunov A, Khleifat AA, Opie-Martin S, Shaw C, Morrison K, Shaw P, McLaughlin R, Hardiman O, Al-Chalabi A, Van Den Berg L, Mill J, Veldink JH. Cross-reactive probes on Illumina DNA methylation arrays: a large study on ALS shows that a cautionary approach is warranted in interpreting epigenome-wide association studies. NAR Genom Bioinform 2020; 2:lqaa105. [PMID: 33554115 PMCID: PMC7745769 DOI: 10.1093/nargab/lqaa105] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 10/27/2020] [Accepted: 11/25/2020] [Indexed: 12/11/2022] Open
Abstract
Illumina DNA methylation arrays are a widely used tool for performing genome-wide DNA methylation analyses. However, measurements obtained from these arrays may be affected by technical artefacts that result in spurious associations if left unchecked. Cross-reactivity represents one of the major challenges, meaning that probes may map to multiple regions in the genome. Although several studies have reported on this issue, few studies have empirically examined the impact of cross-reactivity in an epigenome-wide association study (EWAS). In this paper, we report on cross-reactivity issues that we discovered in a large EWAS on the presence of the C9orf72 repeat expansion in ALS patients. Specifically, we found that that the majority of the significant probes inadvertently cross-hybridized to the C9orf72 locus. Importantly, these probes were not flagged as cross-reactive in previous studies, leading to novel insights into the extent to which cross-reactivity can impact EWAS. Our findings are particularly relevant for epigenetic studies into diseases associated with repeat expansions and other types of structural variation. More generally however, considering that most spurious associations were not excluded based on pre-defined sets of cross-reactive probes, we believe that the presented data-driven flag and consider approach is relevant for any type of EWAS.
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Affiliation(s)
- Paul J Hop
- Department of Neurology, UMC Utrecht Brain Center, 3584 CG, Utrecht, the Netherlands
| | - Ramona A J Zwamborn
- Department of Neurology, UMC Utrecht Brain Center, 3584 CG, Utrecht, the Netherlands
| | - Eilis J Hannon
- University of Exeter Medical School, University of Exeter, Exeter EX2 5DW, UK
| | - Annelot M Dekker
- Department of Neurology, UMC Utrecht Brain Center, 3584 CG, Utrecht, the Netherlands
| | - Kristel R van Eijk
- Department of Neurology, UMC Utrecht Brain Center, 3584 CG, Utrecht, the Netherlands
| | - Emma M Walker
- University of Exeter Medical School, University of Exeter, Exeter EX2 5DW, UK
| | - Alfredo Iacoangeli
- Department of Basic and Clinical Neuroscience, King’s College London, Maurice Wohl Clinical Neuroscience Institute, London SE5 9RS, UK
- Department of Biostatistics and Health Informatics, King’s College London, London SE5 8AF, UK
| | - Ashley R Jones
- Department of Basic and Clinical Neuroscience, King’s College London, Maurice Wohl Clinical Neuroscience Institute, London SE5 9RS, UK
| | - Aleksey Shatunov
- Department of Basic and Clinical Neuroscience, King’s College London, Maurice Wohl Clinical Neuroscience Institute, London SE5 9RS, UK
| | - Ahmad Al Khleifat
- Department of Basic and Clinical Neuroscience, King’s College London, Maurice Wohl Clinical Neuroscience Institute, London SE5 9RS, UK
| | - Sarah Opie-Martin
- Department of Basic and Clinical Neuroscience, King’s College London, Maurice Wohl Clinical Neuroscience Institute, London SE5 9RS, UK
| | - Christopher E Shaw
- Department of Basic and Clinical Neuroscience, King’s College London, Maurice Wohl Clinical Neuroscience Institute, London SE5 9RS, UK
- UK Dementia Research Institute, King’s College London, London WC2R 2LS, UK
| | - Karen E Morrison
- Faculty of Medicine, Health & Life Sciences, Queen’s University Belfast, 90 Lisburn Road, Belfast, BT9 6AG, Northern Ireland, UK
| | - Pamela J Shaw
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield S10 2HQ, UK
| | - Russell L McLaughlin
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, Dublin D02 DK07, Republic of Ireland
| | - Orla Hardiman
- Academic Unit of Neurology, Trinity College Dublin, Trinity Biomedical Sciences Institute, Dublin D02 PN40, Republic of Ireland
- Department of Neurology, Beaumont Hospital, Dublin D02 PN40, Republic of Ireland
| | - Ammar Al-Chalabi
- Department of Basic and Clinical Neuroscience, King’s College London, Maurice Wohl Clinical Neuroscience Institute, London SE5 9RS, UK
- Department of Neurology, King’s College Hospital, Bessemer Road, London, SE5 9RX, UK
| | | | - Jonathan Mill
- University of Exeter Medical School, University of Exeter, Exeter EX2 5DW, UK
| | - Jan H Veldink
- Department of Neurology, UMC Utrecht Brain Center, 3584 CG, Utrecht, the Netherlands
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34
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Vives-Usano M, Hernandez-Ferrer C, Maitre L, Ruiz-Arenas C, Andrusaityte S, Borràs E, Carracedo Á, Casas M, Chatzi L, Coen M, Estivill X, González JR, Grazuleviciene R, Gutzkow KB, Keun HC, Lau CHE, Cadiou S, Lepeule J, Mason D, Quintela I, Robinson O, Sabidó E, Santorelli G, Schwarze PE, Siskos AP, Slama R, Vafeiadi M, Martí E, Vrijheid M, Bustamante M. In utero and childhood exposure to tobacco smoke and multi-layer molecular signatures in children. BMC Med 2020; 18:243. [PMID: 32811491 PMCID: PMC7437049 DOI: 10.1186/s12916-020-01686-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Accepted: 06/29/2020] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND The adverse health effects of early life exposure to tobacco smoking have been widely reported. In spite of this, the underlying molecular mechanisms of in utero and postnatal exposure to tobacco smoke are only partially understood. Here, we aimed to identify multi-layer molecular signatures associated with exposure to tobacco smoke in these two exposure windows. METHODS We investigated the associations of maternal smoking during pregnancy and childhood secondhand smoke (SHS) exposure with molecular features measured in 1203 European children (mean age 8.1 years) from the Human Early Life Exposome (HELIX) project. Molecular features, covering 4 layers, included blood DNA methylation and gene and miRNA transcription, plasma proteins, and sera and urinary metabolites. RESULTS Maternal smoking during pregnancy was associated with DNA methylation changes at 18 loci in child blood. DNA methylation at 5 of these loci was related to expression of the nearby genes. However, the expression of these genes themselves was only weakly associated with maternal smoking. Conversely, childhood SHS was not associated with blood DNA methylation or transcription patterns, but with reduced levels of several serum metabolites and with increased plasma PAI1 (plasminogen activator inhibitor-1), a protein that inhibits fibrinolysis. Some of the in utero and childhood smoking-related molecular marks showed dose-response trends, with stronger effects with higher dose or longer duration of the exposure. CONCLUSION In this first study covering multi-layer molecular features, pregnancy and childhood exposure to tobacco smoke were associated with distinct molecular phenotypes in children. The persistent and dose-dependent changes in the methylome make CpGs good candidates to develop biomarkers of past exposure. Moreover, compared to methylation, the weak association of maternal smoking in pregnancy with gene expression suggests different reversal rates and a methylation-based memory to past exposures. Finally, certain metabolites and protein markers evidenced potential early biological effects of postnatal SHS, such as fibrinolysis.
