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The genetics of fish behavior
Alison M. Bell
University of Illinois, Urbana-Champaign
alisonmb@life.uiuc.edu
Introduction
This is an exciting time to study the genetics of behavior (Boake et al., 2002;
Fitzpatrick et al., 2005; Robinson et al., 2005). Until recently, the powerful tools needed
to understand genetic influences on behavior were available for just a few model
organisms that were not widely studied by behavioral ecologists (Fitzpatrick et al., 2005;
Robinson et al., 2005). Moreover, the behaviors that could be studied were often simple
and were measured in laboratory environments rather than in the field. From a genetic
perspective, behavior was considered to be too subjective to measure, too susceptible to
environmental influences, too plastic and not repeatable. Fear of accusations of genetic
determinism and carryovers from the troubling political implications of eugenics and
sociobiology might also have contributed to the underrepresentation of behavior among
traits studied from a genetic perspective (Lewontin et al., 1984).
However, behavioral traits are not alone in their sensitivity to environmental
influence: nonbehavioral traits such as morphological traits, which we know are
amenable to genetic dissection, can be plastic and responsive to the environment as well
(e.g. trophic morphology: (Adams et al., 2003; Bell, 2005; Chapman et al., 2000), body
size: (Losos et al., 2000; Wikelski and Thom, 2000)). At the same time, there is growing
appreciation that behavior might not be as plastic as we had assumed (Sih et al., 2004;
Sih et al., 2004; West-Eberhard, 2003).
Moreover, we know that behavioral traits respond to both natural and sexual
selection. Behaviors can be heritable, with heritability estimates for behaviors broadly
comparable to other kinds of traits (reviewed in (Meffert et al., 2002; Roff and Mousseau,
1987; Stirling et al., 2002)), and they show adaptive, heritable geographic variation.
Fishes show particularly good examples of geographic variation in behavior, especially
guppies (Poecilia reticulata, Endler, 1995), sticklebacks (Gasterosteus aculeatus, Bell
and Foster, 1994), and Arctic charr (Salvelinus alpinus, summarized in (Foster and
Endler, 1999)). Finally, increasing evidence that behavior can be studied from a
phylogenetic perspective (Brooks and McLennan, 1991), and an appreciation of
behavior’s role in evolutionary processes such as speciation and reproductive isolation
(Boughman, 2002) have all contributed to interest in bridging the gap between genetics
and behavior.
This chapter is written by and for behavioral ecologists working primarily from
the behavior ‘down’ (or ‘forward’, if you’re a geneticist) to genes. That is, I start with
ecologically-relevant natural variation in behavior and discuss approaches for studying
the genes underlying that variation. This strategy is alternative to approaches which start
with artificially-induced mutations and ask their consequences for behavior. Essentially, I
will try to make a case for why scientists interested in behavior might want to include a
genetic component in their research program and to briefly describe some of the tools
available to them.
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As the most diverse vertebrate taxon, fishes provide fascinating ecologically
relevant behavioral variation to be analyzed from a genetic perspective. For example,
fishes exhibit a greater diversity of mating systems than any other vertebrate taxon
(Helfman et al., 2000). Fish are especially suited for genetic studies because many
species have external fertilization, which makes it relatively easy to create specific
crosses. An additional advantage of studying fishes is that several fish species from
diverse groups have had their genome sequenced. At the moment, assembled genomes of
the following fish species are available: Fugu (Fugu rubripes, 393MB), zebrafish (Danio
rerio 1,688MB), tetraodon (Tetraodon nigroviridis, 402MB) and sticklebacks
(Gasterosteus aculeatus, 675MB).
Why might a behavioral ecologist study the genetics of behavior?
In a classic paper, Alan Grafen proposed that with a few simplifying assumptions,
behavioral ecology might be able to ignore genetics (Grafen, 1984). He argued that the
aim of behavioral ecology is to uncover the selective forces that shape characters, and
that our method will work almost regardless of which genetic system underlies the
character. He proposed ‘the phenotypic gambit’, which was ‘to examine the evolutionary
basis of a character as if the very simplest genetic system controlled it’ (Grafen, 1984).
Here, I take seriously Grafen’s challenge to find cases where an understanding of
the genetic mechanisms makes a difference. I highlight cases where different genetic
mechanisms occur and change the evolutionary dynamics. I make the case that we often
need to understand at least a little about the genetic mechanisms underlying behaviors if
we are really interested in predicting the response to selection, inferring evolutionary
history and understanding how animals cope with a nonequilibrial world.
However, for some questions, the phenotypic gambit might be justified. That is, if
our interest is purely in the current utility of a trait rather than the evolutionary history or
fate of it, the genetic mechanisms underlying the trait might not be informative. For
example, if we want to know whether females prefer males with particular ornaments,
then we do not need to know the genetics underlying female preference or the male
ornament.
But there are several reasons why a behavioral ecologist might be interested in the
genetics of behavior. We cannot understand the selective forces that have produced
adaptive variation without studying genetics in some form. In fact, many of our models
either implicitly or explicitly include a genetic component to the behavior of interest
(e.g., Trivers, 1971). Optimality models, for example, the simplest models in behavioral
ecology, assume the presence of heritable variation for selection to act upon (Orzack and
Sober, 1994). Therefore, if we want to know if the behavior has responded to natural or
sexual selection in the past, and if it has the potential to evolve, we need to exclude the
possibility that variation is not entirely environmentally-induced; we need to know if the
trait is heritable.
If we are interested in obtaining more detailed information about the rate and
direction of past and future evolution, then knowing more about the genetic architecture
underlying a trait becomes important. In what follows, I describe what we can learn about
the past and future evolution of a particular behavior by elucidating its genetic
architecture, including the number of loci, the number of alleles at a given locus, the
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distribution of effect sizes of the genes, whether the trait is genetically correlated with
other traits, the relationship between loci (epistasis) and the relationship between alleles
at a particular locus (dominance). Understanding genetic architecture is important for
behavioral ecologists interested in tracing the evolutionary history of a particular
behavior ( e.g., Ruber et al., 2004), as well as for those trying to predict how animals will
respond to a changing environment, including anthropogenic-induced change (Schlaepfer
et al., 2002).
