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Genetic Meta-Analysis and Mendelian Randomization

Genetic Meta-Analysis and Mendelian Randomization. George Davey Smith MRC Centre for Causal Analyses in Translational Epidemiology, University of Bristol. RCT vs Observational Meta-Analysis: fundamental difference in assumptions.

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Genetic Meta-Analysis and Mendelian Randomization

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  1. Genetic Meta-Analysis and Mendelian Randomization George Davey Smith MRC Centre for Causal Analyses in Translational Epidemiology, University of Bristol

  2. RCT vs Observational Meta-Analysis: fundamental difference in assumptions • In meta-analysis of observational studies confounding, residual confounding and bias: • May introduce heterogeneity • May lead to misleading (albeit very precise) estimates

  3. Trial (Year) Mortality results from 33 trials of beta-blockers in secondary prevention after myocardial infarction Adapted from Freemantle et al BMJ 1999 Barber (1967) Reynolds (1972) Wilhelmsson (1974) Ahlmark (1974) Multicentre International (1975) Yusuf (1979) Andersen (1979) Rehnqvist (1980) Baber (1980) Wilcox Atenolol (1980) Wilcox Propanolol (1980) Hjalmarson (1981) Norwegian Multicentre (1981) Hansteen (1982) Julian (1982) BHAT (1982) Taylor (1982) Manger Cats (1983) Rehnqvist (1983) Australian-Swedish (1983) Mazur (1984) EIS (1984) Salathia (1985) Roque (1987) LIT 91987) Kaul (1988) Boissel (1990) Schwartz low risk (1992) Schwartz high risk (1992) SSSD (1993) Darasz (1995) Basu (1997) Aronow (1997) 0.80 (0.74 - 0.86) Overall (95% CI) 0.1 0.2 0.5 1 2 5 10 Relative risk (95% confidence interval)

  4. Study Allen Barongo Bollinger Bwayo Bwayo Cameron Carael Chao Chiasson Diallo Greenblatt Grosskurth Hira Hunter Konde-Luc Kreiss Malamba Mehendal Moss Nasio Pepin Quigley Sassan Sedlin Seed Simonsen Tyndall Urassa 1 Urassa 2 Urassa 3 Urassa 4 Urassa 5 Van de Perre 0.2 0.5 1 2 5 10 Relative risk (95% confidence interval) Results from 29 studies examining the association between intact foreskin and the risk of HIV infection in men Adapted from Van Howe Int J STD AIDS 1999

  5. 1.0 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

  6. Genetic meta-analysis, while of observational data, may be analogous to RCT meta-analysis NOT conventional observational meta-analysis

  7. Clustered environments and randomised genes (93 phenotypes, 23 SNPs) Davey Smith et al. PLoS Medicine 2007 in press

  8. WTCCC: blood donors versus 1958 birth cohort controls

  9. A leading epidemiologist speaks … “Forget what you learnt at the London School of Hygiene and Tropical Medicine …. just get as many cases as possible and a bunch of controls from wherever you can ..” Paul McKeigue, Nov 2002

  10. Or the polite version … “This approach allows geneticists to focus on collecting large numbers of cases and controls at low cost, without the strict population-based sampling protocols that are required to minimize selection bias in case-control studies of environmental exposures” Am J Human Genetics 2003;72:1492-1504

  11. If not confounding or selection bias, why have genetic association studies such a poor history of replication?

  12. Are genetic association studies replicable? Hirschhorn et al reviewed 166 putative associations for which there were 3 or more published studies and found that only 6 had been consistently replicated (defined as “achieving statistically significant findings in 75% or more of published studies”) Hirschhorn JN et al. Genetics in Medicine 2002;4:45-61

  13. Reasons for inconsistent genotype – disease associations True variation Variation of allelic association between subpopulations Effect modification by other genetic or environmental factors that vary between populations Spurious variation Misclassification of phenotype Confounding by population structure Lack of power Chance Publication bias Colhoun et al, Lancet 2003;361:865-72

  14. True variation in genotype and health outcome between populations

  15. Biases vary between studies

  16. Confounding by population substructure

  17. Case-mix heterogeneity

  18. Absence of power leading to false-negative results and failure to replicate

  19. The Beavis effect If the location of a variant and its phenotypic effect size are estimated from the same data sets, the effect size will be over-estimated, in many cases substantially. Statistical significance and the estimated magnitude of the parameter are highly correlated. H Göring et al. Am J Hum Genetics 2001;69:1357-69

