The use of common genetic polymorphisms to enhance the epidemiologic study of environmental carcinogens

Overwhelming evidence indicates that environmental exposures, broadly defined, are responsible for most cancer. There is reason to believe, however, that relatively common polymorphisms in a wide spectrum of genes may modify the effect of these exposures. We discuss the rationale for using common polymorphisms to enhance our understanding of how environmental exposures cause cancer and comment on epidemiologic strategies to assess these effects, including study design, genetic and statistical analysis, and sample size requirements. Special attention is given to sources of potential bias in population studies of gene--environment interactions, including exposure and genotype misclassification and population stratification (i.e., confounding by ethnicity). Nevertheless, by merging epidemiologic and molecular approaches in the twenty-first century, there will be enormous opportunities for unraveling the environmental determinants of cancer. In particular, studies of genetically susceptible subgroups may enable the detection of low levels of risk due to certain common exposures that have eluded traditional epidemiologic methods. Further, by identifying susceptibility genes and their pathways of action, it may be possible to identify previously unsuspected carcinogens. Finally, by gaining a more comprehensive understanding of environmental and genetic risk factors, there should emerge new clinical and public health strategies aimed at preventing and controlling cancer.     

Evaluation of two methods for assessing gene-environment interactions using data from the Danish case-control study of facial clefts

BACKGROUND: Epidemiological investigations have begun to consider gene-environment (GE) interactions as potential risk factors for many diseases, including several different birth defects. However, traditional methodological approaches for the analysis of case-control data tend to have low power for detection of interaction effects. A log-linear approach that can impose the assumption that the genotype and exposure of interest occur independently in the population has been proposed as a potentially more powerful method for assessing GE interactions but has not been widely applied in the published literature. METHODS: The present analyses were undertaken to compare the results obtained when stratified analyses and a log-linear approach were used to assess potential GE interactions. The analyses were conducted using data from a population-based, case-control study conducted in Denmark and considered associations between nonsyndromic cleft lip with or without cleft palate (CL+/-P), infant genotype for variants of RAR-alpha, TGF-alpha, TGF-beta3, and MSX1, and maternal exposure to smoking, alcohol, and multivitamins. RESULTS: Neither the stratified nor the log-linear analyses provided evidence that that risk of CL+/-P is influenced by any of the GE interactions that were evaluated, despite the potential increase in power offered by the latter approach. Further, the analyses highlight concerns regarding the power to reject the assumption of independence of the genetic and environmental factor of interest in the controls and related concerns regarding the validity of results obtained using the log-linear approach when the underlying assumption is violated. CONCLUSIONS: The potential increase in power offered by the log-linear approach is offset by concerns regarding the validity of this approach when the independence assumption is violated.     

Developmental capacitance, genetic accommodation, and adaptive evolution

The concept of genetic accommodation remains controversial, in part because it remains unclear whether evolution by genetic accommodation forces a revolution, or merely a shift in emphasis, in our understanding of how evolution produces adaptive new traits. Here I outline a perspective that largely favors the latter view. I argue that evolution by genetic accommodation can easily be integrated into traditional evolutionary concepts. At the same time, evolution by genetic accommodation invites novel empirical and theoretical approaches that may allow biologists to push the boundaries of our current understanding of the process of evolution and to solve some long-standing controversies. Specifically, I discuss the role of developmental mechanisms as natural, and likely ubiquitous, capacitors of cryptic genetic variation, and the role of environmental perturbations as mechanisms by which such variation can become visible to selection on an individual to population-wide scale. I argue that in combination, developmental capacitance and large-scale environmental perturbations have the potential to facilitate rapid evolution including the origin of novel adaptive features while circumventing otherwise powerful genetic and population-biological constraints on adaptive evolution. I end by highlighting several promising avenues for future empirical research to explore the mechanisms and significance of evolution by genetic accommodation.     