First, the number of genes affecting a behavior and the distribution of their effect
sizes determines how the behavior can respond to future environmental change. For
example, if a single gene of major effect is responsible for variation in the behavior, the
behavior can quickly respond to selection.
While traits that are affected by just a few genes might respond faster to selection,
it has also been argued that polygenic traits might be more ‘evolvable’ because many
genes of small effect effectively increases the opportunities for a beneficial mutation to
arise. This additional pool can act as a reservoir for genetic variation, possibly allowing a
population to respond to novel selection pressures (Houle et al., 1996).
Second, we frequently want to know whether there is additive genetic variation
(Va, reflected in the number of alleles at a given locus) for a trait because this can tell us
about the past selective regime and the potential for future evolution. Theoretical models
predict that natural selection will erode additive genetic variation, therefore small values
of Va (or CVa , Houle 1992)) can indicate that the trait has been subject to selection in the
past. This reasoning should be treated with some caution, however, because the data
actually suggest that there is still substantial genetic variation for fitness-related traits
(Merila and Sheldon 1999), which presumably have been subject to strong directional
selection in the past. A possible resolution to this paradox is that fitness-related traits are
probably affected by many loci, thereby providing a bigger target for mutation to
maintain genetic variation (Houle et al., 1996; Merila and Sheldon 1999). As far as the
fate of traits under selection goes, a trait will not respond to natural selection unless there
is additive genetic variation for it, so knowing Va can tell us whether the trait can respond
to selection in the future. For example, global warming is causing disruption to animals
that rely on seasonal cues for timing reproduction and migration (Both and Visser, 2001;
Visser and al, 1998). If populations do not harbor sufficient genetic variation for resetting the timing of these critical behavioral decisions, then the viability of these
populations might be threatened.
A third important component of the genetic architecture is how traits are
genetically related to each other. A significant genetic correlation between two traits
might indicate that the traits have been subject to correlational selection in the past.
Correlational selection occurs when a trait’s fitness effect depends on its interaction with
another trait (Lande and Arnold, 1983). Alternatively, a genetic correlation might be the
result of a fundamental constraint that links two traits together, i.e. pleiotropy. Genetic
correlations can also tell us about future responses to selection of a behavior of interest
(for an excellent review, see Arnold, 1994)). The response to selection on a particular
trait depends on more than just the amount of genetic variation present for it; it also
depends on the sign and magnitude of genetic covariance between traits as well as on the
force of selection acting on correlated traits (Lande and Arnold, 1983). In some cases, the
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response to selection on a given trait can actually be in the opposite direction that we
would predict if we did not consider genetic correlations (Grant and Grant, 1995).
There is another reason why researchers studying animal behavior might be
interested in genetic correlations. That is because genetic correlations hold a central place
in sexual selection theory, arguably the most popular and controversial topic in animal
behavior. Different theories have been proposed to explain the evolution of exaggerated
male ornaments used to attract mates. Fisherian models of sexual selection explain
‘runaway’ evolution of male ornaments by a genetic correlation between ornament and
female preference for that ornament. In other words, brothers which have the exaggerated
ornament have sisters with a strong preference for that ornament. It has proven difficult
to empirically test this model (Qvarnström et al., 2006), but some of the best evidence
showing support for the Fisherian process comes from studies of fishes. For example,
female guppies prefer to mate with males with particular coloration patterns (especially
increased orange area), and evidence from both population comparisons (Houde and
Endler, 1990) and selection experiments (Houde, 1994; Brooks and Couldridge, 1999)
support the hypothesis that there is a significant genetic correlation between female
preference and different male ornaments in this species. This correlation has also been
found in threespined sticklebacks (Bakker, 1993).
Fourth, nonadditive effects such dominance and epistasis, which are common for
behavioral traits (Meffert et al., 2002), also affect our ability to infer past selective
regimes and to predict evolutionary potential. In a recent review, Merila and Sheldon
(1999) reported that nonadditive genetic effects (and environmental effects) are the most
important genetic determinants of fitness in nature. Dominance refers to the relationship
between alleles at a given locus; a dominance relationship implies that one allele is
dominant to its partner. At the opposite end of the spectrum, if two alleles contribute
equally, then the relationship between alleles is said to be additive.
Directional selection can favor the evolution of directional dominance for the
allele that corresponds to the favored phenotype (Broadhurst and Jinks, 1979). Therefore,
a dominance relationship between alleles at a given locus might indicate that the gene has
been subject to directional selection in the past. Similarly, because a dominant allele will
reach fixation faster than a recessive allele, the dominance relationship among alleles
influences how quickly the trait can evolve (Falconer, 1989).
Epistasis refers to interactions between loci affecting a trait. Selection can favor
particular interactions between loci, resulting in coadapted epistatic gene complexes
within populations. Epistasis is important for evolution because outbreeding depression
can occur when populations that differ in their coadapted gene complexes are crossed.
The breakdown of coadapted gene complexes can result in reproductive isolation
between populations with different genetic backgrounds (Meffert et al., 2002). Therefore,
epistasis could be a mechanism for maintaining postzygotic reproductive isolation
between populations according to the ‘Dobzhansky-Muller model’ (Dobzhansky, 1937).
For some people studying the genetics of complex traits, the real trophy is to find
genes. If genes associated with adaptation can be identified, that can allow us to trace the
evolutionary history of the gene through time. When did the gene originate? What are the
functions of the gene in phylogenetically-distant relatives? Are similar behaviors related
to the same gene in distant relatives? Is the same gene used over and over again to
accomplish similar functions? Does this occur via de novo mutation in particular ‘hot
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spots’ of the genome, or is there standing genetic variation for it? We are beginning to get
a glimmer of answers to these perennial questions for nonbehavioral traits (Colosimo et
al., 2005; Hoekstra et al., 2004) and it is only a question of time until we can answer
those kinds of questions about behavioral traits as well (Fitzpatrick and Sokolowski,
2004).
