In a variety of application areas, there is interest in assessing evidence of differences in the intensity of event realizations between groups. For example, in cancer genomic studies collecting data on rare variants, the focus is on assessing whether and how the variant profile changes with the disease subtype. Motivated by this application, we develop multiresolution nonparametric Bayes tests for differential mutation rates across groups. The multiresolution approach yields fast and accurate detection of spatial clusters of rare variants, and our nonparametric Bayes framework provides great flexibility for modeling the intensities of rare variants. Some theoretical properties are also assessed, including weak consistency of our Dirichlet Process-Poisson-Gamma mixture over multiple resolutions. Simulation studies illustrate excellent small sample properties relative to competitors, and we apply the method to detect rare variants related to common variable immunodeficiency from whole exome sequencing data on 215 patients and over 60,027 control subjects.

翻译：在众多应用领域中，评估不同组别间事件发生强度差异的证据具有重要意义。例如，在收集稀有变异数据的癌症基因组研究中，核心问题是评估变异模式是否以及如何随疾病亚型变化。受此应用启发，我们开发了用于跨组差异突变率的多分辨率非参数贝叶斯检验。该多分辨率方法能够快速准确地检测稀有变异的空间聚类，而我们的非参数贝叶斯框架为建模稀有变异强度提供了极大的灵活性。本文还评估了若干理论性质，包括我们的狄利克雷过程-泊松-伽马混合模型在多分辨率上的弱一致性。模拟研究表明，相较于竞争方法，本方法在小样本条件下具有优异表现。我们将该方法应用于全外显子组测序数据（包含215名患者和超过60,027名对照组受试者），以检测与常见变异免疫缺陷相关的稀有变异。