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Ensink JBM, Keding TJ, Henneman P, Venema A, Papale LA, Alisch RS, Westerman Y, van Wingen G, Zantvoord J, Middeldorp CM, Mannens MMAM, Herringa RJ, Lindauer RJL. Differential DNA Methylation Is Associated With Hippocampal Abnormalities in Pediatric Posttraumatic Stress Disorder. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2021; 6:1063-1070. [PMID: 33964519 DOI: 10.1016/j.bpsc.2021.04.016] [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: 01/19/2021] [Revised: 04/01/2021] [Accepted: 04/26/2021] [Indexed: 11/18/2022]
Abstract
BACKGROUND Recent findings in neuroimaging and epigenetics offer important insights into brain structures and biological pathways of altered gene expression associated with posttraumatic stress disorder (PTSD). However, it is unknown to what extent epigenetic mechanisms are associated with PTSD and its neurobiology in youth. METHODS In this study, we combined a methylome-wide association study and structural neuroimaging measures in a Dutch cohort of youths with PTSD (8-18 years of age). We aimed to replicate findings in a similar independent U.S. cohort. RESULTS We found significant methylome-wide associations for pediatric PTSD (false discovery rate p < .05) compared with non-PTSD control groups (traumatized and nontraumatized youths). Methylation differences on nine genes were replicated, including genes related to glucocorticoid functioning. In both cohorts, methylation on OLFM3 gene was further associated with anterior hippocampal volume. CONCLUSIONS These findings point to molecular pathways involved in inflammation, stress response, and neuroplasticity as potential contributors to neural abnormalities and provide potentially unique biomarkers and treatment targets for pediatric PTSD.
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Affiliation(s)
- Judith B M Ensink
- Genome Diagnostics Laboratory, Department of Clinical Genetics, Amsterdam University Medical Center, location AMC, Amsterdam, the Netherlands; Department of Child and Adolescent Psychiatry, Amsterdam University Medical Center, location AMC, Amsterdam, the Netherlands; Academic Centre for Child and Adolescent Psychiatry, De Bascule, Amsterdam, the Netherlands; Amsterdam Reproduction and Development Research Institute, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Taylor J Keding
- Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin; Neuroscience Training Program, University of Wisconsin-Madison, Madison, Wisconsin
| | - Peter Henneman
- Genome Diagnostics Laboratory, Department of Clinical Genetics, Amsterdam University Medical Center, location AMC, Amsterdam, the Netherlands; Amsterdam Reproduction and Development Research Institute, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Andrea Venema
- Genome Diagnostics Laboratory, Department of Clinical Genetics, Amsterdam University Medical Center, location AMC, Amsterdam, the Netherlands; Amsterdam Reproduction and Development Research Institute, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Ligia A Papale
- Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin; Department of Neurological Surgery, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Reid S Alisch
- Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin; Department of Neurological Surgery, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Yousha Westerman
- Academic Centre for Child and Adolescent Psychiatry, De Bascule, Amsterdam, the Netherlands
| | - Guido van Wingen
- Department of Psychiatry, Amsterdam University Medical Center, location AMC, Amsterdam, the Netherlands
| | - Jasper Zantvoord
- Department of Psychiatry, Amsterdam University Medical Center, location AMC, Amsterdam, the Netherlands
| | - Christel M Middeldorp
- Children's Health Research Centre, University of Queensland, Brisbane, Queensland, Australia
| | - Marcel M A M Mannens
- Genome Diagnostics Laboratory, Department of Clinical Genetics, Amsterdam University Medical Center, location AMC, Amsterdam, the Netherlands; Amsterdam Reproduction and Development Research Institute, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Ryan J Herringa
- Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin; Neuroscience Training Program, University of Wisconsin-Madison, Madison, Wisconsin.
