Polygenic Control of Idiopathic Generalized Epilepsy Phenotypes in the Genetic Absence Rats from Strasbourg (GAERS)


Address correspondence and reprint requests to Dr. D. Gauguier at The Wellcome Trust Centre for Human Genetics, University of Oxford, OX3 7BN Oxford, U.K. E-mail: gdomi@well.ox.ac.uk


Summary: Purpose: Generalized nonconvulsive absence seizures are characterized by the occurrence of synchronous and bilateral spike-and-wave discharges (SWDs) on electroencephalographic recordings, concomitant with behavioral arrest. The GAERS (genetic absence rats from Strasbourg) strain, a well-characterized inbred model for idiopathic generalized epilepsy, spontaneously develops EEG paroxysms that resemble those of typical absence seizures. The purpose of this study was to investigate the genetic control of SWD variables by using a combination of genetic analyses and electrophysiological measurements in an experimental cross derived from GAERS and Brown Norway (BN) rats.

Methods: SWD subphenotypes were quantified on EEG recordings performed at both 3 and 6 months in a cohort of 118 GAERS × BN F2 animals. A genome-wide scan of the F2 progenies was carried out with 146 microsatellite markers that were used to test each marker locus for evidence of genetic linkage to the SWD quantitative traits.

Results: We identified three quantitative trait loci (QTLs) in chromosomes 4, 7, and 8 controlling specific SWD variables in the cross, including frequency, amplitude, and severity of SWDs. Age was a major factor influencing the detection of genetic linkage to the various components of the SWDs.

Conclusions: The identification of these QTLs demonstrates the polygenic control of SWDs in the GAERS strain. Genetic linkages to specific SWD features underline the complex mechanisms contributing to SWD development in idiopathic generalized epilepsy.

Epilepsy is a common neurologic disorder characterized by recurring spontaneous seizures. Symptomatic and idiopathic epilepsies represent the main forms of the disease (1). In symptomatic epilepsy, seizures are the consequence of identifiable lesions or distinct etiologies. Idiopathic epilepsy is characterized by recurring focal or generalized seizures with no detectable brain lesions. Both genetic predisposition and environmental influences play a major role in its etiology (2). Idiopathic generalized epilepsies (IGEs) show genetic heterogeneity and incomplete penetrance. Patients with IGE develop different types of generalized seizures defining specific syndromes, such as childhood absence epilepsy (CAE), juvenile absence epilepsy (JAE), or juvenile myoclonic epilepsy (JME).

Absence epilepsies are a group of IGEs that vary in their age at onset, seizure frequency, and pattern of evolution. Typical absence is a nonconvulsive epileptic seizure characterized on ictal electroencephalogram (EEG) by bilateral, synchronous, and regular 3-Hz spike-and-wave discharges (SWDs) that start and end abruptly and occur as frequently as several hundred times a day. They are concomitant with behavioral arrest, which can be accompanied by automatisms or moderate tonic or clonic components affecting the limbs, eyeballs, or eyelids. Patients do not demonstrate other neurologic or neuropsychological disorders.

The molecular pathogenesis and the etiologic components of IGE remain poorly understood. Like that of other common idiopathic epilepsies, the inheritance of IGE is complex, and multiple genes may be involved in the development of the disease. Although genetic studies have pointed to several chromosomal regions contributing to IGE (3–7), they had limited impact on the characterization of pathophysiologic mechanisms underlying the disease because of its low penetrance and a strong phenotypic heterogeneity. Because typical absence epilepsies affect mainly children and adolescents and have moderate consequences, for ethical reasons, investigations of their underlying pathophysiologic and etiologic mechanisms are difficult in human subjects.

