Candidate genes and their regulatory elements: alcohol preference and tolerance
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QTL analysis of behavioral traits and mouse brain gene expression studies were combined to identify candidate genes involved in the traits of alcohol preference and acute functional alcohol tolerance. The systematic application of normalization and statistical analysis of differential gene expression, behavioral and expression QTL location, and informatics methodologies resulted in identification of 8 candidate genes for the trait of alcohol preference and 22 candidate genes for acute functional tolerance. Pathway analysis, combined with clustering by ontology, indicated the importance of transcriptional regulation and DNA and protein binding elements in the acute functional tolerance trait, and protein kinases and intracellular signal transduction elements in the alcohol preference trait. A rudimentary search for transcription control elements that could indicate coregulation of the panels of candidate genes produced modest results, implicating SMAD-3 in the regulation of four of the eight candidate genes for alcohol preference. However, the realization of the many caveats related to transcription factor binding site analysis, and attempts to correlate between transcription factor binding and function, forestalled any definitive global analysis of transcriptional control of differentially expressed candidate genes.
KeywordsQuantitative Trait Locus Transcription Factor Binding Site Quantitative Trait Locus Analysis Recombinant Inbred Identify Candidate Gene
The authors acknowledge the financial support of the National Institute on Alcohol Abuse and Alcoholism and particularly the Integrative Neuroscience Initiative on Alcoholism; Lohocla Research Corporation and the Banbury Fund.
- Affymetrix (2001) Statistical algorithms reference guide (Santa Clara, CA: Affymetrix, Inc.)Google Scholar
- Affymetrix (2004) GeneChip ® expression analysis. Data analysis fundamentals (Santa Clara, CA: Affymetrix, Inc.)Google Scholar
- Behm A, Lumeng L, Li T-K, Grahame N (2003) Selective breeding for replicate line 2 high and low alcohol preferring mice. Alcohol Clin Exp Res 27:49AGoogle Scholar
- Dudoit S, Yang YH, Callow MJ, Speed TP (2000) Statistical methods for identifying differentially expressed genes in replicate cDNA microarray experiments. Available at http://www.stat.berkeley.edu/users/terry/zarray/TechReport/578.pdf
- Falconer DS (1996) Introduction to Quantitative Genetics (Essex, UK: Longman)Google Scholar
- Jurka J (1994) Approaches to identification and analysis of interspersed repetitive DNA sequences. In Automated DNA Sequencing and Analysis, Adams MD, Fields C, Venter JC (eds) (San Diego, CA: Academic Press), pp 294–298Google Scholar
- Kirstein SL, Davidson KL, Ehringer MA, Sikela JM, Erwin VG, et al. (2002) Quantitative trait loci affecting initial sensitivity and acute functional tolerance to ethanol-induced ataxia and brain cAMP signaling in BXD recombinant inbred mice. J Pharmacol Exp Ther 302:1238–1245PubMedCrossRefGoogle Scholar
- McClearn GE, Wilson JR, Meredith W (1970) The use of isogenic and heterogenic mouse stocks in behavioral research. In Contributions to Behavior-Genetic Analysis: The Mouse As a Prototype, Lindzey G, Thiessen DD. (eds) (New York: Appleton–Century–Crofts), pp 3–22Google Scholar
- Thoday JM (1961) Location of polygenes. Nature 191:368–370Google Scholar