Kohler Jared R, Cutler David J
McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
Am J Hum Genet. 2007 Oct;81(4):684-99. doi: 10.1086/520823. Epub 2007 Aug 14.
Copy-number variation (CNV), and deletions in particular, can play a crucial, causative role in rare disorders. The extent to which CNV contributes to common, complex disease etiology, however, is largely unknown. Current techniques to detect CNV are relatively expensive and time consuming, making it difficult to conduct the necessary large-scale genetic studies. SNP genotyping technologies, on the other hand, are relatively cheap, thereby facilitating large study designs. We have developed a computational tool capable of harnessing the information in SNP genotype data to detect deletions. Our approach not only detects deletions with high power but also returns accurate estimates of both the population frequency and the transmission frequency. This tool, therefore, lends itself to the discovery of deletions in large familial SNP genotype data sets and to simultaneous testing of the discovered deletion for association, with the use of both frequency-based and transmission/disequilibrium test-based designs. We demonstrate the effectiveness of our computer program (microdel), available for download at no cost, with both simulated and real data. Here, we report 693 deletions in the HapMap 16c collection, with each deletion assigned a population frequency.
拷贝数变异（CNV），尤其是缺失，在罕见疾病中可能发挥关键的致病作用。然而，CNV在常见复杂疾病病因中所起的作用在很大程度上尚不清楚。目前检测CNV的技术相对昂贵且耗时，这使得开展必要的大规模基因研究变得困难。另一方面，单核苷酸多态性（SNP）基因分型技术相对便宜，从而便于进行大规模研究设计。我们开发了一种计算工具，能够利用SNP基因型数据中的信息来检测缺失。我们的方法不仅能高效检测缺失，还能准确估计群体频率和传递频率。因此，该工具适用于在大型家族性SNP基因型数据集中发现缺失，并通过基于频率和基于传递/不平衡检验的设计，对发现的缺失进行关联的同步检测。我们用模拟数据和真实数据证明了我们可免费下载的计算机程序（微缺失检测程序）的有效性。在此，我们报告了HapMap 16c数据集中的693个缺失，并为每个缺失指定了群体频率。
