Applications of Human Genome Epidemiology to Environmental Health
Samir N. Kelada, David L. Eaton, Sophia S. Wang, Nathaniel R. Rothman, Muin J. Khoury

Tables | Appendix | References

Introduction
Research exploring the role of genetics in determining susceptibility to environmentally-induced disease has grown considerably over the last few decades. Many recent epidemiologic investigations have examined associations between polymorphic genes that code for enzymes involved in xenobiotic biotransformation (i.e. metabolism) and disease and have generated interesting findings. These results imply that genetic variability may affect the response to exposure to environmental health hazards. However, without the use of exposure assessment methods traditionally employed in environmental health science research, these studies have not been able to investigate and characterize gene-environment interactions with environmental health hazards. In addition, many of the results from gene-disease association studies have not been replicated in subsequent studies, casting doubt on their validity, and leaving the environmental health community with uncertain results with which to proceed.

In this chapter, we assess the integration of genetics into environmental health research using the same exposure —> disease paradigm traditionally used by environmental health scientists, adding genetics to the existing paradigm as a potential modifier of dose or effect of the initial exposure. To identify gaps in current knowledge, we classify examples of gene-environment interaction into one of three categories on the basis of evidence from laboratory and epidemiologic data. Finally, we describe the benefits of applying this model to future research efforts, and we discuss issues to consider for investigators wishing to pursue this type of endeavor.

Environmental Exposures and Human Genetic Variation
Much of the impetus for this area of research has come from the field of pharmacogenetics, which is primarily concerned with the study of genetic variation in drug efficacy and toxicity. It has been recognized for many decades that individual differences in response to pharmacological treatment, exhibited as drug toxicity or a lack of therapeutic effect, are often due to genetic differences that result in altered rates of biotransformation (metabolism). Notable examples include nerve damage among individuals homozygous for some variants of the N-acetyltransferase 2 gene (“slow acetylators”) given isoniazid as an antituberculosis therapy, hemolytic anemia among glucose 6-phosphate dehydrogenase-deficient patients given aminoquinoline antimalarial drugs, and varied rates of biotransformation of debrisoquine, an antihypertensive drug, due to genetic variation at the CYP2D6 locus.(1)

The process of biotransformation, i.e., the enzymatic alteration of foreign or xenobiotic compounds, is conventionally divided into two phases. Phase I enzymes introduce new (or modify existing) functional groups (e.g., -OH, -SH, -NH3) to xenobiotics, and are primarily catalyzed by the Cytochrome P450 enzymes (CYPs), although numerous other oxidases, reductases, and dehydrogenases may also participate. These intermediates are then conjugated with endogenous ligands during Phase II, increasing the hydrophilic nature of the compound, facilitating excretion. Enzymes involved in Phase II include the N-acetyltransferases (NATs), Glutathione S-Transferases (GSTs), UDP Glucuronosyltransferases, epoxide hydrolases and methyltransferases. Phase I and II reactions are catalyzed by enzymes collectively known as xenobiotic metabolism enzymes (XMEs). XMEs are most abundant in the liver, although most tissues have some XME activity. A balance between Phase I and II enzymes is generally necessary to promote the efficient detoxification and elimination of xenobiotics, thereby protecting the body from injury caused by exposure.(2)

In addition to XMEs, exogenous agents may also interact with other cellular structures, potentially resulting in changes in normal cellular processes. Proteins involved in homeostasis (e.g., DNA repair enzymes), cellular signal transduction, transport, cell division, and gene expression are important targets of xenobiotics to consider. Mutations in the genes encoding these enzymes and other proteins result from stochastic genetic processes and may accumulate in the population depending on selective pressures. If the frequency of a specific genetic variant reaches 1% or more in the population, it is referred to as a polymorphism. A polymorphism may have no effect (i.e., is “silent”), or it may be considered functional if it results in altered catalytic function, stability, and/or level of expression of the resulting protein. Functional polymorphisms in XMEs include: (1) point mutations in coding regions of genes resulting in amino acid substitutions, which may alter catalytic activity, enzyme stability, and/or substrate specificity; (2) duplicated or multiduplicated genes, resulting in higher enzyme levels; (3) completely or partially deleted genes, resulting in no gene product; and (4) splice site variants that result in truncated or alternatively spliced protein products.3 Mutations in the regulatory regions of genes may affect the amount of protein expression as well, and mutations in other non-coding regions may affect mRNA stability or mRNA splicing. Most research in genetics in environmental health has focused on these types of functional genetic variation. Nevertheless, even “non-functional” SNPs might be useful in some cases either because of a subtle function (such as mRNA stability) that has yet to be found or because such SNPs may be in linkage disequilibrium with functional polymorphisms in the same region, thus serving as markers for phenotypic effects.

