Epigenetics and psychiatric illness

1. Epigenetics and psychiatric illness Riccardo Marioni Chancellor’s Fellow Centre for Genomic and Experimental Medicine University of Edinburgh riccardo.marioni@ed.ac.uk Centre for Genomic & Experimental Medicine MRC Institute of Genetics & Molecular Medicine at the University of Edinburgh www.ed.ac.uk/cgem

2. Learning Objectives • Describe DNA methylation, how it is measured, and how it varies by tissue type. • Explain the analysis protocol for an epigenome-wide association study and interpret EWAS output. • Recognise the strengths and weaknesses of various epigenetic clock measures and other biomarkers of ageing. • Describe the difference between polygenic and polyepigenic prediction of quantitiative traits • List the pros and cons of looking at blood-based epigenetic markers of brain-related traits.

3. Introduction to dnamethylation

4. DNA Methylation • An epigenetic mark • Involved in gene regulation • Occurs across the genome • Addition of methyl group to C nucleotide (CpG) • Changes over time Interested in the proportion of methylated DNA molecules at each CpG site

5. Technical details • Bisulphite conversion of DNA • Methylated locus C → C • Non-methylated locus C → U → T • Two chemistries • 135,000 probes from Infinium I array • One probe methylated locus, one for non-methylated • 350,000 probes from Infinium II array • One probe for both loci, different colours for meth/non-meth Bibikova et al. Genomics, 2011; 98(4): 288-295

6. Beta-values & M-values 2

7. Tissue Issue • Cell composition eosinophil basophil monocyte neutrophil lymphocyte

8. Quality Control • exclude samples with detection p-values <0.01 for <95% of probes • exclude probes with detection p-values >0.01 in >5% of samples. • Tend not to remove X and Y probes or cross-reactive or polymorphic probes.

9. Genetics Epigenetics Cognitive Function Health

10. Epigenome wide association studies (EWAS)

11. EWAS • ~450,000 CpGs • CpG ~ Trait + Age + Sex + WBCs + (1 | Technical Covariates) • CpG ~ Trait + Age + Sex + WBCs + smoking + BMI + (1 | Technical Covariates) • Cause and effect?

12. Smoking EWAS Zeilinger et al. Plos One 2013; 8(5): e63812

13. Smoking EWAS Joehanes*, Just,* Marioni* et al. Circ Card Gen 2016

14. Alcohol EWAS Methylation signatures built from LASSO regression in one cohort, used to predict heavy from non-drinkers in independent cohorts Liu,* Marioni* et al. Mol Psychiat 2016

15. BMI EWAS • Dick et al. • 479 people, whole bld • Replication in 339 people • 2nd replication in 1,789 people • Replication in adipose tissue (n=635) and skin (n=395) • GEx for hits • Three hits in blood • One replicated in adipose tissue • Inverse association with GEx Dick et al. Lancet 2014; 383(9933): 1990-98

16. BMI Mendelson*, Marioni* et al. PLoS Med 2017

17. EPIGENETIC CLOCK

18. Epigenetic Clock http://labs.genetics.ucla.edu/horvath/dnamage/

19. Methylation Age Elastic net regression on 450k probes Hannum et al. Molecular Cell 2013, 49(2): 359-367 Horvath Genome Biol 2013, 14(10): R115

21. Epigenetic age predicts all-cause mortality High Δage Low Δage High Δage Low Δage Marioni et al. Genome Biol 2015

22. Epigenetic clock and longevity Horvath et al. Aging, 2015; 7(5): 294-306

23. Other epigenetic clock correlates • Obesity (specific to liver) • Down Syndrome • Alzheimer’s disease pathology • HIV • Frailty • Prenatal and early life factors Horvath et al. PNAS 2014 Horvath et al. Aging Cell 2015 Levine et al. Aging 2015 Gross et al. Mol Cell 2016 Breitling et al Clin Epigenetics 2016 Simpkin et al. Hum Mol Gen 2016

24. Cognitive and physical fitness & the epigenetic clock • Cross-sectional but not longitudinal associations gf Grip Strength FEV1 6m walk speed Marioni et al. Int J Epidemiol 2015

25. Other clocks: telomere length Lapham et al. Genetics 2015

26. Biological Clocks in Parallel Marioni et al. Int J Epidemiol 2016

27. Biological Clocks in Parallel • Does the epigenetic clock correlate with telomere length? • Do changes in the epigenetic clock associate with concurrent changes in telomere length? • Do both biological clocks correlate with age independently? • Do both biological clocks associate with mortality risk independently?

28. Polygenic and polyepigenic Prediction

29. EWAS/GWAS predictions of BMI • Methylation and genetic profile scores created using EWAS/GWAS hits • Scores then used as predictors of BMI in other cohorts Shah, Bonder, Marioni et al. AJHG, 2015

30. Models • Model 1: trait ~ GWAS_score • Model 2: trait ~ EWAS_score • Model 3: trait ~ GWAS_score + EWAS_score • Model 4: trait ~ GWAS_score * EWAS_score

31. Polygenic Scores • GWAS from large discovery cohort or meta-analysis • Use βs to create weighted score in independent cohort • Genetic Score = β1*SNP1 + β2*SNP2 + ...

32. Polygenic Scores

33. Polygenic Scores • EWAS Scores • 9 CpGs in LBC (n=1,366) • 5 CpGs in Lifelines (n=752)

34. Cross cohort predictors

35. EWAS/GWAS height prediction

36. Epigenetic prediction of AD • Evidence for a blood-based DNA methylation signature for AD • Are there EWAS signals and epigenetic clock differences in those at high genetic risk of AD? • APOE • AD PGS Lunnon et al. Nature Neurosc 2014

37. Longitudinal modelling • Novel genetic loci • Are these SNPs eQTLs or methQTLs? • Does expression or methylation at these sites change over time? • Novel epigenetic loci • Are these CpGs under genetic control? • Are they linked to gene expression? • Do they change with age? • Do they correlate with changes in other markers of decline – brain atrophy or cognitive decline?

38. Lothian Birth Cohort 1936 (n=1,091) • Exponential increase in AD risk over 8th decade • Methylation at 70,73,76,79 • MRI at 73,76,79 • LCL expression at 70,76 • WGS on all participants • Telomeres at 70,73,76,79 • Inflammasomics at 70 • Lipidomics at 73

39. MethQTLs • 57,070 cis hits • 1,985 trans hits McRae et al. In preparation