Detection for pathway effect contributing to disease in systems epidemiology with a case-control design.

Abstract

Identification of pathway effects responsible for specific diseases has been one of the essential tasks in systems epidemiology. Despite some advance in procedures for distinguishing specific pathway (or network) topology between different disease status, statistical inference at a population level remains unsolved and further development is still needed. To identify the specific pathways contributing to diseases, we attempt to develop powerful statistics which can capture the complex relationship among risk factors. Acute myeloid leukaemia (AML) data obtained from 133 adults (98 patients and 35 controls; 47% female). Simulation studies indicated that the proposed Pathway Effect Measures (PEM) were stable; bootstrap-based methods outperformed the others, with bias-corrected bootstrap CI method having the highest power. Application to real data of AML successfully identified the specific pathway (Treg→TGFβ→Th17) effect contributing to AML with p values less than 0.05 under various methods and the bias-corrected bootstrap CI (-0.214 to -0.020). It demonstrated that Th17-Treg correlation balance was impaired in patients with AML, suggesting that Th17-Treg imbalance potentially plays a role in the pathogenesis of AML. The proposed bootstrap-based PEM are valid and powerful for detecting the specific pathway effect contributing to disease, thus potentially providing new insight into the underlying mechanisms and ways to study the disease effects of specific pathways more comprehensively.