B2624 - Causal Relationship between variation in DNA methylation and Type 2 Diabetes linking epigenetic and gene expression evidence - 11/05/2017

B number: 
B2624
Principal applicant name: 
Diana Juvinao Quintero | MRC, IEU, SSCM (United Kingdom)
Co-applicants: 
Miss Diana Juvinao Quintero
Title of project: 
Causal Relationship between variation in DNA methylation and Type 2 Diabetes: linking epigenetic and gene expression evidence
Proposal summary: 

Type 2 diabetes (T2D) is an important complex disease caused by environmental and genetic factors. Its current growing rates translate into economical loses, with a reduction of the workforce and higher costs to the healthcare system. High cholesterol, obesity and a sedentary life are all lifestyle factors strongly associated with T2D, although they are not perfect indicators of disease risk. Family history of T2D is also an important factor to predict disease risk. Study of common genetic variants in the population has led to the discovery of more than 80 loci (genetic regions) associated with the disease, but they only explain 5% of the genetic risk. The aim of the present project is to strengthen the evidence of the genetic contribution to T2D by testing the association between levels of methylation in the DNA with prevalent and incident cases of T2D, the latter coming from the offspring of affected mums during the gestational period. Associations identified will be verified by the use of causative analyses. The advantage of using DNA methylation over common genetic variants is that they provide information about the cellular response to potentially stressful conditions, and also DNA methylation can archive priming signals of disease risk that could have been established early-on in development. Therefore, DNA methylation can provide more reliable data about a changing molecular environment associated with the development of T2 diabetes.

Date proposal received: 
Thursday, 4 February, 2016
Date proposal approved: 
Wednesday, 17 February, 2016
Keywords: 
Epidemiology, Diabetes, Hypertension, Pregnancy - e.g. reproductive health, postnatal depression, birth outcomes, etc., coronary heart disease, Cell culture, Epigenetics, Gene expression, GWAS, Microarrays, RNA, Statistical methods, in situ prediction of gene expression Extraction of Genomic DNA Pyrosequencing targeted sequencing cellular transfection design of DNA constructs , Cohort studies - attrition, bias, participant engagement, ethics, Genetics - e.g. epigenetics, mendelian randomisation, UK10K, sequencing, etc., Methods - e.g. cross cohort analysis, data mining, mendelian randomisation, etc., Physical - activity, fitness, function, diabetes risk intrauterine environment methylation biomarkers methylation score