Behavior Genetics, Vol. 31, No. 3, May 2001 (©2001)
Quantitative Trait Loci Associated with Reversal Learning and Latent Inhibition in Honeybees (Apis mellifera) Sathees B. C., Chandra,1 Gregory J. Hunt,2 Susan Cobey,1 and Brian H. Smith1,3 Received 19 May 2000—Final 24 Apr. 2001
A study was conducted to identify quantitative trait loci (QTLs) that affect learning in honeybees. Two F1 supersister queens were produced from a cross between two established lines that had been selected for differences in the speed at which they reverse a learned discrimination between odors. Different families of haploid drones from two of these F1 queens were evaluated for two kinds of learning performance—reversal learning and latent inhibition—which previously showed correlated selection responses. Random amplified polymorphic DNA markers were scored from recombinant, haploid drone progeny that showed extreme manifestations of learning performance. Composite interval mapping procedures identified two QTLs for reversal learning (lrn2 and lrn3: LOD, 2.45 and 2.75, respectively) and one major QTL for latent inhibition (lrn1: LOD, 6.15). The QTL for latent inhibition did not map to either of the linkage groups that were associated with reversal learning. Identification of specific genes responsible for these kinds of QTL associations will open up new windows for better understanding of genes involved in learning and memory. KEY WORDS: Quantitative trait loci; honeybees; Apis mellifera; reversal learning; latent inhibition; random amplified polymorphic DNA (RAPD) markers.
INTRODUCTION
ual differences in performance on a variety of learning and memory paradigms in both fruit files and honeybees (Tully and Hirsch, 1982; Zawistowskiand Hirsch, 1984; Brandes and Menzel, 1990; Chandra et al., 2000). However, with the exception of the foraging gene ( for) in Drosophila (De Belle, 1989; Osborne et al., 1997), genes that give rise to naturally occurring polymorphisms in behavioral traits remain relatively uncharacterized. Nevertheless, it is possible that studies of insects will help to reveal the genetic bases for the more subtle, quantitative genetic variation in a wide phylogenetic spectrum of animals. Identification of regions of DNA that account for strain and individual differences can be augmented by the use of saturated genetic maps onto which behavioral characters can be placed relative to identified regions of the genome (Paterson et al., 1988; Lander and Botstein, 1989; Tanksley, 1993). In particular, genetic mapping using randomly generated DNA-based markers has recently been employed to identify quantitative trait loci (QTLs) that affect expression of learning and memory traits in mice (Caldarone et al., 1997; Wehner et al.,
Detection and characterization of genes that affect expression of behavioral characters is of considerable importance to studies of learning in animals. Genes that have major effects on expression of learning and memory in insects and mammals have been identified by mutation of targeted genes (Dura et al., 1993; Boynton and Tully, 1992; Dubnau and Tully, 1998; Chen and Tonegawa, 1997). These types of major genes typically have profound phenotypic effects and are thus likely to be fixed within and across species, which is perhaps why genes show functional conservation in fruit flies and mammals (Dubnau and Tully, 1998). Quantitative genetic studies have also revealed a considerable heritable component to within-species strain and individ1
Department of Entomology, 1735 Neil Avenue, The Ohio State University, Columbus, Ohio 43210-1220. 2 Department of Entomology, Purdue University, West Lafayette, Indiana 47907-1158. 3 To whom correspondence should be addressed. Fax: 614-292-5237. E-mail:
[email protected]
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1997). Several studies of insects have also revealed genetic variation in performance on several defined learning problems (Brandes et al., 1988; Tully and Hirsch, 1982; Bhagavan et al., 1994; Benetar et al., 1995; Chandra et al., 2000). Therefore it is possible that the use of insect models for studying quantitative genetic variation could reveal important genes and gene–environment interactions that are involved in this type of variation, as has been the case for mutational analyses of major gene effects (Dubnau and Tully, 1998). In recent years, the use of molecular markers has made it feasible to develop saturated genetic maps of insects such as the honeybee, Apis mellifera (Hunt and Page, 1995; Hunt et al., 1998). The honeybee genome has a haploid chromosome number of 16 and an approximate size of 180 megabase pairs (Jordan and Brosemer, 1974). The honeybee, in particular, is an excellent experimental organism for studying behavioral genetics. First, the haplodiploid mode of sex determination facilitates robust quantitative and molecular genetic analyses (Laidlaw and Page, 1984). Selection on drones (males), which are haploid, has distinct advantages. Drones learn as well as workers on defined learning tasks (Bhagavan et al., 1994; Chandra et al., 2000). Selection of individual drones also avoids confounding interactions between alleles at single loci. Second, honeybees have a low level of repetitive DNA sequences; about 8–11% of the genome consists of moderate- to high-copy repetitive DNA