Spanish National Cancer Research Center (CNIO), Madrid, E-28029, Spain.
BMC Genomics. 2012 Jul 20;13:326. doi: 10.1186/1471-2164-13-326.
Structural variations such as copy number variants (CNV) influence the expression of different phenotypic traits. Algorithms to identify CNVs through SNP-array platforms are available. The ability to evaluate well-characterized CNVs such as GSTM1 (1p13.3) deletion provides an important opportunity to assess their performance.
773 cases and 759 controls from the SBC/EPICURO Study were genotyped in the GSTM1 region using TaqMan, Multiplex Ligation-dependent Probe Amplification (MLPA), and Illumina Infinium 1 M SNP-array platforms. CNV callings provided by TaqMan and MLPA were highly concordant and replicated the association between GSTM1 and bladder cancer. This was not the case when CNVs were called using Illumina 1 M data through available algorithms since no deletion was detected across the study samples. In contrast, when the Log R Ratio (LRR) was used as a continuous measure for the 5 probes contained in this locus, we were able to detect their association with bladder cancer using simple regression models or more sophisticated methods such as the ones implemented in the CNVtools package.
This study highlights an important limitation in the CNV calling from SNP-array data in regions of common aberrations and suggests that there may be added advantage for using LRR as a continuous measure in association tests rather than relying on calling algorithms.
结构变异，如拷贝数变异（CNV），会影响不同表型特征的表达。目前已有通过 SNP 芯片平台识别 CNV 的算法。评估 GSTM1（1p13.3）缺失等特征明确的 CNV 的能力为评估其性能提供了重要机会。
使用 TaqMan、多重连接依赖性探针扩增（MLPA）和 Illumina Infinium 1M SNP 芯片平台，对 SBC/EPICURO 研究中的 773 例病例和 759 例对照进行了 GSTM1 区域的基因分型。TaqMan 和 MLPA 提供的 CNV 调用高度一致，并复制了 GSTM1 与膀胱癌之间的关联。然而，当使用 Illumina 1M 数据通过现有算法调用 CNV 时，情况并非如此，因为在整个研究样本中未检测到缺失。相比之下，当使用 LRR 作为该基因座中包含的 5 个探针的连续度量时，我们能够使用简单的回归模型或更复杂的方法（如 CNVtools 包中实现的方法）检测它们与膀胱癌的关联。
本研究强调了 SNP 芯片数据中常见异常区域的 CNV 调用的一个重要局限性，并表明在关联测试中使用 LRR 作为连续度量可能比依赖于调用算法具有更大的优势。
