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

Abstract 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.</p

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

Abstract 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

Methodological issues in detecting gene-gene interactions in breast cancer susceptibility: a population-based study in Ontario-1

<p><b>Copyright information:</b></p><p>Taken from "Methodological issues in detecting gene-gene interactions in breast cancer susceptibility: a population-based study in Ontario"</p><p>http://www.biomedcentral.com/1741-7015/5/22</p><p>BMC Medicine 2007;5():22-22.</p><p>Published online 7 Aug 2007</p><p>PMCID:PMC1976420.</p><p></p> The best partition found by CART for the two-locus genotypes. Shaded cells are classified as high-risk and non-shaded cells as low-risk. This corresponds to a ratio of cases versus controls higher or lower than 1, respectively. The four partitions of the two-locus genotypes found by MDR showed two cells with different assignments. In (f), CART can partition the two-locus genotypes in more than two groups, but for the purpose of comparison with MDR, we used the same high-risk/low-risk grouping

Polymorphisms cMyc-N11S and p27-V109G and breast cancer risk and prognosis

Abstract Background cMyc and p27 are key genes implicated in carcinogenesis. Whether polymorphisms in these genes affect breast cancer risk or prognosis is still unclear. In this study, we focus on a rare non-synonymous polymorphism in cMyc (N11S) and a common polymorphism in p27 (V109G) and determine their role in risk and prognosis using data collected from the Ontario Breast Cancer Family Registry. Methods Risk factor data was collected at baseline on a large group of women (cases = 1,115 and population-based controls = 710) and clinical data (including treatment and follow-up) were collected prospectively by periodic review of medical records for a subset of cases (N = 967) for nearly a decade. A centralized pathology review was conducted. Unconditional logistic regression was used to determine the association of polymorphisms with breast cancer risk and the Cox proportional hazards model was used to determine their association with survival. Results Our results suggest that while cMyc-N11S can be considered a putatively functional polymorphism located in the N-terminal domain, it is not associated with risk, tumor characteristics or survival. The p27-G109 allele was associated with a modest protective effect in adjusted analyses and higher T stage. We found no evidence to suggest that p27-V109G alone or in combination with cMyc-N11S was associated with survival. Age at onset and first-degree family history of breast or ovarian cancer did not significantly modify the association of these polymorphisms with breast cancer risk. Conclusion Further work is recommended to understand the potential functional role of these specific non-synonymous amino acid changes and a larger, more comprehensive investigation of genetic variation in these genes (e.g., using a tagSNP approach) in combination with other relevant genes is needed as well as consideration for treatment effects when assessing their potential role in prognosis.</p