On the integration of alcohol-related quantitative trait loci and gene expression analyses

Robert Hitzemann, Cheryl Reed, Barry Malmanger, Maureen Lawler, Barbara Hitzemann, Brendan Cunningham, Shannon McWeeney, John Belknap, Christina (Chris) Harrington, Kari Buck, Tamara Phillips, John Jr Crabbe

Research output: Contribution to journalArticle

46 Citations (Scopus)

Abstract

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 -6 to 10-15. 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 ≥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.

Original languageEnglish (US)
Pages (from-to)1437-1448
Number of pages12
JournalAlcoholism: Clinical and Experimental Research
Volume28
Issue number10
DOIs
StatePublished - Oct 2004

Fingerprint

Quantitative Trait Loci
Gene expression
Ethanol
Alcohols
Gene Expression
Genes
Chromosomes
Brain
Chemical activation
Throughput
Chromosomes, Human, Pair 4
Genetic Models
Genomics
Amygdala
Hippocampus
Databases
Phenotype

Keywords

  • Brain
  • Ethanol
  • Gene Expression
  • Mice
  • QTL

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Toxicology

Cite this

On the integration of alcohol-related quantitative trait loci and gene expression analyses. / Hitzemann, Robert; Reed, Cheryl; Malmanger, Barry; Lawler, Maureen; Hitzemann, Barbara; Cunningham, Brendan; McWeeney, Shannon; Belknap, John; Harrington, Christina (Chris); Buck, Kari; Phillips, Tamara; Crabbe, John Jr.

In: Alcoholism: Clinical and Experimental Research, Vol. 28, No. 10, 10.2004, p. 1437-1448.

Research output: Contribution to journalArticle

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abstract = "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 -6 to 10-15. 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 ≥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.",
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author = "Robert Hitzemann and Cheryl Reed and Barry Malmanger and Maureen Lawler and Barbara Hitzemann and Brendan Cunningham and Shannon McWeeney and John Belknap and Harrington, {Christina (Chris)} and Kari Buck and Tamara Phillips and Crabbe, {John Jr}",
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T1 - On the integration of alcohol-related quantitative trait loci and gene expression analyses

AU - Hitzemann, Robert

AU - Reed, Cheryl

AU - Malmanger, Barry

AU - Lawler, Maureen

AU - Hitzemann, Barbara

AU - Cunningham, Brendan

AU - McWeeney, Shannon

AU - Belknap, John

AU - Harrington, Christina (Chris)

AU - Buck, Kari

AU - Phillips, Tamara

AU - Crabbe, John Jr

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N2 - 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 -6 to 10-15. 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 ≥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.

AB - 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 -6 to 10-15. 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 ≥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.

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KW - Gene Expression

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KW - QTL

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