DESCRIPTION:

QTL associated with methamphetamine response QTL 1. This interval was obtained by using a fixed interval width of 25 Mbp around the peak marker (125309605)

LABEL:

QTL-Marq1-Mouse-Chr 5

SCORE TYPE:

Binary

DATE ADDED:

2012-04-02

DATE UPDATED:

2024-04-25

SPECIES:

URI:

http://www.informatics.jax.org/searches/accession_report.cgi?id=MGI:3590510

AUTHORS:

Palmer AA, Verbitsky M, Suresh R, Kamens HM, Reed CL, Li N, Burkhart-Kasch S, McKinnon CS, Belknap JK, Gilliam TC, Phillips TJ

TITLE:

Gene expression differences in mice divergently selected for methamphetamine sensitivity.

JOURNAL:

Mammalian genome : official journal of the International Mammalian Genome Society May 2005, Vol 16, pp. 291-305

ABSTRACT:

In an effort to identify genes that may be important for drug-abuse liability, we mapped behavioral quantitative trait loci (bQTL) for sensitivity to the locomotor stimulant effect of methamphetamine (MA) using two mouse lines that were selectively bred for high MA-induced activity (HMACT) or low MA-induced activity (LMACT). We then examined gene expression differences between these lines in the nucleus accumbens, using 20 U74Av2 Affymetrix microarrays and quantitative polymerase chain reaction (qPCR). Expression differences were detected for several genes, including Casein Kinase 1 Epsilon (Csnkle), glutamate receptor, ionotropic, AMPA1 (GluR1), GABA B1 receptor (Gabbr1), and dopamine- and cAMP-regulated phosphoprotein of 32 kDa (Darpp-32). We used the www.WebQTL.org database to identify QTL that regulate the expression of the genes identified by the microarrays (expression QTL; eQTL). This approach identified an eQTL for Csnkle on Chromosome 15 (LOD = 3.8) that comapped with a bQTL for the MA stimulation phenotype (LOD = 4.5), suggesting that a single allele may cause both traits. The chromosomal region containing this QTL has previously been associated with sensitivity to the stimulant effects of cocaine. These results suggest that selection was associated with (and likely caused) altered gene expression that is partially attributable to different frequencies of gene expression polymorphisms. Combining classical genetics with analysis of whole-genome gene expression and bioinformatic resources provides a powerful method for provisionally identifying genes that influence complex traits. The identified genes provide excellent candidates for future hypothesis-driven studies, translational genetic studies, and pharmacological interventions. PUBMED: 16104378
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