An overview of the utility of population simulation software in molecular ecology

Stochastic simulation software that simultaneously model genetic, population and environmental processes can inform many topics in molecular ecology. These include forecasting species and community response to environmental change, inferring dispersal ecology, revealing cryptic mating, quantifying past population dynamics, assessing in situ management options and monitoring neutral and adaptive biodiversity change. Advances in population demographic–genetic simulation software, especially with respect to individual life history, landscapes and genetic processes, are transforming and expanding the ways that molecular data can be used. The aim of this review is to explain the roles that such software can play in molecular ecology studies (whether as a principal component or a supporting function) so that researchers can decide whether, when and precisely how simulations can be incorporated into their work. First, I use seven case studies to demonstrate how simulations are employed, their specific advantage/necessity and what alternative or complementary (nonsimulation) approaches are available. I also explain how simulations can be integrated with existing spatial, environmental, historical and genetic data sets. I next describe simulation features that may be of interest to molecular ecologists, such as spatial and behavioural considerations and species' interactions, to provide guidance on how particular simulation capabilities can serve particular needs. Lastly, I discuss the prospect of simulation software in emerging challenges (climate change, biodiversity monitoring, population exploitation) and opportunities (genomics, ancient DNA), in order to emphasize that the scope of simulation‐based work is expanding. I also suggest practical considerations, priorities and elements of best practice. This should accelerate the uptake of simulation approaches and firmly embed them as a versatile tool in the molecular ecologist's toolbox.     

Sex‐specific heritability of cell‐mediated immune response in the blue tit nestlings (Cyanistes caeruleus)

Here, we aimed at estimating sex‐specific heritabilities of cell‐mediated immune response (CMI) in the blue tit nestlings (Cyanistes caeruleus). To separate genetic and environmental components of the phenotypic variance in CMI (measured using phytohaemagglutinin assay), we performed a cross‐fostering experiment. Additionally, controlled environmental variation was introduced by enlarging some broods. Our analyses revealed a significant genetic component (as approximated by the nest‐of‐origin term) of the phenotypic variance in immune response. More importantly, these genetic effects differed between sexes and experimentally manipulated brood sizes, as indicated by significant genotype‐by‐sex and genotype‐by‐environment interactions. We discuss possible causes of such sexual dimorphism in gene expression and suggest that sex‐ and environment‐specific genetic interactions may contribute to the maintenance of genetic variability in traits related to immune functions.     

Simple and efficient analysis of disease association with missing genotype data

Missing genotype data arise in association studies when the single-nucleotide polymorphisms (SNPs) on the genotyping platform are not assayed successfully, when the SNPs of interest are not on the platform, or when total sequence variation is determined only on a small fraction of individuals. We present a simple and flexible likelihood framework to study SNP-disease associations with such missing genotype data. Our likelihood makes full use of all available data in case-control studies and reference panels (e.g., the HapMap), and it properly accounts for the biased nature of the case-control sampling as well as the uncertainty in inferring unknown variants. The corresponding maximum-likelihood estimators for genetic effects and gene-environment interactions are unbiased and statistically efficient. We developed fast and stable numerical algorithms to calculate the maximum-likelihood estimators and their variances, and we implemented these algorithms in a freely available computer program. Simulation studies demonstrated that the new approach is more powerful than existing methods while providing accurate control of the type I error. An application to a case-control study on rheumatoid arthritis revealed several loci that deserve further investigations.     

Next-generation sequencing approaches in genetic rodent model systems to study functional effects of human genetic variation

Rapid advances in DNA sequencing improve existing techniques and enable new approaches in genetics and functional genomics, bringing about unprecedented coverage, resolution and sensitivity. Enhanced toolsets can facilitate the untangling of connections between genomic variation, environmental factors and phenotypic effects, providing novel opportunities, but may also pose challenges in data interpretation, especially in highly heterogeneous human populations. Laboratory rodent strains, however, offer a variety of tailored model systems with controlled genetic backgrounds, facilitating complex genotype/phenotype relationship studies. In this review we discuss the advent of massively parallel sequencing, its methodological advantage for molecular analysis in model organisms and the expectation of increased understanding of biologically relevant consequences of human genetic variation.     

Generation of synthetic data and experimental designs in evaluating interactions for association studies

Complex diseases, by definition, involve multiple factors, including gene-gene interactions and gene-environment interactions. Researchers commonly rely on simulated data to evaluate their approaches for detecting high-order interactions in disease gene mapping. A publicly available simulation program to generate samples involving complex genetic and environmental interactions is of great interest to the community. We have developed a software package named gs1.0, which has been widely used since its publication. In this article, we present an upgraded version gs2.0, which not only inherits its capacity to generate realistic genotype data but also provides great functionality and flexibility to simulate various interaction models. In addition to a standalone version, a user-friendly web server (http://cbc.case.edu/gs) has been set up to help users to build complex interaction models. Furthermore, by utilizing three three-locus models as an example, we have shown how realistic model parameters can be chosen in generating simulated data.     

