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Low- and Moderate-Penetrance Genes Associated With Breast and / or Ovarian Cancer


    Several meta-analyses have investigated the association between the RAD51 135G→C polymorphism and breast cancer risk. There is significant overlap in the studies reported in these meta-analyses, significant variability in the characteristics of the populations included, and significant methodologic limitations to their findings.[93,94,95,96] A meta-analysis of nine epidemiologic studies involving 13,241 cases and 13,203 controls of unknown BRCA1/2 status found that women carrying the CC genotype had an increased risk of breast cancer compared to women with the GG or GC genotype (OR [odds ratio], 1.35; 95% CI, 1.04–1.74). A meta-analysis of 14 case-control studies involving 12,183 cases and 10,183 controls confirmed an increased risk only for women who were known BRCA2 carriers (OR, 4.92; 95% CI, 1.10–21.83).[97] Another meta-analysis of 12 studies included only studies of known BRCA-negative cases and found no association between RAD51 135G→C and breast cancer.[98]

    In summary, among this conflicting data there is substantial evidence for a weak association between germline mutations in RAD51C and breast cancer and ovarian cancer. There is also evidence of an association between polymorphisms in RAD51 135G→C among women with homozygous CC genotypes and breast cancer, particularly among BRCA2 carriers. These associations are plausible given the known role of RAD51 in the maintenance of genomic stability.


    Mutations in the BRCA1-interacting gene Abraxas were found in three Finnish breast cancer families and no controls.[99] The significance of this finding outside of this population is not yet known.

    Genome-Wide Searches

    In contrast to assessing candidate genes and/or alleles, genome wide association studies involve comparing a very large set of genetic variants spread throughout the genome. The current paradigm uses sets of up to 5 million SNPs that are chosen to capture a large portion of common variation within the genome based on the HapMap and the 1000 Genomes Project.[100,101] By comparing allele frequencies between a large number of cases and controls, typically 1,000 or more of each, and validating promising signals in replication sets of subjects, very robust statistical signals of association have been obtained.[102,103,104] The strong correlation between many SNPs that are physically close to each other on the chromosome (linkage disequilibrium) allows one to "scan" the genome for susceptibility alleles even if the biologically relevant variant is not within the tested set of SNPs. While this between-SNP correlation allows one to interrogate the majority of the genome without having to assay every SNP, when a validated association is obtained, it is not usually obvious which of the many correlated variants is causal.

    Genome-wide searches are showing great promise in identifying common, low-penetrance susceptibility alleles for many complex diseases,[105] including breast cancer.[106,107,108,109] The first study involved an initial scan in familial breast cancer cases followed by replication in two large sample sets of sporadic breast cancer, the final being a collection of over 20,000 cases and 20,000 controls from the BCAC, an international group of investigators.[106] Five distinct genomic regions were identified that were within or near the FGFR2, TNRC9, MAP3K1, and LSP1 genes or at the chromosome 8q region. The 8q region and others may harbor multiple independent loci associated with risk, but these regions are included only once in Table 8. Subsequent genome-wide studies have replicated these loci and identified additional ones, as summarized in Table 8.[107,108,110,110,111,112,113,114,115] Numerous SNPs identified through large studies of sporadic breast cancer appear to be associated more strongly with estrogen receptor–positive disease;[116] however, some are associated primarily or exclusively with other subtypes, including triple-negative disease.[117,118] An online catalog of SNP-trait associations from published genome-wide association studies for use in investigating genomic characteristics of trait/disease-associated SNPs (TASs) is available.


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