Case-control studies of common alleles and environmental factors

Linkage Disequilibrium between MSX1 and Non-syndromic Cleft Lip/Palate in the Chilean Population

Non-syndromic cleft lip/palate (NSCLP) is a complex genetic trait. Linkage and association studies have suggested that a clefting locus could be located on chromosome 4p. Sixty Chilean families were recruited for this study; from these, we used unrelated trios to evaluate the possible linkage disequilibrium between MSX1 and NSCLP. An intragenic marker, MSX1-CA, and an extragenic marker, D4S432 at a distance of 0.8 cM from MSX1, were analyzed by means of polymerase chain-reaction with fluorescent-labeled forward primers, followed by electrophoresis on a laser-fluorescent sequencer. We carried out a transmission/disequilibrium test (TDT) for multiple alleles to evaluate the presence of linkage disequilibrium. Results showed a preferential transmission of the 169-bp allele of MSX1 (p = 0.03). Although there was no preferential transmission for the D4S432 marker, the overall extended TDT (ETDT) showed a significant result (p = 0.01). The authors’ findings support the hypothesis of the contribution of MSX1 in the etiology of NSCLP in the Chilean population.

Design and analysis issues in gene and environment studies

Both nurture (environmental) and nature (genetic factors) play an important role in human disease etiology. Traditionally, these effects have been thought of as independent. This perspective is ill informed for non-mendelian complex disorders which result as an interaction between genetics and environment. To understand health and disease we must study how nature and nurture interact. Recent advances in human genomics and high-throughput biotechnology make it possible to study large numbers of genetic markers and gene products simultaneously to explore their interactions with environment. The purpose of this review is to discuss design and analytic issues for gene-environment interaction studies in the "-omics" era, with a focus on environmental and genetic epidemiological studies. We present an expanded environmental genomic disease paradigm. We discuss several study design issues for gene-environmental interaction studies, including confounding and selection bias, measurement of exposures and genotypes. We discuss statistical issues in studying gene-environment interactions in different study designs, such as choices of statistical models, assumptions regarding biological factors, and power and sample size considerations, especially in genome-wide gene-environment studies. Future research directions are also discussed.

Association between bone morphogenetic protein 4 gene polymorphisms with nonsyndromic cleft lip with or without cleft palate in a chilean population

Nonsyndromic cleft lip with or without cleft palate (NSCLP) is one of the most common birth defects in humans with both genetic and environmental components involved in its expression. Experimental evidences have postulated that bone morphogenetic protein 4 gene (Bmp4) is involved in the etiology of cleft lip with or without cleft palate (CL/P) in mice. In our study we analyzed the association between BMP4 and NSCLP in a sample of 150 unrelated trios ascertained through affected probands. Three BMP4 polymorphisms were analyzed, two intronic (rs762642 and rs2855532) and rs1957860, located 5.7 kb upstream from BMP4. Transmission/disequilibrium tests were performed at the allele and haplotype levels. Our results did not detect preferential transmission for individual single-nucleotide polymorphisms. Significant transmission distortion was observed for haplotypes rs1957860-rs762642 (p = 0.018), especially for C-T (p = 0.015) and T-T (p = 0.018) which include the genomic region where the promoter and an enhancer of BMP4 are located. Thus, despite the positive association detected between these haplotypes and NSCLP they probably do not have a functional effect on BMP4 expression or protein activity but possibly reflect NSCLP susceptibility changes which are in linkage disequilibrium with these polymorphisms. The findings of our study support a role for BMP4 in NSCLP in the admixed Chilean population.

Assessment of DNA damage and its modulation by dietary and genetic factors in smokers using the Comet assay: a biomarker model

Methods are needed to assess exposure to genotoxins in humans and to improve understanding of dietary cancer prevention. The Comet assay was used to detect smoking-related exposures and dietary modulations in target tissues. Buccal scrapings, blood and faeces were collected from 38 healthy male volunteers (smokers and non-smokers) during a dietary intervention study with bread supplemented with prebiotics+/-antioxidants. GSTM1-genotype was determined with PCR. Buccal and peripheral lymphocytes were analysed for DNA damage using the Comet assay. Genotoxicity of faecal water (FW) was assayed in human colon HT29 clone 19A cells. 'Tail intensity' (TI) was used as a quantitative indicator of DNA damage in the Comet assay. Intervention with bread reduced DNA damage in lymphocytes of smokers (8.3+/-1.7% TI versus 10.2+/-4.1% TI, n=19), but not of non-smokers (8.6+/-2.8% TI versus 8.3+/-2.7% TI, n=15). Faecal water genotoxicity was reduced only in non-smokers (9.4+/-2.9% TI versus 18.9+/-13.1% TI, n=15) but not in smokers (15.5+/-10.7% TI versus 20.4+/-14.1% TI, n=13). The Comet assay was efficient in the detection of both smoking-related exposure (buccal cells) and efficacy of dietary intervention (faecal samples). Smokers and non-smokers profited differently from the intervention with prebiotic bread+/-antioxidants. Stratification of data by genotype enhanced specificity/sensitivity of the intervention effects and contributed important information on the role of susceptibility.

