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作者： Yacas, Clifford McMaster University

学位级别：硕士

DNA methylation plays a key role in disease analysis, especially for studies that compare known large scale differences in CpG sites, such as cancer/normal studies or between-tissues studies. However, before any analysis can be done, data normalization and preprocessing of methylation data are required. A useful data preprocessing pipeline for large scale comparisons is Functional Normalization (FunNorm), (Fortin et al., 2014) implemented in the minfi package in R. In FunNorm, the univariate quantiles of the methylated and unmethylated signal values in the raw data are used to preprocess the data. However, although FunNorm has been shown to outperform other preprocessing and data normalization processes for these types of studies, it does not account for the correlation between the methylated and unmethylated signals into account; the focus of this paper is to improve upon FunNorm by taking this correlation into account. The concept of a bivariate quantile is used in this study as an attempt to take the correlation between the methylated and unmethylated signals into consideration. From the bivariate quantiles found, the partial least squares method is then used on these quantiles in this preprocessing. The raw datasets used for this research were collected from the European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI) website. The results from this preprocessing algorithm were then compared and contrasted to the results from FunNorm. Drawbacks, limitations and future research are then discussed.

关键词： methylation methylation data partial least squares bivariate bivariate quantile applied statistics preprocessing normalization machine learning

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Quantum squirrel inspired algorithm for gene selection in methylation and expression data of prostate cancer

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APPLIED SOFT COMPUTING 2021年 105卷 107221-107221页

作者： Ghosh, Manosij Sen, Sagnik Sarkar, Ram Maulik, Ujjwal Jadavpur Univ Comp Sci & Engn Dept Kolkata India

Prostate cancer is the second most common type of cancer among men after skin cancer In this work, we present a comprehensive view on genomic and epigenomic changes following the incremental biological functionality. For gene selection, a new Feature Selection algorithm called Quantum Squirrel inspired Feature Selection is proposed here. While exploring the feature space, the proposed algorithm exploits the benefits of Squirrel Search Algorithm (a recently proposed swarm intelligence algorithm) along with Quantum mechanics. Moreover, a modified version of the end of winter concept is used to achieve effective dimension reduction capacity. Quantum Squirrel inspired Feature Selection is executed on both expression and methylation data of prostate cancer. The major challenge in gene selection is to bring down the number of selected features without compromising on accuracy. The proposed algorithm consistently achieves this goal and outperforms other state-of-the-art algorithms. The proposed algorithm has steadily attained 100% accuracy while selecting a much lower number of features (around 4), which is a major improvement over others. The top selected genes are biologically validated in terms of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontologies (GO), which further demonstrates the usefulness of the proposed method. The genes selected by Quantum Squirrel inspired Feature Selection show an association with prostate carcinoma and most are known biomarkers. A few novel biomarkers selected by proposed algorithm have also been detailed in this work. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Expression data methylation data Prostate cancer Squirrel Search Algorithm Quantum mechanics Feature Selection

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Robust joint score tests in the application of DNA methylation data analysis

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BMC BIOINFORMATICS 2018年第1期19卷 1-11页

作者： Li, Xuan Fu, Yuejiao Wang, Xiaogang Qiu, Weiliang York Univ Dept Math & Stat 4700 Keele St Toronto ON M3J1P3 Canada Harvard Med Sch Brigham & Womens Hosp Channing Div Network Med 181 Longwood Ave Boston MA 02115 USA

Background: Recently differential variability has been showed to be valuable in evaluating the association of DNA methylation to the risks of complex human diseases. The statistical tests based on both differential methylation level and differential variability can be more powerful than those based only on differential methylation level. Anh and Wang (2013) proposed a joint score test (AW) to simultaneously detect for differential methylation and differential variability. However, AW's method seems to be quite conservative and has not been fully compared with existing joint tests. Results: We proposed three improved joint score tests, namely ***, ***, and ***, and have made extensive comparisons with the joint likelihood ratio test (jointLRT), the Kolmogorov-Smirnov (KS) test, and the AW test. Systematic simulation studies showed that: 1) the three improved tests performed better (i.e., having larger power, while keeping nominal Type I error rates) than the other three tests for data with outliers and having different variances between cases and controls;2) for data from normal distributions, the three improved tests had slightly lower power than jointLRT and AW. The analyses of two Illumina Humanmethylation27 data sets GSE37020 and GSE20080 and one Illumina Infinium methylationEPIC data set GSE107080 demonstrated that three improved tests had higher true validation rates than those from jointLRT, KS, and AW. Conclusions: The three proposed joint score tests are robust against the violation of normality assumption and presence of outlying observations in comparison with other three existing tests. Among the three proposed tests, *** seems to be the most robust and effective one for all simulated scenarios and also in real data analyses.

