1. THE USE OF GENETIC VARIANTS AS TOOLS FOR EPIDEMIOLOGISTS George Davey Smith MRC Integrative Epidemiology Unit University of Bristol

2. The principle of Mendelian randomization EY G U (A) CRP CHD CRP SNP U (B)

4. CHD risk according to duration of current Vitamin E supplement use compared to no use Rimm et al NEJM 1993; 328: 1450-6 RR

6. Vitamin E supplement use and risk of Coronary Heart Disease Stampfer et al NEJM 1993; 328: 144-9; Rimm et al NEJM 1993; 328: 1450-6; Eidelman et al Arch Intern Med 2004; 164:1552-6 1.0

7. Vitamin E levels and risk factors: Women’s Heart Health Study Childhood SES Manual social class No car access State pension only Smoker Daily alcohol Exercise Low fat diet Obese Height Leg length Lawlor et al, Lancet 2004

8. “Well, so much for antioxidants.”

10. Mendelian randomization Mendel in 1862 In genetic association studies the laws of Mendelian genetics imply that comparison of groups of individuals defined by genotype should only differ with respect to the locus under study (and closely related loci in linkage disequilibrium with the locus under study) Genotypes can proxy for some modifiable risk factors, and there should be no confounding of genotype by behavioural, socioeconomic or physiological factors (excepting those influenced by alleles at closely proximate loci or due to population stratification)

11. Modifiable exposure (e.g. CRP) Outcome (e.g. CHD) Confounders (Factors associated with both exposure and outcome, including unmeasured confounders) and/or Reverse causation (Disease alters the modifiable exposure of interest, rather than vice versa) Conventional observational epidemiology

12. Modifiable exposure (e.g. CRP) Outcome (e.g. CHD) Confounders (Factors associated with both exposure and outcome, including unmeasured confounders) and/or Reverse causation (Disease alters the modifiable exposure of interest, rather than vice versa) Conventional observational epidemiology It is often impossible to exclude confounding and /or reverse causation as an explanation for observed exposure/outcome associations

13. Modifiable exposure (e.g. CRP) Outcome (e.g. CHD) Confounders (Factors associated with both exposure and outcome, including unmeasured confounders) and/or Reverse causation (Disease alters the modifiable exposure of interest, rather than vice versa) Mendelian randomization approach Instrumental variable (Genetic variant e.g. SNP in CRP gene)

14. Modifiable exposure (e.g. CRP) Outcome (e.g. CHD) Confounders (Factors associated with both exposure and outcome, including unmeasured confounders) and/or Reverse causation (Disease alters the modifiable exposure of interest, rather than vice versa) Mendelian randomization approach Instrumental variable (Genetic variant e.g. SNP in CRP gene)

15. Modifiable exposure (e.g. CRP) Outcome (e.g. CHD) Confounders (Factors associated with both exposure and outcome, including unmeasured confounders) and/or Reverse causation (Disease alters the modifiable exposure of interest, rather than vice versa) Instrumental variable analysis in MR study Instrumental variable (Genetic variant e.g. SNP in CRP gene)

16. Associations of IL6, CRP and fibrinogen (top panel) and their prediction of CHD (bottom panel) IL6R Genetics Consortium Emerging Risk Factors Collaboration. Interleukin-6 receptor pathways in coronary heart disease: a collaborative meta-analysis of 82 studies. Lancet 2012;379:1205–1213

17. C-Reactive Protein Coronary Heart Disease Genetics Collaboration (CCGC). Association between C reactive protein and coronary heart disease: mendelian randomisation analysis based on individual participant data. BMJ 2011;342:d548

18. CRP CHD Genetics Consortium. Association between C reactive protein and coronary heart disease: mendelian randomisation analysis. BMJ 2011 FOR FIBRINOGEN see Davey Smith et al Does Elevated Plasma Fibrinogen Increase the Risk of Coronary Heart Disease?: Evidence from a Meta-Analysis of Genetic Association Studies. Arterioscler Thromb Vasc Biol 2005; 25: 2228-2233.

19. Uric acid and IHD using SLC2A9 as an instrument Palmer T et al. Association of plasma uric acid with ischaemic heart disease and blood pressure: mendelian randomisation analysis of two large cohorts BMJ2013;347:f4262

20. BMI and uric acid using FTO, TMEM and MC4R as instruments Palmer T et al. Association of plasma uric acid with ischaemic heart disease and blood pressure: mendelian randomisation analysis of two large cohorts BMJ2013;347:f4262

21. Genetic Effects vs. cross-sectional observations

23. Limitations Reintroduced confounding through pleiotropy

24. Two categories of pleiotropy Spurious Relational Vertical Type II Genuine Mosaic Horizontal Type I Gruneberg H. An analysis of the “pleiotropic” effects of a lethal mutation in the rat. Proc R Soc London, B 1938:125:123-44 Hadorn E. Developmental genetics and lethal factors. Methuen and Company, London 1961 Wagner GP, Zhang J. The pleiotropic structure of the genotype – phenotype map: the evolvability of complex organisms. Nature Reviews Genetics 2011; 12: 204-213. Tyler AL, Asselbergs FW, Williams SM, Moore JH. Shadows of complexity: what biological networks reveal about epistasis and pleiotropy. Bio Essays 2009; 31: 220- 227. Hodgkin J. Seven Types of Pleiotropy. Int. J. Dev. Biol. 1998;42: 501-505.

