关键词： 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

Mesh ： Humans Polymorphism, Single Nucleotide Principal Component Analysis Probability

来  源：   DOI：10.1016/j.gene.2013.09.041   PDF(Sci-hub)

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.
摘要：
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