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Bayesian inference of gene-environment interaction from incomplete data: what happens when information on environment is disjoint from data on gene and disease?
- Source :
-
Statistics in medicine [Stat Med] 2011 Apr 15; Vol. 30 (8), pp. 877-89. Date of Electronic Publication: 2011 Jan 13. - Publication Year :
- 2011
-
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.<br /> (Copyright © 2011 John Wiley & Sons, Ltd.)
- Subjects :
- Arylamine N-Acetyltransferase genetics
Biostatistics
Data Interpretation, Statistical
Genetic Predisposition to Disease
Genotype
Humans
Models, Statistical
Random Allocation
Retrospective Studies
Risk Factors
Smoking adverse effects
Urinary Bladder Neoplasms etiology
Urinary Bladder Neoplasms genetics
Bayes Theorem
Environmental Exposure adverse effects
Models, Genetic
Subjects
Details
- Language :
- English
- ISSN :
- 1097-0258
- Volume :
- 30
- Issue :
- 8
- Database :
- MEDLINE
- Journal :
- Statistics in medicine
- Publication Type :
- Academic Journal
- Accession number :
- 21432881
- Full Text :
- https://doi.org/10.1002/sim.4176