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The Journal of Neuroscience, January 15, 1999, 19(2):549-561
Identification of an Acute Ethanol Response Quantitative Trait
Locus on Mouse Chromosome 2
Kristin
Demarest,
James
McCaughran Jr,
Elham
Mahjubi,
Laura
Cipp, and
Robert
Hitzemann
Departments of Psychiatry and Neurobiology and Behavior, State
University of New York at Stony Brook, Stony Brook, New York
11794-8101, and Research and Psychiatry Services, Veterans
Administration Medical Center, Northport, New York 11768
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ABSTRACT |
A two-stage strategy was used to identify and confirm quantitative
trait loci (QTLs) associated with the changes in locomotor activity
induced by a 1.5 gm/kg ethanol challenge. For stage 1, putative QTLs
were identified by analysis of the strain means for 25 strains of the
BXD recombinant inbred (RI) series (males only). QTLs were identified
on chromosomes 1, 2, 4, and 6. The activity response to
chlordiazepoxide generated similar QTLs on chromosomes 2 and 6. None of
the QTLs were similar to those generated from analysis of the saline
response data. For stage 2, 900 male C57BL/6J (B6) × DBA/2J (D2)
F2 intercross animals were phenotyped for ethanol response,
and the phenotypic extremes (those animals > and <1 SD from the
mean) were identified. These extremes differed by >10,000 cm/15 min in
their response to ethanol. The extreme progeny were used for a
genome-wide scan both to confirm the putative RI-generated QTLs and to
detect new QTLs. The F2 analysis generated no new QTLs with
logarithm of the likelihood for linkage (LOD) scores >3. For
RI-generated QTLs, only the QTL on chromosome 2 was confirmed (LOD = 5.3). The position of the peak LOD was estimated to be 47 cM with a
20 cM 1 LOD support interval; this QTL accounted for 6% of the
phenotypic variance. The 1 LOD support interval overlaps with QTLs
previously identified for alcohol preference and acute ethanol
withdrawal (Melo et al., 1996 ; Buck et al., 1997; Phillips et al.,
1999).
Key words:
ethanol; mouse; genes; recombinant inbred; QTL; activity
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INTRODUCTION |
The current study asks the question,
"What genes contribute to the variability in the ethanol-induced
locomotor response?" The study builds on previous observations of
genetics and ethanol locomotor responses. (1) Among inbred strains of
mice it is now clearly established that moderate doses of ethanol (1-2
gm/kg) markedly stimulate locomotor activity in some strains, e.g.,
DBA/2J (D2), but only mildly affect activity in other strains, e.g., C57BL/6J (B6) (Crabbe et al., 1982, 1983; Crabbe, 1986; Dudek and
Phillips, 1990; Dudek et al., 1991; Dudek and Tritto, 1994). (2) Dudek
and Tritto (1994) completed a full Mendelian analysis of the locomotor
response for the B6D2 genotypes. These authors found an additive mode
of inheritance for the total distance measure (of activity) and
determined that there was no evidence of sex linkage or sex effects.
Narrow sense heritability was estimated at 0.35. (3) The locomotor
response to ethanol has been subject to three different quantitative
trait locus (QTL) analyses involving the BXD recombinant inbred (RI)
series. Crabbe et al. (1983) (but analyzed by Gora-Maslak et al., 1991)
reported significant QTLs on chromosomes 2, 4, 9, and 13. Cunningham
(1995) studied a different phenotype from that of Crabbe et al. (1983),
in that the focus was on the initial ethanol response (first 5 min
after injection) as opposed to beginning measurements 10 min after
injection. Cunningham (1995) detected two QTLs at p < 0.01 on chromosomes 7 and 18. Phillips et al. (1995), using a yet
different behavioral paradigm but still focusing on the initial ethanol
response, confirmed the presence of a significant
(p < 0.01) QTL on the distal part of chromosome
18 but also found a significant (p < 0.01) QTL
on chromosome 3. For all QTLs significant at p < 0.05, Cunningham (1995) and Phillips et al. (1995) agreed on four
(chromosomes 3, 12, 17, and 18).
Because the BXD RI series contains only 26 strains, the smallest QTL
effect size that can be detected at p < 0.01 is
associated with 25% of the genotypic variance. At this level of
significance it is necessary to use an additional strategy for
verification (Belknap et al., 1996). In the current study we have
phenotyped the BXD RI strains for their locomotor response to a 1.5 gm/kg ethanol challenge; a B6D2 F2 intercross genome-wide
scan was used both to confirm the RI generated QTLs and to detect
additional QTLs. Of the three RI studies noted above, the behavioral
paradigm was closest to that of Crabbe et al. (1983) and was patterned after the testing procedure initially developed for the selection of
the FAST and SLOW selected lines (Crabbe et al., 1987) and subsequently
used in the biometric analysis of Dudek and Tritto (1994). To compare
across RI studies (see above), data for ethanol response were collected
over a broad time interval. A secondary goal of the current study was
to use the RI strains to test the hypothesis that a genetic correlation
exists between the ethanol- and benzodiazepine-induced effects on
locomotor activity.
