Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes

Personalized medicine is expected to benefit from combining genomic information with regular monitoring of physiological states by multiple high-throughput methods. Here, we present an integrative personal omics profile (iPOP), an analysis that combines genomic, transcriptomic, proteomic, metabolomic, and autoantibody profiles from a single individual over a 14 month period. Our iPOP analysis revealed various medical risks, including type 2 diabetes. It also uncovered extensive, dynamic changes in diverse molecular components and biological pathways across healthy and diseased conditions. Extremely high-coverage genomic and transcriptomic data, which provide the basis of our iPOP, revealed extensive heteroallelic changes during healthy and diseased states and an unexpected RNA editing mechanism. This study demonstrates that longitudinal iPOP can be used to interpret healthy and diseased states by connecting genomic information with additional dynamic omics activity. [Display omitted] ► Physiological states analyzed by integrative personal omics profiling ► Extensive molecular changes revealed during different health states ► Individual disease risk predicted from integrated omics data ► Extensive heteroallele and RNA editing during healthy and disease states A personalized medicine pilot study samples a patient's transcriptome, proteome, and metabolome multiple times over the course of 14 months and integrates this information with whole-genome sequence data to predict risk and provide a comprehensive view of healthy and disease states, including two viral infections and the onset of type 2 diabetes.