Association between common variation in 120 candidate genes and breast cancer risk

Association studies in candidate genes have been widely used to search for common low penetrance susceptibility alleles, but few definite associations have been established. We have conducted association studies in breast cancer using an empirical single nucleotide polymorphism (SNP) tagging approach to capture common genetic variation in genes that are candidates for breast cancer based on their known function. We genotyped 710 SNPs in 120 candidate genes in up to 4,400 breast cancer cases and 4,400 controls using a staged design. Correction for population stratification was done using the genomic control method, on the basis of data from 280 genomic control SNPs. Evidence for association with each SNP was assessed using a Cochran-Armitage trend test (p-trend) and a two-degrees of freedom chi(2) test for heterogeneity (p-het). The most significant single SNP (p-trend = 8 x 10(-5)) was not significant at a nominal 5% level after adjusting for population stratification and multiple testing. To evaluate the overall evidence for an excess of positive associations over the proportion expected by chance, we applied two global tests: the admixture maximum likelihood (AML) test and the rank truncated product (RTP) test corrected for population stratification. The admixture maximum likelihood experiment-wise test for association was significant for both the heterogeneity test (p = 0.0031) and the trend test (p = 0.017), but no association was observed using the rank truncated product method for either the heterogeneity test or the trend test (p = 0.12 and p = 0.24, respectively). Genes in the cell-cycle control pathway and genes involved in steroid hormone metabolism and signalling were the main contributors to the association. These results suggest that a proportion of SNPs in these candidate genes are associated with breast cancer risk, but that the effects of individual SNPs is likely to be small. Large sample sizes from multicentre collaboration will be needed to identify associated SNPs with certainty.