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MATERIALS AND METHODS |
Animals and sample sizes. Male and female C57BL/6
(B6), DBA/2 (D2), and B6D2 F1 mice and male BXD RI mice (25 strains) were obtained from The Jackson Laboratory (Bar Harbor, ME).
All of the RI strains were obtained over a 3 month period from December 1997 to March 1998. We attempted to obtain an N of 15 per
strain, although this was not always possible. Mice were housed two to four per cage in a constant-temperature colony room with a 12 hr
light/dark cycle. Food and water were provided ad libitum
throughout the study. All testing was conducted during the light cycle
and between 9 A.M. and 3:30 P.M. All mice were allowed a minimum
of 10 d to acclimatize to the colony conditions before testing.
All animal care and testing protocols were approved by the Laboratory Animal Users Committee at the State University of New York at Stony
Brook and conformed to National Institutes of Health Guidelines for
Using Animals in Intramural Research.
For production of the F2 mice, D2B6 F1 mice
were bred locally from the Jackson parental lines. Reciprocal
F1 crosses were used to generate a total of 900 male
F2 mice from June 1, 1996 to August 1, 1997. Sample size
for the genome-wide scan was estimated as described by Soller et al.
(1976) and Lander and Botstein (1989) for an F2 population
from n = (Z + Z )2/(sQTL2/sRES2),
where Z and Z are the normal variates for
the desired values of and , sQTL2 is
the variance associated with or explained by the QTL, and sRES2 is the residual unexplained variance.
The minimum hQTL2 was arbitrarily set at
0.05. It was recognized that the effect size for most QTLs is <0.05;
however, the data from other behavioral phenotypes, e.g., Kanes et al.
(1996), suggested that some QTLs of this size or larger were likely to
be present. For
(sQTL2/sRES2) = 0.053 (from 0.05/0.95), = 0.0001 [Z = 3.89 (two-tailed)], and = 0.05 (Z = 1.64), the estimated
sample size was 575. However, because only the extreme phenotypes (> and <1 SD from the mean) would be genotyped, to maintain power the
sample size must be increased by 1/0.81 or 1.23 (Lander and Botstein,
1989). This correction is relatively small because most of the genetic
information is found in the extreme phenotypes. We also included an
additional correction of 1.15 to account for nonadditive genetic
effects. Sample size estimates were based on the assumption that all
QTLs have equal and additive effects (Fisher, 1918; Barton and
Turelli, 1989). Because this assumption most certainly was
violated, some correction was necessary. Finally, a correction of 1.11 was included to account for the loss of animals from data censoring.
Overall, these correction factors caused the sample size to increase
from 575 to 900.
Measurement of ethanol and chlordiazepoxide effects on locomotor
activity. Mice were removed from the home cage and placed individually in the testing arena; the arena floor was covered with
standard laboratory bedding. Thirty minutes later, the mice were
administered saline and returned to the testing arena, and activity was
monitored for 20 min. On the next day, the procedure was repeated,
except that the animals were administered 1.5 gm/kg ethanol dissolved
in saline (20% v/v). One week later the saline/ethanol days were
repeated for the F2 mice only. For the RI strains, the second week of testing included saline on day 1 and chlordiazepoxide (10 mg/kg) on day 2. Locomotor activity was assessed in a San Diego
Instruments Flex Field locomotor system. The apparatus comprised a
four-by-eight array of photo cells mounted in a 25 × 47 cm metal frame, situated 1 cm off the floor, and surrounding a 22 × 42 × 20-cm-high plastic arena. Activity was recorded over four 5 min blocks. The distance traveled during each block was used as the
measure of activity.
For the F2 sample, ethanol response (day2  day1) data were obtained from two trials, and these
data were used to identify the 10% of individuals showing the poorest
test-retest reliability. These individuals, with a test-retest
reliability of < 0.1, were censored from the data set. The
remaining individuals were ranked from least to greatest
ethanol-induced activation, and the phenotypic extremes (those
individuals > and <1 SD from the mean) were identified. The
extremes were denoted for historical reasons (Demarest et al., 1997) as
very responsive (RR) and very nonresponsive (NN), although the NN group
actually showed a marked inhibition of locomotor activity.
The phenotypic data generated from the RI strains and the
F2 sample were analyzed using standard ANOVA techniques;
the Student-Neuman-Keuls test was used for the post hoc
analysis. Because of the multiple tests being performed for each
phenotype, the threshold for significance was set at p < 0.01.
DNA isolation. High molecular weight genomic DNA was
isolated from liver samples as follows: 250-500 mg of liver tissue was minced with a sterile razor blade, transferred to a 15 ml polypropylene Falcon tube with 5 ml of lysis buffer (100 mM Tris-HCl, pH
8.0, 5 mM EDTA, 100 µg/ml proteinase K, and 200 mM NaCl), and incubated with rocking at 55°C overnight.
