Data-driven integration of epidemiological and toxicological data to select candidate interacting genes and environmental factors in association with disease.
Complex diseases, such as Type 2 Diabetes Mellitus (T2D), result from the interplay of both environmental and genetic factors. However, most studies investigate either the genetics or the environment and there are a few that study their possible interaction in context of disease. One key challenge in documenting interactions between genes and environment includes choosing which of each to test jointly. Here, we attempt to address this challenge through a data-driven integration of epidemiological and toxicological studies. Specifically, we derive lists of candidate interacting genetic and environmental factors by integrating findings from genome-wide and environment-wide association studies. Next, we search for evidence of toxicological relationships between these genetic and environmental factors that may have an etiological role in the disease. We illustrate our method by selecting candidate interacting factors for T2D.
Division of Systems Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Chirag J
CJ
Patel
Rong
R
Chen
Atul J
AJ
Butte
D019295:Computational Biology / Q000379:methods; D030541:Databases, Genetic; D003924:Diabetes Mellitus, Type 2 / Q000235:genetics*; D004777:Environment; D059647:Gene-Environment Interaction; D055106:Genome-Wide Association Study; D006801:Humans; D043922:Toxicogenetics / Q000379:methods