EWAS: The new kid on the block for epigenome-wide association studies

Transcription

1 Cancer Genomics and Epigenomics EWAS: The new kid on the block for epigenome-wide association studies Stephan Beck Medical Genomics UCL Cancer Institute University College London

2 GWAS

3 Gwas what next?

4 RNA modifications 5mC C5- methylcytosine 6mA N6- methyladenosine Epigenetic modifications DNA modifications 3mC N3- methylcytosine 5mC C5- methylcytosine 5hmC C5- hydroxymethylcytosine 5fC C5-formylcytosine 5caC C5- carboxylcytosine Histone modifications ca 400 unique combinations

5 DNA methylation 5-methylcytosine (5mC) First described in 1948 (Hotchkiss J. Biol. Chem. 168:315) First linked to disease in 1983 (Feinberg & Vogelstein Nature 301:89)

6 variation types & Pathways Bhutani et al. Cell : C 5mC 5hmC 5fC 5caC MVP methylation variable position DMR differentially methylated region VMR variably methylated region

7 EWAS

8 ewas EWAS is different from GWAS Study Design EWAS variants are usually acquired not inherited EWAS variants are tissue/cell type specific selection of study material is critical Cause vs Consequence GWAS associations are causal or linked to causal variants by LD EWAS associations can also be consequence of phenotype (reverse causation) analysis of multiple and longitudinal cohorts is essential

9 EWAS cohorts GWAS EWAS EWAS Rakyan et al. Nature Review Genetics :

10 Ewas power large medium small Rakyan et al. Nature Review Genetics :

11 Ewas power simulations methor: odds of a random DNA strand from a random case to be methylated divided by the same odds for controls Rakyan et al. Nature Review Genetics :

12 technol0gy Illumina 450k platform ChAMP (Chip Analysis Methylation Pipeline) Normalisation (Infinium I/II probes) Batch effects (singlular value decomposition, SVD) SNP flagging/filtering CNV analysis Segmentation Computational of MVPs Epigenomics into DMRs Workshop Tiffany Morris

13 Integrated approach GWAS eqtl EWAS Inflammatory Bowel Disease (IBD) Type 1 Diabetes (T1D) Type 2 Diabetes (T2D)

14 Common disease ewas target tissue IBD effector cells T1D surrogate cells T2D MeDIP-chip / 27K Infinium analysis disease-associated MVPs / DMRs / HSM

15 Common disease ewas target tissue IBD (UC) N = 40 (dmz) T1D T2D MeDIP-chip / 27K Infinium 61 MVP/DMRs (P < 0.05) MeDIP-chip / 27K Infinium analysis disease-associated DMRs/MVPs

16 Common disease ewas effector cells (CD14) IBD T1D N = 30 dmz T2D 132 T1D-MVPs:NOT due to: Twinning Genetic heterogeneity Insulin treatment Metabolic dysfunction Long-term immune effects 27K Infinium analysis disease-specific MVPs

17 Common disease ewas surrogate PBMC cells IBD T1D-N N = 192 T2D 27K Infinium MVP analysis disease-associated MVPs

18 Common disease ewas surrogate cells & GWAS IBD T1D T2D N = 60 U C E MeDIP-chip F T O RNA N6mA demethylase (Jia et a; Nat Chem Biol 2011) DNAm HSM analysis P = 1.33x10-7 disease-associated HSM AA AC CC

19 Animal Models Top10 GO and KEGG categories >70% overlap between identified DMRs and fatness QTLs in PigQTL

20 Common disease ewas David Leslie, UK Vardhman Rakyan, UK Bernhard Boehm, DE Åke Lernmark, SE

21 CGI E v o l u t I o n cpg beacons - - C G Macaque - - C G Chimpanzee - - C G - - C G Human Genesis of new CGIs: seeded by one or more beacons Genesis of tissue-specific CGIs Genesis of constitutively active CGIs DNAm Probalility CpG Beacons CpG Density Role of beacons in human disease? Chris Bell

22 Beacon clusters ncrna HAR1A found to be fastest evolving human gene involved in Cortical development (Pollard et a. Nature 2 neurological and psychiatric disorders (P B-H = 6.03 x 10 -

23 Beacon clusters ANKRD11: conserved across primates and implicated in a range of developmental and neurological disorders MeDIP-seq: human, chimpanzee and macaque

24 Evolutionary selective Epigenetics ANKRD11 gene Beacon-mediated evolution of ANKRD11 regulation from tissue-specific to constitutively

25 Conclusions 1. Technologies for EWAS are available and working 2. EWAS associations (MVP, DMR and HSM) exist in common disease in addition to cancer 3. DNA methylation differences in common disease EWAS hits are small (<10%) and their effect size remains unknown 4. Integrated (epi)genomic approach allows to dissect GWAS risk haplotypes for functional analysis 5. CpG Beacons represent a new feature to study

26 acknowledgements Medical Genomics Chris Bell T1D/T1D-N//T2D EWAS Liselotte Bäckdahl IBD EWAS Lee Butcher Helena Caren Pawan Dhami Andrew Feber Paul Guilhamon Matthias Lechner Tiffany Morris EWAS Analysis Sabrina Stewart Tosin Taiwo Andrew Teschendorff EWAS Analysis Chrissie Thirlwell Collaborators UCL Adrienne Flanagan Chris Boshoff David Balding QMUL Vardhman Rakyan T1D EWAS David Leslie T1D EWAS Graham Hitman T2D EWAS Oxford U Mark McCarthy T2D EWAS Gurdon I Thomas Down Kiel U Robert Häsler IBD EWAS Philip Rosenstiel IBD EWAS Stefan Schreiber IBD Funders