Cohort studies for characterizing measured genes

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.

Genome-wide association scans for secondary traits using case-control samples

Genome-wide association studies (GWAS) require considerable investment, so researchers often study multiple traits collected on the same set of subjects to maximize return. However, many GWAS have adopted a case-control design; improperly accounting for case-control ascertainment can lead to biased estimates of association between markers and secondary traits. We show that under the null hypothesis of no marker-secondary trait association, naïve analyses that ignore ascertainment or stratify on case-control status have proper Type I error rates except when both the marker and secondary trait are independently associated with disease risk. Under the alternative hypothesis, these methods are unbiased when the secondary trait is not associated with disease risk. We also show that inverse-probability-of-sampling-weighted (IPW) regression provides unbiased estimates of marker-secondary trait association. We use simulation to quantify the Type I error, power and bias of naïve and IPW methods. IPW regression has appropriate Type I error in all situations we consider, but has lower power than naïve analyses. The bias for naïve analyses is small provided the marker is independent of disease risk. Considering the majority of tested markers in a GWAS are not associated with disease risk, naïve analyses provide valid tests of and nearly unbiased estimates of marker-secondary trait association. Care must be taken when there is evidence that both the secondary trait and tested marker are associated with the primary disease, a situation we illustrate using an analysis of the relationship between a marker in FGFR2 and mammographic density in a breast cancer case-control sample.

ABO blood group and the risk of pancreatic cancer

BACKGROUND

Other than several rare, highly penetrant familial syndromes, genetic risk factors for sporadic pancreatic cancer are largely unknown. ABO blood type is an inherited characteristic that in previous small studies has been associated with the risk of gastrointestinal malignancies.

METHODS

We separately examined the relationship between ABO blood type and the risk of incident pancreatic cancer in two large, independent, prospective cohort studies (the Nurses' Health Study and Health Professionals Follow-up Study) that collected blood group data on 107 503 US health professionals. Hazard ratios for pancreatic cancer by ABO blood type were calculated using Cox proportional hazards models with adjustment for other known risk factors, including age, tobacco use, body mass index, physical activity, and history of diabetes mellitus. All statistical tests were two-sided.

RESULTS

During 927 995 person-years of follow-up, 316 participants developed pancreatic cancer. ABO blood type was associated with the risk of developing pancreatic cancer (P = .004; log-rank test). Compared with participants with blood group O, those with blood groups A, AB, or B were more likely to develop pancreatic cancer (adjusted hazard ratios for incident pancreatic cancer were 1.32 [95% confidence interval {CI} = 1.02 to 1.72], 1.51 [95% CI = 1.02 to 2.23], and 1.72 [95% CI = 1.25 to 2.38], respectively). The association between blood type and pancreatic cancer risk was nearly identical in the two cohorts (P(interaction) = .97). Overall, 17% of the pancreatic cancer cases were attributable to inheriting a non-O blood group (blood group A, B, or AB). The age-adjusted incidence rates for pancreatic cancer per 100 000 person-years were 27 (95% CI = 23 to 33) for participants with blood type O, 36 (95% CI = 26 to 50) for those with blood type A, 41 (95% CI = 31 to 56) for those with blood type AB, and 46 (95% CI = 32 to 68) for those with blood type B.

CONCLUSIONS

In two large, independent populations, ABO blood type was statistically significantly associated with the risk of pancreatic cancer. Further studies are necessary to define the mechanisms by which ABO blood type or closely linked genetic variants may influence pancreatic cancer risk.

ABO blood group and the risk of pancreatic cancer

BACKGROUND

Other than several rare, highly penetrant familial syndromes, genetic risk factors for sporadic pancreatic cancer are largely unknown. ABO blood type is an inherited characteristic that in previous small studies has been associated with the risk of gastrointestinal malignancies.

METHODS

We separately examined the relationship between ABO blood type and the risk of incident pancreatic cancer in two large, independent, prospective cohort studies (the Nurses' Health Study and Health Professionals Follow-up Study) that collected blood group data on 107 503 US health professionals. Hazard ratios for pancreatic cancer by ABO blood type were calculated using Cox proportional hazards models with adjustment for other known risk factors, including age, tobacco use, body mass index, physical activity, and history of diabetes mellitus. All statistical tests were two-sided.