After incubation, 20 µl/ml 5 M NaCl was added with gentle
inversion. The tissue digest was extracted twice with equilibrated
phenol, once with equal volumes of phenol and chloroform/isoamyl
alcohol (chisam, 24:1) and once with chisam alone. DNA was precipitated
with 0.5 vol of 7.5 M ammonium acetate and 2 vol of ice
cold ethanol. Dried DNA pellets were resuspended in double distilled
water. Purity and concentration of the final samples were evaluated by
UV spectroscopy, and only samples with a 260:280 ratio >1.4 were used
for genotyping.
Genotyping the microsatellite polymorphisms. All of the
genotyping involved the -(CA)n- repeating microsatellites
first described by Dietrich et al. (1992). The PCR primer sets were
obtained from the Massachusetts Institute of Technology-Whitehead
catalog (Research Genetics). One to 5 ng of genomic DNA was amplified
with an 18 pM concentration of each primer, 0.5 U of
Taq polymerase (AmpliTaq, Perkin-Elmer, Emeryville, CA; or
Taq DNA polymerase, Boehringer Mannheim, Indianapolis, IN),
and 100 nM dNTPs in a 20 µl reaction under the standard
conditions recommended by the manufacturer. All reactions were
amplified in a Perkin-Elmer thermal cycler. Products were visualized by
electrophoresis in 1× Tris borate-EDTA buffer on a 3% agarose
gel (3:1 Metaphor agarose; FMC Bioproducts, Rockland, ME). Bands were
visualized by ethidium bromide staining.
Detection and mapping of QTLs. Candidate QTLs were generated
from the BXD RI data essentially as described elsewhere (Kanes et al.,
1996). Briefly, the analysis was performed by correlating the strain
means with the strain distribution patterns of 1500 marker gene loci,
each mapped to a chromosome region and showing allelic differences
between the B6 and D2 strains. By convention, the B6 allele is scored
as 0, and the D2 allele is scored as 1; the correlation coefficient has
the same meaning as a t test for significant differences
between strains with the B6 and D2 alleles. Because of linkage
disequilibrium, the actual number of independent tests is <1500 but
>100 (Belknap, 1992). Thus, although the threshold for acceptance of a
candidate QTL was set at p < 0.01, some of these QTLs
will be significant because of chance. Belknap et al. (1996) have
estimated the false-positive rate to be 50%. We recognize that it is
common, even in our own work (Kanes et al., 1996), to report QTL data
generated from RI analyses that are significant at p < 0.05 but not p < 0.01. However, because no behavioral
QTL detected in this range has been independently confirmed using a
non-RI strategy, we feel there is little value in reporting these data.
For the interested reader, the strain means reported in Tables 1-3 are
sufficient to conduct such analyses.
For analysis of the F2 intercross, 25 each of the extreme
phenotypes (approximately one-fifth of each extreme sample) were randomly selected for the genome-wide scan, except in the
regions of the RI-generated QTLs, where a minimum N of 100 per extreme was used. For QTL detection, markers were spaced at ~20
cM (Darvasi et al., 1993). The screening threshold for a
significant segregation of the alleles was set at p < 0.05. For markers meeting the screening threshold, 25 each of the
extreme phenotypes were again randomly selected and
genotyped to confirm a significant effect. For markers meeting the
threshold of p < 0.01, the entire sample of phenotypic extremes was genotyped, and additional markers were added in the region
of interest. The data from this more dense map were then subjected to
analysis using the MAPMAKER-QTL program (Lander and Botstein, 1989) to
determine the position of the peak logarithm of the likelihood for
linkage (LOD) and 1 LOD support intervals.
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RESULTS |
RI strains: saline response
The saline-induced locomotor response among 25 BXD RI strains is
summarized in Table 1 for the four 5 min
intervals and the collapsed 5-20 min interval. Data are also provided
for the B6 and D2 inbred strains and the B6D2 F1 hybrids
(but not the reciprocal D2B6 cross). The correlation matrix for the
activity intervals is presented in the legend to Table 1. All of the
intervals were highly correlated; the correlation coefficients ranged
from 0.82 to 0.97. The ANOVA for both the 0-5 and 5-20 min intervals
revealed highly significant (p < 0.00001)
strain effects (F(24,295) = 10 and 8.4, respectively). The split-halves reliabilities (Blizard, 1992) for the
RI strain means were 0.94 (0-5 min) and 0.93 (5-20 min). The
narrow-sense heritability [DeFries et al., 1989;
h2 = 0.5VA/(0.5VA + VW)] was 0.59 for the 0-5 min interval
and 0.38 for the 5-20 min interval. The data in Table 1 illustrate
that the average activity in the RI strains was similar to that seen in
the D2 strain and markedly less than the activity of the B6 strain.