Much of the recent information regarding the pathophysiology of absences derives from investigations in various rodent models spontaneously exhibiting the main features of the human disorder. These models include numerous mouse strains (8,9), as well as inbred rat strains such as the genetic absence epilepsy rats from Strasbourg (GAERS) (10,11) and the Wistar Albino Glaxo rat (WAG/Rij) (12–14), which both provide powerful tools for extensive and accurate physiological investigations including EEG recordings. Animals of the GAERS strain spontaneously display many of the characteristics of human absence epilepsy. The epilepsy phenotype in GAERS is genetically determined (11,15). SWDs start and end abruptly on a normal EEG background. The mean frequency of spike-and-wave complexes within discharges is 7–11 Hz, and in a state of quiet wakefulness, which is, however, greater than in human epilepsy (3 Hz). SWDs in the GAERS occur 1.3 times/min on average, last for ∼17 s, and their mean cumulated duration per minute is ∼25 s (10,11). SWDs are concomitant with behavioral immobility, and responsiveness to mild sensory stimuli is abolished. In the GAERS strain, SWDs are suppressed by the main antiepileptic drugs (AEDs) that are effective against absences in humans (11,15). Absence seizures in GAERS are generated in a specific neuronal network involving cortical and thalamic areas (16–18). This thalamocortical circuitry is under the control of several specific inhibitory and excitatory systems arising from the forebrain and brainstem.

The purpose of this study was to identify genetic factors triggering SWDs by using a combination of genetic analyses and electrophysiological measurements in F2 progenies derived from GAERS and Brown Norway rats. We identified genetic quantitative trait loci (QTLs) in chromosomes 4, 7, and 8 influencing SWD intermediate phenotypes in the cross. These results represent significant advances in our fundamental knowledge of the complex mechanisms underlying SWDs in IGE.


Animals and experimental cross

The GAERS strain was derived in our laboratory over several generations of repeated selective breeding of outbred Wistar rats spontaneously exhibiting SWDs, followed by 12 repeated brother × sister matings for inbreeding. A colony of inbred GAERS rats was maintained in our laboratory. Brown Norway (BN/CrlBR) rats were purchased from Charles River (Saint Aubin lès Elbeuf, France). Two F1 populations were initially obtained by mating male GAERS and female BN rats, and in the reciprocal cross, by mating male BN and female GAERS rats. Subsequently, F2 animals were produced by independently mating rats of the two F1 reciprocal populations. Animals of the resulting two reciprocal F2 crosses were used for EEG recordings and genotype analysis. The total number of F2 animals was 59 males and 59 females. Tail tips were collected and stored at –80°C for genomic DNA preparation.

All rats were maintained under a 12 h/12h L-D regimen with white lights off at 7 p.m. Food and water were provided ad lib. Animal procedures were conducted in accordance with the European Communities Council Directives (86/609/EEC).

Phenotype analysis

Standard EEG electrodes were permanently implanted under pentobarbital (PYB) anesthesia in 10-week-old F1 and F2 rats. Each rat received four single-contact electrodes placed over the left and right frontoparietal cortex, as previously described (10). The electrodes were secured into the skull and soldered to a microconnector embedded in dental acrylic resin with anchoring screws on the skull. An electrode over the surface of the cerebellum served as ground for all derivations. All rats were allowed to recover for a period of ≥2 weeks until EEG recordings started. Twelve-week-old animals were then familiarized with the EEG recording situation for ≥2 h. Next, the EEGs were recorded for 1 h in freely moving animals from the ipsilateral frontoparietal derivations (Reega Minidix-TR; Alvar Electronic, Montreuil-Paris, France). EEG recordings were repeated in 24-week-old animals. In addition to the measurement of the total number, amplitude, frequency, and total duration of SWDs during the recording period, we applied a grading score system, by using criteria reported earlier (12,19,20), to classify animals in the F2 population according to the EEG hallmark and the severity of SWDs (Fig. 1).

Figure 1.

Spike–wave discharge morphology grading in the F2 (GAERS × BN) cross during an EEG. A: Primarily wave burst. B: Spike-and-wave burst with polarity inversion. C: Typical spike-and-wave burst recorded in GAERS rats. Bar, 1 s, 200 μV.

Grade 0, EEG background without SWDs.

Grade 1 (Fig. 1A), Wave burst.

