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National Research Council (US) Committee on Advances in Collecting and Utilizing Biological Indicators and Genetic Information in Social Science Surveys; Weinstein M, Vaupel JW, Wachter KW, editors. Biosocial Surveys. Washington (DC): National Academies Press (US); 2008.

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16Mendelian Randomization: Genetic Variants as Instruments for Strengthening Causal Inference in Observational Studies

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The incorporation of biomarkers into population-based health surveys is generally intended to improve categorization of exposures or health outcome measures (National Research Council, 2001). An unintended consequence of the growing use of biomarkers—for example, in assessing nutritional status—is that investigators are less aware of the continued threats to validity of their findings caused by measurement error, confounding, and reverse causality, which affect biomarkers in the same way as exposures and outcomes measured using less precise methods. This chapter briefly outlines when and why conventional observational approaches have been misleading and then introduces the Mendelian randomization approach, a form of the use of genes as instrumental variables as briefly discussed by Douglas Ewbank in the earlier Cells and Surveys volume (Ewbank, 2001). The variety of inferences that can be drawn from this approach is illustrated and then potential limitations and ways to address these limitations are outlined. The chapter concludes by summarizing the ways in which Mendelian randomization approaches differ from other methodologies that depend on the use of genetic markers in population-based research.


To investigators interested in the health consequences of a modifiable environmental exposure—say, a particular aspect of diet—the obvious approach would be to directly study dietary intake and how this relates to the risk of disease. Why, then, should an alternative approach be advanced? The impetus for thinking of new approaches is that conventional observational study designs have yielded findings that have failed to be confirmed by randomized controlled trials (Davey Smith and Ebrahim, 2002). Observational studies demonstrated that beta carotene intake was associated with a lower risk of lung cancer mortality, and this stimulated an already active market for vitamin supplements that was based on the notion that they substantially influence chronic disease risk (Figure 16-1). The scientists involved in conducting the observational studies advocated taking supplements in material intended for the public (Willett, 2001) and also, relying on observational data, concluded “Available data thus strongly support the hypothesis that dietary carotenoids reduce the risk of lung cancer” (Willett, 1990). However large-scale randomized controlled trials reported disappointing findings: beta carotene supplementation produced no reduction in risk of lung cancer (Alpha-Tocopherol and Beta-Carotene Cancer Prevention Study Group, 1994).

FIGURE 16-1. Advertisement from the Boston Globe.


Advertisement from the Boston Globe.

With respect to cardiovascular disease, observational studies suggesting that beta carotene (Manson et al., 1991), vitamin E supplements (Rimm et al., 1993; Stampfer et al., 1993), vitamin C supplements (Osganian et al., 2003), and hormone replacement therapy (Stampfer and Colditz, 1991) were protective were followed by large trials showing no such protection (Omenn et al., 1996; Alpha-Tocopherol and Beta-Carotene Cancer Prevention Study Group, 1994; Lancet, 1999; Heart Protection Study Collaborative Group, 2002; Beral, Banks, and Reeves, 2002). In each case special pleading was advanced to explain the discrepancy: Were the doses of vitamins given in the trials too high or too low to be comparable to the observational studies? Did hormone replacement therapy use start too late in the trials? Were differences explained by the duration of follow-up or other design aspects? Were interactions with other factors, such as smoking or alcohol consumption, key? Rather than such particular explanations being true (with the happy consequence that both the observational studies and the trials had got the right answers, but to different questions), it is likely that a general problem of confounding—by lifestyle and socioeconomic factors, or by baseline health status and prescription policies—is responsible. Indeed, in the vitamin E supplements example, the observational studies and the trials tested precisely the same thing. Figures 16-2a and 16-2b show the findings from observational studies of taking vitamin E supplements (Rimm et al., 1993; Stampfer et al., 1993) and a meta-analysis of trials of supplements (Eidelman, Hollar, Hebert, Lamas, and Hennekens, 2004). The point here is that the observational studies specifically investigated the effect of taking supplements for a short period (2-5 years) and found an apparent, robust, and large protective effect, even after adjustment for confounders. The trials tested randomization to essentially the same supplements for the same period and found no protective effect. Importantly, the trial findings cannot be attributed to confounding or self-selection of healthier people into a vitamin-taking group, as taking or not taking vitamin E was determined randomly, which (providing it is done properly) avoids these sources of bias.

FIGURE 16-2a. Vitamin E supplement use and risk of coronary heart disease in two observational studies (Rimm et al., 1993; Stampfer et al., 1993) and in a meta-analysis of randomized controlled trials (RCTs) (Eidelman et al., 2004).

FIGURE 16-2a

Vitamin E supplement use and risk of coronary heart disease in two observational studies (Rimm et al., 1993; Stampfer et al., 1993) and in a meta-analysis of randomized controlled trials (RCTs) (Eidelman et al., 2004).

FIGURE 16-2b. Health Professional Follow-up Study (Rimm et al., 1993).

FIGURE 16-2b

Health Professional Follow-up Study (Rimm et al., 1993). NOTE: Observed effect of duration of vitamin E use compared with no use on coronary heart disease events in the Health Professional Follow-up Study.