Finally, we might want to find genes so that we can manipulate them. This is
desirable, of course, for clinical applications, including psychopharmacology (Stahl and
Muntner, 2000) but it could also be a powerful experimental tool (a la ‘phenotypic
engineeering’ (Ketterson et al., 1996; Sinervo and Huey, 1990)) for the behavioral
ecologists’ toolbox.
Approaches for studying the genetics of behavior and who can use them
In this section, I describe in order of increasing complexity how a researcher
might approach the genetics of a behavior of interest. For each method, I describe its aim,
the general approach, its strength and weaknesses and the kinds of traits and animals for
which it is most suited.
‘Low tech’ approaches
The first step in approaching the genetics of a behavioral trait is to determine if
the behavior is repeatable, which involves measuring the same behavior on the same
individual several times (Lessels and Boag, 1987). Repeatability is the proportion of
phenotypic variance attributable to the individual, which could be caused by additive
genetic variance or environmental variance with long-lasting effects.
Evidence that a behavior is repeatable indicates that it might have a heritable
component and is amenable for further genetic dissection. The repeatability of a trait sets
an ‘upper bound’ to heritability (Boake, 1994). However, it is worth considering that
nongenetic effects can also produce stable behavior; repeated reinforcement including
learning can also lead to stability (Stamps, 2003). Another indication that there might be
a heritable component to the behavior is if it is consistent across contexts; in this case, the
stability occurs via a correlation between individual behaviors in different contexts, or a
behavioral syndrome, which are discussed further, below (Sih et al., 2004; Sih et al.,
2004).
Another relatively low-tech approach to the genetics of a behavior is to ask if it
differs across populations that occur in different kinds of environments (Foster and
Endler, 1999). If so, then population differences in the behavior could reflect an evolved
response to differing selective pressures. Alternatively, the differences could reflect
environmentally-induced changes within individuals. A way to disentangle these two
hypotheses is to rear animals from the different populations in a ‘common garden’, or in
the same environment. If population differences in the behavior are preserved under
common environmental conditions, then the difference between populations might be
genetic in origin. A common garden experiment represents the first step toward
approaching the genetics of complex traits such as behavior, but there are at least two
important factors to consider before concluding that population differences are genetic in
origin. The first is that F1s reared in a common garden could still experience parental
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effects from their wild-caught parents. To control for such environmental effects, it is
preferable to rear the offspring of lab-born individuals and to compare the phenotype of
the F2s. Second, GxE interactions should also be considered when populations do not
differ in a common environment. For example, individuals from both populations might
converge in their response to the common garden environment, but the extent of response
might have a genetic component. A reciprocal transplant experiment might reveal genetic
differences that are not apparent in a common environment.
An advantage to using population differences as a clue to which behaviors are
likely to be heritable is that traits that differ across populations in different environments
are probably linked to fitness. Several common garden studies on fishes have shown that
population differences in behavior are often preserved in a common garden (Grether et
al., 2001; Lahti et al., 2001; Pakkasmaa and Piironen, 2001; Palm and Ryman, 1999). For
example, Lahti et al. 2001 found evidence for a genetic basis to aggressive behavior in
brown trout by comparing different types of populations. The authors reared fish from 10
populations in a common environment. The populations can be grouped into the
following types: resident (nonmigratory), sea-run (migratory) or lake-run (migratory), so
there were replicate populations for each type. Contrary to the expectation that resident
trout are more aggressive than migratory forms, they found that sea-run populations were
consistently more overtly aggressive toward opponents than the other types of
populations.
There is a rich literature comparing domesticated and wild populations of
salmonids, which has revealed genetic influences on several types of behavior (reviewed
in (Huntingford, 2004)). When reared in a common garden, hatchery fish show reduced
antipredator responses (Alvarez and Nicieza, 2003; Malavasi et al., 2004; Petersson and
Järvi, 2006), are more bold toward a novel object (Sundström et al., 2004) and are
frequently more aggressive (Lepage et al., 2000). However, wild Atlantic salmon (Salmo
salar) became dominant over hatchery salmon if they were given an opportunity to
establish residence (Metcalfe et al., 2003), suggesting that the expression of genetic
differences between hatchery and wild salmon depends on the environmental context.
Quantitative genetic approaches
There is a long history of examining the genetic basis of continuously distributed
traits using quantitative genetic techniques, which are based on the phenotypic
resemblance among relatives due to shared genes. Unlike some of the reverse methods
described below, quantitative genetic techniques measure phenotypes, not genes, and rely
on the resemblance among relatives to infer genetic effects. Quantitative genetic
approaches assume that many genes of small effect influence the phenotype, thereby
producing a continuous distribution (however, recent QTL studies have called this
assumption into question). By measuring the phenotype on individuals of known
relatedness, we can partition the phenotypic variation into environmental, genetic and
gene x environment variance components (Falconer, 1989).
The simplest quantitative genetic approach is to ask whether the trait of interest
runs in families, which would suggest that there might be a genetic component to it.
However, families often share a common environment so cross fostering experiments are
useful here (Boake, 1994; Boake et al., 2002). Like repeatability, a demonstration of full-
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sib resemblance sets an upper limit to the heritability. More complicated breeding designs
involve estimating the resemblance of parents and offspring using regression, or
generating full and half-sib families to disentangle parental effects from genetic effects
using ANOVA-based approaches, which are thoroughly described elsewhere (Falconer,
1989; Lynch and Walsh, 1998). These approaches estimate heritabilities, genetic
correlations, parental effects and non-additive genetic variance.
