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A fundamental challenge for any complex nervous system is to regulate behavior in response to environmental challenges. Three measures of behavioral-regulation were tested in a panel of eight inbred rat strains. These measures were: (1) sensation seeking as assessed by locomotor response to novelty and the sensory reinforcing effects of light onset, (2) attention and impulsivity, as measured by a choice reaction time task and (3) impulsivity as measured by a delay discounting task. Deficient behavioral-regulation has been linked to a number of psychopathologies, including ADHD, Schizophrenia, Autism, drug abuse and eating disorders. Eight inbred rat strains (August Copenhagen Irish, Brown Norway, Buffalo, Fischer 344, Wistar Kyoto, Spontaneous Hypertensive Rat, Lewis, Dahl Salt Sensitive) were tested. With n = 9 for each strain, we observed robust strain differences for all tasks; heritability was estimated between 0.43 and 0.66. Performance of the eight inbred rat strains on the choice reaction time task was compared to the performance of outbred Sprague Dawley (n = 28) and Heterogeneous strain rats (n = 48). The results indicate a strong genetic influence on complex tasks related to behavioral-regulation and indicate that some of the measures tap common genetically driven processes. Furthermore, our results establish the potential for future studies aimed at identifying specific alleles that influence variability for these traits. Identification of such alleles could contribute to our understanding of the molecular genetic basis of behavioral-regulation, which is of fundamental importance and likely contributes to multiple psychiatric disorders.
A fundamental challenge for any complex nervous system is to regulate behavior in response to environmental challenges. Examples of poor behavioral-regulation has been linked to a number of different psychopathologies, including ADHD, Schizophrenia, Autism and drug abuse (Barch & Braver 2009; Semrud-Clikeman et al. 2010). The environmental contingencies present during human evolution may have selected for different temperaments and personalities, some of which may not align well with the requirements of the modern world. For example, modern problems of behavioral-regulation such as drug abuse and obesity may result from genetic predispositions that were well adapted in prior environmental conditions. In this article we examine behavior regulation in rats using procedures that measure sensation seeking, attention and choice between delayed and immediate rewards.
Two behavioral paradigms that have been hypothesized to model aspects of sensation seeking are locomotor response to a novel environment (LRN) (Dellu et al. 1993, 1996) and responding to produce a sensory reinforcer (SR) (Gancarz et al. 2012c; Olsen & Winder 2009). In the LRN procedure a novel environment evokes locomotor activity, thus exposure to novel stimulation is involuntary (Bardo et al. 1996; Meyer et al. 2010). In the SR procedure exposure to novel stimulation is voluntary. Both of these procedures measure the magnitude of the response to novel stimulation and the rapidity of habituation to novel stimulation, though the procedures differ in that the LRN procedure measures both approach and avoidance of novel stimuli and the SR procedure measures only approach responses to novel stimuli.
Foraging for important resources is an important aspect of behavioral-regulation. Two laboratory behavioral paradigms that measure important components of foraging behavior are choice reaction time (CRT) and delay discounting (DD). The CRT task measures stimulus control. Rats were required to regulate their behavior in response to a visual stimulus (VS) in order to obtain water reinforcers. Slow reaction times and high variability indicate poor stimulus control, perhaps due to impaired attention. Premature and incorrect responses indicate poor stimulus control due to poor inhibition and impulsivity. Slow, variable reaction times and premature responses are core symptoms of attention deficit hyperactivity disorder (ADHD), and ADHD is associated with drug addiction (Charach et al. 2011). Efficient regulation of behavior by stimuli increases the likelihood of obtaining important resources.
In DD, rats chose between small but immediate or large but delayed amounts of water. The DD procedure used in this article presents a sequential choice situation that was designed to be similar to those that animals would encounter while foraging for important resources in patchy environments (Kacelnik et al. 2011; Stevens & Stephens 2010). Rats chose between an immediate but small reward by staying in a rapidly depleting patch and a delayed but larger reward by investing the time needed to change to a full patch. Longer delays between patches make staying in the old depleting patch a better choice, while shorter delays between patches make staying in the old depleting patch longer a worse choice. Greater DD is indicated by longer stays in the depleting patch with smaller rewards. Optimal foraging theory predicts that evolutionary processes will select animals that make choices on this task that maximize the long term rate of intake (Stevens & Stephens 2010). Performance on DD tasks has been used as a measure of impulsivity and has been shown to be associated with drug abuse in humans (Mackillop et al. 2011) and drug self-administration in animals (Perry & Carroll 2008; Perry et al. 2005).
