17 research outputs found
Top 20 differentially methylated CpG positions in monozygotic twins discordant for CWP (N = 66) from the TwinsUK discovery sample.
<p>Included are CpG positions ranked on combination of p-values and Δβ. Within the table these positions are for consistensy arranged based on their p-values.</p
Functional annotation cluster analysis of hypermethylated genes in the CWP-affected MZ twins from the TwinsUK sample (N = 33).
<p>Functional annotation cluster analysis of hypermethylated genes in the CWP-affected MZ twins from the TwinsUK sample (N = 33).</p
Top 20 differentially methylated CpG positions between unrelated individuals who screened positive (N = 81) or negative (N = 200) for CWP from the TwinsUK discovery sample.
<p>Top 20 differentially methylated CpG positions between unrelated individuals who screened positive (N = 81) or negative (N = 200) for CWP from the TwinsUK discovery sample.</p
Q-Q plots in the unrelated UK sample.
<p>Before normalization (A) and after normalization (B) of the data.</p
SNP Heatmap.
<p>A SNP heatmap of the 65 genotyping probes on the Illumina 450k array for the 33 CWP discordant monozygotic twin pairs.</p
Epigenetics meets proteomics in an epigenome-wide association study with circulating blood plasma protein traits
DNA methylation and blood circulating proteins have been associated with many complex disorders, but the underlying disease-causing mechanisms often remain unclear. Here, we report an epigenome-wide association study of 1123 proteins from 944 participants of the KORA population study and replication in a multi-ethnic cohort of 344 individuals. We identify 98 CpG-protein associations (pQTMs) at a stringent Bonferroni level of significance. Overlapping associations with transcriptomics, metabolomics, and clinical endpoints suggest implication of processes related to chronic low-grade inflammation, including a network involving methylation of NLRC5, a regulator of the inflammasome, and associated pQTMs implicating key proteins of the immune system, such as CD48, CD163, CXCL10, CXCL11, LAG3, FCGR3B, and B2M. Our study links DNA methylation to disease endpoints via intermediate proteomics phenotypes and identifies correlative networks that may eventually be targeted in a personalized approach of chronic low-grade inflammation.Other Information Published in: Nature Communications License: https://creativecommons.org/licenses/by/4.0See article on publisher's website: http://dx.doi.org/10.1038/s41467-019-13831-w</p
Manhattan plot.
<p>Association between MTHFR 677C>T (rs1801133) and genome-wide DNA methylation in 9,894 samples, with 35 <i>cis</i>-meQTLs at chromosome 1 (black/grey) and 1 <i>trans</i>-meQTL at chromosome 6 (green) with FDR<0.05.</p
Regional manhattan plot (chr1: 11824095–12184574).
<p>35 (black) and 16 (green) cis-meQTLs of the MTHFR 677C>T and GRS model respectively, in 9,894 samples. The overlap involved a small region of 238 kb (green rectangular line).</p
Association of <i>MTHFR</i> 677C>T and Genetic Risk Score on mean global methylation levels.
<p>Association of <i>MTHFR</i> 677C>T and Genetic Risk Score on mean global methylation levels.</p
Genome-wide <i>trans</i>-CpGs with FDR<0.05; associated with the <i>MTHFR</i> 677C>T model or Genetic Risk Score of 18 Hcy-associated variants.
<p>Genome-wide <i>trans</i>-CpGs with FDR<0.05; associated with the <i>MTHFR</i> 677C>T model or Genetic Risk Score of 18 Hcy-associated variants.</p