关键词： methylation data Joint score tests Variability

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An adaptive gene-based test for methylation data

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BMC proceedings 2018年第SUPPL 9期12卷 60页

作者： Chong Wu Jun Young Park Weihua Guan Wei Pan Division of Biostatistics University of Minnesota 420 Delaware St SE Minneapolis MN 55455 USA.

DNA methylation plays an important role in normal human development and disease. In epigenome-wide association studies (EWAS), a univariate test for association between a phenotype and each cytosine-phosphate-guanine (CpG) site has been widely used. Given the number of CpG sites tested in EWAS, a stringent significance cutoff is required to adjust for multiple testing; in addition, multiple nearby CpG sites may be associated with the phenotype, which is ignored by a univariate test. These two factors may contribute to the power loss of a univariate test. As an alternative, we propose applying an adaptive gene-based test that is powerful in genome-wide association studies (GWAS), called , to EWAS for simultaneous testing on multiple CpG sites within or near a gene. We show its application to the GAW20 methylation data set.

关键词： methylation data Epigenome-wide Association Studies (EWAS) Normal Human Development Selected Single Nucleotide Polymorphisms (SNPs) Genetics Of Lipid Lowering Drugs And Diet Network (GOLDN)

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Modeling methylation data as an additional genetic variance component

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BMC proceedings 2018年第SUPPL 9期12卷 29页

作者： Marcio Almeida Juan Peralta Jose Garcia Vincent Diego Harald Goring Sarah Williams-Blangero John Blangero 1South Texas Diabetes and Obesity Institute University of Texas Rio Grande Valley School of Medicine One West University Blvd. STDOI Modular Building #100 Brownsville TX 78520 USA. 2Menzies Institute for Medical Research University of Tasmania 17 Liverpool Street Hobart TAS Australia.

High-throughput platforms allow the characterization of thousands of previously known methylation sites. These platforms have great potential for investigating the epigenetic effects that are partially responsible for gene expression control. methylation sites provide a bridge for the investigation of real-time environmental contributions on genomic events by the alteration of methylation status of those sites. Using the data provided by GAW20's organization committee, we calculated the heritability estimates of each cytosine-phosphate-guanine (CpG) island before and after the use of fenofibrate, a lipid-control drug. Surprisingly, we detected substantially high heritability estimates before drug usage. This somewhat unexpected high sample correlation was corrected by the use of principal components and the distributions of heritability estimates before and after fenofibrate treatment, which made the distributions comparable. The methylation sites located near a gene were collected and a genetic relationship matrix estimated to represent the overall correlation between samples. We implemented a random-effect association test to screen genes whose methylation patterns partially explain the observable high-density lipoprotein (HDL) heritability. Our leading association was observed for the gene that encodes a transmembrane protein, and is largely expressed in the liver, had not been previously associated with HDL until this manuscript. Using a variance component decomposition framework with the linear mixed model allows the integration of data from different sources, such as methylation, gene expression, metabolomics, and proteomics. The decomposition of the genetic variance component decomposition provides a flexible analytical approach for the challenges of this new omics era.

