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Vol. 12, Issue 8, 1257-1268, August 2002
LETTER
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ABSTRACT |
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 TOP
 ABSTRACT
 INTRODUCTION
RESULTS
DISCUSSION
METHODS
WEB SITE REFERENCES
REFERENCES
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Consumption of ethanol solutions by rodents in two-bottle choice tests is a model to study human alcohol intake. Mice of the C57BL/6ByJ strain have higher ethanol preferences and intakes than do mice of the 129P3/J strain. F2 hybrids between these two strains were phenotyped using two-bottle tests involving a choice between water and either 3% or 10% ethanol. High ethanol preferences and intakes of the B6 mice were inherited as additive or dominant traits in the F2 generation. A genome screen using these F2 mice identified three significant linkages. Two loci, on distal chromosome 4 (Ap3q) and proximal chromosome 7 (Ap7q), strongly affected 10% ethanol intake and weakly affected 3% ethanol intake. A male-specific locus on proximal chromosome 8 (Ap8q) affected 3% ethanol preference, but not indexes of 10% ethanol consumption. In addition, six suggestive linkages (on chromosomes 2, 9, 12, 13, 17, and 18) affecting indexes of 3% and/or 10% ethanol consumption were detected. The loci with significant and suggestive linkages accounted for 35-44% of the genetic variation in ethanol consumption phenotypes. No additive-by-additive epistatic interactions were detected for the primary loci with significant and suggestive linkages. However, there were a few modifiers of the primary linkages and a number of interactions among unlinked loci. This demonstrates a significant role of the genetic background in the variation of ethanol consumption.
[Supplementary material is available online at www.genome.org.]
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INTRODUCTION |
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TOP
ABSTRACT
INTRODUCTION
RESULTS
DISCUSSION
METHODS
WEB SITE REFERENCES
REFERENCES
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Human alcohol consumption is a complex trait
regulated by multiple mechanisms. Consistent with this, the inheritance
of ethanol consumption by humans and animals is multigenic. Studies in
humans have detected several candidate genes associated with human
alcoholism, although the evidence is still somewhat controversial (for
review, see Foroud and Li 1999
). Another approach, using genome-wide
linkage analyses in humans, also yielded several loci contributing to human alcoholism (Long et al. 1998; Foroud et al. 2000). Human studies
have had limited statistical power and reproducibility, but genetic
analyses using animal models complement them and help to overcome their
limitations (Foroud and Li 1999). Several ethanol-related phenotypes in
experimental animals reproduce different aspects of the complex effects
of ethanol (Crabbe et al. 1994, 1999). One of the phenotypes, ethanol
preference (also referred to as "voluntary consumption"), is
assessed using two-bottle choice tests with an ethanol solution
presented in one bottle and water presented in the second bottle.
Although, like any model, mouse ethanol preference does not replicate
the full spectrum of mechanisms involved in human alcohol consumption,
it is considered relevant to human alcohol drinking (e.g., Lester and
Freed 1973; Li et al. 1979; Dole and Gentry 1984; Dole 1986; Crabbe et
al. 1994; Erickson 1996; Myers 1996; Arola et al. 1997) and is widely
used in experimental studies on alcoholism.
Several laboratories have mapped quantitative trait loci (QTL)
affecting ethanol preference in animal models using parental strains
with contrasting phenotypes (for review, see Crabbe et al. 1999; Foroud
and Li 1999; Belknap and Atkins 2001). In mice, the C57BL/6 strain has
been most commonly used as one parent because of its high ethanol
consumption. In most studies, the low-preferring strain used has been
DBA/2 (e.g., Phillips et al. 1994, 1998; Melo et al. 1996; Belknap et
al. 1997; Tarantino et al. 1998; Whatley et al. 1999), with a few
exceptions (Gill et al. 1998; Vadasz et al., 2000). In this study, we
produced an F2 intercross of the high ethanol-preferring
C57BL/6ByJ (B6) strain with the 129P3/J (129) strain, which has low
ethanol consumption (Belknap et al. 1993; Bachmanov et al. 1996b, 2000;
Logue et al. 1998) and had not been used previously in ethanol-related
mapping studies.
In the previous studies on mapping ethanol preference, a single 10%
ethanol concentration was used. Although strain differences in ethanol
consumption extend over a range of concentrations (Belknap et al.
1993), it is possible that responses to different concentrations involve different mechanisms (e.g., sensory, metabolic, or
pharmacological) and thus may have a different genetic architecture. To
investigate this, we tested the F2 mice sequentially with 3%
and 10% ethanol solutions paired with water.