| | - Ramon J L Lindauer
- Department of Child and Adolescent Psychiatry, Amsterdam University Medical Center, location AMC, Amsterdam, the Netherlands; Academic Centre for Child and Adolescent Psychiatry, De Bascule, Amsterdam, the Netherlands
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Data-Driven-Based Approach to Identifying Differentially Methylated Regions Using Modified 1D Ising Model. BIOMED RESEARCH INTERNATIONAL 2019; 2018:1070645. [PMID: 30581840 PMCID: PMC6276520 DOI: 10.1155/2018/1070645] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Revised: 10/15/2018] [Accepted: 10/31/2018] [Indexed: 12/19/2022]
Abstract
Background DNA methylation is essential for regulating gene expression, and the changes of DNA methylation status are commonly discovered in disease. Therefore, identification of differentially methylation patterns, especially differentially methylated regions (DMRs), in two different groups is important for understanding the mechanism of complex diseases. Few tools exist for DMR identification through considering features of methylation data, but there is no comprehensive integration of the characteristics of DNA methylation data in current methods. Results Accounting for the characteristics of methylation data, such as the correlation characteristics of neighboring CpG sites and the high heterogeneity of DNA methylation data, we propose a data-driven approach for DMR identification through evaluating the energy of single site using modified 1D Ising model. Applied to both simulated and publicly available datasets, our approach is compared with other popular methods in terms of performance. Simulated results show that our method is more sensitive than competing methods. Applied to the real data, our method can identify more common DMRs than DMRcate, ProbeLasso, and Wang's methods with a high overlapping ratio. Also, the necessity of integrating the heterogeneity and correlation characteristics in identifying DMR is shown through comparing results with only considering mean or variance signals and without considering relationship of neighboring CpG sites, respectively. Through analyzing the number of DMRs identified in real data located in different genomic regions, we find that about 90% DMRs are located in CGI which always regulates the expression of genes. It may help us understand the functional effect of DNA methylation on disease.
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Wang C, Shen Q, Du L, Xu J, Zhang H. armDNA: A functional beta model for detecting age-related genomewide DNA methylation marks. Stat Methods Med Res 2018; 27:2627-2640. [PMID: 30103660 DOI: 10.1177/0962280216683571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
DNA methylation has been shown to play an important role in many complex diseases. The rapid development of high-throughput DNA methylation scan technologies provides great opportunities for genomewide DNA methylation-disease association studies. As methylation is a dynamic process involving time, it is quite plausible that age contributes to its variation to a large extent. Therefore, in analyzing genomewide DNA methylation data, it is important to identify age-related DNA methylation marks and delineate their functional relationship. This helps us to better understand the underlying biological mechanism and facilitate early diagnosis and prognosis analysis of complex diseases. We develop a functional beta model for analyzing DNA methylation data and detecting age-related DNA methylation marks on the whole genome by naturally taking sampling scheme into account and accommodating flexible age-methylation dynamics. We focus on DNA methylation data obtained through the widely used bisulfite conversion technique and propose to use a beta model to relate the DNA methylation level to the age. Adjusting for certain confounders, the functional age effect is left completely unspecified, offering great flexibility and allowing extra data dynamics. An efficient algorithm is developed for estimating unknown parameters, and the Wald test is used to detect age-related DNA methylation marks. Simulation studies and several real data applications were provided to demonstrate the performance of the proposed method.
