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Review
The genetic basis of neuroticism
Jonathan Flint*
Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
Abstract
Gray has drawn upon genetic evidence to argue for the existence of rodent emotionality, a model of human neuroticism. With the advent of
molecular mapping techniques it has become possible to test this hypothesis. Here I review the progress that has been made, largely in animal
genetic studies, demonstrating that a common set of genes act pleiotropically on measures of emotionality. More recently, evidence has
emerged supporting the view that the same genes influence variation in both rodent and human phenotypes.
q 2004 Elsevier Ltd. All rights reserved.
Keywords: Neuroticism; Open-field activity; Quantitative trait loci
Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307
2. Identifying the genetic variants that contribute to rodent emotionality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309
3. Experimental evidence for pleiotropic action. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310
4. High resolution mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311
5. Mouse and human comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315
1. Introduction
Attempts to measure personality variation in humans
have not always arrived at the same conclusions: there is
long, often disputatious, history of what the main factors
of personality are, and indeed if they exist at all. One
relatively robust measure that has achieved a modicum of
consensus is neuroticism, or emotional stability, a
measure that emerges from the most widely accepted
three and five-factor models of personality [7,21,63].
Neuroticism manifests at one extreme as anxiety,
depression, moodiness, low self-esteem and diffidence,
but the domain of neuroticism probably contains many
elements. Factor analysis of questionnaire data has been
the main tool for identifying neuroticism, a statistical
method that detects correlations between answers. What
those correlations mean is still a matter of debate, but the
finding that high neuroticism scores are, in some way,
related to depressive illness [18,36,37] has given impetus
to determine whether the personality measure has a
biological correlate.
My work on the genetic basis of the human
personality trait neuroticism and emotionality in rodents,
has been largely driven by Jeffrey Gray’s argument for a
congruence between animal models of trait anxiety and
human neuroticism [27–29]. To move from animal to
human biology using genetics is ambitious enough even
for a physiological phenotype. To perform the maneuver
for a personality trait appears at the least foolhardy,
particularly when there is dispute about how to measure
that trait in humans and when we could reasonably argue
that animals have no personality traits. One way of
dealing with this problem is to argue that emotionality
and neuroticism reflect activity of the same (or at least
similar) brain processes.
Gray put forward the view that there are two interrelated
but separable brain systems that subserve anxiety in both
humans and in rodents. The first system, which he terms the
fight/flight system (FFS), subserves flight, defensive
aggression, freezing and associated autonomic activity.
The second system, termed the behavioural inhibition
system (BIS), subserves the cognitive and information
0149-7634/$ - see front matter q 2004 Elsevier Ltd. All rights reserved.
doi:10.1016/j.neubiorev.2004.01.004
Neuroscience and Biobehavioral Reviews 28 (2004) 307–316
www.elsevier.com/locate/neubiorev
* Tel.: þ44-1865-287512; fax: þ44-1865-287501.
E-mail address: [email protected] (J. Flint).
processing aspects of anxiety. The systems differ anatomi-
cally, pharmacologically and in the behavioural responses
they mediate.
Anatomically, the FFS includes the amygdala and
hypothalamus, while the BIS includes the hippocampal
formation, septal area and related cortico-limbic structures.
Pharmacologically, the BIS is more sensitive to benzo-
diazepines than the FFS, which is in turn more sensitive to
opiates. The psychological characterization of the two
systems derives from Gray’s interpretation of a long series
of experiments into the nature of conditioning paradigms
and can be summarized as follows: fear responses, elicited
by secondary punishing stimuli, and conditioned frustration,
arising from secondary frustrative stimuli, are functionally
equivalent, partly because the BIS mediates both. Gray uses
anxiety to subsume both fear responses and conditioned
frustration. In addition, the BIS responds to novel stimuli, a
class of stimuli that feature predominantly in the exper-
iments described later in this chapter, and to innate fear
stimuli. By contrast, the FFS mediates the behavioural
effects of unconditioned punishment or non-reward. Despite
the allocation of anxiety responses to the BIS, Gray argues
that human anxiety disorders involve activity in both BIS
and FFS, so that successful animal models of human anxiety
should include measures of activity in both systems
[27–29].
