Skip directly to local search Skip directly to A to Z list Skip directly to navigation Skip directly to site content Skip directly to page options
CDC Home

Resources

“The findings and conclusions in this book are those of the author(s) and do not
necessarily represent the views of the funding agency.”

 

These chapters were published with modifications by Oxford University Press (2004)

 

Human Genome Epidemiology: A Scientific Foundation for Using Genetic Information to Improve Health and Prevent Disease

 

 


Part II:
Methods and Approaches 1: Assessing Disease Associations and Interactions


 

Chapter 9


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, Web site 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 Effect 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 Web site 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 Web sites 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.ncbi.nlm.nih.gov/pubmed/11710898?dopt=Abstract). 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 Web site.

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.

Tables

 

Appendix

References
  1. Weber WW. Pharmacogenetics. New York: Oxford University Press, 1997.
  2. Parkinson A. Chapter 6. Biotransformation. In: Louis J. Casarett JD, ed. Toxicology : the basic science of poisons. New York: Macmillan, 1997.
  3. Ingelman-Sundberg M, Oscarson M, McLellan RA. Polymorphic human cytochrome P450 enzymes: an opportunity for individualized drug treatment. Trends Pharmacol Sci 1999;20:342-9.
  4. Brookes AJ. The essence of SNPs. Gene 1999;234:177-86.
  5. Cargill M, Altshuler D, Ireland J, et al. Characterization of single-nucleotide polymorphisms in coding regions of human genes. Nat Genet 1999;22:231-8.
  6. Perera FP. Environment and cancer: who are susceptible? Science 1997;278:1068-73.
  7. Levy RH. Metabolic drug interactions. Philadelphia: Lippincott Williams & Wilkins, 2000.
  8. Caporaso N, Goldstein A. Cancer genes: single and susceptibility: exposing the difference. Pharmacogenetics 1995;5:59-63.
  9. Olden K, Wilson S. Environmental health and genomics: visions and implications. Nat Rev Genet 2000;1:149-53.
  10. Lower GM, Nilsson T, Nelson CE, Wolf H, Gamsky TE, Bryan GT. N-acetyltransferase phenotype and risk in urinary bladder cancer: approaches in molecular epidemiology.
    Preliminary results in Sweden and Denmark. Environ Health Perspect 1979;29:71-9.
  11. Cartwright RA, Glashan RW, Rogers HJ, et al. Role of N-acetyltransferase phenotypes in bladder carcinogenesis: a pharmacogenetic epidemiological approach to bladder cancer. Lancet 1982;2:842-5.
  12. Hanke J, Krajewska B. Acetylation phenotypes and bladder cancer. J Occup Med 1990;32:917-8.
  13. Marcus PM, Hayes RB, Vineis P, et al. Cigarette smoking, N-acetyltransferase 2 acetylation status, and bladder cancer risk: a case-series meta-analysis of a gene-environment interaction. Cancer Epidemiol Biomarkers Prev 2000;9:461-7.
  14. Hein DW, Doll MA, Rustan TD, et al. Metabolic activation and deactivation of arylamine carcinogens by recombinant human NAT1 and polymorphic NAT2 acetyltransferases. Carcinogenesis 1993;14:1633-8.
  15. Mattano SS, Land S, King CM, Weber WW. Purification and biochemical characterization of hepatic arylamine N-acetyltransferase from rapid and slow acetylator mice: identity with arylhydroxamic acid N,O-acyltransferase and N-hydroxyarylamine O-acetyltransferase. Mol Pharmacol 1989;35:599-609.
  16. Trinidad A, Hein DW, Rustan TD, et al. Purification of hepatic polymorphic arylamine N-acetyltransferase from homozygous rapid acetylator inbred hamster: identity with polymorphic N-hydroxyarylamine-O-acetyltransferase. Cancer Res 1990;50:7942-9.
  17. Autrup H. Genetic polymorphisms in human xenobiotica metabolizing enzymes as susceptibility factors in toxic response. Mutat Res 2000;464:65-76.