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Affiliation(s)
- Marta Vives-Usano
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Carles Hernandez-Ferrer
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Léa Maitre
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Carlos Ruiz-Arenas
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Sandra Andrusaityte
- Department of Environmental Sciences, Vytautas Magnus University, K. Donelaicio Street 58, 44248, Kaunas, Lithuania
| | - Eva Borràs
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Ángel Carracedo
- Grupo de Medicina Xenómica, Fundación Pública Galega de Medicina Xenómica, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), SERGAS, Rúa Choupana s/n, 15706, Santiago de Compostela, Spain
- Centro de Investigación en Red de Enfermedades Raras (CIBERER) y Centro Nacional de Genotipado (CEGEN-PRB3-ISCIII), Universidade de Santiago de Compostela, Praza do Obradoiro s/n, 15782, Santiago de Compostela, Spain
| | - Maribel Casas
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Leda Chatzi
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, 1540 Alcazar Street, Los Angeles, 90033, USA
| | - Muireann Coen
- Oncology Safety, Clinical Pharmacology and Safety Sciences, R&D Biopharmaceuticals, AstraZeneca, 1 Francis Crick Avenue, Cambridge, CB2 0RE, UK
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK
| | - Xavier Estivill
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Quantitative Genomics Medicine Laboratories (qGenomics), Esplugues del Llobregat, Barcelona, Catalonia, Spain
| | - Juan R González
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Regina Grazuleviciene
- Department of Environmental Sciences, Vytautas Magnus University, K. Donelaicio Street 58, 44248, Kaunas, Lithuania
| | - Kristine B Gutzkow
- Department af Environmental Health, Norwegian Institute of Public Health, Lovisenberggt 6, 0456, Oslo, Norway
| | - Hector C Keun
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK
- Cancer Metabolism and Systems Toxicology Group, Division of Cancer, Department of Surgery and Cancer, Imperial College London, Hammersmith Hospital Campus, London, W12 0NN, UK
| | - Chung-Ho E Lau
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK
| | - Solène Cadiou
- University Grenoble Alpes, Inserm, CNRS, Team of Environmental Epidemiology Applied to Reproduction and Respiratory Health, IAB, 38000, Grenoble, France
| | - Johanna Lepeule
- University Grenoble Alpes, Inserm, CNRS, Team of Environmental Epidemiology Applied to Reproduction and Respiratory Health, IAB, 38000, Grenoble, France
| | - Dan Mason
- Bradford Institute for Health Research, Bradford Royal Infirmary, Bradford, BD9 6RJ, UK
| | - Inés Quintela
- Grupo de Medicina Xenómica, Centro Nacional de Genotipado (CEGEN-PRB3-ISCIII), Universidade de Santiago de Compostela, Praza do Obradoiro s/n, 15782, Santiago de Compostela, Spain
| | - Oliver Robinson
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, St. Mary's Hospital Campus, London, W21PG, UK
| | - Eduard Sabidó
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Gillian Santorelli
- Bradford Institute for Health Research, Bradford Royal Infirmary, Bradford, BD9 6RJ, UK
| | - Per E Schwarze
- Department af Environmental Health, Norwegian Institute of Public Health, Lovisenberggt 6, 0456, Oslo, Norway
| | - Alexandros P Siskos
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK
- Cancer Metabolism and Systems Toxicology Group, Division of Cancer, Department of Surgery and Cancer, Imperial College London, Hammersmith Hospital Campus, London, W12 0NN, UK
| | - Rémy Slama
- University Grenoble Alpes, Inserm, CNRS, Team of Environmental Epidemiology Applied to Reproduction and Respiratory Health, IAB, 38000, Grenoble, France
| | - Marina Vafeiadi
- Department of Social Medicine, School of Medicine, University of Crete, Heraklion, Crete, Greece
| | - Eulàlia Martí
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Departament de Biomedicina, Institut de Neurociències, Universitat de Barcelona, Barcelona, Spain
| | - Martine Vrijheid
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Mariona Bustamante
- ISGlobal, Barcelona, Spain.
- Universitat Pompeu Fabra (UPF), Barcelona, Spain.
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.
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35
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Sugden K, Hannon EJ, Arseneault L, Belsky DW, Corcoran DL, Fisher HL, Houts RM, Kandaswamy R, Moffitt TE, Poulton R, Prinz JA, Rasmussen LJH, Williams BS, Wong CCY, Mill J, Caspi A. Patterns of Reliability: Assessing the Reproducibility and Integrity of DNA Methylation Measurement. PATTERNS 2020; 1:S2666-3899(20)30014-3. [PMID: 32885222 PMCID: PMC7467214 DOI: 10.1016/j.patter.2020.100014] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
DNA methylation plays an important role in both normal human development and risk of disease. The most utilized method of assessing DNA methylation uses BeadChips, generating an epigenome-wide “snapshot” of >450,000 observations (probe measurements) per assay. However, the reliability of each of these measurements is not equal, and little consideration is paid to consequences for research. We correlated repeat measurements of the same DNA samples using the Illumina HumanMethylation450K and the Infinium MethylationEPIC BeadChips in 350 blood DNA samples. Probes that were reliably measured were more heritable and showed consistent associations with environmental exposures, gene expression, and greater cross-tissue concordance. Unreliable probes were less replicable and generated an unknown volume of false negatives. This serves as a lesson for working with DNA methylation data, but the lessons are equally applicable to working with other data: as we advance toward generating increasingly greater volumes of data, failure to document reliability risks harming reproducibility. Measurements of DNA methylation made using BeadChip probes are differentially reliable Unreliable probes were less heritable, less replicable, and less functionally relevant This has serious implications for reporting and evaluating DNA methylation findings Reliability joins replicability and reproducibility to make three fundamental Rs of STEM
Although DNA methylation data are used widely by researchers in many fields, the reliability of these data are surprisingly variable. Our findings remind us that, in an age of increasingly big data, research is only as robust as its foundations. We hope that our findings will improve the integrity of DNA methylation studies. We also hope that our findings serve as a cautionary reminder for those generating and implementing big data of any type: reliability is a fundamental aspect of replicability. Conducting analysis with reliable data will improve chances of replicable findings, which might lead to more actionable targets for further research. To the extent that reliable data improve replicability, the knock-on effect will be more public confidence in research and less effort spent trying to replicate findings that are bound to fail.
