Laundering CNV data for candidate process prioritization in brain disorders
Abstract Background Prioritization of genomic data has become a useful tool for uncovering the phenotypic effect of genetic variations (e.g. copy number variations or CNV) and disease mechanisms. Due to the complexity, brain disorders represent a major focus of genomic research aimed at revealing pa...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2019-12-01
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| Series: | Molecular Cytogenetics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13039-019-0468-7 |
| _version_ | 1818622775562076160 |
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| author | Maria A. Zelenova Yuri B. Yurov Svetlana G. Vorsanova Ivan Y. Iourov |
| author_facet | Maria A. Zelenova Yuri B. Yurov Svetlana G. Vorsanova Ivan Y. Iourov |
| author_sort | Maria A. Zelenova |
| collection | DOAJ |
| description | Abstract Background Prioritization of genomic data has become a useful tool for uncovering the phenotypic effect of genetic variations (e.g. copy number variations or CNV) and disease mechanisms. Due to the complexity, brain disorders represent a major focus of genomic research aimed at revealing pathologic significance of genomic changes leading to brain dysfunction. Here, we propose a “CNV data laundering” algorithm based on filtering and prioritizing of genomic pathways retrieved from available databases for uncovering altered molecular pathways in brain disorders. The algorithm comprises seven consecutive steps of processing individual CNV data sets. First, the data are compared to in-house and web databases to discriminate recurrent non-pathogenic variants. Second, the CNV pool is confined to the genes predominantly expressed in the brain. Third, intergenic interactions are used for filtering causative CNV. Fourth, a network of interconnected elements specific for an individual genome variation set is created. Fifth, ontologic data (pathways/functions) are attributed to clusters of network elements. Sixth, the pathways are prioritized according to the significance of elements affected by CNV. Seventh, prioritized pathways are clustered according to the ontologies. Results The algorithm was applied to 191 CNV data sets obtained from children with brain disorders (intellectual disability and autism spectrum disorders) by SNP array molecular karyotyping. “CNV data laundering” has identified 13 pathway clusters (39 processes/475 genes) implicated in the phenotypic manifestations. Conclusions Elucidating altered molecular pathways in brain disorders, the algorithm may be used for uncovering disease mechanisms and genotype-phenotype correlations. These opportunities are strongly required for developing therapeutic strategies in devastating neuropsychiatric diseases. |
| first_indexed | 2024-12-16T18:30:32Z |
| format | Article |
| id | doaj.art-e6c98c81f52c4862b08e3b039a2f333b |
| institution | Directory Open Access Journal |
| issn | 1755-8166 |
| language | English |
| last_indexed | 2024-12-16T18:30:32Z |
| publishDate | 2019-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Molecular Cytogenetics |
| spelling | doaj.art-e6c98c81f52c4862b08e3b039a2f333b2022-12-21T22:21:19ZengBMCMolecular Cytogenetics1755-81662019-12-011211610.1186/s13039-019-0468-7Laundering CNV data for candidate process prioritization in brain disordersMaria A. Zelenova0Yuri B. Yurov1Svetlana G. Vorsanova2Ivan Y. Iourov3Mental Health Research CenterMental Health Research CenterMental Health Research CenterMental Health Research CenterAbstract Background Prioritization of genomic data has become a useful tool for uncovering the phenotypic effect of genetic variations (e.g. copy number variations or CNV) and disease mechanisms. Due to the complexity, brain disorders represent a major focus of genomic research aimed at revealing pathologic significance of genomic changes leading to brain dysfunction. Here, we propose a “CNV data laundering” algorithm based on filtering and prioritizing of genomic pathways retrieved from available databases for uncovering altered molecular pathways in brain disorders. The algorithm comprises seven consecutive steps of processing individual CNV data sets. First, the data are compared to in-house and web databases to discriminate recurrent non-pathogenic variants. Second, the CNV pool is confined to the genes predominantly expressed in the brain. Third, intergenic interactions are used for filtering causative CNV. Fourth, a network of interconnected elements specific for an individual genome variation set is created. Fifth, ontologic data (pathways/functions) are attributed to clusters of network elements. Sixth, the pathways are prioritized according to the significance of elements affected by CNV. Seventh, prioritized pathways are clustered according to the ontologies. Results The algorithm was applied to 191 CNV data sets obtained from children with brain disorders (intellectual disability and autism spectrum disorders) by SNP array molecular karyotyping. “CNV data laundering” has identified 13 pathway clusters (39 processes/475 genes) implicated in the phenotypic manifestations. Conclusions Elucidating altered molecular pathways in brain disorders, the algorithm may be used for uncovering disease mechanisms and genotype-phenotype correlations. These opportunities are strongly required for developing therapeutic strategies in devastating neuropsychiatric diseases.https://doi.org/10.1186/s13039-019-0468-7AutismBioinformaticsBrainCNVIntellectual disabilityPathways |
| spellingShingle | Maria A. Zelenova Yuri B. Yurov Svetlana G. Vorsanova Ivan Y. Iourov Laundering CNV data for candidate process prioritization in brain disorders Molecular Cytogenetics Autism Bioinformatics Brain CNV Intellectual disability Pathways |
| title | Laundering CNV data for candidate process prioritization in brain disorders |
| title_full | Laundering CNV data for candidate process prioritization in brain disorders |
| title_fullStr | Laundering CNV data for candidate process prioritization in brain disorders |
| title_full_unstemmed | Laundering CNV data for candidate process prioritization in brain disorders |
| title_short | Laundering CNV data for candidate process prioritization in brain disorders |
| title_sort | laundering cnv data for candidate process prioritization in brain disorders |
| topic | Autism Bioinformatics Brain CNV Intellectual disability Pathways |
| url | https://doi.org/10.1186/s13039-019-0468-7 |
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