1. Gene-lifestyle interaction in obesity and diabetes Robert Scott MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine 07/11/14

2. Diagnosis of Diabetes

3. Diabetes Epidemiology More than 220 million people worldwide have diabetes. In 2004, an estimated 3.4 million people died from consequences of high blood sugar. More than 80% of diabetes deaths occur in low- and middle-income countries. WHO projects that diabetes deaths will double between 2005 and 2030. Healthy diet, regular physical activity, maintaining a normal body weight and avoiding tobacco use can prevent or delay the onset of type 2 diabetes. WHO Fact sheet N°312, January 2011 T2D is a chronic disease, characterised by hyperglycaemia, resulting from defects in insulin secretion, insulin action or both

4. Diabetes Aetiology Diabetes is a lifestyle-related disease Obesity Energy intake - diet Energy expenditure – physical inactivity Diabetes is a genetic disease Familial aggregation Heritability of intermediate phenotypes (Narayan et al., Diab care, 2007) (DPP, NEJM, 2002)

6. 0 5 10 15 20 25 MenWomen Sex European South Asian African/ Caribbean Southall & Brent studies; n=4809 Known & new diabetes: OGTT Prevalence ( % ) Greater diabetes prevalence in ethnic groups of both sexes

7. Family History and T2D Family Maternal Paternal Parental Biparental Sibling FHx category Overall Men Women All Group 13869 5817 8052 10379 9531 11416 8494 7825 N 3607 1358 2249 1790 1098 2682 206 980 FHx + 2.72 (2.48, 2.99) 2.64 (2.22, 3.14) 2.77 (2.49, 3.07) 2.88 (2.58, 3.22) 3.17 (2.76, 3.64) 2.87 (2.61, 3.15) 5.14 (3.74, 7.07) 2.99 (2.54, 3.52) HR (95% CI) 1 1 23 59 Hazard Ratio (Scott et al. 2013)

8. Genetics of Type 2 Diabetes Frayling T Nature Reviews Genetics 2007

9. How has GWAS informed genetics of T2D? KCNJ11 PPARG 2000-3 TCF7L2 2006 TCF2 WFS1 CDKN2B/A IGF2BP2 CDKAL1 HHEX SLC30A8 2007 Candidates Linkage and follow-up GWAS GW M-A JAZF1 NOTCH2 THADA ADAMTS9 CDC123 KCNQ1 TSPAN8 IRS1 2008/9 PROX1 GCKR, THADA, BCL11A RBMS1, GRB14, UBE2E2, ADCY5 ST64GAL1, ANKRD55 ZBED3, DGKB GCK, KLF14 ANK1, TP53INP1 SLC30A8, PTPRD TLE4, TLE1 VPS26A, ZMIZ1 HCCA2, ARAP1 MTNR1B, CCND2 KLHDC5, HMGA2 HNF1A, SPRY2 C2CD4A, HMG20A ZFAND6, AP3S2 PRC1, BCAR1 SRR, MC4R CILP2, GIPR, HNF4A Now

10. Conventional GWAS approach 1.Studies perform test of association between SNP and outcome 1.Outcome = SNP + Covariates 2.2.5M SNPs (1M independent tests) 3.Seek replication at interesting SNPs 4.Those that reach P<5x10 -8 (0.05/1M) are significant

11. Morris et al. Nature Genetics 2012

12. T2D associations in Europeans

13. Gene-lifestyle interaction Genetic main effects can identify biology relevant to diabetes and related traits Gene-lifestyle interaction seeks to identify why certain individuals respond differently to particular environments or lifestyles? Why don’t all overweight individuals develop diabetes? Why do people respond differently to lifestyle intervention? Can identify biology relevant to response to lifestyle intervention May identify individuals most likely to respond to lifestyle intervention – stratified medicine Can link genetics and public health

14. Approaches to study G*E Candidate gene analyses of G*E Discovery analyses in consortia Candidate gene analyses in multi-centre studies Discovery analyses in multi-centre studies

15. BMI according to Genetic Predisposition and Physical Activity Obesity Predisposition Resistant Prone BMI “Obesogenic” environment Restrictive environment Ravussin and Bouchard 2000

