Stability of inbred mouse strain differences in
behavior and brain size between laboratories
and across decades
Douglas Wahlsten*†, Alexander Bachmanov‡, Deborah A. Finn§, and John C. Crabbe§
*Department of Psychology, University of Alberta, Edmonton, AB, Canada T6G 2E9;‡Monell Chemical Senses Center, Philadelphia, PA 19104-3308;
and§Department of Behavioral Neuroscience, Oregon Health and Science University, and Portland Alcohol Research Center and Veterans Affairs Hospital,
Portland, OR 97239-3098
Edited by Joseph S. Takahashi, Northwestern University, Evanston, IL, and approved September 7, 2006 (received for review June 26, 2006)
If we conduct the same experiment in two laboratories or repeat
a classical study many years later, will we obtain the same results?
Recent research with mice in neural and behavioral genetics
yielded different results in different laboratories for certain phe-
notypes, and these findings suggested to some researchers that
behavior may be too unstable for fine-scale genetic analysis. Here
we expand the range of data on this question to additional
laboratories and phenotypes, and, for the first time in this field, we
formally compare recent data with experiments conducted 30–50
years ago. For ethanol preference and locomotor activity, strain
differences have been highly stable over a period of 40–50 years,
and most strain correlations are in the range of r ? 0.85–0.98, as
high as or higher than for brain weight. For anxiety-related
behavior on the elevated plus maze, on the other hand, strain
means often differ dramatically across laboratories or even when
the same laboratory is moved to another site within a university.
When a wide range of phenotypes is considered, no inbred strain
appears to be exceptionally stable or labile across laboratories in
any general sense, and there is no tendency to observe higher
correlations among studies done more recently. Phenotypic drift
over decades for most of the behaviors examined appears to be
agonistic behavior ? anxiety ? ethanol preference ? gene–environment
interaction ? locomotor activity
results? Recent research with mice in neural and behavioral
genetics yielded somewhat different results in different labora-
tories for certain phenotypes (1–4), and these findings evoked
be too unstable for fine-scale genetic analysis. Here we expand
the range of data on this question to additional laboratories and
phenotypes, and, for the first time in this field, we formally
compare recent data with experiments conducted 30–50 years
ago. We find that several kinds of behavior are genetically quite
It is now common practice to backcross targeted mutations in
mice onto the same standard, inbred strain background in
different laboratories. If the background genotype on which
mutations are placed does not behave consistently across labo-
ratories, our understanding of the effects of gene manipulations
on behaviors may be compromised. Comprehensive information
on a wide range of inbred strain characteristics is now being
compiled in the Mouse Phenome Database (6) in a manner that
facilitates comparisons among different phenotypes studied in
different laboratories. Many investigators are now using gene
expression information for brain tissue of recombinant inbred
strains from the Gene Network (www.genenetwork.org) to com-
pare phenotypes across strains (7). What constitutes a high
between-trait strain correlation depends critically on the mag-
a classical study many years later, will we obtain the same
nitude of the strain correlation for the same trait measured in
different laboratories or at different times.
To repeat a genetic experiment, the genotype of the animals
must be replicable. Brother-by-sister mating can achieve genetic
purity in an inbred strain after 60 generations (8). Derivation of
standard strains of mice commenced with the DBA strain in
1909, BALB in 1913, and C57BL in 1921 (9). By 1950, many
strains maintained by The Jackson Laboratory (Bar Harbor,
ME) had undergone ?40 generations of full sibling mating.
Today, when two laboratories obtain samples of the same strain,
they can be confident that the genotypes are almost identical, but
genetic contamination can occur when rigorous inbreeding is not
practiced (10–12). Consequently, in this analysis we compare
only data reported for the same substrains from one supplier, the
The Jackson Laboratory (laboratory code J), in almost all cases.
Only simultaneous study of identical tests given to identical
mouse strains can evaluate replicability in the strict sense, and
any differences in results must arise from the inevitable differ-
ence between the environments in the research facilities external
to the test situation itself. Comparisons of reasonably similar
tests given to nearly identical strains in different years in
different laboratories, however, evaluate the robustness of be-
havioral test results to methodological differences, including
small genetic differences, that exist between most published
reports. Details of the test situation usually differ to some extent
between laboratories (13), and the field is principally concerned
with this category.
