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

Common variation in ISL1 confers genetic susceptibility for human congenital heart disease

Congenital heart disease (CHD) is the most common birth abnormality and the etiology is unknown in the overwhelming majority of cases. ISLET1 (ISL1) is a transcription factor that marks cardiac progenitor cells and generates diverse multipotent cardiovascular cell lineages. The fundamental role of ISL1 in cardiac morphogenesis makes this an exceptional candidate gene to consider as a cause of complex congenital heart disease. We evaluated whether genetic variation in ISL1 fits the common variant-common disease hypothesis. A 2-stage case-control study examined 27 polymorphisms mapping to the ISL1 locus in 300 patients with complex congenital heart disease and 2,201 healthy pediatric controls. Eight genic and flanking ISL1 SNPs were significantly associated with complex congenital heart disease. A replication study analyzed these candidate SNPs in 1,044 new cases and 3,934 independent controls and confirmed that genetic variation in ISL1 is associated with risk of non-syndromic congenital heart disease. Our results demonstrate that two different ISL1 haplotypes contribute to risk of CHD in white and black/African American populations

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

An oncocytic adrenal tumour in a patient with Birt‐Hogg‐Dubé syndrome

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106917/1/cen12292.pd

Polymorphisms in alcohol metabolism genes ADH1B and ALDH2, alcohol consumption and colorectal cancer

Background: Colorectal cancer (CRC) is a leading cause of cancer death worldwide. Epidemiological risk factors for CRC included alcohol intake, which is mainly metabolized to acetaldehyde by alcohol dehydrogenase and further oxidized to acetate by aldehyde dehydrogenase; consequently, the role of genes in the alcohol metabolism pathways is of particular interest. The aim of this study is to analyze the association between SNPs in ADH1B and ALDH2 genes and CRC risk, and also the main effect of alcohol consumption on CRC risk in the study population. Methodology/Principal Findings: SNPs from ADH1B and ALDH2 genes, included in alcohol metabolism pathway, were genotyped in 1694 CRC cases and 1851 matched controls from the Molecular Epidemiology of Colorectal Cancer study. Information on clinicopathological characteristics, lifestyle and dietary habits were also obtained. Logistic regression and association analysis were conducted. A positive association between alcohol consumption and CRC risk was observed in male participants from the Molecular Epidemiology of Colorectal Cancer study (MECC) study (OR = 1.47; 95%CI = 1.18-1.81). Moreover, the SNPs rs1229984 in ADH1B gene was found to be associated with CRC risk: under the recessive model, the OR was 1.75 for A/A genotype (95%CI = 1.21-2.52; p-value = 0.0025). A path analysis based on structural equation modeling showed a direct effect of ADH1B gene polymorphisms on colorectal carcinogenesis and also an indirect effect mediated through alcohol consumption. Conclusions/Significance: Genetic polymorphisms in the alcohol metabolism pathways have a potential role in colorectal carcinogenesis, probably due to the differences in the ethanol metabolism and acetaldehyde oxidation of these enzyme variants