1 research outputs found
A Novel Statistic for Genome-Wide Interaction Analysis
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