BACKGROUND Current sequencing technologies have provided for a more comprehensive genome-wide assessment and have increased genotyping accuracy of rare variants. Scan statistic approaches have previously been adapted to genetic sequencing data. Unlike currently-employed association tests, scan-statistic-based approaches can both localize clusters of disease-related variants and, subsequently, examine the phenotype association within the resulting cluster. In this study, we present a novel Quantitative Phenotype Scan Statistic (QPSS) that extends an approach for dichotomous phenotypes to continuous outcomes in order to identify genomic regions where rare quantitative-phenotype-associated variants cluster. RESULTS We demonstrate the performance and practicality of QPSS with extensive simulations and an application to a whole-genome sequencing (WGS) study of cerebrospinal fluid (CSF) biomarkers from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Using QPSS, we identify regions of rare variant enrichment associated with levels of AD-related proteins, CSF Aβ1-42 and p-tau181P. CONCLUSIONS QPSS is implemented under the assumption that causal variants within a window have the same direction of effect. Typical self-contained tests employ a null hypothesis of no association between the target variant set and the phenotype. Therefore, an advantage of the proposed competitive test is that it is possible to refine a known region of interest to localize disease-associated clusters. The definition of clusters can be easily adapted based on variant function or annotation.

中文翻译：

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定量表型扫描统计 (QPSS) 揭示了与阿尔茨海默病内表型的罕见变异关联。

背景技术当前的测序技术已经提供了更全面的全基因组评估并且提高了稀有变体的基因分型准确性。扫描统计方法以前已适用于基因测序数据。与目前使用的关联测试不同，基于扫描统计的方法既可以定位与疾病相关的变异集群，又可以随后检查结果集群中的表型关联。在这项研究中，我们提出了一种新的定量表型扫描统计 (QPSS)，将二分表型的方法扩展到连续结果，以识别罕见的定量表型相关变异聚集的基因组区域。结果 我们通过广泛的模拟和对来自阿尔茨海默氏病神经影像学倡议 (ADNI) 的脑脊液 (CSF) 生物标志物的全基因组测序 (WGS) 研究的应用证明了 QPSS 的性能和实用性。使用 QPSS，我们确定了与 AD 相关蛋白、CSF Aβ1-42 和 p-tau181P 水平相关的稀有变异富集区域。结论 QPSS 是在一个窗口内的因果变异具有相同的影响方向的假设下实施的。典型的独立测试采用目标变体集和表型之间没有关联的零假设。因此，所提出的竞争性测试的一个优点是可以改进已知的感兴趣区域以定位与疾病相关的集群。