Grade 2 (Fig. 1B), Spike-and-wave burst (spikes directed downward).

Grade 3 (Fig. 1C), Spike-and-wave burst as present with a high incidence in the EEG of GAERS rats (spikes are directed upward).

Genotype analysis

For the genome-wide search of genetic loci controlling absence epilepsy phenotypes, 611 microsatellite markers were tested for existence of allele variations between the BN and GAERS strains. A total of 454 (74%) markers exhibited evidence of polymorphism between the two strains and could be used for the genetic analysis. Results from polymorphism tests are available in our public database (http://www.well.ox.ac.uk/rat_mapping_resources). A subset of 146 markers already genetically mapped (21) and/or localized in the rat genome by using the T55 rat radiation hybrid panel (22) were selected for the genetic study of SWDs in the cross derived from GAERS and BN rats. They were chosen to cover the 21 chromosomes of the rat genome with an average spacing of ∼10 cM between loci. Genotypes were determined after polymerase chain reaction (PCR) amplification and gel electrophoresis of PCR products, as previously described (23). Oligonucleotides were synthesized commercially by Genosys Biotechnologies (Pampisford, U.K.).

Construction of the genetic maps

Genetic maps were constructed by using the JoinMap version 2.0 package (24), as previously described (23). In brief, JoinMap modules were used to assign markers in linkage groups and to calculate single-factor segregation ratios for each marker in a linkage group. Initial maps were then created, and double-recombination events were identified by using the jmchk module, and the data corresponding to these were verified. The JoinMap jmchk module calculates for all loci and for all individuals the probability of obtaining the present genotype, conditional on both the genotypes at the two flanking loci and on map distances. We considered unexplained genotypes to be those having a threshold of >3 for the test statistic of log10 (1/p).

Linkage analysis and statistical analysis

For assessing the effect of age and sex on the various EEG parameters measured in the F1 and F2 populations, two-way analyses of variance (ANOVAs), with age and sex as factors, and Fisher's post hoc test were carried out. The Statview statistical program package was used for statistical calculations.

Before QTL analysis in the F2 cross, all phenotypes determined in the F2 hybrids were standardized for sex and cross (BN × GAERS and GAERS × BN) by regression, and subsequent analyses of genetic linkage were performed on standardized residuals. Linkage between genetic markers and SWD phenotypes was initially evaluated with the MAPMAKER/QTL computer package (25). Nonparametric linkage analysis was performed by using an analysis of variance (ANOVA) test (26) followed by a permutation test (n = 10,000) to evaluate the threshold of significance for each pair of genetic–phenotypic markers (27). Correlations between marker genotypes and phenotypes were calculated by using the SPSS version 11.0 package.


Phenotype analysis in BN and GAERS rats and in F1 and F2 progenies

In a preliminary analysis, we verified the absence of SWDs during a 1-h EEG recording in three male and three female BN rats at 3 and 6 months. Under similar recording conditions, we also confirmed the existence of SWDs in three males and three females of the GAERS strain. These rats developed high numbers of SWDs (39.2 ± 17.8 at 3 months and 69.2 ± 9.7 at 6 months) characterized by long duration (888 ± 441s at 3 months and 1,182 ± 203 at 6 months) and high amplitude (777 ± 99 Hz at 3 months and 649 ± 158 Hz at 6 months). SWD frequency was as previously reported (7.7 ± 0.4 at 3 months and 7.1 ± 0.2 at 6 months) (10,11).

In F1 and F2 populations, SWD variables were not significantly affected by the direction of the cross (BN × GAERS and GAERS × BN), and phenotypes determined in the two subsets of animals were pooled for the calculation of mean values in the F1 (n = 27) and F2 (n = 118) animals.