Other processes in addition to confounding can generate robust, but noncausal, associations in observational studies. Reverse causation—in which the disease influences the apparent exposure, rather than vice versa—may generate strong and replicable associations. For example, many studies have found that people with low circulating cholesterol levels are at increased risk of several cancers, including colon cancer. If causal, this is an important association, as it might mean that efforts to lower cholesterol levels would increase the risk of cancer. However, it is possible that the early stages of cancer may, many years before diagnosis or death, lead to a lowering in cholesterol levels, rather than low cholesterol levels increasing the risk of cancer. Similarly in studies of inflammatory markers, such as C-reactive protein and cardiovascular disease risk, it is possible that early stages of atherosclerosis—which is an inflammatory processes—elevate circulating inflammatory markers, and since people with atherosclerosis are more likely to experience cardiovascular events, a robust but noncausal association between levels of inflammatory markers and incident cardiovascular disease is generated. A form of reverse causation can also occur through reporting bias, with the presence of disease influencing reporting disposition. In case-control studies, people with the disease under investigation may report on their prior exposure history in a different way than do controls—perhaps because the former will think harder about potential reasons that account for why they have developed the disease.

These problems of confounding and bias produce associations in observational studies that are not reliable indicators of the true causation. Furthermore, the strength of associations between truly causal risk factors and disease in observational studies is underestimated due to random measurement imprecision in indexing the exposure. A century ago, Charles Spearman demonstrated mathematically how such measurement imprecision would lead to what he termed the “attenuation by errors” of associations (Spearman, 1904; Davey Smith and Phillips, 1996), later renamed “regression dilution bias.”

Observational studies can and do produce findings that either spuriously enhance or downgrade estimates of causal associations between modifiable exposures and disease. For these reasons, alternative approaches—including those within the Mendelian randomization framework—need to be applied.


The basic principle utilized in Mendelian randomization is that genetic variants that either alter the level of, or mirror the biological effects of, a modifiable environmental exposure that itself alters disease risk should be related to disease risk to the extent predicted by their influence on exposure to the environmental risk factor. Common genetic polymorphisms that have a well-characterized biological function (or are markers for such variants) can therefore be utilized to study the effect of a suspected environmental exposure on disease risk (Davey Smith and Ebrahim, 2003, 2004, 2005; Davey Smith, 2006). The exploitation of situations in which genotypic differences produce effects similar to environmental factors (and vice versa) clearly resonates with the concepts of phenocopy (Goldschmidt, 1938) and genocopy (Schmalhausen, 1938, cited by Gause, 1942) in developmental genetics. Phenocopy refers to the situation in which an environmental effect produces the same effect as that produced by a genetic mutation. Genocopy, the reverse of phenocopy, is when genetic variation generates an outcome that could be produced by an environmental stimulus (Jablonka-Tavory, 1982).

Why use genetic variants as proxies for environmental exposures rather than measure the exposures themselves? First, unlike environmental exposures, genetic variants are not generally associated with the wide range of behavioral, social, or physiological factors that, for example, confound the association between vitamin C and coronary heart disease. This means that if a genetic variant is used to proxy for an environmentally modifiable exposure, it is unlikely to be confounded in the way that direct measures of the exposure will be. Furthermore, aside from the effects of population structure (see Palmer and Cardon, 2005, for a discussion of the likely impact of this), such variants will not be associated with other genetic variants, except those with which they are in linkage disequilibrium. This latter assumption follows from the law of independent assortment (sometimes referred to as Mendel's second law), hence the term “Mendelian randomization.” We illustrate this powerful aspect of Mendelian randomization in Tables 16-1a and 16-b, showing the strong associations between a wide range of variables and blood C-reactive protein (CRP) levels, but no association of the same factors with genetic variants in the CRP gene. The only factor related to genotype is the expected, biological influence of the genetic variant on CRP levels.

TABLE 16-1a. Means or Proportions of Blood Pressure, Pulse Pressure, Hypertension, and Potential Confounders by Quarters of C-Reactive Protein (CRP) N = 3,529.

TABLE 16-1a

Means or Proportions of Blood Pressure, Pulse Pressure, Hypertension, and Potential Confounders by Quarters of C-Reactive Protein (CRP) N = 3,529.

TABLE 16-1b. Means or Proportions of CRP Systolic Blood Pressure, Hypertension, and Potential Confounders by 1059G/C Genotype.

TABLE 16-1b

Means or Proportions of CRP Systolic Blood Pressure, Hypertension, and Potential Confounders by 1059G/C Genotype.

Second, we have seen how inferences drawn from observational studies may be subject to bias due to reverse causation. Disease processes may influence exposure levels, such as alcohol intake, or measures of intermediate phenotypes, such as cholesterol levels and C-reactive protein. However germ line genetic variants associated with average alcohol intake or circulating levels of intermediate phenotypes will not be influenced by the onset of disease. This will be equally true with respect to reporting bias generated by knowledge of disease status in case-control studies or of differential reporting bias in any study design.

