5.1. Inherited Copy Number Polymorphisms and Breast Cancer Risk
Analysis of large genome-wide association studies carried out by the Wellcome Trust Case Control Consortium suggested that common CNVs were unlikely to play a major role in breast cancer susceptibility [70]. This study used a 105K probe Agilent CGH array design containing probes tagging for copy number loci previously identified from (1) the Genome Structural Variation (GSV) Consortium [39]; (2) CNV studies using the SNP arrays Affymetrix 6.0, Illumina 1M, and Affymetrix 500k; (3) novel sequence absent from the reference sequence; 4) candidate genes; and 5) additional risk-associated loci. However, this study was not sufficiently powered to detect the effects of low-penetrant alleles with a minor allele frequency (MAF) less than 5%. Moreover, the genomic regions assessed by this study were limited by the design of the arrays used to generate genotype information across the genome. More recently, a genome-wide association study of common CNVs (MAF ≥ 5%) conducted among Chinese women using high-resolution data from the Affymetrix SNP Array 6.0 identified a deletion in the APOBEC3 gene cluster associated with breast cancer risk. Within this population, the deletion was identified in 65% cases and 45% of controls, conferring odds ratios (ORs) of 1.3 and 1.8 for a hemizygous and homozygous deletion, respectively (p = 2.0 × 10−24) [6]. Subsequent investigations of women with European ancestry using quantitative-PCR also observed the deletion, albeit at a much lower population frequency [71]. Comparable to the study of Chinese women, a higher proportion of breast cancer affected European women (12.4% vs. 10.4%, respectively) because they carried the APOBEC3 allele, thereby conferring low to moderate risk of disease (ORs of 1.2 and 2.3 (p = 0.005) for a hemizygous and homozygous deletion, respectively). Interestingly, the same deletion (CNV ID: CNVR8164.1) was originally identified by the Wellcome Trust Case Control Consortium; however, replication experiments did not show a significant association with breast cancer.
As mentioned above, there is now a wealth of array data available from SNP-based genome-wide association studies that can be utilised for assessing the contribution of CNVs to breast cancer risk. Furthermore, the huge number of cases and controls available for future CNV association studies will provide sufficient power to evaluate many CNVs that occur at low frequency. A major limitation with using these array data is the inability to genotype highly repetitive copy number-variable regions. More than 1000 regions across the human genome have been found overlapping CNVs with three or more segregating alleles [72]. Non-array-based technologies that can resolve multicopy integer states, such as qPCR, Nanostring and massively parallel sequencing, will therefore be necessary to determine the clinical significance of these multiallelic variants in breast cancer and other human diseases.