Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in Rheumatoid Arthritis
By Yun Liu, Martin J. Aryee, L. Padyukov, M. Fallin, E. Hesselberg, Á. Rúnarsson, Lovisa E. Reinius, N. Acevedo, M. Taub, Marcus Ronninger and 7 more. Nature Biotechnology, 2013
932 citations recorded by the source corpus.
Epigenetic mechanisms integrate genetic and environmental causes of disease, but comprehensive genome-wide analyses of epigenetic modifications have not yet demonstrated robust association with common diseases. Using Illumina HumanMethylation450 arrays on 354 anti-citrullinated protein antibody–associated rheumatoid arthritis cases and 337 controls, we identified two clusters within the major histocompatibility complex (MHC) region whose differential methylation potentially mediates genetic risk for rheumatoid arthritis. To reduce confounding factors that have hampered previous epigenome-wide studies, we corrected for cellular heterogeneity by estimating and adjusting for cell-type proportions in our blood-derived DNA samples and used mediation analysis to filter out associations likely to be a consequence of disease. Four CpGs also showed an association between genotype and variance of methylation. The associations for both clusters replicated at least one CpG (P < 0.01), with the rest showing suggestive association, in monocyte cell fractions in an independent cohort of 12 cases and 12 controls. Thus, DNA methylation is a potential mediator of genetic risk.
Subfields: Genetics, Hepatology, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/33941ec7c69518fbc8a51c62698595f1f1838ba8
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Genetics
- Naomi R. Wray
- Andrew E. Teschendorff
- Olivier Francois
- Stephan Beck
- Peter M. Visscher
- Ian J. Deary
- Charles E. Breeze
- Yan V. Sun
In Molecular Biology
- Andrew E. Teschendorff
- Andrew E. Jaffe
- Stephan Beck
- Andrea A. Baccarelli
- Luigi Ferrucci
- Charles E. Breeze
- Laura L. Elo
- Shijie C. Zheng
Citations in the corpus
- A survey of best practices for RNA-seq data analysis (2016, methodology): “General linear models [141–143], logistic regression models [143] and empirical Bayes model [144] have been attempted among other modeling approaches.”
- DNA methylation age of human tissues and cell types (2013, background): “Data set 44 consists of whole blood from [64].”
- Methods of integrating data to uncover genotype–phenotype interactions (2015, background): “These causal approaches 43,44 allow the dissection of the genotype-to-phenotype process in a clear, linear manner.”
- Epigenetic Signatures of Cigarette Smoking (2016, background): “We find it noteworthy that there is enrichment of smoking-associated CpGs for genes associated with rheumatoid arthritis because DNA methylation is one of the proposed molecular mechanisms underlying this disease.(37) It is also interesting that the most significant association of smoking with methy”
- Reference-free cell mixture adjustments in analysis of DNA methylation data (2014, methodology): “A recently published set of statistical methods exploits this association to infer changes in cell mixture proportions, and these methods are presently being applied to adjust for cell mixture effect in the context of epigenome-wide association studies.”
- eFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data (2016, methodology): “…we assessed the ability of eFORGE to predict disease-relevant cell types from EWAS conducted on immune blood cells for three autoimmune diseases: rheumatoid arthritis (RA) (Liu et al., 2013), systemic lupus erythematosus (SLE) (Coit et al., 2013), and Sjögren’s syndrome (Altorok et al., 2014).”
- ENmix: a novel background correction method for Illumina HumanMethylation450 BeadChip (2015, background): “Complex diseases can be associated with very small differences in DNA methylation profiles (14,18).”
- Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016, background): “…techniques has only been available and affordable recently, numerous studies have successfully identified leukocyte-based DNAm markers associated with disease traits (Demerath et al., 2015; Dick et al., 2014; Hidalgo et al., 2014; Irvin et al., 2014; Liang et al., 2015; Liu, Aryee, et al., 2013).”
- Accounting for cellular heterogeneity is critical in epigenome-wide association studies (2014, methodology): “With many of these studies completed, few disease-associated loci have been reported outside of cancer [3], type 1 diabetes [4], and rheumatoid arthritis [5].”
- LFMM 2: Fast and Accurate Inference of Gene-Environment Associations in Genome-Wide Studies (2019, methodology): “The RA data are publicly available and were downloaded from the GEO database under the accession number GSE42861 (Liu et al. 2013).”
- A comparison of reference-based algorithms for correcting cell-type heterogeneity in Epigenome-Wide Association Studies (2017, positive): “EpiDISH improves sensitivity in an EWAS of smoking in whole blood In order to further validate EpiDISH and to illustrate its application to EWAS, we applied it to an EWAS of smoking in a cohort of 152 women, all aged 53, for which whole blood samples were taken and for which DNAm profiles using the ”