1. No category

Journal of Sea Research 51 (2004) 167 – 182
www.elsevier.com/locate/seares
How to detect polymorphisms undergoing selection in marine
fishes? A review of methods and case studies,
including flatfishes
Bruno Guinand, Christophe Lemaire, Francßois Bonhomme *
Ge´nome, Populations, Interactions, Adaptation, Universite´ Montpellier 2, IFREMER CNRS UMR 5171,
Station Me´diterrane´enne de l’Environnement Littoral, 1 Quai de la Daurade, 34200 Se`te, France
Received 30 May 2003; accepted 20 October 2003
Abstract
Populations of marine organisms are potentially affected by numerous selective pressures such as temperature and salinity,
or anthropogenic pressures such as xenobiotics that may preclude adaptation to particular habitats. Such selective pressures may
also affect their demography. Examples include modifications of the population dynamics through shifts in growth rate, and in
life history traits affecting fitness such as size or age of first reproduction. However, the documentation of variation in
phenotypically plastic traits specific to distinct environments cannot be taken as the ultimate proof that natural selection has
occurred. Measurement of the impact of selection and subsequent local adaptation of fish populations based exclusively on
morphological or physiological characters is one of the most difficult things to achieve because it depends on the use of
phenotypic characters that closely match the genotype. Molecular markers can help to overcome this problem and, under some
circumstances, can record the footprints of selection. A combination of polymorphisms that are under selection and those that
are not can provide complementary information. In this paper, we review how and why selection can be detected at the
molecular level, using genetic markers analysed in a population genetic framework. We then report and discuss case studies in
fish.
D 2004 Elsevier B.V. All rights reserved.
Keywords: Selection; Molecular markers; Statistical tests; Adaptation
1. Introduction
The exact tribute paid to selection in natural
populations (among which marine organisms are
no exception) has been the object of heated controversies (see for instance Frank and Leggett, 1994;
* Corresponding author.
E-mail address: [email protected] (F. Bonhomme).
1385-1101/$ - see front matter D 2004 Elsevier B.V. All rights reserved.
doi:10.1016/j.seares.2003.10.002
Hutchings, 2000). Animals like fish are permanently
facing exposure to external physical factors such as
temperature, salinity, xenobiotics and other environmental conditions. All of them may act as putative
natural or artificial selective agents that may influence demographic parameters. The effects of selective forces on species differing in survival from
birth to maturity and experiencing strong mortality
differentials among each age class in each generation have been demonstrated (Hutchings, 2000).
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B. Guinand et al. / Journal of Sea Research 51 (2004) 167–182
Recently, several authors have discussed the potential of fishing pressures and practices as selective
agents. Different exploitation strategies could result
in different evolutionary outcomes, which is a
fundamental issue for fisheries sciences (Conover,
2000; Hutchings, 2000; Law, 2000). As evolution
consists basically in gene frequencies changes
resulting from interactions between organisms and
their environment, selection is hypothesised to act
against genotypes presenting lower fitness when
facing particular environmental conditions. However, if numerous examples of variable shifts have
accumulated (see Stergiou, 2002), most of them
primarily concern phenotypic changes along reaction
norms (i.e. the set of phenotypes that each distinct
genotype may express across environments) rather
than strict genetic changes.
Disentangling the genetic effects of selection from
the plastic changes acting upon phenotypic traits is
difficult, and requires the control of the environmental
variables on which natural selection will act. The
heritability of a trait (in broad-sense: the proportion
of phenotypic variance among individuals in a population that is accounted for by genetic effects), then
the reality of adaptation, may thus be estimated.
However, controlling responses of organisms to peculiar environmental changes (e.g. global warming) in
the marine environment is a very difficult task. As
poikilotherms with indeterminate growth, fish display
a very strong variance in their phenotypic response to
fluctuating environmental conditions, probably leading to imprecise estimation of the distribution of the
reaction norms. This phenomenon is known by aquaculturists, who regularly observe extreme dispersion
of characters as growth, metabolism, and ultimately
survival in offspring of the same breeding pair.