25. Approaches from econometrics Interact instrument with a second exogenous variable which modifies (optimally qualitatively) effect of instrument on intermediate phenotype Card D. Using geographic variation in college proximity to estimate the return from schooling. NBER Working Paper 4483, 1993

26. GxE in an exposure propensity example: how does alcohol intake influence the risk of disease?

28. Metabolism of alcohol Ethanol ADH CYP2E1 AcetaldehydeAcetic acid ALDH * Mainly occurs in the liver, but some activity is also present in the oral cavity and digestive tract

29. ALDH2 genotype by alcohol consumption, g/day: 5 studies, n=6815 Men Women Chen, Davey Smith et al, PLoS Med 2008

30. Relationship between characteristics and ALDH2 genotype Takagi et al, Hypertens Res 2002;25:677-81 Age Years Smoker Percent BMI kg/m 2 Cholesterol mg/dl

31. ALDH2 genotype and systolic blood pressure Chen et al, PLoS Medicine 2008

32. Risk of upper aerodigestive cancer by ADH1B genetic variation, stratified by drinking intensity, rare allele carriers versus common allele homozygous genotype Hashibe et al, Nature Genetics 2008

33. Meta-analysis of association between CHRNA RS1051730 variant and BMI stratified by smoking status (Freathy et al, 2011). 0.05 kg/m2 [95%CI: -0.05, 0.18] -0.23kg/m2 [95%CI: -0.13, -0.31] -0.10 kg/m2 [95%CI: -0.03, -0.18] P for interaction = 0.0001

34. Approaches from econometrics Interact instrument with a second exogenous variable which modifies (optimally qualitatively) effect of instrument on intermediate phenotype Use of multiple instruments Card D. Using geographic variation in college proximity to estimate the return from schooling. NBER Working Paper 4483, 1993 Murray MP. Avoiding Invalid Instruments and Coping with Weak Instruments. Journal of Economic Perspectives 2006;20:111–132

35. ExposureOutcomes Pleiotropy? Use of multiple instruments in Mendelian randomization approaches … Confounders; reverse causation; bias Gene 1Gene 2

36. Percent body fat BMD Does body fat increase bone mineral density? Confounders; reverse causation; bias FTOMC4R Timpson, Davey Smith and Tobias, JBMR 2009

37. Ference BA et al. Effect of Long-Term Exposure to Lower Low-Density Lipoprotein Cholesterol Beginning Early in Life on the Risk of Coronary Heart Disease : A Mendelian Randomization Analysis. JACC 2012 doi: 10.1016/j.jacc.2012.09.017 Effect of 9 SNPs from 6 genes on LDL cholesterol and on CHD risk

38. SNP associations with uric acid and gout Yang Q, Kottgen A, Dehghan A, et al. Multiple Genetic Loci Influence Serum Urate Levels and Their Relationship With Gout and Cardiovascular Disease Risk Factors. Circulation Cardiovascular Genetics 2010;3:523-30

39. Approaches from econometrics Interact instrument with a second exogenous variable which modifies (optimally qualitatively) effect of instrument on intermediate phenotype Use of multiple instruments Sensitivity analysis using standard methods (bias is to OLS) and SSIV/JIVE (bias to null) Card D. Using geographic variation in college proximity to estimate the return from schooling. NBER Working Paper 4483, 1993 Murray MP. Avoiding Invalid Instruments and Coping with Weak Instruments. Journal of Economic Perspectives 2006;20:111–132

40. Approaches from econometrics and beyond Interact instrument with a second exogenous variable which modifies (optimally qualitatively) effect of instrument on intermediate phenotype Use of multiple instruments Sensitivity analysis using standard methods (bias is to OLS) and SSIV/JIVE (bias to null) Bidirectional instrumentation

41. Bidirectional or Reciprocal MR Possible to interrogate pathways from both directions

42. Timpson NJ et al. International Journal of Obesity 2011; 35, 300–308. BMI (exposure) and CRP (outcome)

43. Timpson NJ et al. International Journal of Obesity 2011; 35, 300–308. CRP (exposure) and BMI (outcome)

44. Limitations Reintroduced confounding through pleiotropy Reintroduced confounding through LD

45. Limitations Reintroduced confounding through pleiotropy Reintroduced confounding through LD Low statistical power (and weak instrument bias) - Multiple instruments (using e.g. CUE, LIML) -Allele scores -Two sample MR / IV

46. Limitations Reintroduced confounding through pleiotropy Reintroduced confounding through LD Low statistical power Lack of variants to serve as proxy measures Canalization / developmental compensation Complexity of associations and failure to understand what is being instrumented for

47. Developments of MR Multiphenotype MR Non-linear associations “Biomarker demendalization” Mediation, e.g.epigenomic MR (two step, two step two sample, etc) Anonymous MR Data mining with MR instruments Hypothesis free causality