that is arranged in the longperiod interspersion pattern that is typical of Drosophila (Crain et al., 1976). Third, honeybees have a very high rate of recombination (Hunt and Page 1995), which should facilitate map-based cloning by increasing map resolution for specific QTLs. Learning is particularly important for honeybees, which must learn about many aspects of their environment, especially when foraging outside the nest (Seeley, 1994). There is a great deal of phenotypic variation in the learning performance among individual workers from the same queen (Bhagavan et al., 1994; Chandra et al., 2000). Environmental effects contribute to these differences (Ferguson et al., 2001). But the individual differences within a single colony also reflect a strong contribution of genotype (Brandes, 1988; Brandes and Menzel, 1990; Bhagavan et al., 1994; Benatar et al., 1995; Chandra et al., 2000). In natural situations, the queen mates with up to 17 drones (Adams et al., 1977). Therefore, the variance among workers in part reflects differences in the paternal “drone” genotype. Our research focuses on genetic variation for performance on latent inhibition and reversal learning
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(Chandra et al., 2000; Ferguson et al., 2001). In reversal learning, honeybees are required to reverse a learned discrimination between two odors. The speed of reversal in this task might reflect the degree to which subjects attend to the stimuli (Bitterman 1972). Less attention devoted to the conditioned stimulus (CS) would tend to slow the speed at which reversal occurs. Latent inhibition is a phenomenon in which subjects show delayed acquisition to a CS after they have been exposed to that CS without reinforcement (Lubow, 1973; Lubow et al., 1983). Basically, latent inhibition is a learned inattention mechanism in which subjects learn to ignore stimuli that are meaningless (Lubow, 1997). This paradigm has recently been used to investigate attentional disorders in a variety of animals including humans (Lubow et al., 1983). Our interest in these two tasks derives in part from a correlated selection response between them. When Ferguson et al. (2001) and Chandra et al. (2000) selected lines of honeybees for one of the two tasks, they found a correlated response in learning performance on the remaining task. Ferguson et al. (2001) selected lines for fast and slow reversal learning and then tested their progeny for latent inhibition. Only the progeny of fast reversers showed significant latent inhibition. However, when Chandra et al. (2000) selected lines for latent inhibition and then tested their progeny for reversal learning performance, they found the opposite correlation— the progeny that showed significant latent inhibition reversed relatively slowly. The correlation between the two traits might be due to pleiotropy, linkage, or epistatic interactions between genes that affect both traits (Chandra et al., 2000). Alternatively, the correlation could have arisen from spurious genetic correlations from the small founder populations that were used to establish the two lines. Our purpose here was to apply a QTL mapping to isolate molecular markers that are linked to the genes that affect phenotypic expression of reversal learning and latent inhibition. This would provide inroads toward eventual identification of these genes. In addition, we hypothesized that identification of QTLs might provide insight into the basis for the correlated selection response. MATERIALS AND METHODS Subjects. In previous work we have found that is was necessary to collect drones in the process of making mating flights to ensure that they were motivated to respond in the conditioning paradigm and to
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increase the probability that the drones were sexually mature (Bhagavan et al., 1994; Benatar et al., 1995; Chandra et al., 2000). Therefore, drones were collected on the day prior to conditioning as they returned from mating flights during their normal flight time, between 1500 and 1700 h. Drones were then placed in small cages and placed in a colony overnight so that workers could feed them. The next morning we placed these drones into harnesses as explained below. Virgin queens (2 weeks old) were conditioned on the same day that they were collected from the colony and set up in restraining harnesses. Virgin queens were reared by transferring newly hatched (0- to 12-h-old) larvae into queen rearing caps (Harbo, 1986). These “grafts” were then introduced into queenless colonies for rearing to adult queens, which took approximately 3 weeks before they emerged as adults. On the day of conditioning we selected only those queens and drones that showed appetitive responses to sucrose stimulation of the antennae during the pretest, which was performed 30 min before the start of the training. The pretest ensured that all the animals selected for training were motivated to feed on the unconditioned stimulus (US). Behavioral Testing. We used proboscis extension response (PER) conditioning procedures (Menzel and Bitterman, 1983; Menzel, 1990; http://iris.biosci.ohiostate.edu / honeybee) to study reversal learning and latent inhibition paradigms in honeybees. This procedure utilizes subjects restrained in harnesses in such a way