Sengupta Chattopadhyay A, Hsiao CL, Chang CC, Lian IeB, Fann CS. Summarizing techniques that combine three non-parametric scores to detect disease-associated 2-way SNP-SNP interactions.
Gene 2014;
533:304-12. [PMID:
24076437 DOI:
10.1016/j.gene.2013.09.041]
[Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2013] [Revised: 08/30/2013] [Accepted: 09/09/2013] [Indexed: 10/26/2022]
Abstract
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.
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