A genome-wide "pleiotropy scan" does not identify new susceptibility loci for estrogen receptor negative breast cancer.
PLoS ONE 2013 ; 9: e85955.
Campa D, Barrdahl M, Tsilidis KK, Severi G, Diver WR, Siddiq A, Chanock S, Hoover RN, Ziegler RG, Berg CD, Buys SS, Haiman CA, Henderson BE, Schumacher FR, Le Marchand L, Flesch-Janys D, Lindström S, Hunter DJ, Hankinson SE, Willett WC, Kraft P, Cox DG, Khaw KT, Tjønneland A, Dossus L, Trichopoulos D, Panico S, van Gils CH, Weiderpass E, Barricarte A, Sund M, Gaudet MM, Giles G, Southey M, Baglietto L, Chang-Claude J, Kaaks R, and Canzian F
PubMed ID : 24523857
PMCID : PMC3921107
Approximately 15-30% of all breast cancer tumors are estrogen receptor negative (ER-). Compared with ER-positive (ER+) disease they have an earlier age at onset and worse prognosis. Despite the vast number of risk variants identified for numerous cancer types, only seven loci have been unambiguously identified for ER-negative breast cancer. With the aim of identifying new susceptibility SNPs for this disease we performed a pleiotropic genome-wide association study (GWAS). We selected 3079 SNPs associated with a human complex trait or disease at genome-wide significance level (P<5 × 10(-8)) to perform a secondary analysis of an ER-negative GWAS from the National Cancer Institute's Breast and Prostate Cancer Cohort Consortium (BPC3), including 1998 cases and 2305 controls from prospective studies. We then tested the top ten associations (i.e. with the lowest P-values) using three additional populations with a total sample size of 3509 ER+ cases, 2543 ER- cases and 7031 healthy controls. None of the 3079 selected variants in the BPC3 ER-GWAS were significant at the adjusted threshold. 186 variants were associated with ER- breast cancer risk at a conventional threshold of P<0.05, with P-values ranging from 0.049 to 2.3 × 10(-4). None of the variants reached statistical significance in the replication phase. In conclusion, this study did not identify any novel susceptibility loci for ER-breast cancer using a "pleiotropic approach".