  18. Williams JA. Single nucleotide polymorphisms, metabolic activation and environmental carcinogenesis: why molecular epidemiologists should think about enzyme expression. Carcinogenesis 2001;22:209-14.
  19. Colvin ME, Hatch FT, Felton JS. Chemical and biological factors affecting mutagen potency. Mutat Res 1998;400:479-92.
  20. Ross RK, Yuan JM, Yu MC, et al. Urinary aflatoxin biomarkers and risk of hepatocellular carcinoma. Lancet 1992;339:943-6.
  21. Eaton DL, Groopman JD. The Toxicology of aflatoxins : human health, veterinary, and agricultural significance. San Diego: Academic Press, 1994.
  22. London WT, Evans AA, Buetow K, et al. Molecular and genetic epidemiology of hepatocellular carcinoma: studies in China and Senegal. Princess Takamatsu Symp 1995;25:51-60.
  23. McGlynn KA, Rosvold EA, Lustbader ED, et al. Susceptibility to hepatocellular carcinoma is associated with genetic variation in the enzymatic detoxification of aflatoxin B1. Proc Natl Acad Sci U S A 1995;92:2384-7.
  24. Wild CP, Turner PC. Exposure biomarkers in chemoprevention studies of liver cancer. IARC Sci Publ 2001;154:215-22.
  25. Evans WE, Relling MV. Pharmacogenomics: translating functional genomics into rational therapeutics. Science 1999;286:487-91.
  26. Wilson JF, Weale ME, Smith AC, et al. Population genetic structure of variable drug response. Nat Genet 2001;29:265-269.
  27. Taylor JA, Umbach DM, Stephens E, et al. The role of N-acetylation polymorphisms in smoking-associated bladder cancer: evidence of a gene-gene-exposure three-way interaction. Cancer Res 1998;58:3603-10.
  28. Cascorbi I, Roots I, Brockmoller J. Association of NAT1 and NAT2 polymorphisms to urinary bladder cancer: significantly reduced risk in subjects with NAT1*10. Cancer Res 2001;61:5051-6.
  29. Rothman N, Wacholder, S, Caporaso, NE, Garcia-Closas, M, Buetow, K, Fraumeni Jr, J.F. The use of common genetic polymorphisms to enhance the epidemiologic study of environmental carcinogens. BBA - Reviews on Cancer, 1471 (2) (2001) pp. C1-C10 2000;1471:C1-C10.
  30. Khoury M, Beaty T, Cohen B. Fundamentals of Genetic Epidemiology. New York: Oxford University Press, 1993.
  31. Arbour NC, Lorenz E, Schutte BC, et al. NAT2 mutations are associated with endotoxin hyporesponsiveness in humans. Nat Genet 2000;25:187-91.
  32. Carr BA, Franklin MR. Drug-metabolizing enzyme induction by 2,2'-dipyridyl, 1,7-phenanthroline, 7,8-benzoquinoline and oltipraz in mouse. Xenobiotica 1998;28:949-56.
  33. Kensler TW, Curphey TJ, Maxiutenko Y, Roebuck BD. Chemoprotection by organosulfur inducers of phase 2 enzymes: dithiolethiones and dithiins. Drug Metabol Drug Interact 2000;17:3-22.
  34. Langouet S, Coles B, Morel F, et al. Inhibition of CYP1A2 and CYP3A4 by oltipraz results in reduction of aflatoxin B1 metabolism in human hepatocytes in primary culture. Cancer Res 1995;55:5574-9.
  35. National Report on Human Exposure to Environmental Chemicals. Atlanta: Centers for Disease Control and Prevention, National Center for Environmental Health, 2001.
  36. Caporaso N, Rothman N, Wacholder S. Case-control studies of common alleles and environmental factors. J Natl Cancer Inst Monogr 199925-30.
  37. Langholz B, Rothman N, Wacholder S, Thomas DC. Cohort studies for characterizing measured genes. J Natl Cancer Inst Monogr 199939-42.
  38. Rothman N, Garcia-Closas, M., Setwart, W.T., Lubin J. Chapter 9. The impact of misclassification in case-control studies of gene-environment interactions. IARC Scientific Publications. Vol. 148. Lyon: IARC, 1999:89-96.
  39. Rothman N, Stewart WF, Schulte PA. Incorporating biomarkers into cancer epidemiology: a matrix of biomarker and study design categories. Cancer Epidemiol Biomarkers Prev 1995;4:301-11.