On the culture-independent assessment of the diversity and distribution of Prochlorococcus

Much of our current knowledge on Prochlorococcus has derived from research on various genetic strains that have successfully been brought into culture. In particular, analyses of the complete genomes of 12 of those isolates have revealed the extent to which these strains differ from each other and the genetic means by which they are adapted to specific environmental niches. However, based on culture-independent studies it is now clear that the strains currently available in diverse culture collections do not represent the true diversity of Prochlorococcus geno- and phenotypes. Potential alternatives to overcome the limitations caused by difficulties in isolating Prochlorococcus may be provided by the whole-genome amplification of flow cytometrically isolated individual cells. The new information obtained in this way on various genetic types would, in turn, facilitate the correct identification of Prochlorococcus-derived sequences within metagenomic sequence data sets. However, culture-independent molecular population genetic approaches have also greatly furthered our understanding of the ecology and physiological capability of Prochlorococcus genetic groups. Based on this, I support the notion that such population genetic approaches to reveal the Prochlorococcus microdiversity are still of great value if appropriate marker genes providing a high genetic resolution are used. The comparison of the results from those culture-independent analyses of environmental marine samples with those from the measurement of relevant environmental and biotic parameters still has potential to uncover further parameters that control Prochlorococcus diversity and distribution. In this context, I will discuss those markers that have so far been used in population genetic studies of Prochlorococcus, which is followed by outlining a general approach to evaluate their use for resolving microbial microdiversity and for phylogenetic identification.     

Models for navigating biological complexity in breeding improved crop plants

Progress in breeding higher-yielding crop plants would be greatly accelerated if the phenotypic consequences of making changes to the genetic makeup of an organism could be reliably predicted. Developing a predictive capacity that scales from genotype to phenotype is impeded by biological complexities associated with genetic controls, environmental effects and interactions among plant growth and development processes. Plant modelling can help navigate a path through this complexity. Here we profile modelling approaches for complex traits at gene network, organ and whole plant levels. Each provides a means to link phenotypic consequence to changes in genomic regions via stable associations with model coefficients. A unifying feature of the models is the relatively coarse level of granularity they use to capture system dynamics. Much of the fine detail is not directly required. Robust coarse-grained models might be the tool needed to integrate phenotypic and molecular approaches to plant breeding.     

Detecting the macroevolutionary signal of species interactions

Species interactions lie at the heart of many theories of macroevolution, from adaptive radiation to the Red Queen. Although some theories describe the imprint that interactions will have over long timescales, we are still missing a comprehensive understanding of the effects of interactions on macroevolution. Current research shows strong evidence for the impact of interactions on macroevolutionary patterns of trait evolution and diversification, yet many macroevolutionary studies have only a tenuous relationship to ecological studies of interactions over shorter timescales. We review current research in this area, highlighting approaches that explicitly model species interactions and connect them to broad‐scale macroevolutionary patterns. We also suggest that progress has been made by taking an integrative interdisciplinary look at individual clades. We focus on African cichlids as a case study of how this approach can be fruitful. Overall, although the evidence for species interactions shaping macroevolution is strong, further work using integrative and model‐based approaches is needed to spur progress towards understanding the complex dynamics that structure communities over time and space.     

Systems genetics: From GWAS to disease pathways

Most common diseases are complex, involving multiple genetic and environmental factors and their interactions. In the past decade, genome-wide association studies (GWAS) have successfully identified thousands of genetic variants underlying susceptibility to complex diseases. However, the results from these studies often do not provide evidence on how the variants affect downstream pathways and lead to the disease. Therefore, in the post-GWAS era the greatest challenge lies in combining GWAS findings with additional molecular data to functionally characterize the associations. The advances in various ~omics techniques have made it possible to investigate the effect of risk variants on intermediate molecular levels, such as gene expression, methylation, protein abundance or metabolite levels. As disease aetiology is complex, no single molecular analysis is expected to fully unravel the disease mechanism. Multiple molecular levels can interact and also show plasticity in different physiological conditions, cell types and disease stages. There is therefore a great need for new integrative approaches that can combine data from different molecular levels and can help construct the causal inference from genotype to phenotype. Systems genetics is such an approach; it is used to study genetic effects within the larger scope of systems biology by integrating genotype information with various ~omics datasets as well as with environmental and physiological variables. In this review, we describe this approach and discuss how it can help us unravel the molecular mechanisms through which genetic variation causes disease. This article is part of a Special Issue entitled: From Genome to Function.     