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Affiliation(s)
- Karen Sugden
- Department of Psychology and Neuroscience, Duke University, Grey Building, 2020 West Main Street, Suite 201, Durham, NC 27705, USA.,Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
| | - Eilis J Hannon
- Complex Disease Epigenetics Group, University of Exeter Medical School, Exeter, UK
| | - Louise Arseneault
- King's College London, Social, Genetic, and Developmental Psychiatry Research Centre, Institute of Psychiatry, Psychology, and Neuroscience, London, UK
| | - Daniel W Belsky
- Department of Epidemiology & Butler Aging Center, Columbia University Mailman School of Public Health, New York, NY, USA
| | - David L Corcoran
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
| | - Helen L Fisher
- King's College London, Social, Genetic, and Developmental Psychiatry Research Centre, Institute of Psychiatry, Psychology, and Neuroscience, London, UK
| | - Renate M Houts
- Department of Psychology and Neuroscience, Duke University, Grey Building, 2020 West Main Street, Suite 201, Durham, NC 27705, USA
| | - Radhika Kandaswamy
- King's College London, Social, Genetic, and Developmental Psychiatry Research Centre, Institute of Psychiatry, Psychology, and Neuroscience, London, UK
| | - Terrie E Moffitt
- Department of Psychology and Neuroscience, Duke University, Grey Building, 2020 West Main Street, Suite 201, Durham, NC 27705, USA.,Center for Genomic and Computational Biology, Duke University, Durham, NC, USA.,King's College London, Social, Genetic, and Developmental Psychiatry Research Centre, Institute of Psychiatry, Psychology, and Neuroscience, London, UK.,Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Richie Poulton
- Dunedin Multidisciplinary Health and Development Research Unit, University of Otago, Dunedin, New Zealand
| | - Joseph A Prinz
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
| | - Line J H Rasmussen
- Department of Psychology and Neuroscience, Duke University, Grey Building, 2020 West Main Street, Suite 201, Durham, NC 27705, USA.,Clinical Research Centre, Copenhagen University Hospital Amager and Hvidovre, Hvidovre, Denmark
| | - Benjamin S Williams
- Department of Psychology and Neuroscience, Duke University, Grey Building, 2020 West Main Street, Suite 201, Durham, NC 27705, USA.,Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
| | - Chloe C Y Wong
- King's College London, Social, Genetic, and Developmental Psychiatry Research Centre, Institute of Psychiatry, Psychology, and Neuroscience, London, UK
| | - Jonathan Mill
- Complex Disease Epigenetics Group, University of Exeter Medical School, Exeter, UK
| | - Avshalom Caspi
- Department of Psychology and Neuroscience, Duke University, Grey Building, 2020 West Main Street, Suite 201, Durham, NC 27705, USA.,Center for Genomic and Computational Biology, Duke University, Durham, NC, USA.,King's College London, Social, Genetic, and Developmental Psychiatry Research Centre, Institute of Psychiatry, Psychology, and Neuroscience, London, UK.,Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
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36
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Westerman K, Fernández‐Sanlés A, Patil P, Sebastiani P, Jacques P, Starr JM, J. Deary I, Liu Q, Liu S, Elosua R, DeMeo DL, Ordovás JM. Epigenomic Assessment of Cardiovascular Disease Risk and Interactions With Traditional Risk Metrics. J Am Heart Assoc 2020; 9:e015299. [PMID: 32308120 PMCID: PMC7428544 DOI: 10.1161/jaha.119.015299] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Accepted: 03/10/2020] [Indexed: 12/16/2022]
Abstract
Background Epigenome-wide association studies for cardiometabolic risk factors have discovered multiple loci associated with incident cardiovascular disease (CVD). However, few studies have sought to directly optimize a predictor of CVD risk. Furthermore, it is challenging to train multivariate models across multiple studies in the presence of study- or batch effects. Methods and Results Here, we analyzed existing DNA methylation data collected using the Illumina HumanMethylation450 microarray to create a predictor of CVD risk across 3 cohorts: Women's Health Initiative, Framingham Heart Study Offspring Cohort, and Lothian Birth Cohorts. We trained Cox proportional hazards-based elastic net regressions for incident CVD separately in each cohort and used a recently introduced cross-study learning approach to integrate these individual scores into an ensemble predictor. The methylation-based risk score was associated with CVD time-to-event in a held-out fraction of the Framingham data set (hazard ratio per SD=1.28, 95% CI, 1.10-1.50) and predicted myocardial infarction status in the independent REGICOR (Girona Heart Registry) data set (odds ratio per SD=2.14, 95% CI, 1.58-2.89). These associations remained after adjustment for traditional cardiovascular risk factors and were similar to those from elastic net models trained on a directly merged data set. Additionally, we investigated interactions between the methylation-based risk score and both genetic and biochemical CVD risk, showing preliminary evidence of an enhanced performance in those with less traditional risk factor elevation. Conclusions This investigation provides proof-of-concept for a genome-wide, CVD-specific epigenomic risk score and suggests that DNA methylation data may enable the discovery of high-risk individuals who would be missed by alternative risk metrics.
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Affiliation(s)
- Kenneth Westerman
- JM‐USDA Human Nutrition Research Center on Aging at Tufts UniversityBostonMA
| | - Alba Fernández‐Sanlés
- Cardiovascular Epidemiology and Genetics Research GroupREGICOR Study GroupIMIM (Hospital del Mar Medical Research Institute)BarcelonaCataloniaSpain
- Pompeu Fabra University (UPF)BarcelonaCataloniaSpain
| | - Prasad Patil
- Department of BiostatisticsBoston University School of Public HealthBostonMA
| | - Paola Sebastiani
- Department of BiostatisticsBoston University School of Public HealthBostonMA
| | - Paul Jacques
- JM‐USDA Human Nutrition Research Center on Aging at Tufts UniversityBostonMA
| | - John M. Starr
- Department of PsychologyUniversity of EdinburghUnited Kingdom
- Centre for Cognitive Ageing and Cognitive EpidemiologyUniversity of EdinburghUnited Kingdom
| | - Ian J. Deary
- Department of PsychologyUniversity of EdinburghUnited Kingdom
- Centre for Cognitive Ageing and Cognitive EpidemiologyUniversity of EdinburghUnited Kingdom
| | - Qing Liu
- Department of EpidemiologyBrown University School of Public HealthProvidenceRI
| | - Simin Liu
- Department of EpidemiologyBrown University School of Public HealthProvidenceRI
| | - Roberto Elosua
- Cardiovascular Epidemiology and Genetics Research GroupREGICOR Study GroupIMIM (Hospital del Mar Medical Research Institute)BarcelonaCataloniaSpain
- CIBER Cardiovascular Diseases (CIBERCV)MadridSpain
- Medicine DepartmentMedical SchoolUniversity of Vic‐Central University of Catalonia (UVic‐UCC)VicCataloniaSpain
| | - Dawn L. DeMeo
- Channing Division of Network MedicineDepartment of MedicineBrigham and Women’s HospitalBostonMA
| | - José M. Ordovás
- JM‐USDA Human Nutrition Research Center on Aging at Tufts UniversityBostonMA
- IMDEA AlimentaciónCEIUAMMadridSpain
- Centro Nacional de Investigaciones Cardiovasculares (CNIC)MadridSpain
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37
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Huang J, Bai L, Cui B, Wu L, Wang L, An Z, Ruan S, Yu Y, Zhang X, Chen J. Leveraging biological and statistical covariates improves the detection power in epigenome-wide association testing. Genome Biol 2020; 21:88. [PMID: 32252795 PMCID: PMC7132874 DOI: 10.1186/s13059-020-02001-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 03/17/2020] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Epigenome-wide association studies (EWAS), which seek the association between epigenetic marks and an outcome or exposure, involve multiple hypothesis testing. False discovery rate (FDR) control has been widely used for multiple testing correction. However, traditional FDR control methods do not use auxiliary covariates, and they could be less powerful if the covariates could inform the likelihood of the null hypothesis. Recently, many covariate-adaptive FDR control methods have been developed, but application of these methods to EWAS data has not yet been explored. It is not clear whether these methods can significantly improve detection power, and if so, which covariates are more relevant for EWAS data. RESULTS In this study, we evaluate the performance of five covariate-adaptive FDR control methods with EWAS-related covariates using simulated as well as real EWAS datasets. We develop an omnibus test to assess the informativeness of the covariates. We find that statistical covariates are generally more informative than biological covariates, and the covariates of methylation mean and variance are almost universally informative. In contrast, the informativeness of biological covariates depends on specific datasets. We show that the independent hypothesis weighting (IHW) and covariate adaptive multiple testing (CAMT) method are overall more powerful, especially for sparse signals, and could improve the detection power by a median of 25% and 68% on real datasets, compared to the ST procedure. We further validate the findings in various biological contexts. CONCLUSIONS Covariate-adaptive FDR control methods with informative covariates can significantly increase the detection power for EWAS. For sparse signals, IHW and CAMT are recommended.
