Presence Of Gene-environment Interaction Research Articles

Plasma levels of polychlorinated biphenyl, genetic polymorphisms, and the risk of advanced stage endometriosis

Both environmental and genetic factors interact and play a critical role in the pathogenesis of endometriosis. We analyzed the plasma levels of 12 polychlorinated biphenyl (PCB) congeners with genetic polymorphisms of glutathione-S-transferase M1 (GSTM1), glutathione-S-transferase T1 (GSTT1), and aryl hydrocarbon receptor repressor (AhRR) codon 185. Total sum of the 12 congeners was significantly higher in the controls compared with endometriosis group. Women without C/C genotype in AhRR codon 185 had a significantly increased risk of endometriosis compared with those with C/C genotype. Total sum of the 12 congeners was significantly higher in women without C/C genotype compared with those with C/C genotype. Adjusting for age and AhRR codon 185 genotype, there was no significant association between exposure to PCBs and the risk of endometriosis. These findings suggest a possible presence of gene-environment interaction, however, we could not see any clear association between exposure to PCBs and the risk of endometriosis.

Estimating genetic effect sizes under joint disease-endophenotype models in presence of gene-environment interactions.

Effects of genetic variants on the risk of complex diseases estimated from association studies are typically small. Nonetheless, variants may have important effects in presence of specific levels of environmental exposures, and when a trait related to the disease (endophenotype) is either normal or impaired. We propose polytomous and transition models to represent the relationship between disease, endophenotype, genotype and environmental exposure in family studies. Model coefficients were estimated using generalized estimating equations and were used to derive gene-environment interaction effects and genotype effects at specific levels of exposure. In a simulation study, estimates of the effect of a genetic variant were substantially higher when both an endophenotype and an environmental exposure modifying the variant effect were taken into account, particularly under transition models, compared to the alternative of ignoring the endophenotype. Illustration of the proposed modeling with the metabolic syndrome, abdominal obesity, physical activity and polymorphisms in the NOX3 gene in the Quebec Family Study revealed that the positive association of the A allele of rs1375713 with the metabolic syndrome at high levels of physical activity was only detectable in subjects without abdominal obesity, illustrating the importance of taking into account the abdominal obesity endophenotype in this analysis.

Methodological Challenges in Mendelian Randomization

We give critical attention to the assumptions underlying Mendelian randomization analysis and their biological plausibility. Several scenarios violating the Mendelian randomization assumptions are described, including settings with inadequate phenotype definition, the setting of time-varying exposures, the presence of gene-environment interaction, the existence of measurement error, the possibility of reverse causation, and the presence of linkage disequilibrium. Data analysis examples are given, illustrating that the inappropriate use of instrumental variable techniques when the Mendelian randomization assumptions are violated can lead to biases of enormous magnitude. To help address some of the strong assumptions being made, three possible approaches are suggested. First, the original proposal of Katan (Lancet. 1986;1:507-508) for Mendelian randomization was not to use instrumental variable techniques to obtain estimates but merely to examine genotype-outcome associations to test for the presence of an effect of the exposure on the outcome. We show that this more modest goal and approach can circumvent many, though not all, the potential biases described. Second, we discuss the use of sensitivity analysis in evaluating the consequences of violations in the assumptions and in attempting to correct for those violations. Third, we suggest that a focus on negative, rather than positive, Mendelian randomization results may turn out to be more reliable.

Testing in semiparametric models with interaction, with applications to gene-environment interactions.

Motivated from the problem of testing for genetic effects on complex traits in the presence of gene-environment interaction, we develop score tests in general semiparametric regression problems that involves Tukey style 1 degree-of-freedom form of interaction between parametrically and non-parametrically modelled covariates. We find that the score test in this type of model, as recently developed by Chatterjee and co-workers in the fully parametric setting, is biased and requires undersmoothing to be valid in the presence of non-parametric components. Moreover, in the presence of repeated outcomes, the asymptotic distribution of the score test depends on the estimation of functions which are defined as solutions of integral equations, making implementation difficult and computationally taxing. We develop profiled score statistics which are unbiased and asymptotically efficient and can be performed by using standard bandwidth selection methods. In addition, to overcome the difficulty of solving functional equations, we give easy interpretations of the target functions, which in turn allow us to develop estimation procedures that can be easily implemented by using standard computational methods. We present simulation studies to evaluate type I error and power of the method proposed compared with a naive test that does not consider interaction. Finally, we illustrate our methodology by analysing data from a case-control study of colorectal adenoma that was designed to investigate the association between colorectal adenoma and the candidate gene NAT2 in relation to smoking history.