Therefore, measuring the impact of selection on fish
populations by monitoring morphological or physiological phenotypes is extremely hard to achieve. For
example, Rijnsdorp (1993) attempted to disentangle
both phenotypic and genetic effects related to maturation and reproduction in North Sea populations of
plaice (Pleuronectes platessa), but results were still
phenotypically biased. Conover and Munch (2001)
reported quantitative genetics experiments on Atlantic
silverside Menidia menidia that have shown responses
of fish as measured by standard mean length to levels
of harvesting over as few as four generations.
Detecting the action of selection requires that there
are detectable fitness differentials (Fig. 1). If this
happens during the course of one generation, it
implies that the number of individuals present in the
location where the selection took place was reduced
(e.g. half the fish entering a given estuary died of
heavy metal pollution because they lack the correct
detoxifying genes – or a correct combination of
alleles necessary to express those genes - while the
other half did not). These types of observations should
be taken into account in population dynamics models,
especially if it happens differentially among locations.
Obviously the scope for survival of a given larval
cohort will vary greatly according to where it comes
from and where it eventually recruits. Moreover, if
fish are not equally adapted to the various environmental conditions they can encounter, and if these
conditions vary across their geographical range, one
can expect larval dispersal and subsequent gene flow
to export ‘maladapted’ genes. Maladapted genes will
lessen the possibilities for further local adaptation.
Conover and Schultz (1995) and Conover (1998)
proposed that local adaptation of life history traits
might be prevalent. It is thus highly desirable to have
some idea of what is going on in natural fish populations in terms of natural selection to develop
‘Darwinian management practices’ (Conover, 2000).
As long as heritability for a trait cannot be firmly
established in the field, it will remain difficult to prove
anything about local adaptation in natural populations.
Ecological markers such as trace elements (Campana,
1999), for example, do not provide proof, nor do
sparsely reported morphological or other phenotypic
traits. The use of characters showing a more direct
genotype-phenotype correspondence across generations is, indeed, a strong prerequisite. Molecular polymorphisms seem ideally suited to this end, and there is
some hope that they indeed might help to do so.
Thanks to those markers, a more precise identification
of discrete genetic stocks has been made in the past
few years (Ruzzante et al., 1999 for cod; Bailey, 1997,
Hoarau et al., 2002, for reviews of flatfish studies).
Molecular genetic studies on marine fishes have
generally reported that species with dispersive larvae
are well mixed over large, genetically homogeneous
areas (review in e.g. Hauser and Ward, 1998; Waples,
1998; but see Taylor and Hellberg, 2003). While these
results are important, they also signal poor ability to
B. Guinand et al. / Journal of Sea Research 51 (2004) 167–182
169
Fig. 1. Diagrammatical representation of how a genotype translates to a phenotype through patterns of gene expression and environmental
pressures in a local habitat. Across a single generation, environmental pressures act on population dynamics by shaping Darwinian fitness of
each kind of phenotypically diverse individuals. As a function of their relative fitness, individuals with successful phenotypes will be selected
for greater contribution to the next generation. Successful phenotypes may only represent a part of or may represent another distribution of
former genotype diversity (e.g. changes in genotypic or allele frequencies). Genotypes with modified polymorphism distribution across the
genome would enter the next generation and would respond to new environmental pressures. Repeated action of such selective processes may
lead to fixation of beneficial substitutions (mutations) in the DNA sequences. Distributions of polymorphisms (more generally allele frequencies
at the population level, more generally DNA sequences at the species level) represent raw material for detection of selection at distinct temporal
scales.
recognise localised areas that may necessitate specific
management, on the basis of neutral variation alone.
Luckily, not all molecular markers are necessarily
neutral. Some do correspond to variation inside
expressed genes and may behave differently in terms
of realised gene flow. Indeed, selection is potentially
able to rapidly drag alleles across the species range
where they are favourable as well as limit their spread
to a given milieu when facing environment-dependent
selection. Thus, to correctly address all of the abovementioned issues using molecular studies, we need to
recognise which polymorphisms are subjected to
selection and which ones are not.
The aim of the present paper is, therefore, to
review tools used by population geneticists to detect
the action of selection on fish populations, including
flatfish. We try to provide keys for understanding the
corresponding literature. We also review a few case
studies where the action of selection has been proposed to be at play in flat and not so flat fish. Finally,
we suggest potentially fruitful future lines of research
in this field.