How do these brain systems relate to personality
dimensions? In a sentence, neuroticism or emotional
stability is a measure of sensitivity to reinforcing events.
An ever-simpler explanation is to see neuroticism as a trait
measure of anxiety, which is in turn the consequence of
activity in the BIS. So a highly neurotic person has a
constitutionally overactive behavioural inhibition system.
This is an oversimplification, but it has the value of
emphasizing how theories about human personality arise
from consideration of animal experiments. The critical
insight is that if the same brain systems operate in both
humans and rodents, then the same genetic mechanisms are
also likely to operate in both organisms. This in turn means
that, potentially, we can carry out genetic experiments in
animals, which are relatively cheap and relatively easy, in
place of expensive and difficult human genetic
experiments.
To demonstrate the existence of common brain processes
in animals and humans, we have to show first that the brain
processes exist, and then show that they are, in some way,
homologous between species. Genetic evidence could be
used to support both points if we could show that genetic
action is consistent with mediation by the expected, but
hypothetical, brain processes. Furthermore, if we find that
the same genes operate in the same manner in another
species, then we would have evidence for the existence of a
homologous biological process. Put in genetic terminology,
we need to demonstrate pleiotropic genetic action on a set of
phenotypes that we predict measure the same brain
processes. Much of my review deals with the search for
pleiotropic action.
Part of Gray’s argument rested on the results of a series
of artificial selection experiments, carried out in mice and
rats, which appeared to provide evidence for a common
genetic basis for a number of measures of emotionality. In
brief, artificial selection for one measure of emotionality
gives rise to correlated changes in another measure,
indicating that both measures have a common genetic, and
hence biological, basis.
Calvin Hall introduced the open-field apparatus, a
brightly lit arena, to measure emotional defecation and
activity [30,31]. The amount of open-field defecation (OFD)
and open-field activity (OFA) are indices of the animal’s
emotional state: a frightened rodent will tend to freeze and
to defecate. Hall then used artificial selection to show that
there was a genetic component to the individual variation in
emotional behaviour [32]. That was almost 30 years later,
Broadhurst continued the work genetically selecting rats
for differences in open-field defecation to generate the
Maudsley reactive and non-reactive strains [3,4]. Broad-
hurst pointed to another result from the selection
experiments: the change in activity in response to selection.
Animals that defecate more were found to be less active,
suggesting that the artificial selection had operated on a
something common to both activity and defecation, such as
emotionality. In short, he argued for a pleiotropic action
on both OFA and OFD.
The trouble with this argument is that correlated changes
can occur because for a number of genetic reasons, not all of
them reflecting the biology of the selected phenotype. When
there are a relatively small number of mating pairs in each
generation of selection, allele frequencies are more likely to
fluctuate widely by chance and occasionally go to fixation
(that is to say one allele is lost from the population).
Consequently a population under selection may show
phenotypic changes due to a stochastic alteration in alleles
compared to the founder population. Furthermore, alleles at
loci that are physically close to loci under selection will
themselves undergo a change in frequency, since recombi-
nation events are less likely to sever them from their
neighbours than will be the case for other, more distantly
situated loci (for example those on other chromosomes).
Correlated changes in allele frequencies could thus reflect
physical linkage rather than common biology.
One experiment that goes a long way to establishing a
genetic basis for the correlation between OFA and OFD was
carried out in mice by John DeFries and colleagues, again
using an artificial selection experiment, this time selecting
for OFA [11–13]. The experimental design included full
replication (two high and two low lines), the inclusion of
control lines (not subject to selection), and an extended
period of selection. After 30 generations, OFA scores of the
high lines were 30 times greater than those of the low lines,
consistent with Hall’s and Broadhurst’s rat findings that
OFA is under genetic control. DeFries estimated that about
J. Flint / Neuroscience and Biobehavioral Reviews 28 (2004) 307–316308
20% of the phenotypic variation could be attributed to
genetic variation, while Broadhurst’s estimate was some-
what larger (about 50%).
More importantly, DeFries also noted a correlated
response to defecation. His results are particularly impress-
ive because they almost rule out stochastic allele fluctuation
as a cause: the coincident results of the replicate experiment,
the size of the population maintained under selection and the
failure to see anything similar in the control lines argue
against genetic drift. Theoretically, we expect that the
selection process should have lost relatively little genetic
diversity. With 10 mating pairs in each of the closed lines
and within-litter selection, the expected increase in the co-
efficient of inbreeding is less than 1.5% per generation [22].
