Extension of the survival dimensionality reduction algorithm to detect epistasis in competing risks models (SDR-CR).

Background: The discovery and the description of the genetic background of common human diseases is hampered by their complexity and dynamic behavior. Appropriate bioinformatic tools are needed to account all the facets of complex diseases and to this end we recently described the survival dimensionality reduction (SDR) algorithm in the effort to model gene-gene interactions in the context of survival analysis. When one event precludes the occurrence of another event under investigation in the 'competing risk model', survival algorithms require particular adjustment to avoid the risk of reporting wrong or biased conclusions.

Methods: The SDR algorithm was modified to incorporate the cumulative incidence function as well as an adapted version of the Brier score for mutually exclusive outcomes, to better search for epistatic models in the competing risk setting. The applicability of the new SDR algorithm (SDR-CR) was evaluated using synthetic lifetime epistatic datasets with competing risks and on a dataset of scleroderma patients.

Results/conclusions: The SDR-CR algorithms retains a satisfactory power to detect the causative variants in simulated datasets under different scenarios of sample size and degrees of type I or type II censoring. In the real-world dataset, SDR-CR was capable of detecting a significant interaction between the IL-1α C-889T and the IL-1β C-511T single-nucleotide polymorphisms to predict the occurrence of restrictive lung disease vs. isolated pulmonary hypertension. We provide an useful extension of the SDR algorithm to analyze epistatic interactions in the competing risk settings that may be of use to unveil the genetic background of complex human diseases.

Availability: http://sourceforge.net/projects/sdrproject/files/.