Results from the analysis of SWD phenotypes in F1 animals are shown in Table 1; 78% of F1 rats presented SWDs at age 3 months, whereas 98% showed evidence of SWDs at 6 months. A two-way ANOVA carried out with sex and age as covariates demonstrated no sex effect on any of the EEG parameters. However, a significant age effect was evidenced for the duration (F= 10.85; n = 27; p = 0.001), number (F= 12.72; n = 27; p = 0.0008), and frequency (F= 17.53; n = 27; p < 0.0001) of SWDs. A post hoc Fisher test showed (n = 27) that SWD duration, number of seizures, and frequency increased in an age-dependent manner in the F1 progeny (Table 1).

Table 1. Description of SWD phenotypic variables in male (F1, n = 11; F2, n = 59) and female (F1, n = 16; F2, n = 59) GAERS × BN progeny
Phenotype F1 (3 mo)F1 (6 mo)F2 (3 mo)F2 (6 mo)
  1. Data expressed as mean ± SD.

SWD duration (s)Males111 ± 153341 ± 30378.6 ± 143 135 ± 242
 Females 86 ± 143344 ± 378 69 ± 128215 ± 293
Nb seizuresMales14.5 ± 17.139.1 ± 27.8 8.6 ± 11.514.4 ± 16.7
 Females11.6 ± 17.128.4 ± 21.511.7 ± 16.920.4 ± 20.1
EEG amplitude (μV)Males454 ± 274356 ± 226321 ± 302306 ± 200
 Females184 ± 214399 ± 204295 ± 200317 ± 182
Frequency (Hz)Males5.47 ± 3.527.15 ± 2.454.85 ± 3.706.77 ± 2.25
 Females3.69 ± 3.827.61 ± 2.155.82 ± 3.207.02 ± 2.20

As observed in F1 animals, 72% of F2 hybrids showed SWDs at age 3 months, and 98%, at 6 months. A two-way ANOVA carried out to test sex and age influences on the phenotypes in the F2 cross demonstrated that the number of seizures was significantly higher in females than in males (F = 4.75; n = 118; p = 0.03; Table 1). No other EEG parameters were influenced by sex. A statistically significant age effect was detected for the duration of SWDs (F = 13.88; n = 118; p = 0.0002), number of seizures (F = 11.96; n = 118; p = 0.0006), and SWD frequency (F = 17.58; n = 118; p < 0.0001). Post hoc analysis indicated that, as observed in F1 animals, these EEG parameters increased in an age-dependent manner in the F2 hybrids.

Genetic mapping

We constructed a genetic map in the reciprocal F2 populations derived from GAERS and BN rats, by using genotype data from the 146 markers typed in the hybrid rats. The average spacing between adjacent markers was <15 cM. The genetic length of the chromosomes was consistent with previously published maps (average chromosomal-length variations, 107 ± 5%) (21). Marker order in the resulting maps and genetic distances between markers were consistent with published maps of the rat genome (21,22).

QTL analysis

The dissection of SWDs in quantitative subphenotypes allowed us to identify genetic loci in a broad region of rat chromosome 4 cosegregating with the frequency (maximum LOD, 4.62; p = 0.000024), amplitude (maximum LOD, 3.02; p = 0.0009), and grading (maximum LOD, 4.30; p = 0.00005) of SWDs in 3-month-old F2 rats (Fig. 2A). In 6-month-old F2 animals, significant linkages between markers localized in the same region of chromosome 4 and both the frequency (maximum LOD, 3.27; p = 0.0005) and amplitude (maximum LOD, 3.70; p = 0.0002) of SWDs were confirmed, whereas the linkage to SWD grading was marginal (maximum LOD, 2.58; p = 0.0026; Fig. 2B). This QTL accounts for a maximum of 13.7% of the variance in the cross and was denoted Swd/gaers1. Markers mapped to the QTL showed no evidence of genetic linkage to either the total number or the duration of SWDs in both 3- and 6-month-old F2 hybrids (maximum LOD, 1.78).

Figure 2.

