Application of a Novel Score Test for Genetic Association Incorporating Gene-Gene Interaction Suggests Functionality for Prostate Cancer Susceptibility Regions

Abstract

Aims: We introduce an innovative multilocus test for disease association. It is an extension of an existing score test that gains power over alternative methods by incorporating a parsimonious one-degree-of-freedom model for interaction. We use our method in applications designed to detect interactions that generate hypotheses about the functionality of prostate cancer (PRCA) susceptibility regions. Methods: Our proposed score test is designed to gain additional power through the use of a retrospective likelihood that exploits an assumption of independence between unlinked loci in the underlying population. Its performance is validated through simulation. The method is used in conditional scans with data from stage II of the Cancer Genetic Markers of Susceptibility PRCA genome-wide association study. Results: Our proposed method increases power to detect susceptibility loci in diverse settings. It identified two high-ranking, biologically interesting interactions: (1) rs748120 of NR2C2 and subregions of 8q24 that contain independent susceptibility loci specific to PRCA and (2) rs4810671 of SULF2 and both JAZF1 and HNF1B that are associated with PRCA and type 2 diabetes. Conclusions: Our score test is a promising multilocus tool for genetic epidemiology. The results of our applications suggest functionality for poorly understood PRCA susceptibility regions. They motivate replication study.

Challenges and opportunities in genome-wide environmental interaction (GWEI) studies

“…Strategy for testing can then be defined by averaging the effect of a given locus over other factors (Ferreira et al 2007) or by testing the maximum joint test over a range of possible model (Chapman and Clayton 2007). It has been also suggested that degree-of-freedom for such joint tests can be reduced using Tukey style one-degrees-of-freedom model for interaction between groups of related genetic or/and environmental variables (Chapman and Clayton 2007; Chatterjee et al 2006; Ciampa et al 2011). …”

The Cumulative Effect of Gene-Gene and Gene-Environment Interactions on the Risk of Prostate Cancer in Chinese Men

“…For example, Tao et al identified 1325 pairs of SNP–SNP interactions with a P cutoff of 1.0 × 10 -8 in 1176 PCa cases and 1101 control subjects from the National Cancer Institute Cancer Genetic Markers of Susceptibility study, although no SNP-SNP interaction reached a genome-wide significance level of 4.4 × 10 -13 [ 4 ]. A study by Ciampa et al showed that two biologically interesting interactions, one between rs748120 of NR2C2 and subregions of 8q24 and that between rs4810671 of SULF2 and both JAZF1 and HNF1B, were associated with PCa [ 5 ]. In cases of Italian heredo-familial PCa, VDR1 T/T genotypes coupled with the VDR2 T/T genotype exhibited a five-fold higher probability of PCa [ 6 ].…”

SULF2 overexpression positively regulates tumorigenicity of human prostate cancer cells

“…A limited number of studies reported the involvement of SULFs in prostate cancer. SULF1 is present in prostatic stromal cells in the transition regions between cancer and stroma and SULF2 chromosome locus is associated to prostate cancer susceptibility regions [ 33 , 34 ].…”

“…Zhao et al [ 33 ] reported that SULF1 is present in prostatic stromal cells in the transition regions but not in benign prostatic hyperplasia. Ciampa et al [ 34 ] identified that SULF2 chromosome locus is associated to prostate cancer susceptibility regions. However, the literature is ambiguous about the function of SULFs in cancer, and the enzymes are reported both as anti and as pro-tumorigenic [ 25 ].…”