A flexible Bayesian model for studying gene-environment interaction

An important follow-up step after genetic markers are found to be associated with a disease outcome is a more detailed analysis investigating how the implicated gene or chromosomal region and an established environment risk factor interact to influence the disease risk. The standard approach to this study of gene–environment interaction considers one genetic marker at a time and therefore could misrepresent and underestimate the genetic contribution to the joint effect when one or more functional loci, some of which might not be genotyped, exist in the region and interact with the environment risk factor in a complex way. We develop a more global approach based on a Bayesian model that uses a latent genetic profile variable to capture all of the genetic variation in the entire targeted region and allows the environment effect to vary across different genetic profile categories. We also propose a resampling-based test derived from the developed Bayesian model for the detection of gene–environment interaction. Using data collected in the Environment and Genetics in Lung Cancer Etiology (EAGLE) study, we apply the Bayesian model to evaluate the joint effect of smoking intensity and genetic variants in the 15q25.1 region, which contains a cluster of nicotinic acetylcholine receptor genes and has been shown to be associated with both lung cancer and smoking behavior. We find evidence for gene–environment interaction (P-value = 0.016), with the smoking effect appearing to be stronger in subjects with a genetic profile associated with a higher lung cancer risk; the conventional test of gene–environment interaction based on the single-marker approach is far from significant.     

HPA-axis activity in alcoholism: examples for a gene-environment interaction.

Genetic and environmental influences are both known to be causal factors in the development and maintenance of substance abuse disorders. This review aims to focus on the contributions of genetic and environmental research to the understanding of alcoholism and how gene-environment interactions result in a variety of addiction phenotypes. Gene-environment interactions have been reviewed by focusing on one of the most relevant environmental risk factors for alcoholism, stress. This is examined in more detail by reviewing the functioning of the hypothalamic-pituitary-adrenal (HPA) axis and its genetic and molecular components in this disorder. Recent evidence from animal and human studies have shown that the effects of stress on alcohol drinking are mediated by core HPA axis genes and are associated with genetic variations in those genes. The findings of the studies discussed here suggest that the collaborations of neuroscience, psychobiology and molecular genetics provide a promising framework to elucidate the exact mechanisms of gene-environment interactions as seen to convene upon the HPA axis and effect phenotypes of addiction.     

Varying coefficient model with unknown within-subject covariance for analysis of tumor growth curves

SUMMARY: In this article we develop a nonparametric estimation procedure for the varying coefficient model when the within-subject covariance is unknown. Extending the idea of iterative reweighted least squares to the functional setting, we iterate between estimating the coefficients conditional on the covariance and estimating the functional covariance conditional on the coefficients. Smoothing splines for correlated errors are used to estimate the functional coefficients with smoothing parameters selected via the generalized maximum likelihood. The covariance is nonparametrically estimated using a penalized estimator with smoothing parameters chosen via a Kullback-Leibler criterion. Empirical properties of the proposed method are demonstrated in simulations and the method is applied to the data collected from an ovarian tumor study in mice to analyze the effects of different chemotherapy treatments on the volumes of two classes of tumors.     

Genetic dissection methods: designs used for tests of gene-environment interaction

Given recent advances in the field of molecular genetics, many have recognized the need to exploit either study designs or analytical methods to test hypotheses with gene-by-environment (G x E) interactions. The partial-collection designs, including case-only, partial case-control, and case-parent trio designs, have been suggested as attractive alternatives to the complete case-control design both for increased statistical efficiency and reduced data needs. However, common problems in genetic epidemiology studies, such as, presence of G x E correlation in the population, population mixture, and genotyping error may reduce the validity of these designs. On the basis of previous simulation studies and empirical data and given the potential limitations and uncertainty of assumptions of partial-collection designs, the case-control design is the optimal choice versus partial-collection designs.     

A non-linear model for visual-vestibular interaction during body rotation in man

A mathematical model for visual-vestibular interaction during body rotation in an illuminated visual surround is obtained by combining a previous model of the optokinetic reflex (OKR) with a simplified model of the vestibulo-ocular reflex (VOR). OKR is activated by the slip of the image of the external world on the retina, and represents a negative feedback loop around VOR. For large retinal slip velocities OKR behaves as a basically non-linear system. The validity of the model is proved via computer simulation by comparing predicted responses with the experimental results obtained in man by Koenig et al. (1978) in different situations of visual-vestibular interaction.Work supported by C.N.R. (Rome, Italy), Special Project on Piomedical Engineering, Grant No. 79.01255.86     

Variance components models for gene-environment interaction in twin analysis.

