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Using the Phenogen website for ‘in silico’ analysis of morphine‐induced analgesia: identifying candidate genes
Author(s) -
Hoffman Paula L.,
Bennett Beth,
Saba Laura M.,
Bhave Sanjiv V.,
CarosoneLink Phyllis J.,
Hornbaker Cheryl K.,
Kechris Katerina J.,
Williams Robert W.,
Tabakoff Boris
Publication year - 2011
Publication title -
addiction biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.445
H-Index - 78
eISSN - 1369-1600
pISSN - 1355-6215
DOI - 10.1111/j.1369-1600.2010.00254.x
Subject(s) - candidate gene , in silico , computational biology , gene , biology , phenotype , identification (biology) , genomics , quantitative trait locus , genetics , bioinformatics , genome , botany
The identification of genes that contribute to polygenic (complex) behavioral phenotypes is a key goal of current genetic research. One approach to this goal is to combine gene expression information with genetic information, i.e. to map chromosomal regions that regulate gene expression levels. This approach has been termed 'genetical genomics', and, when used in conjunction with the identification of genomic regions (QTLs) that regulate the complex physiological trait under investigation, provides a strong basis for candidate gene discovery. In this paper, we describe the implementation of the genetical genomic/phenotypic approach to identify candidate genes for sensitivity to the analgesic effect of morphine in BXD recombinant inbred mice. Our analysis was performed 'in silico', using an online interactive resource called PhenoGen (http://phenogen.ucdenver.edu). We describe in detail the use of this resource, which identified a set of candidate genes, some of whose products regulate the cellular localization and activity of the mu opiate receptor. The results demonstrate how PhenoGen can be used to identify a novel set of genes that can be further investigated for their potential role in pain, morphine analgesia and/or morphine tolerance.