However, note that the DeFries selection does not refute
close linkage as an explanation for the correlated response.
DeFries’ experiments address some doubts about the
existence of a common set of genes acting on both open field
activity and defecation, but they do not deal with concerns
about what the open-field tests measure. For instance, could
it be that the negative correlation between defecation and
activity reflects a gut disorder, which both slows the rodents
and increases defecation? Another selection experiment
adds weight to the view that open-field measures do reflect
the presence of emotionality.
In the 1960s, a bidirectional selection of rat performance
in a shuttle box activity was carried out [1]. A shuttle box
has two compartments, each of which is equipped with a
light (the conditioned stimulus) and a floor that can be used
to deliver mildly aversive electric shocks. Animals are
trained to associate the light with the delivery of shock, and
also that to avoid the shock they must not freeze (a passive
avoidance response) but rather actively avoid shock by
moving into the adjoining compartment, where they will
again be presented with the conditional stimulus. Active
avoidance now consists of returning to the first compart-
ment, in which they previously had an aversive experience.
Note that the apparatus holds no safe areas. Animals that
learn the task, shuttle back and forth between the two
compartments in response to the light signal; they have to
learn both to avoid the shock and to suppress their fear of
returning to the places where they received a shock. Thus
the task requires more of the animal than learning one-way
active avoidance or passive avoidance.
Bignami established two strains of rat, known as Roman
high-avoidance (RHA) and Roman low-avoidance (RLA)
by selecting rats for their speed of acquisition in the shuttle
box: after only five generations of selection the high
avoidance strain were consistently better than the low
avoidance strain at escaping when shown the light [1]. If the
selection experiment had produced animals that differed in
emotionality, RHA rats should show high activity and low
defecation in the open field [5]. Several studies have shown
that indeed novelty induced defecation is higher in RLA
than RHA rats [16,24]. Conversely rodents that differ on the
open field measures of emotionality should differ in
the predicted fashion in tests of active and passive
avoidance. It has been shown that Broadhurst’s Maudsley
reactive rats are poorer at shuttle-box avoidance than their
non-reactive counterpart [27]. Additional evidence that the
Roman rats have been selected for emotional differences has
accumulated over the years, using a variety of behavioural
and physiological measures, reviewed in Ref. [17].
Thus, artificial selection experiments indicate that
genetic variants act pleiotropically on measures of emotion-
ality. However, a final demonstration of the validity of this
observation requires identification of the individual genes
and an assessment of their effect on each component
phenotype. I will next review the progress that has been
made towards this goal, and then return to the question of
the relationship between animal and human phenotypes.
2. Identifying the genetic variants that contribute
to rodent emotionality
Finding genes involved in complex traits like emotion-
ality and neuroticism has proved to be a far more difficult
enterprise than expected. Ironically, one of the best reasons
for arguing that the problem was tractable and worth
undertaking came out of rodent studies carried out over
10 years ago when it was demonstrated that a relatively
small number of loci contributed to most of the phenotypic
variance of hypertension in rats [35] and diabetes suscep-
tibility in the mouse [56]. Unfortunately, for a number of
reasons I will describe below, early studies gave a
deceptively simple picture of the genetic architecture of
complex traits.
The basic experimental design for dissecting the genetic
architecture of complex traits was, and largely remains, the
analysis of an experimental cross between two inbred lines.
There are two commonly used variants: either the offspring
of the cross (the F1 generation) are mated with each other to
produce an F2 intercross, or the offspring are backcrossed to
either of the parental strains. Molecular markers are then
used to determine which chromosomal segments segregate
with the trait. Inbred strain crosses contain just two alleles
(one from each F0 founder), so the simplest way of seeing if
any particular region in the genome contains a genetic
variant contributing to phenotypic variation, is to determine
whether there is a significant difference between the
phenotypic distributions associated with each genotype.
Analysis of variance does the job well, though there is now a
large literature on how to squeeze most information out of
the experiment [15]. Genetic loci that influence variation in
quantitative phenotypes (such as anxiety or emotionality)
are termed quantitative trait loci or QTL.