We selected for historical comparisons studies based on
previously published reports of tests given to inbred strains from
The Jackson Laboratory. First, they had to provide data on at
least five of the same substrains examined by at least one of our
four laboratories. Second, they had to provide sufficient infor-
mation in the published article that we could determine the
sample size, mean score, and standard deviation for each strain,
so that an unweighted-means ANOVA could be performed to
compare studies (14). Third, they had to measure essentially the
same behavior on a scale that allowed meaningful comparisons.
Brain Weight. Unlike behavior, weight of a dead and fixed brain
does not fluctuate from moment to moment or day to day, and
Author contributions: D.W., A.B., D.A.F., and J.C.C. designed research; D.W., A.B., D.A.F.,
The authors declare no conflict of interest.
This article is a PNAS direct submission.
Freely available online through the PNAS open access option.
Data deposition: The data reported in this paper have been deposited in the Mouse
Phenome Database, www.jax.org?phenome (accession no. MPD108).
†To whom correspondence should be sent at the present address: Great Lakes Institute,
University of Windsor, Windsor, ON, Canada N9B 3P4. E-mail: email@example.com.
© 2006 by The National Academy of Sciences of the USA
October 31, 2006 ?
vol. 103 ?
it is measured on the same scale in grams in all studies. Fig. 1A
compares data collected in the Edmonton laboratory with
strain means in four other data sets. ANOVAs comparing all
possible pairs of data sets and details of methods for measuring
brains in each study are provided in Tables 1 and 2 and
Supporting Text, which are published as supporting informa-
tion on the PNAS web site. The Edmonton and Portland strain
means were very similar indeed (r ? 0.98), and average weights
across all strains were almost identical at the two sites (0.452
and 0.456 g, respectively) (15). Methods of fixation were very
similar in the Williams (16) study, and the correlation of
Edmonton data with their strain means was very high (r ?
0.88). The two older studies (17, 18) both weighed unfixed
brains, and fresh brain weights are expected to be slightly
larger than fixed brain weights, but there is no reason to expect
major strain-dependent differences in shrinkage (19).The sim-
ilarity between the Edmonton data and the Roderick et al.
means collected 30 years earlier on the same strains (17) was
very high (r ? 0.84), whereas the Storer (18) and Edmonton
data were notably less similar (r ? 0.54).
There were some discrepancies between older and more
recent data. The clearly greater brain size of C57BL?6J than A?J
in our study and the Williams study was not seen in the Roderick
et al. (17) and Storer (18) studies. On the other hand, the tight
seen in all four studies spanning a range of 35 years. Strain
SWR?J brains were considerably heavier (by 46 mg) than strain
SJL?J in the Storer data, in contrast with the other three studies.
Results from the ANOVAs (Table 1) reveal that the strain-by-
laboratory interaction was in all but one case significant, yet it
was substantial by our criteria (interaction effect size more than
half the strain main effect size; see Methods) only for the
comparisons of the Storer data (18) with brain weights from the
Wahlsten laboratory (15).
the gray line is the best fit of data from the y axis to data on the x axis, plotted for the actual range of data. When this line is above the dashed line, the means
of the figure, with the most common strains shown as colored numerals. Effect sizes from the ANOVAs are shown for the strain main effect and strain-by-
laboratory interaction. The significance (P) of the interaction effect is also indicated. NS denotes an interaction not significant at P ? 0.05. (A) Brain weight
measured in the Edmonton laboratory versus four other laboratories. Data for Edmonton and Portland were collected in 2002 and published in ref. 15. (B)
Preference for 10% ethanol solution versus tap water. Nine additional strains not studied in the D.W. and J.C.C. laboratories were included in this set of
four other laboratories, scaled to centimeters per minute.
Correlations between means of inbred strains observed in different laboratories. The dashed line indicates identical results for two laboratories, and
Wahlsten et al.PNAS ?