Third, associative selection bias in which participants' entry to a study is related to both their exposure level and disease risk can generate spurious associations. This is unlikely to occur with respect to genetic variants. There is empirical evidence that a wide range of genetic variants and participation rates in etiological studies are not associated. Odds ratios for differences in the prevalence of genetic variants between those willing and less willing to participate in studies are generally null, showing no strong evidence to support associative selection bias in genetic studies (Bhatti et al., 2005). As these investigators noted, it is important that researchers test this assumption in their own data, as it is possible that other genotypes, particularly those associated with health-relevant behaviors (e.g., alcohol consumption), may show associations.

Finally, a genetic variant will indicate long-term levels of exposure, and if the variant is taken as a proxy for such exposure, it will not suffer from the measurement error inherent in phenotypes that have high levels of variability and are poorly estimated by a single measure. For example, groups defined by cholesterol level–related genotype will, over a long period, experience the cholesterol difference seen between the groups. Indeed, use of the Mendelian randomization approach predicts a strength of association that is in line with randomized controlled trial findings of effects of cholesterol lowering when the increasing benefits seen over the relatively short trial period are projected to the expectation for differences over a lifetime (Davey Smith and Ebrahim, 2004).

Categories of Mendelian Randomization

Several categories of inference can be drawn from studies utilizing the Mendelian randomization paradigm. In the most direct forms, genetic variants can be related to the probability or level of exposure (“exposure propensity”) or to intermediate phenotypes believed to influence disease risk. Less direct evidence can come from genetic variant–disease associations that indicate that a particular biological pathway may be of importance, perhaps because the variants modify the effects of environmental exposures. Several examples of these categories have been given elsewhere (Davey Smith and Ebrahim, 2003, Davey Smith and Ebrahim, 2004; Davey Smith, 2006); here illustrative cases of the first two categories are briefly outlined.

Exposure Propensity: Alcohol Intake and Health

The possible protective effect of moderate alcohol consumption on risk of coronary heart disease (CHD) remains controversial (Marmot, 2001; Bovet and Paccaud, 2001; Klatsky, 2001). Nondrinkers may be at a higher risk of coronary heart disease because health problems (perhaps induced by previous alcohol abuse) dissuade them from drinking (Shaper, 1993). As well as this form of reverse causation, confounding could play a role, with nondrinkers being more likely to display an adverse profile of socioeconomic or other behavioral risk factors for coronary heart disease (Hart, Davey Smith, Hole, and Hawthorne, 1999). Alternatively, alcohol may have a direct biological effect that lessens the risk of coronary heart disease—for example, by increasing the levels of protective high-density lipoprotein (HDL) cholesterol (Rimm, 2001). It is, however, unlikely that a randomized controlled trial of alcohol intake, able to test whether there is a protective effect of alcohol on CHD events, will be carried out.

Alcohol is oxidized to acetaldehyde, which in turn is oxidized by aldehyde dehydrogenases (ALDHs) to acetate. Half of Japanese people are heterozygotes or homozygotes for a null variant of ALDH2, and peak blood acetaldehyde concentrations post alcohol challenge are 18 times and 5 times higher, respectively, among homozygous null variant and heterozygous individuals compared with homozygous wild type individuals (Enomoto, Takase, Yasuhara, and Takada, 1991). This renders the consumption of alcohol unpleasant through inducing facial flushing, palpitations, drowsiness, and other symptoms. As Figure 16-3a shows, there are very considerable differences in alcohol consumption according to genotype (Takagi et al., 2002). The principles of Mendelian randomization are seen to apply: two factors that would be expected to be associated with alcohol consumption, age and cigarette smoking, which would confound conventional observational associations between alcohol and disease, are not related to genotype despite the strong association of genotype with alcohol consumption (Figure 16-3b).

FIGURE 16-3a. Relationship between characteristics and alcohol consumption.

FIGURE 16-3a

Relationship between characteristics and alcohol consumption. SOURCE: Takagi et al. (2002).

FIGURE 16-3b. Relationship between characteristics and ALDH2 genotype.

FIGURE 16-3b

Relationship between characteristics and ALDH2 genotype. SOURCE: Takagi et al. (2002).

It would be expected that the ALDH2 genotype influences diseases known to be related to alcohol consumption, and as proof of principle it has been shown that ALDH2 null variant homozygosity—associated with low alcohol consumption—is indeed related to a lower risk of liver cirrhosis (Chao et al., 1994). Considerable evidence, including data from randomized controlled trials, suggests that alcohol increases HDL cholesterol levels (Haskell et al., 1984; Burr, Fehily, Butland, Bolton, and Eastham, 1986), which should protect against coronary heart disease. In line with this, ALDL2 genotype is strongly associated with HDL cholesterol in the expected direction (Figure 16-3c). Given the apparent protective effect of alcohol against CHD risk seen in observational studies, possession of the null ALDH2 allele—associated with lower alcohol consumption—should be associated with a greater risk of myocardial infarction, and this is what was seen in a case-control study (Takagi et al., 2002). Men either homozygous or heterozygous for null ALDH2 were at twice the risk of myocardial infarction. Statistical adjustment for HDL cholesterol greatly attenuated the association between ALDH2 genotype and coronary heart disease, indicating that the cardio-protective effect of alcohol is mediated by increased levels of HDL cholesterol.

FIGURE 16-3c. Relationship between HDL cholesterol and ALDH2 genotype.

FIGURE 16-3c

Relationship between HDL cholesterol and ALDH2 genotype. SOURCE: Tagaki et al. (2002).

