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Updated: Sep 24, 2025

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
Published on: July 1, 2020
Pathway-specific population attributable fractions
Maurice M O'Connell1, John P Ferguson1
1Biostatistics Unit, HRB Clinical Research Facility Galway, School of Medicine, NUI Galway, Galway, Ireland.
Introduction:
A population attributable fraction represents the relative change in disease prevalence that one might expect if a particular exposure was absent from the population. Often, one might be interested in what percentage of this effect acts through particular pathways. For instance, the effect of a sedentary lifestyle on stroke risk may be mediated by blood pressure, body mass index and several other intermediate risk factors.
Methods:
We define a new metric, the pathway-specific population attributable fraction (PS-PAF), for mediating pathways of interest. PS-PAFs can be informally defined as the relative change in disease prevalence from an intervention that shifts the distribution of the mediator to its expected distribution if the risk factor were eliminated, and sometimes more simply as the relative change in disease prevalence if the mediating pathway were disabled. A potential outcomes framework is used for formal definitions and associated estimands are derived via relevant identifiability conditions. Computationally efficient estimators for PS-PAFs are derived based on these identifiability conditions.
Results:
Calculations are demonstrated using INTERSTROKE-an international case-control study designed to quantify disease burden attributable to a number of known causal risk factors. The applied results suggest that mediating pathways from physical activity through blood pressure, blood lipids and body size explain comparable proportions of stroke disease burden, but a large proportion of the disease burden due to physical inactivity may be explained by alternative pathways.
Conclusion:
PS-PAFs measure disease burden attributable to differing mediating pathways and can generate insights into the dominant mechanisms by which a risk factor affects disease at a population level.
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