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Affiliation(s)
- Chenyang Wang
- 1 State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, P. R. China.,2 Institute of Biostatistics, School of Life Sciences, Fudan University, P. R. China
| | - Qi Shen
- 3 School of Mathematics, Sun Yat-Sen University, P. R. China
| | - Li Du
- 1 State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, P. R. China.,2 Institute of Biostatistics, School of Life Sciences, Fudan University, P. R. China
| | - Jinfeng Xu
- 4 Department of Statistics and Actuarial Science, The University of Hong Kong, P. R. China
| | - Hong Zhang
- 1 State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, P. R. China.,2 Institute of Biostatistics, School of Life Sciences, Fudan University, P. R. China
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Chen Z, Lu Y, Lin T, Liu Q, Wang K. Gene-based genetic association test with adaptive optimal weights. Genet Epidemiol 2017; 42:95-103. [DOI: 10.1002/gepi.22098] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Accepted: 10/22/2017] [Indexed: 12/13/2022]
Affiliation(s)
- Zhongxue Chen
- Department of Epidemiology and Biostatistics; School of Public Health; Indiana University Bloomington; Bloomington Indiana United States of America
| | - Yan Lu
- Department of Mathematics and Statistics; University of New Mexico; Albuquerque New Mexico United States of America
| | - Tong Lin
- The Key Laboratory of Machine Perception (Ministry of Education); School of EECS; Peking University; Beijing China
| | - Qingzhong Liu
- Department of Computer Science; Sam Houston State University; Huntsville Texas United States of America
| | - Kai Wang
- Department of Biostatistics; College of Public Health; University of Iowa; Iowa City Iowa United States of America
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Schildknecht K, Olek S, Dickhaus T. Simultaneous statistical inference for epigenetic data. PLoS One 2015; 10:e0125587. [PMID: 25965389 PMCID: PMC4428829 DOI: 10.1371/journal.pone.0125587] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2015] [Accepted: 03/24/2015] [Indexed: 11/28/2022] Open
Abstract
Epigenetic research leads to complex data structures. Since parametric model assumptions for the distribution of epigenetic data are hard to verify we introduce in the present work a nonparametric statistical framework for two-group comparisons. Furthermore, epigenetic analyses are often performed at various genetic loci simultaneously. Hence, in order to be able to draw valid conclusions for specific loci, an appropriate multiple testing correction is necessary. Finally, with technologies available for the simultaneous assessment of many interrelated biological parameters (such as gene arrays), statistical approaches also need to deal with a possibly unknown dependency structure in the data. Our statistical approach to the nonparametric comparison of two samples with independent multivariate observables is based on recently developed multivariate multiple permutation tests. We adapt their theory in order to cope with families of hypotheses regarding relative effects. Our results indicate that the multivariate multiple permutation test keeps the pre-assigned type I error level for the global null hypothesis. In combination with the closure principle, the family-wise error rate for the simultaneous test of the corresponding locus/parameter-specific null hypotheses can be controlled. In applications we demonstrate that group differences in epigenetic data can be detected reliably with our methodology.
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Affiliation(s)
| | - Sven Olek
- Ivana Türbachova Laboratory for Epigenetics, Epiontis GmbH, Berlin, Germany
| | - Thorsten Dickhaus
- Institute for Statistics, University of Bremen, Bremen, Germany
- * E-mail:
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Zhang Y, Zhang J. Identification of functionally methylated regions based on discriminant analysis through integrating methylation and gene expression data. MOLECULAR BIOSYSTEMS 2015; 11:1786-93. [DOI: 10.1039/c5mb00141b] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
DNA methylation is essential not only in cellular differentiation but also in diseases.
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Affiliation(s)
- Yuanyuan Zhang
- School of Computer Science and Technology
- Xidian University
- Xi'an 710071
- China
| | - Junying Zhang
- School of Computer Science and Technology
- Xidian University
- Xi'an 710071
- China
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Chen Z, Yang W, Liu Q, Yang JY, Li J, Yang M. A new statistical approach to combining p-values using gamma distribution and its application to genome-wide association study. BMC Bioinformatics 2014; 15 Suppl 17:S3. [PMID: 25559433 PMCID: PMC4304193 DOI: 10.1186/1471-2105-15-s17-s3] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Background Combining information from different studies is an important and useful practice in bioinformatics, including genome-wide association study, rare variant data analysis and other set-based analyses. Many statistical methods have been proposed to combine p-values from independent studies. However, it is known that there is no uniformly most powerful test under all conditions; therefore, finding a powerful test in specific situation is important and desirable. Results In this paper, we propose a new statistical approach to combining p-values based on gamma distribution, which uses the inverse of the p-value as the shape parameter in the gamma distribution. Conclusions Simulation study and real data application demonstrate that the proposed method has good performance under some situations.
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Kuan PF. Covariate adjusted differential variability analysis of DNA methylation with propensity score method. Stat Appl Genet Mol Biol 2014; 13:645-58. [PMID: 25332296 DOI: 10.1515/sagmb-2013-0072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
It has been proposed recently that differentially variable CpG methylation (DVC) may contribute to transcriptional aberrations in human diseases. In large scale epigenetic studies, potential confounders could affect the observed methylation variabilities and need to be accounted for. In this paper, we develop a robust statistical model for differential variability DVC analysis that accounts for potential confounding covariates by utilizing the propensity score method. Our method is based on a weighted score test on strata generated propensity score stratification. To the best of our knowledge, this is the first proposed statistical method for detecting DVCs that adjusts for confounding covariates. We show that this method is robust against model misspecification and achieves good operating characteristics based on extensive simulations and a case study.
