Mouse models are complementary to human genetic studies and offer unique advantages and opportunities (Parker & Palmer 2011). Numerous classes of drugs increase locomotor activity in mice and this response is mediated through the same neurocircuitry that is implicated in drug reward in animals and drug-induced euphoria in humans (Wise & Bozarth 1987; Di Chiara & Imperato 1988; Koshikawa et al. 1989). Therefore, much attention has been focused on examining genetic factors that influence the locomotor response to various drugs, including methamphetamine (Grisel et al. 1997; Downing et al. 2006; Kamens et al. 2005; Palmer et al. 2005; Bryant et al. 2009a, 2009b). In mice, differences in the sensitivity to the locomotor activating effects of methamphetamine are heritable (Phillips et al. 2008), and predictive of later drug self-administration (Kamens et al. 2005).

Genetic mapping studies in mice have traditionally used recombinant inbred lines (RI), backcrosses (BC), F₂ intercrosses (F₂), consomic and congenic mice to identify quantitative trait loci (QTLs) for traits like sensitivity to the locomotor stimulating effects of methamphetamine. Due to a lack of recombination, these techniques are only able to identify large genomic regions and are therefore sub-optimal for identifying the genes that underlie QTLs (Peters et al. 2007; Flint 2011; Parker & Palmer 2011). We have recently begun to address this limitation by using populations with greater numbers of accumulated recombinations such as advanced intercross lines (AILs; Cheng et al. 2010; Samocha et al. 2010; Lionikas et al. 2010; Parker et al. 2011). AILs are created by successive generations of pseudo-random mating after the F₂ generation. Each additional generation leads to the accumulation of new recombinations, which allows for more precise mapping due to a breakdown in linkage disequilibrium. Because AILs are derived from two inbred founders, they maintain the simplicity of more traditional crosses, possess no rare alleles, and every marker that differs between the parental strains is perfectly informative in terms of identifying which inbred strain the allele is inherited from.

Interestingly, one of the QTLs we identified, which was associated with locomotor activity in a novel environment (Act1, 1.8-LOD interval = 172.36 Mb to 173.83 Mb), mapped to the proximal region of known QTL hotspot called Qrr1. Qrr1 extends from 172.5 Mb to 177.5 Mb on chromosome 1, and is highly enriched in QTL that control neural and behavioral phenotypes including basal locomotor behavior, escape latency, emotionality, ethanol-induced locomotor activity, and responses to caffeine, pentobarbital and haloperidol (Mozhui et al. 2008). Qrr1 contains 164 known genes and is thought to contain a highly complex gene expression regulatory interval composed of multiple loci modulating the expression of functionally similar sets of genes. In addition to Act1, a chromosome 1 QTL associated with methamphetamine sensitivity (Meth1, 1.8-LOD interval = 111.83 Mb to 184.52 Mb) also maps to Qrr1. Because Qrr1 consists of multiple regions (each associated with the expression of distinct subsets of genes and QTLs), it is possible that Act1 and Meth1 represent either the same or distinct loci.

Traditionally, F₂ intercrosses are used to identify QTLs underlying phenotypic variation, and fine-mapping is carried out as second step, often in congenic strains. Efforts at subsequent dissection and gene identification are often impeded by the existence of multiple causative loci of small effect located in the same chromosomal region (Mott et al. 2000; Shao et al. 2010). An AIL is an improvement over these traditional methods because of the additional recombinations it accumulates over successive generations. The accumulated recombinations allow identification and fine-mapping to be merged into a single step, which can often discriminate between loci that are due to single versus multiple alleles (Parker et al. 2011). The integration of the F₂ and F₈ AIL population combines the detection power of the F₂ with the precision of the F₈ AIL. In the integrated analysis, we reduced the 1.8-LOD support intervals by approximately 65% over the F₂ analysis alone (Supplemental Figure 1). In several instances significant QTLs identified in the F₂ population were not supported by the F₈ AIL data. These regions are difficult to interpret, as they may be caused by either a false positive result in the F₂ mice (this is very unlikely when the LOD score vastly exceeds the threshold for significance), or a false negative in the F₈ AIL mice, which has less power than a similarly sized F₂ (resulting from the reduced association between genotypes at markers). Alternatively, lack of a significant peak in the F₈ AIL may be due to the presence of multiple loci of small effect located in the same chromosomal region, which segregate as a unit in the F₂ but segregate independently in the F₈ AIL. Because of the ambiguity of QTLs not replicated in the F₈ AIL, we chose to focus our fine-mapping efforts on regions where the F₂ and F₈ AIL QTLs were in agreement.