To determine chromosomal positions of genetic loci affecting ethanol consumption, a genome screen was conducted by genotyping polymorphic markers in the F2 mice. In addition to mapping primary QTL, we conducted a genome-wide search of additive-by-additive epistatic interactions. These interactions between loci in the homozygous condition are present in inbred, recombinant inbred, and congenic strains. Thus, the additive-by-additive interactions detected in F2 should be of practical significance for selection of congenic strains, which we plan to conduct. Using the B6 × 129 cross, two different ethanol concentrations, and genome-wide analysis of epistasis allowed us to discover novel ethanol preference loci.
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RESULTS |
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TOP
ABSTRACT
INTRODUCTION
RESULTS
DISCUSSION
METHODS
WEB SITE REFERENCES
REFERENCES
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Analyses of Phenotypes
Parental Strains
All indexes of ethanol consumption (intakes and preference scores for 3% and 10% ethanol) were overall higher in the B6 mice than in the 129 mice [effect of strain, F(1,35) >32.6, P<0.000003; two-way general analysis of variance (ANOVA)]. Females had higher 3% ethanol intakes and preferences than did males [effect of gender, F(1,35) > 5.5, P<0.03], but there were no significant gender differences for indexes of 10% ethanol consumption. The only significant strain × gender interaction by ANOVA was found for 3% ethanol preferences [F(1,35) = 9.2, P<0.005]; the strain differences were larger in males than in females.F2 Hybrids
The F2 mice from three types of reciprocal crosses were compared using two-way ANOVA with gender and cross as between-group factors. Indexes of ethanol consumption for both 3% and 10% concentrations did not differ between the reciprocal crosses. Therefore, the data for all F2 mice were combined in subsequent analyses. To assess the mode of inheritance (additive and dominant effects characterizing allelic interactions), the indexes of ethanol consumption of the F2 mice were compared with those of the B6 and 129 mice, and with corresponding midparental values, using two-way ANOVA with genotype (B6, 129, or F2) and gender as between-group factors. For all indexes of 3% and 10% ethanol consumption, there were significant differences among the three genotypes [F(2,483) > 10, P<0.001; Fig. 1]. Gender differences were found only for 3% ethanol preferences [they were higher in females than in males; F(1,483) = 5.1, P<0.05]. With the exception of 10% ethanol intake by males, ethanol intakes of the F2 mice were significantly higher than corresponding midparental and 129 values, and they did not differ from the B6 values, which indicates directional dominance of the B6 alleles. Ethanol preferences (and 10% ethanol intake by males) of the F2 mice did not differ significantly from midparental values, which indicates additive inheritance of these traits. These modes of inheritance suggest that the F2 intercross is appropriate for linkage analyses.
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Linkage Analyses
The results of the genome scan for indexes of ethanol consumption
are shown in Figure 4 (unconstrained model)
and Table 2 (locus-specific modes of
inheritance). Loci on chromosomes 4 and 7 (Fig.
5A,B) significantly affected 10% ethanol
intake and also had a weaker effect on 10% ethanol preference and 3%
ethanol intake (Fig. 4). Effects of these loci on 10% ethanol intake
were similar in males and females. A male-specific locus on chromosome
8 affected 3% ethanol preference (significant linkage, Fig. 5C) and
intake (suggestive linkage, Table 2), but not indexes of 10% ethanol consumption. The linkage on distal chromosome 4 most likely corresponds to the Ap3q (alcohol preference 3 QTL) locus (Tarantino et al. 1998). The loci on chromosomes 7 and 8 were novel, and they were assigned symbols Ap7q and Ap8q (alcohol preference 7 and 8 QTL), respectively.
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Suggestive linkages were found on chromosomes 2 (3% and 10% ethanol preferences), 9 (a male-specific locus affecting 3% ethanol intake), 12 (3% ethanol intake), 13 (10% ethanol preference), 17 (3% ethanol intake and preference), and 18 (3% ethanol intake; Table 2). Several potential linkages that did not reach the threshold of suggestive linkage (with logarithm of the odds ratio [LOD] scores above 1.9) were detected on chromosomes 10 (3% and 10% ethanol preference), 15 (3% ethanol intake), 16 (3% ethanol preference), and X (10% ethanol intake; Fig. 4).
In addition to interval mapping using MAPMAKER, linkages to the X chromosome markers were analyzed separately using one-way ANOVA with five genotypes (three for females and two for males). The marginally significant effect of DXMit69 genotypes on 10% ethanol intake [F(4,147) = 2.34, P = 0.054] was the only effect found, which is consistent with the weak X-chromosome linkage detected using interval mapping. In females, intakes of 10% ethanol were 3.3 ± 0.3 mL (B6/B6 homozygotes at DXMit69), 3.2 ± 0.2 mL (B6/129 heterozygotes), and 2.7 ± 0.3 mL (129/129 homozygotes; P>0.1, planned comparison tests). Males with the B6 allele of this marker consumed more ethanol than did males with the 129 allele (3.4 ± 0.2 and 2.7 ± 0.2 mL, respectively; P = 0.016, planned comparison test).