2. The hints that indicate selection in natural
populations
Molecular population genetics is still viewed as
reflecting the old debate whether random genetic drift
or selection is the primary driving force of evolution
as stated for instance by Kimura (1983) or Gillespie
(1991). In the past ten years, the selectionist/neutralist
debate has matured into acknowledging that much
molecular variation is neutral, but at the same time
making an effort to estimate how selection may act
and how selection is distributed to affect genetic
variation (Kreitman, 2000). In this sense, neutral
theory of evolution has become the standard null
hypothesis used to approach molecular evolution.
Emerging from studies with Drosophila (Begun and
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B. Guinand et al. / Journal of Sea Research 51 (2004) 167–182
Aquadro, 1992), it has been shown that, for neutral
variation, a correlation existed between recombination
rate and genetic variability within species. The explanation for this correlation appeared to be the hitchhiking of neutral variation with sites under selection as
hypothesised by Maynard-Smith and Haigh (1974;
see Barton, 2000, for review). For one ecological
geneticist, the major interest of this result is that even
if much of the observed variation is neutral, its
dynamics could be governed more by linkage to
selective sites than by genetic drift. As noted by Ford
(2002), if inferences about natural selection could be
made readily from molecular data this would be of
enormous importance in molecular ecology to disentangle the relative parts of neutral DNA evolution,
from those that are at least potentially selected and
more closely investigated for fitness effects and influence on population dynamics in particular environments (Fig. 1). In this paper, we will focus more
specifically on the short-term mediated effects of
selection, modifying fitness of particular populations
and/or cohorts. Such selective effects change allele
or haplotype frequency (multilocus) distribution(s).
Modifications on the DNA sequences themselves,
which are very important at higher evolutionary
levels, will be only briefly mentioned.
2.1. Allele frequency-based tests
Available tests based on study of allele frequency
distributions are summarised in Table 1. As they were
the first widely available markers, selection was first
tested with data sets for allozyme loci. It is worth
noting that how tests behave in relation to polymorphism of each kind of genetic markers was never
strictly investigated (Table 1). The approach based on
covariation of allele frequency distributions is clearly
a multilocus approach introduced by Lewontin and
Krakauer (1973). They suggested that a test for
natural selection could be based on the fact that
purely neutrally evolving loci should show the same
index Fst (the parameter that estimates between-population differentiation sensu Wright, 1951). Using
allele frequencies estimated in a metapopulation,
Lewontin and Krakauer (1973) proposed a test of
selective neutrality based on the sampling distribution
of Fst. Lewontin and Krakauer (1973) argued that the
variance in Fst is proportional to the square of its
mean value averaged across loci, and derived one
equation for the theoretically expected variance in
Fst,
rexp ¼ kFst2 =ðn 1Þ
ð1Þ
where Fst is the mean index of population differentiation across loci, n the number of subpopulations
sampled, and k a constant specific of the underlying
distribution of allele frequency among subpopulations. For example, for monomorphic (fixed) loci,
the expected variance in Fst is 0, and therefore k = 0.
Lewontin and Krakauer (1973) simulated distributions of allele frequencies among subpopulations and
reached the conclusion that – for neutral loci governed by drift only – k < 2. Expanding over this
result, they established that loci with k >2 were under
natural selection. However, Nei and Maruyama
(1975) and Robertson (1975a,b) disagreed with that
conclusion. Nei and Maruyama (1975) argued that
the test was sensitive to population structure (for
instance the so-called island model where migration
attains every population with the same probability vs.
