GPDTI: a Genetic Programming Decision Tree induction method to find epistatic effects in common complex diseases.

MOTIVATION: The identification of risk-associated genetic variants in common diseases remains a challenge to the biomedical research community. It has been suggested that common statistical approaches that exclusively measure main effects are often unable to detect interactions between some of these variants. Detecting and interpreting interactions is a challenging open problem from the statistical and computational perspectives. Methods in computing science may improve our understanding on the mechanisms of genetic disease by detecting interactions even in the presence of very low heritabilities. RESULTS: We have implemented a method using Genetic Programming that is able to induce a Decision Tree to detect interactions in genetic variants. This method has a cross-validation strategy for estimating classification and prediction errors and tests for consistencies in the results. To have better estimates, a new consistency measure that takes into account interactions and can be used in a genetic programming environment is proposed. This method detected five different interaction models with heritabilities as low as 0.008 and with prediction errors similar to the generated errors. AVAILABILITY: Information on the generated data sets and executable code is available upon request.

Association of PDCD1 polymorphisms with childhood-onset systemic lupus erythematosus.

A regulatory single nucleotide polymorphism (SNP) PD1.3G/A located on programmed cell death 1 (PDCD1) gene, was shown to be involved in susceptibility to systemic lupus erythematosus (SLE) in Swedish, European American, and Mexican cases. However, association to childhood-onset SLE has not been analyzed. The aim of this study was to investigate the association of PDCD1 polymorphisms and haplotypes with susceptibility to childhood-onset SLE in Mexican population. Three PDCD1 SNPs, PD1.3G/A, PD1.5C/T, PD1.6G/A, were analyzed in 250 childhood-onset SLE Mexican patients and 355 healthy controls in a case-control association study. Polymorphisms were genotyped by TaqMan technology. Stratification analysis was performed on the SLE cohort to investigate the SNP association with renal disorder. In addition, haplotypes were constructed with these three SNPs. The PD1.3A allele was significantly associated to childhood-onset SLE (P=0.0019, odds ratio (OR) 2.73, 95% confidence interval (95% CI) 1.35-5.56). The other PDCD1 SNPs did not show association. A total of 155 patients (62%) had nephritis, and no association was observed with PDCD1 SNPs. The ACG haplotype (PD1.3A, PD1.5C, PD1.6G) included almost all PD1.3A alleles, and it was more frequent in SLE patients (5.5%) than in controls (2.1%) (P=0.003; OR 2.73, 95% CI 1.37-5.46). The haplotype structure in Mexican controls was significantly different from those reported in Spanish and Swedish. Our results support association of the PD1.3A SNP to susceptibility of childhood-onset SLE in Mexican population and does not show association to lupus nephritis in this age group.