About 90% of all DNA polymorphisms occur as single nucleotide polymorphisms (SNPs), i.e., single base pair substitutions (the first type of functional polymorphism).(4) More than 1,255,000 SNPs have been identified and catalogued as a result of multiple research efforts (see the SNPs Consortium accessed 1/4/01, website given in Appendix 9-1). There are estimated to be three to four SNPs in the average gene and roughly 120,000 common coding region SNPs, of which ~40% are expected to be functional.(5) These estimates do not include polymorphisms outside the coding region of genes, and thus the total number of SNPs affecting protein function can be expected to be greater.

Functional polymorphisms in XMEs can affect the balance of metabolic intermediates produced during biotransformation, and some of these intermediates can bind and induce structural changes in DNA or binding other critical macromolecules, such as sulfhydryl containing proteins. Similarly, polymorphisms in DNA repair enzymes can affect an individual’s ability to repair DNA damage induced by some exposures, such as ultraviolet radiation. The interindividual differences in these and other components of the human genome that relate to environmental exposures have therefore been predicted to modify environmental disease risk.(6) In addition to polymorphisms, age; sex; hormones; and behavioral factors such as cigarette smoking, alcohol consumption, and nutritional status can influence the expression of Phase I and II biotransformation genes(7) and thus are also important in understanding environmental disease risk.

One can contrast the role of polymorphisms in XMEs and other components of the environmental response system with genetic variants that are highly penetrant (i.e., that almost invariably lead to disease) but have low population frequency. The interest and focus here is on the role of common genetic variants that alter the effect of exposures that may lead to disease states, or their precursors, and hence are of lower penetrance. Though the individual risk associated with these polymorphisms is often low, they potentially have greater public health relevance (i.e., population-attributable risk) because of their high population frequency.(8)

A comprehensive effort to identify genetic polymorphisms in genes involved in environmentally induced disease, known as the Environmental Genome Project (EGP), was initiated by the National Institute of Environmental Health Sciences (NIEHS) in 1998.(9) In addition to the identification of polymorphisms, the EGP aims to characterize the function of these polymorphisms and supports epidemiologic studies of gene-environment interactions as well. Like the Human Genome Project, the EGP has devoted substantial resources to the ethical, legal, and social issues related to this project.

Examples of Genetic Effecr Modifiers
The working hypothesis that has typically been employed is that for the majority of genetic polymorphisms that alter responses to chemical hazards, the genetic difference does not result in a qualitatively different response, but rather induces a shift in the dose-response relationship. Thus a genetic variant in an XME that decreases the catalytic efficiency of an enzyme that detoxifies a particular drug might make the standard dose of that drug toxic. This concept extends not only to the acute effects of drugs, but also potentially to chronic response to non-drug chemicals found in the workplace and general environment. Below we describe several examples of ‘gene-environment interactions’ that illustrate the potential public health implications, as well as difficulties in interpretation, of this type of research.

The relationship between aromatic amine exposure, N-acetylation polymorphism (NAT2), and bladder cancer is a classic illustration of the principle of dose-effect modification of an environmental exposure by genetic polymorphism. An initial study by Lower et al.(10) suggested that the effect of exposure to aromatic amines (bladder cancer), by occupation (e.g., dye industry) or smoking, differed by NAT2 phenotype. A preponderance of slow acetylators existed among exposed persons, and subsequent studies have confirmed these results.(11,12)

Recently, Marcus and colleagues conducted a case-series meta-analysis of 16 studies of the NAT2*smoking interaction in bladder cancer.(13) Across all studies, they calculated an odds ratio (OR) of 1.3 (95% confidence interval (CI): 1.0, 1.6) for smokers who are slow acetylators compared with smokers who are rapid acetylators, verifying that smokers who are slow acetylators have a modestly increased risk.(13) Limiting the study selection to European studies with large sample sizes (number of cases = 150), the OR was 1.7 (95% CI: 1.2, 2.3). Different patterns of tobacco use and tobacco type may account for some of these differences. In addition, using estimates of the prevalence of smoking and NAT2 genotype, they predicted bladder cancer risk for smokers and nonsmokers by acetylator status, designating never-smoker rapid acetylators as the reference category. Nonsmoking slow acetylators were predicted to have no increase in risk (OR = 1.10), ever-smoking rapid acetylators have about two times the risk (OR = 1.95), and ever-smokers who are slow acetylators have about threefold higher risk (OR = 3.21). Marcus et al. also estimated that the population-attributable risk of the gene-environment interaction was 35% for slow acetylators who had ever smoked and 13% for rapid acetylators who had ever smoked.