the stepping-stone model where only adjacent populations are interconnected). Complex scenarios of
population divergence may generally increase variance of Fst (Robertson, 1975a,b). Hence, by not
discriminating between genetic drift due to metapopulation structure and true selection, the LewontinKrakauer test (hereafter LK) is basically inadequate
and has therefore not been very much used recently
(but see Tsakas and Krimbas, 1976, who originally
proposed pairwise comparison of populations, relaxing criticisms about population structure; see also
Bowcock et al., 1991). However, Baer (1999),
screening variation at numerous published allozyme
data sets on freshwater and marine fishes, slightly but stringently - adapted the LK test. Baer’s criterion
was to reject neutrality above a much more stringent
threshold of k = 7.6 computed from observed data
sets. Baer basically reported: (1) that in some cases, k
values were superior to the 7.6 threshold, particularly
in species with low population differentiation (high
gene flow); (2) that relationships between k and
effective population size warranted further investigations (Baer’s results suggest that the larger the
population, the higher is k; this intuitively fulfils
population genetics theory where genetic drift is the
B. Guinand et al. / Journal of Sea Research 51 (2004) 167–182
171
Table 1
Description of tests available in the current literature that may allow detection of selected loci at ecological time scales
Nature
of tests
Name
Data set
Main principle*
Advantages
Caveats
Examples in fish
species
Allele
frequencybased
LewontinKrakauer test
multilocus
based on observed
dispersion of Fst
across loci
– blind
– simple use
– influence of
population
structure and
history
– Baer (1999),
reporting 102 data
sets of both marine
and freshwater
fish species
– 32 marine
species were
investigated; 10
demonstrated
significant results
BeaumontNichols test
multilocus
based on the
distribution of Fst
across loci
conditional on
mutation rate A
and gene diversity.
– blind
– easy detection
of outlier loci
putatively under
selection
– not influenced
by population
structure
– not influenced
by mutation rate
Vitalis et al.
test
multilocus
based on observed
dispersion of
Fst across loci
conditional on
known population
history
Schlo¨tterer test
multilocus
based on the ratio
of the variance in
repeated motifs at
microsatellite loci
in pairwise
populations
comparison
– blind
– easy detection
of outlier loci
putatively under
selection
– corrected for
population history
– blind
– specifically
designed for
microsatellite loci
– not influenced
by mutation rate
– robust to
numerous
parameters
(see text)
– only used on
allozymes, no
published results
with highly
polymorphic
markers
– need high
number of loci
– not corrected
for population
history when
several samples
(n>2) are analyzed
together
– may not
respond
accordingly to
different selective
regimes.
– better if high
number of loci is
used
– principally used
on allozymes, not
checked for other
markers.
– better if high
number of loci is
used
– may not
respond
accordingly to
different kind of
selection
– still need to be
tested on poorly
differentiated
populations
– better if high
number of loci is
used
– Data on cod
(Gadus morhua)
including
allozyme and
RFLP markers
(Pogson et al.,
1995) provided in
Beaumont and
Nichols (1996)
–
–
(continued on next page)
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B. Guinand et al. / Journal of Sea Research 51 (2004) 167–182
Table 1 (continued)
Nature
of tests
DNA
sequencebased#
Name
Data set
Main principle*
Advantages
Caveats
Akey et al. test
multilocus
based on extensive
genome scan
providing
dispersion of Fst
across SNPs, then
comparing
observed
distribution to
purely neutral
distributions
– robust to
parameters such as
demography and
population history
– translate
selected SNP
polymorphisms to
candidate gene
definition
RaufasteBonhomme test
monolocus
based on observed
dispersion of
fixation indices F
across alleles for
each locus
– blind
– monolocus test
that can be
extended to the
multilocus case
– primarily useful
when genome
mapping is known
– more able to
detect directional
selection
– baseline for
neutral
expectation not
corrected for
presence of
selected loci
– possible
influence of
population
structure structure
Tajima’s D test
–
– based on
relative
frequencies of
observed
haplotypes, and on
differences
between
polymorphic DNA
sites and observed
nucleotidic
changes
– simple
Fu’s F test
–
as Tajima’s D
– as Tajima’s D.
– corrected for
population structure
– robust under
different scenarios
of evolutionary
demography
– sensitive to
model of
population
structure
– sensitive to low
sample sizes
– sensitive to long
term population
growth
– sensitive to low
sample sizes
– also used to
detect long term
population
growth, not only
selection
Examples in fish
species
–
– Multilocus case:
Lemaire et al.
(unpubl.data)
demonstrating 16
loci possibly under
selection over 93
screened in sea
bass (Dicentrachus
labrax) including
allozymes, introns,
microsatellites,. . .