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Chen Z, Ng HKT, Li J, Liu Q, Huang H. Detecting associated single-nucleotide polymorphisms on the X chromosome in case control genome-wide association studies. Stat Methods Med Res 2014; 26:567-582. [PMID: 25253574 DOI: 10.1177/0962280214551815] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
In the past decade, hundreds of genome-wide association studies have been conducted to detect the significant single-nucleotide polymorphisms that are associated with certain diseases. However, most of the data from the X chromosome were not analyzed and only a few significant associated single-nucleotide polymorphisms from the X chromosome have been identified from genome-wide association studies. This is mainly due to the lack of powerful statistical tests. In this paper, we propose a novel statistical approach that combines the information of single-nucleotide polymorphisms on the X chromosome from both males and females in an efficient way. The proposed approach avoids the need of making strong assumptions about the underlying genetic models. Our proposed statistical test is a robust method that only makes the assumption that the risk allele is the same for both females and males if the single-nucleotide polymorphism is associated with the disease for both genders. Through simulation study and a real data application, we show that the proposed procedure is robust and have excellent performance compared to existing methods. We expect that many more associated single-nucleotide polymorphisms on the X chromosome will be identified if the proposed approach is applied to current available genome-wide association studies data.
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Affiliation(s)
- Zhongxue Chen
- 1 Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, Bloomington, IN, USA
| | - Hon Keung Tony Ng
- 2 Department of Statistical Science, Southern Methodist University, Dallas, TX, USA
| | - Jing Li
- 1 Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, Bloomington, IN, USA
| | - Qingzhong Liu
- 3 Department of Computer Science, Sam Houston State Uiversity, Huntsville, TX, USA
| | - Hanwen Huang
- 4 Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA, USA
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11
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Chen Z, Huang H, Liu Q. Detecting differentially methylated loci for multiple treatments based on high-throughput methylation data. BMC Bioinformatics 2014; 15:142. [PMID: 24884464 PMCID: PMC4026834 DOI: 10.1186/1471-2105-15-142] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2013] [Accepted: 05/06/2014] [Indexed: 12/31/2022] Open
Abstract
Background Because of its important effects, as an epigenetic factor, on gene expression and disease development, DNA methylation has drawn much attention from researchers. Detecting differentially methylated loci is an important but challenging step in studying the regulatory roles of DNA methylation in a broad range of biological processes and diseases. Several statistical approaches have been proposed to detect significant methylated loci; however, most of them were designed specifically for case-control studies. Results Noticing that the age is associated with methylation level and the methylation data are not normally distributed, in this paper, we propose a nonparametric method to detect differentially methylated loci under multiple conditions with trend for Illumina Array Methylation data. The nonparametric method, Cuzick test is used to detect the differences among treatment groups with trend for each age group; then an overall p-value is calculated based on the method of combining those independent p-values each from one age group. Conclusions We compare the new approach with other methods using simulated and real data. Our study shows that the proposed method outperforms other methods considered in this paper in term of power: it detected more biological meaningful differentially methylated loci than others.
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Affiliation(s)
- Zhongxue Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, 1025 E, 7th street, PH C104, Bloomington, IN 47405, USA.
