[Photo: Dr. Hui-Yi Lin]
During the past decade, genome-wide association studies (GWAS) have successfully identified many inherited genetic variants (or single nucleotide polymorphisms (SNPs)) associated with complex diseases, such as cancer. However, the predictive power for the GWAS-identified SNPs is limited. Testing SNP-SNP interactions is considered key for overcoming bottlenecks of genetic association studies, but related statistical methods for testing SNP-SNP interactions are underdeveloped. Dr. Hui-Yi Lin, PhD, Associate Professor and lead author from Louisiana State University Health Sciences Center, School of Public Health, New Orleans and her study team proposed the SNP Interaction Pattern Identifier (SIPI), which tests 45 biologically meaningful interaction patterns for a binary outcome. SIPI takes non-hierarchical models, inheritance modes, and mode coding direction into consideration. The simulation results show that SIPI has higher power than several related methods. Applying SIPI to the Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PACTICAL) consortium data with approximately 21,000 patients, four SNP pairs with the exact or similar interaction pattern in the discovery and validation sets were found to be associated with prostate cancer aggressiveness. This study demonstrated that SIPI not only searches for more meaningful interaction patterns but can also overcome the unstable nature of interaction patterns. The results are published in a paper titled “SNP Interaction Pattern Identifier (SIPI): An Intensive Search for SNP-SNP Interaction Patterns” in Bioinformatics.
To read more about the study, go to: http://bioinformatics.oxfordjournals.org/content/early/2016/12/30/bioinformatics.btw762.abstract