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Prioritisation of candidate genes for psychiatric disorders
The application of genome-wide association studies and next-generation sequencing has had limited success in identifying causal genes for complex diseases. Bipolar disorder is one such disease whose aetiology has not been elucidated despite the application of these technologies. Candidate gene pr...
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| Format: | Thesis |
| Language: | English |
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Health Sciences
2016
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| _version_ | 1867614317886570496 |
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| access_status_str | Open Access |
| author | Kalweit, Kerry |
| author_browse | Kalweit, Kerry |
| author_facet | Kalweit, Kerry |
| author_sort | Kalweit, Kerry |
| collection | Thesis |
| description | The application of genome-wide association studies and next-generation sequencing has had
limited success in identifying causal genes for complex diseases. Bipolar disorder is one such
disease whose aetiology has not been elucidated despite the application of these technologies.
Candidate gene prioritisation offers a solution to limit the vast amount of possible candidate
genes produced from the combination of data sources. Current prioritisation tools rely heavily on
previous data and thus do not perform well for poorly characterised diseases such as bipolar
disorder. Here we have developed Data Integrated Genetics, DIG, a new candidate gene
prioritisation tool designed specifically for complex genetic diseases. Given a user-specified
disease query, DIG initially data-mines literature, linkage, homolog and sequence data to create
a pool of possible candidates. The tool filters out likely false positives by removing pseudogenes.
A unique data integration method is used to rank the remaining list of genes. Additionally,
ranking is validated by tissue expression and single nucleotide polymorphism annotation. DIG
exhibited comparable performance to existing tools when evaluated with four complex diseases.
Eight novel genes were identified when DIG was applied to bipolar disorder, of which the
Huntingtin gene poses as an exciting avenue for new aetiology research. The ease of use and
realistic number of possible candidates given in the DIG results make this tool highly useful for
research application in the study of complex genetic diseases. DIG is freely available from
http://www.cbio.uct.ac.za/DIG. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/21223 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:50:07.921Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2016 |
| publishDateRange | 2016 |
| publishDateSort | 2016 |
| publisher | Health Sciences |
| publisherStr | Health Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/21223 Prioritisation of candidate genes for psychiatric disorders Kalweit, Kerry The application of genome-wide association studies and next-generation sequencing has had limited success in identifying causal genes for complex diseases. Bipolar disorder is one such disease whose aetiology has not been elucidated despite the application of these technologies. Candidate gene prioritisation offers a solution to limit the vast amount of possible candidate genes produced from the combination of data sources. Current prioritisation tools rely heavily on previous data and thus do not perform well for poorly characterised diseases such as bipolar disorder. Here we have developed Data Integrated Genetics, DIG, a new candidate gene prioritisation tool designed specifically for complex genetic diseases. Given a user-specified disease query, DIG initially data-mines literature, linkage, homolog and sequence data to create a pool of possible candidates. The tool filters out likely false positives by removing pseudogenes. A unique data integration method is used to rank the remaining list of genes. Additionally, ranking is validated by tissue expression and single nucleotide polymorphism annotation. DIG exhibited comparable performance to existing tools when evaluated with four complex diseases. Eight novel genes were identified when DIG was applied to bipolar disorder, of which the Huntingtin gene poses as an exciting avenue for new aetiology research. The ease of use and realistic number of possible candidates given in the DIG results make this tool highly useful for research application in the study of complex genetic diseases. DIG is freely available from http://www.cbio.uct.ac.za/DIG. 2016-08-13T18:34:26Z 2016-08-13T18:34:26Z 2013 2016-08-13T18:27:07Z Master Thesis Masters Bsc (Med)Hons http://hdl.handle.net/11427/21223 eng application/pdf Health Sciences Unknown University of Cape Town University of Cape Town |
| spellingShingle | Kalweit, Kerry Prioritisation of candidate genes for psychiatric disorders |
| thesis_degree_str | Master's |
| title | Prioritisation of candidate genes for psychiatric disorders |
| title_full | Prioritisation of candidate genes for psychiatric disorders |
| title_fullStr | Prioritisation of candidate genes for psychiatric disorders |
| title_full_unstemmed | Prioritisation of candidate genes for psychiatric disorders |
| title_short | Prioritisation of candidate genes for psychiatric disorders |
| title_sort | prioritisation of candidate genes for psychiatric disorders |
| url | http://hdl.handle.net/11427/21223 |
| work_keys_str_mv | AT kalweitkerry prioritisationofcandidategenesforpsychiatricdisorders |