Of the nine significant and suggestive loci affecting indexes of 3% and/or 10% ethanol consumption, five had B6 alleles increasing ethanol consumption (chromosomes 4, 8, 9, 13, and 18) and four had B6 alleles decreasing it (chromosomes 2, 7, 12, and 17). The B6 alleles were dominant or partially dominant at three loci (chromosomes 4, 7, and 12), they were recessive or partially recessive at two loci (chromosomes 9 and 17), and allelic interactions for the loci on chromosomes 8, 13, and 18 were additive. The locus on chromosome 2 had a 129-dominant mode of inheritance for 3% ethanol preference, and an additive mode of inheritance for 10% ethanol preference; LOD peaks on chromosome 2 for these two phenotypes were slightly offset.
The combined effect of the significant and suggestive QTL was determined using MAPMAKER/QTL by fitting all primary QTL (listed in Table 2) simultaneously under the unconstrained (free) model. The significant and suggestive linkages explained 31% (3% ethanol intake), 17% (3% ethanol preference), 25% (10% ethanol intake), and 19% (10% ethanol preference) of phenotypical variance, and they explained respectively 44, 38, 40, and 35% of genetic variance (calculated as "% of phenotypical variance explained by QTLs"/"average heritability for males and females"×100). The remaining unexplained genetic variance must be attributed to the other loci that did not reach levels of suggestive linkage, and to epistatic interactions described below.
Analysis of Epistatic Interactions
Sequential analyses by Epistat and ANOVA (see
details in Methods) detected 5-11 significant additive-by-additive epistatic interactions for each trait (Table
3). No interactions among primary QTL (loci
with significant and suggestive linkages listed in Table 2) were
detected. In a few cases, one locus with no linkage modified the effect
of another linked locus. This was observed for D17Mit51
modified by D2Mit61 (3% ethanol intake), D13Mit35
modified by D6Mit36 (10% ethanol intake and preference), and
D3Mit89 modified by DXMit69 (10% ethanol
preference). In most cases, none of the epistatically interacting loci
alone showed significant linkage (correspondingly, they were not
detected in the linkage analysis described above) and only their
interaction effect was significant.
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Most of the interacting pairs of markers were trait-specific, but some of them affected several phenotypes. An interaction of loci on chromosomes 6 (D6Mit36 or D6Mit55, two linked markers) and 13 (D13Mit35) affected all four indexes of ethanol consumption. An interaction of loci on chromosomes 7 (D7Mit7 or D7Mit15, two linked markers) and 13 (D13Mit44) affected 3% and 10% ethanol intakes. Interactions of loci on chromosomes 1 (D1Mit48) and 12 (D12Mit194 or D12Mit20, two linked markers), and 1 (D1Mit37) and 15 (D15Mit96) affected 3% and 10% ethanol preferences. An interaction of loci on chromosomes 2 (D2Mit61) and 18 (D18Mit144) affected 10% ethanol intakes and preferences.
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DISCUSSION |
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TOP
ABSTRACT
INTRODUCTION
RESULTS
DISCUSSION
METHODS
WEB SITE REFERENCES
REFERENCES
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In this study, using F2 hybrids between the B6 and 129 mouse strains, we characterized the inheritance of indexes of ethanol consumption in two-bottle tests, mapped quantitative trait loci underlying the strain differences, and detected epistatically interacting loci. High ethanol preferences and intakes of the B6 mice
were inherited as additive or dominant traits in the F2, which is consistent with the results of our previous study (Bachmanov et al. 1996a). A genome screen using these F2 mice identified three significant linkages. Two loci, on distal chromosome 4 (Ap3q) and proximal chromosome 7 (Ap7q), strongly
affected 10% ethanol intake and weakly affected 3% ethanol intake. A
male-specific locus on proximal chromosome 8 (Ap8q) affected
3% ethanol preference, but not indexes of 10% ethanol consumption. In
addition, six suggestive linkages (on chromosomes 2, 9, 12, 13, 17, and
18) affecting indexes of 3% and/or 10% ethanol consumption were
detected. The loci with significant and suggestive linkages accounted
for 35-44% of the genetic variation of ethanol consumption
phenotypes. None of the primary loci with significant and suggestive
linkages interacted epistatically. However, a few modifiers for the
primary linkages and a larger number of interactions among unlinked
loci were found, which demonstrates a significant role of genetic
background in the variation of ethanol consumption.