A tree‐based model for allele‐sharing‐based linkage analysis in human complex diseases

By adapting a well-known affected-relative-pair linkage model that can incorporate covariate or sub-phenotype information [Olson, 1999: Am J Hum Genet 65:1760-1769], we have developed a recursive-partitioning (RP) algorithm (tree-based model) for identifying phenotype and covariate groupings that interact with the evidence for linkage. This strategy is designed to identify subgroups of affected relative pairs demonstrating increased evidence for linkage, where subgroups are defined by pair-level or family-level covariates. After growing a full tree, we identified optimal tree size through a form of tree pruning and chose the best covariate at each split by using bootstrap algorithms. Simulation studies showed that power to detect linkage can increase in the presence of gene-environment interactions, depending on the magnitude of the interaction. As expected, however, power can decrease by examining more covariates, despite the pruning to optimize tree size. The RP model correctly identifies tree structure in a large proportion of simulations. We applied the RP model to a dataset of families with bipolar affective disorder (BPAD) where linkage regions on chromosome 18 have been previously identified. Using the all-pairs score in Genehunter, the NPL tests showed no regions with strong linkage evidence on chromosome 18. However, using the RP model, several suggestive regions were found on chromosome 18. Two covariates appeared to influence the degree of linkage: the type II BPAD subtype and a pattern of displaying mania before or after a depressive episode. The RP model has the potential to identify previously unknown gene-environmental interactions; here we have demonstrated the practical utility and potential this new methodology holds.

Population-based and family-based studies on the serotonin transporter gene polymorphisms and bipolar disorder: a systematic review and meta-analysis

The serotonin transporter (5-HTT) is a candidate gene for bipolar disorder (BPD). It has been investigated for association with the illness in a series of studies, but overall results have been inconsistent and its role in the disorder remains controversial. Systematic reviews using meta-analytical techniques are a useful method for objectively and reproducibly assessing individual studies and generating combined results. We performed two meta-analyses of published studies--both population-based and family-based studies--investigating the association between BPD and the 5-HTT gene-linked polymorphic region (5-HTTLPR) and the intron 2 variable numbers of tandem repeats (VNTR) polymorphisms. The literature was searched using Medline and Embase to identify studies for inclusion. We statistically joined population-based and family-based studies into a single meta-analysis. For both polymorphisms, our review revealed significant pooled odds ratios (ORs): 1.12 (95% CI 1.03-1.21) for the 5-HTTLPR and 1.12 (95% CI 1.02-1.22) for the intron 2 VNTR. Meta-regression showed that neither the study type (population-based vs family-based; P=0.41 for the 5-HTTLPR and P=0.91 for the intron 2 VNTR) nor the sample ethnicity (Caucasian vs non-Caucasian; P=0.35 for the 5-HTTLPR and P=0.66 for the intron 2 VNTR) significantly contributed to the heterogeneity of the meta-analyses. The observed ORs could be regarded simply as a very small but detectable effect of the 5-HTT, which has an additive effect when combined with other susceptibility loci. Alternative hypotheses on this finding were also discussed: a stronger effect of the haplotypes involving the two polymorphisms or other SNP markers; a more direct effect of these polymorphisms on specific phenotypes of BPD; and the presence of gene-environment interaction as a mediator of the genetic effects of 5-HTT.

Gene-Environment Interactions and the Affected-Sib-Pair Designs

In genetic mapping of complex traits, the affected-sib-pair method (ASP) and the transmission disequilibrium test (TDT) are two methods of choice. The major appeal of both ASP and TDT is that they do not require the knowledge of mode of inheritance underlying the trait in question. The relative ease and economy for data collection also is the reason for their popularity. The basic idea of the ASP is to identify genes or chromosomal regions through identifying genetic similarity based on phenotypical similarity. TDT, on the other hand, detects susceptibility genes through detecting unusual transmission patterns in families. Since phenotypic similarity can also be caused by environmental similarity, we investigate how the presence of gene-environment interaction (GEI) affects the power of both methods. For a simple one-locus-one-risk-factor model, our results indicate that, in the presence of GEI, methods developed based on marginal penetrance functions (i.e. ignoring the risk factor) can give spurious results. The triangular restriction on allele-sharing probability may no longer be valid. If the environment effect is strong, using exposure-discordant affected sib pairs may have advantage over other designs. Above all, a genetic model involving both genetic and environmental factors behaves differently from a single-locus genetic model.

Population and familial relative risks of disease associated with environmental factors in the presence of gene-environment interaction.

With increasing availability of polymorphic DNA markers, family studies can be used to identify linked susceptibility genes for chronic diseases. Nevertheless, in the presence of gene-environment interaction, the ability of family studies to detect associations between exposures and diseases has not been adequately compared with population studies. The authors derive and compare exposure relative risk measures obtained from population studies with those obtained from studying siblings of affected individuals. For multiplicative joint effects on disease risk between the gene and the exposure, it can be shown that exposure relative risks in siblings are identical to those obtained from the population. For less than multiplicative joint effects (e.g., additive) sibling relative risks are less than population relative risks. However, in the presence of more than multiplicative joint effects, it can be shown that sibling relative risks could be much higher than population relative risks, more so for dominant susceptibility genes than recessive ones, and depend on the model of gene-environment interaction. Under these conditions of strong gene-environment interaction, family studies can be useful in searching for exposure-disease associations, in addition to detecting underlying genetic mechanisms.