The first goal of this study was to determine the impact of genetics on multiple domains of behavioral-regulation in the rat and to determine if there were correlations among them, which could reflect the presence of pleiotropic alleles. The second goal of this study was to collect preliminary data to determine the feasibility of a genome wide association study (GWAS) aimed at identifying genetic polymorphisms associated with these behavioral phenotypes. The proposed GWAS would test measures of behavioral-regulation in outbred heterogeneous stock (HS) rats. HS rats are the most highly recombinant rat intercross available, making them an ideal rat model for GWAS studies (see, Solberg Woods et al. 2010, 2012). The HS colony was initiated in 1984 using eight inbred rat strains: (ACI/N, BN/SsN, BUF/N, F344/N, M520/N, MR/N, WKY/N and WN/N) (Hansen & Spuhler 1984). The panel of inbred rat strains used in this study includes proxies for five (ACI, BN, BUF, F344, WKY) of the eight original founder strains. Although the M520 strain is currently available, the other two original founders of the HS stock no longer are. Three additional strains that are not closely related to the founders of the rats were used in this study (SHR, LEW and SS). The SHR strain was chosen because it has been widely used as an animal model of ADHD (Sagvolden & Johansen 2012), the LEW strain was chosen because it has been reported that there are differences in discounting between LEW and F344 rats (Anderson & Woolverton 2005), and the SS strain was chosen because it is often used as the background strain for the development of genetic knockouts (see: http://rgd.mcw.edu/wg/physgenknockouts). This last strain is important because we plan to create knockout rats based on the results of the proposed GWAS study to experimentally test the results of the GWAS. Finally, in order to further support the use of HS rats in the GWAS, we have also tested outbred HS and Sprague Dawley rats on the CRT task for comparison to the inbred rat strains.
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The inbred rats were tested in the following order: LRN procedure (one session), SR procedure (20 sessions), CRT procedure (37 sessions) and finally DD procedure (12 sessions). One test session was performed per day. Testing on the next procedure began immediately after completion of the previous procedure. The outbred rats were tested only on the CRT procedure.
Locomotor response to a novel environment: Each rat was placed into an activity monitor for 30 min and locomotor activity was recorded. Because we have only eight locomotor test chambers, locomotor activity testing was done in groups of eight spread across three consecutive days.
The primary dependent measures were locomotor counts, operationally defined as the total number of horizontal and vertical beam breaks and within session habituation of locomotor activity. Data from the 30-min test sessions were divided into five 6-min epochs (i.e. 0–6 min, 7–12 min, etc.). This analysis revealed clear within session declines in activity, indicating habituation to the novel environment. For each individual rat, the number of responses that occurred in each epoch was divided by the total responses that occurred in the test session to determine the proportion of responding that occurred in the epoch. As the proportions must sum to 1.0, this analysis describes how the animals distributed their responding across the test session and is independent of the absolute rates of responding. The difference between the proportion of activity in the first and last epochs provided a quantitative measure of the rate of habituation, with larger values indicating greater habituation. Without this proportion conversion differences in habituation are confounded with differences in baseline activity levels (Leussis & Bolivar 2006).
Sensory reinforcement: The animals were first pre-exposed to dark experimental chambers in 30-min test sessions. One test session was performed per day. Snout pokes had no programmed consequences during pre-exposure. Following the ten session pre-exposure/familiarization phase, the animals were tested for light contingent responding for ten 30-min sessions. One test session was performed per day. The test chambers were dark during testing except when a response contingent VS was presented. One of the snout poke holes was designated as active and snout pokes into this hole resulted in illumination of the ceiling stimulus light for 5 seconds according to a variable interval (VI) 1-min schedule of reinforcement. On a VI 1-min schedule snout pokes produced the VS on the average of every 60 seconds. The ceiling light produced a luminance of 53 lux as measured from either the left or right snout poke hole. Snout pokes to the inactive alternative had no programmed consequences. The animals were tested six days a week. See Lloyd et al. (2012a) for a detailed description of the SR procedure.