  40. Rushton G, Lolonis P. Exploratory spatial analysis of birth defect rates in an urban population. Stat Med 1996;15:717-26.
  41. Kulldorff M, Feuer EJ, Miller BA, Freedman LS. Breast cancer clusters in the northeast United States: a geographic analysis. Am J Epidemiol 1997;146:161-70.
  42. Ward MH, Nuckols JR, Weigel SJ, Maxwell SK, Cantor KP, Miller RS. Identifying populations potentially exposed to agricultural pesticides using remote sensing and a Geographic Information System. Environ Health Perspect 2000;108:5-12.
  43. Caporaso N. Chapter 6. Selection of Candidate Genes. IARC Sci Publ. Vol. 148. Lyon: IARC, 1999:23-36.
  44. Terwilliger JD, Ott, J. Handbook of human genetic linkage. Baltimore: Johns Hopkins University Press, 1994.
  45. Schaid DJ, Rowland CM, Tines DE, Jacobson RM, Poland GA. Score tests for association between traits and haplotypes when linkage phase is ambiguous. Am J Hum Genet 2002;70:425-34.
  46. Walker AH, Najarian D, White DL, Jaffe JF, Kanetsky PA, Rebbeck TR. Collection of genomic DNA by buccal swabs for polymerase chain reaction-based biomarker assays. Environ Health Perspect 1999;107:517-20.
  47. Heath EM, Morken NW, Campbell KA, Tkach D, Boyd EA, Strom DA. Use of buccal cells collected in mouthwash as a source of DNA for clinical testing. Arch Pathol Lab Med 2001;125:127-33.
  48. Garcia-Closas M, Egan KM, Abruzzo J, et al. Collection of genomic DNA from adults in epidemiological studies by buccal cytobrush and mouthwash. Cancer Epidemiol Biomarkers Prev 2001;10:687-96.
  49. Beskow LM, Burke W, Merz JF, et al. Informed consent for population-based research involving genetics. Jama 2001;286:2315-21.
  50. Garcia-Closas M, Rothman N, Lubin J. Misclassification in case-control studies of gene-environment interactions: assessment of bias and sample size. Cancer Epidemiol Biomarkers Prev 1999;8:1043-50.
  51. Shi MM. Enabling large-scale pharmacogenetic studies by high-throughput mutation detection and genotyping technologies. Clin Chem 2001;47:164-72.
  52. Botto LD, Khoury MJ. Commentary: facing the challenge of gene-environment interaction: the two-by-four table and beyond. Am J Epidemiol 2001;153:1016-20.
  53. Neter J, Kutner, M.H., Nachtsheim, C.J., Wasserman, W. Applied Linear Statistical Models. Chicago: Irwin, 1996.
  54. Breslow N, Day N. Statistical Methods in Cancer Research, Volume1: The Analysis of Case-Control Studies. Vol. 32. Lyon: IARC, 1980.
  55. De Roos A, Smith, M.T., Chanock, S., Rothman, N. Mechanistic Considerations in the Molecular Epidemiology of Cancer. In: Buffler PA. BM, Rice JM., Boffetta P., ed. IARC Scientific Publications. Lyon: IARC, In press.
  56. Chiou HY, Hsueh YM, Hsieh LL, et al. Arsenic methylation capacity, body retention, and null genotypes of glutathione S-transferase M1 and T1 among current arsenic-exposed residents in Taiwan. Mutat Res 1997;386:197-207.
  57. Vahter M. Genetic polymorphism in the biotransformation of inorganic arsenic and its role in toxicity. Toxicol Lett 2000;112-113:209-17.
  58. Richeldi L, Sorrentino R, Saltini C. HLA-DPB1 glutamate 69: a genetic marker of beryllium disease. Science 1993;262:242-4.
  59. Saltini C, Amicosante M, Franchi A, Lombardi G, Richeldi L. Immunogenetic basis of environmental lung disease: lessons from the berylliosis model. Eur Respir J 1998;12:1463-75.
  60. Richeldi L, Kreiss K, Mroz MM, Zhen B, Tartoni P, Saltini C. Interaction of genetic and exposure factors in the prevalence of berylliosis. Am J Ind Med 1997;32:337-40.
  61. Wetmur JG. Influence of the common human delta-aminolevulinate dehydratase polymorphism on lead body burden. Environ Health Perspect 1994;102:215-9.
  62. Schwartz BS, Lee BK, Stewart W, Ahn KD, Springer K, Kelsey K. Associations of delta-aminolevulinic acid dehydratase genotype with plant, exposure duration, and blood lead and zinc protoporphyrin levels in Korean lead workers. Am J Epidemiol 1995;142:738-45.
