Wellness KP

Description: The Multiomics Wellness KP, created and maintained by the Multiomics Provider, provides observational knowledge derived through computation of correlations between many blood analytes. The original multiomic dataset was created through a wellness study that monitored the blood analytes and general wellness of a cohort of individuals that were largely healthy. Edges in this KP represent significant correlations between pairs of analytes.

Example: The Multiomics Wellness KP shows that the level of mercury observed in blood is strongly (positively) correlated with the level of docosahexanoic acid (DHA), a lipid found in fish and fish oil supplements. This correlation is consistent with mercury intake from contaminated fish. The strength of the correlation (Spearman rho) is 0.527, with p-value of 3E-221, as observed among N = 3197 individuals. The correlation is stronger among males (rho = 0.557, N = 1310) than among women (rho = 0.505, N = 1885). There is also an age dependence, with stronger correlation among individuals younger than 35 (rho = 0.588, N = 540) than among those between 35 and 55 (rho = 0.532, N = 1723) and those older than 55 (rho = 0.479, N = 932).

Key methodologic metrics provided on edges: The KP presents, for each correlation, statistical metrics like the strength of the correlation (Spearman rho), the p-value (Bonferroni-corrected for multiple testing), the number of individuals that contributed to the calculation (N), and details on what stratification was used, if any.

The graph currently uses biolink:correlated_with as predicate. Node categories include biolink:ClinicalFinding, biolink:Protein, and biolink:SmallMolecule.

Modes of access: This BioThings API does not comply to the TRAPI standard. However, in collaboration with the Exploring Agent team, this BioThings API is accessible as a TRAPI KP service through:

• BioThings Explorer: https://biothings.ncats.io/multiomics_wellness_kp
• SmartAPI: https://smart-api.info/ui/02af7d098ab304e80d6f4806c3527027

In these situations, BioThings Explorer acts as a "TRAPI wrapper/transformer" that queries the BioThings APIs and processes their responses into knowledge-graphs that follow TRAPI and biolink-model standards (for node categories, edge predicates, etc).

Data Sources Used -

• ISB's Wellness dataset