BACKGROUND: Quantitative trait loci (QTLs) have been detected for a wide variety of ethanol-related phenotypes, including acute and chronic ethanol withdrawal, acute locomotor activation, and ethanol preference. This study was undertaken to determine whether the process of moving from QTL to quantitative trait gene (QTG) could be accelerated by the integration of functional genomics (gene expression) into the analysis strategy. METHODS: Six ethanol-related QTLs, all detected in C57BL/6J and DBA/2J intercrosses were entered into the analysis. Each of the QTLs had been confirmed in independent genetic models at least once; the cumulative probabilities for QTL existence ranged from 10 to 10. Brain gene expression data for the C57BL/6 and DBA/2 strains (n = 6 per strain) and an F2 intercross sample (n = 56) derived from these strains were obtained by using the Affymetrix U74Av2 and 430A arrays; additional data with the U74Av2 array were available for the extended amygdala, dorsomedial striatum, and hippocampus. Low-level analysis was performed by using multiple methods to determine the likelihood that a transcript was truly differentially expressed. For the 430A array data, the F2 sample was used to determine which of the differentially expressed transcripts within the QTL intervals were cis-regulated and, thus, strong candidates for QTGs. RESULTS: Within the 6 QTL intervals, 39 transcripts (430A array) were identified as being highly likely to be differentially expressed between the C57BL/6 and DBA/2 strains at a false discovery rate of 0.01 or better. Twenty-eight of these transcripts showed significant (logarithm of odds > or =3.6) to highly significant (logarithm of odds >7) cis-regulation. The process correctly detected Mpdz (chromosome 4) as a candidate QTG for acute withdrawal. CONCLUSIONS: Although improvements are needed in the expression databases, the integration of QTL and gene expression analyses seems to have potential as a high-throughput strategy for moving from QTL to QTG.
BACKGROUND: Quantitative trait loci (QTLs) have been detected for a wide variety of ethanol-related phenotypes, including acute and chronic ethanol withdrawal, acute locomotor activation, and ethanol preference. This study was undertaken to determine whether the process of moving from QTL to quantitative trait gene (QTG) could be accelerated by the integration of functional genomics (gene expression) into the analysis strategy. METHODS: Six ethanol-related QTLs, all detected in C57BL/6J and DBA/2J intercrosses were entered into the analysis. Each of the QTLs had been confirmed in independent genetic models at least once; the cumulative probabilities for QTL existence ranged from 10 to 10. Brain gene expression data for the C57BL/6 and DBA/2 strains (n = 6 per strain) and an F2 intercross sample (n = 56) derived from these strains were obtained by using the Affymetrix U74Av2 and 430A arrays; additional data with the U74Av2 array were available for the extended amygdala, dorsomedial striatum, and hippocampus. Low-level analysis was performed by using multiple methods to determine the likelihood that a transcript was truly differentially expressed. For the 430A array data, the F2 sample was used to determine which of the differentially expressed transcripts within the QTL intervals were cis-regulated and, thus, strong candidates for QTGs. RESULTS: Within the 6 QTL intervals, 39 transcripts (430A array) were identified as being highly likely to be differentially expressed between the C57BL/6 and DBA/2 strains at a false discovery rate of 0.01 or better. Twenty-eight of these transcripts showed significant (logarithm of odds > or =3.6) to highly significant (logarithm of odds >7) cis-regulation. The process correctly detected Mpdz (chromosome 4) as a candidate QTG for acute withdrawal. CONCLUSIONS: Although improvements are needed in the expression databases, the integration of QTL and gene expression analyses seems to have potential as a high-throughput strategy for moving from QTL to QTG.
Authors: Laura Saba; Sanjiv V Bhave; Nicholas Grahame; Paula Bice; Razvan Lapadat; John Belknap; Paula L Hoffman; Boris Tabakoff Journal: Mamm Genome Date: 2006-06-12 Impact factor: 2.957
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Authors: Susan E Bergeson; Ari E Berman; Peter R Dodd; Howard J Edenberg; Robert J Hitzemann; Joanne M Lewohl; Kerrie H Lodowski; Wolfgang H Sommer Journal: Alcohol Clin Exp Res Date: 2005-06-01 Impact factor: 3.455
Authors: John K Belknap; Shannon McWeeney; Cheryl Reed; Sue Burkhart-Kasch; Carrie S McKinnon; Na Li; Harue Baba; Angela C Scibelli; Robert Hitzemann; Tamara J Phillips Journal: Mamm Genome Date: 2013-11-13 Impact factor: 2.957
Authors: Abraham A Palmer; Miguel Verbitsky; Rathi Suresh; Helen M Kamens; Cheryl L Reed; Na Li; Sue Burkhart-Kasch; Carrie S McKinnon; John K Belknap; T Conrad Gilliam; Tamara J Phillips Journal: Mamm Genome Date: 2005-05 Impact factor: 2.957
Authors: Tiebing Liang; Mark W Kimpel; Jeanette N McClintick; Ashley R Skillman; Kevin McCall; Howard J Edenberg; Lucinda G Carr Journal: Genome Biol Date: 2010-02-03 Impact factor: 13.583