DNA methylation differences in cortical grey and white matter in schizophrenia

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.

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Genome-wide methylation data mirror ancestry information

10.1101/066340 ◽

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.

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Faculty Opinions recommendation of Genome-wide DNA methylation analysis predicts an epigenetic switch for GATA factor expression in endometriosis.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.718302259.793501767 ◽

2014 ◽

Author(s):

Jian-Jun Wei

Keyword(s):

Dna Methylation ◽

Gata Factor ◽

Methylation Analysis ◽

Epigenetic Switch ◽

Genome Wide ◽

Factor Expression ◽

Dna Methylation Analysis

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Faculty Opinions recommendation of Genome-wide DNA methylation analysis of pseudohypoparathyroidism patients with GNAS imprinting defects.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.726106535.793519408 ◽

2016 ◽

Author(s):

Murat Bastepe ◽

Serap Turan

Keyword(s):

Dna Methylation ◽

Methylation Analysis ◽

Genome Wide ◽

Dna Methylation Analysis

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Genome-wide CpG density and DNA methylation analysis method (MeDIP, RRBS, and WGBS) comparisons

Epigenetics ◽

10.1080/15592294.2021.1924970 ◽

2021 ◽

pp. 1-13

Author(s):

Daniel Beck ◽

Millissia Ben Maamar ◽

Michael K. Skinner

Keyword(s):

Dna Methylation ◽

Analysis Method ◽

Methylation Analysis ◽

Genome Wide ◽

Dna Methylation Analysis

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Gene set enrichment analysis for genome-wide DNA methylation data

Genome Biology ◽

10.1186/s13059-021-02388-x ◽

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.

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