An evaluation of supervised methods for identifying differentially methylated regions in Illumina methylation arrays

Mallik, Saurav; Odom, Gabriel J.; Z, Gao; Gomez, Lissette; Chen, Xi; Wang, Lily

doi:10.1093/bib/bby085

Cited by 89 publications

(75 citation statements)

References 42 publications

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“…The following Comb-p parameters were used: seed p-value = 0.001, maximum distance between probes = 500 bp, and a minimum of three probes allowed in a DMR. This set of parameters is consistent with previous work 42 in the field and recommendations from simulation experiments 43 .…”

Section: Differentially Methylated Region Analysissupporting

confidence: 90%

Large epigenome-wide association study of childhood ADHD identifies peripheral DNA methylation associated with disease and polygenic risk burden

et al. 2020

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Epigenetic variation in peripheral tissues is being widely studied as a molecular biomarker of complex disease and disease-related exposures. To date, few studies have examined differences in DNA methylation associated with attention-deficit hyperactivity disorder (ADHD). In this study, we profiled genetic and methylomic variation across the genome in saliva samples from children (age 7-12 years) with clinically established ADHD (N = 391) and nonpsychiatric controls (N = 213). We tested for differentially methylated positions (DMPs) associated with both ADHD diagnosis and ADHD polygenic risk score, by using linear regression models including smoking, medication effects, and other potential confounders in our statistical models. Our results support previously reported associations between ADHD and DNA methylation levels at sites annotated to VIPR2, and identify several novel disease-associated DMPs (p < 1e-5), although none of them were genome-wide significant. The two top-ranked, ADHD-associated DMPs (cg17478313 annotated to SLC7A8 and cg21609804 annotated to MARK2) are also significantly associated with nearby SNPs (p = 1.2e-46 and p = 2.07e-59), providing evidence that disease-associated DMPs are under genetic control. We also report a genome-wide significant association between ADHD polygenic risk and variable DNA methylation at a site annotated to the promoter of GART and SON (p = 6.71E-8). Finally, we show that ADHD-associated SNPs colocalize with SNPs associated with methylation levels in saliva. This is the first large-scale study of DNA methylation in children with ADHD. Our results represent novel epigenetic biomarkers for ADHD that may be useful for patient stratification, reinforce the importance of genetic effects on DNA methylation, and provide plausible molecular mechanisms for ADHD risk variants.

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Section: Differentially Methylated Region Analysissupporting

confidence: 90%

Large epigenome-wide association study of childhood ADHD identifies peripheral DNA methylation associated with disease and polygenic risk burden

et al. 2020

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“…Interestingly, in a recent study, it has been observed that contiguous regions exist in the epigenome denoted as differentially methylated regions (DMRs) which are significantly associated with the various diseases [30], [31]. In addition, We found the comparative study of various DMR finding methods in [32] that might motivate us to extend our future work.…”

Section: Resultsmentioning

confidence: 83%

“…A recent study states that the epigenome contains contiguous regions denoted as differentially methylated regions (DMRs) that are significantly associated with numerous diseases [30], [31]. Mallik et al [32] conducted a comparative study of different DMR-finding methods; the results of this study might motivate our future work.…”

Section: Literature Reviewmentioning

confidence: 99%

DeMoS: Dense Module based Gene Signature Detection through Quasi-Clique: An Application to Cervical Cancer Prognosis

