Journal
NATURE BIOTECHNOLOGY
Volume 35, Issue 8, Pages 747-756Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/nbt.3870
Keywords
-
Categories
Funding
- Robert Wood Johnson Foundation
- M.J. Murdock Charitable Trust
- NIH [2P50GM076547, P30ES017885, U24CA210967, RC2HG005805]
- Arivale
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available