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A B ayesian Integrative Genomic Model for Pathway Analysis of Complex Traits
Author(s) -
Fridley Brooke L.,
Lund Steven,
Jenkins Gregory D.,
Wang Liewei
Publication year - 2012
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.21628
Subject(s) - computational biology , bayesian probability , pharmacogenomics , computer science , path analysis (statistics) , data type , biology , genetics , machine learning , artificial intelligence , programming language
With new technologies, multiple types of genomic data are commonly collected on a single set of samples. However, standard analysis methods concentrate on a single data type at a time and ignore the relationships between genes, proteins, and biochemical reactions that give rise to complex phenotypes. In this paper, we propose a novel integrative model to incorporate multiple types of genomic data into an association analysis with a complex phenotype. The method combines path analysis and stochastic search variable selection into a B ayesian hierarchical model that simultaneously identifies both direct and indirect genomic effects on the phenotype. Results from a simulation study and application of the B ayesian model to a pharmacogenomic study of the drug gemcitabine demonstrate greater sensitivity to detect genomic effects in some simulation scenarios, when compared to the standard single data type analysis. Further research is required to extend and modify this integrative modeling framework to increase computational efficiency to best capitalize on the wealth of information available across multiple genomic data types. Genet. Epidemiol. 36:352–359, 2012. © 2012 Wiley Periodicals, Inc.