Identification of QTLs controlling gene expression networks defined <it>a priori</it>
<p>Abstract</p> <p>Background</p> <p>Gene expression microarrays allow the quantification of transcript accumulation for many or all genes in a genome. This technology has been utilized for a range of investigations, from assessments of gene regulation in response to ge...
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| Format: | Article |
| Language: | English |
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BMC
2006-06-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/7/308 |
| _version_ | 1818753865434005504 |
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| author | Doerge RW Loudet Olivier van Leeuwen Hans West Marilyn AL Kliebenstein Daniel J St Clair Dina A |
| author_facet | Doerge RW Loudet Olivier van Leeuwen Hans West Marilyn AL Kliebenstein Daniel J St Clair Dina A |
| author_sort | Doerge RW |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Gene expression microarrays allow the quantification of transcript accumulation for many or all genes in a genome. This technology has been utilized for a range of investigations, from assessments of gene regulation in response to genetic or environmental fluctuation to global expression QTL (eQTL) analyses of natural variation. Current analysis techniques facilitate the statistical querying of individual genes to evaluate the significance of a change in response, also known as differential expression. Since genes are also known to respond as groups due to their membership in networks, effective approaches are needed to investigate transcriptome variation as related to gene network responses.</p> <p>Results</p> <p>We describe a statistical approach that is capable of assessing higher-order <it>a priori </it>defined gene network response, as measured by microarrays. This analysis detected significant network variation between two <it>Arabidopsis thaliana </it>accessions, Bay-0 and Shahdara. By extending this approach, we were able to identify eQTLs controlling network responses for 18 out of 20 <it>a priori</it>-defined gene networks in a recombinant inbred line population derived from accessions Bay-0 and Shahdara.</p> <p>Conclusion</p> <p>This approach has the potential to be expanded to facilitate direct tests of the relationship between phenotypic trait and transcript genetic architecture. The use of <it>a priori </it>definitions for network eQTL identification has enormous potential for providing direction toward future eQTL analyses.</p> |
| first_indexed | 2024-12-18T05:14:09Z |
| format | Article |
| id | doaj.art-a7a93af53002463382a84c9343e10e69 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-18T05:14:09Z |
| publishDate | 2006-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-a7a93af53002463382a84c9343e10e692022-12-21T21:19:49ZengBMCBMC Bioinformatics1471-21052006-06-017130810.1186/1471-2105-7-308Identification of QTLs controlling gene expression networks defined <it>a priori</it>Doerge RWLoudet Oliviervan Leeuwen HansWest Marilyn ALKliebenstein Daniel JSt Clair Dina A<p>Abstract</p> <p>Background</p> <p>Gene expression microarrays allow the quantification of transcript accumulation for many or all genes in a genome. This technology has been utilized for a range of investigations, from assessments of gene regulation in response to genetic or environmental fluctuation to global expression QTL (eQTL) analyses of natural variation. Current analysis techniques facilitate the statistical querying of individual genes to evaluate the significance of a change in response, also known as differential expression. Since genes are also known to respond as groups due to their membership in networks, effective approaches are needed to investigate transcriptome variation as related to gene network responses.</p> <p>Results</p> <p>We describe a statistical approach that is capable of assessing higher-order <it>a priori </it>defined gene network response, as measured by microarrays. This analysis detected significant network variation between two <it>Arabidopsis thaliana </it>accessions, Bay-0 and Shahdara. By extending this approach, we were able to identify eQTLs controlling network responses for 18 out of 20 <it>a priori</it>-defined gene networks in a recombinant inbred line population derived from accessions Bay-0 and Shahdara.</p> <p>Conclusion</p> <p>This approach has the potential to be expanded to facilitate direct tests of the relationship between phenotypic trait and transcript genetic architecture. The use of <it>a priori </it>definitions for network eQTL identification has enormous potential for providing direction toward future eQTL analyses.</p>http://www.biomedcentral.com/1471-2105/7/308 |
| spellingShingle | Doerge RW Loudet Olivier van Leeuwen Hans West Marilyn AL Kliebenstein Daniel J St Clair Dina A Identification of QTLs controlling gene expression networks defined <it>a priori</it> BMC Bioinformatics |
| title | Identification of QTLs controlling gene expression networks defined <it>a priori</it> |
| title_full | Identification of QTLs controlling gene expression networks defined <it>a priori</it> |
| title_fullStr | Identification of QTLs controlling gene expression networks defined <it>a priori</it> |
| title_full_unstemmed | Identification of QTLs controlling gene expression networks defined <it>a priori</it> |
| title_short | Identification of QTLs controlling gene expression networks defined <it>a priori</it> |
| title_sort | identification of qtls controlling gene expression networks defined it a priori it |
| url | http://www.biomedcentral.com/1471-2105/7/308 |
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