Case-control Studies Of Gene-environment Interactions Research Articles

Case-control studies of gene-environment interactions. When a case might not be the case.

Case-control Genome-Wide Association Studies (GWAS) provide a rich resource for studying the genetic architecture of complex diseases. A key is to elucidate how the genetic effects vary by the environment, what is traditionally defined by Gene-Environment interactions (GxE). The overlooked complication is that multiple, distinct pathophysiologic mechanisms may lead to the same clinical diagnosis and often these mechanisms have distinct genetic bases. In this paper, we first show that using the clinically diagnosed status can lead to severely biased estimates of GxE interactions in situations when the frequency of the pathologic diagnosis of interest, as compared to other diagnoses, depends on the environment. We then propose a pseudo-likelihood solution to correct the bias. Finally, we demonstrate our method in extensive simulations and in a GWAS of Alzheimer’s disease.

Semiparametric analysis of complex polygenic gene-environment interactions in case-control studies.

Many methods have recently been proposed for efficient analysis of case-control studies of gene-environment interactions using a retrospective likelihood framework that exploits the natural assumption of gene-environment independence in the underlying population. However, for polygenic modelling of gene-environment interactions, which is a topic of increasing scientific interest, applications of retrospective methods have been limited due to a requirement in the literature for parametric modelling of the distribution of the genetic factors. We propose a general, computationally simple, semiparametric method for analysis of case-control studies that allows exploitation of the assumption of gene-environment independence without any further parametric modelling assumptions about the marginal distributions of any of the two sets of factors. The method relies on the key observation that an underlying efficient profile likelihood depends on the distribution of genetic factors only through certain expectation terms that can be evaluated empirically. We develop asymptotic inferential theory for the estimator and evaluate its numerical performance via simulation studies. An application of the method is presented.

Genotype‐based association mapping of complex diseases: gene‐environment interactions with multiple genetic markers and measurement error in environmental exposures

With the advent of dense single nucleotide polymorphism genotyping, population-based association studies have become the major tools for identifying human disease genes and for fine gene mapping of complex traits. We develop a genotype-based approach for association analysis of case-control studies of gene-environment interactions in the case when environmental factors are measured with error and genotype data are available on multiple genetic markers. To directly use the observed genotype data, we propose two genotype-based models: genotype effect and additive effect models. Our approach offers several advantages. First, the proposed risk functions can directly incorporate the observed genotype data while modeling the linkage disequilibrium information in the regression coefficients, thus eliminating the need to infer haplotype phase. Compared with the haplotype-based approach, an estimating procedure based on the proposed methods can be much simpler and significantly faster. In addition, there is no potential risk due to haplotype phase estimation. Further, by fitting the proposed models, it is possible to analyze the risk alleles/variants of complex diseases, including their dominant or additive effects. To model measurement error, we adopt the pseudo-likelihood method by Lobach et al. [2008]. Performance of the proposed method is examined using simulation experiments. An application of our method is illustrated using a population-based case-control study of association between calcium intake with the risk of colorectal adenoma development.

Flexible matching in case-control studies of gene-environment interactions.

Because of the lack of power of case-control study designs to detect gene-environment interactions, flexible matching has recently been proposed as a method of improving efficiency. In this paper, the authors consider a large-sample approximation method that allows estimation of the most efficient matching strategy when genotype and exposure are either independent or associated. The authors provide tables of the sample sizes required to detect gene-environment interactions if this flexible matching strategy is followed, and they make brief comparisons with other study designs.

Flexible matching strategies to increase power and efficiency to detect and estimate gene-environment interactions in case-control studies.

Lack of power is a pertinent problem in many case-control studies of gene-environment interactions. The authors recently introduced the concept of flexible matching strategies with varying proportions of a matching factor among selected controls (degree of matching) to increase the power and efficiency of case-control studies. In this study, they extended the concept of flexible matching strategies to the field of gene-environment interactions. They assessed the power and efficiency of such studies to detect and estimate gene-environment interactions under a variety of assumptions regarding the prevalence and effects of the environmental exposure and the genetic susceptibility as well as their association in the population. For each set of parameters, 10,000 case-control studies were simulated using varying degrees of matching. Traditional frequency matching increased the power and precision in most scenarios, but even greater gains were often obtained by increasing the prevalence of the environmental exposure in controls above the one in cases. The authors concluded that flexible matching strategies can increase the power and efficiency of case-control studies to detect and estimate gene-environment interactions compared with traditional frequency matching and therefore might help to alleviate the notorious lack of power of these studies in specific situations.

Power and sample size calculations for case-control studies of gene-environment interactions with a polytomous exposure variable.

Genetic polymorphisms may appear to the epidemiologist most commonly as different levels of susceptibility to exposure. Epidemiologic studies of heterogeneity in exposure susceptibility aim at estimating the parameter quantifying the gene-environment interaction. In this paper, the authors use a general approach to power and sample size calculations for case-control studies, which is applicable to settings where the exposure variable is polytomous and where the assumption of independence between the distribution of the genotype and the environmental factor may not be met. It was found through exploration of different scenarios that in the cases explored, power calculations were relatively insensitive to assumptions about the odds ratio for the exposure in the referent genotype category and to assumptions about the odds ratio for the genetic factor in the lowest exposure category, yet they were relatively sensitive to assumptions about gene frequency, particularly when gene frequency was low. In general, to detect a small to moderate gene-environment interaction effect, large sample sizes are needed. Because the examples studied represent only a small subset of possible scenarios that could occur in practice, the authors encourage the use of their user-friendly Fortran program for calculating power and sample size for gene-environment interactions with exposures grouped by quantiles that are explicitly tailored to the study at hand.