RESULTS

During 927 995 person-years of follow-up, 316 participants developed pancreatic cancer. ABO blood type was associated with the risk of developing pancreatic cancer (P = .004; log-rank test). Compared with participants with blood group O, those with blood groups A, AB, or B were more likely to develop pancreatic cancer (adjusted hazard ratios for incident pancreatic cancer were 1.32 [95% confidence interval {CI} = 1.02 to 1.72], 1.51 [95% CI = 1.02 to 2.23], and 1.72 [95% CI = 1.25 to 2.38], respectively). The association between blood type and pancreatic cancer risk was nearly identical in the two cohorts (P(interaction) = .97). Overall, 17% of the pancreatic cancer cases were attributable to inheriting a non-O blood group (blood group A, B, or AB). The age-adjusted incidence rates for pancreatic cancer per 100 000 person-years were 27 (95% CI = 23 to 33) for participants with blood type O, 36 (95% CI = 26 to 50) for those with blood type A, 41 (95% CI = 31 to 56) for those with blood type AB, and 46 (95% CI = 32 to 68) for those with blood type B.

CONCLUSIONS

In two large, independent populations, ABO blood type was statistically significantly associated with the risk of pancreatic cancer. Further studies are necessary to define the mechanisms by which ABO blood type or closely linked genetic variants may influence pancreatic cancer risk.

Exploiting gene-environment interaction to detect genetic associations

Complex disease by definition results from the interplay of genetic and environmental factors. However, it is currently unclear how gene-environment interaction can best be used to locate complex disease susceptibility loci, particularly in the context of studies where between 1,000 and 1,000,000 markers are scanned for association with disease. We present a joint test of marginal association and gene-environment interaction for case-control data. We compare the power and sample size requirements of this joint test to other analyses: the marginal test of genetic association, the standard test for gene-environment interaction based on logistic regression, and the case-only test for interaction that exploits gene-environment independence. Although for many penetrance models the joint test of genetic marginal effect and interaction is not the most powerful, it is nearly optimal across all penetrance models we considered. In particular, it generally has better power than the marginal test when the genetic effect is restricted to exposed subjects and much better power than the tests of gene-environment interaction when the genetic effect is not restricted to a particular exposure level. This makes the joint test an attractive tool for large-scale association scans where the true gene-environment interaction model is unknown.

Genes, environment and the value of prospective cohort studies

Case-control studies have many advantages for identifying disease-related genes, but are limited in their ability to detect gene-environment interactions. The prospective cohort design provides a valuable complement to case-control studies. Although it has disadvantages in duration and cost, it has important strengths in characterizing exposures and risk factors before disease onset, which reduces important biases that are common in case-control studies. This and other strengths of prospective cohort studies make them invaluable for understanding gene-environment interactions in complex human disease.

Epidemiology informing clinical practice: from bills of mortality to population laboratories

The earliest observations on population patterns of disease and how they might inform medical practice probably occurred during the 17th century, and they continue to the present day, with increasing relevance to nutritional and infectious diseases, and cancer and other chronic diseases. Chronic-disease methods grew out of infectious-disease epidemiology, in which both field and laboratory methods are used. In diseases where intermediate biology was not initially observable (particularly cancer), record-based and interview-based epidemiology revealed some key exposures (e.g. smoking and radiation). With measurable intermediates (e.g. blood lipids), cardiovascular epidemiology also yielded inferences on causal pathways. Important changes that are remaking the field of epidemiology and will ultimately influence all aspects of medical practice include the following: high-throughput genotyping, allowing genetic and gene-environment causes of disease to be identified; high-throughput proteomics, which should allow the development of early-detection methods; new tools for the measurement of exposures; and a molecular basis for disease taxonomy. These new methods will allow a much better understanding of both the etiology and the intermediate stages of disease; however, new methods do not obviate the necessity for good study design, especially the need to be clear on the difference between observation and experiment. The greatest opportunities to inform medical practice come from the application of new methods to large-scale human observational studies, which include genetics, environment, early-detection markers, molecular classification of outcome, and treatment data. Improved molecular classification of disease will allow smaller, focused clinical trials to be undertaken and, ultimately, the tailoring of treatment to the biological profile of patient and disease.

