Summarizing techniques that combine three non-parametric scores to detect disease-associated 2-way SNP-SNP interactions.
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.
Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei, Taiwan; Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan; Institute of Information Science, Academia Sinica, Taipei, Taiwan; Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.
Amrita
A
Sengupta Chattopadhyay
Ching-Lin
CL
Hsiao
Chien Ching
CC
Chang
Ie-Bin
IeB
Lian
Cathy S J
CS
Fann
D006801:Humans; D020641:Polymorphism, Single Nucleotide; D025341:Principal Component Analysis; D011336:Probability
APD; APD score; APDS; BDNF; C5; CART; CASP 9; CCP; CV; ES; GAW16; GS; GWAS; Gini score; HLA; HLA-DQB1; HLA-DRB1; KEGG; LD; MAF; MDR; Max; NARAC; NN; NTRK2; Non-parametric methods; North American Rheumatoid Arthritis Consortium; PC1; PCS; PIA; PTPN22; QC; RA; RASSUN; RAnked Summarized Scores Using Non-parametric-methods; Rheumatoid arthritis (RA); SNP; SNP-SNP interaction; SS; SSS; Single-nucleotide-polymorphism (SNP); Std Dev; Summary scores; TNF-receptor-associated factor 1; TRAF1; Z-score; Z-sum score; ZS; ZSS; absolute probability difference; brain derived neurotrophic factor; caspase 9; classification and regression trees; compliment component; cross-validation; cyclic citrullinated peptide; entropy score; genetic- analysis-workshop 16; genome wide association study; human leukocyte antigens; kyoto encyclopedia of genes and genomes; linkage disequilibrium; major hiscompatibility complex class II, DQ beta 1; major hiscompatibility complex class II, DR beta 1; maximum; minor allele frequency; multifactor dimensionality reduction; neural networks; neurotrophic tyrosine kinase, receptor, type 2; polymorphism interaction analysis; principal component 1; principle component score; protein tyrosine phosphatase, non-receptor type 22 lymphoid; quality control; rheumatoid arthritis; scaled score; single-nucleotide-polymorphism; standard deviation; sum of scaled scores