Gene-environment interaction is likely to be a common and important source of variation for complex behavioral traits. Often conceptualized as the genetic control of sensitivity to the environment, it can be incorporated in variance components twin analyses by partitioning genetic effects into a mean part, which is independent of the environment, and a part that is a linear function of the environment. The model allows for one or more environmental moderator variables (that possibly interact with each other) that may i). be continuous or binary ii). differ between twins within a pair iii). interact with residual environmental as well as genetic effects iv) have nonlinear moderating properties v). show scalar (different magnitudes) or qualitative (different genes) interactions vi). be correlated with genetic effects acting upon the trait, to allow for a test of gene-environment interaction in the presence of gene-environment correlation. Aspects and applications of a class of models are explored by simulation, in the context of both individual differences twin analysis and, in a companion paper (Purcell & Sham, 2002) sibpair quantitative trait locus linkage analysis. As well as elucidating environmental pathways, consideration of gene-environment interaction in quantitative and molecular studies will potentially direct and enhance gene-mapping efforts.     

Integrating epidemiology and genetic association: the challenge of gene-environment interaction

Recent advances in human genomics have made it possible to better understand the genetic basis of disease. In addition, genetic association studies can also elucidate the mechanisms by which "non-genetic" exogenous and endogenous exposures influence the risk of disease. This is true both of studies that assess the marginal effect of a single gene and studies that look at the joint effect of genes and environmental exposures. For example, gene variants that are known to alter enzyme function or level can serve as surrogates for long-term biomarker levels that are impractical or impossible to measure on many subjects. Evidence that genetic variants modify the effect of an established risk factor may help specify the risk factor's biologically active components. We illustrate these ideas with several examples and discuss design and analysis challenges, particularly for studies of gene-environment interaction. We argue that to increase the power to detect interaction effects and limit the number of false positive results, large sample sizes will be needed, which are currently only available through planned collaborative efforts. Such collaborations also ensure a common approach to measuring variation at a genetic locus, avoiding a problem that has led to difficulties when comparing results from genetic association studies.     

Combining disease models to test for gene-environment interaction in nuclear families

It is useful to have robust gene-environment interaction tests that can utilize a variety of family structures in an efficient way. This article focuses on tests for gene-environment interaction in the presence of main genetic and environmental effects. The objective is to develop powerful tests that can combine trio data with parental genotypes and discordant sibships when parents' genotypes are missing. We first make a modest improvement on a method for discordant sibs (discordant on phenotype), but the approach does not allow one to use families when all offspring are affected, e.g., trios. We then make a modest improvement on a Mendelian transmission-based approach that is inefficient when discordant sibs are available, but can be applied to any nuclear family. Finally, we propose a hybrid approach that utilizes the most efficient method for a specific family type, then combines over families. We utilize this hybrid approach to analyze a chronic obstructive pulmonary disorder dataset to test for gene-environment interaction in the Serpine2 gene with smoking. The methods are freely available in the R package fbati.     

Genetic susceptibility on CagA-interacting molecules and gene-environment interaction with phytoestrogens: a putative risk factor for gastric cancer

Objectives

To evaluate whether genes that encode CagA-interacting molecules (SRC, PTPN11, CRK, CRKL, CSK, c-MET and GRB2) are associated with gastric cancer risk and whether an interaction between these genes and phytoestrogens modify gastric cancer risk.

Methods

In the discovery phase, 137 candidate SNPs in seven genes were analyzed in 76 incident gastric cancer cases and 322 matched controls from the Korean Multi-Center Cancer Cohort. Five significant SNPs in three genes (SRC, c-MET and CRK) were re-evaluated in 386 cases and 348 controls in the extension phase. Odds ratios (ORs) for gastric cancer risk were estimated adjusted for age, smoking, H. pylori seropositivity and CagA strain positivity. Summarized ORs in the total study population (462 cases and 670 controls) were presented using pooled- and meta-analysis. Plasma concentrations of phytoestrogens (genistein, daidzein, equol and enterolactone) were measured using the time-resolved fluoroimmunoassay.