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Affiliation(s)
- Jinyan Huang
- State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, National Research Center for Translational Medicine, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 197 Ruijin Er Road, Shanghai, 200025, China.
| | - Ling Bai
- State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, National Research Center for Translational Medicine, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 197 Ruijin Er Road, Shanghai, 200025, China
| | - Bowen Cui
- State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, National Research Center for Translational Medicine, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 197 Ruijin Er Road, Shanghai, 200025, China
| | - Liang Wu
- State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, National Research Center for Translational Medicine, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 197 Ruijin Er Road, Shanghai, 200025, China
| | - Liwen Wang
- Department of General Surgery, Rui-Jin Hospital, Shanghai Jiao Tong University, 197 Ruijin Er Road, Shanghai, 200025, China
| | - Zhiyin An
- State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, National Research Center for Translational Medicine, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 197 Ruijin Er Road, Shanghai, 200025, China
| | - Shulin Ruan
- State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, National Research Center for Translational Medicine, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 197 Ruijin Er Road, Shanghai, 200025, China
| | - Yue Yu
- Division of Digital Health Sciences, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA
| | - Xianyang Zhang
- Department of Statistics, Texas A&M University, Blocker 449D, College Station, TX, 77843, USA.
| | - Jun Chen
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research and Center for Individualized Medicine, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA.
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38
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Li S, Wong EM, Nguyen TL, Joo JHE, Stone J, Dite GS, Giles GG, Saffery R, Southey MC, Hopper JL. Causes of blood methylomic variation for middle-aged women measured by the HumanMethylation450 array. Epigenetics 2018; 12:973-981. [PMID: 29099274 DOI: 10.1080/15592294.2017.1384891] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
Abstract
To address the limitations in current classic twin/family research on the genetic and/or environmental causes of human methylomic variation, we measured blood DNA methylation for 479 women (mean age 56 years) including 66 monozygotic (MZ), 66 dizygotic (DZ) twin pairs and 215 sisters of twins, and 11 random technical duplicates using the HumanMethylation450 array. For each methylation site, we estimated the correlation for pairs of duplicates, MZ twins, DZ twins, and siblings, fitted variance component models by assuming the variation is explained by genetic factors, by shared and individual environmental factors, and by independent measurement error, and assessed the best fitting model. We found that the average (standard deviation) correlations for duplicate, MZ, DZ, and sibling pairs were 0.10 (0.35), 0.07 (0.21), -0.01 (0.14) and -0.04 (0.07). At the genome-wide significance level of 10-7, 93.3% of sites had no familial correlation, and 5.6%, 0.1%, and 0.2% of sites were correlated for MZ, DZ, and sibling pairs. For 86.4%, 6.9%, and 7.1% of sites, the best fitting model included measurement error only, a genetic component, and at least one environmental component. For the 13.6% of sites influenced by genetic and/or environmental factors, the average proportion of variance explained by environmental factors was greater than that explained by genetic factors (0.41 vs. 0.37, P value <10-15). Our results are consistent with, for middle-aged woman, blood methylomic variation measured by the HumanMethylation450 array being largely explained by measurement error, and more influenced by environmental factors than by genetic factors.
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Affiliation(s)
- Shuai Li
- a Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health , University of Melbourne , Parkville , Victoria , Australia
| | - Ee Ming Wong
- b Genetic Epidemiology Laboratory, Department of Pathology , University of Melbourne , Parkville , Victoria , Australia.,c Precision Medicine, School of Clinical Sciences at Monash Health , Monash University , Clayton , Victoria , Australia
| | - Tuong L Nguyen
- a Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health , University of Melbourne , Parkville , Victoria , Australia
| | - Ji-Hoon Eric Joo
- b Genetic Epidemiology Laboratory, Department of Pathology , University of Melbourne , Parkville , Victoria , Australia.,c Precision Medicine, School of Clinical Sciences at Monash Health , Monash University , Clayton , Victoria , Australia
| | - Jennifer Stone
- d Centre for Genetic Origins of Health and Disease , Curtin University and the University of Western Australia , Perth , Western Australia , Australia
| | - Gillian S Dite
- a Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health , University of Melbourne , Parkville , Victoria , Australia
| | - Graham G Giles
- a Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health , University of Melbourne , Parkville , Victoria , Australia.,e Cancer Epidemiology and Intelligence Division , Cancer Council Victoria , Melbourne , Victoria , Australia
| | - Richard Saffery
- f Murdoch Children's Research Institute , Royal Children's Hospital , Parkville , Victoria , Australia.,g Department of Paediatrics , University of Melbourne , Parkville , Victoria , Australia
| | - Melissa C Southey
- b Genetic Epidemiology Laboratory, Department of Pathology , University of Melbourne , Parkville , Victoria , Australia.,c Precision Medicine, School of Clinical Sciences at Monash Health , Monash University , Clayton , Victoria , Australia
| | - John L Hopper
- a Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health , University of Melbourne , Parkville , Victoria , Australia
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LogLoss-BERAF: An ensemble-based machine learning model for constructing highly accurate diagnostic sets of methylation sites accounting for heterogeneity in prostate cancer. PLoS One 2018; 13:e0204371. [PMID: 30388122 PMCID: PMC6214495 DOI: 10.1371/journal.pone.0204371] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 09/06/2018] [Indexed: 12/23/2022] Open
Abstract
Although modern methods of whole genome DNA methylation analysis have a wide range of applications, they are not suitable for clinical diagnostics due to their high cost and complexity and due to the large amount of sample DNA required for the analysis. Therefore, it is crucial to be able to identify a relatively small number of methylation sites that provide high precision and sensitivity for the diagnosis of pathological states. We propose an algorithm for constructing limited subsamples from high-dimensional data to form diagnostic panels. We have developed a tool that utilizes different methods of selection to find an optimal, minimum necessary combination of factors using cross-entropy loss metrics (LogLoss) to identify a subset of methylation sites. We show that the algorithm can work effectively with different genome methylation patterns using ensemble-based machine learning methods. Algorithm efficiency, precision and robustness were evaluated using five genome-wide DNA methylation datasets (totaling 626 samples), and each dataset was classified into tumor and non-tumor samples. The algorithm produced an AUC of 0.97 (95% CI: 0.94-0.99, 9 sites) for prostate adenocarcinoma and an AUC of 1.0 (from 2 to 6 sites) for urothelial bladder carcinoma, two types of kidney carcinoma and colorectal carcinoma. For prostate adenocarcinoma we showed that identified differential variability methylation patterns distinguish cluster of samples with higher recurrence rate (hazard ratio for recurrence = 0.48, 95% CI: 0.05-0.92; log-rank test, p-value < 0.03). We also identified several clusters of correlated interchangeable methylation sites that can be used for the elaboration of biological interpretation of the resulting models and for further selection of the sites most suitable for designing diagnostic panels. LogLoss-BERAF is implemented as a standalone python code and open-source code is freely available from https://github.com/bioinformatics-IBCH/logloss-beraf along with the models described in this article.