  63. Kelada SN, Shelton E, Kaufmann RB, Khoury MJ. Delta-aminolevulinic acid dehydratase genotype and lead toxicity: a HuGE review. Am J Epidemiol 2001;154:1-13.
  64. Schwartz BS, Lee BK, Stewart W, et al. delta-Aminolevulinic acid dehydratase genotype modifies four hour urinary lead excretion after oral administration of dimercaptosuccinic acid. Occup Environ Med 1997;54:241-6.
  65. Fleming DE, Chettle DR, Wetmur JG, et al. Effect of the delta-aminolevulinate dehydratase polymorphism on the accumulation of lead in bone and blood in lead smelter workers. Environ Res 1998;77:49-61.
  66. Schwartz BS, Stewart WF, Kelsey KT, et al. Associations of tibial lead levels with BsmI polymorphisms in the vitamin D receptor in former organolead manufacturing workers. Environ Health Perspect 2000;108:199-203.
  67. Schwartz BS, Lee BK, Lee GS, et al. Associations of blood lead, dimercaptosuccinic acid-chelatable lead, and tibia lead with polymorphisms in the vitamin D receptor and [delta]-aminolevulinic acid dehydratase genes. Environ Health Perspect 2000;108:949-54.
  68. Rosipal R, Lamoril J, Puy H, et al. Systematic analysis of coproporphyrinogen oxidase gene defects in hereditary coproporphyria and mutation update. Hum Mutat 1999;13:44-53.
  69. Grandchamp B, Lamoril J, Puy H. Molecular abnormalities of coproporphyrinogen oxidase in patients with hereditary coproporphyria. J Bioenerg Biomembr 1995;27:215-9
  70. Mendez M, Sorkin L, Rossetti MV, et al. Familial porphyria cutanea tarda: characterization of seven novel uroporphyrinogen decarboxylase mutations and frequency of common hemochromatosis alleles. Am J Hum Genet 1998;63:1363-75.
  71. Moran-Jimenez MJ, Ged C, Romana M, et al. Uroporphyrinogen decarboxylase: complete human gene sequence and molecular study of three families with hepatoerythropoietic porphyria. Am J Hum Genet 1996;58:712-21.
  72. Hori H, Kawano T, Endo M, Yuasa Y. Genetic polymorphisms of tobacco- and alcohol-related metabolizing enzymes and human esophageal squamous cell carcinoma susceptibility. J Clin Gastroenterol 1997;25:568-75.
  73. Chao YC, Wang LS, Hsieh TY, Chu CW, Chang FY, Chu HC. Chinese alcoholic patients with esophageal cancer are genetically different from alcoholics with acute pancreatitis and liver cirrhosis. Am J Gastroenterol 2000;95:2958-64.
  74. Tanabe H, Ohhira M, Ohtsubo T, Watari J, Yokota K, Kohgo Y. Genetic polymorphism of aldehyde dehydrogenase 2 in patients with upper aerodigestive tract cancer. Alcohol Clin Exp Res 1999;23:17S-20S.
  75. Yokoyama A, Ohmori T, Muramatsu T, et al. Cancer screening of upper aerodigestive tract in Japanese alcoholics with reference to drinking and smoking habits and aldehyde dehydrogenase-2 genotype. Int J Cancer 1996;68:313-6.
  76. Yokoyama A, Muramatsu T, Omori T, et al. Alcohol and aldehyde dehydrogenase gene polymorphisms influence susceptibility to esophageal cancer in Japanese alcoholics. Alcohol Clin Exp Res 1999;23:1705-10.
  77. Eaton DL, Gallagher EP, Bammler TK, Kunze KL. Role of cytochrome P4501A2 in chemical carcinogenesis: implications for human variability in expression and enzyme activity. Pharmacogenetics 1995;5:259-74.
  78. Gallagher EP, Kunze KL, Stapleton PL, Eaton DL. The kinetics of aflatoxin B1 oxidation by human cDNA-expressed and human liver microsomal cytochromes P450 1A2 and 3A4. Toxicol Appl Pharmacol 1996;141:595-606.
  79. Lang NP, Chu DZ, Hunter CF, Kendall DC, Flammang TJ, Kadlubar FF. Role of aromatic amine acetyltransferase in human colorectal cancer. Arch Surg 1986;121:1259-61.
  80. Hein DW, Doll MA, Fretland AJ, et al. Molecular genetics and epidemiology of the NAT1 and NAT2 acetylation polymorphisms. Cancer Epidemiol Biomarkers Prev 2000;9:29-42.
