ABSTRACT
Risk classification for prostate cancer (PCa) aggressiveness and underlying mechanisms remain inadequate. Interactions between single nucleotide polymorphisms (SNPs) may provide a solution to fill these gaps. To identify SNP-SNP interactions in the four pathways (the angiogenesis-, mitochondria-, miRNA-, and androgen metabolism-related pathways) associated with PCa aggressiveness, we tested 8587 SNPs for 20,729 cases from the PCa consortium. We identified 3 KLK3 SNPs, and 1083 (P < 3.5 × 10-9) and 3145 (P < 1 × 10-5) SNP-SNP interaction pairs significantly associated with PCa aggressiveness. These SNP pairs associated with PCa aggressiveness were more significant than each of their constituent SNP individual effects. The majority (98.6%) of the 3145 pairs involved KLK3. The 3 most common gene-gene interactions were KLK3-COL4A1:COL4A2, KLK3-CDH13, and KLK3-TGFBR3. Predictions from the SNP interaction-based polygenic risk score based on 24 SNP pairs are promising. The prevalence of PCa aggressiveness was 49.8%, 21.9%, and 7.0% for the PCa cases from our cohort with the top 1%, middle 50%, and bottom 1% risk profiles. Potential biological functions of the identified KLK3 SNP-SNP interactions were supported by gene expression and protein-protein interaction results. Our findings suggest KLK3 SNP interactions may play an important role in PCa aggressiveness.
Subject(s)
Kallikreins/genetics , Prostate-Specific Antigen/genetics , Prostatic Neoplasms/genetics , Biomarkers, Tumor/genetics , Epistasis, Genetic , Genetic Predisposition to Disease , Genotype , Humans , Male , Polymorphism, Single Nucleotide , Prostatic Neoplasms/pathologyABSTRACT
BACKGROUND: African American (AA) men have a higher risk of developing prostate cancer than white men. SNPs are known to play an important role in developing prostate cancer. The impact of PVT1 and its neighborhood genes (CASC11 and MYC) on prostate cancer risk are getting more attention recently. The interactions among these three genes associated with prostate cancer risk are understudied, especially for AA men. The objective of this study is to investigate SNP-SNP interactions in the CASC11-MYC-PVT1 region associated with prostate cancer risk in AA men. METHODS: We evaluated 205 SNPs using the 2,253 prostate cancer patients and 2,423 controls and applied multiphase (discovery-validation) design. In addition to SNP individual effects, SNP-SNP interactions were evaluated using the SNP Interaction Pattern Identifier, which assesses 45 patterns. RESULTS: Three SNPs (rs9642880, rs16902359, and rs12680047) and 79 SNP-SNP pairs were significantly associated with prostate cancer risk. These two SNPs (rs16902359 and rs9642880) in CASC11 interacted frequently with other SNPs with 56 and 9 pairs, respectively. We identified the novel interaction of CASC11-PVT1, which is the most common gene interaction (70%) in the top 79 pairs. Several top SNP interactions have a moderate to large effect size (OR, 0.27-0.68) and have a higher prediction power to prostate cancer risk than SNP individual effects. CONCLUSIONS: Novel SNP-SNP interactions in the CASC11-MYC-PVT1 region have a larger impact than SNP individual effects on prostate cancer risk in AA men. IMPACT: This gene-gene interaction between CASC11 and PVT1 can provide valuable information to reveal potential biological mechanisms of prostate cancer development.
Subject(s)
Black or African American/genetics , Prostatic Neoplasms/genetics , RNA, Long Noncoding/genetics , White People/genetics , Biomarkers, Tumor/genetics , Biomarkers, Tumor/metabolism , Case-Control Studies , Epistasis, Genetic , Follow-Up Studies , Genes, myc , Genetic Predisposition to Disease , Genome-Wide Association Study , Humans , Male , Polymorphism, Single Nucleotide , Prognosis , Prospective Studies , Prostatic Neoplasms/metabolism , Prostatic Neoplasms/pathology , Proto-Oncogene Proteins c-myc/genetics , Proto-Oncogene Proteins c-myc/metabolism , RNA, Long Noncoding/metabolism , Risk FactorsABSTRACT
Motivation: The use of single nucleotide polymorphism (SNP) interactions to predict complex diseases is getting more attention during the past decade, but related statistical methods are still immature. We previously proposed the SNP Interaction Pattern Identifier (SIPI) approach to evaluate 45 SNP interaction patterns/patterns. SIPI is statistically powerful but suffers from a large computation burden. For large-scale studies, it is necessary to use a powerful and computation-efficient method. The objective of this study is to develop an evidence-based mini-version of SIPI as the screening tool or solitary use and to evaluate the impact of inheritance mode and model structure on detecting SNP-SNP interactions. Results: We tested two candidate approaches: the 'Five-Full' and 'AA9int' method. The Five-Full approach is composed of the five full interaction models considering three inheritance modes (additive, dominant and recessive). The AA9int approach is composed of nine interaction models by considering non-hierarchical model structure and the additive mode. Our simulation results show that AA9int has similar statistical power compared to SIPI and is superior to the Five-Full approach, and the impact of the non-hierarchical model structure is greater than that of the inheritance mode in detecting SNP-SNP interactions. In summary, it is recommended that AA9int is a powerful tool to be used either alone or as the screening stage of a two-stage approach (AA9int+SIPI) for detecting SNP-SNP interactions in large-scale studies. Availability and implementation: The 'AA9int' and 'parAA9int' functions (standard and parallel computing version) are added in the SIPI R package, which is freely available at https://linhuiyi.github.io/LinHY_Software/. Supplementary information: Supplementary data are available at Bioinformatics online.