Intermediate Phenotypes

Genetic variants can influence such circulating biochemical factors as cholesterol, homocysteine, or fibrinogen levels. This provides a method for assessing causality in associations observed between these measures (intermediate phenotypes) and disease, and thus whether interventions to modify the intermediate phenotype could be expected to influence disease risk. Proof of principle for this approach is provided by familial hypercholesterolemia genetic variants that are associated with higher circulating cholesterol levels, which increase risk of coronary heart disease. These observational data are in line with the randomized controlled trial evidence confirming that lowering cholesterol reduces the risk of coronary heart disease (Davey Smith and Ebrahim, 2004).

C-Reactive Protein and Coronary Heart Disease

Strong associations of CRP, an acute phase inflammatory marker, with hypertension, insulin resistance, and coronary heart disease have been repeatedly observed (Danesh et al., 2004; Wu, Dorn, Donahue, Sempos, and Trevisan, 2002; Pradhan, Manson, Rifai, Buring, and Ridker, 2001; Han et al., 2002; Sesso et al., 2003; Hirschfield and Pepys, 2003; Hu, Meigs, Li, Rifai, and Manson, 2004), with the obvious inference that CRP is a cause of these conditions (Ridker et al., 2005; Sjöholm and Nystöm, 2005; Verma, Szmitko, and Ridker, 2005). A Mendelian randomization study has examined the association between polymorphisms of the CRP gene and demonstrated that, although serum CRP differences were highly predictive of blood pressure and hypertension, the CRP variants—which are related to sizeable serum CRP differences—were not associated with these same outcomes (Davey Smith et al., 2005b). It is likely that these divergent findings are explained by the extensive confounding between serum CRP and outcomes (as shown in Table 16-1). Current evidence on this issue, although statistically underpowered, also suggests that CRP levels do not lead to elevated risk of insulin resistance (Timpson et al., 2005) or coronary heart disease (Casas et al., 2006). Again, confounding, and reverse causation—in which existing coronary disease or insulin resistance may influence CRP levels—could account for this discrepancy. Similar findings have been reported for serum fibrinogen, variants in the beta fibrinogen gene, and coronary heart disease (Davey Smith et al., 2005a; Keavney et al., 2006). The CRP and fibrinogen examples demonstrate that Mendelian randomization can increase evidence for a causal effect of an environmentally modifiable factor (as in the cases of alcohol and cholesterol levels discussed earlier) as well as provide evidence against causal effects, which can help direct efforts away from targets of no preventative or therapeutic relevance.

Implications of Mendelian Randomization Study Findings

Establishing the causal influence of environmentally modifiable risk factors from Mendelian randomization designs informs policies for improving population health through population-level interventions. They do not imply that the appropriate strategy is genetic screening to identify those at high risk and application of selective exposure reduction policies. For example, establishing the association between genetic variants (such as familial defective ApoB) associated with elevated cholesterol level and CHD risk strengthens causal evidence that elevated cholesterol is a modifiable risk factor for coronary heart disease for the whole population. Thus even though the population attributable risk for coronary heart disease of this variant is small, it usefully informs public health approaches to improving population health. It is this aspect of Mendelian randomization that illustrates its distinction from conventional risk identification and genetic screening purposes of genetic epidemiology.

Mendelian Randomization and Randomized Controlled Trials

Randomized controlled trials are clearly the definitive means of obtaining evidence on the effects of modifying disease risk processes. There are similarities in the logical structure of randomized controlled trials and Mendelian randomization, however. Figure 16-4 illustrates this, drawing attention to the unconfounded nature of exposures proxied for by genetic variants (analogous to the unconfounded nature of a randomized intervention), the lack of possibility of reverse causation as an influence on exposure-outcome associations in both Mendelian randomization and randomized controlled trial settings, and the importance of intention to treat analyses—that is, comparisons of groups defined by genetic variant, irrespective of associations between the genetic variant and the proxied for exposure within any particular individual.

FIGURE 16-4. Mendelian randomization and randomized controlled trial designs compared.


Mendelian randomization and randomized controlled trial designs compared.

The analogy with randomized controlled trials is also useful in understanding why an objection to Mendelian randomization—that the environmentally modifiable exposure proxied for by the genetic variants (such as alcohol intake or circulating CRP levels) are influenced by many other factors in addition to the genetic variants (Jousilahti and Salomaa, 2004)—while true, is of no consequence. Consider a randomized controlled trial of blood pressure–lowering medication. Blood pressure is influenced mainly by factors other than taking blood pressure–lowering medication. Obesity, alcohol intake, salt consumption and other dietary factors, smoking, exercise, physical fitness, genetic factors, and early life developmental influences are all of importance. However, the randomization that occurs in trials ensures that these factors are balanced between the group that receives the blood pressure–lowering medication and the control group that does not. Thus the fact that many other factors are related to the modifiable exposure does not vitiate the power of randomized controlled trials; neither does it vitiate the strength of Mendelian randomization designs.