October 31, 2006 ?
vol. 103 ?
no. 44 ?
Ethanol Preference. The remarkable preference of the C57BL?6J
mouse strain for a 10% ethanol solution versus tap water and the
distaste of DBA?2J for ethanol was originally discovered in 1959
(20), and the technique of measuring ethanol preference was
thoroughly explored by Fuller (21). In 1966 Rodgers (22) used
the two-bottle preference test to document a wide range of
preference scores for 15–19 inbred strains, and another large
survey of 15 strains was reported 27 years later by Belknap et al.
(23). The older data are compared here with two new inbred
strain surveys done by A.B. (28 strains) and D.A.F. (22 strains)
(Table 3, which is published as supporting information on the
PNAS web site). We present data from each study where a
two-bottle preference test of 10% ethanol versus tap water was
done with bottle position changed systematically over a period of
at least 4 days. Methods of testing preference are described in
detail in Table 4, which is published as supporting information
on the PNAS web site. The results of D.A.F. could not be
compared with Belknap et al. (23) because the two studies
included only two identical substrains (C57BL?6J and DBA?2J).
The strain correlations among the four studies done 30 or more
years apart are impressive indeed (Fig. 1B and Table 1) and for
three data sets versus Rodgers (22) are slightly greater than the
strain correlations seen for brain weight in independent studies.
A considerable number of other studies with fewer inbred strains
has been published over the years, and, to our knowledge, the
high ethanol preference of C57BL?6J versus the pronounced
aversion to ethanol of DBA?2J has been confirmed in every
report. Thus, this behavioral difference is highly robust with
respect to laboratory environments and the fine details of
methods used to assess preference.
Although strain correlations are generally very high, the
ANOVAs (Table 1) point to several significant strain-by-
laboratory interactions. These two indicators of replicability are
not in conflict, because the interaction effects are invariably
much smaller that the massive main effects of strain. Neverthe-
less, closer inspection of strain distributions (Fig. 1B) reveals
some noteworthy differences for certain pairs of strains. If an
investigator works with only a few inbred strains and happens to
include one or two that are especially sensitive to conditions of
rearing and?or testing, these kinds of interaction effects could
Locomotor Activity.Depending on the test conditions, locomotion
is used to index complex traits such as general health, explor-
atory drive, novelty seeking, anxiety, and the potential future
preference for drugs of abuse. The first large survey of 15 inbred
strains in 1953 by Thompson (24) involved a simple count by a
human of the lines crossed by the mouse in a box divided into 5 ?
5-inch squares. Fifteen years later, Southwick and Clark (25)
used the same size enclosure but counted entries into 6 ? 6-inch
squares. Fifty years after Thompson, Liu and Gershenfeld (26)
used a computer to monitor beam breaks of photocells spaced
2.5 cm apart, whereas the J.C.C. and D.W. laboratories used
in all laboratories are specified in Table 5, which is published as
supporting information on the PNAS web site. Results are
compared in Fig. 1C and Table 1. To aid comparisons among
studies, we converted all data to a common measurement scale,
centimeters traveled per minute.
Fig. 1C and Table 1 indicate that results of studies done 40–50
years apart are remarkably similar. Every laboratory found that
A?J was the least active and C57BL?6J was among the most
active strains, and the same result has been reported in virtually
every other published study involving fewer strains. In only one
comparison was the correlation notably lower and the interac-
tion term really large relative to the strain main effect: Liu and
Gershenfeld (26) versus Thompson (24). Thompson (24) found
large differences between C3H?HeJ and A?J and between
BALB?cJ and C57BL?6J, whereas Liu and Gershenfeld (26)
observed only minor differences between these strain pairs. This
was not a consequence of the great time span between the
studies, because the Thompson data (24) were highly correlated
with results of Southwick and Clark (25) as well as our own very
Elevated Plus Maze. Anxiety or anxiety-like behavior, as assessed
by the elevated plus maze (27, 28), is very sensitive to environ-
mental variation. In our previous study of eight mouse strains on
the elevated plus maze in three laboratories, average anxiety-like
behavior levels were much lower in Edmonton than Portland for
almost all strains (1), even though we used physically identical
maze. Lines, effect sizes, and strain numbers in A and B have the same
the value for the wild-derived inbred strain PERA?Ei (number 15 in yellow
circle) in Portland was considerably greater than in Edmonton, but other
means were generally very similar for the two laboratories. (B) There were
remarkable discrepancies for the strains BALB?cByJ (no. 3), C3H?HeJ (no. 5),
and C57BL?6J (no. 6) in the Edmonton laboratory versus the study of Trullas
in 2002 as part of the Mouse Phenome Project using the same apparatus and
protocols as in 1998, but the actual laboratories had moved to new locations
in each university, resulting in substantially different data, especially in Edm-
onton, where every strain was above 30% (blue line) in 1998. (D) Four inbred
surname of the first author; complete references (30–35) are provided in the
text. The substrains were not identical across all laboratories shown in D.