The most powerful quantitative genetic experiments estimate the G matrix. The G
matrix refers to the multivariate matrix of genetic variances and covariances between
several different traits. Although laborious to quantify, obtaining a G matrix allows us to
predict the consequences of selection on any given trait, and, with a few assumptions
about the stability of the G matrix through time, allows us to retrospectively analyze
selection in the past (Jones et al., 2003; Lande, 1979). In addition to satisfying the
assumption of stability required for retrospective selection analysis, it is also desirable to
compare G matrices to determine whether genetic constraints are limiting. If genetic
correlations can be uncoupled, then the relationship between traits might respond to
selection, and the G matrix will, itself, evolve. Alternatively, genetic correlations might
limit the number of different configuration of traits that are possible, which would be
reflected in an invariant G matrix through time. Fortunately, there are several different
techniques available to compare the structure of G matrices (Steppan, 2002) but there is
debate about the best method.
A relatively new powerful technique known as the animal model allows us to
estimate genetic variances and covariances without performing breeding experiments in
the lab (Kruuk, 2003). Therefore this method is particularly useful for field studies. The
animal model is powerful because it takes advantage of all types of resemblance between
relatives to partition variance components; it uses all the available information about
relatedness from a pedigree. Moreover, this method is suitable for unbalanced datasets,
and can accommodate missing values.
Another approach for estimating genetic variances and covariances is to perform
an artificial selection experiment, which involves selective breeding of individuals.
Selection experiments have shown that many behavioral traits can respond to selection
(Boake, 1994). Artificial selection experiments are only suitable for organisms that can
be kept in the lab, and which ideally have a short generation time. Selection experiments
should only be undertaken when different selected lines can be replicated and when
sample sizes are sufficient to control for drift and inbreeding. The pitfalls and perils of
selection experiments are reviewed in (Fuller et al., 2005).
Artificial selection experiments can be particularly insightful when they vary the
environmental context in which selection occurs. For example, in a very interesting series
of selection experiments on medaka (Oryzias latipes, Ruzzante and Doyle ( 1991, 1993)
showed that the correlated response of aggressiveness to selection for fast growth
depended on the ecological context in which the selection took place. In these
experiments, Ruzzante and Doyle selected for fast growth under two different conditions:
when food was clumped and could be defended, and when food was dispersed. The same
limited amount of food was added to each treatment. Selection for fast growth produced a
correlated response to selection on aggressiveness, but only when food was defensible.
Interestingly, levels of aggressiveness decreased in the fast-growth lines, which they
interpret as reflecting indirect selection for ‘social tolerance’.
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One of the strengths of a selection experiment is that it can tell us whether there
are packages of traits that are linked together and respond to selection in concert. For
example, selection for increased and decreased stress responsiveness, as measured by the
change in circulating concentrations of cortisol in response to handling stress, produced
several correlated effects on physiological and behavioral measures in rainbow trout
(Oncorhynchus mykiss, Pottinger and Carrick, 1999). Relative to trout that did not release
very much cortisol in response to handling stress, ‘high responding’ trout were more
aggressive in a new environment (Schjolden et al., 2005), were more active in the
presence of an intruder and took longer to acclimate to a new environment (Øverli et al.,
2002). Moreover, these behavioral differences were also accompanied by changes in the
brain monoaminergic systems (Øverli et al., 2001). These results suggest that an entire
suite of physiological and behavioral changes accompanied modifications to stress
responsiveness. Interestingly, the packages of physiological and behavioral traits that
changed together are analogous to different ‘coping styles’ that have been identified in
other vertebrates (Koolhaas et al., 1999).
Candidate gene approaches
Over the past few decades, there has been increasing evidence that the molecular
functions of many genes are highly conserved across species. For example, studies on
transgenics have revealed that genes from one species accomplish similar functions in
distantly-related species (Jaenisch, 1988; Manzanares et al., 2000). The incredible
conservation of gene function in living things allows us to apply genetic information
about other species to the organism of interest. This means that we can apply the genetic
information gained from studies on model organisms to nontraditional models for which
genomic information is not available.
Genes could become candidates based on genetic studies in other species (i.e.
when polymorphism has already been identified and associated with behavioral variation,
Fitzpatrick et al., 2005), or because a physiological pathway leading to behavior of
interest is well-understood, suggesting hypotheses about which genes to examine. For
example, the neuroendocrine mechanisms underlying circadian rhythms are well
described for several mammalian species (Reppert and Weaver, 2002). Therefore, we can
ask whether the expression or structure of genes along those pathways differ among
individuals or among groups. Candidate gene expression approaches typically involve
measuring the abundance of mRNA transcript in a particular tissue (usually brain, in the
case of behavior) using quantitative real-time PCR or Northern blots.
One of the attractions of the candidate gene approach is that detailed genomic
information on the species is not necessary, therefore this technique can be applied to
non-model organisms by designing degenerate primers based on other species
(Fitzpatrick et al., 2005). Another attraction of the candidate gene approach is that unlike
quantitative genetic approaches, it does not require complicated breeding designs or data
on the relationships among individuals – comparisons can be made across individuals of
unknown relatedness because the genes are being measured directly. Therefore this
approach is suitable for organisms that do not readily breed in the lab.
Summaries of using the candidate gene approach for studying behavior are welldescribed elsewhere, see (Choleris et al., 2004; Fitzpatrick et al., 2005; Robinson et al.,
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2005). Notable examples include work on the for gene and foraging behavior (Debelle et
al., 1987; Debelle and Sokolowski, 1989; Debelle et al., 1993; Graf and Sokolowski,
1989; Ingram et al., 2005; Osborne et al., 1997; Pereira and Sokolowski, 1993), and the
‘fruitless’ gene and mating behavior in Drosophila (Kimura et al., 2005; Villella et al.,
2005) and the promoter region of the vasopressin gene and parental behavior (Hammock
and Young, 2002). The candidate gene approach for studying the genetics of fish
behaviour is a relatively unexplored but promising future research direction.
While the candidate gene approach is very attractive and has great appeal
especially for nonmodel organisms, there are some drawbacks. First, candidate gene
studies are biased; what if the ultimate source of genetic differences between groups lies
further up or downstream of the particular candidate gene? One approach to this issue is
to choose a pathway that is probably associated with the behavior and look at gene
expression at several points in the pathway. Second, candidate gene studies are purely
correlative. Concluding that the gene is really associated with the behavior requires
further experimentation (described below). Perhaps most seriously, candidate gene
approaches work best when the trait is influenced by just few genes of major effect,
which is probably the exception rather than the rule for behavioral traits (Boake, 1994;
Boake et al., 2002).