Activity in the F1 cross was similar to that of the D2 strain.
RI strains: ethanol response (difference score)
The ethanol response was calculated as the difference in activity
between day2 [ethanol (1.5 gm/kg) challenge] and
day1 (saline challenge). The ethanol locomotor responses
among the 25 BXD RI strains, the B6 and D2 inbred strains, and the B6D2
F1 hybrids are summarized in Table
2 and presented graphically in Figure 1 for the 0-5 min interval and the
collapsed 5-20 min interval. The correlation matrix for the activity
intervals is presented in the legend to Table 2. All of the intervals
were highly correlated; the correlation coefficients ranged from 0.61 to 0.95. However, the correlations among the 5-10, 10-15, and 15-20
min intervals were stronger and in two cases significantly
(p < 0.01) so when compared with the
correlations between the 0-5 min interval and the subsequent
intervals. The ANOVA for both the 0-5 and 5-20 min intervals revealed
highly significant strain effects: 0-5 min
(F(23,288) = 2.4; p < 0.0003)
and 5-20 min (F(24,288) = 9.0; p < 0.00001). Post hoc analysis revealed
numerous significant differences among the strain means, some of which
are illustrated in Figure 1.
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Figure 1.
Strain means for the activity response to ethanol
(1.5 gm/kg) among the BXD RI series (n = 25 strains). On the day after a saline challenge, animals were
administered 1.5 gm/kg ethanol intraperitoneally, and activity was
monitored for the next 20 min. Data in this figure are expressed as the
difference score (day2 day1)
and reported for the 0-5 and 5-20 min intervals after ethanol
administration. In addition to data for the RI strains, data are also
reported for the B6 and D2 inbred strains and the B6D2 F1
hybrids. Black bars illustrate the range of significant
(p < 0.01) steps (of difference) for the RI
strains as determined by the Student-Neuman-Keuls test. Thus for the
5-20 min data, strains 24 and 8 are significantly different, but
strains 14 and 27 are not. All data are reported as the mean ± SE
for the distance traveled.
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The split-halves reliabilities for the RI strain means were 0.65 (0-5
min) and 0.88 (5-20 min). The narrow-sense heritabilities were 0.19 (0-5 min) and 0.32 (5-20 min). The data in Table 2 and Figure 1
illustrate that the average ethanol response in the RI strains for the
0-5 min interval was intermediate to that seen in the D2 and B6
strains; in contrast, the response in the F1 cross was
identical to that found in the D2 strain (marked activation). A
different pattern of ethanol responses was observed for the 5-20 min
interval. Over this time frame, ethanol responses in the RI strains
(average response), in the B6 strain and in the F1 cross
were identical and markedly less than the response found in the D2
strain. The 5-20 min interval was characterized by several RI strains
that showed a marked decrease in activity after the ethanol challenge.
RI strains: chlordiazepoxide response
In a series of preliminary studies, B6 and D2 mice
(n = 6-8 per strain per dose) were challenged with
saline (day1) and 3, 10, or 30 mg/kg of
chlordiazepoxide (CDP) (day2) and the
difference scores calculated. The 10 mg/kg dose produced the maximum
activation in the D2 strain (Table 3),
which was observed over the 5-20 min interval. In contrast, no
significant effect was observed in the B6 strain. At 30 mg/kg, CDP
significantly decreased activity in both the B6 and D2 strains (data
not shown). Thus, the 10 mg/kg CDP dose was used to challenge the RI
strains.
The CDP locomotor responses among the 25 BXD RI strains and the B6 and
D2 inbred strains are summarized in Table 3 for the four 5 min blocks
and presented graphically in Figure 2 for
the 0-5 min interval and the collapsed 5-20 min interval. The
correlation matrix for the activity blocks is presented in the legend
to Table 3. There were no significant correlations between the 0-5 min interval and the subsequent intervals. However, the 5-10, 10-15, and
15-20 min intervals were all highly correlated (range of r values, 0.79-0.91). The ANOVA for the 0-5 min interval did not meet
the study criterion (p < 0.01) for a
significant strain effect (F(24,283) = 1.7;
p < 0.028) and was not considered for further analysis. In contrast, the ANOVA for the 5-20 min interval was highly
significant (F(24,283) = 3.8; p < 0.0001); the post hoc analysis detected strain mean
differences significant at p < 0.01 or better (see
Fig. 2). The split-halves reliability for the RI strain means (5-20
min) was 0.91, and the narrow-sense heritability was 0.14. The data in
Table 3 and Figure 2 illustrate that the average CDP response in the RI
strains for 5-20 min interval was similar to that seen in the B6
strain. Overall, the RI strain means showed a trend toward expansion in
the direction of an inhibitory effect on activity.
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Figure 2.