The primary dependent measures were active and inactive responding and within session habituation of active responding. Active responding was the number of responses to the alternative that produced the SR. Inactive responding was the number of responses to the alternative that had no programmed effect. Habituation was measured as described above for locomotor activity. The averaged data for test sessions 2–10 of the light contingent testing phase were used for analysis. The data were averaged across tests sessions 2–10 because for some strains (i.e. BN and WKY) the number of responses that occurred in individual test sessions was very low. Session 1 was not included in this analysis because the animals had not had the opportunity to learn that responding to the active alternative produced light onset.
Choice reaction time: We have previously used similar versions of this procedure to test the effects of methamphetamine (Sabol et al. 2003) and prenatal ethanol exposure (Hausknecht et al. 2005) on attention. Rats initiated test trials by holding their snout in the center snout hole until either the left or right stimulus light was turned on. The amount of time required for the rat to hold its snout in the center snout-poke hole before the onset of the imperative stimulus (left or right stimulus lights) was called the hold time. Once the hold time criterion was reached and the imperative stimulus was presented, the rat had 3 seconds to respond (by removing its snout and inserting in into one of the two feeder holes), or the trial ended (the imperative stimulus was turned off) and the response was counted as an omission. If the rat made a correct response, the rat received a water reinforcer (30 µl) and the trial ended. If the rat made an incorrect response, the trial ended without reinforcement. The stimulus lights were the only sources of illumination in the test box. Training occurred 6 days a week. Sessions lasted for 30 min or 100 trials, whichever occurred first.
The hold time was progressively increased during initial training to have a final average value of 2.6 seconds. The hold time on any particular trial was variable ranging from a minimum of 0.06 seconds to a maximum of 10.5 seconds as determined by an exponential distribution with a mean of 2.6 seconds (Fleshler & Hoffman 1962). The hold time was cumulative; for example, if the hold time was 4 seconds, the rat could meet this requirement by holding its snout in the hole for 2 seconds on two different occasions. A 2900 CPS tone was turned on for the duration of each snout poke response into the center hole. The feedback tone occurred independently of any other contingency and was in effect for the duration of the test session.
Preliminary training consisted of 20 sessions with a minimal hold time, so that only a brief snout poke into the center hole was required to initiate the trial. During this initial training period the animals also learned to respond correctly to the left and right stimulus lights. During sessions 20–31 the average hold time was gradually increased to 2.6 seconds and made variable. Sessions 32–36 were used for analysis.
The primary dependent variables were the mean reaction time (RT), reaction time standard deviation (RT-SD), deviation from the mode (DevM), premature responses and proportion correct. RT was the mean time elapsed from onset of the imperative stimulus to withdrawal of the snout from the center snout poke hole for each rat. RT-SD was the standard deviation of the RT for each rat. DevM was the difference between the RT mean and the MODE of each rat distribution. The Half-Range Mode method was used to calculate the mode. Originally described by Hedges and Shah (2003), use of the Half-Range Mode method to determine the DevM of RT distributions is discussed in Spencer et al. (2009, p. 810). The DevM metric was designed to measure lapses of attention (see Hausknecht et al. 2005; Sabol et al. 2003; Spencer et al. 2009 for further explanation). Premature responses (false alarms) were defined as a withdrawal from the center hole followed by a snout poke response into the left or right water feeder holes prior to the presentation of the stimulus. The number of premature responses was divided by the total number of trials completed to provide an estimate of premature responses that was not biased by the number of trials completed. Proportion correct was the number of correct responses in a session divided by the total number of completed trials.