Unconditional analyses can increase efficiency in assessing gene-environment interaction of the case-combined-control design

BACKGROUND

A design combining both related and unrelated controls, named the case-combined-control design, was recently proposed to increase the power for detecting gene-environment (GxE) interaction. Under a conditional analytic approach, the case-combined-control design appeared to be more efficient and feasible than a classical case-control study for detecting interaction involving rare events.

METHODS

We now propose an unconditional analytic strategy to further increase the power for detecting gene-environment (GxE) interactions. This strategy allows the estimation of GxE interaction and exposure (E) main effects under certain assumptions (e.g. no correlation in E between siblings and the same exposure frequency in both control groups). Only the genetic (G) main effect cannot be estimated because it is biased.

RESULTS

Using simulations, we show that unconditional logistic regression analysis is often more efficient than conditional analysis for detecting GxE interaction, particularly for a rare gene and strong effects. The unconditional analysis is also at least as efficient as the conditional analysis when the gene is common and the main and joint effects of E and G are small.

CONCLUSIONS

Under the required assumptions, the unconditional analysis retains more information than does the conditional analysis for which only discordant case-control pairs are informative leading to more precise estimates of the odds ratios.

Increased power to detect gene-environment interaction using siblings controls

PURPOSE

Interest is increasing in studying gene-environment (G x E) interaction in disease etiology. Study designs using related controls as a more appropriate control group for evaluating G x E interactions have been proposed but often assume unrealistic numbers of available relative controls. To evaluate a more realistic design, we studied the relative efficiency of a 1:0.5 case-sibling-control design compared with a classical 1:1 case-unrelated-control design and examined the effect of the analysis strategy.

METHODS

Simulations were performed to assess the efficiency of a 1:0.5 case-sibling-control design relative to a classical 1:1 case-unrelated-control design under a variety of assumptions for estimating G x E interaction. Both matched and unmatched analysis strategies were examined.

RESULTS

When using a matched analysis, the 1:1 case-unrelated-control design was almost always more powerful than the 1:0.5 case-sibling-control design. In contrast, when using an unmatched analysis, the 1:0.5 case-sibling-control design was almost always more powerful than the 1:1 case-unrelated-control design. The unconditional analysis of the case-sibling-control design to estimate G x E interaction, however, requires no correlation in E between siblings.

CONCLUSIONS

In most settings, a matched analysis may be required and a 1:1 case-unrelated-control design will be more powerful than a 1:0.5 case-sibling-control design.

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

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

Modern cancer epidemiological research: genetic polymorphisms and environment

Individual cancer susceptibility seems to be related to factors such as changes in oncogenes and tumor suppressor genes expression, and differences in the action of metabolic enzymes and DNA repair regulated by specific genes. Epidemiological studies on genetic polymorphisms of human xenobiotics metabolizing enzymes and cancer have revealed low relative risks. Research considering genetic polymorphisms prevalence jointly with environmental exposures could be relevant for a better understanding of cancer etiology and the mechanisms of carcinogenesis and also for new insights on cancer prognosis. This study reviews the approaches of molecular epidemiology in cancer research, stressing case-control and cohort designs involving genetic polymorphisms, and factors that could introduce bias and confounding in these studies. Similarly to classical epidemiological research, genetic polymorphisms requires considering aspects of precision and accuracy in the study design.

The case-combined-control design was efficient in detecting gene-environment interactions

OBJECTIVE

The interest in studying gene-environment (GxE) interaction is increasing for complex diseases. A design combining both related and unrelated controls (e.g., population-based and siblings) is proposed to increase the power to detect GxE interaction.

STUDY DESIGN AND SETTING

We used simulations to assess the efficiency of the case-combined-control design relative to a classical case-control study under a variety of assumptions.

RESULTS

The case-combined-control design appears more efficient and feasible than a classical case-control study for detecting interaction involving rare exposures and/or genetic factors. The number of available sibling controls per case and the frequencies of the risk factors are the most important parameters for determining relative efficiency. Relative efficiencies decrease as the frequency of the gene (G) increases. A positive correlation in exposure (E) between siblings decreases relative efficiency.

CONCLUSIONS

Although the case-combined-control design may not be efficient for common genes with moderate effects, it appears to be a useful alternative in certain situations where classical approaches remain unrealistic.

