As a fundamental step in understanding the causes of epigenetic variation we will generate data of key epigenetic modifications in collections of two blood cell types from 100 healthy individuals. These measurements will be combined with whole genome sequencing and transcriptome sequencing in order to dissect the interplay between common DNA sequence variation and the epigenome. This will allow us to estimate the degree by which certain modifications at the cellular level, primarily driven by epigenetic modifications, find their causes at the level of the heritable genome (i.e. DNA) and what fraction of cellular function and variation is driven by transient or stable non-genetic effects. This will provide a unique framework to understand the interplay between the genetic code and environment and how the latter contributes to complex diseases and will ultimately show the direction of prevention and medical intervention that needs to be adopted to achieve better health.
BLUEPRINT also aims to quantify the variation in epigenomes using inbred strains of mice, an excellent animal model for interrogating the genotype-epigenotype-phenotype relationship. The advantage of using inbred strains of mice is the ability to control mating conditions between mice of identical DNA sequence, thus providing genetic homogeneity in experimental cells, and, where appropriate, experimental F1 hybrids of controlled genotype. BLUEPRINT will conduct the first study that combines RNA-seq, DNA methylation and hydroxymethylation profiling with ChIP-seq for seven further epigenetic features. We will interrogate the epigenomes of three strains of mouse whose sequence is completed (C57BL/6J, C3H/HeJ and CAST/EiJ) using two easily purified cell types directly isolated from mouse peripheral blood. These data will be validated, and then the extent to which differences in epigenotype correlate with sequence variation will be quantified and related to the transcriptome. In these defined genetic backgrounds, these data will inform the human datasets by determining how much variation is genotype-dependent, how much may be caused by non-genetic components and how much is functional. As the chosen cell types are comparable to those analysed from human, inter-species comparisons in the data-sets can be made. The mouse model programme will also provide an opportunity to identify any autosomal differences between males and females and the extent of parental origin effects, for example those that might lie outside known imprinted regions.
Research Area Leader: Stephan Beck
|DNA methylation variation in T1DM||Leader: David Leslie|
|Biomarker development||Leader: Christoph Bock|
|The effect of common sequence variation on the epigenome landscape||Leader: Nicole Soranzo|
|Mouse models to quantify variation in reference epigenomes||Leader: Anne Ferguson-Smith|