Population genetic effects of human-mediated plant range expansions on native phytophagous insects

Anthropogenic changes in organismal distributions have affected many, if not all, terrestrial ecosystems. Anthropogenic plant range expansions (APREs) may have profound effects on the population genetics of native phytophagous insects exploiting recent changes in plant distributions. Here I focus on the important, although often overlooked, effects on insect species which feed on such plants, but do not show evidence of host race formation. This article investigates geographic range expansion and increased interpopulation gene flow, which may occur as a result of phytophagous insects exploiting APREs. The two effects have clear predicted genetic signatures: (1) geographic range expansion should result in low levels of genetic diversity in new portions of the insect species' range, relative to older, pre-APRE portions of the range and (2) increased interpopulation gene flow will result in reduced population genetic differentiation. Data from the literature are qualitatively consistent with the predictions of one or both population genetic change scenarios. Higher order effects, such as reduced local adaptation, increased spread of resistance in agricultural systems, and hybridization among formerly isolated lineages, as evidenced by case studies, and the economic and conservation implications thereof are also discussed. Finally, I outline future approaches for addressing the impact of APREs on native phytophagous insect ecology and discuss the application of these concepts of population genetic change and resultant outcomes to research outside plant–insect interactions.     

Chromatin and heritability: how epigenetic studies can complement genetic approaches

Despite the success in using genome-wide association studies to identify many loci associated with human disease, there are several gaps in understanding of how common genetic diseases are manifested. Epigenetic studies, which focus on DNA and chromatin modifications, have the potential to complement genetic approaches and provide more insight into mechanism, environmental effects and modes of inheritance, including the potential for non-DNA-based heritability. However, there are considerable challenges in designing and interpreting epigenetic studies associated with disease. Here, I review recent studies focused on individual variation in chromatin, and outline how epigenome-based studies can be used to complement genetic studies. In particular, I see more benefit to epigenetic studies being performed in the context of genetic studies, rather than as separate investigations.     

Impact of metabolic genotypes on levels of biomarkers of genotoxic exposure

Phase I and Phase II xenobiotic-metabolising enzyme families are involved in the metabolic activation and detoxification of various classes of environmental carcinogens. Particular genetic polymorphisms of these enzymes have been shown to influence individual cancer risk. A brief overview is presented about recent research of the relationship between metabolic genotypes and internal dose, biologically effective dose and cytogenetic effects of complex and specific genotoxic exposures of human study populations, and we report our new results from two molecular epidemiological studies. We investigated the effects of multiple interactions among CYP1A1 Ile462Val, CYP1A1 MspI, CYP1B1 Leu432Val, CYP2C9 Arg144Cys, CYP2C9 Ile359Leu, NQO1 Pro189Ser, GSTM1 gene deletion and GSTP1 Ile105Val genotypes on the levels of carcinogen-DNA adducts determined by (32)P-postlabelling and PAH-DNA immunoassay in peripheral blood lymphocytes from workers occupationally exposed to polycyclic aromatic hydrocarbons in aluminium plants, and in bronchial tissue from smoking lung patients. A statistically significant positive linear correlation was observed between white blood cell aromatic DNA adduct and urinary 1-hydroxypyrene (1-OHPY) levels from potroom workers with GSTM1 null genotype (P=0.011). Our results suggest interactions between GSTM1 and GSTP1 alleles in modulation of urinary 1-OHPY levels and white blood cell DNA adduct levels in the PAH-exposed workers. Interactions between GSTM1 and GSTP1 alleles, in association with particular genotype combinations of CYPs, were also recognised in bronchial aromatic DNA adduct levels of smoking lung patients. The impact of single metabolic genotypes and their combinations on biomarkers of exposure was usually weak, if any, in both our studies and reports of the literature. The effect of special metabolic gene interactions may be better recognised if the compared groups of individuals are stratified for multiple potential modulators of the observable biomarker end-point, and/or if chemical structure-specific biomarker methods are applied.     