The 9.0-cM confidence interval (75.0-84.0 cM from the centromere) for
the Ap3q locus on distal chromosome 4 includes 67 genes, cDNA
segments, and phenotypical loci included in the Mouse Genome Database
([MGD], www.informatics.jax.org). It corresponds to 13 Mb of genomic
sequence in the Celera database (www.celera.com), including 338 transcripts. This region contains the saccharin preference
(Sac) locus (Phillips et al. 1994; Lush et al. 1995; Bachmanov
et al. 1997; Blizard et al. 1999; Li et al. 2001), which recently has
been cloned positionally (Bachmanov et al. 2001b) and corresponds to a
sweet-taste receptor gene, Tas1r3 (Kitagawa et al. 2001; Max
et al. 2001; Montmayeur et al. 2001; Nelson et al. 2001; Sainz et al.
2001). There is considerable evidence showing genetic association
between consumption of ethanol and sweeteners (for review, see
Kampov-Polevoy et al. 1999). This association was detected in the B6 × 129 F2 intercross (Bachmanov et al. 1996a). Therefore, the
Sac and Ap3q loci probably are identical, suggesting that the Tas1r3 gene is a candidate for the Ap3q
locus (Bachmanov et al. 2001a). This region has conserved synteny with
a subtelomeric region of a short arm of human chromosome 1 (1p36). A
locus influencing vulnerability to alcoholism has been mapped to human
chromosome 1 (1p13-35) (Nurnberger et al. 2001), however the
linkage peak for this locus is substantially more centromeric than the
region of conserved synteny for the Ap3q locus.
The 14.1-cM confidence interval (11.4-25.5 cM from the centromere) for
the Ap7q locus on proximal chromosome 7 includes 83 genes,
cDNA segments and phenotypical loci (MGD). It corresponds to 18 Mb of
genomic sequence, including 497 transcripts (Celera). The only QTL with
neurological effects mapped to this region is Bis4
(beta-carboline induced seizures 4) (Gershenfeld et al. 1999). This
region has conserved synteny with human chromosomes 11p and 19q. A
locus influencing alcohol dependence has been mapped to human
chromosome 11p15 (Long et al. 1998) and thus may be a human ortholog of
the mouse Ap7q locus.
The 27.0-cM confidence interval (0.0-27.0 cM from the centromere) for
the Ap8q locus on proximal chromosome 8 includes 90 genes,
cDNA segments, and phenotypical loci (MGD). It corresponds to 39 Mb of
genomic sequence, including 605 transcripts (Celera). This region has
conserved synteny with human chromosomes 4q, 8p, 13q, and 19p. Linkages
of alcohol-related phenotypes to human chromosomes 4 (Long et al. 1998;
Reich et al. 1998) and 13 (Schuckit et al. 2001) may be orthologs of
the mouse Ap8q locus. The Ap8q locus exerts its
effect only in males, which adds another example to gender-specific
ethanol consumption loci detected in previous studies (Melo et al.
1996; Peirce et al. 1998; Fernandez et al. 1999).
In previous studies, at least eight QTL with significant effects on
voluntary 10% ethanol consumption have been described, including those
on chromosomes 1 (proximal and middle), 2 (proximal and distal), 3, 4, 9, and 11 (Table 4; for review, see Crabbe et al. 1999; Belknap and
Atkins 2001). Of the three significant linkages identified in this study, only one, on distal chromosome 4, also was found to be linked to ethanol intake in the previous studies
(Tarantino et al. 1998). The suggestive linkage on chromosome 2 (3%
and 10% ethanol preference) may be equivalent to
Alcp1/Pref2 (Phillips et al. 1994, 1998; Melo et al.
1996; Belknap et al. 1997; Whatley et al. 1999; Belknap and Atkins
2001), and the suggestive linkage on chromosome 9 (3% ethanol intake)
may be equivalent to Ap5q/Pref1 (Phillips et al.
1994, 1998; Belknap et al. 1997; Tarantino et al. 1998; Belknap and
Atkins 2001). Several epistatically interacting loci (Table 3) also may
correspond to some of these previously detected linkages.
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The moderate correspondence between the results of our study and
previous studies is consistent with having one common parental strain
(B6), and the other strain being different in our (129) and the other
(DBA/2 or A) studies. Mechanisms underlying ethanol avoidance may be
different in different low-preferring strains; correspondingly, loci
detected in crosses of the B6 strain with different strains also may be
different. Furthermore, loci determining ethanol preference of the B6
mice may have different effects on different genetic backgrounds (i.e.,
in crosses with different strains), and thus can be detected only in
some crosses. In addition, mapping using backcross onto the B6 strain
(Melo et al. 1996) must have precluded detection of B6-dominant loci,
but provided higher statistical power to detect B6-recessive loci
compared with F2 intercross; this also may account for
differences in identified sets of loci. Age potentially can modify the
effects of alcohol-related QTL (McClearn et al. 1998). In most ethanol
preference QTL studies, ~2-3-month-old mice were phenotyped
(Phillips et al. 1994; Belknap et al. 1997; Gill et al. 1998; Tarantino
et al. 1998; Whatley et al. 1999), although sometimes up to 9-month-old
mice were used (Peirce et al. 1998). Here, we used ~7-month-old mice,
so this also may have contributed to the different loci detected
compared with the other studies. This possibility, however, appears to be remote because ethanol intakes and preferences of the B6 and 129 mice remained stable over a period of 1-12 months (Tordoff and
Bachmanov, www.monell.org/MMTPP). Overall, our choices of the parental
strains and the phenotyping procedure were efficient for discovering
new linkages for ethanol preference.