Genomics
‘Genomics’ refers to the study of the structure, content and evolution of genomes,
including the analysis of the expression and function of genes and proteins (Gibson and
Muse, 2002). What distinguishes genomics from other branches of genetics is that it
looks at the whole genome simultaneously, rather than focusing on one gene at a time.
Luckily, doing genomics does not require a full genome sequence, and there are
already several good examples of applying whole-genome approaches to nonmodel
organisms (Charlesworth et al., 2001; Feder and Mitchell-Olds, 2003) and ecologicallyrelevant traits, including behavior (Robinson et al., 2005). Genomics has been hailed as
an opportunity to integrate mechanistic and evolutionary approaches to studying behavior
because information about genes provides neuroscientists and behavioral ecologists with
a ‘common language’ (Robinson et al., 2005).
Quantitative trait locus mapping
The goal of quantitative trait locus (QTL) mapping is to find regions of the
genome that are associated with variation in a phenotypic trait (reviewed in (Slate,
2005)). Results from QTL analyses can include the number and location of QTL affecting
a trait, their effect size, and whether QTL interact with each other. Those regions contain
genes influencing the trait. Determining the precise location of genes within a QTL
depends on the density of markers in a genome-wide linkage map.
The basic procedure for performing a QTL analysis is to correlate polymorphic
markers distributed throughout the genome with a phenotype (Slate, 2005). QTL analysis
is a very powerful and thorough approach for finding genes. It is unbiased, in that it starts
with the phenotype and blindly searches for correlated genes irrespective of their function
(unlike the candidate gene approach). However, QTL analysis has several drawbacks and
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might not be suitable for most organisms. First, the approach requires that the
investigator is able to perform controlled laboratory crosses or has detailed pedigree
information. Second, depending on the generation time of the species, QTL analysis can
take a long time (up to 3 years for annual species). Third, the success of the method in
finding genes is contingent on the level of detail within the linkage map; the resolution of
the map will strongly affect how many genes lie within a QTL. This is a key issue not
only for finding genes but also for inferring the effect sizes of a QTL; if a QTL in fact
consists of linked genes, the effect size of any one of the genes within that QTL will be
overestimated. This is particularly a problem with small sample sizes (the so-called
‘Beavis effect’) (Slate, 2005). QTL with tens to hundreds of thousands of linked genes
are not unexpected because we know that selection can favor the evolution of gene
clustering (discussed below).
Finally, QTL studies are especially suited for studying extreme, discrete variation
rather than continuously varying traits. As the majority of behaviors of interest to
behavioral ecologists are continuously-distributed, QTL analyses might not be generally
useful. However, QTL analysis is well-suited for studying categorical behaviors which
probably have a simple genetic basis such as disperse/no disperse, or alternative mating
types (Liu, 1997). An important factor to keep in mind is that even if there is evidence
that the behavior of interest is heritable and amenable to evolutionary analyses, that does
not necessarily mean that the behavior is going to be mappable because a heritability of
0.75 could reflect the action of just one major gene accounting for 75% of the variation,
which could be easily mapped, or of 75 different genes, each accounting for 1% of the
variation, which would be difficult to map.
Despite these caveats, the QTL approach has been successfully applied to study
behavioral variation, mostly in mouse, honeybees and drosophila (Anholt and Mackay,
2004; Flint, 2003; Flint and Mott, 2001; Flint et al., 2005; Gleason et al., 2002;
Henderson et al., 2004; Hitzemann et al., 2003; Hunt et al., 1999; Hunt et al., 1998;
Macdonald and Goldstein, 1999; Mackay, 2004; Mackay et al., 2005; Page et al., 2000;
Rueppell et al., 2006; Rueppell et al., 2004; Ruppell et al., 2004; Shaw and Parsons,
2002; Turri et al., 2004; Yalcin et al., 2004), with one recent fish study.
For example, wild zebrafish (Danio rerio) are more bold toward novel objects and
do not shoal as readily as domesticated zebrafish and those behavioral differences can be
mapped (Wright et al., 2006). Wright et al. crossed wild and domesticated zebrafish,
intercrossed the resulting F1s, and measured the F2s for behavior. They genotyped 84
F2s at 66 loci, and measured their shoaling tendency and boldness toward a novel object,
and found three QTLs that were correlated with behavioral differences.
One of the most surprising but consistent results to come out of the spate of QTL
studies over the past 10 years is that genes of major effect appear to be common, e.g.
(Bradshaw et al., 1998). However, genes of major effect are also the ones that are easiest
to detect, the so-called ‘low lying fruit’ (Walsh, 2001), so it is premature to conclude that
Mendel was right all along.
Microarray approaches
One of the most exciting new techniques for studying the genetics of behavior
involves using microarrays to monitor the expression of thousands of genes
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simultaneously. A microarray consists of a glass slide spotted with oligonucleotides or
cDNAs of known genes. The experimenter can measure the amount of mRNA present in
a particular tissue by applying RNA from a sample onto the plate and then quantifying
how much of the sample binds to each oligo (see (Churchill, 2002) for more details on
experimental design of microarray experiments).
Behavioral experiments using microarrays might compare gene expression
following different kinds of experiences (Lawniczak and Begun, 2004), across different
behavioral types (Atlantic salmon, Aubin-Horth et al., 2005; Aubin-Horth et al., 2005;
see also Whitfield et al., 2003), or among individuals from different populations or strains
(reviewed in (Hofmann, 2003; Paratore et al., 2006; Ranz and Machado, 2006)).