A related objection is that genetic variants often explain only a trivial proportion of the variance in the environmentally modifiable risk factor that is being proxied for (Glynn, 2006). Again, consider a randomized controlled trial of blood pressure–lowering medication, in which 50 percent of participants receive the medication and 50 percent receive a placebo. If the antihypertensive therapy reduced blood pressure by a quarter of a standard deviation, which is approximately the situation for such pharmacotherapy, then within the whole study group the treatment assignment (i.e., antihypertensive use versus placebo) will explain 1.5 percent of the variance in blood pressure. In the example of CRP haplotypes used as markers for CRP levels, these haplotypes explain 1.7 percent of the variance in CRP levels in the population (Lawlor, Harbord, Sterne, and Davey Smith, 2007). As can be seen, the quantitative association of genetic variants as proxies can be similar to that of randomized treatments with respect to biological processes that such treatments modify. Both logic and quantification fail to support criticisms of the Mendelian randomization approach on the basis of either the obvious fact that many factors influence most phenotypes of interest or that particular genetic variants account for only a small proportion of variance in the phenotype.

Mendelian Randomization and Instrumental Variable Approaches

As well as the analogy with randomized controlled trials, Mendelian randomization can also be likened to instrumental variable approaches that have been heavily utilized in econometrics and social science. In this approach, the instrument is a variable that is related to the outcome only through its association with the modifiable exposure of interest. The instrument is not related to confounding factors, nor is its assessment biased in a manner that would generate a spurious association with the outcome. Furthermore the instrument will not be influenced by the development of the outcome (i.e., there will be no reverse causation). Figure 16-5 presents this basic schema, where the dotted line between genotypeand the outcome provides an unconfounded and unbiased estimate of the causal association between the exposure that the genotype is proxying for and the outcome. The development of instrumental variable methods in econometrics, in particular, has led to a sophisticated range of statistical methods for estimating causal effects, and these have now been applied in Mendelian randomization studies (e.g., Davey Smith et al., 2005a, 2005b; Timpson et al., 2005). The parallels between Mendelian randomization and instrumental variable approaches are discussed in more detail elsewhere (Thomas and Conti, 2004; Didelez and Sheehan, 2007; Lawlor et al., 2007).

FIGURE 16-5. Mendelian randomization as an instrumental variables approach.


Mendelian randomization as an instrumental variables approach.

Mendelian Randomization and Gene-Environment Interaction

Mendelian randomization is one way in which genetic epidemiology can inform understanding about environmental determinants of disease. A more conventional approach has been to study interactions between environmental exposures and genotype (Perera, 1997; Mucci, Wedren, Tamimi, Trichopoulos, and Adami, 2001). From epidemiological and Mendelian randomization perspectives, several issues arise with gene-environment interactions.

The most reliable findings in genetic association studies relate to the main effects of polymorphisms on disease risk (Clayton and McKeigue, 2001). The power to detect meaningful gene-environment interaction is low (Wright, Carothers, and Campbell, 2002), resulting in a large number of reports of spurious gene-environment interactions in the medical literature (Colhoun, McKeigue, and Davey Smith, 2003). The presence or absence of statistical interactions depends on the scale used (i.e., linear or logarithmic association between the exposure and disease outcome) and the difficulty in defining whether deviation from either an additive or multiplicative model exists, given the imprecision of estimation. Measurement error—particularly if differential with respect to other factors influencing disease risk—makes interactions both difficult to detect and often misleading when they are apparently found (Clayton and McKeigue, 2001). Furthermore, the biological implications of interactions (however defined) are generally uncertain (Thompson, 1991).

The situation may be different with exposures that differ qualitatively rather than quantitatively between individuals. Consider the possible influence of smoking tobacco on bladder cancer risk. Observational studies suggest an association, but clearly confounding, and a variety of biases could generate such an association. The potential carcinogens in tobacco smoke of relevance to bladder cancer include aromatic and heterocyclic amines, which are detoxified by N-acetyl transferase 2 (NAT2). Genetic variation in NAT2 enzyme levels leads to slower or faster acetylation states. If the carcinogens in tobacco smoke do increase the risk of bladder cancer, then it would be expected that slow acetylators, who have a reduced rate of detoxification of these carcinogens, would be at an increased risk of bladder cancer if they were smokers, whereas if they were not exposed to these carcinogens (and the major exposure route for those outside of particular industries is through tobacco smoke) then an association of genotype with bladder cancer risk would not be anticipated. Table 16-2 tabulates findings from the largest study to date reported in a way that allows analysis of this simple hypothesis (Garcia-Closas et al., 2005). As can be seen, the influence of the NAT2 slow acetylation genotype is appreciable only among those exposed to heavy smoking. Since the geno-type will be unrelated to confounders, it is difficult to reason why this situation should arise, unless smoking is a causal factor with respect to bladder cancer. Thus the presence of a sizable effect of genotype in heavy smokers but not nonsmokers provides evidence of the causal nature of an environmentally modifiable risk factor, in this example, smoking.

TABLE 16-2. Association of NAT2 Slow Acetylation Genotype with Bladder Cancer in Never and Ever Smokers and Overall. Odds Ratio (95% confidence intervals).

TABLE 16-2

Association of NAT2 Slow Acetylation Genotype with Bladder Cancer in Never and Ever Smokers and Overall. Odds Ratio (95% confidence intervals).

