Genome-Wide Interaction-Based Association Analysis Identified Multiple New Susceptibility Loci for Common Diseases

Abstract

Genome-wide interaction-based association (GWIBA) analysis has the potential to identify novel susceptibility loci. These interaction effects could be missed with the prevailing approaches in genome-wide association studies (GWAS). However, no convincing loci have been discovered exclusively from GWIBA methods, and the intensive computation involved is a major barrier for application. Here, we developed a fast, multi-thread/parallel program named “pair-wise interaction-based association mapping” (PIAM) for exhaustive two-locus searches. With this program, we performed a complete GWIBA analysis on seven diseases with stringent control for false positives, and we validated the results for three of these diseases. We identified one pair-wise interaction between a previously identified locus, C1orf106, and one new locus, TEC, that was specific for Crohn's disease, with a Bonferroni corrected P<0.05 ( P = 0.039). This interaction was replicated with a pair of proxy linked loci ( P = 0.013) on an independent dataset. Five other interactions had corrected P<0.5. We identified the allelic effect of a locus close to SLC7A13 for coronary artery disease. This was replicated with a linked locus on an independent dataset ( P = 1.09×10 ⁻⁷). Through a local validation analysis that evaluated association signals, rather than locus-based associations, we found that several other regions showed association/interaction signals with nominal P<0.05. In conclusion, this study demonstrated that the GWIBA approach was successful for identifying novel loci, and the results provide new insights into the genetic architecture of common diseases. In addition, our PIAM program was capable of handling very large GWAS datasets that are likely to be produced in the future.

Author Summary

Recent studies on the genetic basis of common diseases have identified many loci that confer disease susceptibility. However, much of the heritability of these diseases remains unexplained. Loci involved in gene–gene interactions are considered cryptic, because they confer susceptibility, but may not generate a detectable signal on their own. These interactions may account for the “missing heritability” of common diseases. Theoretically, these interactions can be identified with the genome-wide interaction-based association analysis. But, in reality, very few gene–gene interactions have been identified with that method, and most were based on prior biological knowledge. Here, we applied a parallel computing technique that facilitated the identification of multiple new cryptic susceptibility loci involved in common diseases. We applied stringent control for false positives, and we validated our findings with independent datasets. This study demonstrated that interactions between gene loci could be successfully identified with the genome-wide interaction-based approach. With this approach, we also identified cryptic loci with moderate single-locus effects. The identified loci and interactions merit further investigations for fine mapping and functional analyses. Our results extend the current knowledge of common diseases for future studies in genetic mapping. This approach is applicable to current and future genome-wide association datasets.