Strain means for the activity response to
chlordiazepoxide (10 mg/kg) among the BXD RI series
(n = 25 strains). Seven to 14 d after the
ethanol challenge, animals were challenged with saline
(day1) and chlordiazepoxide
(day2). The data presented are for the difference
score (day2 day1). All other details
are the same as in the legend to Figure 1.
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RI strains: correlations among phenotypes
For the 5-20 min data, baseline (saline response) locomotor
activity was not significantly (p > 0.2)
correlated with either the ethanol or the CDP response. However, it was
observed that there was a significant correlation between the ethanol
and CDP response for this interval (r = 0.60;
p < 0.01).
Phenotypic characterization of the F2 sample
A total of 900 male B6D2 F2 mice were phenotyped for
their response to saline (day1) and ethanol (1.5 gm/kg; day2). As with the RI strains, the ethanol
response was defined as the difference in activity between
day2 and day1. The entire procedure was
repeated one week later (trial2). The results
obtained are summarized in Table 4. For
the saline response it is useful to compare the results in Table 4 with
those in Table 1. The data illustrate that the average activity in the
RI strains and the F2 sample were quite similar: the saline
response phenotype is similar to that seen in the D2 strain. As
expected (see Markel et al., 1995), the average activity obtained from
the test-retest design significantly reduced the sample variance
without affecting activity. For both the 0-5 and 5-20 min intervals,
the F test was 1.3 (p < 0.01). The
test-retest reliability from trial1 to trial2
was 0.54 (p < 0.0001) for both the 0-5 and
5-20 min intervals. The correlation matrix for each of the 5 min
intervals (average data) is presented in Table 4; all of the intervals
were highly correlated (range of r values, 0.63-0.84).
Animals were rank-ordered on the basis of the test-retest reliability.
The effect of censoring the data set of the bottom 10% (those
individuals showing the poorest reliability) is also illustrated in
Table 4. Censoring significantly improved the test-retest reliability
of the remaining sample for both the 0-5 and 5-20 min intervals from
0.54 to 0.71 (p < 0.01) without affecting the
sample means or variances.
The ethanol response data for the F2 sample are also found
in Table 4. The average response in the F2 sample was
similar to the average response in the RI strains (Table 2). A
potential confound in using the test-retest design was the
sensitization effect from trial1 to
trial2; i.e., on average there was a greater activation response in trial2 compared with
trial1. However, sensitization and locomotor activity
appear to be phenotypically independent. No significant relationship
was detected between the average ethanol effect and the difference in
the ethanol effect from trial1 to trial2; for the 0-5 min interval, r was
0.017 and for the 5-20 min interval, r was 0.08. For all
intervals, the test-retest design significantly reduced the sample
variance (F > 1.6; p < 0.0001). The
test-retest reliability of the uncensored data were poor and ranged
from 0.15 for the 0-5 min interval to 0.25 for the 5-20 min interval;
this difference was significant (p < 0.01). The correlation matrix for each of the 5 min intervals (average data) is
presented in Table 4; all of the intervals were significantly correlated (range of r values, 0.41-0.84). However, the
correlations among the 5-10, 10-15, and 15-20 min intervals were
significantly (p < 0.01) stronger when compared
with the correlations between the 0-5 min interval and the subsequent
intervals. The effect of censoring the data set of the bottom 10% is
also illustrated in Table 4. Censoring significantly improved the
test-retest reliability of the remaining sample for the 0-5 min
sample from 0.15 to 0.38 (p < 0.001) and for
5-20 min intervals from 0.25 to 0.51 (p < 0.01) without affecting the sample means or variances. For the censored
data, the reliability of the 5-20 min interval remained significantly
(p < 0.01) better than the reliability of the
0-5 min data.
The distributions of the censored data sets for the 0-5 min and 5-20
min intervals are shown in Figures 3
(saline response) and 4 (ethanol
response). The data in Figure 3 illustrate that the saline responses
were generally normally distributed, despite the floor effect (activity
cannot be <0). For the parental strains, there was no significant
change in activity from trial1 to
trial2; the average (of trial1 and
trial2) D2 and B6 data are shown in Figure 3. For
the F2 sample, only a few animals showed a saline response
that exceeded that seen in the B6 strain, but numerous animals were
less active than the D2 strain. The data in Figure 4 illustrate that
the F2 sample ethanol responses were also approximately normally distributed. For the parental D2 strain, there was no significant change in the ethanol response from trial1 to
trial2; however, there was a significant
sensitization in the B6 strain (activity increased from 1107 to 1940 cm/15 min).The average (of trial1 and
trial2) D2 and B6 data are shown in Figure 4 for
comparison with the F2 sample. The F2 extreme
phenotypes were defined as those individuals > and <1 SD from
the mean and were denoted RR and NN. For the 0-5 min interval,
activity in the RR and NN extremes was 3100 ± 60 and 400 ± 60 cm, respectively. For the 5-20 min interval, activity in the RR
and NN extremes was 7310 ± 190 and 2910 ± 110 cm,
respectively. A comparison of these data for the 5-20 min interval
with the mean ± SE values for the D2 and B6 strains (3850 ± 225 and 1940 ± 175 cm, respectively) illustrates the expansion in
the F2 sample of the ethanol response phenotype beyond the
parental boundaries. For both the 0-5 min interval (r = 0.09) and the 5-20 min interval (r = 0.01), there
was no significant relationship between the average saline response and the average ethanol response.