that they can easily move their head and antennae. Each individual bee (either queen or drone) was set up in a restraining harness and conditioned for proboscis extension using odor as the CS according to the established protocols (Ferguson et al., 2001; Chandra et al., 2000). We used either geraniol or 1-hexanol as the CS. Sucrose (1.5 M)– or salt (3 M NaCl)–water solutions were used as US. A conditioning trial began after we placed a subject into a conditioning arena through which air was constantly drawn over the subject’s antennae and into an exhaust vent. The exhaust ensured that odor was removed quickly from the arena. The odor CS was delivered 30–40 s after subject was placed into the arena and was presented for 4 s. Odorant delivery was accomplished by activation of a valve that was controlled via a parallel port on a computer. When it was activated, the valve carried air through a 1-ml glass syringe that contained 3 l of pure odorant placed onto a small strip of filter paper. If a subject extended its proboscis after the onset of odor but before presen-
tation of the US, a positive response was recorded. Otherwise a negative response was registered. Three seconds after the onset of odor delivery a 0.4-l droplet of the US was manually applied to the subject’s antennae, which, in the case of sucrose, elicited proboscis extension. Delivery of the US was timed for 3 s. However, consumption of the sucrose droplet always occurred before the end of the feeding period. Each trial lasted for approximately 60 s, after which the next subject was placed in the conditioning arena and continued the training. Selection and Establishment of F1 Queens. We evaluated the reversal learning performance of both queens and drones. These animals were chosen from fast and slow reversal lines that had been selected over two generations in a previous study (Ferguson et al., 2001). Five to ten queens or drones were conditioned every day. The reversal learning protocol involved two phases of conditioning—initial discrimination training, followed by reversal training. In discrimination training, subjects were exposed to geraniol and 1-hexanol counterbalanced as A and B across days. In this phase, subjects were conditioned to discriminate odor A, which was associated with sucrose reinforcement, from odor B, which was associated with a 3 M salt solution. Salt enhances discrimination performance in this paradigm (Getz et al., 1986). The sequence of odor presentation across trials was pseudo-randomized and identical for each subject (ABBABAABABBA). The intertrial interval was fixed at 8 min. After the first 12 trials (6 with A⫹ and 6 with B⫺) the pattern of reinforcement was reversed. Odor B was paired with sucrose and A with salt for an additional 12 trials during the reversal training phase. Queens and drones selected for breeding F1 offspring were evaluated according to the criteria that we established in previous studies (Ferguson et al., 2001; Chandra et al., 2000). First, subjects selected for evaluation in the second, reversal phase had to show nearperfect discrimination performance in the first (discrimination) phase of training. This involved consistent responding to odor A (associated with sucrose reinforcement) on all but the first trial and no response to odor B. The second selection criterion involved selecting subjects for fast and slow reversal performance in the reversal conditioning phase. We defined reversal performance according to performance during the last two of six trials with each odorant during the reversal phase. Animals that successfully reversed the initial discrimination revealed no response to A and two
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responses to B over two trials with each odorant. We defined slow reversal performance as two or more total deviations (errors) from those response patterns to A and B during those same trials. Four colonies were established by inseminating a fast reverser queen with the sperm collected in each case from a single slow-reverser drone. Insemination of virgin queens was performed according to an established procedure (Harbo, 1986). Progeny from such single-drone inseminations produced supersisters, of which all share an average of 75% of their genes by descent (Page and Laidlaw, 1988). Virgin supersister F1 queens were reared from fertilized eggs produced by the selected queens according to the procedure described above. These F1 queens were allowed to make mating flights, during which they mated with up to 20 drones in the local population (Adams et al., 1977). This open-mating procedure increases the longevity of the F1 queens. We could allow open mating in this series of experiments because our intent was to evaluate only the drone progeny of these queens, which inherit their genotypes only from the queen. Collection of Drone Progeny. Drone progeny of one of the F1 queens, which was selected at random, were evaluated for reversal learning. To ensure that collected drones were in fact the progeny of the selected F 1 queen, the following procedure was followed. F 1 colonies were monitored to establish when the queens began laying eggs. Two to three weeks after the onset of oviposition, several hundred drone offspring from one of the F 1 queen colonies were allowed to emerge in incubator cages. Then