In the laboratory setting, complementary experiments can be designed to gain understanding of the biologic basis of the observed effect. This ultimately contributes to the argument of causality. Primary human cell lines, transient and stable transfection assays in cell lines, and transgenic animal models have frequently been used to investigate these questions. With respect to aromatic amines, NAT2, and bladder cancer, in vitro and in vivo studies have demonstrated that polymorphic N-acetylation of some aromatic amines can result in the bioactivation of these procarcinogens in the bladder.(14-16) After N-oxidation of aromatic amines such as 4-aminobiphenyl or 2-naphthylamine by CYP1A2 in the liver, O-acetylation of the resulting hydroxylamine by NAT2 can produce unstable acetoxy esters that decompose to form highly electrophilic aryl nitrenium ion species. In addition, the formation of the acetoxy ester, a proximate carcinogen, can proceed through N-acetylation and N-oxidation reactions that yield N-hydroxy-N-acetyl aromatic amines, which then form the acetoxy ester through N,O-acetyltransfer catalyzed by NAT2. In slow acetylators, initial acetylation in the liver is less efficient, and hence biotransformation of the aromatic amine is more likely to proceed through the CYP1A2 route. Subsequently, the hydroxylated aromatic amine can be further bioactivated in the bladder, either enzymatically or nonenzymatically, potentially leading to DNA binding and point mutations. This is considered a likely mechanism of initiation of bladder carcinogenesis.(17-19) Thus, after the early findings by Lower et al., the concerted efforts of epidemiologic and toxicologic studies have quantitatively evaluated this gene-environment interaction and elucidated a probable mechanism.

Recent research exploring genetic modifiers of other common exposures with significant public health importance have begun to yield interesting findings. In addition to gene-environment interactions that link exposures, polymorphisms, and disease states, associations of particular exposures with biomarkers of exposure or effect and polymorphisms have been evaluated. A nonexhaustive list of these exposures and biomarkers or diseases with their potential genetic effect modifiers is given in Table 9-1 (please see Appendix 9-2 for additional information about the genes). The evidence for these relationships has been classified according to the whether the associations were proposed from basic scientific laboratory evidence (classified as 2) or from laboratory evidence with suggestive epidemiologic data in some studies (classified as 1). We acknowledge that even for those relationships classified as 1, proof of causality may not be necessarily inferred. The purpose of using this classification system is to identify gaps in knowledge about the exposure-disease association and effect modification that merit further investigation. The sources of these potential modifiers come from several different fields, including biochemistry, genetics, physiology, pharmacology, and pharmaceutics.

Table 9-1 shows several different types of exposures, including exposures to industrially produced compounds and byproducts (e.g., butadiene and dioxin), substances in the diet (e.g., alcohol and aflatoxin B₁), and both voluntary and involuntary examples of exposure (e.g., tobacco smoke and environmental tobacco smoke). As would be expected, some genes appear to be associated with several different exposures. This can be partially attributed to the relatively nonspecific roles of their gene products in biotransformation of exogenous substrates. It is also likely that once genotyping methods for a particular gene have been developed and streamlined, its role in several pathways will be explored. In total, few examples in Table 9-1 have the “1” classification, indicating that evidence clearly demonstrating effect modification by polymorphisms is quite limited (e.g., due to small sample size, study design issues).

An example of the evolving knowledge of effect modification by polymorphisms is that of exposure to aflatoxin B₁, a mycotoxin found in some foodstuffs, and an established risk for hepatocellular carcinoma (HCC), especially when combined with hepatitis virus exposure.(20) The biotransformation of aflatoxin B1 proceeds through a CYP450 mediated oxidation and then through a glutathione S-transferase, epoxide hydrolase, and/or glucuronosyl transferase catalyzed reactions to yield excretable metabolites.(21) For exposed persons, having NAT2 and EPHX1 genotypes conferring a lack of enzyme and less active enzyme, respectively, was shown to result in increased HCC risk (22-23). Similarly, functional polymorphisms in CYP1A2 and CYP3A4, both of which catalyze the Phase I metabolism (epoxidation) of aflatoxin B1, would be expected to modify HCC risk in exposed persons as well, though epidemiologic data for this have not yet been gathered. Biomarker studies of urinary aflatoxin metabolites and aflatoxin-albumin adducts in peripheral blood have validated their use as indicators of HCC risk at the group level, and polymorphisms in NAT2 and EPHX1 yielded higher levels of adducts.(24) Thus, in the case of aflatoxin, exposure-specific, validated biomarkers can be used in lieu of clinical disease measures to estimate the effect modification by specific polymorphisms. Even for this example, however, only a few studies exist and they have limited statistical power; hence, the magnitude of the modifying effect of genetic polymorphism remains highly uncertain. Future efforts to determine the predictive value of biomarkers of other exposures will facilitate the analysis of the effects of common polymorphisms in modifying the effects of those exposures.