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Chen Z, Nadarajah S. On the optimally weighted z-test for combining probabilities from independent studies. Comput Stat Data Anal 2014. [DOI: 10.1016/j.csda.2013.09.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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Zhang Y, Zhang J, Shang J. Quantitative identification of differentially methylated loci based on relative entropy for matched case–control data. Epigenomics 2013; 5:631-43. [DOI: 10.2217/epi.13.58] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Aim: As an important epigenetic modification, DNA methylation plays a critical role in regulating multiple biological processes and diseases. Many efforts have been devoted to identifying differentially methylated loci (DML) between cases and controls. Materials & methods: However, most present methods are statistical and are limited in handling methylation data with characteristics of high heterogeneity and non-normal distribution. Here, a quantitative method, quantitative DML (QDML), based on modified relative entropy is introduced to face these challenges, which can identify DML, hypermethylated loci and hypomethylated loci simultaneously. QDML, compared with some statistical methods, does not require a presupposed distribution of methylation data. Furthermore, QDML is more powerful in handling highly heterogeneous data, owing to the difference in sensitivity on every matched sample pair in case–control groups rather than the overall difference of all samples. Results: Simulation studies and real-data application show that QDML has a higher accuracy and a lower false-positive rate when identifying DML than statistical methods. Conclusion: QDML is developed to identify DML based on relative entropy that can quantify the difference in methylation status between cases and controls. Its applications are not limited to methylation data and can be extended to other case–control studies.
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Affiliation(s)
- Yuanyuan Zhang
- School of Computer Science & Technology, Xidian University, Xi’an 710071, China
| | - Junying Zhang
- School of Computer Science & Technology, Xidian University, Xi’an 710071, China
| | - Junliang Shang
- School of Computer Science, Qufu Normal University, Rizwhao 276826, China
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Cruickshank MN, Oshlack A, Theda C, Davis PG, Martino D, Sheehan P, Dai Y, Saffery R, Doyle LW, Craig JM. Analysis of epigenetic changes in survivors of preterm birth reveals the effect of gestational age and evidence for a long term legacy. Genome Med 2013; 5:96. [PMID: 24134860 PMCID: PMC3978871 DOI: 10.1186/gm500] [Citation(s) in RCA: 94] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2013] [Accepted: 09/26/2013] [Indexed: 02/08/2023] Open
Abstract
Background Preterm birth confers a high risk of adverse long term health outcomes for survivors, yet the underlying molecular mechanisms are unclear. We hypothesized that effects of preterm birth can be mediated through measurable epigenomic changes throughout development. We therefore used a longitudinal birth cohort to measure the epigenetic mark of DNA methylation at birth and 18 years comparing survivors of extremely preterm birth with infants born at term. Methods Using 12 extreme preterm birth cases and 12 matched, term controls, we extracted DNA from archived neonatal blood spots and blood collected in a similar way at 18 years of age. DNA methylation was measured at 347,789 autosomal locations throughout the genome using Infinium HM450 arrays. Representative methylation differences were confirmed by Sequenom MassArray EpiTYPER. Results At birth we found 1,555 sites with significant differences in methylation between term and preterm babies. At 18 years of age, these differences had largely resolved, suggesting that DNA methylation differences at birth are mainly driven by factors relating to gestational age, such as cell composition and/or maturity. Using matched longitudinal samples, we found evidence for an epigenetic legacy associated with preterm birth, identifying persistent methylation differences at ten genomic loci. Longitudinal comparisons of DNA methylation at birth and 18 years uncovered a significant overlap between sites that were differentially-methylated at birth and those that changed with age. However, we note that overlapping sites may either differ in the same (300/1,555) or opposite (431/1,555) direction during gestation and aging respectively. Conclusions We present evidence for widespread methylation differences between extreme preterm and term infants at birth that are largely resolved by 18 years of age. These results are consistent with methylation changes associated with blood cell development, cellular composition, immune induction and age at these time points. Finally, we identified ten probes significantly associated with preterm individuals and with greater than 5% methylation discordance at birth and 18 years that may reflect a long term epigenetic legacy of preterm birth.