Delay discounting: The patch depletion procedure is based on the more complex adjusting amount procedure that we previously developed (Richards et al. 1997). The patch depletion procedure was designed to rapidly determine the effect of delay on reward value. In the patch depletion procedure rats consume water at both the left and right water feeders (patches). Rats receive successively smaller amounts of water every 4 seconds by remaining at the same feeder. The amount of water is initially 0.15 ml and is then decreased by 20% after each delivery from the same feeder. For example, if the animals remain in the same patch (indicated by detection of the head in the feeder) they would receive 0.150 ml at 0 seconds, 0.120 ml at 4 seconds, 0.096 ml at 8 seconds, 0.077 ml at 12 seconds, 0.061 ml at 16 seconds, etc. The rats can reset the amount of water to the initial maximum of 0.15 ml by switching to the alternative water feeder. However, changing to the opposite patch results in a delay to activation of the alternative feeder. During the delay, water is not available at either feeder.
A change in patch is indicated by a snout poke into the alternative nonactive feeder. If a 0-second delay is being tested, the stimulus light associated with the old feeder is turned off and the stimulus light associated with the new feeder is illuminated simultaneously with the delivery of 0.15 ml of water. The obtained patch change time is the time from the last head detection in the previously active patch to the head poke into the currently active patch. If an 8-seconds delay is being tested, the stimulus light associated with the previous patch is turned off and the pulsed 1.9 kHz tone is presented. The delay to activation of the new feeder lasts for 8 seconds (starting from the time of the last head detection in the previously active patch) before the stimulus light above the new feeder is illuminated. The first snout poke into the new feeder after onset of the stimulus light results in the delivery of 0.15 ml of water.
Delays of 0, 8 and 16 seconds were tested. A different delay was tested during each session. The delays were tested in the following sequence (0, 8, 16, 0, 16, 8 seconds). This sequence was repeated for a total of 12 test sessions. Sessions lasted for 10 min.
The primary dependent variables were area under curve (AUC) and k. The AUC measure provides a simple measure of discounting which is not tied to a particular discount function (Myerson et al. 2001). However, much importance has been given to the hypothesis that discount functions are hyperbolic in shape (Ainslie 1975). Hyperbolic discount functions indicate a varying rate of discounting while exponential discount functions indicate a constant rate of discounting. Discount functions for single parameter hyperbolic and exponential discount functions were obtained by fitting the median change points for each of the eight strains at the 0, 8 and 16-seconds delays. For the 0-second delay, the median obtained delay for the strain was used as the delay value. The hyperbolic equation was V = 150/1 + kD and the exponential equation was V = 150e –kD . For both equations V is the value of the water available at the alternative feeder, 150 is the amount of water available at the alternative feeder, D is the length of the experimenter imposed delay to obtaining water at the alternative feeder and k is a free parameter that describes the steepness of the discount function. Larger values of k indicate greater discounting.
A nonlinear curve-fitting program (Microcal Origin, version 6.0) was used to fit the hyperbolic and exponential discount functions and obtain the best fitting values of k. This program used an iterative Levenberg–Marqardt algorithm to determine the values of k that minimized χ 2-values describing the difference between predicted and obtained points. Smaller χ 2-values indicated better fits.
Locomotor response to a novel environment: Total locomotor activity and locomotor habituation were analyzed using a one factor between subject analysis of variance with strain as the between subject factor. Significant F-values were followed by Tukey post-hoc tests to determine homogeneous subsets. Genetic effect size (h 2) was estimated by dividing the sum of squares between strains by the total sum of squares (Meyer et al. 2010; Wilhelm & Mitchell 2009).
Sensory reinforcement: Active responding and habituation of active responding were analyzed to determine homogeneous subsets and h 2 as described above for locomotor activity.
Choice reaction time: Five dependent measures derived from the CRT task (RT, RT-SD, DevM, premature responses and proportion correct) were analyzed to determine homogeneous subsets and h 2 as described above for locomotor activity.
Delay discounting: The AUC values for each rat were analyzed to determine homogeneous subsets and h 2 as described above for locomotor activity. The goodness of fit for k values obtained with hyperbolic and exponential equations fitted to the medians of the discount points obtained for each of the eight strains were evaluated by comparing the obtained χ 2-values.
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Locomotor response to a novel environment: Average locomotor activity for each strain across the 30-min test session is shown in terms of 6-min epochs in Fig. 1a. Examination of the locomotor activity across the test session indicates obvious within session declines in activity. There was a significant effect of strain and high heritability for both total locomotor activity (F 7,64 = 18.19, P < .001; h 2 = 0.67; see Fig. 1b) and within session habituation (F 7,64 = 10.77, P < .001; h 2 = 0.54; see Fig. 1c) of locomotor activity.