  81. Gil JP, Lechner MC. Increased frequency of wild-type arylamine-N-acetyltransferase allele NAT2*4 homozygotes in Portuguese patients with colorectal cancer. Carcinogenesis 1998;19:37-41.
  82. Brockton N, Little J, Sharp L, Cotton SC. N-acetyltransferase polymorphisms and colorectal cancer: a HuGE review. Am J Epidemiol 2000;151:846-61.
  83. Deitz AC, Zheng W, Leff MA, et al. N-Acetyltransferase-2 genetic polymorphism, well-done meat intake, and breast cancer risk among postmenopausal women. Cancer Epidemiol Biomarkers Prev 2000;9:905-10.
  84. Zheng W, Xie D, Cerhan JR, Sellers TA, Wen W, Folsom AR. Sulfotransferase 1A1 polymorphism, endogenous estrogen exposure, well-done meat intake, and breast cancer risk. Cancer Epidemiol Biomarkers Prev 2001;10:89-94.
  85. Landi S, Hanley NM, Warren SH, Pegram RA, DeMarini DM. Induction of genetic damage in human lymphocytes and mutations in Salmonella by trihalomethanes: role of red blood cells and GSTT1-1 polymorphism. Mutagenesis 1999;14:479-82.
  86. Pegram RA, Andersen ME, Warren SH, Ross TM, Claxton LD. Glutathione S-transferase-mediated mutagenicity of trihalomethanes in Salmonella typhimurium: contrasting results with bromodichloromethane off chloroform. Toxicol Appl Pharmacol 1997;144:183-8.
  87. Rothman N, Smith MT, Hayes RB, et al. Benzene poisoning, a risk factor for hematological malignancy, is associated with the NQO1 609C-->T mutation and rapid fractional excretion of chlorzoxazone. Cancer Res 1997;57:2839-42.
  88. Ross D, Traver RD, Siegel D, Kuehl BL, Misra V, Rauth AM. A polymorphism in NAD(P)H:quinone oxidoreductase (NQO1): relationship of a homozygous mutation at position 609 of the NQO1 cDNA to NQO1 activity. Br J Cancer 1996;74:995-6.
  89. Xu X, Wiencke JK, Niu T, et al. Benzene exposure, glutathione S-transferase theta homozygous deletion, and sister chromatid exchanges. Am J Ind Med 1998;33:157-63.
  90. Bruning T, Lammert M, Kempkes M, Thier R, Golka K, Bolt HM. Influence of polymorphisms of NAT2 and GSTT1 for risk of renal cell cancer in workers with long-term high occupational exposure to trichloroethene. Arch Toxicol 1997;71:596-9.
  91. Sweeney C, Farrow DC, Schwartz SM, Eaton DL, Checkoway H, Vaughan TL. Glutathione S-transferase M1, T1, and P1 polymorphisms as risk factors for renal cell carcinoma: a case-control study. Cancer Epidemiol Biomarkers Prev 2000;9:449-54.
  92. Nebert DW, McKinnon RA, Puga A. Human drug-metabolizing enzyme polymorphisms: effects on risk of toxicity and cancer. DNA Cell Biol 1996;15:273-80.
  93. Stresser DM, Kupfer D. Human cytochrome P450-catalyzed conversion of the proestrogenic pesticide methoxychlor into an estrogen. Role of CYP2C19 and CYP1A2 in O-demethylation. Drug Metab Dispos 1998;26:868-74.
  94. Landi MT, Sinha R, Lang NP, Kadlubar FF. Chapter 16. Human cytochrome P4501A2. IARC Sci Publ, 1999:173-95.
  95. Au WW, Sierra-Torres CH, Cajas-Salazar N, Shipp BK, Legator MS. Cytogenetic effects from exposure to mixed pesticides and the influence from genetic susceptibility. Environ Health Perspect 1999;107:501-5.
  96. Eaton DL. Biotransformation enzyme polymorphism and pesticide susceptibility. Neurotoxicology 2000;21:101-11.
  97. Sams C, Mason HJ, Rawbone R. Evidence for the activation of organophosphate pesticides by cytochromes P450 3A4 and 2D6 in human liver microsomes. Toxicol Lett 2000;116:217-21.
  98. Kelsey KT, Wiencke JK, Ward J, Bechtold W, Fajen J. Sister-chromatid exchanges, glutathione S-transferase theta deletion and cytogenetic sensitivity to diepoxybutane in lymphocytes from butadiene monomer production workers. Mutat Res 1995;335:267-73.
  99. Norppa H, Hirvonen A, Jarventaus H, et al. Role of GSTT1 and NAT2 genotypes in determining individual sensitivity to sister chromatid exchange induction by diepoxybutane in cultured human lymphocytes. Carcinogenesis 1995;16:1261-4.
