Motivation Conservation is broadly used to identify biologically important (epi)genomic regions. In the case of tumor growth, preferential conservation of DNA methylation can be used to identify areas of particular functional importance to the tumor. However, reliable assessment of methylation conservation based on multiple tissue samples per patient requires the decomposition of methylation variation at multiple levels. Results We developed a Bayesian hierarchical model that allows for variance decomposition of methylation on three levels: between-patient normal tissue variation, between-patient tumor-effect variation and within-patient tumor variation. We then defined a model-based conservation score to identify loci of reduced within-tumor methylation variation relative to between-patient variation. We fit the model to multi-sample methylation array data from 21 colorectal cancer (CRC) patients using a Monte Carlo Markov Chain algorithm (Stan). Sets of genes implicated in CRC tumorigenesis exhibited preferential conservation, demonstrating the model’s ability to identify functionally relevant genes based on methylation conservation. A pathway analysis of preferentially conserved genes implicated several CRC relevant pathways and pathways related to neoantigen presentation and immune evasion. Our findings suggest that preferential methylation conservation may be used to identify novel gene targets that are not consistently mutated in CRC. The flexible structure makes the model amenable to the analysis of more complex multi-sample data structures. Availability and implementation The data underlying this article are available in the NCBI GEO Database, under accession code GSE166212. The R analysis code is available at https://github.com/kevin-murgas/DNAmethylation-hierarchicalmodel. Supplementary information Supplementary data are available at Bioinformatics online.

中文翻译：

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估计结直肠肿瘤中 DNA 甲基化保守性的贝叶斯分层模型

动机保护广泛用于识别生物学上重要的 (epi) 基因组区域。在肿瘤生长的情况下，DNA 甲基化的优先保护可用于识别对肿瘤具有特殊功能重要性的区域。然而，基于每位患者的多个组织样本对甲基化保护的可靠评估需要在多个水平上分解甲基化变异。结果我们开发了一个贝叶斯分层模型，允许在三个水平上对甲基化进行方差分解：患者之间的正常组织变异、患者之间的肿瘤效应变异和患者肿瘤内的变异。然后，我们定义了一个基于模型的保守评分来识别相对于患者间变异减少的肿瘤内甲基化变异的位点。我们使用蒙特卡罗马尔可夫链算法 (Stan) 将模型拟合到来自 21 名结直肠癌 (CRC) 患者的多样本甲基化阵列数据。与 CRC 肿瘤发生有关的基因组表现出优先保守性，表明该模型能够根据甲基化保守性识别功能相关基因。优先保守基因的通路分析涉及几个 CRC 相关通路和与新抗原呈递和免疫逃避相关的通路。我们的研究结果表明，优先甲基化保护可用于识别在 CRC 中未始终发生突变的新基因靶标。灵活的结构使模型能够分析更复杂的多样本数据结构。可用性和实施​​ 本文所依据的数据可在 NCBI GEO 数据库中获得，登录号为 GSE166212。R 分析代码可在 https://github.com/kevin-murgas/DNAmethylation-hierarchicalmodel 获得。补充信息 补充数据可在 Bioinformatics 在线获取。