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Figure 3.
Distribution of the average saline response in the
B6D2 F2 intercross. Data are presented for the average from
trials 1 and 2 (censored data set). In addition, the mean activity in
the parental B6 and D2 strains (n = 18 per strain)
are also illustrated. For both the 0-5 and 5-20 min intervals, the
average intercross activity was similar to that seen in the D2 strain.
Both distributions were not significantly different from normal.
n = 810.
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Figure 4.
Distribution of the average ethanol response in
the B6D2 F2 intercross. Difference score data are presented
for the average from trials 1 and 2 (censored data set). In addition,
the mean activities in the parental B6 and D2 strains
(n = 18 per strain) are also illustrated. For both
the 0-5 and 5-20 min intervals, a significant proportion of the
animals showed an ethanol response below that seen in the B6 strain;
for the 5-20 min interval, significant inhibition of activity was
observed. Both distributions were not significantly different from
normal. n = 810.
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QTL analysis of the RI strain means
Table 5 lists the QTLs detected from
analysis of the RI strain means (Tables 1-3) and found to be
significant at p < 0.01 or better. For the saline
response, a total of four QTLs were identified on chromosomes 1, 3, 5, 10, and 18. With the possible exception of the QTL on chromosome 1, these QTLs appear to be distinct from those detected for the ethanol or
CDP responses. The QTL on chromosome 5 appears to be the same as a
baseline activity QTL identified by Phillips et al. (1995, 1998) for
the BXD RI series and also by K. Buck, T. Lisehka, J. Dorow, and J. C. Crabbe (unpublished observations).
Significant QTLs for ethanol response were identified on chromosomes 1, 2, 4, and 6; the relationships between RI strain genotype and phenotype
(5-20 min data) are illustrated in Figure
5. These data demonstrate both the
genetic homogeneity of the extreme phenotypes and for some QTLs the
reversal of genotype based on the parental phenotype. Thus, on
chromosomes 1 and 6, it is the B6 allele that contributes to increased
locomotor activity and the D2 allele that contributes to decreased
activity.
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Figure 5.
Relationship between phenotype and genotype for
the activity response QTL (5-20 min) data. Data are presented for the
four QTLs described in Table 5. For each marker the graph illustrates
whether a particular RI strain carried the B6 or D2 genotype.
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The QTLs on chromosomes 1 and 2 were detected for both the 0-5 min and
the 5-20 min intervals. The data for chromosome 2 suggest that the QTL
interval stretches from ~50 to 100 cM. Four significant QTLs were
detected for the CDP response (chromosomes 2, 4, 6, and 15). The QTLs
on chromosomes 2 and 6 appear to be similar to those found for the
ethanol response.
QTL confirmation and detection of additional QTLs
Approximately 100 each of the F2 ethanol response
phenotypic extremes from the 0-5 and 5-20 min intervals were used to
confirm the RI-generated ethanol response QTLs described in Table 5. Given the expansion of the genetic map in the RI strains and the associated difficulty in directly comparing map distances in the RI and
F2 samples, summary data are provided for chromosomes 1, 2, 4, and 6 (Fig. 6). The QTLs on
chromosomes 1, 4, and 6 were not confirmed in this analysis, although
there was a trend (LOD = 1.6) for confirmation of the 0-5 min QTL
on chromosome 1. On chromosome 2, the data illustrate that the QTLs for
the 0-5 and 5-20 min data were probably identical, but the effect
sizes for the 5-20 min data were greater. For the 0-5 min data, the
peak LOD was 3.0, which is below the suggested criteria for acceptance (Lander and Kruglyak, 1995); however, combining the RI and
F2 data would yield an LOD > 4. For the 5-20 min
data, additional markers were added for fine mapping, and the data were
analyzed used the MAPMAKER/QTL program. Because there was generally
good agreement between the primary linkage map and the consensus map for chromosome 2 (Peters et al., 1998), the consensus map was used to
place the markers in Figure 7. This
figure illustrates that a peak LOD of 5.3 was obtained at ~48 cM with
either the free or additive QTL model. The 1 LOD support interval was
20 cM, and the peak LOD was associated with 6% of the phenotypic variance.
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Figure 6.
Summary of the screening results for ethanol
response QTLs on chromosomes 1, 2, 4, and 6. The 0-5 and 5-20 min
phenotypic extremes of the F2 intercross were genotyped for
microsatellite markers on each of the four chromosomes identified from
analysis of the RI strain means to contain ethanol response QTLs. The
relative placement of each marker (based on the consensus map) is
indicated. Each marker was analyzed independently (Kanes et al., 1996)
and the  2 value obtained was transformed to an LOD score
(Lynch and Walsh, 1998).