关键词： Additive Genetic Variance Component methylation data methylation Sites Genetic Relationship Matrix (GRM) Fenofibrate Treatment

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A Bayesian hierarchical model for improving measurement of 5mC and 5hmC levels: Toward revealing associations between phenotypes and methylation states

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GENETIC EPIDEMIOLOGY 2022年第7期46卷 446-462页

作者： Jiang, Lai Greenlaw, Keelin Ciampi, Antonio Canty, Angelo J. Gross, Jeffrey Turecki, Gustavo Greenwood, Celia M. T. Jewish Gen Hosp Lady Davis Inst Med Res Montreal PQ Canada McGill Univ Dept Epidemiol Biostat & Occupat Hlth Montreal PQ Canada McMaster Univ Dept Math & Stat Hamilton ON Canada McGill Univ Dept Psychiat Douglas Inst Montreal PQ Canada McGill Univ Gerald Bronfman Dept Oncol Montreal PQ Canada McGill Univ Dept Human Genet Montreal PQ Canada

5-hydroxymethylcytosine (5hmC) is a methylation state linked with gene regulation, commonly found in cells of the central nervous system. 5hmC is associated with demethylation of cytosines from 5-methylcytosine (5mC) to the unmethylated state. The presence of 5hmC can be inferred by a paired experiment involving bisulfite and oxidation-bisulfite treatments on the same sample, followed by a methylation assay using a platform such as the Illumina Infinium methylationEPIC BeadChip (EPIC). Existing methods for analysis of the resulting EPIC data are not ideal. Most approaches ignore the correlation between the two experiments and any imprecision associated with DNA damage from the additional treatment. Estimates of 5mC/5hmC levels free from these limitations are desirable to reveal associations between methylation states and phenotypes. We propose a hierarchical Bayesian method called Constrained HYdroxy methylation Estimation (CHYME) to simultaneously estimate 5mC/5hmC signals as well as any associations between these signals and covariates or phenotypes, while accounting for the potential impact of DNA damage and dependencies induced by the experimental design. Simulations show that CHYME has valid type 1 error and better power than a range of alternative methods, including the popular OxyBS method and linear models on transformed proportions. Other methods we examined suffer from hugely inflated type 1 error for inference on 5hmC proportions. We use CHYME to explore genome-wide associations between 5mC/5hmC levels and cause of death in postmortem prefrontal cortex brain tissue samples. These analyses indicate that CHYME is a useful tool to reveal phenotypic associations with 5mC/5hmC levels.

关键词： 5-hydroxymethylcytosine (5hmC) 5-methylcytosine (5mC) hierarchical Bayesian methods methylation data paired bisulfite and oxidation-bisulfite treatments phenotype-methylation associations prefrontal cortex brain samples

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Detecting differentially methylated loci for Illumina Array methylation data based on human ovarian cancer data

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BMC MEDICAL GENOMICS 2013年第1-sup期6卷 S9-S9页

作者： Chen, Zhongxue Huang, Hanwen Liu, Jianzhong Ng, Hon Keung Tony Nadarajah, Saralees Huang, Xudong Deng, Youping Indiana Univ Sch Publ Hlth Dept Epidemiol & Biostat Bloomington IN 47405 USA Univ Texas Hlth Sci Ctr Houston Ctr Clin & Translat Sci Houston TX 77030 USA Chem21 Grp Inc Lake Forest IL 60045 USA So Methodist Univ Dept Stat Sci Dallas TX 75275 USA Univ Manchester Sch Math Manchester M13 9PL Lancs England Massachusetts Gen Hosp Dept Psychiat Neurochem Lab Charlestown MA 02129 USA Harvard Univ Sch Med Charlestown MA 02129 USA Rush Univ Canc Ctr Dept Internal Med Chicago IL 60612 USA Rush Univ Med Ctr Chicago IL 60612 USA

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.

关键词： Nonparametric Method methylation data Empirical Power Welch Test Ovarian Cancer data

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Meta-Analysis for Epigenome-Wide Association Studies

Meta-Analysis for Epigenome-Wide Association Studies

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作者： Nan Wang Shuilin Jin

With the rapid development of methylation profiling technology, many datasets are generated to quantify genome-wide methylation patterns. Given the heavy burden of multiple testing of hundreds of thousands of DNA methylation markers, individual studies often have limited sample sizes and power. The EWAS meta-analysis is an approach that combines results from multiple studies on the same scientific question. It helps to improve statistical power by combining information from individual studies and reduce the chances of false positives. This chapter introduces commonly used meta-analysis methods and analytical tools with application to EWAS data. less

关键词： Statistical Calculation Meta-analysis Analytical tools methylation data

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Interpretation of genome-wide infinium methylation data from ligated DNA in formalin-fixed, paraffin-embedded paired tumor and normal tissue