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40
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Zaimi I, Pei D, Koestler DC, Marsit CJ, De Vivo I, Tworoger SS, Shields AE, Kelsey KT, Michaud DS. Variation in DNA methylation of human blood over a 1-year period using the Illumina MethylationEPIC array. Epigenetics 2018; 13:1056-1071. [PMID: 30270718 PMCID: PMC6342169 DOI: 10.1080/15592294.2018.1530008] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Revised: 08/02/2018] [Accepted: 09/22/2018] [Indexed: 12/29/2022] Open
Abstract
Assessing DNA methylation profiles in human blood has become a major focus of epidemiologic inquiry. Understanding variability in CpG-specific DNA methylation over moderate periods of time is a critical first step in identifying CpG sites that are candidates for DNA methylation-based etiologic, diagnostic and prognostic predictors of pathogenesis. Using the Illumina MethylationEPIC [850K] BeadArray, DNA methylation was profiled in paired whole blood samples collected approximately 1 year apart from 35 healthy women enrolled in the Nurses Study II cohort. The median intraclass correlation coefficient (ICC) across all CpG loci was 0.19 [Interquartile Range (IQR) 0.00-0.50]; 74.8% of ICCs were in the low range (0-0.5), 16.9% in the mid-range of ICCs (0.5-0.8), and 8.3% in the high-range of ICCs (0.8-1). ICCs were similar for CpG probes on the 450K Illumina array (median 0.17) and the new probes added to the 850K array (median 0.21). ICCs for CpG loci on the sex chromosomes and known metastable epialleles were high (median 0.71, 0.97, respectively), and ICCs among methylation quantitative trait loci (mQTL) CpGs were significantly higher as compared to non-mQTL CpGs (median 0.73, 0.16, respectively, P < 2 × 10-16). We observed wide variation in DNA methylation stability over a 1-year period. Probes considered non-stable, due to substantial variation over a moderate period of time and with minimal variability across individuals could be removed in large epidemiological studies. Moreover, adjusting for technical variation that arises from using high-dimensional arrays is critical.
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Affiliation(s)
- Ina Zaimi
- a Department of Public Health & Community Medicine, Tufts University School of Medicine , Tufts University , Boston , USA
| | - Dong Pei
- b Department of Biostatistics , University of Kansas Medical Center , Kansas City , USA
- c University of Kansas Cancer Center , The University of Kansas Medical Center , Kansas City , USA
| | - Devin C Koestler
- b Department of Biostatistics , University of Kansas Medical Center , Kansas City , USA
- c University of Kansas Cancer Center , The University of Kansas Medical Center , Kansas City , USA
| | - Carmen J Marsit
- d Department of Environmental Health and Department of Epidemiology, Rollins School of Public Health , Emory University , Atlanta , USA
| | - Immaculata De Vivo
- e Channing Division of Network Medicine, Department of Medicine , Brigham and Women's Hospital and Harvard Medical School , Boston , USA
| | - Shelley S Tworoger
- f Department of Cancer Epidemiology , Moffitt Cancer Center , Tampa , USA
- g Department of Epidemiology , Harvard T.H. Chan School of Public Health , Boston , USA
| | - Alexandra E Shields
- h Department of Medicine , Harvard Medical School , Boston , MA , USA
- k Harvard/MGH Center on Genomics, Vulnerable Populations, and Health Disparities , Massachusetts General Hospital , Boston , MA , USA
| | - Karl T Kelsey
- i Department of Epidemiology , Brown University , Providence , USA
- j Department of Pathology and Laboratory Medicine , Brown University , Providence , USA
| | - Dominique S Michaud
- a Department of Public Health & Community Medicine, Tufts University School of Medicine , Tufts University , Boston , USA
- i Department of Epidemiology , Brown University , Providence , USA
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41
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Joshu CE, Barber JR, Coresh J, Couper DJ, Mosley TH, Vitolins MZ, Butler KR, Nelson HH, Prizment AE, Selvin E, Tooze JA, Visvanathan K, Folsom AR, Platz EA. Enhancing the Infrastructure of the Atherosclerosis Risk in Communities (ARIC) Study for Cancer Epidemiology Research: ARIC Cancer. Cancer Epidemiol Biomarkers Prev 2018; 27:295-305. [PMID: 29263187 PMCID: PMC5835193 DOI: 10.1158/1055-9965.epi-17-0696] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Revised: 10/05/2017] [Accepted: 12/19/2017] [Indexed: 01/03/2023] Open
Abstract
Background: We describe the expansion of the Atherosclerosis Risk in Communities (ARIC) Study into a cancer cohort. In 1987 to 1989, ARIC recruited 15,792 participants 45 to 64 years old to be sex (55% female), race (27% black), and geographically diverse. ARIC has exceptional data collected during 6 clinical visits and calls every 6 months, repeated biospecimens, and linkage to Medicare claims data.Methods: We established a Cancer Coordinating Center to implement infrastructure activities, convened a Working Group for data use, leveraged ARIC staff and procedures, and developed protocols. We initiated a cancer-specific participant contact, added questions to existing contacts, obtained permission to collect medical records and tissue, abstracted records, linked with state cancer registries, and adjudicated cases and characterizing data.Results: Through 2012, we ascertained and characterized 4,743 incident invasive, first, and subsequent primary cancers among 4,107 participants and 1,660 cancer-related deaths. We generated a total cancer incidence and mortality analytic case file, and analytic case files for bladder, breast, colorectal, liver, lung, pancreas, and prostate cancer incidence, mortality, and case fatality. Adjudication of multiple data sources improved case records and identified cancers not identified via registries. From 2013 onward, we ascertain cases from self-report coupled with medical records. Additional cancer registry linkages are planned.Conclusions: Compared with starting a new cohort, expanding a cardiovascular cohort into ARIC Cancer was an efficient strategy. Our efforts yielded enhanced case files with 25 years of follow-up.Impact: Now that the cancer infrastructure is established, ARIC is contributing its unique features to modern cancer epidemiology research. Cancer Epidemiol Biomarkers Prev; 27(3); 295-305. ©2017 AACR.
