Joint Analysis for Integrating Two Related Studies of Different Data Types and Different Study Designs Using Hierarchical Modeling Approaches
Author(s) -
Rui Li,
David V. Conti,
David Díaz-Sánchez,
Frank D. Gilliland,
Duncan C. Thomas
Publication year - 2012
Publication title -
human heredity
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.423
H-Index - 62
eISSN - 1423-0062
pISSN - 0001-5652
DOI - 10.1159/000345181
Subject(s) - computational biology , bayesian probability , gene , computer science , biology , genetics , artificial intelligence
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.
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