Comparing the coordinated expression of thousands of genes simultaneously is
revealing several unexpected insights. For instance, using cDNA microarrays, Giger et al
compared the expression of 900 genes between three different groups: migratory brown
trout populations, non-migratory brown trout populations and one population of Atlantic
salmon (Giger et al., 2006). They also determined the evolutionary relatedness among
these groups using microsatellite markers. Among the brown trout populations, they
found that genetic relatedness had little effect on the pattern of gene expression;
populations that were more closely related to each other did not show more similar
patterns of gene expression relative to distantly-related populations. Instead, the biggest
source of variation was life history strategy: 45% of the total variability in gene
expression could be attributed to differences between migratory and non-migratory
forms. In fact, 268 out of the 900 genes differed in expression between the migratory and
non-migratory forms. However, when the other species (Atlantic salmon) was included in
the analysis, more than half of the total variance in gene expression was explained by
genetic differences between groups, while less than 3% was explained by life-history
differences. Therefore, these results suggest that interspecific differences in gene
expression are mostly attributable to genetic differences between the species, but
intraspecific differences in gene expression is mostly attributable to ecological, life
history and behavioral events that are experienced by individuals. The study by Giger et
al. is a particularly good example of a hypothesis-driven microarray experiment which
did much more than simply describe patterns of gene expression. The experimental
design allowed the authors to test for the relationship between genetic relatedness and
gene expression similarity, while at the same time comparing gene expression patterns
within and between species.
The strengths of a microarray approach are that it is: 1) unbiased, in that it is
scanning the entire genome for relevant genes rather than cherry-picking one gene at a
time; 2) open-ended and therefore good for finding candidate genes; 3) considers the
coordinated action of the entire genome, which is probably good for polygenic behavioral
traits. Another argument for using microarrays is that they are efficient – each microarray
is self-contained experiment, so comparing relative expression across genes is
straightforward. Microarrays are also good for nonmodel organisms in which it is not
efficient to use traditional forward genetic approaches (Feder and Mitchell-Olds, 2003;
Robinson et al., 2005).
However, there are several drawbacks to the microarray approach. The first, and
most obvious, is that it requires a microarray. This limitation might not be as serious as it
appears, though, because several studies have successfully employed microarray
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technology developed for other species on a species of interest, such as Atlantic salmon
(Aubin-Horth et al., 2005) and rainbow trout (Sneddon et al., 2005), for other taxa see
(Hofmann, 2003; Hofmann et al., 1999; Renn et al., 2004), and progress is being made
toward developing universal microarrays (Roth and al, 2004), but see (Karssen, 2006).
This is an active area of research and at the time of this writing it is too early to determine
if there are any general guidelines regarding the expected error rate and bias toward
highly conserved genes (Vasemägi and Primmer, 2005).
The next big step after a microarray experiment is to show the evolutionary and
ecological significance of variation in transcript levels (note that the candidate gene
expression approach has the same problem). Differences in transcript abundance could be
a consequence, rather than a cause of differences in behavior. Moreover, identifying the
causative genetic polymorphisms underlying the difference in gene expression can be
difficult (Vasemägi and Primmer, 2005). That is, the real root of the variation could be
occurring upstream in the pathway. This is an active area of research within genomics
(‘genetical genomics’ (Jansen and Nap, 2001; Ranz and Machado, 2006; Vasemagi and
Primmer, 2005)).
Another consideration to bear in mind when using micorarrays to study behavior
is that the timing of measurement of gene expression is critical. Finally, another thing to
consider is that there is often considerable variation in gene expression across even
genetically-identical individuals (Oleksiak et al., 2002; Pritchard et al., 2006).
Other whole-genome approaches
There are several other whole-genome tools for studying the genetic basis for
quantitative variation that are accessible to organisms without a complete genome
sequence. These approaches are well-described elsewhere (Gibson and Muse, 2002) and
the technology is rapidly changing, so they will be discussed just briefly here.
If a microarray is not available, other gene expression comparisons such as
subtractive hybridization and RNA differential display are useful for identifying
differentially expressed genes (Gibson and Muse, 2002). For example, Sneddon et al.
(2005) used suppression subtraction hybridization to create a cDNA library that
contained genes that were differentially expressed between rainbow trout of differing
dominance status. They then used this cDNA library to construct a microarray to compare
gene expression in dominant and subordinate trout.
It has become obvious to both scientists and laypeople that having a complete
genome sequence is not ‘the answer’. The fact that organisms, including ourselves, share
so much of the same DNA code with phenotypically and phylogenetically divergent
organisms drew attention to the inadequacy of the precise sequence itself in
differentiating groups or explaining biological variation.
So what, then, is the big deal about having a whole genome sequence? There are
many reasons, but the basic answer is that it makes doing molecular biology a lot easier.
Finding genes, for example, is greatly facilitated with a complete genome sequence;
without a genome sequence, localizing a particular gene from within a QTL can require
laborious chromosome walking, which can be done in silico, or electronically, if the full
sequence is available. Other benefits of a whole genome sequence are that it becomes
straightforward to design primers for amplifying a gene of interest, and it allows quick
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identification of microsatellite or SNP (single nucleotide polymorphism) markers (Morin
et al., 2004). Another advantage is that with a whole genome sequence and a computer, it
is easy to determine if a candidate gene has a homolog in your species, and where the
gene is located in the genome.
‘Hot topics’ in the genetics of behaviour
In this section, I draw attention to two particularly exciting topics in the genetics
of behavior. Admittedly, this is a very biased selection, but includes a representative topic
from two general categories. The first category is longstanding issues in animal behavior
to which a genomic approach has a lot to offer (the role of the environment). The second
category is current ‘hot’ topics in behavioral ecology that could benefit from a genetic
approach (behavioral syndromes).
The environment
While this chapter has focused on genetic effects, like many complex traits, the
expression of many behaviors is sensitive to environmental conditions, so a complete
genetic understanding of behavior requires a formulation that includes environmental
inputs over developmental time. A purely genes-based approach to understanding the
evolution of behavior clearly does not have all the answers (Hofmann, 2003) because the
environment affects when and where genes are expressed. One of the most promising
offerings of the genomic revolution for animal behavior is a potential tool for integrating
the effects of genetics and the environment on behavior (GxE). Gene expression
(‘transcriptomics’) might be a key integrative link between genetic and environmental
cues because it reflects the actions of both genetic and environmental inputs while at the
same time feeding back on them (e.g. via genomic imprinting and epigenetic
programming (Weaver et al., 2004).
