Biology subjects

Relton, C.

Publications and source records attributed to Relton, C..

7 recordsLinked to original sources

bioRxiv2018

Systematic evaluation of the causal relationship between DNA methylation and C-reactive protein

Elevated C-reactive protein (CRP) levels are an indicator of chronic low-grade inflammation. Epigenetic modifications, including DNA methylation, have been linked to CRP, but systematic investigations into potential underlying causal relationships have not yet been performed.\n\nWe systematically performed two-sample Mendelian randomization and colocalization analysis between CRP and DNA methylation levels, using GWAS and EWAS summary statistics as well as individual level data available through the ARIES subset of the Avon Longitudinal Study of Parents and Children (ALSPAC; 1,616 participants).\n\nWe found no convincing examples for a causal association from CRP to DNA methylation. Testing for the reverse (a putative causal effect of DNA methylation on CRP), we found three CpG sites that had shared genetic effects with CRP levels after correcting for multiple testing (cg26470501 (offspring: beta=0.07 [0.03, 0.11]; mothers: beta=0.08 [0.04, 0.13]), cg27023597 (offspring: beta=0.18 [0.10, 0.25]; mothers: beta=0.20 [0.12, 0.28]) and cg12054453 (offspring: beta=0.09 [0.05, 0.13])) influenced CRP levels. For all three CpG sites, linked to the genes TMEM49, BCL3 and MIR21, increased methylation related to an increase in CRP levels. Two CpGs (cg27023597 and cg12054453) were influenced by SNPs in genomic regions that had not previously been implicated in CRP GWASs, implicating them as novel genetic associations.\n\nOverall, our findings suggest that CRP associations with DNA methylation are more likely to be driven by either confounding or causal influences of DNA methylation on CRP levels, rather than the reverse.

genetics↗

bioRxiv2018

The effect of body mass index on smoking behaviour and nicotine metabolism: a Mendelian randomization study

BackgroundGiven clear evidence that smoking lowers weight, it is possible that individuals with higher body mass index (BMI) smoke in order to lose or maintain their weight.\n\nMethods and FindingsWe undertook Mendelian randomization analyses using 97 genetic variants associated with BMI. We performed two sample Mendelian randomization analyses of the effects of BMI on smoking behaviour in UK Biobank (N=335,921) and the Tobacco and Genetics consortium genomewide association study (GWAS) (N[≤]74,035) respectively, and two sample Mendelian randomization analyses of the effects of BMI on cotinine levels (N[≤]4,548) and nicotine metabolite ratio (N[≤]1,518) in published GWAS, and smoking-related DNA methylation in the Avon Longitudinal Study of Parents and Children (N[≤]846).\n\nIn inverse variance weighted Mendelian randomization analysis, there was evidence that higher BMI was causally associated with smoking initiation (OR for ever vs never smoking per one SD increase in BMI: 1.19, 95% CI: 1.11 to 1.27) and smoking heaviness (1.45 additional cigarettes smoked per day per SD increase in BMI, 95% CI: 1.03 to 1.86), but little evidence for a causal effect with smoking cessation. Results were broadly similar using pleiotropy robust methods (MR-Egger, median and weighted mode regression). These results were supported by evidence for a causal effect of BMI on DNA methylation at the aryl-hydrocarbon receptor repressor (AHRR) locus. There was no strong evidence that BMI was causally associated with cotinine, but suggestive evidence for a causal negative association with the nicotine metabolite ratio.\n\nConclusionsThere is a causal bidirectional association between BMI and smoking, but the relationship is likely to be complex due to opposing effects on behaviour and metabolism. It may be useful to consider BMI and smoking together when designing prevention strategies to minimise the effects of these risk factors on health outcomes.

epidemiology↗

bioRxiv2018

Appraising the causal relevance of DNA methylation for risk of lung cancer

DNA methylation changes in peripheral blood have been identified in relation to lung cancer risk. However, the causal nature of these associations remains to be fully elucidated. Meta-analysis of four epigenome-wide association studies (918 cases, 918 controls) revealed differential methylation at 16 CpG sites (FDR < 0.05) in relation to lung cancer risk. A two-sample Mendelian randomization analysis, using genetic instruments for methylation at 14 of the 16 CpG sites, and 29,863 cases and 55,586 controls from the TRICL-ILCCO lung cancer consortium, was performed to appraise the causal role of methylation at these sites on lung cancer. This approach provided little evidence that DNA methylation in peripheral blood at the 14 CpG sites play a causal role in lung cancer development, including for cg05575921 AHRR, where methylation is strongly associated with lung cancer risk. Further studies are needed to investigate the causal role played by DNA methylation in lung tissue.

epidemiology↗

bioRxiv2018

Identification of loci where DNA methylation potentially mediate genetic risk of type 1 diabetes