Impact of changing drug treatment and malaria endemicity on the heritability of malaria phenotypes in a longitudinal family-based cohort study

Despite considerable success of genome wide association (GWA) studies in identifying causal variants for many human diseases, their success in unraveling the genetic basis to complex diseases has been more mitigated. Pathogen population structure may impact upon the infectious phenotype, especially with the intense short-term selective pressure that drug treatment exerts on pathogens. Rigorous analysis that accounts for repeated measures and disentangles the influence of genetic and environmental factors must be performed. Attempts should be made to consider whether pathogen diversity will impact upon host genetic responses to infection.We analyzed the heritability of two Plasmodium falciparum phenotypes, the number of clinical malaria episodes (PFA) and the proportion of these episodes positive for gametocytes (Pfgam), in a family-based cohort followed for 19 years, during which time there were four successive drug treatment regimes, with documented appearance of drug resistance. Repeated measures and variance components analyses were performed with fixed environmental, additive genetic, intra-individual and maternal effects for each drug period. Whilst there was a significant additive genetic effect underlying PFA during the first drug period of study, this was lost in subsequent periods. There was no additive genetic effect for Pfgam. The intra-individual effect increased significantly in the chloroquine period.The loss of an additive genetic effect following novel drug treatment may result in significant loss of power to detect genes in a GWA study. Prior genetic analysis must be a pre-requisite for more detailed GWA studies. The temporal changes in the individual genetic and the intra-individual estimates are consistent with those expected if there were specific host-parasite interactions. The complex basis to the human response to malaria parasite infection likely includes dominance/epistatic genetic effects encompassed within the intra-individual variance component. Evaluating their role in influencing the outcome of infection through host genotype by parasite genotype interactions warrants research effort.     

Use of genetic effects and genotype by environmental interactions for the classification of mexican races of maize

To examine the questions of whether the additive and dominance effects present for morphological characters in racial crosses are of sufficient consistency and magnitude to allow such genetic effects to be used for racial classification, we used a diallel experiment among the 25 well-defined Mexican races of maize, which include the ancestral stocks of most commercial and genetic maize types. With such an experiment, genetic effects and genotype by environmental interactions for one or more characters can be used to measure genetic and adaptational or environmental similarity. We used average parental effects (general combining abilities), specific effects, and genotype by environmental effects of 21 characters from the diallel (grown at three locations) to group the Mexican races of maize. The groupings based upon average genetic effects and upon genotype by environmental interactions are more satisfactory than groupings based upon specific effects. The standard errors for genetic distances based upon specific (largely dominance) effects seem to be too high for practical use. Principal components analyses of the same data suggest a similar conclusion.-The groupings based upon average genetic effects are in general agreement with previous studies, with the exception of Maíz Dulce, which is grouped with the Cónicos, rather than being isolated from the other Mexican races of maize.     

Heritability in the genome-wide association era

Heritability, the fraction of phenotypic variation explained by genetic variation, has been estimated for many phenotypes in a range of populations, organisms, and time points. The recent development of efficient genotyping and sequencing technology has led researchers to attempt to identify the genetic variants responsible for the genetic component of phenotype directly via GWAS. The gap between the phenotypic variance explained by GWAS results and those estimated from classical heritability methods has been termed the "missing heritability problem". In this work, we examine modern methods for estimating heritability, which use the genotype and sequence data directly. We discuss them in the context of classical heritability methods, the missing heritability problem, and describe their implications for understanding the genetic architecture of complex phenotypes.     

Phenotypic assortative mating among the Peruvian Cashinahua

The persistence of behaviorally deleterious genes in the human population poses an interesting question for population genetics: If certain alleles at these loci are deleterious, why have they survived in the population? We consider evidence for phenotypic capacitance and/or frequency-dependent selection for an allele that has been putatively shown to have negative associations with human behaviors (the “short” 5-HTT promoter region allele) yet has persisted in human and nonhuman primate populations. Using data from the National Longitudinal Study of Adolescent Health, we compare sibling and twin variation in depression by 5-HTT genotype (specified in several ways) and investigate sibship-level cross-person gene-gene interactions. In support of the “orchid/dandelion” hypothesis, we find evidence that the short allele increases variation in phenotypes in response to environmental (or genetic) differences (i.e., acts as a perturbation of a phenotypic capacitor). Further, we also find some evidence that the effects of allelic variation at this locus are moderated by the genetic environment of the sibship unit (i.e., effects may be susceptible to frequency-dependent selection). We discuss implications of these findings for genetic models in general, specifically with respect to stable unit treatment value assumption violations (i.e., nonindependence of units of analysis).