IBD Genetic Risk Profile in Healthy First-Degree Relatives of Crohn's Disease Patients.

BACKGROUND: Family history provides important information on risk of developing inflammatory bowel disease [IBD], and genetic profiling of first-degree relatives [FDR] of Crohn's disease [CD]- affected individuals might provide additional information. We aimed to delineate the genetic contribution to the increased IBD susceptibility observed in FDR. METHODS: N = 976 Caucasian, healthy, non-related FDR; n = 4997 independent CD; and n = 5000 healthy controls [HC]; were studied. Genotyping for 158 IBD-associated single nucleotide polymorphisms [SNPs] was performed using the Illumina Immunochip. Risk allele frequency [RAF] differences between FDR and HC cohorts were correlated with those between CD and HC cohorts. CD and IBD genetic risk scores [GRS] were calculated and compared between HC, FDR, and CD cohorts. RESULTS: IBD-associated SNP RAF differences in FDR and HC cohorts were strongly correlated with those in CD and HC cohorts, correlation coefficient 0.63 (95% confidence interval [CI] 0.53 - 0.72), p = 9.90 x 10(-19). There was a significant increase in CD-GRS [mean] comparing HC, FDR, and CD cohorts: 0.0244, 0.0250, and 0.0257 respectively [p < 1.00 x 10(-7) for each comparison]. There was no significant difference in the IBD-GRS between HC and FDR cohorts [p = 0.81]; however, IBD-GRS was significantly higher in CD compared with FDR and HC cohorts [p < 1.00 x 10(-10) for each comparison]. CONCLUSION: FDR of CD-affected individuals are enriched with IBD risk alleles compared with HC. Cumulative CD-specific genetic risk is increased in FDR compared with HC. Prospective studies are required to determine if genotyping would facilitate better risk stratification of FDR.

Methodological issues in detecting gene-gene interactions in breast cancer susceptibility: a population-based study in Ontario.

BACKGROUND: There is growing evidence that gene-gene interactions are ubiquitous in determining the susceptibility to common human diseases. The investigation of such gene-gene interactions presents new statistical challenges for studies with relatively small sample sizes as the number of potential interactions in the genome can be large. Breast cancer provides a useful paradigm to study genetically complex diseases because commonly occurring single nucleotide polymorphisms (SNPs) may additively or synergistically disturb the system-wide communication of the cellular processes leading to cancer development. METHODS: In this study, we systematically studied SNP-SNP interactions among 19 SNPs from 18 key genes involved in major cancer pathways in a sample of 398 breast cancer cases and 372 controls from Ontario. We discuss the methodological issues associated with the detection of SNP-SNP interactions in this dataset by applying and comparing three commonly used methods: the logistic regression model, classification and regression trees (CART), and the multifactor dimensionality reduction (MDR) method. RESULTS: Our analyses show evidence for several simple (two-way) and complex (multi-way) SNP-SNP interactions associated with breast cancer. For example, all three methods identified XPD-[Lys751Gln]*IL10-[G(-1082)A] as the most significant two-way interaction. CART and MDR identified the same critical SNPs participating in complex interactions. Our results suggest that the use of multiple statistical approaches (or an integrated approach) rather than a single methodology could be the best strategy to elucidate complex gene interactions that have generally very different patterns. CONCLUSION: The strategy used here has the potential to identify complex biological relationships among breast cancer genes and processes. This will lead to the discovery of novel biological information, which will improve breast cancer risk management.

SNP-SNP interactions in breast cancer susceptibility.

BACKGROUND: Breast cancer predisposition genes identified to date (e.g., BRCA1 and BRCA2) are responsible for less than 5% of all breast cancer cases. Many studies have shown that the cancer risks associated with individual commonly occurring single nucleotide polymorphisms (SNPs) are incremental. However, polygenic models suggest that multiple commonly occurring low to modestly penetrant SNPs of cancer related genes might have a greater effect on a disease when considered in combination. METHODS: In an attempt to identify the breast cancer risk conferred by SNP interactions, we have studied 19 SNPs from genes involved in major cancer related pathways. All SNPs were genotyped by TaqMan 5'nuclease assay. The association between the case-control status and each individual SNP, measured by the odds ratio and its corresponding 95% confidence interval, was estimated using unconditional logistic regression models. At the second stage, two-way interactions were investigated using multivariate logistic models. The robustness of the interactions, which were observed among SNPs with stronger functional evidence, was assessed using a bootstrap approach, and correction for multiple testing based on the false discovery rate (FDR) principle. RESULTS: None of these SNPs contributed to breast cancer risk individually. However, we have demonstrated evidence for gene-gene (SNP-SNP) interaction among these SNPs, which were associated with increased breast cancer risk. Our study suggests cross talk between the SNPs of the DNA repair and immune system (XPD-[Lys751Gln] and IL10-[G(-1082)A]), cell cycle and estrogen metabolism (CCND1-[Pro241Pro] and COMT-[Met108/158Val]), cell cycle and DNA repair (BARD1-[Pro24Ser] and XPD-[Lys751Gln]), and within carcinogen metabolism (GSTP1-[Ile105Val] and COMT-[Met108/158Val]) pathways. CONCLUSION: The importance of these pathways and their communication in breast cancer predisposition has been emphasized previously, but their biological interactions through SNPs have not been described. The strategy used here has the potential to identify complex biological links among breast cancer genes and processes. This will provide novel biological information, which will ultimately improve breast cancer risk management.