Figure 1. Locomotor activity in a novel environment. (a) Within session pattern of locomotor activity in a novel environment plotted as five 6-min epochs. (b) Total locomotor activity for each rat. (c) Degree to which locomotor activity decreased between the first epoch of the session and the last epoch of the test session for each rat. Circles indicate the data from individual animals. The x-axis indicates strain. The rectangles in plots (b) and (c) indicate homogenous subsets as indicated by Tukey post-hoc tests. The significance of the F-test for the single factor of strain and heritability (h2 ) are indicated.
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Sensory reinforcement: Introduction of response contingent light onset increased the rate of active responding (Fig. 2) but did not significantly affect the rate of inactive responding (Data not shown). The selective effects of response contingent light onset on active responding replicate previous results obtained in outbred Sprague Dawley rats (Gancarz et al. 2012a,2012b,2012c; Lloyd et al. 2012a,2012b) This indicates that the VS was a reinforcer. Similar to locomotor activity in the novel environment, responding for the VSR generally decreased from the beginning of the test session to the end of the test session (Fig. 2a). There was a significant effect of strain and high heritability for both active responding (F 7,64 = 9.40, P < .001; h 2 = 0.50; see Fig. 2b) and habituation (F 7,64 = 12.21, P < .001; h 2 = 0.57; see Fig. 2c).
Figure 2. Sensory reinforcement. (a) Within session pattern of active responding to produce a visual stimulus reinforcer (VSR) plotted as five 6-min epochs. (b) Total active responses for each rat. (c) Degree to which active responding decreased between the first epoch of the session and the last epoch of the test session for each rat. Circles indicate the data from individual animals. The x-axis indicates strain. The rectangles in plots (b) and (c) indicate homogenous subsets as indicated by Tukey post-hoc tests. The significance of the F-test for the single factor of strain and heritability (h2 ) are indicated.
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Choice reaction time: Six WKY rats, three BUF rats and one LEW rat failed to complete an average of 20 trials per test session and were excluded from the analysis. The data from the CRT task were divided into two subsets hypothesized to be related to attention deficit symptoms and hyperactivity/impulsivity deficit symptoms of ADHD. Significant effects of strain and high heritability were found for all three measures related to attention and for both hyperactivity/impulsivity-related measures. The attention-related measures were RT (F 7,54 = 14.86, P < .001; h 2 = 0.66; see Fig. 3a), RT-SD (F 7,54 = 7.03, P < .001; h 2 = 0.48; see Fig. 3b) and DEVM (F 7,54 = 5.84, P < .001; h 2 = 0.43; see Fig. 3c). The hyperactivity/impulsivity-related measures were premature responses (F 7,54 = 10.11, P < .001; h 2 = 0.51; see Fig. 4a) and proportion correct (F 7,54 = 6.06, P < .001; h 2 = 0.44; see Fig. 4b).
Figure 3. Attention-related measures from the CRT task. (a) Reaction time (RT) for each rat. (b) Reaction time standard deviation (RT-SD) for each rat. (c) The DevM for each rat. Circles indicate the data of individual animals from the eight inbred strains. The x-axis indicates strain. Values for 28 Sprague Dawley (dark stars) and 48N/NIH (open stars) outbred rats are shown to the right of the dashed line for comparison with the eight inbred strains. The rectangles in plots indicate homogenous subsets as indicated by Tukey post-hoc tests. The significance of the F-test for the single factor of strain and heritability (h2 ) are indicted.
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Figure 4. Hyperactivity/impulsivity-related measures from the CRT task are shown. (a) Premature responses for each rat. (b) The proportion of correct responses. Circles indicate the data from individual animals. The x-axis indicates strain. Values for 28 Sprague Dawley (dark stars) and 48 HS (open stars) outbred rats are shown to the right of the dashed line for comparison with the eight inbred strains. The rectangles in plots indicate homogenous subsets as indicated by Tukey post-hoc tests. The significance of the F-test for the single factor of strain and heritability (h2 ) are indicted.