Genetic mapping of the locus Swd/gaers1 controlling spike–wave discharge (SWD) phenotypes in 3- (A) and 6-month-old (B) hybrids of the (GAERS × BN) F2 cross. LOD scores are plotted against map distance in centimorgans (cM), as determined in the GAERS × BN cross, for the amplitude (♦), frequency (▪), duration (▾), and total number (•) of SWDs, as well as the SWD grading scores (▴). Regression for both sex and cross was applied to all phenotypes. Permutation tests (n = 10,000) (27) were used to establish critical threshold values for significant linkage (LOD > 3.3 in an F2 cross), as determined by Lander and Kruglyak (51).

A further two regions of rat chromosomes 7 and 8 denoted Swd/gaers2 and Swd/gaers3, respectively, showed evidence of genetic linkage to one or several SWD variables in an age-dependent manner (Table 2). They each explain a maximum of 12% of the variance of the most significantly linked phenotype in the cross. The marker locus D7Mgh5 was significantly linked to the frequency of SWDs at 3 months (LOD, 3.05; p = 0.0009), and both the grading (LOD, 3.68; p = 0.0002) and total duration (LOD, 3.06; p = 0.0009) of SWDs at 6 months. Linkage between this marker and other SWD variables at 3 months was not statistically significant (maximum LOD, 2.24; p = 0.0058 for the total duration of SWDs), and linkages to the amplitude and total number of SWDs at 6 months remained either not significant or marginal (LOD, 2.20; p = 0.0063; and LOD, 2.72; p = 0.0019, respectively). Among markers mapped to rat chromosome 8, D8Got49 showed the strongest evidence of linkage to a SWD parameter. This marker is specifically associated with the frequency of SWDs in 6-month-old F2 hybrids (LOD, 3.45; p = 0.00036).

Table 2. Correlations between genotypes at marker loci D4Mgh16 (Swd/gaers1), D7Mgh5 (Swd/gaers2), and D8Got49 (Swd/gaers3) and SWD phenotypes (grading, number, total duration, amplitude, frequency) measured in 3- and 6-month-old GAERS × BN F2 hybrids
D4Mgh16LOD ScoreLOD ThresholdGenotypep ValueF
Grading 3 mo4.303.201.64 ± 0.22 1.59 ± 0.150.78 ± 0.190.00454.57
Grading 6 mo2.362.972.23 ± 0.18 1.78 ± 0.131.34 ± 0.210.011 3.87
Total number 3 mo0.312.709.48 ± 1.9911.47 ± 2.037.84 ± 2.710.69  0.49
Total number 6 mo1.262.9721.65 ± 3.63 18.76 ± 2.6510.72 ± 2.14 0.086 2.25
Total duration 3 mo (s)0.283.0359.5 ± 18.4 86.4 ± 19.358.6 ± 25.00.67  0.51
Total duration 6 mo (s)1.423.23243.3 ± 62.4 194.3 ± 35.873.9 ± 22.70.06  2.53
Amplitude 3 mo (μV)3.022.97343 ± 49 353 ± 33171 ± 37 0.00434.62
Amplitude 6 mo (μV)3.702.97410 ± 33 354 ± 21235 ± 31 0.00066.22
Frequency 3 mo (Hz)4.622.91 5.9 ± 0.59 5.89 ± 0.413.37 ± 0.700.00294.92
Frequency 6 mo (Hz) ± 0.26 7.27 ± 0.195.49 ± 0.620.00155.46
D7Mgh5LOD ScoreLOD ThresholdGenotypep ValueF
Grading 3 mo1.503.131.81 ± 0.20 1.27 ± 0.171.06 ± 0.220.034 2.99
Grading 6 mo3.683.032.34 ± 0.17 1.69 ± 0.171.25 ± 0.170.00066.25
Total number 3 mo0.592.8611.53 ± 2.20 11.22 ± 2.216.81 ± 2.560.44  0.91
Total number 6 mo2.722.9621.19 ± 2.54 20.69 ± 3.218.34 ± 1.590.00744.18
Total duration 3 mo (s)2.242.78209.1 ± 43.9 128.9 ± 26.858.4 ± 19.70.021 3.35
Total duration 6 mo (s)3.062.75327.9 ± 63.8 196.7 ± 37.480.7 ± 22.80.00155.45
Amplitude 3 mo (μV)1.832.99402 ± 44 250 ± 30254 ± 50 0.012 3.79
Amplitude 6 mo (μV)2.202.95369 ± 32 346 ± 21238 ± 34 0.049 2.69
Frequency 3 mo (Hz)3.052.936.76 ± 0.48 5.11 ± 0.523.70 ± 0.630.00155.46
Frequency 6 mo (Hz)1.372.987.65 ± 0.09 6.55 ± 0.366.24 ± 0.540.053 2.63
D8Got49LOD ScoreLOD ThresholdGenotypep ValueF
  1. For all phenotypes, means ± SD were calculated for each genotype of the animals at the locus. Number of observations is in parentheses. LOD scores, p values, and F scores were determined from phenotypic values standardized by sex and cross. Analysis of variance was applied to test for linkage. LOD threshold level indicates values obtained after 10,000 permutations (27) and determining statistical significance threshold (p = 0.001).

