Journal article
Bayesian inference of gene-environment interaction from incomplete data: what happens when information on environment is disjoint from data on gene and disease?
Statistics in medicine, v 30(8), pp 877-889
15 Apr 2011
PMID: 21432881
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Inference in gene-environment studies can sometimes exploit the assumption of mendelian randomization that genotype and environmental exposure are independent in the population under study. Moreover, in some such problems it is reasonable to assume that the disease risk for subjects without environmental exposure will not vary with genotype. When both assumptions can be invoked, we consider the prospects for inferring the dependence of disease risk on genotype and environmental exposure (and particularly the extent of any gene-environment interaction), without detailed data on environmental exposure. The data structure envisioned involves data on disease and genotype jointly, but only external information about the distribution of the environmental exposure in the population. This is relevant as for many environmental exposures individual-level measurements are costly and/or highly error-prone. Working in the setting where all relevant variables are binary, we examine the extent to which such data are informative about the interaction, via determination of the large-sample limit of the posterior distribution. The ideas are illustrated using data from a case-control study for bladder cancer involving smoking behaviour and the NAT2 genotype.
Metrics
Details
- Title
- Bayesian inference of gene-environment interaction from incomplete data: what happens when information on environment is disjoint from data on gene and disease?
- Creators
- Paul Gustafson - Department of Statistics, University of British Columbia, 333-6356 Agricultural Road, Vancouver, BC, Canada V6T 1Z2. gustaf@stat.ubc.caIgor Burstyn
- Publication Details
- Statistics in medicine, v 30(8), pp 877-889
- Publisher
- Wiley; England
- Grant note
- 62863 / Canadian Institutes of Health Research
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Environmental and Occupational Health
- Web of Science ID
- WOS:000288859500007
- Scopus ID
- 2-s2.0-79953022073
- Other Identifier
- 991014878045804721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Mathematical & Computational Biology
- Medical Informatics
- Medicine, Research & Experimental
- Public, Environmental & Occupational Health
- Statistics & Probability