Abstract
Pathway analysis has become popular as a secondary analysis strategy for genome-wide association studies (GWAS). Most of the current pathway analysis methods aggregate signals from the main effects of single nucleotide polymorphisms (SNPs) in genes within a pathway without considering the effects of gene-gene interactions. However, gene-gene interactions can also have critical effects on complex diseases. Protein-protein interaction (PPI) networks have been used to define gene pairs for the gene-gene interaction tests. Incorporating the PPI information to define gene pairs for interaction tests within pathways can increase the power for pathway-based association tests. We propose a pathway association test, which aggregates the interaction signals in PPI networks within a pathway, for GWAS with case-control samples. Gene size is properly considered in the test so that genes do not contribute more to the test statistic simply due to their size. Simulation studies were performed to verify that the method is a valid test and can have more power than other pathway association tests in the presence of gene-gene interactions within a pathway under different scenarios. We applied the test to the Wellcome Trust Case Control Consortium GWAS datasets for seven common diseases. The most significant pathway is the chaperones modulate interferon signaling pathway for Crohn’s disease (p-value = 0.0003). The pathway modulates interferon gamma, which induces the JAK/STAT pathway that is involved in Crohn’s disease. Several other pathways that have functional implications for the seven diseases were also identified. The proposed test based on gene-gene interaction signals in PPI networks can be used as a complementary tool to the current existing pathway analysis methods focusing on main effects of genes. An efficient software implementing the method is freely available at http://puppi.sourceforge.net.
Highlights
We performed simulation studies to verify that the PUPPI has correct type I errors for pathways with different numbers of genes and for different sample sizes
As Protein-protein interaction (PPI) information is independent from the statistical tests, it is important to note that using PPI information in the PUPPI does not bias the test statistics
The power simulation results suggested that the PUPPI can have higher or comparable power to that of PLINK, HYST, and SKAT in some models when there were both main effects and interaction effects
Summary
Genome-wide association studies (GWAS) have identified thousands of single nucleotide polymorphisms (SNPs) significantly associated with complex diseases [1], such as Crohn’s disease and type 2 diabetes [2, 3]. For SNPs with modest effects, GWAS has low power to detect such SNPs because of the high multiple testing correction burden resulting from the large number of tests (e.g., 1 million tests) typically performed in GWAS. More than 5,000 cases and the same number of controls are required for a GWAS to achieve power > 80% at the genome-wide significance level for SNPs with effect sizes between 1.3 and 1.5 [4]. Pathway analysis hypothesizes that SNPs in genes in the same pathway have a joint effect on the disease.
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