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Title: MicroRNA processing and binding site polymorphisms are not replicated in the Ovarian Cancer Association Consortium.
Authors: Permuth-Wey J,  Chen Z,  Tsai YY,  Lin HY,  Chen YA,  Barnholtz-Sloan J,  Birrer MJ,  Chanock SJ,  Cramer DW,  Cunningham JM,  Fenstermacher D,  Fridley BL,  Garcia-Closas M,  Gayther SA,  Gentry-Maharaj A,  Gonzalez-Bosquet J,  Iversen E,  Jim H,  McLaughlin J,  Menon U,  Narod SA,  Phelan CM,  Ramus SJ,  Risch H,  Song H,  Sutphen R,  Terry KL,  Tyrer J,  Vierkant RA,  Wentzensen N,  Lancaster JM,  Cheng JQ,  Berchuck A,  Pharoah PD,  Schildkraut JM,  Goode EL,  Sellers TA,  Ovarian Cancer Association Consortium (OCAC)
Journal: Cancer Epidemiol Biomarkers Prev
Date: 2011 Aug
Branches: CGR, HREB, LTG
PubMed ID: 21636674
PMC ID: PMC3153581
Abstract: BACKGROUND: Single nucleotide polymorphisms (SNP) in microRNA-related genes have been associated with epithelial ovarian cancer (EOC) risk in two reports, yet associated alleles may be inconsistent across studies. METHODS: We conducted a pooled analysis of previously identified SNPs by combining genotype data from 3,973 invasive EOC cases and 3,276 controls from the Ovarian Cancer Association Consortium. We also conducted imputation to obtain dense coverage of genes and comparable genotype data for all studies. In total, 226 SNPs within 15 kb of 4 miRNA biogenesis genes (DDX20, DROSHA, GEMIN4, and XPO5) and 23 SNPs located within putative miRNA binding sites of 6 genes (CAV1, COL18A1, E2F2, IL1R1, KRAS, and UGT2A3) were genotyped or imputed and analyzed in the entire dataset. RESULTS: After adjustment for European ancestry, no overall association was observed between any of the analyzed SNPs and EOC risk. CONCLUSIONS: Common variants in these evaluated genes do not seem to be strongly associated with EOC risk. IMPACT: This analysis suggests earlier associations between EOC risk and SNPs in these genes may have been chance findings, possibly confounded by population admixture. To more adequately evaluate the relationship between genetic variants and cancer risk, large sample sizes are needed, adjustment for population stratification should be carried out, and use of imputed SNP data should be considered.