The risk of Type 1 Diabetes (T1D) comprises both genetic and environmental components. We investigated whether genetic susceptibility to T1D could be mediated by changes in DNA methylation, an epigenetic mechanism that potentially plays a role in autoimmune diabetes. Using data from a non-diabetic population comprising blood samples taken at birth (n=844), childhood (n=911) and adolescence (n=907), we evaluated the associations between 65 top GWAS single nucleotide polymorphisms (SNPs) and genome-wide DNA methylation levels. We identified 159 proximal SNP-cytosine phosphate guanine (CpG) pairs (cis), and 7 distal SNP-CpG associations (trans) at birth, childhood, and adolescence. There was also a systematic enrichment for methylation related SNPs to be associated with T1D across the genome, after controlling for genomic characteristics of the SNPs, implying that methylation could either be on the causal pathway to T1D or a non-causal biomarker. Combining the principles of Mendelian Randomization and genetic colocalization analysis, we provided evidence that at 5 loci, ITGB3BP, AFF3, PTPN2, CTSH and CTLA4, DNA methylation is potentially on the causal pathway to T1D.

genomics↗

bioRxiv2017

DNA Methylation As A Marker For Prenatal Smoke Exposure In Adults

Prenatal cigarette smoke is an environmental stressor that has a profound effect on DNA methylation in the exposed offspring. We have previously shown that some of these effects persist throughout childhood and into adolescence. Of interest is whether these signals persist into adulthood.\n\nWe conducted an analysis to investigate associations between reported maternal smoking in pregnancy and DNA methylation in peripheral blood of women in the Avon Longitudinal Study of Parents and Children (ALSPAC) (n=754; mean age 30 years). We observed associations at 15 CpG sites in 11 gene regions, MYO1G, FRMD4A, CYP1A1, CNTNAP2, ARL4C, AHRR, TIFAB, MDM4, AX748264, DRD1, FTO (FDR < 5%). All but two of these CpG sites have previously been identified in relation to prenatal smoke exposure in the offspring at birth and the majority showed persistent hypermethylation among the offspring of smokers.\n\nWe confirmed that most of these associations were not driven by own smoking and that they were still present 18 years later (N = 656; mean age 48 years). In addition, we replicated findings of a persistent methylation signal related to prenatal smoke exposure in peripheral blood among men in the ALSPAC cohort (N = 230; mean age 53 years). For both participant groups, there was a strong signal of association above that expected by chance at CpG sites previously associated with prenatal smoke exposure in newborns (Wilcoxon rank sum p-value < 2.2 x 10-4). Furthermore, we found that a prenatal smoking score, derived by combining methylation values at these CpG sites, could predict whether the mothers of the ALSPAC women smoked during pregnancy with an AUC 0.69 (95% 0.67, 0.73).

epidemiology↗

bioRxiv2017

MELODI - Mining Enriched Literature Objects to Derive Intermediates

MotivationThe scientific literature contains a wealth of information from different fields on potential disease mechanisms. However, prioritising mechanisms for further analytical evaluation presents enormous challenges in terms of the quantity and diversity of published research. The application of data mining approaches to the literature offers the potential to identify and prioritise mechanisms for more focused and detailed analysis.\n\nResultsHere we present MELODI, a literature mining platform that can identify mechanistic pathways between any two biomedical concepts. Two case studies demonstrate the potential uses of MELODI and how it can generate hypotheses for further investigation. Firstly, an analysis of ERG and prostate cancer derives the intermediate transcription factor SP1, recently confirmed to be physically interacting with ERG. Secondly, examining the relationship between a new potential risk factor for pancreatic cancer identifies possible mechanistic insights which can be studied in vitro.\n\nAvailabilityMELODI has been implemented as a Python/Django web application, and is freely available to use at www.melodi.biocompute.org.uk\n\nContactmelodi@biocompute.org.uk

bioinformatics↗

bioRxiv2016

MR-Base: a platform for systematic causal inference across the phenome using billions of genetic associations

Published genetic associations can be used to infer causal relationships between phenotypes, bypassing the need for individual-level genotype or phenotype data. We have curated complete summary data from 1094 genome-wide association studies (GWAS) on diseases and other complex traits into a centralised database, and developed an analytical platform that uses these data to perform Mendelian randomization (MR) tests and sensitivity analyses (MR-Base, http://www.mrbase.org). Combined with curated data of published GWAS hits for phenomic measures, the MR-Base platform enables millions of potential causal relationships to be evaluated. We use the platform to predict the impact of lipid lowering on human health. While our analysis provides evidence that reducing LDL-cholesterol, lipoprotein(a) or triglyceride levels reduce coronary disease risk, it also suggests causal effects on a number of other non-vascular outcomes, indicating potential for adverse-effects or drug repositioning of lipid-lowering therapies.

epidemiology↗