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  1. Epigenetic Studies in ALSPAC Caroline Relton CAiTE Symposium 12th 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 Average beta (Me/unMe) Avg Beta, Phenol 114 probes more methylated in guanidine extracted DNA (> 1.5-fold) Buffy coat White cells Whole blood Avg Beta, Guanidine

  4. Quantify changes in DNA methylation over time • SequenomEpiTyper • 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 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 Methylation at CpGvs rs231840 BMI Fat mass Lean mass Methylation (%) Change in outcome / 1% in methylation Genotype (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 ChristophBock, EleniM Tomazou, ArieB Brinkman, FabianMüller, FemkeSimmer, HongcangGu, Natalie Jäger, Andreas Gnirke, HendrikG Stunnenberg & Alexander Meissner Nature Biotechnology : 28: 1106–1114 (2010)

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

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

  9. Develop bioinformatic and statistical approaches Defining where in the genome to look for differential methylation • In silicotools • 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 • IlluminaVeraCode 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) • Geneticalepigenomics (GDS) • Other longitudinal studies (MCS, NSHD (1946), Bto20, IMS, APCAPS, Barshi)

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