scholarly journals Genome-wide methylation data mirror ancestry information

2016 ◽  
Author(s):  
Elior Rahmani ◽  
Liat Shenhav ◽  
Regev Schweiger ◽  
Paul Yousefi ◽  
Karen Huen ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Genetic Data ◽  
Data Sets ◽  
Methylation Data ◽  
Methylation Array ◽  
Information Signal ◽  
Genome Wide ◽  
Public Data ◽  
Dna Methylation Analysis ◽  
450K Methylation

AbstractGenetic data are known to harbor information about human demographics, and genotyping data are commonly used for capturing ancestry information by leveraging genome-wide differences between populations. In contrast, it is not clear to what extent population structure is captured by whole-genome DNA methylation data. We demonstrate, using three large cohort 450K methylation array data sets, that ancestry information signal is mirrored in genome-wide DNA methylation data, and that it can be further isolated more effectively by leveraging the correlation structure of CpGs with cis-located SNPs. Based on these insights, we propose a method, EPISTRUCTURE, for the inference of ancestry from methylation data, without the need for genotype data. EPISTRUCTURE can be used to infer ancestry information of individuals based on their methylation data in the absence of corresponding genetic data. Although genetic data are often collected in epigenetic studies of large cohorts, these are typically not made publicly available, making the application of EPISTRUCTURE especially useful for anyone working on public data. Implementation of EPISTRUCTURE is available in GLINT, our recently released toolset for DNA methylation analysis at: http://glint-epigenetics.readthedocs.io.

scholarly journals MethylToSNP: identifying SNPs in Illumina DNA methylation array data

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Brenna A. LaBarre ◽  
Alexander Goncearenco ◽  
Hanna M. Petrykowska ◽  
Weerachai Jaratlerdsiri ◽  
M. S. Riana Bornman ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Data Interpretation ◽  
Data Sets ◽  
Methylation Data ◽  
Methylation Array ◽  
Array Data ◽  
Functional Regions ◽  
A Genome ◽  
Probe Site ◽  
Dna Methylation Array

Abstract Background Current array-based methods for the measurement of DNA methylation rely on the process of sodium bisulfite conversion to differentiate between methylated and unmethylated cytosine bases in DNA. In the absence of genotype data this process can lead to ambiguity in data interpretation when a sample has polymorphisms at a methylation probe site. A common way to minimize this problem is to exclude such potentially problematic sites, with some methods removing as much as 60% of array probes from consideration before data analysis. Results Here, we present an algorithm implemented in an R Bioconductor package, MethylToSNP, which detects a characteristic data pattern to infer sites likely to be confounded by polymorphisms. Additionally, the tool provides a stringent reliability score to allow thresholding on SNP predictions. We calibrated parameters and thresholds used by the algorithm on simulated and real methylation data sets. We illustrate findings using methylation data from YRI (Yoruba in Ibadan, Nigeria), CEPH (European descent) and KhoeSan (southern African) populations. Our polymorphism predictions made using MethylToSNP have been validated through SNP databases and bisulfite and genomic sequencing. Conclusions The benefits of this method are threefold. First, it prevents extensive data loss by considering only SNPs specific to the individuals in the study. Second, it offers the possibility to identify new polymorphisms in samples for which there is little known about the genetic landscape. Third, it identifies variants as they exist in functional regions of a genome, such as in CTCF (transcriptional repressor) sites and enhancers, that may be common alleles or personal mutations with potential to deleteriously affect genomic regulatory activities. We demonstrate that MethylToSNP is applicable to the Illumina 450K and Illumina 850K EPIC array data and is also backwards compatible to the 27K methylation arrays. Going forward, this kind of nuanced approach can increase the amount of information derived from precious data sets by considering samples of the project individually to enable more informed decisions about data cleaning.

DNA methylation differences in cortical grey and white matter in schizophrenia

Epigenomics ◽  
2021 ◽  
Author(s):  
Amber Berdenis van Berlekom ◽  
Nina Notman ◽  
Marjolein AM Sneeboer ◽  
Gijsje JLJ Snijders ◽  
Lotte C Houtepen ◽  
...  
Keyword(s):  
Dna Methylation ◽  
White Matter ◽  
Postmortem Brain ◽  
Tissue Type ◽  
Cortical Tissue ◽  
Methylation Array ◽  
Genome Wide ◽  
Postmortem Brain Tissue ◽  
And Control ◽  
Dna Methylation Analysis

Aim: Identify grey- and white-matter-specific DNA-methylation differences between schizophrenia (SCZ) patients and controls in postmortem brain cortical tissue. Materials & methods: Grey and white matter were separated from postmortem brain tissue of the superior temporal and medial frontal gyrus from SCZ (n = 10) and control (n = 11) cases. Genome-wide DNA-methylation analysis was performed using the Infinium EPIC Methylation Array (Illumina, CA, USA). Results: Four differentially methylated regions associated with SCZ status and tissue type (grey vs white matter) were identified within or near KLF9, SFXN1, SPRED2 and ALS2CL genes. Gene-expression analysis showed differential expression of KLF9 and SFXN1 in SCZ. Conclusion: Our data show distinct differences in DNA methylation between grey and white matter that are unique to SCZ, providing new leads to unravel the pathogenesis of SCZ.

scholarly journals Gene set enrichment analysis for genome-wide DNA methylation data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jovana Maksimovic ◽  
Alicia Oshlack ◽  
Belinda Phipson
Keyword(s):  
Dna Methylation ◽  
Enrichment Analysis ◽  
R Package ◽  
Gene Set Enrichment Analysis ◽  
Methylation Array ◽  
Gene Set ◽  
Genome Wide ◽  
Genome Methylation ◽  
Unbiased Gene ◽  
Gene Set Testing

AbstractDNA methylation is one of the most commonly studied epigenetic marks, due to its role in disease and development. Illumina methylation arrays have been extensively used to measure methylation across the human genome. Methylation array analysis has primarily focused on preprocessing, normalization, and identification of differentially methylated CpGs and regions. GOmeth and GOregion are new methods for performing unbiased gene set testing following differential methylation analysis. Benchmarking analyses demonstrate GOmeth outperforms other approaches, and GOregion is the first method for gene set testing of differentially methylated regions. Both methods are publicly available in the missMethyl Bioconductor R package.

scholarly journals Evaluation of affinity-based genome-wide DNA methylation data: Effects of CpG density, amplification bias, and copy number variation

2010 ◽  
Vol 20 (12) ◽  
pp. 1719-1729 ◽  
Author(s):  
M. D. Robinson ◽  
C. Stirzaker ◽  
A. L. Statham ◽  
M. W. Coolen ◽  
J. Z. Song ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Copy Number Variation ◽  
Copy Number ◽  
Methylation Data ◽  
Amplification Bias ◽  
Genome Wide ◽  
Number Variation

scholarly journals Genome-wide DNA methylation analysis of the porcine hypothalamus-pituitary-ovary axis

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Xiao-Long Yuan ◽  
Zhe Zhang ◽  
Bin Li ◽  
Ning Gao ◽  
Hao Zhang ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Methylation Analysis ◽  
Genome Wide ◽  
Dna Methylation Analysis

scholarly journals Genome-Wide DNA Methylation Analysis in Male Methamphetamine Users With Different Addiction Qualities

2020 ◽  
Vol 11 ◽  
Author(s):  
Liang Liu ◽  
Tao Luo ◽  
Huixi Dong ◽  
Chenxi Zhang ◽  
Tieqiao Liu ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Methylation Analysis ◽  
Genome Wide ◽  
Dna Methylation Analysis
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