– Pogson (2001)
on cod (Gadus
morhua)
interpreting results
as selection at the
nuclear nuclear
pantophysin locus
– Chikhi (1995)
on Sardinella
spp. for one
mitochondrial locus,
interpreting results as
population growth,
but not as selection
– Pogson (2001)
on cod reported
opposite results
when using F
instead of D
(see above)
* Statistical details are not provided in this review.
#
Numerous methods available (see text), we only mentioned methods that may be used to detect short term selective processes within
species (Fig. 1).
prominent force acting in small populations whereas
selection is more actively acting in large ones). We
cannot expand upon those results, but it is striking
that marine species - and especially fish – are poorly
differentiated and present high gene flow estimates
(e.g. Waples, 1998). Baer (1999) reported that 10
B. Guinand et al. / Journal of Sea Research 51 (2004) 167–182
over 32 (31%) marine species displayed k > 7.6, the
stringent criterion empirically retained by the author
for inferring the presence of selection (e.g., sharks
[Carcharhinus spp.], damselfishes [Eupomacentrus
partitus and Stegastes fasciolatus], silverside [Menidia peninsulae], ocean perch [Sebastes alutus], and
yellowfin tuna [Thunnus albacares]). The proportion
for freshwater species with k >7.6 was, however,
similar. Flatfishes presented low k values (k = 1.79
and 5.35 for the common sole, Solea solea, and the
American plaice, Hippoglossoides platessoides, respectively). Baer’s attempt is only an empirical approach of dealing with the inter-locus variance of the
Fst index of population differentiation. It can only
detect outlier loci that are eventually more differentiated than the rest (hence eventually undergoing
disruptive selection), but not with less differentiated
ones (that would be under the action of homogenising
selective forces).
Expanding over the LK test, Akey et al. (2002)
also used one allele frequency-based test based on
global estimations and distributions of Fst at the levels
of the genome, the chromosome, and individual
genes. These authors contrasted Fst of each individual
polymorphism (26,530 single-nucleotide polymorphisms or SNPs; SNPs represent single nucleotidic
presence/absence polymorphisms) with the empirical
genome-wide distribution of Fst to identify polymorphisms. Loci possibly influenced by selection would
be represented as outliers. Contrary to the original LK
test, Akey et al. (2002) only considered distribution of
multilocus Fst, but not explicitly its variance or
parameter k as in Eq. (1). Akey et al. (2002) have
demonstrated that the results of their method were not
confounded with factors affecting the LK test, but
they further noted as a drawback that the method is
more powerful to detect directional selection (Table
1). The empirical genome-wide distribution of Fst
used as baseline by Akey et al. (2002) for testing
outliers was probably itself influenced by estimation
of Fst as those SNPs (i.e. polymorphisms putatively
under selection were used to define the significance
level representing neutral expectations without considering any correction factor). This represents also a
possible bias.
Beaumont and Nichols (1996; hereafter BN) proposed a method based on the distribution of Fst
conditional on gene diversity rather than allele fre-
173
quency. They generated by simulation the 95% confidence intervals (C.I.) for Fst under distinct genetic
models, then Fst estimates at different loci were
plotted against their expected gene diversities. Outlier
loci were considered under selective regime. The BN
test has desirable properties over the LK test (Table 1).
This test has been applied to the data of Pogson et al.
(1995) on cod (Gadus morhua), including both allozymes and restriction fragment length polymorphisms
(RFLPs) (21 loci total). Beaumont and Nichols (1996)
concluded in the same direction as these authors on
the occurrence of loci under selection. Vitalis et al.
(2001) have developed a method based on pairwise
comparisons of populations that incorporated population divergence by the means of partial pairwise
Fst’s – loosely speaking, history - and provide estimators to identify loci that are likely to have responded to
selection. By considering pairwise comparisons, this
method allows us to know which particular population(s) is (are) driven by selection. Vitalis et al. (2001)
have found that their method outperformed the BN
test. The methods appeared very similar in detecting
selected loci when multilocus Fst were large over all
populations. However, processes that would cause
apparent decrease of genetic variation at one locus
in a single population, without leading to observable
decrease of the genetic variation over all populations,
would not be detected by the BN test. In the BN test,
the rejection zone for loci with Fst smaller than
expected is extremely small. Hence, only loci with
excess differentiation (i.e. under differential rather
than balancing or stabilising selection) are likely to
be detected. In other words, if selection acts on one
locus at a local scale, pairwise comparisons of populations are more likely to be efficient in detecting
outlier loci (Vitalis et al., 2001). We are not aware of
applications of this method. Vitalis et al. (2001) have
illustrated their approach using a Drosophila data set
including 43 polymorphic allozymic loci, but the
reliability of the method when used with a smaller
number of loci is unknown.