they were marked on the thorax with a dot of enamel paint, after which they were introduced into a single colony headed by an unrelated queen. The paint markings allowed us to differentiate selected drones from those produced by the colony itself. Evaluation of Drone Progeny for Reversal Learning. Three weeks after they were introduced into a colony, marked drones were observed making mating flights from that colony. They were collected and set up in harnesses as described above. On the day after collection they were tested for their reversal learning performance, which involved two training phases in the same manner as for their parents, as described above. A total of 412 drones was evaluated using this procedure. We calculated the number of “errors” each drone made to each odor. An error to the reinforced odor “B” involved a lack of a response to the odor on any trial but the first. An error to the nonreinforced “A” odor involved a response on any trial after the first. Imme-
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diately after the evaluation of their performance, each drone was placed in an Eppendorf tube and frozen at ⫺70°C. Evaluation of Drones for Latent Inhibition. Since the F1 queen that was used in the reversal learning experiment died, the drone progeny of one of her supersisters was used for evaluation of latent inhibition. The conditioning procedure for latent inhibition (Lubow, 1973; Abramson and Bitterman, 1986) involved three phases (Ferguson et al., 2001; Chandra et al., 2000). In the first phase, three trials performed with each subject involved forward pairing of odor A with sucrose reinforcement. The intertrial interval was 5 min This phase was designed to eliminate subjects that might have poor olfactory acuity or low motivational states. Thus all subjects that were subsequently evaluated for latent inhibition were motivated and could learn the association of odor with the reinforcement. We chose only subjects that exhibited rapid acquisition of the response (at least two of three possible responses to the CS) for evaluation in the second and third phases. In the second phase, subjects chosen in the first phase were placed on a wheel connected to and controlled by a computer. Subjects were rotated into a slowly moving airstream that vented into an exhaust system. This phase was designed to produce latent inhibition to a particular odor (here labeled “B”), which was always presented without subsequent sucrose reinforcement. An odor other than the one used in the first phase was always used in this phase. Thirty seconds after a conditioning trial began the odor was injected for 4 s into the conditioning arena and across the subject’s antennae. Subjects were then exposed to 36 such trials followed by no reinforcement at a 5-min intertrial interval (ITI). Subjects were then taken off of the wheel and left undisturbed for 15 min. The final phase was designed to select for individuals that showed fast versus slow acquisition to the odor they experienced in the second phase. They were then brought back to the original training station and conditioned to the same odor (B) used in the second phase. But this time, each trial involved forward pairing with odorant delivery and sucrose reinforcement as described above. Each subject experienced six trials at a fixed ITI of 5 min. Responses of the subjects were recorded as described above. A total of 210 drones, which were chosen from a single F1 queen that was a supersister of the first queen, was evaluated using this procedure. After evaluation each drone was placed in an Eppendorf tube and frozen at ⫺70°C for molecular genetic analysis.
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To check whether the distribution of errors in reversal learning and distribution of responses in latent inhibition deviated from a normal distribution, we determined whether the values for standard errors of kurtosis and skewness overlapped with zero (Sokal and Rohlf, 1997). Detection of QTLs. After one generation Ferguson et al. (2001) managed to establish fast and slow reversal lines. An F1 queen was produced by single-drone insemination between the selected fast-reversal queen and the drone derived from the slow-reversal queen. The drones, which were developed from unfertilized eggs, therefore represented gametes of hybrid queens and were used as the mapping population for determining linkage relationships. DNA Extraction. Individual frozen drones were ground with an electric drill in Eppendorf tubes that contained 350 l of CTAB extraction buffer [1% hexadecyltrimethyl ammonium bromide, 1.1 M Nacl, 50 mM Tris–Cl (pH 8), 10 mM EDTA, and 100 g/ml proteinase K]. After incubating at 65°C for 2–3 h, samples were extracted first with phenol/chloroform and then with only chloroform. DNA was then precipitated with 0.1 vol of 3 M sodium acetate (pH 5.2) and 2 vol of cold ethanol. Following centrifugation for 10 min at 14,000 rpm, the precipitate was then washed with 70% cold ethanol and resuspended in 500 l of distilled water. The DNA was then quantified with a fluorometer and diluted to 5 ng/l in distilled water. Polymerase Chain Reactions. Ten nucleotide primers of arbitrary sequence were used to generate RAPD markers in polymerase chain reactions (Williams et al., 1990). Primers were obtained