%0 Journal Article %1 teschendorff2018statistical %A Teschendorff, Andrew E. %A Relton, Caroline L. %D 2018 %J Nature Reviews Genetics %K methylation review shouldread software %N 3 %P 129--147 %R 10.1038/nrg.2017.86 %T Statistical and integrative system-level analysis of DNA methylation data %U https://doi.org/10.1038/nrg.2017.86 %V 19 %X Cell-type heterogeneity can be a major source of confounding and reverse causation in epigenome-wide association studies (EWAS). Adjustment for cell-type composition is therefore critical for an improved interpretation and understanding of EWAS.For a given study, the best choice of cell-type deconvolution algorithm depends not only on the tissue and phenotype of interest but also on the presence of other confounders and the desired output.Most variation in DNA methylation (DNAm) is driven by genetic factors and cell-type heterogeneity, with corresponding features — methylation quantitative trait loci (mQTLs) and cell-type-specific differentially methylated cytosines (DMCs) — readily identifiable using linear modelling.Identification and interpretation of DNAm changes that accrue with age or exposure to environmental disease risk factors may benefit from differential variance statistics.Analysing patterns of covariation in DNAm at regulatory elements can help to identify disrupted regulatory networks and gene modules in disease.The inverse association between DNAm at regulatory elements and transcription factor binding can be exploited to elucidate the functional role of non-coding genome-wide association study (GWAS) single-nucleotide polymorphisms (SNPs) or functional effects caused by exposure to environmental disease risk factors.Mendelian randomization can help to clarify the role of DNAm as a causal mediator between exposure to risk factors and disease.

@article{teschendorff2018statistical, abstract = {Cell-type heterogeneity can be a major source of confounding and reverse causation in epigenome-wide association studies (EWAS). Adjustment for cell-type composition is therefore critical for an improved interpretation and understanding of EWAS.For a given study, the best choice of cell-type deconvolution algorithm depends not only on the tissue and phenotype of interest but also on the presence of other confounders and the desired output.Most variation in DNA methylation (DNAm) is driven by genetic factors and cell-type heterogeneity, with corresponding features — methylation quantitative trait loci (mQTLs) and cell-type-specific differentially methylated cytosines (DMCs) — readily identifiable using linear modelling.Identification and interpretation of DNAm changes that accrue with age or exposure to environmental disease risk factors may benefit from differential variance statistics.Analysing patterns of covariation in DNAm at regulatory elements can help to identify disrupted regulatory networks and gene modules in disease.The inverse association between DNAm at regulatory elements and transcription factor binding can be exploited to elucidate the functional role of non-coding genome-wide association study (GWAS) single-nucleotide polymorphisms (SNPs) or functional effects caused by exposure to environmental disease risk factors.Mendelian randomization can help to clarify the role of DNAm as a causal mediator between exposure to risk factors and disease.}, added-at = {2020-01-22T22:28:11.000+0100}, author = {Teschendorff, Andrew E. and Relton, Caroline L.}, biburl = {https://www.bibsonomy.org/bibtex/2ab8e340073036026df4baaca90af21ce/marcsaric}, description = {Statistical and integrative system-level analysis of DNA methylation data | Nature Reviews Genetics}, doi = {10.1038/nrg.2017.86}, interhash = {30b98b6bb7822d0287e5c06f4d09f187}, intrahash = {ab8e340073036026df4baaca90af21ce}, issn = {14710064}, journal = {Nature Reviews Genetics}, keywords = {methylation review shouldread software}, number = 3, pages = {129--147}, refid = {Teschendorff2018}, timestamp = {2020-01-22T22:28:11.000+0100}, title = {Statistical and integrative system-level analysis of DNA methylation data}, url = {https://doi.org/10.1038/nrg.2017.86}, volume = 19, year = 2018 }