Contradictory findings are often found in the literature. Similar issues have been encountered in pharmacogenetic studies. Evans and Relling (25) have commented that the use of different endpoints in assessing response to drugs, the heterogeneous nature of diseases studied, and the polygenic nature of many drug effects all contribute to the study-to-study variation often observed. These same factors will also be important in types of studies discussed here. In addition, discrepant findings may be attributable to ethnic differences in the prevalence of a polymorphism, as population genetic structure can affect the average effect of a gene-environment interaction detected.(26)

The examples of gene-environment interaction presented thus far have been fairly simple. More realistically, chronic disease risk is a function of multiple genes in multiple biologic pathways interacting with each other and with cumulative environmental factors over a lifetime. Taylor et al. provided evidence for a three-way interaction between NAT2, NAT1, and smoking that modifies bladder cancer risk such that individuals who smoke and have NAT2 slow acetylator alleles in combination with the high activity NAT1*10 allele (homozygotes or heterozygotes) have heightened bladder cancer risk.(27) Contrasting findings, however, have been reported more recently.(28)

Advantages of Incorporating Genetic Polymorphisms into Health Effects Studies
The addition of genetic polymorphisms affords several noteworthy opportunities to health effects studies of exposures to environmental toxicants and toxins. Stratification of a studied health outcome or biomarker by relevant genotype (or phenotype) may allow for detection of different levels of risk among subgroups of exposed persons.(29) Collectively, the studies on aromatic amine exposure, NAT2 genotype, and bladder cancer demonstrate this point. Investigations that assess bladder cancer risk associated with exposure to aromatic amines alone would observe a magnitude of effect that represents the average risk for rapid and slow acetylators combined. This estimate would not suggest that aromatic amines are as etiologically significant, i.e., are potent carcinogens, for particular subpopulations as a stratified analysis would indicate. This has been referred to as effect dilution.(30) Effect dilution may be especially important for common exposures—to dietary constituents or air pollution, for example—whose association to a disease outcome is often weak.

Second, evidence of effect modification by genotype yields insights into the potential biologic processes of toxicity or carcinogenicity, as substrates or targets of candidate gene products are identified as potential causative agents.(29) The effect of lipopolysaccaride ([LPS], also known as endotoxin), a component of particulate matter in rural areas, on lung function parameters may turn out to be a modern example of this. Arbour et al. have shown that response to LPS, measured by decrease in forced expiratory volume in the first second (FEV₁), differed by TLR4 genotype.(31) TLR4 codes for the toll-like receptor that binds LPS and initiates a signal transduction pathway that leads to inflammation of the lung. Their data suggest that individuals with the variant TLR4 genotype may be resistant to LPS-induced lung inflammation but may be more susceptible to a systemic inflammatory response. These findings may aid in answering the difficult question of what component(s) of particulate matter is (are) responsible for the range of health effects observed, particularly in rural areas where LPS levels are appreciable.

Finally, enhanced understanding of pathologic mechanism gained by the concerted efforts of epidemiologic and toxicologic studies may allow for the development of drugs or dietary interventions that prevent disease onset or progression. As an example, Oltipraz (OPZ, 5-(2-pyrazinyl)-4-methyl-1,2-dithiole-3-thione) is a drug that induces Phase II XMEs, notably the GSTs.(32) Early evidence showed that OPZ can protect against the hepatocarcinogenic effects of aflatoxin B1 in rats, and subsequent efforts have demonstrated that administration of OPZ to humans significantly enhanced excretion of a Phase II product, aflatoxin-mercapturic acid.(33) Interestingly, there is also evidence that OPZ may act by competitively inhibiting CYP1A2, thereby preventing the activation of aflatoxin.(34) In total, the understanding of aflatoxin biotransformation pathways from animal models and in vitro human tissue studies led to the hypothesis-based epidemiologic studies and ultimately contributed to the development of a chemoprevention strategy for aflatoxin-induced HCC.