Dashed lines show that the profile of strain means was very similar for three
studies [Ducottet and Belzung (32), Griebel et al. (33), and Khrapova et al.
(34)], but remarkable differences between other laboratories are evident.
Percent time spent exploring the two open arms of the elevated plus
www.pnas.org?cgi?doi?10.1073?pnas.0605342103Wahlsten et al.
apparatus and test protocols. Five years later we ran the same
test in Edmonton and Portland using the same mazes and
protocols from the previous study, expanding the sample to 21
inbred strains. This time, our two laboratories obtained very
similar results (Fig. 2A). ANOVA indicated very large differ-
ences among strains, no significant laboratory difference, and a
very small strain-by-laboratory interaction (Table 1).
Four of the strains in the current study were also tested in our
earlier study (1). As shown in Fig. 2C, open arm exploration
increased markedly for strain BALB?cByJ in Portland, whereas
it decreased considerably for strains A?J, BALB?cByJ, and
DBA?2J in Edmonton. The apparatus and protocols were the
sites. The colony and testing rooms in Edmonton were moved to
another wing of the same building, into an area where several
other mammalian species were housed in a bustling, centralized
animal facility, whereas the testing laboratory in Portland was
moved from a large facility where many mouse studies were
being done at the same time to a more isolated location in
same room with the test apparatus.
The elevated plus maze test was originally devised for use with
rats and later was adapted for use with mice (29), then applied
to several inbred strains by Trullas and Skolnick (30). Our
physical maze and procedures and those used by Trullas and
Skolnick (30) were almost the same (Table 6, which is published
as supporting information on the PNAS web site), because we
results of our current study and the Trullas and Skolnick (30)
study were drastically different for strains C3H?HeJ, BALB?
cByJ, and C57BL?6J (Fig. 2B), and the strain-by-laboratory
interaction was large relative to the main effect of strain.
Studies with 6–11 inbred strains on the elevated plus maze
have been reported from several laboratories (31–35), but
unfortunately the number of specific substrains in common with
or fewer in every instance. By treating substrains from different
suppliers as effectively the same strain, it was possible to
compute correlations involving three to six strains in common.
Mean scores for four strains that were examined in all eight
laboratories are shown in Fig. 2D. Three studies yielded strain
means that were positively correlated for four to six strains in
common (32–34), whereas the results of Yilmazer-Hanke et al.
(35) were negatively correlated with each of these three studies
but positively correlated with the data from Edmonton and
Many factors can influence mouse anxiety (36). Consequently,
the experimenter needs to choose test parameters very carefully.
At the same time, the great similarity of apparatus and proce-
dures in the synchronized Edmonton and Portland experiments
and the independent test by Trullas and Skolnick (30) did not
yield similar data (Fig. 2B). Furthermore, we could detect no
major differences or similarities in methods, as described in
Table 6, that could account for the patterns of strain correlations
among the eight studies (Fig. 2D). This suggests that variations
in the local laboratory environments had important influences
on the manifestation of mouse anxiety.
