Commentary: Genes as instruments for evaluation of markers and causes

Use of genetic markers and gene-diet interactions for interrogating population-level causal influences of diet on health

Differences in diet appear to contribute substantially to the burden of disease in populations, and therefore changes in diet could lead to major improvements in public health. This is predicated on the reliable identification of causal effects of nutrition on health, and unfortunately nutritional epidemiology has deficiencies in terms of identifying these. This is reflected in the many cases where observational studies have suggested that a nutritional factor is protective against disease, and randomized controlled trials have failed to verify this. The use of genetic variants as proxy measures of nutritional exposure-an application of the Mendelian randomization principle-can contribute to strengthening causal inference in this field. Genetic variants are not subject to bias due to reverse causation (disease processes influencing exposure, rather than vice versa) or recall bias, and if obvious precautions are applied are not influenced by confounding or attenuation by errors. This is illustrated in the case of epidemiological studies of alcohol intake and various health outcomes, through the use of genetic variants related to alcohol metabolism (in ALDH2 and ADH1B). Examples from other areas of nutritional epidemiology and of the informative nature of gene-environment interactions interpreted within the Mendelian randomization framework are presented, and the potential limitations of the approach addressed.

Use of pathway information in molecular epidemiology

Candidate gene studies are generally motivated by some form of pathway reasoning in the selection of genes to be studied, but seldom has the logic of the approach been carried through to the analysis. Marginal effects of polymorphisms in the selected genes, and occasionally pairwise gene-gene or gene-environment interactions, are often presented, but a unified approach to modelling the entire pathway has been lacking. In this review, a variety of approaches to this problem is considered, focusing on hypothesis-driven rather than purely exploratory methods. Empirical modelling strategies are based on hierarchical models that allow prior knowledge about the structure of the pathway and the various reactions to be included as 'prior covariates'. By contrast, mechanistic models aim to describe the reactions through a system of differential equations with rate parameters that can vary between individuals, based on their genotypes. Some ways of combining the two approaches are suggested and Bayesian model averaging methods for dealing with uncertainty about the true model form in either framework is discussed. Biomarker measurements can be incorporated into such analyses, and two-phase sampling designs stratified on some combination of disease, genes and exposures can be an efficient way of obtaining data that would be too expensive or difficult to obtain on a full candidate gene sample. The review concludes with some thoughts about potential uses of pathways in genome-wide association studies.

Mendelian Randomization for Strengthening Causal Inference in Observational Studies: Application to Gene × Environment Interactions

Identification of environmentally modifiable factors causally influencing disease risk is fundamental to public-health improvement strategies. Unfortunately, observational epidemiological studies are limited in their ability to reliably identify such causal associations, reflected in the many cases in which conventional epidemiological studies have apparently identified associations that randomized controlled trials have failed to verify. The use of genetic variants as proxy measures of exposure -an application of the Mendelian randomization principle-can contribute to strengthening causal inference. Genetic variants are not subject to bias due to reverse causation (disease processes influencing exposure, rather than vice versa) or recall bias, and if simple precautions are applied, they are not influenced by confounding or attenuation by errors. The principles of Mendelian randomization are illustrated with specific reference to studies of the effects of alcohol intake on various health-related outcomes through the utilization of genetic variants related to alcohol metabolism (in ALDH2 and ADH1B). Ways of incorporating Gene × Environment interactions into the Mendelian randomization framework are developed, and the strengths and limitations of the approach discussed.

Population-Based Genomewide Genetic Analysis of Common Clinical Chemistry Analytes

Background: Recent technologies enable genetic association studies of common clinical analytes on a genomewide basis in populations numbering thousands of individuals. The first publications using these technologies are already revealing novel biological functions for both genic and nongenic loci, and are promising to transform knowledge about the biological networks underlying disease pathophysiology. These early studies have also led to development of a set of principles for conducting a successful genomewide association study (GWAS).

Content: This review focuses on these principles with emphasis on the use of GWAS for plasma-based analytes to better understand human disease, with examples from cardiovascular biology.

Conclusions: The correlation of common genetic variation on a genomewide basis with clinical analytes, or any other outcome of interest, promises to reveal how parts of the genome work together in human physiology. Nonetheless, performing a genomewide association study demands an awareness of very specific epidemiologic and analytic principles.

Strengthening causal inference in cardiovascular epidemiology through Mendelian randomization

Observational studies have contributed in a major way to understanding modifiable determinants of cardiovascular disease risk, but several examples exist of factors that were identified in observational studies as potentially protecting against coronary heart disease, that in randomized controlled trials had no such effect. The likely reason for misleading findings from observational epidemiological studies is that associations are influenced by confounding, bias, and reverse causation--where disease influences a risk factor, rather than vice versa. Mendelian randomization utilizes genetic variants that serve as proxy measures for modifiable risk factors to allow estimation of the causal influence of the modifiable risk factor in question. We present examples of the use of the Mendelian randomization approach and discuss both the limitations and potentials of this strategy.