For our purposes, investigating the genetic basis of
emotionality, the critical point we wish to establish is
whether genetic action is indeed pleiotropic. It is critical
because, as I have argued above, concerted genetic action is
taken as evidence for the existence of common brain
J. Flint / Neuroscience and Biobehavioral Reviews 28 (2004) 307–316 309
processes underlying different behavioural measures of
emotionality. Here the difficulty is two-fold, partly with the
phenotype and partly with the genetics.
The phenotypic problem is that the measures we use are
the outcome of many processes, not all of which are relevant
to our aims. For example, we can use changes of activity in a
novel, mildly aversive environment (the open-field arena) as
a measure of emotionality; we can also use the number of
entries into the open arms of the elevated plus maze (another
aversive environment). Unfortunately both measures rely on
the animal’s activity level: a more active animal will
therefore appear as less emotional in both the open-field test
and the elevated plus maze.
The genetic problem, to determine that a single genetic
variant operates on multiple phenotypes, arises from the
design of the inbred strain cross. Because observable
recombinations occur only in one generation, each chromo-
some has a limited number of recombinants (on average,
between one and two). In an experiment with a few hundred
animals searching a relatively small genetic effect (for
instance a locus that accounts for about 10% of the
variance), we obtain limited resolving power. Darvasi
provides an empirical formula for calculating the 95%
confidence interval of a QTL [9,10], the use of which allows
us to estimate that for genetic effects explaining less than
10% of the variance we will need well over 1000 animals to
reduce the interval to less than 10 centimorgans (or 20
megbases of DNA in the mouse). A region this large will
still contain hundreds, perhaps thousands of genes (and very
few studies have used as many as 1000 animals [25]).
Therefore just because two genetic effects map to the same
location in an inbred strain cross is no evidence that the
same genes influence variation in two phenotypes.
3. Experimental evidence for pleiotropic action
We can make some progress with the phenotypic
problem by choosing tests that measure the same underlying
trait from different perspectives. If we then map multiple
tests, we can expect QTL that influence more than one
measure (that act pleiotropically) in a way consistent with
our predictions, to be the loci that influence emotionality.
We would also expect such QTL to have no influence on
measures unrelated to the phenotype. Of course this
approach does not deal with the genetic difficulty, but I
will return to that later.
A simple solution is to control for confounds used in the
tests of emotionality. In an experiment using the DeFries
mouse strains, we asked whether the genetic effects are
specific to the aversive situation common to the ethological
tests of anxiety. We mapped behavioural variation in five
tests of emotionality: the open-field arena, the elevated plus
maze [48,49], square maze [53], the light dark box [8] and
the mirror chamber [57]. We included two controls. In one
case we measured activity in a non-threatening environment
(the home cage). In the second, to determine whether the
genetic effects were common to all aversive stimuli, we
included a tail suspension test in our battery. The tail
suspension induces stress in rodents, as assessed by tail
suspension induced immobility [58]. We also asked whether
the same genes influence behaviour after habituation to the
aversive situations (as measured by re-testing the animals).
Re-testing is expected to reduce the anxiogenic potential of
the apparatus, leading to a relative reduction in the effect of
the QTLs influencing anxiety [14,34,46]. Consequently, in
two tests, the open-field arena and light dark box, animals
were tested twice, on separate days.
Fig. 1 summarizes the mapping results. The QTL fall into
three groups. On chromosomes 1, 7, 12, 14, 15 and 18 are
QTL that influence only one or more of the five tests of
emotionality. Chromosomes 3, 5, 11 and 19 harbour loci
that influence the control measures. Finally there are three
loci, on chromosomes 4, and X, where QTL influence both
types of measure. Similar results emerged from the analysis
of repeated measures. We found a dramatic fall in the
importance of the chromosome 15 effect on emotionality
tests from day 1 to day 2. For example, the negative log
P values associated with the transitions in the light-dark
box, drop from 14, on day 1, to a non-significant value of
0.75 on day 2. These results demonstrate that it is possible to
separate out confounding effects genetically.
