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 In meta-analysis of observational studies confounding, residual confounding and bias: May introduce heterogeneity May lead to misleading (albeit very precise) estimates
Trial (Year) 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) Mortality results from 33 trials of beta-blockers in secondary prevention after myocardial infarction Adapted from Freemantle et al BMJ 1999 Overall (95% CI) 0.80 (0.74-0.86) 0.1 0.2 0.5 1 2 5 10 Relative risk (95% confidence interval)
Results from 29 studies examining the association between intact foreskin Study and the risk of HIV infection in men 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) Adapted from Van Howe Int J STD AIDS 1999
Vitamin E supplement use and risk of Coronary Heart Disease 1.1 1.0 0.9 0.7 0.5 0.3 Stampfer 1993 Rimm 1993 RCTs 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
Genetic meta-analysis, while of observational data, may be analogous to RCT meta-analysis NOT conventional observational meta-analysis
Clustered environments and randomised genes (93 phenotypes, 23 SNPs) Phenotype / phenotype 4278 pairwise associations 43% significant at p<0.01 Phenotype / genotype 2139 pairwise combinations 20 significant at p<0.01 vs 21 expected Genotype / genotype 253 pairwise combinations 4 / 253 significant at p<0.01 vs 3 expected Davey Smith et al. PLoS Medicine 2007 in press
WTCCC: blood donors versus 1958 birth cohort controls
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
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
If not confounding or selection bias, why have genetic association studies such a poor history of replication?
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
Reasons for inconsistent genotype True variation disease associations 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
True variation in genotype and health outcome between populations Disease-causing allele is in LD with a different allele at the marker locus in different groups More likely when disease-causing variant is rare or has been subject to selection pressure Allelic heterogeneity (different variants within the same gene) between ethnic groups The association is modified by other genetic or environmental factors that vary between the groups studied Effect modification by genes unlikely to account for failure to replicate studies in similar populations. Modification by environmental factors more likely, especially when absolute risk of disease varies
Biases vary between studies Differential misclassification of genotypes Avoided by appropriate laboratory procedures Differential misclassification of outcome: possible if genotype is known when outcome is classified Unlikely, because outcome is usually confirmed in advance of genotyping
Confounding by population substructure Population is divided into strata that vary by disease risk and by allele frequencies at the marker locus Unlikely to be a serious problem in most studies: when confounding is a problem, it can be controlled in study design by restriction or use of family-based controls, or in the analysis by quantifying and controlling for substructure
Case-mix heterogeneity Case mix heterogeneity in an apparently homogenous outcome between populations studied: for instance in a study of stroke, mix of haemorrhagic and thrombotic subtypes may vary between populations Unlikely to be an explanation for failure to replicate studies in similar populations with similar case sampling strategies
Absence of power leading to false-negative results and failure to replicate Failure to consider that the initial effect size reported is an inflation of the true effect size Replication studies should be powered to detect effect sizes that are smaller than the initial effect size reported, especially when the initial study had low power
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
False positive results by chance in initial positive studies Multiple testing and publication bias: multiple loci are assessed in each study, many statistical tests are done, and multiple studies are undertaken but only positive results are reported Perhaps the most likely reason for failure to replicate?
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
What percentage of associations that are studied actually exist? 1 in 10? (at 80% power, 5% significance level) Polymorphism really is associated with disease Polymorphism is not associated with disease Total Result of experiment Association declared to exist 80 45 125 Association not declared to exist 20 855 875 Total 100 900 1000 Oakes 1986; Davey Smith 1998; Sterne & Davey Smith 2001
Percentage of significant results that are false positives if 10% of studied associations actually exist Power of study (% of time we reject null hypothesis if it is false) P=0.05 P=0.01 P=0.001 20 69.2 31.0 4.3 50 47.4 15.3 1.8 80 36.0 10.1 1.1 Sterne & Davey Smith BMJ 2001;322:226-231
Percentage of significant results that are false positives if 1% of studied associations actually exist Power of study (% of time we reject null hypothesis if it is false) P=0.05 P=0.01 P=0.001 20 96.1 83.2 33.1 50 90.8 66.4 16.5 80 86.1 55.3 11.0 Sterne & Davey Smith. BMJ 2001;322:226-231
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
Why has replication proved 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?
Why has replication proved 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
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
Association of GNB3 and Hypertension Bagos et al, J Hypertens March 2007 34 Studies Cases = 14,094 Controls = 17,760 Total = 21,654
α β γ A B C a b c?
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
Replicable Studies ABCC8, type 2 diabetes COL1A1, fracture CTLA4, type 1 diabetes DRD3, schizophrenia GSTM1, head/neck cancer HTR2A, schizophrenia PPARG, type 2 diabetes SLC2A1, type 2 diabetes 2.28 (1.27-4.10) 1.59 (1.36-1.86) 1.27 (1.17-1.37) 1.12 (1.02-1.23) 1.20 (1.09-1.33) 1.07 (1.01-1.14) 1.22 (1.08-1.37) 1.76 (1.35-2.31)
Are genetic associations studies replicable: take two? Low hanging fruit and a best-case scenario. Effect size estimates not so widely welcomed..
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 TCF7 Science, June 1, 2007 1.14 1.14
Nature, June 7, 2007 Distribution of OR s for 70 Common Disease Variants 20 18 16 14 12 % 10 8 6 4 2 0 1.05 1.15 1.25 1.35 1.45 1.55 1.65 1.75 1.85 1.95 Odds Ratio
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
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) Mendel in 1862 Gregor Mendel, 1865.
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
Mendelian randomisation and RCTs MENDELIAN RANDOMISATION RANDOMISED CONTROLLED TRIAL RANDOM SEGREGATION OF ALLELES RANDOMISATION METHOD 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