title: A novel statistic for genome-wide interaction analysis.
abstract: Although great progress in genome-wide association studies (GWAS) has been made, the significant SNP associations identified by GWAS account for only a few percent of the genetic variance, leading many to question where and how we can find the missing heritability. There is increasing interest in genome-wide interaction analysis as a possible source of finding heritability unexplained by current GWAS. However, the existing statistics for testing interaction have low power for genome-wide interaction analysis. To meet challenges raised by genome-wide interactional analysis, we have developed a novel statistic for testing interaction between two loci (either linked or unlinked). The null distribution and the type I error rates of the new statistic for testing interaction are validated using simulations. Extensive power studies show that the developed statistic has much higher power to detect interaction than classical logistic regression. The results identified 44 and 211 pairs of SNPs showing significant evidence of interactions with FDR<0.001 and 0.001<FDR<0.003, respectively, which were seen in two independent studies of psoriasis. These included five interacting pairs of SNPs in genes LST1/NCR3, CXCR5/BCL9L, and GLS2, some of which were located in the target sites of miR-324-3p, miR-433, and miR-382, as well as 15 pairs of interacting SNPs that had nonsynonymous substitutions. Our results demonstrated that genome-wide interaction analysis is a valuable tool for finding remaining missing heritability unexplained by the current GWAS, and the developed novel statistic is able to search significant interaction between SNPs across the genome. Real data analysis showed that the results of genome-wide interaction analysis can be replicated in two independent studies.
authors.affiliation: Department of Epidemiology and Statistics, Bengbu Medical College at Bengbu, Anhui, China.
authors.forename: Xuesen
authors.initials: X
authors.lastname: Wu
authors.forename: Hua
authors.initials: H
authors.lastname: Dong
authors.forename: Li
authors.initials: L
authors.lastname: Luo
authors.forename: Yun
authors.initials: Y
authors.lastname: Zhu
authors.forename: Gang
authors.initials: G
authors.lastname: Peng
authors.forename: John D
authors.initials: JD
authors.lastname: Reveille
authors.forename: Momiao
authors.initials: M
authors.lastname: Xiong
mesh_terms: D004194:Disease / Q000235:genetics; D053263:Gene Regulatory Networks / Q000235:genetics; D005799:Genes, Dominant / Q000235:genetics; D005808:Genes, Recessive / Q000235:genetics; D056426:Genetic Loci / Q000235:genetics; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study / Q000706:statistics & numerical data*; D006239:Haplotypes / Q000235:genetics; D006801:Humans; D016015:Logistic Models; D035683:MicroRNAs / Q000378:metabolism; D008957:Models, Genetic; D015233:Models, Statistical; D009154:Mutation / Q000235:genetics; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D011565:Psoriasis / Q000235:genetics
chemical_list: D035683:MicroRNAs