However, gene-environment interactions interpreted within the Mendelian randomization framework are not protected from confounding in the same way as the main genetic effects are.


We consider Mendelian randomization to be one of the brightest current prospects for improving causal understanding in population-based studies. There are, however, several potential limitations to the application of this methodology (Davey Smith and Ebrahim, 2003; Little and Khoury, 2003), which we discuss below.

Failure to Establish Reliable Genotype-Intermediate Phenotype or Genotype-Disease Associations

If the associations between genotype and a potential intermediate phenotype, or between genotype and disease outcome, are not reliably estimated, then interpreting these associations in terms of their implications for potential environmental causes of disease will clearly be inappropriate. This is not an issue peculiar to Mendelian randomization; instead, the nonreplicable nature of perhaps most apparent findings in genetic association studies is a serious limitation to the whole enterprise. In Box 16-1 we summarize possible reasons for the nonreplication of findings (Cardon and Bell, 2001; Colhoun et al., 2003). Population stratification—that is, the confounding of genotype-disease associations by factors related to subpopulation group membership in the overall population in a study—is unlikely to be a major problem in most situations (Wacholder, Rothman, and Caporaso, 2000; Wacholder et al., 2002; Palmer and Cardon, 2005). Genotyping errors can, of course, lead to failures of replication of genotype-disease associations. When intermediate phenotypes can be measured, as in the case of CRP, a demonstration of the expected relationship between genotype and intermediate phenotype in such studies indicates that genotyping errors are not to blame.

Box Icon

BOX 16-1

Reasons for Inconsistent Genotype-Phenotype Associations. Variation of allelic association between subpopulations: (1) disease causing allele in linkage disequilibrium with different marker alleles in different populations; or (2) different variants within (more...)

Regarding failure to replicate results in genetic epidemiology, true variation between studies is clearly possible—for example, people heterozygous for familial hypercholesterolemia seem to experience increased mortality only in populations with substantial dietary fat intake and the presence of other CHD risk factors (Sijbrands et al., 2001; Pimstone et al., 1998). Nevertheless, the major factor for nonreplication is probably inadequate statistical power (generally reflecting limited sample size), coupled with publication bias (Colhoun et al., 2003).

Confounding of Genotype: Environmentally Modifiable Risk Factor–Disease Associations

The power of Mendelian randomization lies in its ability to avoid the often substantial confounding seen in conventional observational epidemiology. However, confounding can be reintroduced into Mendelian randomization studies and needs to be considered when interpreting the results.

Linkage Disequlibrium

It is possible that the locus under study is in linkage disequilibrium with another polymorphic locus. Confounding will result if both the study locus and that with which it is in linkage disequilibrium are both associated with the outcome of interest. It may seem unlikely—given the relatively short distances over which linkage disequilibrium is seen in the human genome—that a polymorphism influencing, say, CHD risk would be associated with another polymorphism influencing CHD risk (and thus producing confounding). There are, nevertheless, cases of different genes influencing the same metabolic pathway being in physical proximity. For example, different polymorphisms influencing alcohol metabolism appear to be in linkage disequilibrium (Osier et al., 2002).

Pleiotropy and the Multifunction of Genes

Mendelian randomization is most useful when it can be used to relate a single intermediate phenotype to a disease outcome. However, polymorphisms may (and probably often will) influence more that one intermediate phenotype, and this may mean that they proxy for more than one environmentally modifiable risk factor. This can be the case through multiple effects mediated by their RNA expression or immediate protein coding, through alternative splicing, in which one polymorphic region contributes to alternative forms of more than one protein (Glebart, 1998), or through other mechanisms. The most robust interpretations will be possible when the functional polymorphism appears to directly influence the level of the intermediate phenotype of interest (as in the CRP example), but such examples are probably going to be less common in Mendelian randomization than in cases in which the polymorphism can influence several systems, with different potential interpretations of how the effect on outcome is generated.

The association of possession of the ApoE-2 allele with cholesterol levels and coronary heart disease might be an example of pleiotropic effects, since carriers of this allele have lower cholesterol levels but do not have the degree of protection against coronary heart disease that would be anticipated from this (Keavney et al., 2004; Song, Stampfer, and Liu, 2004). In addition to lower cholesterol levels, the ApoE-2 allele is associated with less efficient transfer of very low density lipoproteins and chylomicrons from the blood to the liver, greater postprandial lipemia, and an increased risk of type III hyperlipidemia (Smith, 2002; Eichner et al., 2002). These differences will accompany the lower cholesterol levels and may counterbalance the predicted benefits.

Multiple Instruments as an Approach to Confounding in Mendelian Randomization

Linkage disequilibrium and pleiotropy can reintroduce confounding and vitiate the power of the Mendelian randomization approach. Genomic knowledge may help in estimating the degree to which these are likely to be problems in any particular Mendelian randomization study, through, for instance, explication of genetic variants that may be in linkage disequilibrium with the variant under study, or the function of a particular variant and its known pleiotropic effects. Furthermore, genetic variation can be related to measures of potential confounding factors in each study, and the magnitude of such confounding estimated. Empirical studies to date suggest that common genetic variants are largely unrelated to the behavioral and socioeconomic factors considered to be important confounders in conventional observational studies (Smits et al., 2004; Bhatti et al., 2005; Davey Smith et al., 2005b, 2007; Chatterjee, Kalaylioglu, and Carroll, 2005; Umbach and Weinberg, 1997). However, relying on measurement of confounders does, of course, remove the central purpose of Mendelian randomization, which is to balance unmeasured as well as measured confounders (as randomization does in randomized controlled trials).

