Identify the heterogeneity and commonality of chronic overlapping pain conditions (COPCs) through phenotypic and genomic perspectives

项目总结/摘要 据估计，美国成年人的患病率为20%，每年的费用超过5亿美元， 对公众健康造成严重危害。许多患者发展为慢性重叠疼痛状况（COPC）， 颅面疼痛，如颞下颌关节紊乱病（TMD）是一种独特的组成部分， 经常伴有其他慢性疼痛，包括肠易激综合征（IBS）。不仅合并症 复杂的疼痛管理，但病因COPC仍然不清楚。异源性存在于 因此，需要基于机制的分类方案来确定安全有效的治疗方法 对于不同的患者亚组。然而，围绕COPC分类的现有研究还没有完全 在群体水平上整合表型和基因型数据。对疾病特异性研究队列的依赖 严重限制了样本量和多样性。另一方面，信息学和数据科学取得了进展， 生物医学数据的二次利用，为假设驱动的对照研究提供了强有力的替代方案。我们 建议通过挖掘三个不同的基于人群的临床数据集并插补COPC亚组， 通过使用功能基因组知识库，共同发生COPC的生物学基础。我们的方法 包括两个目的：1）从丰富的COPC中鉴定COPC亚组和其他常见的相关表型。 纵向临床数据和记录，包括罗切斯特流行病学项目中超过100万例患者。 将基于以下因素对临床数据集进行COPC和其他共现表型的计算筛选： 诊断代码和自然语言处理。我们将确定具有统计学意义的COPC合并症 和发展轨迹使用新颖和定制的统计数据。所发现的轨迹将被聚集到 使用最先进的图聚类算法的子组。患者将被分配到最匹配的 亚组，每组的其他表型特征将通过最小绝对值确定 收缩和选择操作符。2)通过整合来确定共病COPC的生物学基础 生物库和生物储存库中的表型、遗传和基因组数据。我们将在全基因组范围内， 基于UK Biobank和All of Us的诊断和基因型的全表型关联研究 研究计划，导致识别与COPC相关的其他基因型， 除了NHGRI-EBI GWAS目录中的那些。我们将应用我们的信息理论框架来估算 通过使用GTEx表达定量， 性状基因座数据和基因本体注释。COPC之间共享机制的研究结果将提供 对遗传因素的新见解，特别是在非编码区，和功能联系是关键， 开发COPC的药物再利用等应用。这两个目标相互印证， 基因组-表型组边界，以揭示可解释的亚组，这将推动COPC的精准医学。

PROJECT SUMMARY/ABSTRACT With an estimated prevalence of 20% in American adults and annual costs above $500 million, chronic pains pose a high toll to public health. Many patients developed chronic overlapping pain conditions (COPCs), where craniofacial pains like temporomandibular disorder (TMD) represent a unique component that co-occurs frequently with other chronic pains including irritable bowel syndrome (IBS). Not only do the comorbidities complicate pain management, but the etiology of COPCs remains unclear. Heterogeneity exists within and across COPCs, so mechanism-based classification schemes are needed to identify safe and effective therapies for distinct subgroups of patients. However, existing research surrounding COPC taxonomy has not fully integrated phenotypic and genotypic data at the population level. Reliance on disease-specific study cohorts seriously limited the sample size and diversity. On the other hand, informatics and data science have advanced secondary use of biomedical data, presenting a strong alternative to hypothesis-driven, controlled studies. We propose to elicit COPC subgroups by mining three distinct population-based clinical datasets and imputing the biological underpinnings of co-occurring COPCs by using functional genomic knowledge bases. Our approach consists of two aims: 1) Identify COPC subgroups and other commonly associated phenotypes from rich longitudinal clinical data and notes, including over one million patients in the Rochester Epidemiology Project. The clinical datasets will be computationally screened for COPCs and other co-occurring phenotypes based on diagnosis codes and natural language processing. We will identify statistically significant COPC comorbidities and progression trajectories using novel and tailored statistics. The discovered trajectories will be clustered into subgroups using cutting-edge graph clustering algorithms. The patients will be assigned to the best matched subgroups, for which additional phenotypic characteristics of each group will be determined by least absolute shrinkage and selection operator. 2) Impute biological underpinnings for comorbid COPCs by integrating phenotypic, genetic, and genomic data in biobanks and biorepositories. We will conduct genome-wide and phenome-wide association studies based on the diagnoses and genotypes from the UK Biobank and All of Us Research Program, leading to identification of additional genotypes that are associated with the COPCs and beyond those in the NHGRI-EBI GWAS catalog. We will apply our information-theoretic framework to impute the functional similarity and shared biological mechanisms across COPCs by using GTEx expression quantitative trait loci data and gene ontology annotations. The findings of shared mechanisms among COPCs will provide novel insight into the genetic factors, particularly in noncoding regions, and functional linkages that are pivotal to developing applications such as drug repurposing for COPCs. The two aims corroborate each other across the genome-phenome boundary to unveil interpretable subgroups that will advance precision medicine for COPCs.