We were able to push the argument further still, using
multivariate analyses carried out on phenotypes collected
from a cross of the Roman High and Low Avoidance rats
[23]. Recall that these animals were derived by selection for
extremes of shuttle-box avoidance behaviour in the shuttle
box. As with the DeFries mice, we mapped a series of
measures of emotionality. In addition to the shuttle box, we
used the open-field arena, the elevated plus maze, an
acoustic startle paradigm, and fear conditioning to both cue
and context. The latter two tests measure variation in
passive avoidance. The rats were trained to associate a light
with a shock and we then measured freezing to the
conditioned stimulus and to the context in which the
association was learnt. As before, we included a measure of
home cage activity as a control for the activity components
of the emotionality tests.
We mapped the phenotypes jointly, and identified eight
QTL. We next determined the relative contribution of each
trait to the eight QTL. To estimate the significance of a
locus’s contribution for the detection of a QTL and to test
the significance of the QTL effect for each of the traits, we
used methods that combine multivariate analysis with
permutation techniques [39]. The test works by randomizing
the individual values of each phenotype, relative to the other
traits and genotypes and then re-analyzing the permuted
data set. When this is done many times, we can create a
distribution of results, and ask how often the experimental
result occurs by chance. Table 1 shows the P-values for
these analyses for all eight chromosomes bearing a QTL.
Two columns are shown for each chromosome: the first
J. Flint / Neuroscience and Biobehavioral Reviews 28 (2004) 307–316310
displays the results when all traits are included in the
analysis and the second when all but traits making a
significant contribution have been removed.
The multivariate analysis indicates that only three loci
(on chromosomes 5, 10 and 15) have broad effects across
different test measures. At other locations significant
contributions to the LOD scores derive from a single
(chromosome 19) or two phenotypes (chromosomes 1, 3
and 6). Both defecation and activity in a novel environment
contribute to the LOD score on the X chromosome, but there
is no significant contribution from the other measures of
fear. Of the three potential candidates as loci influencing
fear, that on chromosome 15 has the most circumscribed
effect. The evidence is strongest for an effect on grooming
and there is no significant contribution from shuttle box, fear
conditioning or elevated plus maze to the LOD score.
Having identified three candidate QTL for pleiotropic
action, we applied a multivariate regression method
developed by Knott and Haley [38]. We chose those traits
known to make a significant contribution to the LOD score
for loci on chromosomes 5, 10 and 15 (based on the previous
analyses), and compared the hypothesis that one QTL
influences each trait with the hypothesis that one QTL
influences all traits. The null hypothesis is a single
pleiotropic QTL. Again, we used replicate simulations to
obtain the distribution of the statistic and thereby arrive at a
significance threshold. We were then able to reject the
hypothesis of a single pleiotropic QTL on chromosome 10
at the 5% threshold [23].
4. High resolution mapping
Statistical analyses, while powerful, do not deal with the
problem of inadequate mapping resolution, which we need
to solve in order to identify the genes influencing variation
Fig. 1. Quantitative trait loci that influence behaviour in the DeFries strains of mice. Each panel. Chromosomes which harbour significant genetic effects are
shown as numbers on each panel. The top panel shows a genome scan for two measures that control for putative confounds (home cage activity and tail hang);
the lower three panels show genome scans for five measures of emotionality.
J. Flint / Neuroscience and Biobehavioral Reviews 28 (2004) 307–316 311
in the trait. The approach we have adopted is mapping loci
in genetically heterogeneous stocks (HS) of mice. Because
mapping is carried out many generations after the stock was
founded from a set of inbred progenitor strains, the method
has the potential to map a QTL to a region containing a
small number of genes. There are eight progenitor strains in
the HS animals we use, so QTL analysis is not straightfor-
ward: alleles descended from different progenitor strains are
often identical. We developed a dynamic programming
method to estimate the probability that an allele descended
from each progenitor and showed, using OFA as a
phenotype, that the method could deliver high resolution
mapping [44,54].
We used high resolution mapping to ask whether a locus
on chromosome one contains a pleiotropic QTL. A number
of mapping studies, including our own, have identified a
locus on the distal region of mouse chromosome one that
influences emotionality. Both ethological measures (beha-
viour in the open-field arena and elevated plus maze, light
dark box) and conditioned freezing have been mapped. Each
study provides convincing evidence for the presence of a
genetic effect reporting P-values that meet stringent
thresholds of significance (see Table 2).
