A framework for analyzing DNA methylation data from Illumina Infinium HumanMethylation450 BeadChip

Wang, Zhenxing; Chu, Yanshuo; Wang, Yongtian; Wang, Yadong

doi:10.1109/bibm.2017.8217914

Cited by 6 publications

(7 citation statements)

References 43 publications

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“…In addition, both the 450K and EPIC arrays contain two different types of probes with different dynamic ranges [112]. Several methods have been developed to minimize bias introduced by these potential array effects [113][114][115][116], as well as comparisons across methods, which provide useful frameworks for the design of quality control and normalization of Illumina-based DNA methylation profiles [117][118][119]. Further work has also focused on guidelines for exclusion criteria of low-performing probes [120][121][122], or has explicitly flagged unreliable probes due to cross-reactive events or underlying genetic variation [123].…”

Section: Dna Methylation Profilingmentioning

confidence: 99%

Genetic impacts on DNA methylation: research findings and future perspectives

2021

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Multiple recent studies highlight that genetic variants can have strong impacts on a significant proportion of the human DNA methylome. Methylation quantitative trait loci, or meQTLs, allow for the exploration of biological mechanisms that underlie complex human phenotypes, with potential insights for human disease onset and progression. In this review, we summarize recent milestones in characterizing the human genetic basis of DNA methylation variation over the last decade, including heritability findings and genome-wide identification of meQTLs. We also discuss challenges in this field and future areas of research geared to generate insights into molecular processes underlying human complex traits.

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Section: Dna Methylation Profilingmentioning

confidence: 99%

Genetic impacts on DNA methylation: research findings and future perspectives

2021

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“…Compared to DNAm derived from targeted methods such as bisulfite pyrosequencing, DNAm from array‐based methods requires rigorous quality control, background correction and signal normalization (Morris & Beck, 2015; Wang, Wu, & Wang, 2018). There are numerous programming tools and pipelines for carrying these procedures out, but quality control and normalization should be modified when output will be used for epigenetic clocks.…”

Section: Methods and Implementation Of Epigenetic Clocksmentioning

confidence: 99%

“Epigenetic clocks”: Theory and applications in human biology

Ryan

2020

American J Hum Biol

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All humans age, but how we age—and how fast—differs considerably from person to person. This deviation between apparent age and chronological age is often referred to as “biological age” (BA) and until recently robust tools for studying BA have been scarce. “Epigenetic clocks” are starting to change this. Epigenetic clocks use predictable changes in the epigenome, usually DNA methylation, to estimate chronological age with unprecedented accuracy. More importantly, deviations between epigenetic age and chronological age predict a broad range of health outcomes and mortality risks better than chronological age alone. Thus, epigenetic clocks appear to capture fundamental molecular processes tied to BA and can serve as powerful tools for studying health, development, and aging across the lifespan. In this article, I review epigenetic clocks, especially as they relate to key theoretical and applied issues in human biology. I first provide an overview of how epigenetic clocks are constructed and what we know about them. I then discuss emerging applications of particular relevance to human biologists—those related to reproduction, life‐history, stress, and the environment. I conclude with an overview of the methods necessary for implementing epigenetic clocks, including considerations of study design, sample collection, and technical considerations for processing and interpreting epigenetic clocks. The goal of this review is to highlight some of the ways that epigenetic clocks can inform questions in human biology, and vice versa, and to provide human biologists with the foundational knowledge necessary to successfully incorporate epigenetic clocks into their research.

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“…The RNA‐seq data and latest follow‐up information were downloaded from TCGA GDC Application Programming Interface, which included a total of 185 samples. In addition, Illumina Infinium HumanMethylation450 data were downloaded from the UCSC Cancer Browser (Z. Wang, Wu, & Wang, 2018), which included 202 samples.…”

Section: Methodsmentioning

confidence: 99%

DNA methylation data‐based prognosis‐subtype distinctions in patients with esophageal carcinoma by bioinformatic studies

Chen

Qin

et al. 2020

Journal Cellular Physiology

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Esophageal carcinoma (ESCA) is caused by the accumulation of genetic and epigenetic alterations in esophageal mucosa. Of note, the earliest and the most frequent molecular behavior in the complicated pathogenesis of ESCA is DNA methylation. In the present study, we downloaded data of 178 samples from The Cancer Genome Atlas (TCGA) database to explore specific DNA methylation sites that affect prognosis in ESCA patients. Consequently, we identified 1,098 CpGs that were significantly associated with patient prognosis. Hence, these CpGs were used for consensus clustering of the 178 samples into seven clusters. Specifically, the samples in each group were different in terms of age, gender, tumor stage, histological type, metastatic status, and patient prognosis. We further analyzed 1,224 genes in the corresponding promoter regions of the 1,098 methylation sites, and enriched these genes in biological pathways with close correlation to cellular metabolism, enzymatic synthesis, and mitochondrial autophagy. In addition, nine representative specific methylation sites were screened using the weighted gene coexpression network analysis. Finally, a prognostic prediction model for ESCA patients was built in both training and validation cohorts. In summary, our study revealed that classification based on specific DNA methylation sites could reflect ESCA heterogeneity and contribute to the improvement of individualized treatment and precise prognostic prediction.

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A framework for analyzing DNA methylation data from Illumina Infinium HumanMethylation450 BeadChip

Cited by 6 publications

References 43 publications

Genetic impacts on DNA methylation: research findings and future perspectives

Genetic impacts on DNA methylation: research findings and future perspectives

“Epigenetic clocks”: Theory and applications in human biology

DNA methylation data‐based prognosis‐subtype distinctions in patients with esophageal carcinoma by bioinformatic studies

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