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Affiliation(s)
- Mark N Cruickshank
- Early Life Epigenetics Group, Murdoch Childrens Research Institute (MCRI), Royal Children's Hospital, Flemington Road, Parkville, Victoria 3052, Australia ; Department of Paediatrics, University of Melbourne, Royal Children's Hospital, Flemington Road, Parkville, Victoria 3052, Australia ; Present address: Telethon Institute for Child Health Research, University of Western Australia, 100 Roberts Road, Subiaco, WA 6008, Australia
| | - Alicia Oshlack
- Department of Paediatrics, University of Melbourne, Royal Children's Hospital, Flemington Road, Parkville, Victoria 3052, Australia ; Bioinformatics Group, MCRI, Royal Children's Hospital, Flemington Road, Parkville, Victoria 3052, Australia
| | - Christiane Theda
- Early Life Epigenetics Group, Murdoch Childrens Research Institute (MCRI), Royal Children's Hospital, Flemington Road, Parkville, Victoria 3052, Australia ; Neonatal Services, Royal Women's Hospital, Parkville, Victoria 3052, Australia ; Department of Obstetrics and Gynaecology, University of Melbourne, Royal Women's Hospital, Parkville, Victoria 3052, Australia
| | - Peter G Davis
- Department of Paediatrics, University of Melbourne, Royal Children's Hospital, Flemington Road, Parkville, Victoria 3052, Australia ; Neonatal Services, Royal Women's Hospital, Parkville, Victoria 3052, Australia ; Department of Obstetrics and Gynaecology, University of Melbourne, Royal Women's Hospital, Parkville, Victoria 3052, Australia
| | - David Martino
- Cancer and Developmental Epigenetics Group, MCRI, Royal Children's Hospital, Flemington Road, Parkville, Victoria 3052, Australia
| | - Penelope Sheehan
- Department of Obstetrics and Gynaecology, University of Melbourne, Royal Women's Hospital, Parkville, Victoria 3052, Australia
| | - Yun Dai
- Early Life Epigenetics Group, Murdoch Childrens Research Institute (MCRI), Royal Children's Hospital, Flemington Road, Parkville, Victoria 3052, Australia
| | - Richard Saffery
- Department of Paediatrics, University of Melbourne, Royal Children's Hospital, Flemington Road, Parkville, Victoria 3052, Australia ; Cancer and Developmental Epigenetics Group, MCRI, Royal Children's Hospital, Flemington Road, Parkville, Victoria 3052, Australia
| | - Lex W Doyle
- Department of Paediatrics, University of Melbourne, Royal Children's Hospital, Flemington Road, Parkville, Victoria 3052, Australia ; Neonatal Services, Royal Women's Hospital, Parkville, Victoria 3052, Australia ; Department of Obstetrics and Gynaecology, University of Melbourne, Royal Women's Hospital, Parkville, Victoria 3052, Australia
| | - Jeffrey M Craig
- Early Life Epigenetics Group, Murdoch Childrens Research Institute (MCRI), Royal Children's Hospital, Flemington Road, Parkville, Victoria 3052, Australia ; Department of Paediatrics, University of Melbourne, Royal Children's Hospital, Flemington Road, Parkville, Victoria 3052, Australia
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Xu H, Podolsky RH, Ryu D, Wang X, Su S, Shi H, George V. A method to detect differentially methylated loci with next-generation sequencing. Genet Epidemiol 2013; 37:377-82. [PMID: 23554163 DOI: 10.1002/gepi.21726] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2012] [Revised: 01/09/2013] [Accepted: 02/24/2013] [Indexed: 01/07/2023]
Abstract
Epigenetic changes, especially DNA methylation at CpG loci have important implications in cancer and other complex diseases. With the development of next-generation sequencing (NGS), it is feasible to generate data to interrogate the difference in methylation status for genome-wide loci using case-control design. However, a proper and efficient statistical test is lacking. There are several challenges. First, unlike methylation experiments using microarrays, where there is one measure of methylation for one individual at a particular CpG site, here we have the counts of methylation allele and unmethylation allele for each individual. Second, due to the nature of sample preparation, the measured methylation reflects the methylation status of a mixture of cells involved in sample preparation. Therefore, the underlying distribution of the measured methylation level is unknown, and a robust test is more desirable than parametric approach. Third, currently NGS measures methylation at over 2 million CpG sites. Any statistical tests have to be computationally efficient in order to be applied to the NGS data. Taking these challenges into account, we propose a test for differential methylation based on clustered data analysis by modeling the methylation counts. We performed simulations to show that it is robust under several distributions for the measured methylation levels. It has good power and is computationally efficient. Finally, we apply the test to our NGS data on chronic lymphocytic leukemia. The results indicate that it is a promising and practical test.
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Affiliation(s)
- Hongyan Xu
- Department of Biostatistics and Epidemiology, Georgia Health Sciences University, Augusta, GA 30912-4900, USA.
