A systems biology network model for genetic association studies of nicotine addiction and treatment.
OBJECTIVE
Interpreting genome-scale genetic association data, particularly for complex diseases and phenotypes, requires extensive use of prior knowledge across a broad range of potential biological and environmental influences, spanning many scientific subdisciplines. We suggest that known or hypothesized disease risk factors, and causal mechanisms, can be represented using an ontology, a computational specification of a set of concepts and the relations between them.


METHODS
We have integrated the expertise of multiple investigators in nicotine pharmacokinetics and pharmacodynamics, nicotine dependence, and clinical smoking cessation outcomes, and represented this knowledge in an ontology-based network model. Our model spans multiple scales, from molecules, genes and cellular pathways, to complex behavioral phenotypes and even environmental factors. To leverage previous and ongoing work in the field of ontology development, we adopt, expand upon and relate elements from existing ontologies whenever possible.


RESULTS
We discuss several applications of our ontology: to support interdisciplinary research by graphically representing a complex scientific theory, to facilitate meta-analysis across different studies, to highlight potential interactions, and to support statistical analysis and causal modeling. We demonstrate that our ontology can focus hypothesis testing on areas supported by current theory.


CONCLUSIONS
We describe how an ontology-based computational representation can be applied to disease risk factors and mechanisms, enabling the use of prior knowledge in large-scale genetic association studies in general. In specific, we have developed an initial Smoking Behavior Risk Ontology to support studies related to the pharmacogenetics of nicotine addiction and treatment.
Thomas
Paul D
PD
Evolutionary Systems Biology, Artificial Intelligence Center, SRI International, Menlo Park, CA 94025, USA. paul.thomas@sri.com
Mi
Huaiyu
H
Swan
Gary E
GE
Lerman
Caryn
C
Benowitz
Neal
N
Tyndale
Rachel F
RF
Bergen
Andrew W
AW
Conti
David V
DV
D030541:Databases, Genetic; D020022:Genetic Predisposition to Disease; D006801:Humans; D008099:Liver / Q000378:metabolism; D008957:Models, Genetic; D010641:Phenotype; D015203:Reproducibility of Results; D012907:Smoking / Q000235:genetics; D049490:Systems Biology; D014029:Tobacco Use Disorder / Q000235:genetics* / Q000628:therapy*