Recent developments in genomewide association scans: a workshop summary and review

With the imminent availability of ultra-high-volume genotyping platforms (on the order of 100,000-1,000,000 genotypes per sample) at a manageable cost, there is growing interest in the possibility of conducting genomewide association studies for a variety of diseases but, so far, little consensus on methods to design and analyze them. In April 2005, an international group of >100 investigators convened at the University of Southern California over the course of 2 days to compare notes on planned or ongoing studies and to debate alternative technologies, study designs, and statistical methods. This report summarizes these discussions in the context of the relevant literature. A broad consensus emerged that the time was now ripe for launching such studies, and several common themes were identified--most notably the considerable efficiency gains of multistage sampling design, specifically those made by testing only a portion of the subjects with a high-density genomewide technology, followed by testing additional subjects and/or additional SNPs at regions identified by this initial scan.

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.

Gly972Arg variant in the insulin receptor substrate-1 gene and association with Type 2 diabetes: a meta-analysis of 27 studies

AIMS/HYPOTHESIS

Several case-control studies have examined the association between the Gly972Arg variant in the IRS-1 gene and Type 2 diabetes, but most had limited power and results could therefore be conflicting.

METHODS

We systematically reviewed the literature by means of a meta-analysis and investigated sources of heterogeneity in results of different studies.

RESULTS

The summary risk ratio, based on 3408 cases and 5419 control cases from 27 studies, was 1.25 (95% CI 1.05-1.48). The results, however, differed according to the type of study, method of verifying non-diabetic status of the control subjects, and age of the case subjects. Population-based studies reported lower odds ratios than hospital-based studies (OR 0.98, 95% CI 0.74-1.30 vs OR 1.43, 95% CI 1.17-1.74). Also, the diagnostic test to exclude diabetes amongst control subjects interacted with the association between the IRS-1 Gly972Arg variant and Type 2 diabetes (p=0.03). Finally, the odds ratio reduced with increasing age ( p=0.03).

CONCLUSION/INTERPRETATION

Overall, carriers of the 972Arg variant of the IRS-1 gene are at a 25% increased risk of having Type 2 diabetes compared with non-carriers. The odds ratios are generally higher in hospital-based studies, including relatively young, symptomatic, cases.

DNA banking for epidemiologic studies: a review of current practices

To study genetic risk factors for common diseases, researchers have begun collecting DNA specimens in large epidemiologic studies and surveys. However, little information is available to guide researchers in selecting the most appropriate specimens. In an effort to gather the best information for the selection of specimens for these studies, we convened a meeting of scientists engaged in DNA banking for large epidemiologic studies. In this discussion, we review the information presented at that meeting in the context of recent published information. Factors to be considered in choosing the appropriate specimens for epidemiologic studies include quality and quantity of DNA, convenience of collection and storage, cost, and ability to accommodate future needs for genotyping. We focus on four types of specimens that are stored in these banks: (1) whole blood preserved as dried blood spots; (2) whole blood from which genomic DNA is isolated, (3) immortalized lymphocytes from whole blood or separated lymphocytes, prepared immediately or subsequent to cryopreservation; and (4) buccal epithelial cells. Each of the specimens discussed is useful for epidemiologic studies according to specific needs, which we enumerate in our conclusions.

SNPing away at candidate genes

We develop regression methodology to identify subsets of single nucleotide polymorphisms (SNPs) within candidate genes related to quantitative traits and apply our methods to the simulated Genetic Analysis Workshop (GAW) 12 data set. In the data set we find 694 SNP loci with minimum allele frequencies of at least 0.01. We assume an additive casual model between these SNPs and all five quantitative traits. After initial screening using one-way analysis of variance, we employ a computationally efficient, simulated annealing algorithm to select among all possible subsets of SNP loci, using a generalization of Mallows' Cp as our optimality criterion. The simple transition kernel we develop evaluates new subsets in O(1), by requiring just three arithmetic operations to calculate the proposed RSS based on the Gauss-Jordan pivot. We identify an SNP loci located at 6-5782 related to traits 2 and 3 and several sites on gene 2 related to trait 5 using a subsample of 1,000 individuals and the full data set (n = 8,250) for comparison.

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.