from Operon Technologies (Almeda, CA) or the university of British Columbia Biotechnology Center (Vancouver, Canada). Amplifications in Biometra or MJ Research thermal cyclers were performed with the following parameters: 5 cycles of 94°C/1 min, 35°C/1 min, 2-min ramp to 72°C, and 72°C/2 min, followed by 33 cycles of 94°C/10 s, 35°C/30 s, (and) 72°C/30 s. Polymerase chain reactions were performed in 12.5-l reaction volumes that contained a 1 M concentration of primer, 100 M concentrations each of dATP, dGTP, dCTP, and dTTP (Gibco-BRL), 10 mM Tris–HCl (pH 8.3), 50 mM KCl, 2 mM MgCl2, 0.5 U of Taq polymerase, and 5 ng of genomic DNA. A drop of mineral oil was used to cover reactions. Linkage Analyses of RAPD Markers. For reversal learning, DNA was extracted, as described above, from 96 haploid male progeny of the F1 queen (48 fast reversers and 48 slow reversers) and was used to construct a genomic map with RAPD markers (Hunt and
Page, 1995). For latent inhibition, DNA was extracted from a new set of 96 haploid male progeny of the F1 queen (48 noninhibitors and 48 inhibitors). RAPD markers were produced, as described above, by amplifying honeybee DNA in a polymerase chain reaction using commercially available, 10-base oligonucleotides as primers. Markers were resolved in gels containing 1 to 1.4% Synergel (Diversified Biotech, Newton Center, MA), 0.7% agarose, and 0.5⫻ TBE. The QTL analyses compared the inheritance of specific RAPD markers with learning performance. Interval mapping with MapQTL software (Van Ooijen and Maliepaard, 1996) was used to screen 153 markers for reversal learning belonging to 10 linkage groups that each contained at least 5 RAPD markers, spanning 2100 cM of the honeybee genome; 154 markers were screened for latent inhibition, belongng to 8 linkage groups that each contained at least 4 markers, spanning 2650 cM of the honeybee genome at an average spacing about of 8 cM. Statistical Analysis. RAPD markers were scored in the haploid drone progeny of the F 1 queen. The scores (number of responses) of the two behavioral traits (fast and slow learners) and the data on marker alleles were first analyzed by a linear regression. A p ⫽ .005 threshold was set for inclusion of markers for further QTL analysis, which was accomplished with the software package MapQTL (Van Ooijen and Maliepaard, 1996). First, potential QTLs were identified using standard interval mapping procedures (Lander and Botstein, 1989). Then we used the multiple QTL model (MQM) feature of MapQTL to fit more than one QTL at a time. This feature uses the marker closest to the QTL as a cofactor in the likelihood equation to account for the portion of the variance that can be attributed to that QTL. All the cofactors for putative QTLs with LOD scores above a threshold of 2.0 were used in the analysis. If the multifactor analysis decreased the LOD score to below 2.0, that marker was then dropped from subsequent analysis as a cofactor and the analysis was repeated. If a marker increased to LOD 2.0 in the multifactor analysis, this new marker was added to the cofactor list and the analysis was repeated until there was no further change in cofactors. We chose 2.0 as the inclusion criterion, even though this represents only a provisional QTL (Lander and Kruglyak, 1995), because we wanted to identify all putative QTLs and avoid making a type II error. A LOD score of 2.0 corresponds to the appropriate threshold to control type I error to ␣ ⫽ .05 for a single chromosome scan (Van Ooijen, 1999).
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RESULTS Reversal Learning Performance of Drones. Drones (n ⫽ 412) from a single F 1 queen quickly learned to discriminate the two odorants during the A⫹ B⫺ phase of conditioning (Fig. 1). By the sixth trial with each odorant, 85% of the drones responded to A, while only 25% responded to B. During the reversal phase, responses to B increased rapidly to reach the same level that was achieved with A in the initial phase (Fig. 1). Responses to A decreased slowly across the six reversal trials with that odorant. Figure 2a illustrates the frequency of individuals plotted against number of errors made during discrimination conditioning. Most drones showed robust learning performance during the 12 trials in the discrimination (A⫹ B⫺) phase. On average drones made 3.5 (SE ⫽ 0.09) errors over the 10 trials that comprised this conditioning phase. We selected a subset of the 412 drones that made four or fewer errors during initial discrimination conditioning (Fig. 2a) (n ⫽ 264; overall mean error ⫽ 2.3, SE ⫽ 0.07) These errors occurred almost equally to A (mean error ⫽ 0.9, SE ⫽ 0.06) and B (mean error ⫽ 1.0, SE ⫽ 0.06) odors. These 264 drone progeny showed a continuous distribution of errors during the reversal (A⫺ B⫹) phase (Fig. 2b) (mean error ⫽ 5.4, SE ⫽ 0.12), which suggests that the speed of reversal is polygenically regulated. This distribution was continuous and normal [n ⫽ 264; kurtosis ⫽ 0.196, SE ⫽ 0.299; skewness ⫽ 0.109, SE ⫽ 0.150 (Sokal and Rohlf, 1997)]. Note that
Fig. 1. Reversal learning performance of F1 drone progeny (N ⫽ 412). For discrimination training, subjects were discriminately conditioned to A⫹/B⫺. For reversal training, the pattern of reinforcement was changed to A⫺/B⫹. Vertical lines represent 95% confidence intervals.