It may be possible to identify two separate genetic variants, which are not in linkage disequilibrium with each other, but which both serve as proxies for the environmentally modifiable risk factor of interest. If both variants are related to the outcome of interest and point to the same underlying association, then it becomes much less plausible that reintroduced confounding explains the association, since it would have to be acting in the same way for these two unlinked variants. This can be likened to randomized controlled trials of different blood pressure–lowering agents, which work through different mechanisms and have different potential side effects but lower blood pressure to the same degree. If the different agents produce the same reductions in cardiovascular disease risk, then it is unlikely that this is through agent-specific effects of the drugs; instead, it points to blood pressure lowering as being key. The use of multiple genetic variants working through different pathways has not been applied in Mendelian randomization to date, but it represents an important potential development in the methodology.

Canalization and Developmental Stability

Perhaps a greater potential problem for Mendelian randomization than reintroduced confounding arises from the developmental compensation that may occur through a polymorphic genotype being expressed during fetal or early postnatal development, and thus influencing development in such a way as to buffer against the effect of the polymorphism. Such compensatory processes have been discussed since C.H. Waddington introduced the notion of canalization in the 1940s (Waddington, 1942). Canalization refers to the buffering of the effects of either environmental or genetic forces attempting to perturb development, and Waddingtion's ideas have been well developed both empirically and theoretically (Wilkins, 1997; Rutherford, 2000; Gibson and Wagner, 2000; Hartman, Garrik, and Hartwell, 2001; Debat and David, 2001; Kitami and Nadeau, 2002; Gu et al., 2003; Hornstein and Shomron, 2006). Such buffering can be achieved either through genetic redundancy (more than one gene having the same or similar function) or through alternative metabolic routes, in which the complexity of metabolic pathways allows recruitment of different pathways to reach the same phenotypic end point. In effect, a functional polymorphism expressed during fetal development or postnatal growth may influence the expression of a wide range of other genes, leading to changes that may compensate for the influence of the polymorphism.

In the field of animal genetic engineering studies, such as knockout preparations or transgenic animals manipulated so as to overexpress foreign DNA, the interpretive problem created by developmental compensation is well recognized (Morange, 2001; Shastry, 1998; Gerlai, 2001; Williams and Wagner, 2000). Conditional preparations, in which the level of transgene expression can be induced or suppressed through the application of external agents, are now being utilized to investigate the influence of such altered gene expression after the developmental stages during which compensation can occur (Bolon and Galbreath, 2002). Thus further evidence on the issue of genetic buffering should emerge to inform interpretations of both animal and human studies.

Most examples of developmental compensation relate to dramatic genetic or environmental insults, so it is unclear whether the generally small phenotypic differences induced by common functional polymorphisms will be sufficient to induce compensatory responses. The fact that the large gene-environment interactions that have been observed often relate to novel exposures (e.g., drug interactions) that have not been present during the evolution of a species (Wright et al., 2002) may indicate that homogenization of response to exposures that are widely experienced—as would be the case with the products of functional polymorphisms or common mutations—has occurred; canalizing mechanisms could be particularly relevant in these cases. Further work on the basic mechanisms of developmental stability and how this relates to relatively small exposure differences during development will allow these considerations to be understood. Knowledge of the stage of development at which a genetic variant has functional effects will also allow the potential of developmental compensation to buffer the response to the variant to be assessed.

In some Mendelian randomization designs, developmental compensation is not an issue. For example, when maternal genotype is utilized as an indicator of the intrauterine environment, then the response of the fetus will not differ whether the effect is induced by maternal genotype or by environmental perturbation, and the effect on the fetus can be taken to indicate the effect of environmental influences during the intrauterine period. Also, in cases in which a variant influences an adulthood environmental exposure—for example, ALDH2 variation and alcohol intake—developmental compensation to genotype will not be an issue. In many cases of gene-environment interaction interpreted with respect to causality of the environmental factor, the same applies. However, in some situations there remains the somewhat unsatisfactory position of Mendelian randomization facing a potential problem that cannot currently be adequately assessed.