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Affiliation(s)
- Corinne E Joshu
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, Maryland
| | - John R Barber
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University, Baltimore, Maryland
| | - David J Couper
- Department of Biostatistics, University of North Carolina at Chapel Hill School of Global Public Health, Chapel Hill, North Carolina
| | - Thomas H Mosley
- Division of Geriatrics, University of Mississippi Medical Center, Jackson, Mississippi
- Division of Neurology, University of Mississippi Medical Center, Jackson, Mississippi
| | - Mara Z Vitolins
- Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Kenneth R Butler
- Division of Geriatrics, University of Mississippi Medical Center, Jackson, Mississippi
| | - Heather H Nelson
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota
| | - Anna E Prizment
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota
| | - Elizabeth Selvin
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University, Baltimore, Maryland
| | - Janet A Tooze
- Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Kala Visvanathan
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, Maryland
| | - Aaron R Folsom
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota
| | - Elizabeth A Platz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, Maryland
- James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland
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Forest M, O'Donnell KJ, Voisin G, Gaudreau H, MacIsaac JL, McEwen LM, Silveira PP, Steiner M, Kobor MS, Meaney MJ, Greenwood CMT. Agreement in DNA methylation levels from the Illumina 450K array across batches, tissues, and time. Epigenetics 2018; 13:19-32. [PMID: 29381404 PMCID: PMC5837078 DOI: 10.1080/15592294.2017.1411443] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Epigenome-wide association studies (EWAS) have focused primarily on DNA methylation as a chemically stable and functional epigenetic modification. However, the stability and accuracy of the measurement of methylation in different tissues and extraction types is still being actively studied, and the longitudinal stability of DNA methylation in commonly studied peripheral tissues is of great interest. Here, we used data from two studies, three tissue types, and multiple time points to assess the stability of DNA methylation measured with the Illumina Infinium HumanMethylation450 BeadChip array. Redundancy analysis enabled visual assessment of agreement of replicate samples overall and showed good agreement after removing effects of tissue type, age, and sex. At the probe level, analysis of variance contrasts separating technical and biological replicates clearly showed better agreement between technical replicates versus longitudinal samples, and suggested increased stability for buccal cells versus blood or blood spots. Intraclass correlations (ICCs) demonstrated that inter-individual variability is of similar magnitude to within-sample variability at many probes; however, as inter-individual variability increased, so did ICC. Furthermore, we were able to demonstrate decreasing agreement in methylation levels with time, despite a maximal sampling interval of only 576 days. Finally, at 6 popular candidate genes, there was a large range of stability across probes. Our findings highlight important sources of technical and biological variation in DNA methylation across different tissues over time. These data will help to inform longitudinal sampling strategies of future EWAS.
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Affiliation(s)
- Marie Forest
- a Lady Davis Institute , Jewish General Hospital , Montreal , QC , Canada.,b Ludmer Centre for Neuroinformatics and Mental Health , McGill University , Montreal , QC , Canada
| | - Kieran J O'Donnell
- b Ludmer Centre for Neuroinformatics and Mental Health , McGill University , Montreal , QC , Canada.,c Douglas Hospital Research Centre , McGill University , Montreal , QC , Canada.,d Sackler Program for Epigenetics & Psychobiology , McGill University , Montreal , QC , Canada.,e Canadian Institute for Advanced Research , Child and Brain Development Program , Toronto , ON , Canada
| | - Greg Voisin
- a Lady Davis Institute , Jewish General Hospital , Montreal , QC , Canada
| | - Helene Gaudreau
- b Ludmer Centre for Neuroinformatics and Mental Health , McGill University , Montreal , QC , Canada.,c Douglas Hospital Research Centre , McGill University , Montreal , QC , Canada
| | - Julia L MacIsaac
- f Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics , and BC Children's Hospital Research Institute, University of British Columbia , Vancouver , BC , Canada
| | - Lisa M McEwen
- f Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics , and BC Children's Hospital Research Institute, University of British Columbia , Vancouver , BC , Canada
| | - Patricia P Silveira
- b Ludmer Centre for Neuroinformatics and Mental Health , McGill University , Montreal , QC , Canada.,c Douglas Hospital Research Centre , McGill University , Montreal , QC , Canada.,d Sackler Program for Epigenetics & Psychobiology , McGill University , Montreal , QC , Canada
| | | | - Michael S Kobor
- f Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics , and BC Children's Hospital Research Institute, University of British Columbia , Vancouver , BC , Canada
| | - Michael J Meaney
- b Ludmer Centre for Neuroinformatics and Mental Health , McGill University , Montreal , QC , Canada.,c Douglas Hospital Research Centre , McGill University , Montreal , QC , Canada.,d Sackler Program for Epigenetics & Psychobiology , McGill University , Montreal , QC , Canada.,e Canadian Institute for Advanced Research , Child and Brain Development Program , Toronto , ON , Canada.,h Singapore Institute of Clinical Sciences , Singapore
| | - Celia M T Greenwood
- a Lady Davis Institute , Jewish General Hospital , Montreal , QC , Canada.,b Ludmer Centre for Neuroinformatics and Mental Health , McGill University , Montreal , QC , Canada.,i Departments of Oncology and Human Genetics , McGill University , Montreal , QC , Canada.,j Department of Epidemiology, Biostatistics and Occupational Health , McGill University , Montreal , QC , Canada
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43
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Hemani G, Tilling K, Davey Smith G. Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genet 2017; 13:e1007081. [PMID: 29149188 PMCID: PMC5711033 DOI: 10.1371/journal.pgen.1007081] [Citation(s) in RCA: 1001] [Impact Index Per Article: 143.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Revised: 12/01/2017] [Accepted: 10/18/2017] [Indexed: 12/11/2022] Open
Abstract
Inference about the causal structure that induces correlations between two traits can be achieved by combining genetic associations with a mediation-based approach, as is done in the causal inference test (CIT). However, we show that measurement error in the phenotypes can lead to the CIT inferring the wrong causal direction, and that increasing sample sizes has the adverse effect of increasing confidence in the wrong answer. This problem is likely to be general to other mediation-based approaches. Here we introduce an extension to Mendelian randomisation, a method that uses genetic associations in an instrumentation framework, that enables inference of the causal direction between traits, with some advantages. First, it can be performed using only summary level data from genome-wide association studies; second, it is less susceptible to bias in the presence of measurement error or unmeasured confounding. We apply the method to infer the causal direction between DNA methylation and gene expression levels. Our results demonstrate that, in general, DNA methylation is more likely to be the causal factor, but this result is highly susceptible to bias induced by systematic differences in measurement error between the platforms, and by horizontal pleiotropy. We emphasise that, where possible, implementing MR and appropriate sensitivity analyses alongside other approaches such as CIT is important to triangulate reliable conclusions about causality. Understanding the causal relationships between pairs of traits is crucial for unravelling the causes of disease. To this end, results from genome-wide association studies are valuable because if a trait is known to be influenced by a genetic variant then this knowledge can be used to test the trait’s causal influences on other traits and diseases. Here we discuss scenarios where the nature of the genetic association with the causal trait can lead existing causal inference methods to give the wrong direction of causality. We introduce a new method that can be applied to summary level data and is potentially less susceptible to problems such as measurement error, and apply it to evaluate the causal relationships between DNA methylation levels and gene expression. While our results show that DNA methylation is more likely to be the causal factor, we point out that is it crucial to acknowledge that systematic differences in measurement error between the platforms could influence such conclusions.
