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.
Highlights
In the past three years, about 400 genome-wide association studies (GWAS) that focused largely on individually testing the associations of single single nucleotide polymorphisms (SNPs) with diseases have been conducted [1]
In addition to single nucleotide polymorphisms (SNPs) with a minor allele frequency (MAF) greater than 1%, there are other classes of human genetic variation including: (a) rare variants that are defined as mutations with a MAF of less than 1% and (b) structural variants including copy number variants (CNVs) and copy neutral variation such as inversions and translocations
Null Distribution of Test Statistics In the previous sections, we have shown that when the sample size is large enough to apply large sample theory, the distribution of the statistic TIH for testing the interaction between two loci under the null hypothesis of no interaction between them is asymptotically a central x2(1) distribution
Summary
In the past three years, about 400 genome-wide association studies (GWAS) that focused largely on individually testing the associations of single SNP with diseases have been conducted [1]. These studies have identified more than 531 SNPs associated with different traits or diseases [2] and have provided substantial information for understanding disease mechanisms. Common diseases can be caused by multiple rare mutations, each with a low marginal genetic effect. A more realistic model is that the entire spectrum of genetic variants ranging from rare to common contributes to disease susceptibility
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