Grading 3 mo0.482.801.15 ± 0.26 1.34 ± 0.161.65 ± 0.210.47  0.85
Grading 6 mo0.232.931.80 ± 0.26 1.85 ± 0.141.71 ± 0.200.83  0.30
Total number 3 mo0.203.20 9.6 ± 3.61 9.24 ± 1.7111.71 ± 2.80 0.81  0.32
Total number 6 mo0.033.1818.95 ± 5.40 17.88 ± 2.4415.9 ± 2.550.95  0.12
Total duration 3 mo (s)0.492.7947.9 ± 22.3 59.8 ± 14.0103.3 ± 30.6 0.48  0.83
Total duration 6 mo (s)0.122.78185.0 ± 64.5 184.2 ± 40.5174.3 ± 38.0 0.90  0.19
Amplitude 3 mo (μV)1.033.06233 ± 47 266 ± 31376 ± 50 0.048 2.71
Amplitude 6 mo (μV)0.402.93301 ± 39 353 ± 23356 ± 42 0.046 0.86
Frequency 3 mo (Hz)0.462.744.84 ± 0.83 5.01 ± 0.485.75 ± 0.570.37  1.07
Frequency 6 mo (Hz)3.453.334.81 ± 0.82 7.40 ± 0.287.18 ± 0.470.00045.97

We subsequently calculated the mean values of each phenotype according to the genotypes of the animals for three markers showing strong evidence of linkage to SWD phenotypes (Table 2). At both loci Swd/gaers1 and Swd/gaers2 (markers D4Mgh16 and D7Mgh5, respectively), BN alleles were associated with high SWD grades and increased frequency and amplitude of SWDs in 3- and/or 6-month-old F2 animals (Table 2). In addition, the QTL Swd/gaers2 specifically contributed to the control of the total duration of SWDs at 3 and 6 months and the total number of SWDs at 6 months. At this locus, BN alleles were significantly associated with an increased duration and number of SWDs. In contrast, GAERS alleles at the locus Swd/gaers3 (marker D8Got49) were specifically and significantly associated with an increased frequency of SWDs in 6-month-old animals (Table 2). Rats carrying the homozygous GAERS genotype or the heterozygous genotype at the locus showed a significantly higher frequency of SWDs than did rats carrying the homozygous BN genotype at the locus.


By using an approach combining genetic and physiological experiments, we demonstrated the polygenic inheritance of SWD-related phenotypes in the inbred GAERS model of absence epilepsy. After EEG recordings in an F2 population derived from GAERS and BN rats, we identified QTLs located in chromosomes 4 (Swd/gaers1), 7 (Swd/gaers2), and 8 (Swd/gaers3) that control different components of SWDs. Repeated EEG recordings in 3- and 6-month-old hybrid rats showed an increased severity of SWD traits with age, which appears to be an important factor influencing linkage detection.