Possible association of interleukin 1 gene locus polymorphisms with low back pain

Based on a hypothesis that interleukin 1 (IL-1) activity is associated with low back pain (LBP), we investigated relationships between previously described functional IL-1 gene polymorphisms and LBP. The subjects were a subgroup of a Finnish study cohort. The IL-1alpha(C(889)-T), IL-1beta(C(3954)-T) and IL-1 receptor antagonist (IL-1RN)(G(1812)-A, G(1887)-C and T(11100)-C) polymorphisms were genotyped in 131 middle-aged men from three occupational groups (machine drivers, carpenters and office workers). A questionnaire inquired about individual and lifestyle characteristics and the occurrence of LBP, the number of days with pain and days with limitation of daily activities because of pain, and pain intensity, during the past 12 months. Lumbar disc degeneration was determined with magnetic resonance imaging. Carriers of the IL-1RNA(1812) allele had an increased risk of LBP (OR 2.5, 95% CI 1.0-6.0) and carriers of this allele in combination with the IL-1alphaT(889) or IL-1betaT(3954) allele had a higher risk of and more days with LBP than non-carriers. Pain intensity was associated with the simultaneous carriage of the IL-1alphaT(889) and IL-1RNA(1812) alleles (OR 3.7, 95% CI 1.2-11.9). Multiple regression analyses allowing for occupation and disc degeneration showed that carriage of the IL-1RNA(1812) allele was associated with the occurrence of pain, the number of days with pain and days with limitations of daily activities. Carriage of the IL-1betaT(3954) allele was associated with the number of days with pain. The results suggest a possible contribution of the IL-1 gene locus polymorphisms to the pathogenesis of LBP. The possibility of chance findings cannot be excluded due to the small sample size.

Spouses and unrelated friends of probands as controls for stroke genetics studies

To plan a multisite, ischemic stroke genetic study, stroke patients were surveyed about the availability and characteristics of a convenience sample of spouse/friend controls. 65% of all stroke-affected probands reported a living spouse. A more detailed survey was conducted at the University of Virginia, Charlottesville, Va., USA: 51% of stroke patients reported a living, stroke-free spouse who would be willing to serve as a control, and 49% reported having a stroke-free friend who would be willing to serve as a control. Overall, 75% of stroke patients reported at least 1 individual willing to participate as a control. Cases without an identified control were more likely to be non-white (48%) than were cases with a control (13%; p = 0.00004). Cases were older than controls (67.3 vs. 59.2 years; p = 0.000002), and a greater proportion of cases than controls were male (57 vs. 33%; p = 0.0002). Without proper attention to matching, the use of a spouse/friend convenience sample would result in imbalances in basic demographic characteristics.

The contribution of inherited genotype to breast cancer

The etiology of breast cancer is complex, and is likely to involve the actions of genes at multiple levels along the multistage carcinogenesis process. These inherited genotypes include those that affect the propensity to be exposed to breast carcinogens, and those associated with breast tumorigenesis directly. In addition, inherited genotypes may influence response to breast cancer chemoprevention and treatment. Studies relating inherited genotypes with breast cancer incidence and mortality should consider a broader spectrum of genes and their potential roles in multistage carcinogenesis than have been typically evaluated to date. Understanding the role of inherited genotype at different stages of carcinogenesis could improve our understanding of cancer biology, may identify specific exposures or events that correlate with carcinogenesis, or target relevant biochemical pathways for the development of preventive or therapeutic interventions.

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

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

Counter-matching in studies of gene-environment interaction: efficiency and feasibility

The interest in studying gene-environment interaction is increasing for complex diseases. However, most methods of detecting gene-environment interactions may not be appropriate for the study of interactions involving rare genes (G:) or uncommon environmental exposures (E:), because of poor statistical power. To increase this power, the authors propose the counter-matching design. This design increases the number of subjects with the rare factor without increasing the number of measurements that must be performed. In this paper, the efficiency and feasibility (required sample sizes) of counter-matching designs are evaluated and discussed. Counter-matching on both G: and E: appears to be the most efficient design for detecting gene-environment interaction. The sensitivity and specificity of the surrogate measures, the frequencies of G: and E:, and, to a lesser extent, the value of the interaction effect are the most important parameters for determining efficiency. Feasibility is also more dependent on the exposure frequencies and the interaction effect than on the main effects of G: and E: Although the efficiency of counter-matching is greatest when the risk factors are very rare, the study of such rare factors is not realistic unless one is interested in very strong interaction effects. Nevertheless, counter-matching appears to be more appropriate than most traditional epidemiologic methods for the study of interactions involving rare factors.