Abstract
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.
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Availability of data and materials
The data that support the findings of this study are available as additional material. However, whilst information for the patients results has been removed for confidential reasons. The datasets used and/or analysed during the current study are available from the DECIPHER database under signed agreement. All code underlying the workflow is freely available from https://github.com/fmjabato/PhenFun, written in R and bash script, employing a workflow manager, AutoFlow, to be run on UNIX-like systems. All dependencies are explained in the README file of the Github repository.
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Acknowledgements
The authors would like to thank the Supercomputing and Bioinnovation Center (SCBI) of the University of Malaga for their provision of computational resources and technical support (www.scbi.uma.es/site). This study makes use of data generated by the DECIPHER community. A full list of centers who contributed to the generation of the data is available from http://decipher.sanger.ac.uk and via email from decipher@sanger.ac.uk. Funding for the project was provided by the Wellcome Trust. Those who carried out the original analysis and collection of the data bear no responsibility for the further analysis or interpretation of it by the Recipient or its Registered Users.
Funding
The study was funded by grants from the The Spanish Ministry of Science and Innovation with European Regional Development Fund [PID2019-108096RB-C21 to J.A.G and PID2019-108096RB-C22 to F.P.]; the Andalusian Government with European Regional Development Fund [UMA18-FEDERJA-102]; biomedicine research project [PI-0075-2017] (Fundacion Progreso y Salud); and the Ramon Areces foundation for rare disease investigation (National call for research on life and material sciences, XIX edition). The CIBERER is an initiative from the Carlos III Health Institute (Instituto de Salud Carlos III).
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Phenotype-GO HTML enrichments report with phenotype specific enrichment results. Extended explanation is included into file.
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Phenotype-KEGG HTML enrichments report with phenotype specific enrichment results. Extended explanation is included into file.
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Phenotype-Reactome HTML enrichments report with phenotype specific enrichment results. Extended explanation is included into file.
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Patient-GO HTML enrichments report with patients specific enrichment results. Extended explanation is included into file.
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Jabato, F.M., Seoane, P., Perkins, J.R. et al. Systematic identification of genetic systems associated with phenotypes in patients with rare genomic copy number variations. Hum Genet 140, 457–475 (2021). https://doi.org/10.1007/s00439-020-02214-7
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DOI: https://doi.org/10.1007/s00439-020-02214-7