Results

SRC rs6122566, rs6124914, c-MET rs41739, and CRK rs7208768 showed significant genetic effects for gastric cancer in both the pooled and meta-analysis without heterogeneity (pooled OR = 3.96 [95% CI 2.05–7.65], 1.24 [95% CI = 1.01–1.53], 1.19 [95% CI = 1.01–1.41], and 1.37 [95% CI = 1.15–1.62], respectively; meta OR = 4.59 [95% CI 2.74–7.70], 1.36 [95% CI = 1.09–1.70], 1.20 [95% CI = 1.00–1.44], and 1.32 [95% CI = 1.10–1.57], respectively). Risk allele of CRK rs7208768 had a significantly increased risk for gastric cancer at low phytoestrogen levels (p interaction<0.05).

Conclusions

Our findings suggest that SRC, c-MET and CRK play a key role in gastric carcinogenesis by modulating CagA signal transductions and interaction between CRK gene and phytoestrogens modify gastric cancer risk.     

A mechanism for gene-environment interaction in the etiology of congenital scoliosis

Congenital scoliosis, a lateral curvature of the spine caused by vertebral defects, occurs in approximately 1 in 1,000 live births. Here we demonstrate that haploinsufficiency of Notch signaling pathway genes in humans can cause this congenital abnormality. We also show that in a mouse model, the combination of this genetic risk factor with an environmental condition (short-term gestational hypoxia) significantly increases the penetrance and severity of vertebral defects. We demonstrate that hypoxia disrupts FGF signaling, leading to a temporary failure of embryonic somitogenesis. Our results potentially provide a mechanism for the genesis of a host of common sporadic congenital abnormalities through gene-environment interaction.     

Different data from different labs: lessons from studies of gene-environment interaction

It is sometimes supposed that standardizing tests of mouse behavior will ensure similar results in different laboratories. We evaluated this supposition by conducting behavioral tests with identical apparatus and test protocols in independent laboratories. Eight genetic groups of mice, including equal numbers of males and females, were either bred locally or shipped from the supplier and then tested on six behaviors simultaneously in three laboratories (Albany, NY; Edmonton, AB; Portland, OR). The behaviors included locomotor activity in a small box, the elevated plus maze, accelerating rotarod, visible platform water escape, cocaine activation of locomotor activity, and ethanol preference in a two-bottle test. A preliminary report of this study presented a conventional analysis of conventional measures that revealed strong effects of both genotype and laboratory as well as noteworthy interactions between genotype and laboratory. We now report a more detailed analysis of additional measures and view the data for each test in different ways. Whether mice were shipped from a supplier or bred locally had negligible effects for almost every measure in the six tests, and sex differences were also absent or very small for most behaviors, whereas genetic effects were almost always large. For locomotor activity, cocaine activation, and elevated plus maze, the analysis demonstrated the strong dependence of genetic differences in behavior on the laboratory giving the tests. For ethanol preference and water escape learning, on the other hand, the three labs obtained essentially the same results for key indicators of behavior. Thus, it is clear that the strong dependence of results on the specific laboratory is itself dependent on the task in question. Our results suggest that there may be advantages of test standardization, but laboratory environments probably can never be made sufficiently similar to guarantee identical results on a wide range of tests in a wide range of labs. Interpretations of our results by colleagues in neuroscience as well as the mass media are reviewed. Pessimistic views, prevalent in the media but relatively uncommon among neuroscientists, of mouse behavioral tests as being highly unreliable are contradicted by our data. Despite the presence of noteworthy interactions between genotype and lab environment, most of the larger differences between inbred strains were replicated across the three labs. Strain differences of moderate effects size, on the other hand, often differed markedly among labs, especially those involving three 129-derived strains. Implications for behavioral screening of targeted and induced mutations in mice are discussed.     

Melittin-GM1 interaction: a model for a side-by-side complex

We report here the interaction of melittin with ganglioside GM1 by steady-state fluorescence, one-dimensional (1)H NMR spectroscopy and molecular modeling. In the presence of GM1 the emission maximum of melittin is blue shifted and fluorescence quenching efficiencies of iodide and acrylamide are substantially reduced, indicating a shielding of tryptophan of melittin from aqueous environment. Significant line broadening of NMR resonances of melittin, suggestive of motional restriction, is observed. Molecular modeling indicates a melittin-GM1 complex with N-terminal hydrophobic stretch of melittin associating with the ceramide tail and C-terminal hydrophilic end of melittin having favorable electrostatic interaction with the carbohydrate head group of GM1.     

