Region-based genome-wide scans are usually performed by use of a priori chosen analysis regions. Such an approach will likely miss the region comprising the strongest signal and, thus, may result in increased type II error rates and decreased power. Here, we propose a genomic exhaustive scan approach that analyzes all possible subsequences and does not rely on a prior definition of the analysis regions. As a prime instance, we present a computationally ultraefficient implementation using the rare-variant collapsing test for phenotypic association, the genomic exhaustive collapsing scan (GECS). Our implementation allows for the identification of regions comprising the strongest signals in large, genome-wide rare-variant association studies while controlling the family-wise error rate via permutation. Application of GECS to two genomic data sets revealed several novel significantly associated regions for age-related macular degeneration and for schizophrenia. Our approach also offers a high potential to improve genome-wide scans for selection, methylation, and other analyses.

中文翻译：

---

详尽的基因组扫描方法，应用于稀有变异关联分析。

基于区域的全基因组扫描通常是通过使用先验选择的分析区域来执行的。这种方法可能会错过包含最强信号的区域，因此可能导致II型错误率增加和功率降低。在这里，我们提出了一种基因组穷举扫描方法，该方法可以分析所有可能的子序列，并且不依赖于分析区域的先验定义。作为一个很好的例子，我们提出了一种利用表型关联的稀有变异折叠测试（即基因组穷举折叠扫描（GECS））实现计算效率超高的实现。我们的实现方法允许在大型的，全基因组范围的稀有变异关联研究中识别出包含最强信号的区域，同时通过置换控制家族方式的错误率。GECS在两个基因组数据集上的应用揭示了几个与年龄相关的黄斑变性和精神分裂症的新型显着相关区域。我们的方法在改进全基因组选择，甲基化和其他分析的扫描方面也具有很大的潜力。