Compared with the 129 mice, the B6 mice have higher indexes of ethanol
consumption for a range of ethanol concentrations (Bachmanov et al.
1996b). However, there are indications that the genetic determination
of the strain differences may be concentration-dependent. First,
correlations between indexes of consumption for 3% and 10% ethanol in
F2 were only moderate (+0.36 - +0.57 for corresponding indexes, Table 1), which is consistent with previous data on inbred
strains for these concentrations (r = +0.34,
n = 15, P = 0.2, [Belknap et al. 1993]).
Accordingly, only partially overlapping sets of loci were detected for
these two ethanol concentrations, suggesting that consumption of
different concentrations of ethanol involves different, albeit
overlapping, genetic controls. Finally, all loci involved in 10%
ethanol consumption had similar effects in both genders, whereas
some of the loci involved in 3% ethanol consumption were
male-specific. This is consistent with larger parental strain
differences in 3% ethanol intakes and preferences in males compared
with females, whereas gender had little effect on strain differences in
indexes of 10% ethanol consumption.
Approaches to analyze epistatic interactions among QTL are still
evolving (Cheverud and Routman 1995; Frankel and Schork 1996; Brockmann
et al. 2000; Fernandez et al. 2000; Cheverud et al. 2001; Cordell et
al. 2001; Crabbe 2001; Hood et al. 2001; Jannink and Jansen 2001;
Shimomura et al. 2001). These recent studies demonstrate the importance
of epistatic interactions, as well as methodological difficulties
involved in their detection. One complicated aspect is that
interactions between already detected primary QTL may not encompass all
epistatic interactions present. For example, an individual locus may
affect a trait only when it is combined with a particular genotype at
another locus, or it may have opposite effects depending on the
genotype of another locus, revealing genetic background effects
(Cheverud and Routman 1995). Therefore, when all individuals are
analyzed as a single group, the overall effect of this locus may go
undetected, and it thus would not be identified as a primary QTL.
However, such hidden loci can be unveiled in a genome-wide screen for
epistasis, in which the effects of combinations between all pairs of
all genotyped markers are assessed. The problem with genome-wide
screens is that they involve multiple comparisons resulting in higher thresholds of statistical significance, but group sizes are reduced compared with single-locus analyses. We approached the genome-wide analysis of epistatic interactions among QTL using a combination of
specialized software, Epistat, and ANOVA. Our
results show that epistatic interactions represent a substantial
component of the genetic variation in ethanol consumption. This is
consistent with strong effects of genetic background, such as those
observed when targeted mutations are backcrossed on different inbred
partner strains (e.g., Crawley et al. 1997; Thiele et al. 2000; Hoffman et al. 2001). The B6 and 129 strains used in this study are also commonly used in "knockout" experiments, forming mixed genetic background for mutant mice, and thus loci identified in this cross may
modify effects of the genes with targeted mutations.
In conclusion, our mapping study using a novel cross and phenotypes of voluntary consumption of two different ethanol concentrations detected the presence of several primary loci and a substantial contribution of genetic background as revealed in multiple epistatic interactions. This study completes an initial step toward positional cloning of ethanol consumption QTL.
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ABSTRACT
INTRODUCTION
RESULTS
DISCUSSION
METHODS
WEB SITE REFERENCES
REFERENCES
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Animals
129P3/J (129) and C57BL/6ByJ (B6) inbred mice were obtained from The Jackson Laboratory. The mice were housed in a temperature-controlled vivarium at 23°C on a 12:12 h light:dark cycle and had free access to water and Teklad Rodent Diet 8604. The F1 and F2 hybrids between the B6 and 129 strains were bred at Monell. Pups were weaned at 21-30 d of age and reared in groups of the same gender (in most cases, four to six mice per cage, but never more than six in one cage). During two-bottle tests, the mice were housed in individual cages.
450 (224 female and 226 male) F2 mice were obtained from
three types of reciprocal crosses: (129
× B6
)
F1 × (129 × B6) F1 (92 females
and 101 males), (B6 × 129)
F1 × (B6 × 129) F1 (103 females
and 91 males), and (B6 × 129)
F1 × (129 × B6) F1 (29 females
and 34 males). Mice from the parental B6 (9 females and 10 males) and
129 (10 females and 10 males) strains were tested together with the
F2 mice with the same stimuli. When two-bottle tests with
ethanol began, the F2 mice were on average 7.26 ± 0.04 mo
old (with ages ranging from 5.7 to 8.6 mo); the B6 and 129 mice were
5.79 ± 0.14 mo old (ranging from 4.8 to 6.7 mo).
