Creation and implications of a phenome-genome network.
Although gene and protein measurements are increasing in quantity and comprehensiveness, they do not characterize a sample's entire phenotype in an environmental or experimental context. Here we comprehensively consider associations between components of phenotype, genotype and environment to identify genes that may govern phenotype and responses to the environment. Context from the annotations of gene expression data sets in the Gene Expression Omnibus is represented using the Unified Medical Language System, a compendium of biomedical vocabularies with nearly 1-million concepts. After showing how data sets can be clustered by annotative concepts, we find a network of relations between phenotypic, disease, environmental and experimental contexts as well as genes with differential expression associated with these concepts. We identify novel genes related to concepts such as aging. Comprehensively identifying genes related to phenotype and environment is a step toward the Human Phenome Project.
Butte
Atul J
AJ
Stanford Medical Informatics, Department of Medicine, Stanford University School of Medicine, 251 Campus Drive, Room X-215, Stanford, California 94305-5479, USA. abutte@stanford.edu
Kohane
Isaac S
IS
D000375:Aging / Q000502:physiology; D000818:Animals; D001185:Artificial Intelligence; D016000:Cluster Analysis; D030562:Databases, Protein; D004777:Environment; D005786:Gene Expression Regulation / Q000502:physiology*; D023281:Genomics / Q000379:methods*; D051379:Mice; D008810:Mice, Inbred C57BL; D008954:Models, Biological; D009124:Muscle Proteins / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology*; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods; D025941:Protein Interaction Mapping / Q000379:methods; D015398:Signal Transduction / Q000502:physiology*; D017432:Unified Medical Language System
D009124:Muscle Proteins