title: A powerful score-based test statistic for detecting gene-gene co-association.
abstract: BACKGROUND
  The genetic variants identified by Genome-wide association study (GWAS) can only account for a small proportion of the total heritability for complex disease. The existence of gene-gene joint effects which contains the main effects and their co-association is one of the possible explanations for the "missing heritability" problems. Gene-gene co-association refers to the extent to which the joint effects of two genes differ from the main effects, not only due to the traditional interaction under nearly independent condition but the correlation between genes. Generally, genes tend to work collaboratively within specific pathway or network contributing to the disease and the specific disease-associated locus will often be highly correlated (e.g. single nucleotide polymorphisms (SNPs) in linkage disequilibrium). Therefore, we proposed a novel score-based statistic (SBS) as a gene-based method for detecting gene-gene co-association.
  
  
  RESULTS
  Various simulations illustrate that, under different sample sizes, marginal effects of causal SNPs and co-association levels, the proposed SBS has the better performance than other existed methods including single SNP-based and principle component analysis (PCA)-based logistic regression model, the statistics based on canonical correlations (CCU), kernel canonical correlation analysis (KCCU), partial least squares path modeling (PLSPM) and delta-square (δ (2)) statistic. The real data analysis of rheumatoid arthritis (RA) further confirmed its advantages in practice.
  
  
  CONCLUSIONS
  SBS is a powerful and efficient gene-based method for detecting gene-gene co-association.
authors.affiliation: Department of Biostatistics, School of Public Health, Shandong University, 44 Wen Hua Xi Road, PO Box 100, Jinan, 250012, China. xujinghello@163.com.
authors.forename: Jing
authors.initials: J
authors.lastname: Xu
authors.affiliation: Department of Biostatistics, School of Public Health, Shandong University, 44 Wen Hua Xi Road, PO Box 100, Jinan, 250012, China. yuanzhongshang@sdu.edu.cn.
authors.forename: Zhongshang
authors.initials: Z
authors.lastname: Yuan
authors.affiliation: Department of Biostatistics, School of Public Health, Shandong University, 44 Wen Hua Xi Road, PO Box 100, Jinan, 250012, China. jjdjijiadong@163.com.
authors.forename: Jiadong
authors.initials: J
authors.lastname: Ji
authors.affiliation: Department of Biostatistics, School of Public Health, Shandong University, 44 Wen Hua Xi Road, PO Box 100, Jinan, 250012, China. zhxiaoshuai@hotmail.com.
authors.forename: Xiaoshuai
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Department of Biostatistics, School of Public Health, Shandong University, 44 Wen Hua Xi Road, PO Box 100, Jinan, 250012, China. lihongkaiyouxiang@163.com.
authors.forename: Hongkai
authors.initials: H
authors.lastname: Li
authors.affiliation: Department of Epidemiology and Statistics, Bengbu Medical College at Bengbu, Anhui, 233030, China. xuesenwu@163.com.
authors.forename: Xuesen
authors.initials: X
authors.lastname: Wu
authors.affiliation: Department of Biostatistics, School of Public Health, Shandong University, 44 Wen Hua Xi Road, PO Box 100, Jinan, 250012, China. xuefzh@sdu.edu.cn.
authors.forename: Fuzhong
authors.initials: F
authors.lastname: Xue
authors.affiliation: Department of Biostatistics, School of Public Health, Shandong University, 44 Wen Hua Xi Road, PO Box 100, Jinan, 250012, China. liu-yx@sdu.edu.cn.
authors.forename: Yanxun
authors.initials: Y
authors.lastname: Liu
mesh_terms: D001172:Arthritis, Rheumatoid / Q000235:genetics; D003198:Computer Simulation; D053263:Gene Regulatory Networks; D020022:Genetic Predisposition to Disease / Q000235:genetics; D055106:Genome-Wide Association Study; D006801:Humans; D040582:Inheritance Patterns; D008957:Models, Genetic; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide; D025341:Principal Component Analysis
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