Copy number variation (CNV) related disorders tend to show complex phenotypic profiles that do not match known diseases. This makes it difficult to ascertain their underlying molecular basis. A potential solution is to compare the affected genomic regions for multiple patients that share a pathological phenotype, looking for commonalities. Here, we present a novel approach to associate phenotypes with functional systems, in terms of GO categories and KEGG and Reactome pathways, based on patient data. The approach uses genomic and phenomic data from the same patients, finding shared genomic regions between patients with similar phenotypes. These regions are mapped to genes to find associated functional systems. We applied the approach to analyse patients in the DECIPHER database with de novo CNVs, finding functional systems associated with most phenotypes, often due to mutations affecting related genes in the same genomic region. Manual inspection of the ten top-scoring phenotypes found multiple FunSys connections supported by the previous studies for seven of them. The workflow also produces reports focussed on the genes and FunSys connected to the different phenotypes, alongside patient-specific reports, which give details of the associated genes and FunSys for each individual in the cohort. These can be run in “confidential” mode, preserving patient confidentiality. The workflow presented here can be used to associate phenotypes with functional systems using data at the level of a whole cohort of patients, identifying important connections that could not be found when considering them individually. The full workflow is available for download, enabling it to be run on any patient cohort for which phenotypic and CNV data are available.

与拷贝数变异（CNV）相关的疾病往往表现出与已知疾病不匹配的复杂表型特征。这使得难以确定其潜在的分子基础。一种潜在的解决方案是比较具有病理表型的多名患者的受影响基因组区域，以寻找共同点。在这里，我们根据患者数据，根据GO类别，KEGG和Reactome途径，提出了一种将表型与功能系统相关联的新颖方法。该方法使用来自相同患者的基因组和表型数据，在具有相似表型的患者之间找到共享的基因组区域。这些区域被映射到基因以找到相关的功能系统。我们应用了该方法来分析带有de novo CNV的DECIPHER数据库中的患者，常常是由于突变影响了同一基因组区域中的相关基因，因此发现与大多数表型相关的功能系统。人工检查了十个得分最高的表型，发现其中七个对多个FunSys连接得到了先前研究的支持。该工作流程还生成关注于与不同表型相关的基因和FunSys的报告，以及针对患者的报告，该报告提供了队列中每个个体的相关基因和FunSys的详细信息。这些可以在“机密”模式下运行，从而保护患者机密性。此处介绍的工作流程可用于使用整个患者队列中的数据将表型与功能系统相关联，从而确定在单独考虑患者时找不到的重要联系。