Identifying hypothetical genetic influences on complex disease phenotypes
<p>Abstract</p> <p>Background</p> <p>Statistical interactions between disease-associated loci of complex genetic diseases suggest that genes from these regions are involved in a common mechanism impacting, or impacted by, the disease. The computational problem we addres...
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| Format: | Article |
| Language: | English |
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BMC
2009-02-01
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| Series: | BMC Bioinformatics |
| _version_ | 1811319196415426560 |
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| author | Keller Benjamin J McEachin Richard C |
| author_facet | Keller Benjamin J McEachin Richard C |
| author_sort | Keller Benjamin J |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Statistical interactions between disease-associated loci of complex genetic diseases suggest that genes from these regions are involved in a common mechanism impacting, or impacted by, the disease. The computational problem we address is to discover relationships among genes from these interacting regions that may explain the observed statistical interaction and the role of these genes in the disease phenotype.</p> <p>Results</p> <p>We describe a heuristic algorithm for generating hypothetical gene relationships from loci associated with a complex disease phenotype. This approach, called Prioritizing Disease Genes by Analysis of Common Elements (PDG-ACE), mines biomedical keywords from text descriptions of genes and uses them to relate genes close to disease-associated loci. A keyword common to, and significantly over-represented in, a pair of gene descriptions may represent a preliminary hypothesis about the biological relationship between the genes, and suggest the role the genes play in the disease phenotype.</p> <p>Conclusion</p> <p>Our experimentation shows that the approach finds previously published relationships, while failing to find relationships that don't exist. The results also indicate that the approach is robust to differences in keyword vocabulary. We outline a brief case study in which results from a recently published Type 2 Diabetes association study are used to identify potential hypotheses.</p> |
| first_indexed | 2024-04-13T12:38:40Z |
| format | Article |
| id | doaj.art-fa4076b528874cf9a9e914ccd463e5e8 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-13T12:38:40Z |
| publishDate | 2009-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-fa4076b528874cf9a9e914ccd463e5e82022-12-22T02:46:35ZengBMCBMC Bioinformatics1471-21052009-02-0110Suppl 2S1310.1186/1471-2105-10-S2-S13Identifying hypothetical genetic influences on complex disease phenotypesKeller Benjamin JMcEachin Richard C<p>Abstract</p> <p>Background</p> <p>Statistical interactions between disease-associated loci of complex genetic diseases suggest that genes from these regions are involved in a common mechanism impacting, or impacted by, the disease. The computational problem we address is to discover relationships among genes from these interacting regions that may explain the observed statistical interaction and the role of these genes in the disease phenotype.</p> <p>Results</p> <p>We describe a heuristic algorithm for generating hypothetical gene relationships from loci associated with a complex disease phenotype. This approach, called Prioritizing Disease Genes by Analysis of Common Elements (PDG-ACE), mines biomedical keywords from text descriptions of genes and uses them to relate genes close to disease-associated loci. A keyword common to, and significantly over-represented in, a pair of gene descriptions may represent a preliminary hypothesis about the biological relationship between the genes, and suggest the role the genes play in the disease phenotype.</p> <p>Conclusion</p> <p>Our experimentation shows that the approach finds previously published relationships, while failing to find relationships that don't exist. The results also indicate that the approach is robust to differences in keyword vocabulary. We outline a brief case study in which results from a recently published Type 2 Diabetes association study are used to identify potential hypotheses.</p> |
| spellingShingle | Keller Benjamin J McEachin Richard C Identifying hypothetical genetic influences on complex disease phenotypes BMC Bioinformatics |
| title | Identifying hypothetical genetic influences on complex disease phenotypes |
| title_full | Identifying hypothetical genetic influences on complex disease phenotypes |
| title_fullStr | Identifying hypothetical genetic influences on complex disease phenotypes |
| title_full_unstemmed | Identifying hypothetical genetic influences on complex disease phenotypes |
| title_short | Identifying hypothetical genetic influences on complex disease phenotypes |
| title_sort | identifying hypothetical genetic influences on complex disease phenotypes |
| work_keys_str_mv | AT kellerbenjaminj identifyinghypotheticalgeneticinfluencesoncomplexdiseasephenotypes AT mceachinrichardc identifyinghypotheticalgeneticinfluencesoncomplexdiseasephenotypes |