  100. Wiencke JK, Pemble S, Ketterer B, Kelsey KT. Gene deletion of glutathione S-transferase theta: correlation with induced genetic damage and potential role in endogenous mutagenesis. Cancer Epidemiol Biomarkers Prev 1995;4:253-9.
  101. Schaaf BM, Seitzer U, Pravica V, Aries SP, Zabel P. Tumor necrosis factor-alpha -308 promoter gene polymorphism and increased tumor necrosis factor serum bioactivity in farmer's lung patients. Am J Respir Crit Care Med 2001;163:379-82.
  102. Kleeberger SR, Reddy S, Zhang LY, Jedlicka AE. Genetic susceptibility to ozone-induced lung hyperpermeability: role of toll-like receptor 4. Am J Respir Cell Mol Biol 2000;22:620-7.
  103. Wu MT, Huang SL, Ho CK, Yeh YF, Christiani DC. Cytochrome P450 1A1 MspI polymorphism and urinary 1-hydroxypyrene concentrations in coke-oven workers. Cancer Epidemiol Biomarkers Prev 1998;7:823-9.
  104. Nielsen PS, de Pater N, Okkels H, Autrup H. Environmental air pollution and DNA adducts in Copenhagen bus drivers--effect of NAT2 and NAT2 genotypes on adduct levels. Carcinogenesis 1996;17:1021-7.
  105. Merlo F, Andreassen A, Weston A, et al. Urinary excretion of 1-hydroxypyrene as a marker for exposure to urban air levels of polycyclic aromatic hydrocarbons. Cancer Epidemiol Biomarkers Prev 1998;7:147-55.
  106. Knudsen LE, Norppa H, Gamborg MO, et al. Chromosomal aberrations in humans induced by urban air pollution: influence of DNA repair and polymorphisms of glutathione S-transferase M1 and N-acetyltransferase 2. Cancer Epidemiol Biomarkers Prev 1999;8:303-10.
  107. Binkova B, Lewtas J, Miskova I, et al. Biomarker studies in northern Bohemia. Environ Health Perspect 1996;104:591-7.
  108. Whyatt RM, Perera FP, Jedrychowski W, Santella RM, Garte S, Bell DA. Association between polycyclic aromatic hydrocarbon-DNA adduct levels in maternal and newborn white blood cells and glutathione S-transferase P1 and CYP1A1 polymorphisms. Cancer Epidemiol Biomarkers Prev 2000;9:207-12.
  109. Viezzer C, Norppa H, Clonfero E, et al. Influence of NAT2, GSTT1, GSTP1, and EPHX gene polymorphisms on DNA adduct level and HPRT mutant frequency in coke-oven workers. Mutat Res 1999;431:259-69.
  110. Motykiewicz G, Michalska J, Pendzich J, et al. A molecular epidemiology study in women from Upper Silesia, Poland. Toxicol Lett 1998;96-97:195-202.
  111. Lan Q, He X, Costa DJ, et al. Indoor coal combustion emissions, NAT2 and GSTT1 genotypes, and lung cancer risk: a case-control study in Xuan Wei, China. Cancer Epidemiol Biomarkers Prev 2000;9:605-8.
  112. Adamiak W, Jadczyk P, Kucharczyk J. Application of Salmonella strains with altered nitroreductase and O-acetyltransferase activities to the evaluation of the mutagenicity of airborne particles. Acta Microbiol Pol 1999;48:131-40.
  113. Watanabe T, Kaji H, Takashima M, Kasai T, Lewtas J, Hirayama T. Metabolic activation of 2- and 3-nitrodibenzopyranone isomers and related compounds by rat liver S9 and the effect of S9 on the mutational specificity of nitrodibenzopyranones. Mutat Res 1997;388:67-78.
  114. Dybdahl M, Vogel U, Frentz G, Wallin H, Nexo BA. Polymorphisms in the DNA repair gene XPD: correlations with risk and age at onset of basal cell carcinoma. Cancer Epidemiol Biomarkers Prev 1999;8:77-81.
  115. Lunn RM, Helzlsouer KJ, Parshad R, et al. XPD polymorphisms: effects on DNA repair proficiency. Carcinogenesis 2000;21:551-5.
  116. Fan F, Liu C, Tavare S, Arnheim N. Polymorphisms in the human DNA repair gene XPF. Mutat Res 1999;406:115-20.
  117. Duell EJ, Wiencke JK, Cheng TJ, et al. Polymorphisms in the DNA repair genes XRCC1 and ERCC2 and biomarkers of DNA damage in human blood mononuclear cells. Carcinogenesis 2000;21:965-71.
