Summarizing techniques that combine three non-parametric scores to detect disease-associated 2-way SNP-SNP interactions

• ZSS and PCS combine Gini, APD and entropy to detect gene-gene interactions.
• ZSS and PCS show high power (~> 0.75) under most epistatic models.
• ZSS and PCS control type-I-error rates (< 0.05).
• ZSS and PCS identify rs7745656 (HLA-DQB1) ∗ rs9275572 (HLA-DRB5) from RA dataset.

Identifying susceptibility genes that influence complex diseases is extremely difficult because loci often influence the disease state through genetic interactions. Numerous approaches to detect disease-associated SNP-SNP interactions have been developed, but none consistently generates high-quality results under different disease scenarios. Using summarizing techniques to combine a number of existing methods may provide a solution to this problem. Here we used three popular non-parametric methods—Gini, absolute probability difference (APD), and entropy—to develop two novel summary scores, namely principle component score (PCS) and Z-sum score (ZSS), with which to predict disease-associated genetic interactions. We used a simulation study to compare performance of the non-parametric scores, the summary scores, the scaled-sum score (SSS; used in polymorphism interaction analysis (PIA)), and the multifactor dimensionality reduction (MDR). The non-parametric methods achieved high power, but no non-parametric method outperformed all others under a variety of epistatic scenarios. PCS and ZSS, however, outperformed MDR. PCS, ZSS and SSS displayed controlled type-I-errors (< 0.05) compared to GS, APDS, ES (> 0.05). A real data study using the genetic-analysis-workshop 16 (GAW 16) rheumatoid arthritis dataset identified a number of interesting SNP-SNP interactions.