Panels of genetically diverse inbred strains are a rich resource for detecting the presence of common genetic influences on multiple behavioral, pharmacological or physiological traits 1
. Multiple phenotypes are measured in a number of inbred strains, and strain means for each trait are used to compare strain performance in a given test. The strain means can then be used to calculate correlation coefficients between all pairs of traits (correlation matrix), which can then be subjected to multivariate analyses. Each individual within an inbred strain is genetically identical to all other individuals of that strain, while individual differences within a strain reflect non-genetic or environmental influences. Because such correlations are based on inbred strain means, they are estimates of genetic correlations, which index the degree to which any pair of traits share common genetic influences, a condition termed pleiotropy 1
. The matrix of all such correlations is a genetic correlation matrix.
Some examples of this approach include identification of similar genetic influences on withdrawal severity induced by drugs of abuse with similar pharmacological mechanisms 2
, which in turn led to the identification of a chromosomal region with genes affecting severity of withdrawal from pentobarbital, ethanol and zolpidem. This region of mouse chromosome 11 harbors several GABA-A receptor subunit genes 3
. Similar genetic influences affect multiple bone strength parameters 4
, and multiple cardiovascular indices related to blood pressure covary among strains 5,6
. Strain covariation in kainate and serotonin receptor densities in amygdala are correlated with fear-sensitized acoustic startle response 7
. In another recent example, commonalities among 22 different assays of pain sensitivity were assessed in a panel of 11-12 standard inbred mouse strains by first estimating the (bivariate) genetic correlations among all pairs of assays. These were subjected to multivariate methods which suggested several clusters of common genetic influence among some of the assays, and thus presumably also common mechanisms 8,9
. This approach can also be taken to study common genetic influences among diverse responses to a single drug, such as locomotor stimulation and reduction in body temperature induced by pentobarbital 10
. In another analysis in inbred mouse strains, genetic correlations among many different responses to ethanol thought to reflect intoxication were generally small, suggesting that each assay (e.g., rotarod, balance beam) was reflecting a genetically unique trait and therefore could not easily be thought to represent the broad domain of “ethanol-induced intoxication.” 11
Each correlation coefficient in a genetic correlation matrix represents the bivariate case (one pair of variables), but all of them in the aggregate (the covariance matrix) provides the starting point for most multivariate analyses where variables are analyzed in greater-than-pairwise fashion. Among the advantages of multivariate methods are the reduction in redundancy among variables (data reduction), and to reveal groups of related variables at higher levels of association than the bivariate. Studies involving a relatively large number of inbred strains offer a number of advantages over studying only one or two strains. First, they allow a more accurate assessment of the heritability, which estimates the proportion of the observed variability that is genetically determined. Second, they allow an assessment of genetic correlations, or the degree to which two traits show common genetic influences 1
. For example, alcohol withdrawal severity and alcohol preference drinking are known to be genetically negatively correlated in a variety of populations derived from the C57BL/6 and DBA/2 inbred strains 12
. In the analysis reported here, we sought to determine whether this same relationship also exists for the other three drugs of abuse we tested, and whether it exists when sampling alleles from 15 inbred strains rather than just two. Third, they allow the identification of genotypes (strains) that possess useful and/or interesting characteristics, such as those exhibiting very high or very low drug-seeking behavior. This can lead to further studies of the trait extremes, which may serve to model some genetic determinants of high and low abuse risk in humans. Opposite-scoring strains are often good choices for creating two-strain crosses for gene mapping efforts. Fourth, they can be useful for gene mapping studies directly rather than through crosses because of the large number of marker loci, especially SNPs, that are now available for most standard inbred strains 13-17
. In addition, the fine haplotype structure mapping this new method provides can be valuable for increasing map resolution of already mapped genome regions [e.g., 14-18
We have previously assessed the genetic influences on behavioral responses to four drugs of abuse - ethanol, pentobarbital, diazepam, and morphine. We selected four drugs that shared some common neuropharmacological actions but which also had unique actions. Ethanol acts on many neurotransmitter receptors, inhibiting N-methyl-d-aspartate (NMDA) type glutamate receptors, and potentiating glycine receptors, type-A γ-aminobutyric acid receptors (GABAA
receptors), 5-hydroxytryptamine (serotonin) type-3 receptors, and neuronal nicotinic acetylcholine (neuronal nACh) receptors. Further, ethanol inhibits voltage-gated L-type calcium channels, and potentiates G-protein-coupled inward rectifier potassium (GIRK) channels 19
. Like ethanol, pentobarbital potentiates GABAA
receptors, but in contrast to ethanol it inhibits neuronal nACh receptors and additionally inhibits α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) type glutamate receptors and voltage-gated sodium and calcium channels 20
. It is not clear from these studies whether the calcium channels inhibited by pentobarbital are the same as those inhibited by ethanol. Morphine is an agonist at μ-opioid, κ-opioid, and δ-opioid receptors, with highest binding to the μ-opioid receptor 21
. Diazepam is a full agonist at the benzodiazepine binding site on GABAA receptors 22
, similar to pentobarbital and ethanol.
All four drugs exert pronounced dose-dependent effects on locomotor activity. Locomotor stimulation, thought principally to reflect mesolimbic dopamine release, has been proposed as a behavioral surrogate for the euphoric response to drugs of abuse 23
. Nearly all drugs affect thermoregulation, but the mechanisms by which they do so are myriad, and could be either genetically unique or due to common genes. All drugs will be taken orally by mice with varying degrees of preference when compared to water, and this offered a tractable method to assess reinforcement. Finally, all four drugs result in physical dependence, which can be indexed by the severity of a drug-specific withdrawal reaction. The choice of drugs allowed us to study the same group of behavioral responses - had we included a psychostimulant, for example, we could not have analyzed withdrawal easily.
We applied multivariate analyses to these published data to seek evidence for common gene action. Besides these data, few other studies have explored the drugs analyzed here in multiple inbred strains. The major exception is ethanol, for which there is a long tradition of genetic studies, and those studies have made it clear that the patterns of mouse strain differences in ethanol preference drinking are highly stable over many laboratories and over a 45+ year period 24
. These analyses allowed us to address several questions, including: a) Is withdrawal severity influenced by common genes across all drugs? b) Is the locomotor stimulant response similar across drugs? c) Do stimulant and thermal sensitivity covary genetically? d) Do either stimulant or thermal responses predict withdrawal severity? e) Do withdrawal severity and reinforcement assessed from self-administration show coordinate, negatively-coupled genetic control? We assessed these response across a panel of 14-15 inbred strains. Because there were also available preference drinking data for many tastants, including these data also allowed us to ask whether preference for ethanol and sweet solutions were co-regulated, and to consider the role of taste in preference drinking of drugs of abuse.