  118. Hu JJ, Smith TR, Miller MS, Mohrenweiser HW, Golden A, Case LD. Amino acid substitution variants of APE1 and XRCC1 genes associated with ionizing radiation sensitivity. Carcinogenesis 2001;22:917-22.
  119. Xu X, Kelsey KT, Wiencke JK, Wain JC, Christiani DC. Cytochrome P450 CYP1A1 MspI polymorphism and lung cancer susceptibility. Cancer Epidemiol Biomarkers Prev 1996;5:687-92.
  120. Houlston RS. CYP1A1 polymorphisms and lung cancer risk: a meta-analysis. Pharmacogenetics 2000;10:105-14.
  121. Bartsch H, Nair U, Risch A, Rojas M, Wikman H, Alexandrov K. Genetic polymorphism of CYP genes, alone or in combination, as a risk modifier of tobacco-related cancers. Cancer Epidemiol Biomarkers Prev 2000;9:3-28.
  122. Houlston RS. Glutathione S-transferase M1 status and lung cancer risk: a meta-analysis. Cancer Epidemiol Biomarkers Prev 1999;8:675-82.
  123. McWilliams JE, Sanderson BJ, Harris EL, Richert-Boe KE, Henner WD. Glutathione S-transferase M1 (NAT2) deficiency and lung cancer risk. Cancer Epidemiol Biomarkers Prev 1995;4:589-94.
  124. Bouchardy C, Mitrunen K, Wikman H, et al. N-acetyltransferase NAT1 and NAT2 genotypes and lung cancer risk. Pharmacogenetics 1998;8:291-8.
  125. Benhamou S, Reinikainen M, Bouchardy C, Dayer P, Hirvonen A. Association between lung cancer and microsomal epoxide hydrolase genotypes. Cancer Res 1998;58:5291-3.
  126. Ratnasinghe D, Yao SX, Tangrea JA, et al. Polymorphisms of the DNA repair gene XRCC1 and lung cancer risk. Cancer Epidemiol Biomarkers Prev 2001;10:119-23.
  127. Marcus PM, Vineis P, Rothman N. NAT2 slow acetylation and bladder cancer risk: a meta-analysis of 22 case-control studies conducted in the general population. Pharmacogenetics 2000;10:115-22.
  128. Engel LS, Taioli, E., Pfeiffer, R., Garcia-Closas, M., Marcus, P.M., Lan, Q., Boffetta, P., Vineis, P., Autrup, H., Bell, D.A., Branch, R.A., Brockmöller, J., Daly, A.K., Heckbert, S.R., Kalina, I., Kang, D., Katoh, T., Lafuente, A., Lin, H.J., Romkes, M., Taylor, J.A., Rothman, N. Pooled analysis and meta-analysis of NAT2 and bladder cancer: A HuGE Review. American Journal of Epidemiology In press.
  129. Koyama H, Geddes DM. Genes, oxidative stress, and the risk of chronic obstructive pulmonary disease. Thorax 1998;53:S10-4.
  130. Bennett WP, Alavanja MC, Blomeke B, et al. Environmental tobacco smoke, genetic susceptibility, and risk of lung cancer in never-smoking women. J Natl Cancer Inst 1999;91:2009-14.
  131. Spurr NK, Gough AC, Stevenson K, Wolf CR. Msp-1 polymorphism detected with a cDNA probe for the P-450 I family on chromosome 15. Nucleic Acids Res 1987;15:5901.
  132. Persson I, Johansson I, Ingelman-Sundberg M. In vitro kinetics of two human CYP1A1 variant enzymes suggested to be associated with interindividual differences in cancer susceptibility. Biochem Biophys Res Commun 1997;231:227-30.
  133. Sachse C, Brockmoller J, Bauer S, Roots I. Functional significance of a C-->A polymorphism in intron 1 of the cytochrome P450 CYP1A2 gene tested with caffeine. Br J Clin Pharmacol 1999;47:445-9.
  134. Chida M, Yokoi T, Fukui T, Kinoshita M, Yokota J, Kamataki T. Detection of three genetic polymorphisms in the 5'-flanking region and intron 1 of human CYP1A2 in the Japanese population. Jpn J Cancer Res 1999;90:899-902.
  135. Marchand LL, Wilkinson GR, Wilkens LR. Genetic and dietary predictors of CYP2E1 activity: a phenotyping study in Hawaii Japanese using chlorzoxazone. Cancer Epidemiol Biomarkers Prev 1999;8:495-500.
  136. Hayashi S, Watanabe J, Kawajiri K. Genetic polymorphisms in the 5'-flanking region change transcriptional regulation of the human cytochrome P450IIE1 gene. J Biochem (Tokyo) 1991;110:559-65.
  137. Rebbeck TR, Jaffe JM, Walker AH, Wein AJ, Malkowicz SB. Modification of clinical presentation of prostate tumors by a novel genetic variant in CYP3A4. J Natl Cancer Inst 1998;90:1225-9.