In order to determine whether there is a pleiotropic locus
on chromosome one, we mapped fear conditioning in the HS
and compared the results with an analysis of open-field
behaviours [55]. Fig. 2 shows the results: two peaks indicate
the locations of QTL influencing OFA, surrounding one
peak over the conditioned fear QTL. We were able to
determine that there was no overlap in the location estimates
using a permutation test.
High resolution mapping data therefore indicate that
QTL influencing variation in OFA and conditioned fear can
be separated, at least in the 20 cm region we examined on
chromosome 1. The results do not exclude the existence
elsewhere of pleiotropic loci, influencing both open-field
behaviours and fear conditioning; nor can we exclude a
small pleiotropic effect attributable to the loci we have
characterised. Nevertheless, the data indicate that the
genetic basis of emotionality is more complex than we
first appreciated. It should be pointed out that high
resolution studies of other phenotypes have also often
found that a single locus in the inbred strain cross consisted
of a number of smaller effect loci [2,41,50].
Unfortunately the HS mice do not provide sufficient
mapping resolution to identify the actual genes involved.
You can see in Fig. 2, that the QTL peaks cover a couple of
centimorgans, still containing about 4 megabases of DNA.
In some cases, there may be up to 100 genes in such an
interval. Methodologies to deal with the remaining
problems of gene identification are still under development
so this part of the story remains incomplete. However, if the
data from the current HS work is an indication of what we
will find, then we may expect an even more complex picture
to emerge as we dissect the genetic architecture at the
highest level of resolution.
5. Mouse and human comparisons
The last part of the story is also incomplete, but provides
a tantalizing glimpse of a possible coincidence between the
genetic basis of rodent emotionality and human neuroticism.
Over the last few years we have been attempting to map the
genetic basis of neuroticism in humans and we are now in
the position where we can begin to compare human and
mouse data. There are two problems with carrying out the
human studies: the first is to find a strategy to detect loci; the
second is to obtain high-resolution mapping. We have gone
Table 1
Permutation tests of significance of the contribution to a multitrait LOD score of individual measures
CHR 1 CHR3 CHR 5 CHR 6 CHR 10 CHR 15 CHR 19 CHR X
Shuttle box
Avoidances 0.662 0.888 0.021 0.000 0.561 0.000 0.000 0.747 0.765 0.621
Fear conditioning
Cue 0.800 0.731 0.121 0.000 0.732 0.471 0.562 0.881 0.881
Context 0.682 0.661 0.228 0.002 0.790 0.555 0.000 0.444 0.831 0.923
Elevated plus maze
Pct open arm time 0.552 0.551 0.203 0.000 0.832 0.647 0.447 0.518 0.921
Closed arm entries 0.202 0.981 0.711 0.411 0.522 0.161 0.759 0.929
Open field
Activity in periphery 0.842 0.222 0.000 0.129 0.000 0.953 0.059 0.645 0.800 0.038 0.000
Activity in centre 0.732 0.718 0.841 0.289 0.133 0.060 0.021 0.561 0.677
Acoustic startle response 0.478 0.732 0.879 0.154 0.027 0.000 0.412 0.011 0.627 0.621
Spontaneous activity 0.017 0.030 0.863 0.861 0.014 0.000 0.237 0.761 0.929 0.510 0.019
Grooming 0.427 0.232 0.093 0.000 0.143 0.521 0.000 0.000 0.033 0.000 1.000
Rearing 0.000 0.000 0.920 0.678 0.920 0.242 0.012 0.145 0.666 0.321
Defecation 0.118 0.000 0.000 0.456 0.000 0.000 0.027 0.000 0.432 0.479 0.000 0.000
Two columns are shown for each QTL. In the first, the probabilities of the contribution when all measures are included in the analysis are shown. The
second displays the probabilities after all non-significant contributors have been excluded in stepwise fashion, as explained in the text. The results are based on
10,000 permutations.
J. Flint / Neuroscience and Biobehavioral Reviews 28 (2004) 307–316312
some way now towards dealing with the first problem, but
we have yet no results that address the second issue.