Fig. 2. Frequency distribution of reversal learning performance for drone progeny from a single F1 queen that was reared from a cross between fast and slow reversal lines (N ⫽ 412). (a) The graph shows the proportion of the total number of drones that committed the number of errors indicated on the X axis during the initial discrimination phase. Drones selected for reversal training (N ⫽ 264; hatched bars) made four or fewer errors. (b) Frequency distribution of drone (N ⫽ 264) learning performance during the reversal training phase. The graph shows the proportion of the total number of drones that committed the number of errors indicated on the X axis. (c) Frequency distribution of learning performance during the reversal training phase showing the proportion of the total number of drones that committed the number of errors to odor A and odor B.
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most of the errors occurred as responses to odor A (mean error ⫽ 3.8, SE ⫽ 0.10), which was not reinforced in this phase. Most drones quickly learned to respond to odor B in this phase, which led to fewer errors to this odor (mean error ⫽ 1.6, SE ⫽ 0.08). Separation of errors to each of the two odors also failed to reveal a bimodal distribution (Fig. 2c). Instead, distributions were skewed toward high (A) or low (B) numbers of errors toward each odor. For molecular analyses, we selected subjects representing the extremes of the distribution for reversal learning. Those that made 8 or more errors (of a maximum possible 10 errors) during the reversal phase were classified as “slow reversers” (n ⫽ 41; mean error ⫽ 8.7, SE ⫽ 0.77), because these drones tended to not reverse the learned discrimination from the first phase. Subjects that made fewer than four errors, that is, quickly reversed the learned discrimination from the first phase, were categorized as “fast reversers” (n ⫽ 37; mean error ⫽ 2.1, SE ⫽ 0.83). Learning Performance of Drones on Latent Inhibition. We then evaluated a new set of drone progeny (from the supersister of the original F1 queen) for latent inhibition. In Fig. 3, the frequency of individuals is plotted against the number of responses made by drones in the third phase of conditioning (n ⫽ 208; mean error ⫽ 3.1, SE ⫽ 1.81). In contrast to reversal learning, the distribution was not continuous (n ⫽ 208; kurtosis ⫽ 1.05, SE ⫽ 0.336; skewness ⫽ 0.503, SE ⫽ 0.169), with most individuals lying at either extreme. This type of distribution suggests that expression of latent inhibition may be influenced in part by one or a few genes with major phenotypic effects.
For molecular analyses, we categorized drones that showed more than four correct responses (of a possible five trials) as “noninhibitors” (n ⫽ 48; mean response ⫽ 5.06, SE ⫽ 0.035). These drones quickly learned to respond to odor B in the third phase, perhaps because they were not inhibited by exposure to odor in the second phase. Twenty-eight percent (56 of 208) of the subjects showed at least five responses over six trials. Only 4 of 56 subjects responded spontaneously on the first trial. Drones that showed fewer than three responses were categorized as “inhibitors” (n ⫽ 48; mean responses ⫽ 0.31, SE ⫽ 0.073). Twenty-three percent (48 of 208) showed either no or one response to odor across the six trials in phase III. None of the inhibitors responded spontaneously on the first trial. Thus inhibitors and noninhibitors reveal a difference in the tendency to inhibit their response to odor in the latent inhibition paradigm. Previous analyses, which incorporated different control procedures, have attributed this difference to latent inhibition (Chandra et al., 2000). QTL Maps for Reversal Learning and Latent Inhibition. The 96 haploid drone progeny of the F1 hybrid queen that were selected based on reversal learning performance were analyzed for RAPD markers to detect linkage to genes that influence learning performance. QTL analyses for reversal learning showed that one marker, X12-.87 (Fig. 4A), had an associated LOD score (logarithm of odds ratio) for the likelihood of having a linked QTL of 2.45 and accounted for 12.9% of the phenotypic variance. This putative QTL was designated lrn2. Another marker, A10-.70, had a LOD score of 2.75 (df ⫽ 1) and explained 14.1% of the total phenotypic variance (Fig. 4B). This putative QTL was designated lrn3. In combination, the two loci explained 27% of the phenotypic variance. Together, these two QTLs exceed the 95% threshold for a single chromosome (Jansen, 1994; Lander and Kruglyak, 1995; Van Ooijen, 1999). The new set of 96 haploid drone progeny of the supersister of the F 1 hybrid queen was analyzed for RAPD markers. QTL analyses for latent inhibition showed that only one marker, R3-.86 on linkage group X (Fig. 5), had a significantly high LOD score, 6.15 (df ⫽ 1). No other genomic region had a LOD score higher than 2. This QTL explained 28.1% of the phenotypic variance and was designated lrn1. In addition, this putative QTL is independent of those associated with reversal learning performance. Since genotyping the extremes will bias our estimates for explained variance, we also genotyped an additional 96 drones with intermediate phenotypes and repeated the QTL analyses. These analyses showed a LOD score of 5.41 (df ⫽ 1) for lrn1 that accounted for 14.1% of the phenotypic
Fig. 3. Frequency distribution of learning performance during phase III of the latent inhibition protocol of a set of drone progeny from a supersister of the original F1 queen. The graph shows the proportion of the total number of drones that showed the number of responses indicated on the X axis.
