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.

Figure 1. Quantile-quantile plots for the test statistic .

(A) Quantile-quantile plots for the test statistic in dataset 1. The P-values (<) for the test are plotted (as −log10 values) as a function of its expected p values. (B) Quantile-quantile plots for the test statistic in dataset 2. The P-values (<) for the test are plotted (as −log10 values) as a function of its expected p values.

Figure 2. Power of the statistics for testing interaction between two linked loci under recessive disease model.

(A) The power of the test statistic , the “fast-epistasis” in PLINK and logistic regression analysis for testing interaction between two linked loci as a function of traditional odds-ratio under a two-locus recessiverecessive disease model, where the number of individuals in both the case and control groups is 2,000, the significance level is 0.05, and the odds-ratios at two loci were . (B) The power of the test statistic , the “fast-epistasis” in PLINK and logistic regression analysis for testing interaction between two linked loci as a function of traditional odds-ratio under a two-locus recessiverecessive disease model, where the number of individuals in both the case and control groups is 2,000, the significance level is 0.01, and the odds-ratios at two loci were . (C) The power of the test statistic , the “fast-epistasis” in PLINK and logistic regression analysis for testing interaction between two linked loci as a function of traditional odds-ratio under a two-locus recessiverecessive disease model, where the number of individuals in both the case and control groups is 2,000, the significance level is 0.001, and the odds-ratios at two loci were .

Figure 3. Power of the statistics for testing interaction between two linked loci under dominant disease model.

(A) The power of the test statistic , the “fast-epistasis” in PLINK and logistic regression analysis for testing interaction between two linked loci as a function of traditional odds-ratio under a two-locus dominantdominant disease model, where the number of individuals in both the case and control groups is 1,000, the significance level is 0.05, and the odds-ratios at two loci were . (B) The power of the test statistic , the “fast-epistasis” in PLINK and logistic regression analysis for testing interaction between two linked loci as a function of traditional odds-ratio under a two-locus dominantdominant disease model, where the number of individuals in both the case and control groups is 1,000, the significance level is 0.01, and the odds-ratios at two loci were . (C) The power of the test statistic , the “fast-epistasis” in PLINK and logistic regression analysis for testing interaction between two linked loci as a function of traditional odds-ratio under a two-locus dominantdominant disease model, where the number of individuals in both the case and control groups is 1,000, the significance level is 0.001, and the odds-ratios at two loci were .

Figure 4. Power of the statistics for testing interaction between two linked loci under additive disease model.

(A) The power of the test statistic , the “fast-epistasis” in PLINK and logistic regression for testing interaction between two linked loci analysis as a function of traditional odds-ratio under a two-locus additiveadditive disease model, where the number of individuals in both the case and control groups is 1,000, the significance level is 0.05, and the odds-ratios at two loci were . (B) The power of the test statistic , the “fast-epistasis” in PLINK and logistic regression for testing interaction between two linked loci analysis as a function of traditional odds-ratio under a two-locus additiveadditive disease model, where the number of individuals in both the case and control groups is 1,000, the significance level is 0.01, and the odds-ratios at two loci were . (C) The power of the test statistic , the “fast-epistasis” in PLINK and logistic regression for testing interaction between two linked loci analysis as a function of traditional odds-ratio under a two-locus additiveadditive disease model, where the number of individuals in both the case and control groups is 1,000, the significance level is 0.001, and the odds-ratios at two loci were .

Figure 5. Power of the statistics for testing interaction between two unlinked loci.

(A) The power of the test statistic , the “fast-epistasis” in PLINK and logistic regression analysis for testing interaction between two unlinked loci as a function of traditional odds-ratio under a two-locus recessiverecessive disease model, where the number of individuals in both the case and control groups is 2,000, the significance level is 0.001, and the odds-ratios at two loci were . (B) The power of the test statistic , the “fast-epistasis” in PLINK and logistic regression analysis for testing interaction between two unlinked loci as a function of traditional odds-ratio under a two-locus dominantdominant disease model, where the number of individuals in both the case and control groups is 1,000, the significance level is 0.001, and the odds-ratios at two loci were . (C) The power of the test statistic , the “fast-epistasis” in PLINK and logistic regression analysis for testing interaction between two unlinked loci as a function of traditional odds-ratio under a two-locus additiveadditive disease model, where the number of individuals in both the case and control groups is 1,000, the significance level is 0.001, and the odds-ratios at two loci were .

Figure 6. Interacting SNPs that were located in 19 pathways formed a network.

Each pathway was represented by an ellipse with the number. The SNPs were represented by nodes and placed insight their located pathways. Nearby each SNP there was its RS number and the name of its located gene. The pathway and its harbored SNPs were labeled by the same color. The interacting SNPs were connected by the solid light green lines.