  138. Walker AH, Jaffe JM, Gunasegaram S, et al. Characterization of an allelic variant in the nifedipine-specific element of CYP3A4: ethnic distribution and implications for prostate cancer risk. Mutations in brief no. 191. Online. Hum Mutat 1998;12:289.
  139. Smart J, Daly AK. Variation in induced CYP1A1 levels: relationship to CYP1A1, Ah receptor and NAT2 polymorphisms. Pharmacogenetics 2000;10:11-24.
  140. Hassett C, Aicher L, Sidhu JS, Omiecinski CJ. Human microsomal epoxide hydrolase: genetic polymorphism and functional expression in vitro of amino acid variants. Hum Mol Genet 1994;3:421-8.
  141. Moran JL, Siegel D, Ross D. A potential mechanism underlying the increased susceptibility of individuals with a polymorphism in NAD(P)H:quinone oxidoreductase 1 (NQO1) to benzene toxicity. Proc Natl Acad Sci U S A 1999;96:8150-5.
  142. Raftogianis RB, Wood TC, Otterness DM, Van Loon JA, Weinshilboum RM. Phenol sulfotransferase pharmacogenetics in humans: association of common SULT1A1 alleles with TS PST phenotype. Biochem Biophys Res Commun 1997;239:298-304.
  143. Seidegard J, Vorachek WR, Pero RW, Pearson WR. Hereditary differences in the expression of the human glutathione transferase active on trans-stilbene oxide are due to a gene deletion. Proc Natl Acad Sci U S A 1988;85:7293-7.
  144. Ali-Osman F, Akande O, Antoun G, Mao JX, Buolamwini J. Molecular cloning, characterization, and expression in Escherichia coli of full-length cDNAs of three human glutathione S-transferase Pi gene variants. Evidence for differential catalytic activity of the encoded proteins. J Biol Chem 1997;272:10004-12.
  145. Pemble S, Schroeder KR, Spencer SR, et al. Human glutathione S-transferase theta (GSTT1): cDNA cloning and the characterization of a genetic polymorphism. Biochem J 1994;300:271-6.
  146. Wiebel FA, Dommermuth A, Thier R. The hereditary transmission of the glutathione transferase hGSTT1-1 conjugator phenotype in a large family. Pharmacogenetics 1999;9:251-6.
  147. Humbert R, Adler DA, Disteche CM, Hassett C, Omiecinski CJ, Furlong CE. The molecular basis of the human serum paraoxonase activity polymorphism. Nat Genet 1993;3:73-6.
  148. Cooper GS, Umbach DM. Are vitamin D receptor polymorphisms associated with bone mineral density? A meta analysis. J Bone Miner Res 1996;11:1841-9.
  149. Shen MR, Jones IM, Mohrenweiser H. Nonconservative amino acid substitution variants exist at polymorphic frequency in DNA repair genes in healthy humans.
    Cancer Res 1998;58:604-8.
  150. Hadi MZ, Coleman MA, Fidelis K, Mohrenweiser HW, Wilson ID. Functional characterization of Ape1 variants identified in the human population. Nucleic Acids Res 2000;28:3871-9.
  151. Abraham LJ, Kroeger KM. Impact of the -308 TNF promoter polymorphism on the transcriptional regulation of the TNF gene: relevance to disease.
    J Leukoc Biol 1999;66:562-6.

 

 

Address correspondence to Dr. Khoury at
Office of Genomics and Disease Prevention
Centers for Disease Control and Prevention
6 Executive Park, Mail Stop E-82
Atlanta, Georgia 30329


 

Contact Us:
  • Centers for Disease Control and Prevention
    1600 Clifton Rd.
    Atlanta, GA 30333 USA
    800-CDC-INFO (800-232-4636)
  • Additional information for Public Health Genomics is available on our contact page.
USA.gov: The U.S. Government's Official Web PortalDepartment of Health and Human Services
Centers for Disease Control and Prevention   1600 Clifton Road Atlanta, GA 30329-4027, USA
800-CDC-INFO (800-232-4636) TTY: (888) 232-6348 - Contact CDC–INFO
A-Z Index
  1. A
  2. B
  3. C
  4. D
  5. E
  6. F
  7. G
  8. H
  9. I
  10. J
  11. K
  12. L
  13. M
  14. N
  15. O
  16. P
  17. Q
  18. R
  19. S
  20. T
  21. U
  22. V
  23. W
  24. X
  25. Y
  26. Z
  27. #