Numerous studies have examined the genetic basis of
neuroticism in humans and have arrived at similar estimates
of the trait’s genetic basis: additive genetic variance is
estimated to be between 27 and 31% and non-additive
effects between 14 and 17% [20,40,43,52]. The heritability
of neuroticism is thus comparable to other complex traits
that have been subject to genome scans to identify
susceptibility loci.
One of the few clear messages to have emerged from
genetic studies of complex traits in humans is that the
majority of genetic loci contribute to only a very small
proportion of the phenotypic variance, perhaps on the
order of 5–10% of the total. In order to stand a good
chance of identifying loci that influence neuroticism in
humans the study design has to be able to detect loci of
small effect. In effect this means very large cohorts are
needed to identify susceptibility loci, which renders such
studies suitable for various phenotypic selection schemes
that reduce the genotyping burden while maintaining
statistical power [19,51].
Linkage analysis of sibling pairs uses deviation from an
expected value of allele sharing at a given point in the
genome as evidence for the presence of a QTL. At any
marker, a sibling pair will share one, two or no alleles. Once
we have this information across the genome (which means
genotyping with hundreds of markers) we can ask if there
are any regions where siblings who are phenotypically
similar (for example where both have high N scores) are
also genetically similar (that is, share two alleles). We
would also expect that phenotypically dissimilar sibling
pairs would be genetically dissimilar (share no alleles) at
these same locations. The expectation for those with one
allele in common depends on whether the genetic effect is
recessive or dominant. We can combine these expectations
into a linear model and then test for the presence of a genetic
effect, as is done in a regression analysis [33]. It turns out
that the amount of genotyping required to detect a QTL can
be considerably reduced by selecting the most genetically
Table 2
Genetic loci on mouse chromosome one that influence fear-related phenotypes
Phenotype Strains Method Number Pos. Log P %var Reference
EPM % open arm entries DeFries high and low F2 1636 80 15.4 5.6 [59]
LD latency DeFries high and low F2 1636 80 6.8 2.5 [59]
LD transitions (3 days) A/J C57BL/6 F2 518 67 2.5 2.5 [26]
OF activity (5 min) DeFries high and low F2 1636 74 27.4 9.9 [59]
OF activity (5 min) A/J C57BL/6 F2 518 100 7.1 6.3 [26]
OF Activity (last 5 min of the second 15 min trial) A/J C57BL/6 F2 518 69 3.1 4 [26]
OF centre time (day 1 first 5 min) A/J C57BL/6 F2 518 73 7.7 6.8 [26]
OF defecation DeFries high and low F2 1636 74 14.5 5.2 [59]
OF vertical movement T1 A/J C57BL/6 F2 518 79 4.5 5.9 [26]
OF vertical movement T2 A/J C57BL/6 F2 518 102 5.8 5.4 [26]
Fear conditioning: altered context C3H/HeJ C57BL/6 BC 473 70 3.4 [6]
Fear conditioning: altered context C3H/HeJ C57BL/6 BC 473 30 2.4 [6]
Fear conditioning: contextual C3H/HeJ C57BL/6 BC 473 30 5.1 6.2 [6]
Fear conditioning: contextual C3H/HeJ C57BL/6 BC 473 75 4.3 [6]
Fear conditioning: contextual C57BL/6 DBA/2 F2 479 75 3.8 4.8 [61]
Fear conditioning: cued C3H/HeJ C57BL/6 BC 473 75 2 [6]
Fear conditioning: cued C57BL/6 DBA/2 RI 25 .1 [47]
Fear conditioning: cued C57BL/6 DBA/2 F2 479 78 5.6 6.3 [61]
The columns contain the following information. Phenotype: the test used to measure a fear-related behaviour. Abbreviations are: OF, open-field arena;
EPM, elevated plus maze; LD, light dark box. Strains: the inbred strains used in the mapping experiment. DeFries high and low are inbred strains derived from
a cross between C57BL/6 and BALB/cJ (see Turri, et al., 2001b). Method: F2 for F2 intercross, BC for backcross and RI for recombinant inbred strain analysis.
Number: the number of animals used in the study, or the number of strains if the method is RI. Pos: gives the position in centimorgans for the locus on
chromosome 1. Log P: negative logarithm of the P-value for the analysis. In F2 studies this is identical to a LOD score. %var: percentage of the phenotypic
variances explained by the locus.