Genetic studies in human and rodent models of absence epilepsy have successfully provided evidence for a polygenic control of IGEs (7,28–34). However, the determination of specific phenotypic features characterizing the disease remains a key component of genetic investigations. Rat models, including the GAERS and WAG/Rij inbred strains, allow accurate and repeated EEG recordings that are often technically difficult in mice. In the GAERS model, interictal and ictal EEG patterns, as well as behavioral manifestations, show many similarities with typical human absence. The GAERS strain spontaneously develops specific features of absence epilepsy, including the persistence of absences into adulthood, variability in the electrocortical SWD pattern, high frequencies of SWDs, and absence of polyspikes during SWDs. The higher mean frequency of SWDs in the GAERS (7–11 Hz) than in human epilepsy (3 Hz) may underlie differences in biologic rhythmic oscillator involved in the thalamocortical loop in rats. Such information was crucial for the phenotype screening in the GAERS × BN F2 rats. Although statistical significance of the QTLs identified in the cross was modest, as usually observed in genetic studies of complex traits, the QTLs were clearly associated with specific phenotypes derived from repeated EEG recordings. The limitation in statistical significance was mostly due to the relatively modest size of the F2 cohort, which conversely allowed accurate phenotype profiling of all hybrids.

Results from the dissection of SWD variables in GAERS × BN F2 progeny showed that the various components participating in the SWD pattern have either strong or moderate power in QTL detection. For example, genetic linkage to the total number of SWDs was not significant or remained marginally significant (maximum LOD, 2.72 at the locus Swd/gaers2). In contrast, all three QTLs characterized in the F2 cross were strongly linked to SWD frequency, which appears to be a key component in the altered EEG profile. The differential effect of GAERS and BN alleles at the QTLs in promoting SWDs suggests that SWDs recorded in the GAERS strain stem from the effects of multiple genes having opposite effects on distinct SWD subphenotypes. The fact that GAERS alleles at the loci Swd/gaers1 and Swd/gaers2 are associated with a reduction in SWD frequency suggests that either GAERS rats carry SWD “resistant” alleles or that this phenotypic effect results from interactions between GAERS and BN alleles. The choice of the strain combination in our genetic study was driven by the high genetic-polymorphism rate between GAERS and BN strains, which facilitates genome-wide scans, and the absence of SWD-related traits in the BN rat. Replication of the SWD QTLs in other experimental crosses derived from the GAERS and rat strains that show absence or low frequency of SWD would validate the existence and robustness of genetic linkages reported here. However, results from genetic studies in rodent models of human complex traits, including, for example, blood pressure (35), have highlighted the importance of the genetic background of control strains in QTL detection. Results from our genetic study suggest that each QTL reflects the effect of gene(s) that specifically control a subset of phenotypic variables contributing to SWDs and may involve different structures of the central nervous system. Absence seizures in most of rodent models are generated in a complex circuitry that involves the cerebral cortex and the thalamus. In the GAERS strain, SWDs are consistently recorded over the lateral frontoparietal cortex and the posterolateral thalamus (15,16). Electrophysiological studies have shown that low-voltage–activated (LVA) T-type calcium currents in thalamocortical relay neurons as well as γ-aminobutyric acid (GABA)ergic neurons of the thalamic relay neurons (nRT) are implicated in the generation of SWDs (36,37). In this respect, amplitude of the T-type Ca2+ current is increased in GAERS nRT (38), and significant elevations in mRNA levels for two subunits of T-type calcium channel genes, α1G in thalamocortical neurons and α1H in nRT, have been reported in the GAERS (39).