Additionally, studies on the health effects of exposure to regulated environmental contaminants that incorporate genetic susceptibilities will enlarge the body of knowledge pertaining to the range of human variability in response to these contaminants. For example, the CDC National Report on Human Exposure to Environmental Chemicals (35) reports body burden among NHANES subjects for 27 chemicals. Studies developed to look at the effect of these chemicals should include genes that might confer susceptibility. In this way, the risk assessment process may be improved by using refined estimates of human variability instead of the default assumptions conventionally used (i.e., uncertainty factor of 10), potentially improving public health protection and the regulation of industry through redefinition of acceptable exposure levels. This advantage has been touted for some time, but no clear example yet exists of how this can be done, especially in the face of numerous ethical, legal and social issues around the use of genetic information. Still, the promise holds, and the potential continues to grow as more functional polymorphisms are discovered and their role in effect modification is deduced.

Considerations for Human Genome Epidemiology Studies

Finally, for environmental health scientists interested in pursuing health effects research that incorporates genetic effect modifiers, we list considerations for health studies that include genetic polymorphisms. Many of these considerations are explored in depths in other chapters in this book. This discussion also assumes that the investigator(s) already have chosen the study design. Case-control and cohort studies are most often used to evaluate gene-environment interaction, and their benefits and drawbacks have been compared and contrasted.(36,37)

1. Exposure assessment
Exposure assessment is of paramount importance in studies of gene-environment interaction. Typically, efforts aim to characterize the type, duration, intensity and timing of exposure. Exposure misclassification is a major concern, since it can bias the estimate of the effect of exposures as well as the estimate of the joint genotype-exposure effect.(38) New methods such as biomonitoring approaches (39) and geographic information systems (40-42) can be used to achieve more precise exposure assessments.
2. Candidate gene selection
The selection of candidate genes is one of the first methodologic issues encountered. Generally, one can investigate the role of a gene whose product is hypothesized to be involved in the biotransformation, cell signal transduction, repair, or disease process relevant to a specific exposure. Sources of toxicologic or other biomedical data that can be used to identify candidate genes include previously published literature (PubMed), the Agency for Toxic Substances and Disease Registry’s Tox Profiles, the National Library of Medicine’s ToxNet, the National Institute for Occupational Safety and Health's Registry of Toxic Effects of Chemical Substances (RTECS), the National Toxicology Program Reports on Carcinogens, On-line Mendelian Inheritance in Man (OMIM), and the HuGE NET Database (please see Appendix 9-1 for selected website addresses).

Once candidate genes have been selected, sources of genetic information can be used to identify important polymorphisms in candidate gene(s). These sources include websites for specific gene families (e.g., CYPs, NATs), OMIM, NIEHS’ Environmental Genome Project Database, the National Cancer Institute’s Cancer Genome Anatomy Project (CGAP), and polymorphism databases (e.g., the SNPs consortium and the National Center for Biotechnology Information’s dbSNPs database) [See Appendix 9-1 for a listing of relevant URLs]. Focusing on polymorphisms with known functional effects is, of course, advantageous.

Efforts to study complex gene-environment interactions are tempered by the difficulty in obtaining adequate sample size (29). Two primary factors to consider are the prevalence of the polymorphism in the population and the magnitude of effect modification. As Caporaso has pointed out (43), there is a trade-off between the prevalence of a polymorphism and the magnitude of effect that may be detected. On the one hand, common variants are less likely to exhibit a strong effect; on the other hand, there is more statistical power in studying these polymorphisms because they are more common. Furthermore, the population- attributable risk of common variants will be greater, even if the penetrance is modest.

More recently, investigators have expanded their study design to include analysis of multiple polymorphisms in single genes that co-segregate (i.e., haplotypes). Haplotype analysis is advantageous in that more information about variation in a gene is captured by this approach relative to single SNPs. Inferring haplotypes from genotype data requires using specific algorithms (e.g., reference 44), and methods are evolving to include adjustment for covariates in the analysis.45
3. Selection of a method to obtain samples for genotyping
Collection of DNA samples from the study population is an area of technological evolution. Besides venous blood samples, from which DNA can be extracted, buccal cell collection brushes46 or mouth washes (47,48) have been employed and offer increased convenience to the study participant, but DNA yield can be substantially lower.

