1. Epigenetic Studies in ALSPAC Caroline Relton CAiTE Symposium 12 th January 2010

2. Objectives Define DNA methylation variation due to DNA source and extraction method Quantify changes in DNA methylation over time Validate differential DNA methylation at selected target loci in serial DNA samples Undertake pilot MeDIP-sequencing of the entire methylome Quantify differential DNA methylation in pre- and post-menopausal women Apply a Mendelian randomisation approach to strengthen evidence for a causal relationship between environmental exposures, DNA methylation and childhood outcomes Develop bioinformatic and statistical approaches for handling DNA methylation data

3. Define DNA methylation variation due to DNA source and extraction method Buffy coat White cells Whole blood Average beta (Me/unMe) Avg Beta, Guanidine 114 probes more methylated in guanidine extracted DNA (> 1.5-fold) #TimeTubeExtraction T1BirthHeparinPhenol T243 monthsEDTAPhenol T361 monthsEDTAPhenol T47 yearsEDTASalting out T5a9 yearsCPD/ACDGuanidine hydrochloride T5b9 yearsCell ineGuanidine hydrochloride T0PregnancyEDTA or heparinPhenol T0+17+17y follow-upEDTAGuanidine hydrochloride Avg Beta, Phenol

4. Quantify changes in DNA methylation over time Sequenom EpiTyper 6 amplicons 12-21 CpG sites per amplicon Birth and 7y DNA N=90 Correlation is much lower than that observed in adults at 2 time points Additional samples are being analysed AmpliconSpearman’s rho (B) 95% CI (B)Spearman p FTO_160.3410.099, 0.5680.006 P16_380.2920.075, 0.4900.008 P16_40.2650.058, 0.4580.014 IGF2BP2_11-0.242-0.439, -0.040.024 IGF2BP2_290.2370.003, 0.4640.039 PPARg_290.2150.020, 0.3980.040 P16_250.2110.014, 0.3970.047 IGF2BP2_260.216-0.017, 0.4430.047 Highest intra-probe correlations B = bootstrapped

5. Validate differential DNA methylation at selected target loci Methylation at birth and body composition in childhood SNP-dependent locus associated with insulin resistance BMIFat massLean mass Change in outcome / 1%  in methylation Genotype (rs231840) Methylation (%) Methylation at CpG vs rs231840

6. Undertake pilot MeDIP-sequencing of the entire methylome High vs normal BMI Aged 0y, 7y and 15y N=10 per group MeDIP-seq pilot Illumina 27K array Quantitative comparison of genome-wide DNA methylation mapping technologies Christoph Bock, Eleni M Tomazou, Arie B Brinkman, Fabian Müller, Femke Simmer, Hongcang Gu, Natalie Jäger, Andreas Gnirke, Hendrik G Stunnenberg & Alexander Meissner Nature Biotechnology : 28: 1106–1114 (2010)

7. Quantify differential DNA methylation in pre- and post-menopausal women Avg beta Post MP Avg beta Pre MP Pregnancy vs +17y ▫1032 CpG sites differ +/- 5% Pre vs Post menopause ▫199 CpG sites differ +/- 5% Average methylation for 5 largest methylation shifts in each direction with SD error bars

8. Apply a Mendelian randomisation approach Reverse causation Confounded Independent and both causal CpG On causal pathway CpG BMI CVD CpG CVD BMI ADH1B Socio-economic position Nutritional status Smoking CpG HNSCC Alcohol Alternative non-epigenetic pathway

9. Develop bioinformatic and statistical approaches Defining where in the genome to look for differential methylation In silico tools ▫CGI Explorer ▫Data mining tools ▫Transcription factor binding sites Gene expression data Whole methylome analysis ▫MeDIP-seq Genome-wide site-specific analysis ▫Illumina 450k array Targeted approaches ▫Illumina VeraCode Analysing DNA methylation data Large data sets Highly correlated Non-normal distribution Outlier effects Temporal variation Tissue specificity Differences in DNA source and method used ▫Illumina ▫Sequenom ▫Pyrosequencing Relationship between genotype and epigenotype

10. Grant submissions and future plans Grants awarded ▫WT/MRC Strategic Award (GDS) ▫WT ALSPAC Mums (DAL) ▫MRC Fellowship (LZ) Grant submitted ▫NIH Conduct problem trajectories (EB) ▫NIH Obesity and epigenetics (CR) ▫MRC ALSPAC Mums (DAL) ▫BBSRC BBR (GDS) Grants in preparation ▫MRC Obesity and epigenetics (CR) ▫NIH methylation trajectories in development and ageing (CR) Future directions ▫Prostate cancer (RM) ▫Insulin resistance/T2D (CR) ▫Air pollution and respiratory phenotypes (JH, PV, PE) ▫UV exposure (JT, AS) ▫Genetical epigenomics (GDS) ▫Other longitudinal studies (MCS, NSHD (1946), Bto20, IMS, APCAPS, Barshi)

11. Acknowledgements George DS Debbie L Sue R Wendy M Beate StP Tom G Adrian S Jon T Luisa Z Nic T Kate T Kate N Hannah Elliott Alix Groom