Another recurring theme in the genetics of complex traits is that GxE interactions
are ubiquitous and important (Mackay, 2004; Merila and Sheldon, 1999). Whereas
plasticity occurs when the environment affects the expression of the phenotype, GxE
interactions go one step further by indicating that there is genetic variation for
responsiveness to the environment. In other words, GxE interactions occur when different
genotypes respond differently to the same set of environments. While GxE interactions
can be a source of annoyance and noise to strictly genetic studies because they can make
it more difficult to find genes, for those of us interested in plasticity, the fact that genetic
studies are turning up more and more evidence for them is good news. That is because
GxE interactions reflect genetic variation for plasticity. Therefore a GxE interaction
indicates that plasticity, as a trait, might respond to selection.
A promising strategy for investigators interested in both genetics and plasticity is
to conduct experiments that examine both genetic and environmental effects
simultaneously rather than separately. A researcher might, for example, utilize an
experimental design in which full sibs are reared in different kinds of environments, or
simultaneously compare gene expression in different genetic groups (e.g. strains or
populations) as well as under different environmental conditions (e.g. social/nonsocial,
predator/no predator, etc).
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Experiments which examine both genetic and environmental effects
simultaneously have two additional advantages. The first is that they are efficient – they
can test three hypotheses (the role of environment, genetics and GxE) rather than just
one. Second, varying the environment can increase the chances of detecting a genetic
effect. That is, in many cases, the effect of genotype is only apparent in certain kinds of
environments. In fact, in some cases it can be more accurate to view environmental
effects as a generator of genetic variation rather than noise; environmental stimuli can
uncover parts of the norm of reaction that are only expressed under certain conditions
(West-Eberhard, 2003).
There are several excellent examples of GxE interactions for behavioral traits.
Relatively low-tech experiments on fishes which compared the effect of different
environmental conditions on genetically-differentiated populations have revealed that
sticklebacks and minnows from ‘high predation’ localities are more responsive to
experience with predators relative to stickleback and minnows from low predation
localities (Huntingford et al., 1994; Magurran, 1990).
Behavioral syndromes
There is a long history of studying the genetics of several traits simultaneously in
evolutionary biology. Multivariate approaches to selection analyses using the G matrix
explicitly take correlations among traits into account when inferring the past or future
course of evolution (Arnold, 1994). In addition, artificial selection experiments have
consistently produced correlated responses to selection on other traits (Fuller et al.,
2005). Therefore, the fact that traits do not evolve independently of one another is widely
appreciated in evolutionary biology.
Recently, the significance of this insight for animal behavior has come to the
forefront. Animal behavior as a discipline has tended to emphasize distinctions between
different functional categories of behavior rather than the overlap between them (the table
of contents of Animal Behavior textbooks illustrates this point well). However, there is
accumulating evidence that behaviors tend to be correlated across different contexts, and
form behavioral syndromes. In fish, behavioral syndromes have been found, for example,
in three-spined stickleback (Bell, 2005; Bell and Stamps, 2004), brown trout Salmo trutta
(Sundström et al, 2004) and pumpkinseed sunfish Lepomis gibbosus, Wilson et al. 1993;
1998; for other taxa see Carere et al., 2001; Dall et al., 2004; Dingemanse et al., 2004;
Dingemanse et al., 2002; Drent et al., 2003; Gosling, 2001; Hedrick, 2000; Johnson and
Sih 2005; Koolhaas et al., 1999; Reale and Festa-Bianchet, 2003; Reale et al., 2000; Sih
et al., 2004; Sih et al., 2004; Stamps, 2003; Verbeek et al., 1996; ; Wilson et al., 1994; ).
One of the reasons why behavioral syndromes have been attracting attention is
because correlations among behaviors might constrain the ability of behaviors to change
and evolve independently of one another (Sih et al., 2004; Sih et al., 2004). Within the
lifetime of an individual, behavioral syndromes might produce limited plasticity; if
individuals have a ‘tendency’ to behave a certain way in several different situations, and
different levels of behavior are favored in different circumstances, then individuals might
not express the optimal behavior in every situation. For example, an aggressive individual
might do well during competition for resources but might be overly aggressive toward
offspring (Ketterson and Nolan, 1999). Therefore behavioral syndromes might be able to
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explain why animals do not always express the optimal behavior for any given situation
(Johnson, 2003). If different behaviors are influenced by the same genes (pleiotropy),
those genes might constrain optimal behavior. Over evolutionary time, genetic
correlations between behaviors might prevent single behaviors from evolving
independently of each other.
While there is good evidence that traits, including behavioral ones, often occur
together in packages, the extent to which those links constrain adaptation is less clear
(Bell, 2005). Much has been written about the extent to which correlations among traits
reflect the product of natural selection or if they are a constraint on it (Pigliucci and
Preston, 2004). On one hand, genetic correlations might constrain a trait’s evolution. If
two traits are influenced by the same gene (pleiotropy), then this could result in a tradeoff
between the two traits. On the other hand, rigorous selection experiments have revealed
that even tightly-linked, genetically correlated traits can be uncoupled (Beldade et al.,
2002; Weber, 1992).
One way to test for the genetic constraints is to compare the relationship between
traits in different kinds of populations. If the relationship between traits varies across
populations, that suggests that genetic correlations are not constraining the number of
combinations possible and that the different combinations might represent adaptations to
different selective conditions. I applied this reasoning to a behavioral syndrome in order
to test whether behavioral syndromes can act as an evolutionary constraint. Previous
work had shown that threespined sticklebacks could be characterized by the ‘boldness
aggressiveness behavioral syndrome’: individuals sticklebacks that were more bold
toward predators were also more aggressive toward conspecifics (Huntingford, 1976).