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Performance of all of the inbred rats regardless of strain was compared to the performance of outbred SD and HS rats (See Figs. 3, 4). The resulting means and standard errors for each of the five measures are shown in Table 1. For the attention-related measures, a significant effect of rat type (Inbred, SD or HS) was found for IRT (F 2,131 = 3.33, P < .05), RTSD (F 2,131 = 7.27, P < .01) and DevM (F 2,131 = 8.77, P < .001). Tukey tests indicated that inbred rats had faster IRT times than the SD rats but were not significantly different from the HS rats, inbred rats had less variability (smaller RTSDs) than the HS rats and were not significantly different from the SD rats and inbred rats had smaller DevM than SD rats but were not significantly different from HS rats. For the impulsivity/hyperactivity measures of premature responses and proportion correct, only premature responses produced a significant effect of rat type (F 2,131 = 11.93, P < .001). Post-hoc Tukey tests indicated that inbred rats had had significantly fewer premature responses than the HS rats but significantly more than the SD rats. As expected, a high degree of variation was found within both outbred strains. Importantly, the variation within the HS rats generally encompassed the variation found between the inbred strains, indicating this resource will likely be useful for fine-mapping genetic loci involved in these traits (see, Solberg Woods et al. 2010, 2012).
Table 1. CRT performance of inbred rats averaged across strains compared to outbred Sprague Dawley and HS rats
|Inbred strains|| 23.72 || 10.902 || 1.770 || 0.375 || 0.934 |
| 0.860 || 0.933 || 0.557 || 0.031 || 0.008 |
|SD|| 28.13 * || 14.092 || 6.238 * || 0.222 *,† || 0.918 |
|Outbred|| 1.478 || 1.603 || 0.957 || 0.053 || 0.014 |
|HS|| 24.83 || 16.244 * || 3.793 || 0.520 *,† || 0.914 |
|Outbred|| 0.978 || 1.060 || 0.633 || 0.035 || 0.010 |
Delay discounting: All of the rats quickly learned to switch between the two feeders. There was a significant effect of strain and high heritability for the AUC measure (F 7,64 = 8.96, P < .001; h 2 = 0.50; see Fig. 5). Figure 6 shows that the rats proved to be sensitive to the imposed delays of 8 and 16 seconds. The hyperbolic discount function fit the data for each strain better than the exponential discount function. For each strain, the χ 2-values for the hyperbolic fits were smaller than the χ 2-values for the exponential fits. The χ 2-values for the hyperbolic fit ranged between 4.4 and 434 with a median value of 72.2 and the χ 2-values for the exponential fit ranged between 65.5 and 781 with a median value of 271.9. A Wilcoxon signed-rank test confirmed that the difference between the χ 2-values for the hyperbolic and exponential fits were significantly different (P = 0.012).
Figure 5. Area under the curve ( AUC ) is plotted as function of strain. The x-axis indicates strain. Circles indicate the data from individual animals. Rectangles in plots indicate homogenous subsets as indicated by Tukey post-hoc tests. The significance of the F-test for the single factor of strain and heritability (h2 ) are indicted.
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Figure 6. Best fitting hyperbolic and exponential discount functions. Individual animal change values for each strain are shown when the programmed delays were 0, 8 and 16 seconds. The x-axis indicates how long it took the animal to switch between the two water sources. Solid squares indicate data for individual animals when the programmed delay was 0 second, open circles indicate data for individual animals when the programmed delay was 8 seconds and solid triangles indicate data when the programmed delay was 16 seconds. Large open circles indicate median change values for each strain. For the 0-second delay the values vary along the x-axis indicating how long it took the animals to switch when no delay was imposed (0 second). All animals switched in less than 8 seconds so that the time to switch was determined by the experimenter imposed delays of 8 and 16 seconds. Hyperbolic (solid lines) and exponential (dashed lines) curves were fit to the medians of the individual change values for each strain. Individual k values resulting from the hyperbolic fit are shown for each strain.
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Between task correlations: Table 2 shows the between task correlation computed using the average values for each strain (n = 8). The significance values in the table are meant only to indicate association that may be of importance. Interpretation of the significance values in Table 2 needs to take into account that 54 correlations are presented and the probability of one of them reaching a high level of significance by chance alone is high.