Fig. 2. High resolution mapping of open-field activity and contextual fear in
genetically heterogeneous mice. Two phenotypes are shown: open field
activity (OFA) and contextual fear (context).
J. Flint / Neuroscience and Biobehavioral Reviews 28 (2004) 307–316 313
similar and dissimilar sibling pairs (those sharing either no
alleles or two alleles) [19,51]. The required pairs are
concordant for either extremely high or low scores or
discordant, with one member of the pair having an
extremely low and the other an extremely high score.
We have recently conducted a population-based study of
personality in Southwest England, in which over 88,000
individuals completed the EPQ. We identified more than
34,000 sibling pairs from whom we selected extreme pairs
for genotyping. To find evidence for linkage we used a
regression-based approach [60] and Fig. 3 shows the
regression results evaluated at every 5 cm across the
genome. The statistic shown is the negative logarithm
(base 10) of the P-value obtained from the regression
analysis. The figure shows loci on chromosomes 1, 4, 7 and
13 that exceed a 5% genome-wide significance threshold of
3.8, and one locus on chromosome 12 that exceeds a 1%
threshold of 4.7.
The locus on chromosome 1 is intriguing because it may
be syntenic with loci discovered in animal studies. The locus
in the middle of rat chromosome 5 [23], discussed above,
that influences behaviour in a number of tests of rodent
emotionality, is syntenic with chromosome 1p in humans,
but the low resolution of both human and rat mapping
studies makes it impossible to say whether the same genes
influence the trait in both species.
Mapping in the HS mice detected a number of loci that
influence emotionality in a region syntenic with human
chromosome 1q [44,54,55]. Association testing, using
candidate genes discovered in the 0.8 cm region containing
the mouse locus, will be able to determine whether the same
genes influence neuroticism in the human subjects and
variation in emotionality in the mouse. The congruence of
human and animal studies may at last provide a way of
determining whether there are a common set of genes that
contribute to susceptibility to neuroticism in humans and
emotionality in rodents.
However, genetic association studies of human
behaviour have often provided contradictory results.
There have been a considerable number of association
studies of personality, since the report in 1996 of an
association between variation in the serotonin transporter
gene (5HTT) [42]. When we analysed all publications
(almost 80) in a meta-analysis of genetic association
studies of personality reported, we were unable to find
convincing evidence that any of the loci examined had a
significant effect [45]. This result is perhaps not
surprising, given the small number of loci examined.
One of the main lessons is that the genetic effects on
individual variation in behaviour are small and that, in
consequence, sample sizes of many thousands of
individuals are needed [62]. Studies of human personality
published to date are not adequately powered to detect
small genetic effects unambiguously, but large studies,
currently in progress, are likely to do so in the near
future.
Finally, we should stress that even when we have good
evidence that a molecular variant influences a behaviour,
interpreting how that variant has its effect will be
extremely difficult, requiring a greater approximation
between systems and molecular neuroscience than has so
far occurred. Pleiotropic action, which I have discussed at
length, could arise at a number of levels within the brain,
some of which will be much harder to work out than
others. For instance, following Gray’s arguments we would
expect that if a genetic effect operates on the anatomical
basis of emotionality then it would involve the hippocam-
pal formation, septal area and related cortico-limbic
structures. The large numbers of anatomical structures
involved and the complexity of their interaction could
make it impossible to identify how the genetic effect
operates. By contrast, if the genetic effect acts directly, via
the GABAergic system, to modulate anxiety, it will be
easier to detect the site of genetic action and thereby work
out the responsible physiology. Either way, advances in
genetic analysis and molecular neuroscience are soon
likely to provide new insights into the biological basis
of personality.
Fig. 3. A genome scan for neuroticism in human sibling pairs. The negative logarithm of the P-value is plotted on the vertical axis and the distance along the
genome (in centimorgans) on the horizontal axis. Vertical lines demarcate the boundaries of chromosomes, which are numbered along the top of the panel. The
5% genome-wide significance level is 3.8.
J. Flint / Neuroscience and Biobehavioral Reviews 28 (2004) 307–316314
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