Case–Control Studies of Gene–Environment Interaction: Bayesian Design and Analysis

With increasing frequency, epidemiologic studies are addressing hypotheses regarding gene-environment interaction. In many well-studied candidate genes and for standard dietary and behavioral epidemiologic exposures, there is often substantial  prior  information available that may be used to analyze current data as well as for designing a new study. In this article, first, we propose a proper full Bayesian approach for analyzing studies of gene–environment interaction. The Bayesian approach provides a natural way to incorporate uncertainties around the assumption of gene–environment independence, often used in such an analysis. We then consider Bayesian sample size determination criteria for both estimation and hypothesis testing regarding the multiplicative gene–environment interaction parameter. We illustrate our proposed methods using data from a large ongoing case–control study of colorectal cancer investigating the interaction of N-acetyl transferase type 2 (NAT2) with smoking and red meat consumption. We use the existing data to elicit a design prior and show how to use this information in allocating cases and controls in planning a future study that investigates the same interaction parameters. The Bayesian design and analysis strategies are compared with their corresponding frequentist counterparts.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/78584/1/j.1541-0420.2009.01357.x.pd

Familial Medullary Thyroid Carcinoma Associated with Cutaneous Lichen Amyloidosis

Background: This is a report of a patient with a novel genotype phenotype relationship of a c804 mutation of the RET proto-oncogene manifesting as medullary thyroid carcinoma (MTC) and cutaneous lichen amyloidosis (CLA). Summary: Clinical data were obtained for patient appearance and laboratory results. Analyzed were histopathology of the skin lesion and thyroid gland, genetic mutation, and family pedigree. Skin histology and histochemistry were consistent with CLA. Serum calcitonin levels were moderately elevated. Thyroid histology demonstrated a 4mm focus of MTC. Measurements of serum parathormone, calcium, and plasma metanephrines were normal. DNA analysis demonstrated a mutation in codon 804 of the RET proto-oncogene resulting in a Valine to Methionine (V804M) substitution. Genetic testing in two siblings revealed the same mutation. Conclusions: This is the first description of a patient with CLA not associated with a mutation in codon 634. The patient is one of the few with a V804M mutation in whom the clinical expression did not fully conform to the definition of familial MTC.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/78146/1/thy.2009.0021.pd

Nonsteroidal Anti-Inflammatory Drugs and Risk of Melanoma

Because nonsteroidal anti-inflammatory drugs (NSAIDs) inhibit tumor growth in vitro, we investigated the association between NSAIDs and melanoma to determine if there was epidemiologic evidence of a chemopreventive effect from these medications. Three hundred twenty-seven subjects with incident melanoma and 119 melanoma-free controls completed a structured interview assessing melanoma risk factors. The unadjusted odds ratio (OR) for use of nonaspirin NSAIDs was 0.58 (95% CI 0.31–1.11), in a comparison of subjects with melanoma to controls. After adjustment for melanoma risk factors, the OR was 0.71 (95% CI 0.23–2.02). Aspirin users had an unadjusted OR of 0.85 (95% CI 0.45–1.69) and an adjusted OR of 1.45 (95% CI 0.44–4.74). In this pilot study, we found no evidence of a significant association between analgesic use and melanoma risk when potential confounders are assessed. Based on conflicting reports in the literature, meta-analysis may be appropriate

Tests for Gene-Environment Interactions and Joint Effects with Exposure Misclassification

The number of methods for genome-wide testing of gene-environment interactions (GEI) continues to increase with the hope of discovering new genetic risk factors and obtaining insight into the disease-gene-environment relationship. The relative performance of these methods based on family-wise type 1 error rate and power depends on underlying disease-gene-environment associations, estimates of which may be biased in the presence of exposure misclassification. This simulation study expands on a previously published simulation study of methods for detecting GEI by evaluating the impact of exposure misclassification. We consider seven single step and modular screening methods for identifying GEI at a genome-wide level and seven joint tests for genetic association and GEI, for which the goal is to discover new genetic susceptibility loci by leveraging GEI when present. In terms of statistical power, modular methods that screen based on the marginal disease-gene relationship are more robust to exposure misclassification. Joints tests that include main/marginal effects of a gene display a similar robustness, confirming results from earlier studies. Our results offer an increased understanding of the strengths and limitations of methods for genome-wide search for GEI and joint tests in presence of exposure misclassification. KEY WORDS: case-control; genome-wide association; gene discovery, gene-environment independence; modular methods; multiple testing; screening test; weighted hypothesis test. Abbreviations: CC, case-control; CC(EXP), CC in the exposed subgroup; CO, case-only; CT, cocktail; DF, degree of freedom; D-G, disease-gene; EB, empirical Bayes; EB(EXP), EB in the exposed subgroup; EDGxE, joint marginal/association screening; FWER, family-wise error rate; G-E, gene-environment; GEI, gene-environment interaction; GEWIS, Gene Environment Wide Interaction Study; H2, hybrid two-step; LR, likelihood ratio; MA, marginal; OR, odds ratio; SE, sensitivity; SP, specificity; TS, two-step gene-environment screening

Estimating probability of germline mismatch repair mutations in colorectal cancer patients with microsatellite stable tumors

http://deepblue.lib.umich.edu/bitstream/2027.42/112938/1/13053_2011_Article_206.pd