Other Behaviors. Agonistic behavior of male mice appears to be
strongly influenced by rearing and testing conditions (37). Two
early studies found opposite rank orderings of two strains (38,
39), an interaction that Ginsburg (40) later attributed to forceps
handling of young mice before weaning in one of the laborato-
ries. Maxson et al. (41) demonstrated Y chromosome effects on
agonistic behavior and their interaction with genetic background
(41), but the consomic line differences vanished when the strains
were moved into a specific pathogen-free laboratory, a gene–
environment interaction that was eventually traced to the acid-
ified water (37). Selective breeding is very effective in creating
large line differences if the males are reared in isolation, but the
line difference largely disappears when they are instead reared
in groups (42–44). Recent inbred strain surveys (45–48) used
very different test methods and?or different substrains, and they
differed substantially in many details from the 1968 multiple-
strain comparison by Southwick and Clark (25). The available
data did not warrant a formal statistical comparison between
studies, and we could not evaluate the stability of scores over
A number of multiple strain surveys of behavior have been
done recently and are reported in the Mouse Phenome Database
(www.jax.org?phenome). At the present time, however, the
Mouse Phenome Database includes few instances where the
same phenotype was examined by different laboratories. We
have compared data on the accelerating rotarod and water
considerably different from previous tasks. In the future, it will
be informative to assess replicability by carefully copying the
apparatus and protocols of earlier studies.
It is evident from these comparisons among studies that inbred
strain differences for ethanol preference and locomotor activity
are highly replicable across laboratories and even decades,
whereas strain differences in elevated plus maze exploration are
strongly dependent on local conditions. Data on agonistic be-
havior suggest that it is also very sensitive to pretesting envi-
ronment, but stability over decades cannot yet be fairly judged.
Thus, there is no basis for viewing mouse behavior as inherently
unstable or unsuitable for genetic analysis in any general sense.
Some of the classical strain differences in behavior that were
discovered 40–50 years ago are highly robust and still evident
today. Other kinds of tests are more labile. These findings do not
imply that tests of anxiety and agonistic interactions are bad
tests. On the contrary, the tests are very sensitive to genuine
individual and strain differences in the constructs they are
designed to assess, but the processes underlying those constructs
are also very sensitive to environmental conditions.
The data do not indicate that any one strain is especially stable
or labile across a wide range of behavioral phenotypes. Certain
extreme strain differences on certain tasks, such as C57BL?6J
versus DBA?2J on ethanol preference and C57BL?6J versus A?J
on motor activity, are highly replicable over laboratories and
decades, but those strains show considerable variation for other
phenotypes such as anxiety-related and agonistic behaviors.
C57BL?6J quickly develops obesity and diabetes on a high-fat
diet (49) and as a neonate is easily primed by loud noise so that
it later suffers audigenic seizures (50). C57BL?6J serves as a
good choice when backcrossing a new mutation onto a standard
strain background, not because it is markedly stable phenotyp-
ically, but because it breeds reasonably well and is so widely used
as a de facto standard in different laboratories.
Comparisons of current and classical data suggest that minor
genetic changes over several decades have been of little conse-
quence for mouse behavioral testing and, ipso facto, demonstrate
the robustness of carefully conducted behavioral assays. It
appears that substrain differentiation is generally not a major
threat to replicability of behavioral data over several decades,
provided the substrains are formed after the root strain has
already been inbred for many generations. A study of 1,638
single-nucleotide polymorphisms observed that differences
among several C57BL?6 substrains represented residual het-
erozygosity at the time substrains were separated as well as new
mutations (51). Allelic differences at 12 of 342 microsatellite loci
were noted between the C57BL?6J and C57BL?6NTac sub-
strains that were separated in 1951, ?150 generations ago (52).
A C57BL?6J mouse from The Jackson Laboratory today will not
Wahlsten et al.PNAS ?
October 31, 2006 ?
vol. 103 ?
no. 44 ?
be identical nucleotide-for-nucleotide to the same strain 50 years
ago. It will nevertheless be far more similar to its own ancestor
in 1950 than to substrains separated early in the history of a
major strain, for example C57BL?10, C57L, and C58 (51), or to
another substrain separated around 1950.
