Bayesian semiparametric analysis for two-phase studies of gene-environment interaction

The two-phase sampling design is a cost-efficient way of collecting expensive covariate information on a judiciously selected subsample. It is natural to apply such a strategy for collecting genetic data in a subsample enriched for exposure to environmental factors for gene-environment interaction (G x E) analysis. In this paper, we consider two-phase studies of G x E interaction where phase I data are available on exposure, covariates and disease status. Stratified sampling is done to prioritize individuals for genotyping at phase II conditional on disease and exposure. We consider a Bayesian analysis based on the joint retrospective likelihood of phases I and II data. We address several important statistical issues: (i) we consider a model with multiple genes, environmental factors and their pairwise interactions. We employ a Bayesian variable selection algorithm to reduce the dimensionality of this potentially high-dimensional model; (ii) we use the assumption of gene-gene and gene-environment independence to trade off between bias and efficiency for estimating the interaction parameters through use of hierarchical priors reflecting this assumption; (iii) we posit a flexible model for the joint distribution of the phase I categorical variables using the nonparametric Bayes construction of Dunson and Xing [J. Amer. Statist. Assoc. 104 (2009) 1042-1051].Comment: Published in at http://dx.doi.org/10.1214/12-AOAS599 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Missing Exposure Data in Stereotype Regression Model: Application to Matched Case–Control Study with Disease Subclassification

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87006/1/j.1541-0420.2010.01453.x.pd

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

Pediatric duodenal cancer and biallelic mismatch repair gene mutations

Gastrointestinal malignancies are extremely rare in the pediatric population, and duodenal cancers represent an even more unusual entity. Intestinal cancers in young adults and children have been observed to be associated with functional deficiencies of the mismatch repair (MMR) system causing a cancer-predisposition syndrome. We report the case of a 16-year-old female with duodenal adenocarcinoma and past history of medulloblastoma found to have a novel germline bialleleic truncating mutation (c.[949C>T]+[949C>T]) of the PMS2 gene. Pediatr Blood Cancer 2009;53:116–120. © 2009 Wiley-Liss, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62997/1/21957_ftp.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