METHODS: We evaluated the potential of gene-based aggregation in the Breast Cancer Association Consortium cohorts including 83,471 cases and 59,199 controls. Low-frequency variants were aggregated for individual genes' coding and regulatory regions. Association results in European ancestry samples were compared to single-marker association results in the same cohort. Gene-based associations were also combined in meta-analysis across individuals with European, Asian, African, and Latin American and Hispanic ancestry.
RESULTS: In European ancestry samples, 14 genes were significantly associated (q
METHODS: Seven single-nucleotide polymorphisms (SNPs) in IKZF1, three SNPs in DDC, two SNPs in CDKN2A, two SNPs in CEBPE, and three SNPs in LMO1 were genotyped in 289 Yemeni children (136 cases and 153 controls), using the nanofluidic Dynamic Array (Fluidigm 192.24 Dynamic Array). Logistic regression analyses were used to estimate ALL risk, and the strength of association was expressed as odds ratios with 95% confidence intervals.
RESULTS: We found that the IKZF1 SNP rs10235796 C allele (p = 0.002), the IKZF1 rs6964969 A>G polymorphism (p = 0.048, GG vs. AA), the CDKN2A rs3731246 G>C polymorphism (p = 0.047, GC+CC vs. GG), and the CDKN2A SNP rs3731246 C allele (p = 0.007) were significantly associated with ALL in Yemenis of Arab-Asian descent. In addition, a borderline association was found between IKZF1 rs4132601 T>G variant and ALL risk. No associations were found between the IKZF1 SNPs (rs11978267; rs7789635), DDC SNPs (rs3779084; rs880028; rs7809758), CDKN2A SNP (rs3731217), the CEBPE SNPs (rs2239633; rs12434881) and LMO1 SNPs (rs442264; rs3794012; rs4237770) with ALL in Yemeni children.
CONCLUSION: The IKZF1 SNPs, rs10235796 and rs6964969, and the CDKN2A SNP rs3731246 (previously unreported) could serve as risk markers for ALL susceptibility in Yemeni children.
METHODS: Genomic DNAs were extracted from the blood samples followed by whole-genome sequencing. The reads were aligned to the reference human genome hg19 and variants in the CYP2D6 gene were analyzed. CYP2D6*5 and duplication of CYP2D6 were analyzed using previously established methods.
RESULTS: A total of 72 single nucleotide polymorphisms were identified. CYP2D6*1, *2, *4, *5, *10,*41, and duplication of the gene were found in the Orang Asli, whereby CYP2D6*2 and *41 alleles are reported for the first time in the Malaysian population.
CONCLUSION: The findings in this study provide insights into the genetic polymorphisms of CYP2D6 in the Orang Asli of Peninsular Malaysia.
OBJECTIVE: To test if SNPs associated with other traits may also affect the risk of aggressive prostate cancer.
DESIGN, SETTING, AND PARTICIPANTS: SNPs implicated in any phenotype other than prostate cancer (p≤10(-7)) were identified through the catalog of published GWAS and tested in 2891 aggressive prostate cancer cases and 4592 controls from the Breast and Prostate Cancer Cohort Consortium (BPC3). The 40 most significant SNPs were followed up in 4872 aggressive prostate cancer cases and 24,534 controls from the Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium.
OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Odds ratios (ORs) and 95% confidence intervals (CIs) for aggressive prostate cancer were estimated.
RESULTS AND LIMITATIONS: A total of 4666 SNPs were evaluated by the BPC3. Two signals were seen in regions already reported for prostate cancer risk. rs7014346 at 8q24.21 was marginally associated with aggressive prostate cancer in the BPC3 trial (p=1.6×10(-6)), whereas after meta-analysis by PRACTICAL the summary OR was 1.21 (95% CI 1.16-1.27; p=3.22×10(-18)). rs9900242 at 17q24.3 was also marginally associated with aggressive disease in the meta-analysis (OR 0.90, 95% CI 0.86-0.94; p=2.5×10(-6)). Neither of these SNPs remained statistically significant when conditioning on correlated known prostate cancer SNPs. The meta-analysis by BPC3 and PRACTICAL identified a third promising signal, marked by rs16844874 at 2q34, independent of known prostate cancer loci (OR 1.12, 95% CI 1.06-1.19; p=4.67×10(-5)); it has been shown that SNPs correlated with this signal affect glycine concentrations. The main limitation is the heterogeneity in the definition of aggressive prostate cancer between BPC3 and PRACTICAL.
CONCLUSIONS: We did not identify new SNPs for aggressive prostate cancer. However, rs16844874 may provide preliminary genetic evidence on the role of the glycine pathway in prostate cancer etiology.
PATIENT SUMMARY: We evaluated whether genetic variants associated with several traits are linked to the risk of aggressive prostate cancer. No new such variants were identified.
METHODS: We conducted a gene-environment interaction (GxE) analysis including 8,255 cases and 11,900 controls from four pancreatic cancer genome-wide association study (GWAS) datasets (Pancreatic Cancer Cohort Consortium I-III and Pancreatic Cancer Case Control Consortium). Obesity (body mass index ≥30 kg/m2) and diabetes (duration ≥3 years) were the environmental variables of interest. Approximately 870,000 SNPs (minor allele frequency ≥0.005, genotyped in at least one dataset) were analyzed. Case-control (CC), case-only (CO), and joint-effect test methods were used for SNP-level GxE analysis. As a complementary approach, gene-based GxE analysis was also performed. Age, sex, study site, and principal components accounting for population substructure were included as covariates. Meta-analysis was applied to combine individual GWAS summary statistics.
RESULTS: No genome-wide significant interactions (departures from a log-additive odds model) with diabetes or obesity were detected at the SNP level by the CC or CO approaches. The joint-effect test detected numerous genome-wide significant GxE signals in the GWAS main effects top hit regions, but the significance diminished after adjusting for the GWAS top hits. In the gene-based analysis, a significant interaction of diabetes with variants in the FAM63A (family with sequence similarity 63 member A) gene (significance threshold P < 1.25 × 10-6) was observed in the meta-analysis (P GxE = 1.2 ×10-6, P Joint = 4.2 ×10-7).
CONCLUSIONS: This analysis did not find significant GxE interactions at the SNP level but found one significant interaction with diabetes at the gene level. A larger sample size might unveil additional genetic factors via GxE scans.
IMPACT: This study may contribute to discovering the mechanism of diabetes-associated pancreatic cancer.