Survival dimensionality reduction (SDR): development and clinical application of an innovative approach to detect epistasis in presence of right-censored data

• Main Authors: Beretta Lorenzo, Santaniello Alessandro, van Riel Piet LCM, Coenen Marieke JH, Scorza Raffaella
• Format: Article
• Language: English
• Published: BMC 2010-08-01
• Series: BMC Bioinformatics
• Online Access: http://www.biomedcentral.com/1471-2105/11/416

<p>Abstract</p> <p>Background</p> <p>Epistasis is recognized as a fundamental part of the genetic architecture of individuals. Several computational approaches have been developed to model gene-gene interactions in case-control studies, however, none of them is suitable for time-dependent analysis. Herein we introduce the Survival Dimensionality Reduction (SDR) algorithm, a non-parametric method specifically designed to detect epistasis in lifetime datasets.</p> <p>Results</p> <p>The algorithm requires neither specification about the underlying survival distribution nor about the underlying interaction model and proved satisfactorily powerful to detect a set of causative genes in synthetic epistatic lifetime datasets with a limited number of samples and high degree of right-censorship (up to 70%). The SDR method was then applied to a series of 386 Dutch patients with active rheumatoid arthritis that were treated with anti-TNF biological agents. Among a set of 39 candidate genes, none of which showed a detectable marginal effect on anti-TNF responses, the SDR algorithm did find that the rs1801274 SNP in the FcγRIIa gene and the rs10954213 SNP in the IRF5 gene non-linearly interact to predict clinical remission after anti-TNF biologicals.</p> <p>Conclusions</p> <p>Simulation studies and application in a real-world setting support the capability of the SDR algorithm to model epistatic interactions in candidate-genes studies in presence of right-censored data.</p> <p>Availability: <url>http://sourceforge.net/projects/sdrproject/</url></p>