Two-Bottle Tests
Construction of the drinking tubes and other experimental
procedures have been described previously (Bachmanov et al. 1996b, in
press) and are given in detail on our web site (Tordoff and Bachmanov,
www.monell.org/MMTPP). Individually housed mice were presented with one tube containing ethanol solution in deionized water
and the other tube containing deionized water. Daily measurements were
made in the middle of the light period by reading fluid volume to the
nearest 0.2 mL. The positions of the tubes were switched every 24 h to
control for side preferences. Ethanol solutions were tested for 8 d:
during the first 4 d, mice received 3% ethanol, and during the next 4 d they received 10% ethanol. As part of another experiment, the mice
were phenotyped in 4-d, 2-bottle tests with 30 mM glycine; 30 mM
D-phenylalanine; 20 and 1 mM saccharin; 120, 300, and 3 mM sucrose; and
1 and 300 mM monosodium glutamate before the tests with ethanol. The
tests were designed to avoid any possible "carry-over" effects from
taste solutions on ethanol intake.
Analyses of Phenotypical Data
Based on daily solution and water intakes, 4-d averages were calculated for each mouse for each solution concentration. In preliminary statistical and linkage analyses, averages for the first two days and last two days of each 4-d test were analyzed separately. Results for the 2-d averages were similar to each other and to those for the 4-d averages, with the 4-d averages typically showing more significant effects. In another study, we have shown that the 4-d test duration provides optimal statistical power for detecting genetic differences (Tordoff and Bachmanov, in press). Therefore, only 4-d averages are reported here. Preference scores were calculated as the ratio of the average daily solution intake to average daily total fluid (solution + water) intake, in percent. The phenotype data were analyzed using Pearson correlation coefficients, ANOVA, and planned comparisons. When appropriate, Bonferroni corrections for multiple comparisons were used. Significance of deviation of experimental distributions from the normal distribution was tested using Kolmogorov-Smirnov tests. The analyses were conducted using STATISTICA software (StatSoft, Inc.).
To offset any difference between males and females, ethanol intakes and preferences were standardized within each gender to a group mean (0) and standard deviation (1). This standardization was conducted for all 450 F2 mice (224 females and 226 males); the standardized scores for a subset of 169 F2 mice (88 females and 81 males) were used in linkage analyses.
Because fluid intakes can depend on body weight (Bachmanov et al., in
press; National Center for Environmental Assessment, www.epa.gov/ncea/pdfs/allometr.pdf), and because B6, 129, and F2 mice differed in body weights, in preliminary analyses we
assessed the relationships between ethanol intake and body weight.
Mouse body weight was measured before and after the tests with ethanol, averaged, and used to calculate adjusted intakes (mL per 30 g of body
weight, the approximate weight of an adult mouse). Average body weights
of females were 28.4 ± 0.4 (B6), 23.3 ± 0.8 (129), and
29.9 ± 0.3 (F2), and those of males were 35.1 ± 0.4,
27.1 ± 0.6, and 39.9 ± 0.4, respectively [effects of genotype
F(2, 483) = 29.8, P<0.001; gender F(1, 483) = 31.6,
P<0.001; and genotype × gender interaction F(2,
483) = 3.0, n.s.; two-way ANOVA]. Adjustment of ethanol intakes for
body weight had little effect on the differences among B6, 129, and
F2 mice. There were no positive correlations between body
weights and unadjusted intakes in the F2 mice (r calculated for males and females separately ranged from 
0.16 to
0.01, P
0.018, n.s. after Bonferroni correction). Intakes adjusted for body weight showed significant negative correlations with
body weights (r ranged from 0.41 to 0.32,
P<0.001, significant after Bonferroni correction), indicating
that the ratio became dependent on the denominator. Because of this
dependence, linkages of intakes corrected for body weight may be from
loci primarily affecting body size rather than ethanol consumption, and
so unadjusted intakes were used in linkage analyses along with
preference scores, which are independent of body size. As an additional
measure to assess the possibility that linkages for ethanol intakes may
be affected by body size, we also conducted a genome screen for body weight. Body weight was affected by loci on chromosomes 2, 9 (significant linkages), and 6 (suggestive linkages); see more details
in D.R. Reed et al. (in prep.). Although confidence
intervals for body weight and ethanol intake linkages on chromosome 9 overlapped, their distinct modes of inheritance showed that different
loci affected these two traits.
Dominance in the F2 generation was detected when the
F2 value significantly differed from a midparental value.
