Walker Logan C, Wiggins George A R, Pearson John F
Mackenzie Cancer Research Group, Department of Pathology, University of Otago, Christchurch 8140, New Zealand.
Biostatistics and Computational Biology Unit, University of Otago, Christchurch 8140, New Zealand.
Microarrays (Basel). 2015 Sep 8;4(3):407-23. doi: 10.3390/microarrays4030407.
Constitutional copy number variants (CNVs) include inherited and de novo deviations from a diploid state at a defined genomic region. These variants contribute significantly to genetic variation and disease in humans, including breast cancer susceptibility. Identification of genetic risk factors for breast cancer in recent years has been dominated by the use of genome-wide technologies, such as single nucleotide polymorphism (SNP)-arrays, with a significant focus on single nucleotide variants. To date, these large datasets have been underutilised for generating genome-wide CNV profiles despite offering a massive resource for assessing the contribution of these structural variants to breast cancer risk. Technical challenges remain in determining the location and distribution of CNVs across the human genome due to the accuracy of computational prediction algorithms and resolution of the array data. Moreover, better methods are required for interpreting the functional effect of newly discovered CNVs. In this review, we explore current and future application of SNP array technology to assess rare and common CNVs in association with breast cancer risk in humans.
染色体拷贝数变异（CNV）包括在特定基因组区域内与二倍体状态的遗传和新生偏差。这些变异对人类的遗传变异和疾病有重大贡献，包括乳腺癌易感性。近年来，乳腺癌遗传风险因素的识别主要依赖于全基因组技术，如单核苷酸多态性（SNP）阵列，且重点显著放在单核苷酸变异上。尽管这些大型数据集为评估这些结构变异对乳腺癌风险的贡献提供了大量资源，但迄今为止，它们在生成全基因组CNV图谱方面尚未得到充分利用。由于计算预测算法的准确性和阵列数据的分辨率，在确定CNV在人类基因组中的位置和分布方面仍然存在技术挑战。此外，还需要更好的方法来解释新发现的CNV的功能效应。在本综述中，我们探讨了SNP阵列技术在评估与人类乳腺癌风险相关的罕见和常见CNV方面的当前和未来应用。
