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

Guilford County Solution to the Opioid Problem (GCSTOP): A Model for University/Community Partnerships

There were over 700 overdoses and 180 deaths from opioids in Guilford County, NC in 2017. The Guilford Solution to the Opioid Problem (GSTOP) project leverages funds allocated by the STOP-Act to design, implement, and evaluate a rapid response program intended to decrease mortality from opioid overdoses. The program engages citizens who overdose in harm reduction practices, distributes naloxone kits to high-risk users, conducts community health education, coordinates community resources through the CURE Triad collaborative, and builds relationships focused on ending opioid overdose. This presentation will review the development of the partnership between Guilford County Emergency Medical Services and the University of North Carolina at Greensboro that has resulted in the GSTOP demonstration project. The presentation included background on the opioid epidemic in Guilford County, the development of CURE Triad (a community coalition to address overdoses) and the implementation of GSTOP, the unique features of hosting such a program within a university, the evaluation design, and preliminary outcomes of the program

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

A wing expressed sequence tag resource for Bicyclus anynana butterflies, an evo-devo model

BACKGROUND: Butterfly wing color patterns are a key model for integrating evolutionary developmental biology and the study of adaptive morphological evolution. Yet, despite the biological, economical and educational value of butterflies they are still relatively under-represented in terms of available genomic resources. Here, we describe an Expression Sequence Tag (EST) project for Bicyclus anynana that has identified the largest available collection to date of expressed genes for any butterfly. RESULTS: By targeting cDNAs from developing wings at the stages when pattern is specified, we biased gene discovery towards genes potentially involved in pattern formation. Assembly of 9,903 ESTs from a subtracted library allowed us to identify 4,251 genes of which 2,461 were annotated based on BLAST analyses against relevant gene collections. Gene prediction software identified 2,202 peptides, of which 215 longer than 100 amino acids had no homology to any known proteins and, thus, potentially represent novel or highly diverged butterfly genes. We combined gene and Single Nucleotide Polymorphism (SNP) identification by constructing cDNA libraries from pools of outbred individuals, and by sequencing clones from the 3' end to maximize alignment depth. Alignments of multi-member contigs allowed us to identify over 14,000 putative SNPs, with 316 genes having at least one high confidence double-hit SNP. We furthermore identified 320 microsatellites in transcribed genes that can potentially be used as genetic markers. CONCLUSION: Our project was designed to combine gene and sequence polymorphism discovery and has generated the largest gene collection available for any butterfly and many potential markers in expressed genes. These resources will be invaluable for exploring the potential of B. anynana in particular, and butterflies in general, as models in ecological, evolutionary, and developmental genetics