All of the phenotypes examined here rank as highly complex
traits. Quantitative trait locus analyses have indicated influences
of multiple loci for brain weight (16) and for the behavioral traits
studied (53). A minor change in one or two of many pertinent
genes should not shift the mean score of a specific strain
appreciably when it is tested several decades later. There does
not seem to be any tendency in the data reviewed here for strains
to differ more substantially when the studies were conducted
many years apart. For example, the ethanol preference data of
A.B. were more similar to those of Rodgers (22) from 40 years
earlier than those of Belknap et al. (23) just 10 years earlier.
Although the Thompson (24) activity data were not highly
correlated with the data from Liu and Gershenfeld (26), they
were very similar to more recent data from the Edmonton
The importance of many fine details of husbandry and testing
is recognized on the basis of carefully controlled experiments
conducted within a single laboratory. Phytoestrogens in the diet
(54), cage enrichment (4), cage position in the colony room (55),
size of the drinking spout orifice (56), shipping before testing
(57), and even the specific experimenter administering the test
(4, 58) have been found to affect laboratory mouse physiology
and behavior. Nevertheless, just as a single gene usually accounts
for little variance on its own, it is highly unlikely that a multi-
dimensional laboratory difference in behavioral data can be
traced to a single environmental variant. A small environmental
effect might be evident within a carefully controlled experiment,
whereas the same effect might not be audible amidst the
cacophony of multiple laboratory-specific parameters in rearing
The sample of phenotypes in this systematic comparison of
recent and classical data sets is not sufficient to warrant strong
conclusions about what kinds of behavior should be most and
least robust across laboratories. Very tentatively, we suggest that
things more closely associated with sensory input and motor
output will tend to be less affected by minor variants in the
laboratory environment, whereas behaviors related to emotional
and social processes will be more labile. A similar classification
regarding degree of genetic influence has been proposed on
theoretical grounds by Lipp (59).
Animals and Laboratories. Data on brain weight, open field activ-
ity, and elevated plus maze were collected simultaneously in the
J.C.C. laboratory in Portland and the D.W. laboratory in Edm-
onton, whereas data on ethanol preference were collected in the
A.B. and D.A.F. laboratories as separate studies done within a
few months of each other. We all obtained the animals from The
Jackson Laboratory at 4–6 weeks of age. The D.W. and J.C.C.
laboratories evaluated the same 21 inbred strains, consisting of
priority lists A and B of the Mouse Phenome Project (129S1?
SvImJ, A?J, AKR?J, BALB?cByJ, C3H?HeJ, C57BL?6J,
C57L?J, C58?J, CAST?EiJ, DBA?2J, FVB?NJ, MOLF?EiJ,
NOD?LtJ, NZB?BlNJ, PERA?EiJ, PL?J, SJL?J, SM?J,
SPRET?EiJ, and SWR?J) plus the strain BTBR T? tf?J from
list D (www.jax.org?phenome), which has an interesting but
viable loss of forebrain commissures (15). The A.B. laboratory
studied 28 strains, and the D.A.F. laboratory studied 22 strains
(Table 3), 10 of which were common to at least two studies but
were in addition to those studied by the J.C.C. and D.W.
laboratories (129P3?J, BALB?cJ, BUB?BnJ, C57BL?KsJ,
CBA?J, CE?J, I?LnJ, LP?J, RIIIS?J, and SEA?GnJ). No at-
tempt was made to equate the housing conditions in the four
laboratories, but conditions were nevertheless quite similar, as
described in Table 7, which is published as supporting informa-
tion on the PNAS web site.
Data Analysis. There currently is no standard in the field for what
Instead, we rely strongly on close inspection of the data by those
speaking, replication of results in two laboratories requires a
large strain difference but no strain-by-laboratory interaction
effect. The magnitude of a strain difference can be expressed as
partial ?2, the proportion of variance attributable to the differ-
ences among strain means when laboratory differences are
removed from the equation. The partial ?2can also be estimated
for an interaction effect (1). If ?2for an interaction effect is
considerably less than ?2for a strain main effect, then we
consider the interaction to be relatively small, even if its statis-
tical significance (P value) is beyond reproach. Likewise, to be
considered large or substantial in a strain-by-laboratories study,
the interaction effect size should be at least half the strain main
effect size when data are compared across two laboratories.
