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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