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

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Resumo

CKGROUND: 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-1a C-889T and the IL-1? 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/.

Resumo Limpo

ckground discoveri descript genet background common human diseas hamper complex dynam behavior appropri bioinformat tool need account facet complex diseas end recent describ surviv dimension reduct sdr algorithm effort model genegen interact context surviv analysi one event preclud occurr anoth event investig compet risk model surviv algorithm requir particular adjust avoid risk report wrong bias conclusionsmethod sdr algorithm modifi incorpor cumul incid function well adapt version brier score mutual exclus outcom better search epistat model compet risk set applic new sdr algorithm sdrcr evalu use synthet lifetim epistat dataset compet risk dataset scleroderma patientsresultsconclus sdrcr algorithm retain satisfactori power detect causat variant simul dataset differ scenario sampl size degre type type ii censor realworld dataset sdrcr capabl detect signific interact ila ct il ct singlenucleotid polymorph predict occurr restrict lung diseas vs isol pulmonari hypertens provid use extens sdr algorithm analyz epistat interact compet risk set may use unveil genet background complex human diseasesavail httpsourceforgenetprojectssdrprojectfil

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