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Li P, Demirci F, Mahalingam G, Demirci C, Nakano M, Meyers BC. An integrated workflow for DNA methylation analysis. J Genet Genomics 2013; 40:249-60. [PMID: 23706300 DOI: 10.1016/j.jgg.2013.03.010] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2013] [Revised: 03/25/2013] [Accepted: 03/25/2013] [Indexed: 10/27/2022]
Abstract
The analysis of cytosine methylation provides a new way to assess and describe epigenetic regulation at a whole-genome level in many eukaryotes. DNA methylation has a demonstrated role in the genome stability and protection, regulation of gene expression and many other aspects of genome function and maintenance. BS-seq is a relatively unbiased method for profiling the DNA methylation, with a resolution capable of measuring methylation at individual cytosines. Here we describe, as an example, a workflow to handle DNA methylation analysis, from BS-seq library preparation to the data visualization. We describe some applications for the analysis and interpretation of these data. Our laboratory provides public access to plant DNA methylation data via visualization tools available at our "Next-Gen Sequence" websites (http://mpss.udel.edu), along with small RNA, RNA-seq and other data types.
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Affiliation(s)
- Pingchuan Li
- Department of Plant & Soil Sciences, Delaware Biotechnology Institute, University of Delaware, Newark, DE 19711, USA
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Huang H, Chen Z, Huang X. Age-adjusted nonparametric detection of differential DNA methylation with case-control designs. BMC Bioinformatics 2013; 14:86. [PMID: 23497201 PMCID: PMC3599607 DOI: 10.1186/1471-2105-14-86] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2012] [Accepted: 02/20/2013] [Indexed: 12/31/2022] Open
Abstract
Background DNA methylation profiles differ among disease types and, therefore, can be used in disease diagnosis. In addition, large-scale whole genome DNA methylation data offer tremendous potential in understanding the role of DNA methylation in normal development and function. However, due to the unique feature of the methylation data, powerful and robust statistical methods are very limited in this area. Results In this paper, we proposed and examined a new statistical method to detect differentially methylated loci for case control designs that is fully nonparametric and does not depend on any assumption for the underlying distribution of the data. Moreover, the proposed method adjusts for the age effect that has been shown to be highly correlated with DNA methylation profiles. Using simulation studies and a real data application, we have demonstrated the advantages of our method over existing commonly used methods. Conclusions Compared to existing methods, our method improved the detection power for differentially methylated loci for case control designs and controlled the type I error well. Its applications are not limited to methylation data; it can be extended to many other case–control studies.
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Affiliation(s)
- Hanwen Huang
- Department of Epidemiology and Biostatistics, University of Georgia, Athens, GA 30605, USA
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Chen Z, Huang H, Liu J, Tony Ng HK, Nadarajah S, Huang X, Deng Y. Detecting differentially methylated loci for Illumina Array methylation data based on human ovarian cancer data. BMC Med Genomics 2013; 6 Suppl 1:S9. [PMID: 23369576 PMCID: PMC3552689 DOI: 10.1186/1755-8794-6-s1-s9] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Background It is well known that DNA methylation, as an epigenetic factor, has an important effect on gene expression and disease development. Detecting differentially methylated loci under different conditions, such as cancer types or treatments, is of great interest in current research as it is important in cancer diagnosis and classification. However, inappropriate testing approaches can result in large false positives and/or false negatives. Appropriate and powerful statistical methods are desirable but very limited in the literature. Results In this paper, we propose a nonparametric method to detect differentially methylated loci under multiple conditions for Illumina Array Methylation data. We compare the new method with other methods using simulated and real data. Our study shows that the proposed one outperforms other methods considered in this paper. Conclusions Due to the unique feature of the Illumina Array Methylation data, commonly used statistical tests will lose power or give misleading results. Therefore, appropriate statistical methods are crucial for this type of data. Powerful statistical approaches remain to be developed. Availability R codes are available upon request.
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Affiliation(s)
- Zhongxue Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, 1025 E, 7th Street, Bloomington, IN 47405, USA.