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Fig. 4. Putative quantitative trait loci that influence reversal learning: (A) lrn2 and (B) lrn3. Numbers to the left of the bar are distances (cM) between markers. Numbers to the right designate specific RAPD markers. Numbers preceded by a letter represent primers produced by Operon Technologies, California, while those without a letter were produced by University of British Columbia Biotechnology Laboratory. Numbers following the dash represent the size of the RAPD marker fragment (kb). The line to the right shows the LOD score for the likelihood that a QTL exists at each location along the linkage group.
variance. The score support interval was 20.6 cM. Thus, even though lrn1 represents an unconfirmed QTL, it exceeds an experimentwise error rate in a genome scan for ␣ ⫽ .01, even when the larger number of drones is included (Van Ooijen, 1999). DISCUSSION The primary focus of our research was to explore the genetics underlying performance on reversal learning and latent inhibition learning tasks in honeybees. A single queen and drone were chosen from fast and slow reversal lines that had been selected over two generations in a previous study (Ferguson et al., 2001). The primary vehicle for behavioral and genetic analyses were drones from two F1 queen supersisters that
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Fig. 5. A quantitative trait locus that influences latent inhibition. Linkage group containing the most likely location of the QTL (lrn1) for latent inhibition. All else as in Fig. 4.
were established from a cross between the two lines. In essence, honeybee drones are the queen’s gametes, since the drones arise from unfertilized eggs. Thus one particular advantage of the use of drone honeybees is the ability to evaluate phenotypic characteristics of individuals that possess recombinant haploid genotypes. Our earlier studies had identified correlated selection responses between reversal learning and latent inhibition (Chandra et al., 2000; Ferguson et al., 2001). Here we tested whether these correlated responses might have arisen by way of pleiotropic effects from a common set of genes. Specifically, we hypothesized that the distribution of behavioral response patterns as well as the molecular loci that are correlated with perfor-
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mance on the two tasks would be similar across drones from F1 supersister queens. Behavioral analyses of drones from the F1 queens produced two distribution patterns for reversal learning and latent inhibition, and QTLs influencing these traits were unlinked. The distribution of errors during reversal learning was quasi-normal and continuous. The phenotypic distribution would be consistent with the conclusion that the genetic predisposition of one or another kind of performance on reversal learning might be polygenic, that is, it is influenced by two or possibly many genes, each having small, cumulative effects (Lai et al., 1994; Falconer and Mackay, 1996). The majority of gametes under these conditions would exhibit genotypes that are characterized by a mixture of alleles from the two original lines. By chance, a small fraction of gametes would inherit a large number of alleles from one line or the other, and their performance would be reflected in the two extremes of the distribution. Because of the wide distribution, we selected 48 drones from each extreme and analyzed their genotypes for 153 molecular markers. Our molecular analysis discovered two QTLs (lrn2 and lrn3) that cumulatively explained 27% of the phenotypic variance for reversal learning. However, these two QTLs do not map to linkage groups that could be recognized in the honeybee maps (Hunt and Page, 1995; Hunt et al., 1998) and are independent of each other. Clearly, more than one gene is influencing the reversal learning task, which is consistent with the conclusion from the distribution of errors in the behavioral data. Interestingly, the distribution of responses was significantly different for latent inhibition. In this case, the phenotypic variation was discontinuous. This distribution would be consistent with the conclusion that learning performance on latent inhibition might be influenced by a single locus that has a major phenotypic effect. This locus might consist of a single gene or a family of tightly linked genes. As a consequence, approximately half of the animals under these conditions would be expected to exhibit genotypes similar to that of one parent and the other half would exhibit genotypes similar to that of the other parent. In our study the numbers of drones that showed five versus zero responses were not equivalent. More of the F1 progeny fell into the “noninhibitor” group than fell into the “inhibitor” group, particularly if the two lowest (zero or one) and two highest (four or five) categories are taken into account. The unequal distribution may be due epistatic interactions that modify the expression of this locus, or to environmental effects.