16. BMI according to Genetic Predisposition and Physical Activity Li S PLoS Med. 2010

17. Forest plot of the effect of the interaction between the FTO rs9939609 SNP and physical activity on BMI in a random effects meta-analysis of 218,166 adults. Kilpeläinen TO, Qi L, Brage S, Sharp SJ, et al. (2011) Physical Activity Attenuates the Influence of FTO Variants on Obesity Risk: A Meta- Analysis of 218,166 Adults and 19,268 Children. PLoS Med 8(11): e1001116. doi:10.1371/journal.pmed.1001116 http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001116

18. Incidence of Diabetes According to Treatment Group and rs7903146 Florez et al. N Engl J Med. 2006

19. (Wareham et al., Am.J.Epidemiol. 2000) SNPGene Effect (mmol/l)P value rs1260326GCKR0.109.2 x 10 -21 rs2877716ADCY50.071.68 x 10 -11 rs12243326TCF7L20.079.99 x 10 -9 rs17271305VPS13C0.074.33 x 10 -11 rs10423928GIPR0.112.56 x 10 -20 (Saxena et al., Nat.Genet. 2010) 2hr glucose = Genes + Lifestyle

20. A: Effect of SNP is shown on 2-h glucose. Scott R A et al. Diabetes 2012;61:1291-1296 Copyright © 2011 American Diabetes Association, Inc.

21. Are loci detected by main effect GWAS likely to be the most environmentally sensitive ? p-value ~ Effect size / variance

22. Approaches to study G*E Candidate gene analyses of G*E Discovery analyses in consortia Candidate gene analyses in multi-centre studies Discovery analyses in multi-centre studies

23. Method jointly meta-analyses the SNP and SNPxBMI terms to produce a single test statistic with null hypothesis that: β SNP = 0 and β SNPxBMI = 0 Alternative approach 1: GWAS of G and GxE GWAS to GWIS GWAS: Outcome = SNP GWIS: Outcome = SNP + SNPxE

24. Manning et al. Nat Gen 2012 Continuous Glycaemic Traits in Healthy Individuals: Fasting Insulin and Glucose

25. Difficulties with this approach for T2D Lifestyle measurement varies across studies Difficult to harmonise Lifestyle/environment subject to reverse causality / recall bias in case- control analyses Can be overcome using prospective study designs

26. EU funded nested case-cohort study within EPIC Europe Study Large 455,680 individuals at baseline Sufficient elapsed time 12,403 incident cases of T2DM Baseline measures: Diet/Physical activity Stored blood Exposure heterogeneity Discover how genetic and lifestyle behavioural factors interact on risk of developing type 2 diabetes

27. Approaches to study G*E Candidate gene analyses of G*E Discovery analyses in consortia Candidate gene analyses in multi-centre studies Discovery analyses in multi-centre studies

28. Established T2D loci * Environment interaction 49 SNP genetic score Association with incident T2D Investigated interaction with major risk factors Also by strata major risk factors for T2D

29. Langenberg et al., PLOS Med, 2014

30. Langenberg et al. PLOS Med, 2014 Absolute risk of T2D by BMI and genetic risk strata

31. Approaches to study G*E Candidate gene analyses of G*E Discovery analyses in consortia Candidate gene analyses in multi-centre studies Discovery analyses in multi-centre studies

32. EU funded nested case-cohort study within EPIC Europe Study Large 455,680 individuals at baseline Sufficient elapsed time 12,403 incident cases of T2DM Diet/Physical activity Stored blood Exposure heterogeneity Discover how genetic and lifestyle behavioural factors interact on risk of developing type 2 diabetes GWIS IN PROGRESS

33. Conclusions Epidemiological data implicate a role for gene-environment interactions in aetiology of T2D and obesity; progress in understanding the molecular basis has been slow. Consortia-based meta-analysis for quantitative traits is possible but there are limits due to the variability in assessment of the lifestyle exposures. There is little evidence of interaction of T2D loci either in quantitative trait studies, prevention trials or prospective cohort studies- GWIS is underway. Current knowledge of interaction does not provide support for a role of genetic stratification in prevention. Evidence of how genetics would play in role in such preventive strategies is limited.

34. Acknowledgements Nick Wareham Claudia Langenberg Stephen Sharp Lab and Technical Teams DIAGRAM consortium

35. Prediction Talmud et al., BMJ, 2010