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BMC Research Notes 2012年第1期5卷 1-11页

作者： Jasmine, Farzana Rahaman, Ronald Roy, Shantanu Raza, Maruf Paul, Rupash Rakibuz-Zaman, Muhammad Paul-Brutus, Rachelle Dodsworth, Charlotte Kamal, Mohammed Ahsan, Habibul Kibriya, Muhammad G. Department of Health Studies University of Chicago Chicago IL 60637 United States Department of Human Genetics University of Chicago Chicago IL 60637 United States Department of Medicine University of Chicago Chicago IL 60637 United States Department of Comprehensive Cancer Center University of Chicago Chicago IL 60637 United States University of Chicago Research Office in Bangladesh Dhaka United States Department of Pathology Bangabandhu Sheikh Mujib Medical University (BSMMU) Dhaka 1000 Bangladesh

Background: Formalin-fixed, paraffin-embedded (FFPE) samples are a highly desirable resource for epigenetic studies, but there is no suitable platform to assay genome-wide methylation in these widely available resources. Recently, Thirlwell et al. (2010) have reported a modified ligation-based DNA repair protocol to prepare FFPE DNA for the Infinium methylation assay. In this study, we have tested the accuracy of methylation data obtained with this modification by comparing paired fresh-frozen (FF) and FFPE colon tissue (normal and tumor) from colorectal cancer patients. We report locus-specific correlation and concordance of tumor-specific differentially methylated loci (DML), both of which were not previously assessed. Methods. We used Illumina's Infinium methylation 27K chip for 12 pairs of FF and 12 pairs of FFPE tissue from tumor and surrounding healthy tissue from the resected colon of the same individual, after repairing the FFPE DNA using Thirlwell's modified protocol. Results: For both tumor and normal tissue, overall correlation of values between all loci in paired FF and FFPE was comparable to previous studies. Tissue storage type (FF or FFPE) was found to be the most significant source of variation rather than tissue type (normal or tumor). We found a large number of DML between FF and FFPE DNA. Using ANOVA, we also identified DML in tumor compared to normal tissue in both FF and FFPE samples, and out of the top 50 loci in both groups only 7 were common, indicating poor concordance. Likewise, while looking at the correlation of individual loci between FFPE and FF across the patients, less than 10% of loci showed strong correlation (r 0.6). Finally, we checked the effect of the ligation-based modification on the Infinium chemistry for SNP genotyping on an independent set of samples, which also showed poor performance. Conclusion: Ligation of FFPE DNA prior to the Infinium genome-wide methylation assay may detect a reasonable number of loci, but the number

关键词： FFPE Tissue methylation data FFPE Sample methylation Assay FFPE Block

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Comments on: Interpretation of genome-wide infinium methylation data from ligated DNA in formalin-fixed paraffin-embedded paired tumor and normal tissue

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BMC Research Notes 2012年第1期5卷 1-4页

作者： Thirlwell, Christina Feber, Andrew Lechner, Matthias Teschendorff, Andrew E Beck, Stephan Medical Genomics Laboratory UCL Cancer Institute London WC1E 6BT 72 Huntley Street United Kingdom

BMC Research Notes recently published a research article regarding the use of ligated DNA extracted from formalin-fixed paraffin embedded (FFPE) tissue on the Illumina Infinium methylation platform - "Interpretation of genome-wide infinium methylation data from ligated DNA in formalin-fixed, paraffin-embedded paired tumor and normal tissue" Jasmine et al. BMC Research Notes 2012, 5:117. This article repeatedly refers to our previous work and concludes that methylation data obtained from ligated FFPE extracted DNA should be used with great caution. In this Discussion we review the data analysis performed in Jasmine et al's paper and suggest limitations which subsequently lead the authors to draw what we believe are incorrect conclusions. Moreover, we continue to analyse genome-wide methylation data from DNA extracted from FFPE tissue successfully on both the HumMeth27 and 450 K arrays. © 2012 Thirlwell et al.;licensee BioMed Central Ltd.

关键词： methylation data Batch Effect Fresh Freeze Fresh Freeze Sample Differentially Methylated Locus

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