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Ahn S, Wang T. A powerful statistical method for identifying differentially methylated markers in complex diseases. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2013:69-79. [PMID: 23424113 PMCID: PMC3621641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
DNA methylation is an important epigenetic modification that regulates transcriptional expression and plays an important role in complex diseases, such as cancer. Genome-wide methylation patterns have unique features and hence require the development of new analytic approaches. One important feature is that methylation levels in disease tissues often differ from those in normal tissues with respect to both average and variability. In this paper, we propose a new score test to identify methylation markers of disease. This approach simultaneously utilizes information from the first and second moments of methylation distribution to improve statistical efficiency. Because the proposed score test is derived from a generalized regression model, it can be used for analyzing both categorical and continuous disease phenotypes, and for adjusting for covariates. We evaluate the performance of the proposed method and compare it to other tests including the most commonlyused t-test through simulations. The simulation results show that the validity of the proposed method is robust to departures from the normal assumption of methylation levels and can be substantially more powerful than the t-test in the presence of heterogeneity of methylation variability between disease and normal tissues. We demonstrate our approach by analyzing the methylation dataset of an ovarian cancer study and identify novel methylation loci not identified by the t-test.
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Zhang H, Herman AI, Kranzler HR, Anton RF, Zhao H, Zheng W, Gelernter J. Array-based profiling of DNA methylation changes associated with alcohol dependence. Alcohol Clin Exp Res 2013; 37 Suppl 1:E108-15. [PMID: 22924764 PMCID: PMC3511647 DOI: 10.1111/j.1530-0277.2012.01928.x] [Citation(s) in RCA: 78] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2012] [Accepted: 06/05/2012] [Indexed: 12/01/2022]
Abstract
BACKGROUND Epigenetic regulation through DNA methylation may influence vulnerability to numerous disorders, including alcohol dependence (AD). METHODS Peripheral blood DNA methylation levels of 384 CpGs in the promoter regions of 82 candidate genes were examined in 285 African Americans (AAs; 141 AD cases and 144 controls) and 249 European Americans (EAs; 144 AD cases and 105 controls) using Illumina GoldenGate Methylation Array assays. Association of AD and DNA methylation changes was analyzed using multivariate analyses of covariance with frequency of intoxication, sex, age, and ancestry proportion as covariates. CpGs showing significant methylation alterations in AD cases were further examined in a replication sample (49 EA cases and 32 EA controls) using Sequenom's MassARRAY EpiTYPER technology. RESULTS In AAs, 2 CpGs in 2 genes (GABRB3 and POMC) were hypermethylated in AD cases compared with controls (p ≤ 0.001). In EAs, 6 CpGs in 6 genes (HTR3A, NCAM1, DRD4, MBD3, HTR2B, and GRIN1) were hypermethylated in AD cases compared with controls (p ≤ 0.001); CpG cg08989585 in the HTR3A promoter region showed a significantly higher methylation level in EA cases than in EA controls after Bonferroni correction (p = 0.00007). Additionally, methylation levels of 6 CpGs (including cg08989585) in the HTR3A promoter region were analyzed in the replication sample. Although the 6 HTR3A promoter CpGs did not show significant methylation differences between EA cases and EA controls (p = 0.067 to 0.877), the methylation level of CpG cg08989585 was nonsignificantly higher in EA cases (26.9%) than in EA controls (18.6%; p = 0.139). CONCLUSIONS The findings from this study suggest that DNA methylation profile appears to be associated with AD in a population-specific way and the predisposition to AD may result from a complex interplay of genetic variation and epigenetic modifications.
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Affiliation(s)
- Huiping Zhang
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut 06516, USA.
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Abstract
DNA methylation is an epigenetic mark that has suspected regulatory roles in a broad range of biological processes and diseases. The technology is now available for studying DNA methylation genome-wide, at a high resolution and in a large number of samples. This Review discusses relevant concepts, computational methods and software tools for analysing and interpreting DNA methylation data. It focuses not only on the bioinformatic challenges of large epigenome-mapping projects and epigenome-wide association studies but also highlights software tools that make genome-wide DNA methylation mapping more accessible for laboratories with limited bioinformatics experience.
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Affiliation(s)
- Christoph Bock
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria.
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