Lack of Suitable Genetic Variants to Proxy for the Exposure of Interest

An obvious limitation of Mendelian randomization is that it can only examine areas for which there are functional polymorphisms (or genetic markers linked to such functional polymorphisms) that are relevant to the modifiable exposure of interest. In the context of genetic association studies more generally, it has been pointed out that, in many cases, even if a locus is involved in a disease-related metabolic process, there may be no suitable marker or functional polymorphism to allow study of this process (Weiss and Terwillger, 2000). Since one of our examples, used in an earlier paper (Davey Smith and Ebrahim, 2003), of how observational epidemiology appeared to have got the wrong answer related to vitamin C, we considered whether the association between vitamin C and coronary heart disease could have been studied utilizing the principles of Mendelian randomization. We stated that polymorphisms exist that are related to lower circulating vitamin C levels—for example, the haptoglobin polymorphism (Langlois, Delanghe, DeBuyzere, Bernard, and Ouyang, 1997; Delanghe, Langlois, Duprez, DuBuyzere, and Clement, 1999)—but in this case the effect on vitamin C is at some distance from the polymorphic protein and, as in the apolipoprotein E example, other phenotypic differences could have an influence on CHD risk that would distort examination of the influence of vitamin C levels through relating genotype to disease. SLC23A1—a gene encoding for the vitamin C transporter SVCT1, vitamin C transport by intestinal cells— would be an attractive candidate for Mendelian randomization studies (Erichsen, Eck, Levine, and Chanock, 2001). However, by 2003 (the date of our earlier paper) a search for variants had failed to find any common single-nucleotide polymorphism that could be used in such a way. However, since then, functional variation in SLC23A1 that is related to circulating vitamin C levels has been identified (Timpson et al., personal communication). Rapidly developing knowledge of human genomics will identify more variants that can serve as instruments for Mendelian randomization studies.


Mendelian randomization is not predicated on the presumption that genetic variants are major determinants of health and disease in populations. There are many cogent critiques of genetic reductionism and the overselling of “discoveries” in genetics that reiterate obvious truths so clearly (albeit somewhat repetitively) that there is no need to repeat them here (e.g., Berkowitz, 1996; Baird, 2000; Holtzman, 2001; Strohman, 1993; Rose, 1995). Mendelian randomization does not depend on there being genes “for” particular traits, and certainly not in the strict sense of a gene “for” a trait being one that is maintained by selection because of its causal association with that trait (Kaplan and Pigliucci, 2001). The association of genotype and the environmentally modifiable factor that it proxies for will be, like most genotype-phenotype associations, one that is contingent and cannot be reduced to individual-level prediction, but within environmental limits will pertain at a group level (Wolf, 1995). This is analogous to a randomized controlled trial of antihypertensive agents, in which at a collective level the group randomized to active medication will have lower mean blood pressure than the group randomized to placebo, but at an individual level many participants randomized to active treatment will have higher blood pressure than many individuals randomized to placebo. These group-level differences are what create the analogy between Mendelian randomization and randomized controlled trials, outlined in Figure 16-4.

Finally, the associations that Mendelian randomization depend on do need to pertain to a definable group at a particular time, but they do not need to be immutable. Thus ALDH2 variation will not be related to alcohol consumption in a society in which alcohol is not consumed, and the association will vary by gender and by cultural group and may change over time (Higuchi et al., 1994; Hasin et al., 2002). Within the setting of a study of a well-defined group, however, the genotype will be associated with group-level differences in alcohol consumption, and group assignment will not be associated with confounding variables.

Mendelian Randomization and Genetic Epidemiology

Critiques of contemporary genetic epidemiology often focus on two features of findings from genetic association studies: that the population-attributable risk of the genetic variants is low, and that in any case the influence of genetic factors is not reversible (Terwilliger and Weiss, 2003). These evaluations of the role of genetic epidemiology are not relevant when considering the potential contributions of Mendelian randomization. This approach is not concerned with the population attributable risk of any particular genetic variant, but the degree to which associations between the genetic variant and disease outcomes can demonstrate the importance of environmentally modifiable factors as causes of disease. Consider, for example, the case of familial hypercholesterolemia or familial defective ApoB. The genetic mutations associated with these conditions will account for only a trivial percentage of cases of coronary heart disease in the population—that is, the population attributable risk will be low, despite a high relative risk of coronary heart disease (Tybjaerg H et al., 1998). However, by identifying blood cholesterol levels as a causal factor for coronary heart disease, the triangulation between genotype, blood cholesterol, and CHD risk identifies an environmentally modifiable factor with a very high population attributable risk—assuming that 50 percent of the population have raised blood cholesterol above 6.0 mmol/l, and this is associated with a relative risk of two-fold, a population attributable risk of 33 percent is obtained. The same reasoning applies to the nonmodifiable nature of genotype-disease associations. The point of Mendelian randomization approaches is to utilize these associations to strengthen inferences regarding modifiable environmental risks for disease and then reduce disease risk in the population through applying this knowledge.

Mendelian randomization differs from other contemporary approaches to genetic epidemiology in that its central concern is not with the magnitude of genetic variant influences on disease, but rather on what the genetic associations tell us about environmentally modifiable causes of disease. As David B. Abrams, director of the Office of Behavioral and Social Sciences Research at the U.S. National Institutes of Health has said, “The more we learn about genes, the more we see how important environment and lifestyle really are.” Many years earlier, the pioneering geneticist Thomas Hunt Morgan articulated a similar sentiment in his Nobel prize acceptance speech, when he contrasted his views with the then popular genetic approach to disease—eugenics. He thought that “through public hygiene and protective measures of various kinds we can more successfully cope with some of the evils that human flesh is heir to. Medical science will here take the lead—but I hope that genetics can at times offer a helping hand” (Morgan, 1935). More than seven decades later, it might now be time that genetic research can directly strengthen the knowledge base of public health.


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Copyright © 2008, National Academy of Sciences.
Bookshelf ID: NBK62433


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