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Affiliation(s)
- Gibran Hemani
- MRC Integrative Epidemiology Unit (IEU) at the University of Bristol, School of Social and Community Medicine, Bristol, United Kingdom
- * E-mail:
| | - Kate Tilling
- MRC Integrative Epidemiology Unit (IEU) at the University of Bristol, School of Social and Community Medicine, Bristol, United Kingdom
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU) at the University of Bristol, School of Social and Community Medicine, Bristol, United Kingdom
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Raina A, Zhao X, Grove ML, Bressler J, Gottesman RF, Guan W, Pankow JS, Boerwinkle E, Mosley TH, Fornage M. Cerebral white matter hyperintensities on MRI and acceleration of epigenetic aging: the atherosclerosis risk in communities study. Clin Epigenetics 2017; 9:21. [PMID: 28289478 PMCID: PMC5310061 DOI: 10.1186/s13148-016-0302-6] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Accepted: 12/12/2016] [Indexed: 02/03/2023] Open
Abstract
Background Cerebral white matter hyperintensities (WMH) on magnetic resonance imaging (MRI) are part of the spectrum of brain vascular injury accompanying aging and are associated with a substantial risk of stroke and dementia. We investigated the association of cerebral WMH burden on MRI with a DNA methylation-based biomarker of aging, termed DNA methylation age acceleration, which represents the deviation of the DNA methylation-predicted age from the chronologic age. Results In this cross-sectional observational study of 713 African-American participants of the Atherosclerosis Risk in Communities study, aged 51–73 years, estimates of predicted age were obtained based on two algorithms (Hannum et al. and Horvath) from DNA methylation measured using the Illumina HM450 array on genomic DNA extracted from blood. Age acceleration, calculated as the residual values from the regression of each of the predicted age measures onto the chronologic age, was significantly associated with WMH burden after accounting for chronologic age and sex, body mass index, systolic blood pressure, hypertension, diabetes, current smoking, and blood cell composition, and results were similar for either Hannum et al.- or Horvath-derived estimates (P = 0.016 and 0.026). An age acceleration increase by 1 year was associated with an increase of WMH burden by ~1 grade. To shed light on possible biological mechanisms underlying this association, we conducted a genome-wide association study of age acceleration and identified four loci harboring genes implicated in hemostasis, cell proliferation, protein degradation, and histone methylation. However, none of these loci were associated with WMH burden. Conclusions In this population-based study of middle-aged to older African-American adults, we report an association between accelerated epigenetic aging and increased WMH burden, independent of known risk factors, including chronologic age. Additional studies are needed to clarify whether DNA methylation age reflects biological mechanisms implicated in the aging of the cerebral white matter. Electronic supplementary material The online version of this article (doi:10.1186/s13148-016-0302-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Abhay Raina
- Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, 1825 Pressler Street, 77030 Houston, TX USA
| | - Xiaoping Zhao
- Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, 1825 Pressler Street, 77030 Houston, TX USA
| | - Megan L Grove
- Division of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX USA
| | - Jan Bressler
- Division of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX USA
| | - Rebecca F Gottesman
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD USA
| | - Weihua Guan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN USA
| | - James S Pankow
- Division of Epidemiology & Community Health, School of Public Health, University of Minnesota, Minneapolis, MN USA
| | - Eric Boerwinkle
- Division of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX USA
| | - Thomas H Mosley
- Division of Geriatrics, School of Medicine, University of Mississippi Medical Center, Jackson, MS USA
| | - Myriam Fornage
- Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, 1825 Pressler Street, 77030 Houston, TX USA.,Division of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX USA
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45
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Genome-wide measures of DNA methylation in peripheral blood and the risk of urothelial cell carcinoma: a prospective nested case-control study. Br J Cancer 2016; 115:664-73. [PMID: 27490804 PMCID: PMC5023776 DOI: 10.1038/bjc.2016.237] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2016] [Revised: 05/13/2016] [Accepted: 07/08/2016] [Indexed: 12/23/2022] Open
Abstract
Background: Global DNA methylation has been reported to be associated with urothelial cell carcinoma (UCC) by studies using blood samples collected at diagnosis. Using the Illumina HumanMethylation450 assay, we derived genome-wide measures of blood DNA methylation and assessed them for their prospective association with UCC risk. Methods: We used 439 case–control pairs from the Melbourne Collaborative Cohort Study matched on age, sex, country of birth, DNA sample type, and collection period. Conditional logistic regression was used to compute odds ratios (OR) of UCC risk per s.d. of each genome-wide measure of DNA methylation and 95% confidence intervals (CIs), adjusted for potential confounders. We also investigated associations by disease subtype, sex, smoking, and time since blood collection. Results: The risk of superficial UCC was decreased for individuals with higher levels of our genome-wide DNA methylation measure (OR=0.71, 95% CI: 0.54–0.94; P=0.02). This association was particularly strong for current smokers at sample collection (OR=0.47, 95% CI: 0.27–0.83). Intermediate levels of our genome-wide measure were associated with decreased risk of invasive UCC. Some variation was observed between UCC subtypes and the location and regulatory function of the CpGs included in the genome-wide measures of methylation. Conclusions: Higher levels of our genome-wide DNA methylation measure were associated with decreased risk of superficial UCC and intermediate levels were associated with reduced risk of invasive disease. These findings require replication by other prospective studies.
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46
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Reliability of DNA methylation measures from dried blood spots and mononuclear cells using the HumanMethylation450k BeadArray. Sci Rep 2016; 6:30317. [PMID: 27457678 PMCID: PMC4960587 DOI: 10.1038/srep30317] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2016] [Accepted: 07/04/2016] [Indexed: 01/29/2023] Open
Abstract
The reliability of methylation measures from the widely used HumanMethylation450 (HM450K) microarray has not been assessed for DNA from dried blood spots (DBS) or peripheral blood mononuclear cells (PBMC), nor for combined data from different studies. Repeated HM450K methylation measures in DNA from DBS and PBMC samples were available from participants in six case-control studies nested within the Melbourne Collaborative Cohort Study. Reliability was assessed for individual CpGs by calculating the intraclass correlation coefficient (ICC) based on technical replicates (samples repeated in a single study; 126 PBMC, 136 DBS) and study duplicates (samples repeated across studies; 280 PBMC, 769 DBS) using mixed-effects models. Reliability based on technical replicates was moderate for PBMC (median ICC = 0.42), but lower for DBS (median ICC = 0.20). Study duplicates gave lower ICCs than technical replicates. CpGs that were either highly methylated or unmethylated generally had lower ICCs, which appeared to be mostly related to their lower variability. The ICCs for global methylation measures were high, typically greater than 0.70. The reliability of methylation measures determined by the HM450K microarray is wide-ranging and depends primarily on the variability in methylation at individual CpG sites. The power of association studies is low for a substantial proportion of CpGs in the HM450K assay.