This test was achieved using planned comparisons in ANOVA by collapsing
B6 and 129 phenotypical values, assigning coefficient 1 for each of
them, and assigning a coefficient 2 to the F2 value.
Heritability in the broad sense (the degree of genetic determination
[Falconer and Mackay 1996]) was estimated based on variances in the
parental strains and F2. The environmental (nongenetic) variance was calculated as an average between the phenotypical (total) variances for the two parental strains,
VARE = 1/2 (VARB6 + VAR129).
The genetic variance was calculated as a difference between the
phenotypic variance of the F2 generation and the
environmental variance, VARG = VARF2 VARE. The heritability estimate was calculated as a percent
of the genetic variance from the phenotypical variance of F2,
h2 = VARG/VARF2 × 100 (Wright 1968;
Falconer and Mackay 1996).
Genotyping
Mouse genomic DNA was purified from tails by NaOH/Tris (Truett et
al. 2000) or phenol/chloroform (Hogan et al. 1986) extraction. Microsatellite (simple sequence length polymorphism [SSLP]) markers were purchased from Research Genetics Inc. and tested using a standard
protocol (Dietrich et al. 1992), with minor modifications (Bachmanov et
al. 1997; Li et al. 2001). The D18346 marker was genotyped
using single-strand conformation polymorphism (SSCP) protocol (Li et
al. 2001). The polymorphic bands were visualized by autoradiography.
The bands were scored by two independent readers and discrepancies were
resolved by regenotyping.
In addition to the semidominant SSLP and SSCP markers, several dominant
coat and eye color markers were used: agouti (A) on chromosome
2, and tyrosinase (Tyr, formerly albino) and pink-eyed dilution (P) on chromosome 7. The B6 mice have black eyes and fur determined by genotypes a/a, Tyr/Tyr,
and P/P. The 129 mice have pink eyes and albino (genotype
Aw/Aw,
Tyrc/Tyrc, p/p) or cream (light
chinchilla; genotype Aw/Aw,
Tyrc-ch/Tyrc, p/p) fur (Festing
et al., www.informatics.jax.org/mgihome/nomen/strain_129.shtml; Roderick and Guidi 1989; Witham 1990). The F2 mice had
several eye and coat color phenotypes. The F2 mice with pink
eyes were albino (Tyrc/Tyrc), cream
(Tyrc-ch/Tyrc) or light buff
(Tyrc-ch/Tyrc-ch); agouti alleles could
not be determined in these mice and were scored as unknown. The other
variants were: white bellied agouti coat and black eyes
(Aw/-, Tyr/-, P/-), black coat and
black eyes (a/a, Tyr/-, P/-), yellow coat
and pink eyes (Aw/-, Tyr/-, p/p),
blue-gray coat and pink eyes (a/a, Tyr/-,
p/p), chinchilla coat and black eyes (Aw/-,
Tyrc-ch/Tyrc-ch, P/- and
Aw/-, Tyrc-ch/Tyrc,
P/-), and chocolate coat and black eyes (a/a,
Tyrc-ch/Tyrc-ch, P/-) (Silvers 1979).
For each coat and eye color marker, two genotypes could be
distinguished, a homozygote for a recessive allele (nonagouti for the
B6 strain, and albino and pink-eyed dilution for the 129 strain), and a
heterozygote/homozygote for a dominant allele.
For genotyping, we have randomly selected a cohort of 169 F2 mice (88 females and 81 males). The mice were selected randomly, rather than from extremes of the phenotypical distributions because several phenotypes were assessed in the same mice (see Two-Bottle Tests section above). Thus, random selection of the mice for genotyping allowed us to use their genotypes for all tested phenotypes; it also allowed us to avoid problems with non-normal phenotypical distributions of the extreme subsets. We have genotyped 137 markers on all autosomes and the X chromosome (the list of markers is available online at www.genome.org). On average, distances between adjacent markers were 11 cM (based on MIT/whitehead database positions, www.genome.wi.mit.edu).
Linkage Analyses
Interval mapping was conducted using MAPMAKER
software (Lander et al. 1987) with phenotypical scores of the
F2 mice standardized within each gender. Linkages were
analyzed using all 169 F2 mice and separately for the subsets
of 88 females and 81 males. Thresholds for significant and suggestive
linkages were applied as described in Lander and Kruglyak (1995). The
confidence interval was defined as a LOD score drops of 1.0 from the
LOD peak. The mode of inheritance (the character of allelic
interactions) was determined by comparing LOD scores obtained under
unconstrained, additive, recessive, and dominant models. The percent of
phenotypical variance explained by several loci was determined
using MAPMAKER/QTL by fitting all linked loci
simultaneously under the unconstrained model. Using this value and
heritability estimates (described above), the percent of genetic
variance explained by several loci was calculated as: "% of
phenotypical variance explained by QTLs"/"heritability" × 100
(heritability was calculated for males and females separately; mean of
both genders was used in this calculation).