Table 2. Spearman nonparametric correlation coefficients for eight inbred rat strains across the four behavioral challenges
|LHab||Rs||SRHabb||RT||SD||DevM||Correct||Prem||AUC|| k |
|LRN||– 0.929 || 0.643 ||–0.429||0.119||0.095||0.310||–0.036||–0.286||0.476||–0.286|
| 0.001 || 0.086 || 0.289 || 0.779 || 0.823 || 0.456 || 0.933 || 0.493 || 0.233 || 0.493 |
|LHab|| || ||0.524||0.143||–0.119||–0.167||0.132||0.143||–0.584||0.333|
| || 0.071 || 0.183 || 0.736 || 0.779 || 0.693 || 0.756 || 0.736 || 0.160 || 0.420 |
|Rs|| || ||– 0.786 ||0.143||0.190||0.571||–0.240||–0.381||0.548||–0.333|
| || || 0.021 || 0.736 || 0.651 || 0.139 || 0.568 || 0.352 || 0.160 || 0.420 |
|SRHab|| || || ||–0.286||–0.048||–0.357||0.527||–0.048||– 0.833 || 0.690 |
| || || || 0.493 || 0.911 || 0.385 || 0.180 || 0.911 || 0.010 || 0.058 |
|RT|| || || || || 0.881 || 0.667 ||–0.299||–0.381||0.214||–0.286|
| || || || || 0.004 || 0.071 || 0.471 || 0.352 || 0.610 || 0.493 |
|SD|| || || || || || 0.730 ||–0.108||– 0.619 ||–0.071||0.024|
| || || || || || 0.037 || 0.799 || 0.102 || 0.867 || 0.955 |
|DevM|| || || || || || ||–0.228||– 0.690 ||0.143||–0.119|
| || || || || || || 0.588 || 0.058 || 0.736 || 0.779 |
|Correct|| || || || || || || ||–0.527||–0.455||0.395|
| || || || || || || || 0.180 || 0.257 || 0.333 |
|Prem|| || || || || || || || ||0.190||–0.190|
| || || || || || || || || 0.651 || 0.651 |
|AUC|| || || || || || || || || ||– 0.952 |
| || || || || || || || || || 0.001 |
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Our survey of a panel of eight inbred rat strains indicates strong genetic influences on complex tasks thought to measure behavioral-regulation. We also showed that two outbred strains (SD and HS) show high levels of variability for some of these traits. The HS rat has been shown to be useful for fine-mapping multiple complex traits to only a few Megabases (Johannesson et al. 2009; Solberg Woods et al. 2010, 2012). In this study, we show large behavioral differences between the inbred strains that make up the HS colony, as well as a high degree of behavioral variability within the HS colony. These finding indicate that HS rats will likely be a useful resource for fine-mapping genetic loci that underlie these behavioral traits.
This study has some limitations. Although there was a large effect of strain on all of the behavioral measures, the scatter plots showing individual subject performance indicate that there was also considerable inter-subject variability within strains. The source of this variability is presumed to be due to nongenetic differences or measurement error; however, it is also possible that there are polymorphisms that are segregating within one or more of these strains. In addition, as not all strains were obtained from the same vendor, it is possible that some of the effects we are attributing to genetics are in fact due to environmental differences between the vendors. There is also strong possibility that the genotypes of inbred strains may be substantially different between vendors. Finally, we do not know if small procedural differences would alter the rank order of the strains in this study. Indeed, when complimentary data were available from other studies with substantial behavioral expertise, the rank order of the strains appeared to be different. These differences may reflect environmental, procedural or genetic differences between studies, as it is known that some behaviors are very sensitive to such effects (Crabbe et al. 1999).
In summary, we have shown that a number of behavioral-regulation measures that are related to sensation seeking, attention and decision making are highly heritable and can be measured efficiently in large cohorts of rats. The effects of genotype in these studies are much larger than those that would commonly be expected with drugs or other kinds of environmental interventions. These results increase confidence that studies aimed at identifying genetic polymorphisms associated with these behavioral phenotypes would be successful.