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47
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Chen J, Just AC, Schwartz J, Hou L, Jafari N, Sun Z, Kocher JPA, Baccarelli A, Lin X. CpGFilter: model-based CpG probe filtering with replicates for epigenome-wide association studies. Bioinformatics 2015; 32:469-71. [PMID: 26449931 DOI: 10.1093/bioinformatics/btv577] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2015] [Accepted: 09/30/2015] [Indexed: 12/28/2022] Open
Abstract
SUMMARY The development of the Infinium HumanMethylation450 BeadChip enables epigenome-wide association studies at a reduced cost. One observation of the 450K data is that many CpG sites the beadchip interrogates have very large measurement errors. Including these noisy CpGs will decrease the statistical power of detecting relevant associations due to multiple testing correction. We propose to use intra-class correlation coefficient (ICC), which characterizes the relative contribution of the biological variability to the total variability, to filter CpGs when technical replicates are available. We estimate the ICC based on a linear mixed effects model by pooling all the samples instead of using the technical replicates only. An ultra-fast algorithm has been developed to address the computational complexity and CpG filtering can be completed in minutes on a desktop computer for a 450K data set of over 1000 samples. Our method is very flexible and can accommodate any replicate design. Simulations and a real data application demonstrate that our whole-sample ICC method performs better than replicate-sample ICC or variance-based method. AVAILABILITY AND IMPLEMENTATION CpGFilter is implemented in R and publicly available under CRAN via the R package 'CpGFilter'. CONTACT chen.jun2@mayo.edu or xlin@hsph.harvard.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jun Chen
- Division of Biomedical Statistics and Informatics and Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115
| | - Allan C Just
- Department of Environmental Health, Harvard School of Public Health, Boston, MA 02115
| | - Joel Schwartz
- Department of Environmental Health, Harvard School of Public Health, Boston, MA 02115
| | - Lifang Hou
- Department of Preventive Medicine and the Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL 60208 and
| | - Nadereh Jafari
- Center for Genetic Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60208, USA
| | - Zhifu Sun
- Division of Biomedical Statistics and Informatics and Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905
| | - Jean-Pierre A Kocher
- Division of Biomedical Statistics and Informatics and Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905
| | - Andrea Baccarelli
- Department of Environmental Health, Harvard School of Public Health, Boston, MA 02115
| | - Xihong Lin
- Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115
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48
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Dugué PA, English DR, MacInnis RJ, Joo JE, Jung CH, Milne RL. The repeatability of DNA methylation measures may also affect the power of epigenome-wide association studies. Int J Epidemiol 2015; 44:1460-1. [PMID: 26342585 DOI: 10.1093/ije/dyv189] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
| | - Dallas R English
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, VIC, Australia, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
| | - Robert J MacInnis
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, VIC, Australia, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
| | - Jihoon E Joo
- Genetic Epidemiology Laboratory, Department of Pathology, University of Melbourne, Parkville, VIC, Australia and
| | - Chol-Hee Jung
- VLSCI Life Sciences Computation Centre, University of Melbourne, Carlton VIC, Australia
| | - Roger L Milne
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, VIC, Australia, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
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49
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Demerath EW, Guan W, Grove ML, Aslibekyan S, Mendelson M, Zhou YH, Hedman ÅK, Sandling JK, Li LA, Irvin MR, Zhi D, Deloukas P, Liang L, Liu C, Bressler J, Spector TD, North K, Li Y, Absher DM, Levy D, Arnett DK, Fornage M, Pankow JS, Boerwinkle E. Epigenome-wide association study (EWAS) of BMI, BMI change and waist circumference in African American adults identifies multiple replicated loci. Hum Mol Genet 2015; 24:4464-79. [PMID: 25935004 DOI: 10.1093/hmg/ddv161] [Citation(s) in RCA: 230] [Impact Index Per Article: 25.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2015] [Accepted: 04/13/2015] [Indexed: 02/06/2023] Open
Abstract
Obesity is an important component of the pathophysiology of chronic diseases. Identifying epigenetic modifications associated with elevated adiposity, including DNA methylation variation, may point to genomic pathways that are dysregulated in numerous conditions. The Illumina 450K Bead Chip array was used to assay DNA methylation in leukocyte DNA obtained from 2097 African American adults in the Atherosclerosis Risk in Communities (ARIC) study. Mixed-effects regression models were used to test the association of methylation beta value with concurrent body mass index (BMI) and waist circumference (WC), and BMI change, adjusting for batch effects and potential confounders. Replication using whole-blood DNA from 2377 White adults in the Framingham Heart Study and CD4+ T cell DNA from 991 Whites in the Genetics of Lipid Lowering Drugs and Diet Network Study was followed by testing using adipose tissue DNA from 648 women in the Multiple Tissue Human Expression Resource cohort. Seventy-six BMI-related probes, 164 WC-related probes and 8 BMI change-related probes passed the threshold for significance in ARIC (P < 1 × 10(-7); Bonferroni), including probes in the recently reported HIF3A, CPT1A and ABCG1 regions. Replication using blood DNA was achieved for 37 BMI probes and 1 additional WC probe. Sixteen of these also replicated in adipose tissue, including 15 novel methylation findings near genes involved in lipid metabolism, immune response/cytokine signaling and other diverse pathways, including LGALS3BP, KDM2B, PBX1 and BBS2, among others. Adiposity traits are associated with DNA methylation at numerous CpG sites that replicate across studies despite variation in tissue type, ethnicity and analytic approaches.
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Affiliation(s)
- Ellen W Demerath
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN 55454, USA,
| | - Weihua Guan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55454, USA
| | - Megan L Grove
- Human Genetics Center, School of Public Health, University of Texas Health Sciences Center at Houston, Houston, TX 77030, USA
| | | | - Michael Mendelson
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20824, USA, Framingham Heart Study, Framingham, MA 01702, USA, Department of Cardiology, Boston Children's Hospital, Boston, MA 02215, USA
| | - Yi-Hui Zhou
- Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA
| | - Åsa K Hedman
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK, Department of Medical Sciences, Molecular Medicine and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Johanna K Sandling
- Department of Medical Sciences, Molecular Medicine and Science for Life Laboratory, Uppsala University, Uppsala, Sweden, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK
| | - Li-An Li
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | | | - Degui Zhi
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Panos Deloukas
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK, Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders (PACER-HD), King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Liming Liang
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20824, USA, Framingham Heart Study, Framingham, MA 01702, USA, Departments of Epidemiology and Biostatistics, School of Public Health, Harvard University, Boston, MA 02115, USA
| | - Chunyu Liu
- Framingham Heart Study, Framingham, MA 01702, USA, Department of Biostatistics, Boston University, Boston, MA 02118, USA
| | - Jan Bressler
- Human Genetics Center, School of Public Health, University of Texas Health Sciences Center at Houston, Houston, TX 77030, USA
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK
| | - Kari North
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Yun Li
- Department of Genetics, Department of Biostatistics and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Devin M Absher
- Hudson Alpha Institute for Biotechnology, Huntsville, AL 34806, USA
| | - Daniel Levy
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20824, USA, Framingham Heart Study, Framingham, MA 01702, USA
| | | | - Myriam Fornage
- Human Genetics Center, School of Public Health, University of Texas Health Sciences Center at Houston, Houston, TX 77030, USA, Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - James S Pankow
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN 55454, USA
| | - Eric Boerwinkle
- Human Genetics Center, School of Public Health, University of Texas Health Sciences Center at Houston, Houston, TX 77030, USA, Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
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