Saha¹,

Mallik²,

Bandyopadhyay³

2020

Preprint

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Background: Gene signature is useful to represent the molecular alternation in the disease genomes at specified conditions and is often used to distinguish samples into various groups for better research prospective as well as better clinical treatment. There is lack of efficient techniques that can take into account the complex gene expression profile and able to identify the most relevant signatures. Methods: In this article, we presented a new framework to identify Dense Module based gene Signature (DeMoS) and their targeting miRNAs through Quasi-Clique detection algorithm and their application in prognosis survival study. Here we applied a cervical cancer data repository with prognosis clinical data to conduct our experiment. We first performed Empirical Bayes test using Limma method to identify dysregulated genes (or, miRNAs). MiRNA-mediated dysregulated target genes had been extracted from those dysregulated miRNAs. Thereafter, We detected dense co-expressed modules using Quasi-Clique identification technique. The average correlation coefficient was then computed for each resultant module. The module containing the highest correlation was formulated as the resultant gene signature. Next, We applied three well-known classifiers (SVM, PAM and Random Forest (RF)) using 10-fold cross-validation, and obtained AUC. Finally, we performed survival prognosis study for the resultant gene signature. Results: The resultant signature consisted of ten genes, FGF9, FGF18, PPP1R9A, ERBB4, DCDC2, TOX3, ARMC3, DNALI1, RGL3 and ENPP3. In addition, We identified a total of eight dysregulated miRNAs that targeted the aforementioned gene signature. Hsa-mir-34c was found to be strongly associated with the genes signature since its out-degree centrality score was highest. On the other hand, the p-value of the Cox regression in the prognosis study for the resultant gene signature was found to be significant (=4.2e-02). Finally, DeMoS evaluated the highest AUC values (viz., 0.95 for SVM, 0.955 for RF, 0.955 for PAM) for our resultant gene signature in compared to the other state-of-the-art techniques. Conclusions: Our framework estimated most promising gene signature that could classify multiple groups/subtypes of samples with higher AUC as well as statistically significant p-value in regression based prognosis analysis. Our method is useful to determine signature for any RNA-seq profile. Code is available at https://github.com/sahasuparna/DeMoS.

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“…One such challenge with supervised DMR-identification methods is their lack of power when the difference in beta values between two groups was small but consistent (i.e. difference in mean beta values is less than 0.05) (25). This is likely because supervised methods scan the genome to identify regions with adjacent low p-values, so a number of positions that pass a multiple-comparison-corrected significance threshold are required.…”

Section: Discussionmentioning

confidence: 99%

“…A number of statistical methods for identifying DMRs have been proposed (10,11,(17)(18)(19)(20), reviewed (21,22), and compared (23)(24)(25). Methods for DMR identification can be classified into supervised and unsupervised methods.…”

Section: Introductionmentioning

confidence: 99%

coMethDMR: Accurate identification of co-methylated and differentially methylated regions in epigenome-wide association studies

Gomez

et al. 2019

Preprint

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Recent technology has made it possible to measure DNA methylation profiles in a cost-effective and comprehensive genome-wide manner using array-based technology for epigenome-wide association studies. However, identifying differentially methylated regions (DMRs) remains a challenging task because of the complexities in DNA methylation data. Supervised methods typically focus on the regions that contain consecutive highly significantly differentially methylated CpGs in the genome, but may lack power for detecting small but consistent changes when few CpGs pass stringent significance threshold after multiple comparison. Unsupervised methods group CpGs based on genomic annotations first and then test them against phenotype, but may lack specificity because the regional boundaries of methylation are often not well defined. We present coMethDMR, a flexible, powerful, and accurate tool for identifying DMRs. Instead of testing all CpGs within a genomic region, coMethDMR carries out an additional step that selects co-methylated sub-regions first. Next, coMethDMR tests association between methylation levels within the sub-region and phenotype via a random coefficient mixed effects model that models both variations between CpG sites within the region and differential methylation simultaneously. coMethDMR offers well-controlled Type I error rate, improved specificity, focused testing of targeted genomic regions, and is available as an open-source R package.

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An evaluation of supervised methods for identifying differentially methylated regions in Illumina methylation arrays

Cited by 89 publications

References 42 publications

Large epigenome-wide association study of childhood ADHD identifies peripheral DNA methylation associated with disease and polygenic risk burden

Large epigenome-wide association study of childhood ADHD identifies peripheral DNA methylation associated with disease and polygenic risk burden

DeMoS: Dense Module based Gene Signature Detection through Quasi-Clique: An Application to Cervical Cancer Prognosis

coMethDMR: Accurate identification of co-methylated and differentially methylated regions in epigenome-wide association studies

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