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Figure 7.
LOD plot of chromosome 2 for the 5-20 min ethanol
response data based as determined by an interval mapping approach
(MAPMAKER/QTL). The markers used to generate the plot are found on the
x-axis. Data were obtained by genotyping the NN and RR
phenotypic extremes from the F2 intercross and ~100 of
the intermediate phenotypes. Curves are presented for the free,
additive, dominant, and recessive models. The schematic chromosome
indicates the location of some potential candidate genes.
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|
The remainder of the genome in the F2 sample was screened
for additional QTLs using the strategy outlined in Materials and Methods. The distribution of the markers used is given in Figure 8. No QTLs with LOD scores > 3 were
detected. However, it is important to note that the screening strategy
used was biased to detecting additive genetic effects. To detect
dominant genetic effects, which are common for quantitative traits
(Lynch and Walsh, 1998), would require that a substantially greater
proportion of the total F2 sample be used for
screening.
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Figure 8.
Location of the microsatellite markers used in the
genome-wide scan for ethanol response QTLs.
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DISCUSSION |
It is estimated that 40-60% of the risk for developing
alcoholism is explained by genetic factors (Schuckit, 1994). However, neither the genes involved nor the mode of inheritance have been characterized. There are several reasons why progress has been slow.
(1) Although the diagnosis of alcoholism is highly reliable, it is
quite clear that one is dealing with a heterogeneous phenotype, which
markedly reduces the power to detect specific gene effects. (2)
Alcoholism is clearly a polygenic disorder and, thus, will require
large samples to detect relatively small gene effects. For complex
phenotypes under the control of multiple genes (or QTLs), gene effect
sizes of 5-10% are generally considered large (Paterson et al.,
1991). (3) Although recent reports show promise (Camp and Bansal, 1997;
Gu and Rao, 1997; Hoeschele et al., 1997; Iyengar et al., 1997;
Martin et al., 1997), there has been a lag in the execution of clinical
QTL studies (in which one must deal with outbred populations and it is
not possible to manipulate either the genome or the environment).
An alternative approach to finding clinically relevant QTLs is to first
detect and map the QTLs using highly informative animal models and then
look for the QTL in the homologous region of the human genome. Because
the homologous regions of the mouse and human chromosomes are so well
defined, it is possible to identify the chromosomal location of a gene
in humans by mapping it in mice, often with far greater precision than
human mapping studies (Copeland et al., 1993). Validation of this
approach is seen in the recent report of Lembertas et al. (1997), who
identified a putative QTL for human obesity on chromosome 20 based on
homology with mouse chromosome 2, in which a significant obesity QTL
had been mapped. In the application of the animal-to-human approach, alcoholism has a significant advantage over other areas of substance abuse. Studies on the genetics of alcohol-related behaviors have covered a wide variety of phenotypes and experimental strategies. Most
investigations have focused on ethanol preference and choice, ethanol
withdrawal, and/or acute ethanol sensitivity. Several recent studies
have illustrated that QTL analysis can be successfully applied to such
phenotypes, and importantly, some QTLs have been independently
confirmed (Phillips et al., 1994; Rodriguez et al., 1995; Melo et al.,
1996; Belknap et al., 1997). The work of the current study falls in the
category of phenotypes related to acute ethanol sensitivity. QTL data
are available for such diverse phenotypes as loss of the righting
reflex (LORR), hypothermia, and locomotor activity (Crabbe et al.,
1994; Cunningham, 1995; Phillips et al., 1995; Markel et al., 1997).
These phenotypes may be relevant to alcoholism because one of the
factors that appears to be inherited in alcoholic families is a
difference in alcohol sensitivity (Schuckit et al., 1994); family
history-positive subjects are generally less sensitive than family
history-negative subjects to the effects of alcohol.
The data presented here confirm and extend the QTL analysis of the
Crabbe et al. (1983) data set (Gora-Maslak et al., 1991). In this
analysis, four provisional QTLs were detected on chromosomes 2, 4, 9, and 13; only the QTL on chromosome 4 was significant at
p < 0.01. The analysis of the BXD RI data in Table 2
confirmed the QTLs on chromosomes 2 and 4 and detected two additional
QTLs on chromosomes 1 and 6. However, only the QTL on chromosome 2 was
confirmed in the F2 analysis. Although the false-positive rate for the RI analysis was high (75%), it was not unexpected given
the numerous comparisons being made (Belknap et al., 1996) and was
similar to previous results from this laboratory (Kanes et al., 1996).
