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Genetic Meta-Analysis and Mendelian Randomization
George Davey SmithMRC 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
Relative risk(95% confidence interval)
0.1 0.2 0.5 1 2 5 10
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)
Overall (95% CI) 0.80 (0.74 - 0.86)
Mortality results from 33 trials of beta-blockers in secondary prevention after myocardial infarction
Adapted from Freemantle et al BMJ 1999
0.2 0.5 1 2 5 10
Study
AllenBarongoBollingerBwayoBwayoCameronCaraelChaoChiassonDialloGreenblattGrosskurthHiraHunterKonde-LucKreissMalambaMehendalMossNasioPepinQuigleySassanSedlinSeedSimonsenTyndallUrassa 1Urassa 2Urassa 3Urassa 4Urassa 5Van de Perre
Relative risk
(95% confidence interval)
Results from 29 studies examining the association between intact foreskinand the risk of HIV infection in men
Adapted from Van Howe Int J STD AIDS 1999
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
0.3
0.5
0.7
0.9
1.1
Stampfer 1993 Rimm 1993 RCTs
1.0
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)
4 / 253 significant at p<0.01 vs 3 expected
20 significant at p<0.01 vs 21 expected
43% significant at p<0.01
Genotype / genotype253 pairwise combinations
Phenotype / genotype2139 pairwise combinations
Phenotype / phenotype4278 pairwise associations
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 –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 phenotypeConfounding by population structureLack of powerChancePublication bias
Colhoun et al, Lancet 2003;361:865-72
True variation in genotype and health outcome between populations
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
The association is modified by other genetic or environmental factors that vary between the groups studied
More likely when disease-causing variant is rare or has been subject to selection pressure
Disease-causing allele is in LD with a different allele at the marker locus in different groups
Allelic heterogeneity (different variants within the same gene) between ethnic groups
Unlikely, because outcome is usually confirmed in advance of genotyping
Differential misclassification of outcome: possible if genotype is known when outcome is classified
Avoided by appropriate laboratory procedures
Differential misclassification of genotypes
Biases vary between studies
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
Population is divided into strata that vary by disease risk and by allele frequencies at the marker locus
Confounding by population substructure
Unlikely to be an explanation for failure to replicate studies in similar populations with similar case sampling strategies
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
Case-mix heterogeneity
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
Failure to consider that the initial effect size reported is an inflation of the true effect size
Absence of power leading to false-negative results and failure to replicate
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 estimatedmagnitude of the parameter are highly correlated.
H Göring et al. Am J Hum Genetics 2001;69:1357-69
Perhaps the most likely reason for failure to replicate?
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
False positive results by chance in initial positive studies
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
1000900100Total
87585520Association not declared to exist
1254580Association declared to exist
Result of experiment
TotalPolymorphism is not associated with disease
Polymorphism really is associated with disease
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
1.110.136.080
1.815.347.450
4.331.069.220
P=0.001P=0.01P=0.05Power of study (% of time we reject null hypothesis if it is false)
Percentage of “significant” results that are false positives if 10% of studied associations actually exist
Sterne & Davey Smith BMJ 2001;322:226-231
11.055.386.180
16.566.490.850
33.183.296.120
P=0.001P=0.01P=0.05Power of study (% of time we reject null hypothesis if it is false)
Percentage of “significant” results that are false positives if 1% of studied associations actually exist
Sterne & Davey Smith. BMJ 2001;322:226-231
P values often misinterpreted in both genetic and conventional
epidemiologyLow prior probability major issue in genetic epidemiology; meaningless (but real) associations a major issue
in conventional epidemiology
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?
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
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 HypertensionBagos et al, J Hypertens March 2007
34 Studies
Cases = 14,094Controls = 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
1.76 (1.35-2.31)SLC2A1, type 2 diabetes
1.22 (1.08-1.37)PPARG, type 2 diabetes
1.07 (1.01-1.14)HTR2A, schizophrenia
1.20 (1.09-1.33)GSTM1, head/neck cancer
1.12 (1.02-1.23)DRD3, schizophrenia
1.27 (1.17-1.37)CTLA4, type 1 diabetes
1.59 (1.36-1.86)COL1A1, fracture
2.28 (1.27-4.10)ABCC8, type 2 diabetes
Are genetic associations studies replicable: take two?
“Low hanging fruit” and a best-casescenario.
Effect size estimates not so widely welcomed ..
Science, June 1, 2007
All Studies Combined14,585 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
Nature, June 7, 2007
0
2
4
6
8
10
12
14
16
18
20
1.05 1.15 1.25 1.35 1.45 1.55 1.65 1.75 1.85 1.95
Distribution of OR’s for 70 Common Disease Variants
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)
Gregor Mendel, 1865.Mendel in 1862
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
RANDOMISATION METHOD
RANDOMISED CONTROLLED TRIAL
CONFOUNDERS EQUAL BETWEEN
GROUPS
MENDELIAN RANDOMISATION
RANDOM SEGREGATION OF ALLELES
CONFOUNDERS EQUAL BETWEEN
GROUPS
EXPOSED: FUNCTIONAL ALLELLES
EXPOSED:
INTERVENTION
CONTROL: NULL ALLELLES
CONTROL: NO INTERVENTION
OUTCOMES COMPARED BETWEEN GROUPS
OUTCOMES COMPARED BETWEEN GROUPS