Document Detail

Joint analysis for integrating two related studies of different data types and different study designs using hierarchical modeling approaches.
MedLine Citation:
PMID:  23343600     Owner:  NLM     Status:  MEDLINE    
BACKGROUND: A chronic disease such as asthma is the result of a complex sequence of biological interactions involving multiple genes and pathways in response to a multitude of environmental exposures. However, methods to model jointly all factors are still evolving. Some of the current challenges include how to integrate knowledge from different data types and different disciplines, as well as how to utilize relevant external information such as gene annotation to identify novel disease genes and gene-environment inter-actions.
METHODS: Using a Bayesian hierarchical modeling framework, we developed two alternative methods for joint analysis of an epidemiologic study of a disease endpoint and an experimental study of intermediate phenotypes, while incorporating external information.
RESULTS: Our simulation studies demonstrated superior performance of the proposed hierarchical models compared to separate analysis with the standard single-level regression modeling approach. The combined analyses of the Southern California Children's Health Study and challenge study data suggest that these joint analytical methods detected more significant genetic main and gene-environment interaction effects than the conventional analysis.
CONCLUSION: The proposed prior framework is very flexible and can be generalized for an integrative analysis of diverse sources of relevant biological data.
Rui Li; David V Conti; David Diaz-Sanchez; Frank Gilliland; Duncan C Thomas
Publication Detail:
Type:  Journal Article     Date:  2013-01-18
Journal Detail:
Title:  Human heredity     Volume:  74     ISSN:  1423-0062     ISO Abbreviation:  Hum. Hered.     Publication Date:  2012  
Date Detail:
Created Date:  2013-04-03     Completed Date:  2013-09-23     Revised Date:  2014-03-24    
Medline Journal Info:
Nlm Unique ID:  0200525     Medline TA:  Hum Hered     Country:  Switzerland    
Other Details:
Languages:  eng     Pagination:  83-96     Citation Subset:  IM    
Copyright Information:
Copyright © 2013 S. Karger AG, Basel.
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MeSH Terms
Asthma / epidemiology,  genetics
Bayes Theorem
Biological Markers
Data Interpretation, Statistical*
Epidemiologic Studies
Gene-Environment Interaction
Models, Statistical*
Research Design
Grant Support
Reg. No./Substance:
0/Biological Markers

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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