None of the QTLs detected at p < 0.01 in the studies of Cunningham (1995) and Phillips et al. (1995) (chromosomes 3, 17 and
18) was confirmed in the present study. In this regard, it is important
to note that these authors used a higher dose of ethanol (2 gm/kg) and
different apparatus for measuring activity. Erwin et al. (1997b)
examined the locomotor response to various doses of ethanol in the
LS × SS RI series (24 strains). These authors found there was
only one common QTL between those calculated for the ethanol activation
slope and 2.0 gm/kg activity; the activation slope was calculated from
the dose-response data obtained at 1, 1.25 and 1.5 gm/kg ethanol.
Erwin et al. (1997b) concluded that for the LS × SS RI
series, the 1.5 gm/kg dose of ethanol demarcates the end of the mainly
stimulatory effects of ethanol and that the 2 gm/kg dose involves both
the stimulatory and inhibitory influences of ethanol, making it a
mixture of two different quantitative traits, which could be difficult
to disentangle. This conclusion is supported in part by the
observations of Dudek et al. (1991) that the stimulatory and inhibitory
effects of ethanol are genetically independent. The exact dose
conclusions of Erwin et al. (1997b) do not seem applicable to
the BXD RI strains, because numerous strains show inhibition of
activity over a time course very similar to that used to characterize
the LS × SS RI series. However, it is reasonable to assume that
for any particular dose of ethanol, one is observing (from a locomotor
perspective) a mixture of quantitative traits, and this mixture is
dose-dependent. Thus, the behavior seen at 2 gm/kg ethanol probably
differs substantially from that found at lower doses. The argument that
one is studying a mixture of quantitative traits has important
implications for the QTL analysis. If there are separate QTLs for the
activating and inhibitory effects of ethanol on locomotor activity, and
assuming that one always has some mixture of these influences, then the
power of both the RI and F2 analyses will be substantially
reduced. Furthermore, in a genome-wide F2 analyses we could
expect to find QTLs where the genetic segregation is asymmetrical;
i.e., the responders but not the nonresponders (or vice versa) show a
significant deviation from the expected 1:2:1 ratio of genotypes.
However, the QTL on chromosome 2 was reasonably symmetrical; the
genotypic ratios for the RR and NN individuals at D2Mit94
were 2.1:1.4:0.5 and 0.8:1.7:1.5, respectively. These data strongly
suggest that this marker is linked to a gene or genes in which the D2
alleles cause activation, whereas the B6 alleles cause inhibition.
The activity QTL on chromosome 2 is in the same general region
previously identified to contain QTLs associated with ethanol preference and consumption (Melo et al., 1996; Phillips et al., 1999)
and acute ethanol withdrawal (Buck et al., 1997) and appears to be
significantly proximal from the loss of righting reflex QTL
(Lorr2) identified by Markel et al. (1997). The observation that the ethanol and CDP responses in the RI strains are highly correlated and both map to the same region of chromosome 2, suggests the presence of a gene or genes that could provide a link between the
ethanol response and ethanol effects at the GABA-benzodiazepine receptor arrays (see Deitrich et al., 1989). A potential
GABA-related candidate gene that is also likely to affect ethanol
preference and withdrawal is glutamic acid decarboxylase 1 (Gad1) (see Fig. 7). GAD activity among inbred mouse
strains, including the B6 and D2 strains, has been reviewed (Hitzemann
et al., 1995), and in general there is no substantial evidence for a
functional polymorphism; although see Buck (1996). However, it is of
interest to note that stress has been reported to increase GABA levels
in the amygdala of the D2 but not B6 strain (Simler et al., 1982);
recent evidence from this laboratory has suggested that the amydala and
in particular the central nucleus are important in the regulation of
the ethanol activity response (Hitzemann and Hitzemann, 1997; Demarest
et al., 1998, 1999).
In conclusion, the data presented here are the first to confirm a QTL
for the locomotor response to ethanol. This QTL joins a growing list of
alcohol-related QTLs that have met or exceeded the generally accepted
criteria for acceptance of LOD > 4.4 (Lander and Schork, 1994;
Lander and Kruglyak, 1995). This list includes QTLs for alcohol
preference (Melo et al., 1996; Phillips et al., 1998), acute ethanol
withdrawal (Buck et al., 1997), and loss of the righting reflex (Markel
et al., 1997). Although QTL detection for ethanol phenotypes has
generally been quite successful, it is but the first step in the
lengthy process of gene identification. The next step will be the fine
mapping of the QTLs to intervals of  1 cM; for this step it will be
necessary to use strategies such as advanced intercross lines or
interval-specific congenic strains (Darvasi, 1998). The formation of
congenic strains will also allow for a direct test of whether the QTLs,
such as those identified on chromosome 2, are indeed pleitropic.
| |
FOOTNOTES |
Received July 22, 1998; revised Oct. 12, 1998; accepted Oct. 27, 1998.
These studies were supported in part by US Public Health Service Grant
AA 11043.
Correspondence should be addressed to Dr. Robert Hitzemann, Department
of Psychiatry, State University of New York at Stony Brook, Stony
Brook, NY 11794-8101.
| |
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Abstract
Introduction
