Document Type

Article

Publication Date

8-1-2017

Publication Title

Nature biotechnology

Keywords

Biomarkers; Computational Biology; Databases, Factual; Exercise; Genome-Wide Association Study; Humans; Longitudinal Studies; Metabolome; Microbiota; Models, Statistical; Monitoring, Physiologic; Neoplasms; Nutritional Status; Proteome

Abstract

Personal data for 108 individuals were collected during a 9-month period, including whole genome sequences; clinical tests, metabolomes, proteomes, and microbiomes at three time points; and daily activity tracking. Using all of these data, we generated a correlation network that revealed communities of related analytes associated with physiology and disease. Connectivity within analyte communities enabled the identification of known and candidate biomarkers (e.g., gamma-glutamyltyrosine was densely interconnected with clinical analytes for cardiometabolic disease). We calculated polygenic scores from genome-wide association studies (GWAS) for 127 traits and diseases, and used these to discover molecular correlates of polygenic risk (e.g., genetic risk for inflammatory bowel disease was negatively correlated with plasma cystine). Finally, behavioral coaching informed by personal data helped participants to improve clinical biomarkers. Our results show that measurement of personal data clouds over time can improve our understanding of health and disease, including early transitions to disease states.

Department

Institute for Systems Biology

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