Most studies of drug response traits identify QTLs based on summary measures that collapse out the within-subjects factor time. This approach implies that the QTLs are expected to have a uniform effect over the testing period. In order to better determine if a QTL was driven by a particular time point, we split the 30 minute testing period into six bins of five-minutes each. This indicated a temporal nature to our QTLs, although a formal test examining the QTL-by-time interaction would be necessary to definitively state that differences across time-bins are statistically significant. We plotted these results in three dimensions (time x position x LOD score). QTLs for initial locomotor activity in a novel environment as well as QTLs for methamphetamine-induced locomotor activity displayed peak LOD scores in the first half of the testing period. By considering the time-course in greater detail, we were able to observe that in some situations, the peak LOD scores were primarily driven by a particular time-point, as was the case with Act1, ActX, Meth9, and Meth15. In other instances, the LOD scores for the QTLs were consistently high across all time-points; this was the case for Act4 and Meth16.

To further narrow our QTLs and to attempt to identify the underlying genes, we used a series of bioinformatic approaches. First, we identified eQTLs that co-mapped with our QTLs. eQTLs are believed to underlie many QTLs for more complex traits (Nicolae et al. 2010; Li & Deng 2010). We used an existing database (www.GeneNetwork.org) of eQTLs from whole brain and striatum of untreated B6 x D2 F₂ mice and from the nucleus accumbens and prefrontal cortex of saline-injected BXD RI mice. In many cases this identified a smaller number of genes that co-mapped within the 1.8-LOD intervals of our QTLs (Supplemental Table 1). Some of the genes we identified have been implicated in other studies examining the stimulant properties of drugs of abuse, and may be promising candidates for follow-up studies. In the case of Meth15 we replicated our previous finding regarding the gene Csnk1e, which has been shown to influence the locomotor stimulant response to methamphetamine (Palmer et al. 2005; Bryant et al. 2009a). Others have found associations between Csnk1e and methamphetamine dependence as well as heroin addiction (Veenstra-VanderWeele et al. 2006; Levran et al. 2008). In addition, expression of the gene for the galanin 3 receptor (Galr3) also mapped to the Meth15 QTL. Transgenic mice overexpressing galanin were reported to have attenuated amphetamine-induced locomotor activity, as compared to controls (Kuteeva et al. 2005). We also found that the expression of Epha3 and Epha5 mapped to the Meth16 QTL. Sieber et al. (2004) investigated the functional role of Epha signaling by overexpressing a broad-range Epha receptor antagonist in the central nervous system of transgenic mice. Transgenic mice displayed a 40–50% reduction of dopaminergic neurons in the striatum, as well as insensitivity to the locomotor activating effects of amphetamine. While co-mapping of a QTL and an eQTL does not constitute proof that the latter causes the former, it does suggests a clear and testable hypothesis -- the candidate gene can be directly manipulated using a variety of molecular or pharmacological approaches (e.g. Bryant et al. 2009a). In addition, gene expression differences in other brain regions, across multiple developmental time-points, and in a variety of cell types may further aid in the identification of genes underlying these QTLs. Finally, we identified between 12 and 64 genes with “consequential” SNPs within each of our constrained QTL intervals (Supplemental Table 2). A subset of these resulted in premature stop codons, which are especially likely to alter the function of the gene. Follow-up studies will determine if any of these SNPs result in non-conservative amino acid changes, or if they occur in evolutionarily conserved amino acids; as these SNPs are most likely to cause phenotypic differences. Taken together, these bioinformatic approaches allowed us to narrow both the size of the QTLs and to identify a smaller subset of genes that we believe are likely to cause these QTLs.