The molecular analyses detected a single QTL with a LOD score of 6.15 (lrn1; df ⫽ 1). This finding is consistent with the suggestion that this component of learning behavior could be influenced by one locus with a large effect. Furthermore, the QTL for latent inhibition does not map to either of the linkage groups that were correlated with reversal learning. Thus the molecular data suggest that the correlation between the traits in previous studies (Chandra et al., 2000; Ferguson et al., 2001) might not have been due to pleiotropy or linkage because our molecular analyses did not show overlapping or linked QTLs for the two traits. Detecting linkage between reversal learning and latent inhibition QTLs would have indicated pleiotropy. But failure to detect linkage does not exclude the existence of a QTL with pleiotropic effects on these traits. One possibility is that the QTLs responsible for the correlation observed during selection have yet to be identified. It is also possible that the mother of the two F 1 queens from the high-reverser line was heterozygous for one or more of the QTLs that we mapped because we do not maintain an inbred line. The queens inherited the same QTL alleles from their haploid, slow-reverser father. However, if their high-reverser mother was not fixed for lrn1 by two generations of selection (based on reversal learning), the supersister F1 queens would have a 50% chance of inheriting alternative alleles for lrn1. In this case, we might not have had the opportunity to test for effects of lrn1 on reversal learning because the queen used in that study was fixed for the low allele. Besides QTL location, estimation of gene effects and investigation of phenomena such as pleiotropy, our analyses stand to provide an inroad to the identification of genes that influence the expression of learning. In humans, QTLs with effects on learning-related traits such as general cognitive ability (Fisher et al., 1999), Alzheimer’s disease (St. George-Hyslop et al., 1987), and dyslexia (Cardon et al., 1994) have been mapped. QTLs have also been mapped in studies of fear conditioning (Owen et al., 1997; Wehner et al., 1997) and ethanol sensitivity in mice (Markel et al., 1997) and rats (Bice et al., 1998). A number of useful QTLs have also been identified for honeybee foraging (Hunt et al., 1995) and stinging (Hunt et al., 1998) behavior. Various mapping techniques have successfully isolated single genes or loci with substantial effects on behavioral traits of humans, mice, rats, and fruit flies (Cardon et al., 1994; Taylor et al., 1999; Gusella et al., 1983; Crabbe et al., 1989; Hall, 1994; Takahashi et al., 1994). In one case, a genetic locus with a large effect
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on larval foraging behavior has been identified in Drosophila melonogaster (De Belle et al., 1989). Later it was designated as a single foraging gene, “for ”—a gene that has two (rover and sitter) naturally occurring variants in food search behavior. Recently, molecular mapping placed for mutations in the dg2 gene, which encoded a cyclic cGMP-dependent protein kinase (PKG) (Osborne et al., 1997). Examples in which a gene was isolated by mapbased cloning from initial QTLs are not common, which is due primarily to the large size of confidence intervals for QTL position and the subtle connection between genotype and quantitative phenotype associated with these conditions (Symula, 1999; Phillips, 1999). However, many genes involved in human diseases have been isolated with the help of positional information (Estivill et al., 1987; Lander and Schork, 1994). In addition, the honeybee has some advantages for cloning genes based on map position. It has a relatively small genome, about 180 MB, with 8% dispersed repetitive DNA (Jordan and Brosemer, 1974). The honeybee also has the highest rate of recombination reported for a metazoan (Hunt and Page, 1995). Because of the high recombination rate, there is only about 50 kb of DNA per cM. Using the formula of Darvasi and Soller (1997), our current 95% confidence interval for the position of lrn1 is about 23 cM (1 Mb). Reducing this confidence interval to 5 cM would represent a distance that could be spanned by several genomic DNA clones. Hence it may someday be feasible to clone the gene through a map-based cloning procedure. ACKNOWLEDGMENTS Support for this work was provided by grants to B.H.S. from NIH-NIGMS (1 R01 GM52392-03) and NIH-NCRR (9 R01 RR14166-06). The authors also wish to thank Dr. Patricia Parker for technical assistance with RAPD analyses and for her intellectual support throughout the project. REFERENCES Abramson, C. I., and Bitterman, M. E. (1986). Latent inhibition in honeybees. Anim. Learn. Behav. 14:184 –189. Adams, R. J., Kerr, W. E., and Paulino, Z. L. (1977). Estimation of sex alleles and queen matings from diploid male frequencies in a population of Apis mellifera. Genetics 86:583–596. Benatar, S. T., Cobey, S., and Smith, B. H. (1995). Selection on a haploid genotype for discrimination learning performance: Correlation between drone honeybees (Apis mellifera) and their worker progeny (Hymenoptera: Apidae). J. Insect. Behav. 8:637– 652.
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