However, different populations of sticklebacks vary, on average, in boldness and
aggressiveness, and much of the variation across populations has been attributed to
differences in predation pressure (reviewed in (Huntingford, 1994). Using full sib and
parent-offspring resemblance and the animal model, I found that the genetic architecture
underlying boldness and aggressiveness differed in two different populations of
sticklebacks (Bell, 2005). Specifically, boldness and aggressiveness were positively
genetically correlated in one population but not the other one. Ecological comparisons
between the two populations suggests that the population in which the syndrome occurs
is subject to strong selection by bird and fish predators.
Therefore this study provides support for the hypothesis that some behavioral
syndromes might be the result of selection, rather than a constraint on it. In fact, the
genetic correlation between boldness and aggressiveness in some populations might
reflect a history of correlational selection which has favored particular trait value
combinations. Correlational selection will result in linkage disequilibrium between the
co-selected genes (Lande and Arnold, 1983). Understanding the mechanisms underlying
genetic correlations might be able to disentangle the ‘constraint’ and ‘adaptation’
hypotheses about the significance of behavioral syndromes. If the genetic correlation is
due to linkage disequilibrium, and possibly a product of correlational selection, the
genetic correlation will break down in hybrids (Conner, 2002), suggesting that the genetic
correlation can be uncoupled. If the genetic correlation is preserved in hybrids, that
suggests the two traits are affected by the same genes and the genetic correlation will be
more difficult to modify through evolutionary time. In addition to simple crossing
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experiments, access to the actual genes as opposed to just their statistical consequences
will shed some light on the extent to which genetic correlations can act as a constraint.
Finally, while behavioral syndromes have drawn attention to correlated behaviors,
an even broader view is that all aspects of the phenotype (behavior, morphology,
physiology, life history) function as an integrated unit and are best studied as such
(Pigliucci and Preston, 2004). Therefore, genetic studies might be most successful when
they measure several aspects of the phenotype simultaneously rather than dissecting each
piece individually.
Conclusions
Behavioral ecologists will have an important role to play in the genomic
revolution. Understanding a species’ natural variation in behavior can help make sense of
the wealth of genomic variation that confronts us. This is especially an issue for model
organisms such as zebrafish, which have been heralded as a rising model system for
studying the genetics of behavior (Gerlai, 2003; Robison and Rowland, 2005; Wright et
al., 2006; Wright et al., 2003), yet we know very little about the natural behavior of
zebrafish in the field. While they might not be particularly charismatic, zebrafish offer
tremendous opportunities for a young behavioral ecologist trying to choose an organism
to study.
Studying natural variation in behavior and its ecological relevance for model
systems such as zebrafish is important to genetic studies for several reasons. First, basic
ethological information about what the animal does in its natural environment is critical
for determining what traits are relevant to the animal. Second, knowing how an organism
interacts with its environment is key to determine how to measure the behavior in a
laboratory environment, such as which cues or experimental setups will most efficiently
elicit the behavior of interest. Third, knowing what the animal does in its natural
environment and how it varies according to different selective pressures gives hints as to
whether the variation is adaptive and if it might have been subject to past or future
selection. Finally, natural variation in behavior might be a better approximation to the
kind of variation of interest to human health and disease (Koolhaas, 2006). That is, unlike
behavioral variation in laboratory animals which have been protected from ecologically
relevant selective forces, natural variation in behavior can represent adaptive solutions to
particular selective challenges.
Behavioral ecologists can also help direct genetic studies in hypothesis-driven
research directions. An understanding of the ecology of the species in question can help
point to particularly interesting biological issues faced by the organism. Understanding
the natural history of the species will also help interpret the results of genetic
manipulations. For example, if we know the ecological significance of the traits that are
affected by knocking out genes or by blocking their expression using RNAi, that could
suggest functional reasons why the traits are correlated in the first place. Therefore there
is great scope for collaboration between behavioral ecologists and their molecular
colleagues.
If this chapter has not scared off the faint-of-heart, I hope that it has convinced
researchers who were not thinking of including a genetic component in their research
program to reconsider. But if they should choose to embark on such a mission, a
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recurring theme in discussions of the genetics of complex traits (Boake et al., 2002; Feder
and Mitchell-Olds, 2003; Vasemagi and Primmer, 2005) and in this chapter is that no
single genetic approach has all the answers, and research programs which are integrative
and multi-pronged are the way forward. Research strategies which approach the question
from different angles and which use complementary techniques that give different types
of data are probably the best way to approach the genetics of complex traits. Of course no
single research program can do it all; it would be logistically impossible for a single PhD
project to perform experiments which look at a range of environments, which considers
behavioral, physiological and morphological traits, and which carries out QTL, candidate
gene, microarray experiments all at once. However, research strategies that take just two
approaches complementary approaches will be fruitful. For example, a study might carry
out a QTL analysis in parallel with the analysis of candidate gene expression, and then
look to see if the informative candidate genes lie within QTL (Letwin, 2006). Similarly,
doing experiments which explicitly study both genetic and environmental effects
simultaneously, and which measure entire suites of traits rather than just one trait at a
time will be the most lucrative.
In 1984, when Grafen made his original ‘gambit’, we knew very little about the
genetics of complex phenotypes. Grafen’s deal was provisional, revocable when the
assumption that the genetic basis to behavior is simple was no longer tenable and when
genes were more within the reach of behavioral ecologists. Since then, we have learned
that the genetics of complex traits such as behavior are far more complicated than he
originally envisioned (Mackay, 2004). In addition, the genetic tools necessary to
approach this complexity are becoming more available. Luckily, approaching the genetics
of complex traits is within the grasp of behavioral ecologists and a more thorough
understanding of genetic mechanisms will make our studies richer.
Acknowledgements
I thank Tim Caro for challenging me with the phenotypic gambit, Katie Peichel for
informative conversations about QTL analysis, Beverly Ajie, Jason Watters, Ripan Malhi
and Niels Dingemanse for comments on the chapter.
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