Integrated epigenomics and metabolomics analysis in twins

• Main Author: Yet, Idil
• Other Authors: Bell, Jordana Tzenova ; Spector, Timothy David
• Published: King's College London (University of London) 2016
• Subjects: 612.3
• Online Access: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.695758

Epigenetics and metabolomics are rapidly growing areas of research, in part due to recent advances in technology that have allowed for a wide coverage of the human genome. Metabolites are small compounds present in cell and body fluids, and are involved in biochemical processes of the cell. Quantitative trait loci associated with levels of individual metabolites (mQTLs) have been identified from numerous metabolome GWAS. Here, I analysed metabolite levels in twins with the aim of identifying genetic variants that influence metabolomic traits (mQTLs) using two different metabolomics platforms, and consequently compared the results to report stable metabolites on both technologies to ultimately enable combining metabolite profiles across these two platforms. DNA methylation is a biochemical process that is vital for mammalian development. It is present throughout the genome and is the most extensively studied epigenetic mark. Previous studies have explored the heritability of DNA methylation and have identified methylation QTLs (meQTL). Here, I identified meQTLs with the goal of assesing the evidence of genetic effects influence not only DNA methylation levels, but also variability by using MZ-twin discordance as a measure of variance. Epigenetic mechanisms and metabolomic profiles have both been shown to play a role in gene expression and susceptibility for complex human disease. Here, I analysed the association between type 2 diabetes and metabolomic and epigenetic datasets and combined the data to identify connections between these levels of biological data at genetic variants linked to type 2 diabetes to gain more insight into the disease susceptibility and progression. Overall, the results confirmed previous findings of strong genetic influences on metabolites and extend current knowledge about genetic effects underlying several biochemical pathways. Additionally, the results also showed genetic influences on DNA methylation, and give insights into mechanisms by which genetic impacts epigenetic processes. Lastly, the findings show that specific genetic susceptibility variants for type 2 diabetes can also impact epigenetic and metabolomics profiles, and can help improve our understanding of the disease etiology.