We thank Naomi Yoneyama, Andrea Wetzel, Maria Theodorides, Sue
Charlotte D. Wenger, Chia-Hua Yu, Pamela Metten, Brandie Moisan,
Sean F. Cooper, Tera Mosher, Tim Frigon, and Elizabeth Munn for
assistance in collecting the data. This work was supported in part by
National Institutes of Health grants to D.W. (Grant AA12714), A.B.
(Grant AA11028), J.C.C. (Grant AA10270 and Integrative Neuro-
science Initiative on Alcoholism Consortium Grant AA13519), and
D.A.F. (Integrative Neuroscience Initiative on Alcoholism Consortium
Grants AA134785, AA10760, AA12439, and AA13478); Department of
Veterans Affairs grants (to J.C.C. and D.A.F.); and Natural Sciences and
Engineering Research Council Grant 45825 (to D.W.).
1. Crabbe JC, Wahlsten D, Dudek BC (1999) Science 284:1670–1672.
2. Crestani F, Martin JR, Mo ¨hler H, Rudolph U (2000) Nat Neurosci 3:1059.
3. Kafkafi N, Benjamini Y, Sakov A, Elmer GI, Golani I (2005) Proc Natl Acad
Sci USA 102:4619–4624.
4. Lewejohann L, Reinhard C, Schrewe A, Brandewiede J, Haemisch A, Gortz N,
Schachner M, Sachser N (2006) Genes Brain Behav 5:64–72.
5. Pfaff D (2001) Proc Natl Acad Sci USA 98:5957–5960.
6. Grubb SC, Churchill GA, Bogue MA (2004) Bioinformatics 20:2857–
7. Chesler EJ, Lu L, Shou SM, Qu YH, Gu J, Wang JT, Hsu HC, Mountz JD,
Baldwin NE, Langston MA, et al. (2005) Nat Genet 37:233–242.
8. Green EL (1981) Genetics and Probability in Animal Breeding Experiments
(Oxford Univ Press, New York).
9. Beck JA, Lloyd S, Hafexparast M, Lennon-Pierce M, Eppig JT, Festing MFW,
Fisher EMC (2000) Nat Genet 24:23–25.
10. Kahan B, Auerbach R, Alter BJ, Bach FH (1982) Science 217:379–381.
11. Simpson EM, Linder CC, Sargent EE, Davisson MT, Mobraaten LE, Sharp JJ
(1997) Nat Genet 16:19–27.
12. Threadgill DW, Yee D, Matin A, Nadeau JH, Magnuson T (1997) Mamm
13. Wahlsten D, Metten P, Phillips TJ, Boehm SL, II, Burkhart-Kasch S, Dorow
J, Doerksen S, Downing C, Fogarty J, Hen R, et al. (2003) J Neurobiol
14. Winer BJ, Brown DR, Michels KM (1991) Statistical Principles in Experimental
Design (McGraw-Hill, New York), 3rd Ed.
15. Wahlsten D, Metten P, Crabbe JC (2003) Brain Res 971:47–54.
16. Williams RW (2000) in Mouse Brain Development, eds Goffinet AM, Rakic P
(Springer, New York), pp 21–49.
17. Roderick TH, Wimer RE, Wimer C, Schwartzkroin PA (1973) Brain Res
18. Storer JB (1967) Exp Gerontol 2:173–182.
19. Wahlsten D, Hudspeth WJ, Bernhardt K (1975) J Comp Neurol 162:519–532.
20. McClearn GE, Rodgers DA (1959) Q J Stud Alcohol 20:691–695.
21. Fuller JL (1964) J Comp Physiol Psychol 57:85–88.
22. Rodgers DA (1966) Psychosom Med 28:498–513.
23. Belknap JK, Crabbe JC, Young ER (1993) Psychopharmacology 112:503–510.
www.pnas.org?cgi?doi?10.1073?pnas.0605342103 Wahlsten et al.