Varying coefficient models for mapping quantitative trait loci using recombinant inbred intercrosses

There has been a great deal of interest in the development of methodologies to map quantitative trait loci (QTL) using experimental crosses in the last 2 decades. Experimental crosses in animal and plant sciences provide important data sources for mapping QTL through linkage analysis. The Collaborative Cross (CC) is a renewable mouse resource that is generated from eight genetically diverse founder strains to mimic the genetic diversity in humans. The recombinant inbred intercrosses (RIX) generated from CC recombinant inbred (RI) lines share similar genetic structures of F(2) individuals but with up to eight alleles segregating at any one locus. In contrast to F(2) mice, genotypes of RIX can be inferred from the genotypes of their RI parents and can be produced repeatedly. Also, RIX mice typically do not share the same degree of relatedness. This unbalanced genetic relatedness requires careful statistical modeling to avoid false-positive findings. Many quantitative traits are inherently complex with genetic effects varying with other covariates, such as age. For such complex traits, if phenotype data can be collected over a wide range of ages across study subjects, their dynamic genetic patterns can be investigated. Parametric functions, such as sigmoidal or logistic functions, have been used for such purpose. In this article, we propose a flexible nonparametric time-varying coefficient QTL mapping method for RIX data. Our method allows the QTL effects to evolve with time and naturally extends classical parametric QTL mapping methods. We model the varying genetic effects nonparametrically with the B-spline bases. Our model investigates gene-by-time interactions for RIX data in a very flexible nonparametric fashion. Simulation results indicate that the varying coefficient QTL mapping has higher power and mapping precision compared to parametric models when the assumption of constant genetic effects fails. We also apply a modified permutation procedure to control overall significance level.     

Variance components models for gene-environment interaction in quantitative trait locus linkage analysis.

Gene-environment interaction (G x E) is likely to be a common and important source of variation for complex behavioral traits. Gene-environment interaction, or genetic control of sensitivity to the environment, can be incorporated into variance components twin and sib-pair analyses by partitioning genetic effects into a mean part, which is independent of the environment, and a part that is a linear function of the environment. An approach described in a companion paper (Purcell, 2002) is applied to sib-pair variance components linkage analysis in two ways: allowing for quantitative trait locus by environment interaction and utilizing information on any residual interactions detected prior to analysis. As well as elucidating environmental pathways, consideration of G x E in quantitative and molecular studies will potentially direct and enhance gene-mapping efforts.     

A non-linear analysis for spatial structure in a reaction-diffusion model

A non-linear stability analysis using a multi-scale perturbation procedure is carried out on the practical Thomas reaction-diffusion mechanism which exhibits bifurcation to non-uniform states. The analytical results compare favourably with the numerical solutions. The sequential patterns generated by this model by variations in a parameter related to the reaction-diffusion domain indicate its capacity to represent certain key morphogenetic features required in a recent model by Kauffman for pattern formation in theDrosophila embryo.     

Natural and orthogonal interaction framework for modeling gene-environment interactions with application to lung cancer

Objectives: We aimed at extending the Natural and Orthogonal Interaction (NOIA) framework, developed for modeling gene-gene interactions in the analysis of quantitative traits, to allow for reduced genetic models, dichotomous traits, and gene-environment interactions. We evaluate the performance of the NOIA statistical models using simulated data and lung cancer data. Methods: The NOIA statistical models are developed for additive, dominant, and recessive genetic models as well as for a binary environmental exposure. Using the Kronecker product rule, a NOIA statistical model is built to model gene-environment interactions. By treating the genotypic values as the logarithm of odds, the NOIA statistical models are extended to the analysis of case-control data. Results: Our simulations showed that power for testing associations while allowing for interaction using the NOIA statistical model is much higher than using functional models for most of the scenarios we simulated. When applied to lung cancer data, much smaller p values were obtained using the NOIA statistical model for either the main effects or the SNP-smoking interactions for some of the SNPs tested. Conclusion: The NOIA statistical models are usually more powerful than the functional models in detecting main effects and interaction effects for both quantitative traits and binary traits.