All genotyped X chromosome markers were in the non-pseudoautosomal region. To conduct linkage analyses using MAPMAKER, male hemizygotes were coded as homozygotes. Like for the autosomes, the MAPMAKER analysis for the X chromosome markers was conducted in males and females separately, as well as in a pooled set of both genders. Because coding male hemizygotes as homozygotes is an approximation, we also conducted a single-marker analysis of the X chromosome linkages using one-way ANOVA with three female genotypes (B6/B6 and 129/129 homozygotes and B6/129 heterozygotes) and two male genotypes (B6 and 129 hemizygotes) as five gradations of the genotype factor.
Analysis of Epistatic Interactions
Epistatic interactions were analyzed using
Epistat software (Chase et al. 1997; Lark,
www.larklab.4biz.net;) followed by two-way ANOVA. Our primary interest
was in detecting additive-by-additive interactions revealing themselves
in animals homozygous for the interacting markers. These interactions
are of practical importance in inbred, recombinant inbred, and congenic
strains. The Epistat software originally designed
for analyzing recombinant inbred strains is suitable for detecting such
interactions. An automated search for coadaptive interactions among all
possible pairs of the genotyped markers was conducted using a minimal
subgroup number of 5 (there were four subgroups homozygous for each
marker in the pair) and log-likelihood ratio (LLR) threshold of 8.0. This LLR corresponds to a P-value of 0.0000634 (Lark,
www.larklab.4biz.net), which approximates a threshold of statistical
significance for a genome-wide epistasis search based on a Bonferroni
correction (two unlinked regions per chromosome, 40 unlinked regions
for the whole genome, 780 independent comparisons,
P<0.05/780 
0.000064). Significance levels for the
detected interactions were estimated based on Monte Carlo simulations
with 10,000 permutations (this program is included in the
Epistat package). The pairs of markers with significant
(P<0.05) effects on a phenotype were next analyzed using
two-way ANOVA with genotypes of each marker in the pair as
between-group factors (only mice homozygous for marker genotypes were
included in the analysis). Pairs of markers with significant
interaction effects of P<0.01 in ANOVA were reported here as
interacting epistatically. This threshold value was chosen arbitrarily
but has been used in several other studies of epistatic interactions
(Hood et al. 2001; Shimomura et al. 2001).
Bioinformatic Analyses
Genomic regions corresponding to confidence intervals of significantly linked QTL and respective regions of conserved synteny on human chromosomes were defined by consulting the MGD (www.informatics.jax.org). Markers at the ends of the confidence intervals were used to estimate physical size and gene content of the regions using the Celera database (www.celera.com).
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ABSTRACT
INTRODUCTION
RESULTS
DISCUSSION
METHODS
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http://www.celera.com; Celera database.
http://www.epa.gov/ncea/pdfs/allometr.pdf; National Center for Environmental Assessment of the Environmental Protection Agency. Allometric Equations.
http://www.informatics.jax.org; Mouse Genome Database (MGD). The Jackson Laboratory, Mouse Genome Informatics.
http://www.informatics.jax.org/mgihome/nomen/strain_129.shtml; Festing, M.F.W., Simpson, E.M., Davisson, M.T., and Mobraaten, L.E. The Jackson Laboratory. Revised nomenclature for strain 129 mice.
http://www.larklab.4biz.net/; Lark, K.G. The University of Utah. Epistat: A computer program for identifying and testing interactions between pairs of quantitative trait loci.
http://www.monell.org/MMTPP, Tordoff, M.G. and Bachmanov, A.A. Monell Chemical Senses Center. Monell Mouse Taste Phenotyping Project.
www.genome.wi.mit.edu/; Massachusetts Institute of Technology (MIT)/Whitehead Institute for Biomedical Research database.
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ACKNOWLEDGMENTS |
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We thank R. Arlen Price and Maja Bucan for advice on genotyping and genetic analyses, and Josephine Yee, Mala Palkhivala, Jessica Santo, and Ke Lu for technical assistance with genotyping. This work was supported by National Institutes of Health grants R01AA11028 and R01DK46791 (MGT), R01DC00882 (GKB), R03DC03509, R01DC04188 and R01DK55853 (DRR), and R03DC03854 (AAB).
The publication costs of this article were defrayed in part by payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 USC section 1734 solely to indicate this fact.
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FOOTNOTES |
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1 Corresponding author.
E-MAIL bachmanov{at}monell.org; FAX (215) 898-2084.
Article and publication are at http://www.genome.org/cgi/doi/10.1101/gr.129702.
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ABSTRACT
INTRODUCTION
RESULTS
DISCUSSION
METHODS
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