  20. False positive results by chance in initial positive studies

  21. What is being associated in genetic association studies? • Estimates of 15M SNPs in human genome (rare allele frequency >1% in at least one population) • Large number of outcomes (diseases and subcategories of particular disease outcomes) • Large number of potential subgroups • Multiple possible genetic contrasts

  22. What percentage of associations that are studied actually exist? … 1 in 10? (at 80% power, 5% significance level) Oakes 1986; Davey Smith 1998; Sterne & Davey Smith 2001

  23. Percentage of “significant” results that are false positives if 10% of studied associations actually exist Sterne & Davey Smith BMJ 2001;322:226-231

  24. Percentage of “significant” results that are false positives if 1% of studied associations actually exist Sterne & Davey Smith. BMJ 2001;322:226-231

  25. P values often misinterpreted in both genetic and conventional epidemiology Low prior probability major issue in genetic epidemiology; meaningless (but real) associations a major issue in conventional epidemiology

  26. Why has replicationproved to be so difficult? • LOW STATISTICAL POWER • A consistent feature of almost all analyses • Fundamental to many of the explanations or the approach needed to correct for them • If we need 5,000 cases to test for a given aetiological effect with a power of 80%, and with a critical p-value of 0.0001, how much power would there be for a study with 500 cases?

  27. Why has replicationproved to be so difficult? • LOW STATISTICAL POWER!! • A key feature of almost all proffered explanations, and/or of the approach needed to correct for them • If we need 5,000 cases to test for a given aetiological effect with a power of 80%, and with a critical p-value of 0.0001, how much power would there be for a study with 500 cases? 0.008

  28. Deducing “true numerical ratios” requires “the greatest possible number of individual values; and the greater the number of these the more effectively will mere chance be eliminated”. Gregor Mendel 1865/6

  29. Association of GNB3 and Hypertension Bagos et al, J Hypertens March 2007 34 Studies Cases = 14,094 Controls = 17,760 Total = 21,654

  30. ¿|αβγ|A B C|a b c|?

  31. Are genetic associations studies replicable: take two? Joel Hirschhorn’s group selected 25 of the 166 genetic associations that they had studied and performed formal meta-analysis, claiming that 8 of these (one third) were robust. “One third” claim widely welcomed! Lohmueller KE et al. Nature Genetics 2003;33:177-182

  32. Replicable Studies

  33. Are genetic associations studies replicable: take two? “Low hanging fruit” and a best-case scenario. Effect size estimates not so widely welcomed ..

  34. All Studies Combined 14,585 cases 17,968 controls 1.17 1.12 1.13 1.20 1.12 1.14 1.12 1.37 1.14 1.14 TCF7 Science, June 1, 2007

  35. Distribution of OR’s for 70 Common Disease Variants Nature, June 7, 2007 % Odds Ratio

  36. for exposures with small effect sizes it is very difficult to exclude confounding and bias in conventional epidemiology, and level of statistical “significance” does not help statistical deviation from the null more important in genetic epidemiology

  37. Mendel on Mendelian randomization “the behaviour of each pair of differentiating characteristics in hybrid union is independent of the other differences between the two original plants, and, further, the hybrid produces just so many kinds of egg and pollen cells as there are possible constant combination forms” (Sometimes called Mendel’s second law – the law of independent assortment) Gregor Mendel, 1865. Mendel in 1862

  38. Mendelian randomization Genotypes can proxy for some modifiable environmental 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), no bias due to reverse causation, and lifetime exposure patterns can be captured

  39. MENDELIAN RANDOMISATION RANDOMISED CONTROLLED TRIAL RANDOMISATION METHOD RANDOM SEGREGATION OF ALLELES EXPOSED: FUNCTIONAL ALLELLES CONTROL: NULL ALLELLES EXPOSED: INTERVENTION CONTROL: NO INTERVENTION CONFOUNDERS EQUAL BETWEEN GROUPS CONFOUNDERS EQUAL BETWEEN GROUPS OUTCOMES COMPARED BETWEEN GROUPS OUTCOMES COMPARED BETWEEN GROUPS Mendelian randomisation and RCTs

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