We observed an increased severity of the SWD variables with age in both GAERS × BN F1 and F2 progeny, and an age-dependent detection of genetic linkage in F2 progeny. In both F1 and F2 rats, duration, number, and frequency of SWDs are significantly increased in 6-month-old rats when compared with 3-month-old rats, whereas age does not affect the amplitude of SWDs. These results suggest that the structure of SWDs is unaffected in old animals, but increased number, duration, and frequency of SWDs contribute to altered EEG profile in an age-dependent manner. The identification of age-dependent genetic linkages to SWD subphenotypes in the F2 cross suggests interactions between genetic loci (epistasis) affecting EEG profile and/or temporal expression of genes modulating SWDs. Evidence of epistasis between genetic loci could not be reliably tested because of the relatively limited size of the F2 cohort. Studies in inbred rat strains, including GAERS, WAG/Rij, BN, and ACI rats, already demonstrated a deterioration of absence epilepsy phenotypes, including SWDs, with age (40–43). These ontogenetic profiles in rodents suggest either distinct epileptogenic mechanisms at a molecular level, or a multigenic threshold effect controlled by the selective maturation of central neuromodulatory systems. In human epilepsy, age at onset is a key criterion for the classification of IGE syndromes, which may implicate interacting susceptibility loci in the etiology of the disease. A prolonged history of epilepsy may steadily alter gene expression within bursting neural circuits, modify their pattern of excitability and connectivity, and create specific cortical synchronization trait.

Rat gene maps and comparative genome data between the rat and human/mouse genomes (http://www.well.ox.ac.uk/rat_mapping_resources/) (21,22) were used to infer the position of the GAERS QTLs in the mouse and human genomes and to search for potential candidate genes. We were able to determine that these loci are homologous to regions of mouse chromosomes 6 (Swd/gaers1), 9 (Swd/gaers3), and 15 (Swd/gaers2). These regions correspond to human chromosomes 7q31-34 (Swd/gaers1), 7p14-15 (Swd/gaers1), 11q23 (Swd/gaers3), 22q12-q13 (Swd/gaers2), and 12q12-q13 (Swd/gaers2). None of these human loci has been characterized for linkage to epilepsy-related phenotypes. However, the region of mouse chromosome 9 conserved with the locus Swd/gaers3 contains the QTL El4, which is associated with recurrent epileptic seizures in a cross derived from the EL/Suz mouse (44).

Several ion channels, including Cacng2 (Stargazin), KCnj4, and Scn2b, that are potential candidates for epilepsy phenotypes, map to the GAERS QTLs. Cacng2, which is located in the central region of the locus Swd/gaers2, is a voltage-dependent calcium channel subunit. In the Stargazer mouse, spontaneous mutation in this gene results in frequent prolonged, generalized spike–wave cortical discharges with behavioral arrest (45,46). Kcnj4, a brain-specific channel with electrophysiological properties (47), also maps to the locus Swd/gaers2. Scn2b, which is localized in the locus Swd/gaers3, and forms part of a voltage-gated sodium channel expressed in the brain. Expression of this channel subunit increases functional expression of coexpressed α subunits, stabilizes gating, and increases cell capacitance (48). Other putative candidates for epilepsy-related traits at GAERS QTLs include the genes encoding hexosaminidase A (locus Swd/gaers3) and metabotropic glutamate receptor Gprc1h (locus Swd/gaers1). Hexosaminidase A is a lysosomal enzyme that contributes to the degradation of glycoproteins, glycolipids, and glycosaminoglycans (49). Glutamate mediates fast excitatory neurotransmission in the vertebrate brain and activates a group of receptors, including Gprc1h, whose expression occurs in the olfactory bulb and cortical and hindbrain cells (50).

We demonstrated the polygenic control of SWD phenotypes in the GAERS model of human absence epilepsy and identified three QTLs that significantly contribute to various aspects of altered EEG profile in an age-dependent manner. Congenic strains derived for these chromosomal regions represent powerful tools for validating the existence of these QTLs and characterizing the pathophysiologic consequences of gene variants at the loci. Both high-throughput gene transcription–profiling technologies and rat genomic sequence data (http://hgsc.bcm.tmc.edu/projects/rat/) should facilitate the identification of candidate genes at these QTLs that can be searched for causative sequence variants in the GAERS. Ultimately, these results can provide new chromosomal targets for genetic studies of IGE.


Acknowledgment:  Work in Oxford was supported by the Wellcome Trust. D.G. holds a Wellcome Trust senior fellowship in basic biomedical science.