24. Thompson WR (1953) Can J Psychol 7:145–155. Download full-text
25. Southwick CH, Clark LH (1968) Commun Behav Biol 1:49–59.
26. Liu X, Gershenfeld HK (2003) Brain Res Bull 60:223–231.
27. Belzung C, Griebel G (2001) Behav Brain Res 125:141–149.
28. Rodgers RJ (1997) Behav Pharmacol 8:477–496.
29. Lister RG (1987) Psychopharmacology 92:180–185.
30. Trullas R, Skolnick P (1993) Psychopharmacology 111:323–331.
31. Brooks SP, Pask T, Jones L, Dunnett SB (2005) Genes Brain Behav 4:307–317.
32. Ducottet C, Belzung C (2005) Behav Brain Res 156:153–162.
33. Griebel G, Belzung C, Perrault G, Sanger DJ (2000) Psychopharmacology
34. Khrapova MV, Popova NK, Avgustinovich DF (2001) Zh Vyssh Nerv Deiat Im
I P Pavlova 51:324–328.
35. Yilmazer-Hanke DM, Roskoden T, Zilles K, Schwegler H (2003) Behav Brain
36. Hogg S (1996) Pharmacol Biochem Behav 54:21–30.
37. Maxson SC (1992) in Techniques for the Genetic Analysis of Brain and Behavior:
Focus on the Mouse, eds Goldowitz D, Wahlsten D, Wimer RE (Elsevier,
Amsterdam), pp 349–373.
38. Ginsburg B, Allee WC (1942) Physiol Zool 15:485–506.
39. Scott JP (1942) J Hered 33:11–15.
40. Ginsburg BE (1969) in Stimulation in Early Infancy, ed Ambrose JA (Academic,
New York), pp 73–96.
41. Maxson SC, Ginsburg BE, Trattner A (1979) Behav Genet 9:219–226.
42. Lagerspetz KM, Lagerspetz KY (1971) Scand J Psychol 12:241–248.
43. Hood KE, Cairns RB (1989) Aggress Behav 15:361–380.
44. Nyberg J, Sandnabba K, Schalkwyk L, Sluyter F (2004) Genes Brain Behav
45. Roubertoux PL, Le Roy I, Mortaud S, Perez-Diaz F, Tordjman S (1999) in
Handbook of Molecular-Genetic Techniques for Brain and Behavior Research,
eds Crusio WE, Gerlai RT (Elsevier Science, Amsterdam), pp 696–709.
46. Kulikov AV, Osipova DV, Naumenko VS, Popova NK (2005) Genes Brain
47. Guillot PV, Chapouthier G (1996) Behav Brain Res 77:211–213.
48. Tordjman S, Carlier M, Cohen D, Cesselin F, Bourgoin S, Colas-Linhart N, Petiet
A, Perez-Diaz F, Hamon M, Roubertoux PL (2003) Behav Genet 33:529–536.
49. Surwit RS, Kuhn CM, Cochrane C, Mccubbin JA, Feinglos MN (1988) Diabetes
50. Henry KR, Bowman RE (1970) J Comp Physiol Psychol 70:235–241.
51. Petkov PM, Ding YM, Cassell MA, Zhang WD, Wagner G, Sargent EE,
Asquith S, Crew V, Johnson KA, Robinson P, et al. (2004) Genome Res
52. Bothe GWM, Bolivar VJ, Vedder MJ, Geistfeld JG (2004) Genes Brain Behav
53. Flint J (2003) J Neurobiol 54:46–77.
54. Wang H, Tranguch S, Xie H, Hanley G, Das SK, Dey SK (2005) Proc Natl Acad
Sci USA 102:9960–9965.
55. Izidio GS, Lopes DM, Spricigo L, Jr, Ramos A (2005) Genes Brain Behav
56. Dotson CD, Spector AC (2005) Physiol Behav 85:655–661.
57. Tordoff MG, Alarcon LK, Byerly EA, Doman SA (2005) Physiol Behav
58. Chesler EJ, Wilson SG, Lariviere WR, Rodriguez-Zas SL, Mogil JS (2002) Nat
59. Lipp H-P (1995) Behav Processes 35:19–33.
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