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

Amrita Sengupta Chattopadhyay, Ching Lin Hsiao, Chien Ching Chang, Ie Bin Lian, Cathy S.J. Fann

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)304-312
Number of pages9
Issue number1
Publication statusPublished - 2014 Jan 1

All Science Journal Classification (ASJC) codes

  • Genetics

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