4. Informed consent
Informed consent for genetic testing is also an important consideration. Beskow et al.(49) recently described the major issues to consider in obtaining informed consent and developed a general template for researchers to utilize (see http://www.cdc.gov/genomics/info/reports/policy/consentarticle.htm; more information can be found at http://www.cdc.gov/genomics/info/perspectives/infmcnst.htm). In addition, the Department of Health and Human Services (DHHS) provides information about human subjects protection, and templates for informed consent protocols can be accessed at the DHHS website.

5. Selection of a genotyping method
Many different methods can be used to genotype subjects. Choosing an appropriate method and utilizing quality control procedures are critical because even minor genotype misclassification can substantially bias study results.(38,50)The choice of method depends on both the type of polymorphism to be analyzed and the type of sample obtained. DNA sequence analysis is considered the gold standard, but it is time consuming and expensive. PCR methods are ideal for rapid genotyping of large samples. Restriction fragment length polymorphism analysis can be used if the polymorphism of interest is known to result in the addition or deletion of a restriction site. More recent, high-throughput approaches include 5’-nuclease-based fluorescence assays (Taqman), matrix assisted laser desorption/ionization—time of flight mass spectrometry analysis, and DNA microarrays.(51)

6. Data analysis
Khoury and Botto (52) have advocated that, in the context of a case-control study where exposure and genotype are dichotomized, the conventional 2x2 table analysis of exposure and disease be expanded to include genotype, yielding a 2x4 table. In this manner, the raw exposure and genotype data are displayed in such a way that relative risk estimates for each factor alone and their joint effect can be easily generated. Attributable fractions also can be computed from these data. Regression models of interactions can also be employed.(53,54) Though not discussed here, issues regarding multiple comparisons and false positive findings are also important to consider, and the reader is referred to De Roos et al.(55) for guidance.

Conclusions
The role of genetic variations as determinants of health is being explored in many areas of public health research. In environmental health, recently gathered epidemiologic and toxicologic data suggest that the health effects of many different types of exposures can be modified by genetic polymorphisms, although the effect modification may be weak and the power of many studies is inadequate to demonstrate an effect. Current and future efforts to identify new polymorphisms in genes involved in environmental response will broaden the scope of potential genetic effect modifiers. Determining the effect of these polymorphisms (phenotype) will then be of paramount importance.

Though the individual risk associated with the polymorphisms discussed are relatively low, the population-attributable risk may be large, and thus this area of research merits investigation. As newly identified and previously known polymorphisms are incorporated into epidemiologic research, gene-environment interactions can be detected and quantified. Through toxicologic studies, the mechanisms of these interactions can be elucidated. Correlations between biomarkers of exposure and effect with disease outcomes will facilitate the process of identification of polymorphisms that act as effect modifiers. As with any scientific endeavor, intriguing results in this area of research need to be replicated in different studies and populations to confirm the role of a polymorphism as an effect modifier.

Although many ‘gene-environment’ interaction studies on human populations have been completed in the past decade, the number of examples demonstrating important and consistent positive relationships is remarkably small. It now appears that the ‘one gene - one risk factor’ approach to understanding the etiology of environmentally-related chronic diseases is not likely to yield high rewards. Nevertheless, it remains clear that most chronic diseases of public health importance arise from a complex and often poorly understood combination of genetic and environmental factors. New tools for high throughput genotyping of hundreds or thousands of genetic variants in a sample, coupled with very large-scale population-based studies that utilize sensitive biomarkers and comprehensive exposure assessment strategies are likely to be needed to begin to unravel the complex multi-gene-environment interactions responsible for most chronic diseases of public health importance. This will require new paradigms for interdisciplinary collaborative research that involve very large-scale studies, as well as new bioinformatics tools to help scientists make sense of the dizzying array of complex data that will come from such studies. Finally, increasing interest and discussion has been generated about the development of an integrated database that links new findings on exposures, etiologic pathways, relevant genes, polymorphisms in these genes and their function. (55) This database would serve to guide the design of new studies as well as data analysis and interpretation of results.(55)

In summary, the ability to detect different levels of risk within the population and greater understanding of etiologic mechanisms are the primary benefits of incorporating genetics into the existing environmental health research framework. The insights gained by employing this framework should ultimately allow for the development of new disease prevention strategies. The use of this information in risk assessments may also be a viable area of development. Whether the use of this information in disease prevention efforts targeted to genetically susceptible individuals is acceptable is an ethical question that is beginning to be addressed and necessitates considerable attention in the future.

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