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Searching QTL by gene expression: analysis of diabesity
BACKGROUND: Recent developments in sequence databases provide the opportunity to relate the expression pattern of genes to their genomic position, thus creating a transcriptome map. Quantitative trait loci (QTL) are phenotypically-defined chromosomal regions that contribute to allelically variant bi...
Autores principales: | , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2005
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC555939/ https://www.ncbi.nlm.nih.gov/pubmed/15760467 http://dx.doi.org/10.1186/1471-2156-6-12 |
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author | Brown, Aaron C Olver, William I Donnelly, Charles J May, Marjorie E Naggert, Jürgen K Shaffer, Daniel J Roopenian, Derry C |
author_facet | Brown, Aaron C Olver, William I Donnelly, Charles J May, Marjorie E Naggert, Jürgen K Shaffer, Daniel J Roopenian, Derry C |
author_sort | Brown, Aaron C |
collection | PubMed |
description | BACKGROUND: Recent developments in sequence databases provide the opportunity to relate the expression pattern of genes to their genomic position, thus creating a transcriptome map. Quantitative trait loci (QTL) are phenotypically-defined chromosomal regions that contribute to allelically variant biological traits, and by overlaying QTL on the transcriptome, the search for candidate genes becomes extremely focused. RESULTS: We used our novel data mining tool, ExQuest, to select genes within known diabesity QTL showing enriched expression in primary diabesity affected tissues. We then quantified transcripts in adipose, pancreas, and liver tissue from Tally Ho mice, a multigenic model for Type II diabetes (T2D), and from diabesity-resistant C57BL/6J controls. Analysis of the resulting quantitative PCR data using the Global Pattern Recognition analytical algorithm identified a number of genes whose expression is altered, and thus are novel candidates for diabesity QTL and/or pathways associated with diabesity. CONCLUSION: Transcription-based data mining of genes in QTL-limited intervals followed by efficient quantitative PCR methods is an effective strategy for identifying genes that may contribute to complex pathophysiological processes. |
format | Text |
id | pubmed-555939 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2005 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-5559392005-04-03 Searching QTL by gene expression: analysis of diabesity Brown, Aaron C Olver, William I Donnelly, Charles J May, Marjorie E Naggert, Jürgen K Shaffer, Daniel J Roopenian, Derry C BMC Genet Methodology Article BACKGROUND: Recent developments in sequence databases provide the opportunity to relate the expression pattern of genes to their genomic position, thus creating a transcriptome map. Quantitative trait loci (QTL) are phenotypically-defined chromosomal regions that contribute to allelically variant biological traits, and by overlaying QTL on the transcriptome, the search for candidate genes becomes extremely focused. RESULTS: We used our novel data mining tool, ExQuest, to select genes within known diabesity QTL showing enriched expression in primary diabesity affected tissues. We then quantified transcripts in adipose, pancreas, and liver tissue from Tally Ho mice, a multigenic model for Type II diabetes (T2D), and from diabesity-resistant C57BL/6J controls. Analysis of the resulting quantitative PCR data using the Global Pattern Recognition analytical algorithm identified a number of genes whose expression is altered, and thus are novel candidates for diabesity QTL and/or pathways associated with diabesity. CONCLUSION: Transcription-based data mining of genes in QTL-limited intervals followed by efficient quantitative PCR methods is an effective strategy for identifying genes that may contribute to complex pathophysiological processes. BioMed Central 2005-03-10 /pmc/articles/PMC555939/ /pubmed/15760467 http://dx.doi.org/10.1186/1471-2156-6-12 Text en Copyright © 2005 Brown et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methodology Article Brown, Aaron C Olver, William I Donnelly, Charles J May, Marjorie E Naggert, Jürgen K Shaffer, Daniel J Roopenian, Derry C Searching QTL by gene expression: analysis of diabesity |
title | Searching QTL by gene expression: analysis of diabesity |
title_full | Searching QTL by gene expression: analysis of diabesity |
title_fullStr | Searching QTL by gene expression: analysis of diabesity |
title_full_unstemmed | Searching QTL by gene expression: analysis of diabesity |
title_short | Searching QTL by gene expression: analysis of diabesity |
title_sort | searching qtl by gene expression: analysis of diabesity |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC555939/ https://www.ncbi.nlm.nih.gov/pubmed/15760467 http://dx.doi.org/10.1186/1471-2156-6-12 |
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