Finally, Schlo¨tterer (2002a) recently developed a
new method, more specifically designed for the study
of highly polymorphic microsatellite loci. A microsatellite locus linked to a beneficial mutation is
expected to have a reduction in variability below
purely neutral expectations (e.g. Slatkin, 1995). Thus,
a multilocus screen for genomic regions subjected to
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B. Guinand et al. / Journal of Sea Research 51 (2004) 167–182
selection could take advantage of this reduction in
variability. Schlo¨tterer (2002a) designed a test based
on the ratio of the variance of the repeat sequences to
investigate selection at each microsatellite locus in
two groups of populations. Using simulations, he
proved the test statistic relatively robust to variability
in mutation rates that are important to consider with
such loci (Table 1). Behaviour of this test statistic still
needs to be investigated, particularly when populations are closely related (Schlo¨tterer, 2002a; Table 1).
It is also unclear how this test statistic depends on the
selection regime acting on loci (e.g. balancing vs.
diversifying selection).
A possible limitation of the previously described
tests is the need to contrast the history of a rather large
number of loci (at least >10) to know which ones
depart from neutral distribution. One other Fst-based
approach was recently proposed by Raufaste and
Bonhomme (submitted ms; see Arnaud-Haond et al.,
2003, for background of the method). This test compares the distribution of Fst not among loci, but among
the different alleles of a multiallelic locus. Fst values
are estimated for a given locus as a weighted function
of the contributions of individual alleles, with two
estimators using different weightings that are likely to
behave differently in face of selection. The Fst estimator of Robertson and Hill (reported in Weir and
Cockerham, 1984) gives more weight to the contribution of rare alleles than that of Weir and Cockerham
(1984). Under neutral expectations, each allele would
be expected to contribute equally to the locus-wide
Fst, irrespective of whether it is frequent or rare.
However, this may not be true when selection is
acting on those alleles differentially. Indeed, rare
and frequent alleles are differentially affected under
various selective regimes. For instance, if some sort of
frequency-dependent selection is acting to ‘rescue’ a
counter-selected allele, it will be more evenly distributed across subpopulations (and hence contribute less
to Fst) than a randomly drifting average frequency
allele. Conversely, environment-dependent selection
favouring different alleles in different subpopulations
according to local conditions may increase the contribution of frequent alleles as compared to neutral and
rare alleles likely to drift freely. By simulating the
distribution of their difference D according to a given
population structure model, it is possible to test the
departure from neutrality of the observed D for a
given data set. Applying this test to marine species
has already proved useful, suggesting for instance an
uneven distribution of polymorphisms in the pearl
oyster (Pinctada margaritifera) populations (ArnaudHaond et al., 2003; see Table 1).
This monolocus test may also be extended to
multilocus data sets to sequentially detect outlier loci.
If, in a series of Fst values obtained at different
multiallelic loci, some of them are more differentiated
because of the action of selection in a manner that is
detected by the monolocus test, the global multilocus
Fst will be overestimated (a point not considered in
Akey et al., 2002). Hence, this may induce a less
powerful estimation of baseline of neutral expectation
and gene flow. Removing such loci allows a recalculation of the global Fst, which can serve to obtain a
neutral distribution of any of the Fst estimators that
may be used to build a 95% C.I. and exclude the
outliers. This sequential procedure was successfully
applied to a data set concerning 93 loci in the sea bass
Dicentrarchus labrax and allowed (Lemaire et al.
unpublished) to propose that 16 of them were indeed
selectively implied in differential adaptation to lagoon/open sea conditions – thus different salinity
and temperature conditions - as suggested in Lemaire
et al. (2000).
Methods outlined in this section provide a means
to search multilocus (multiallelic) data to identify
those loci that show a deviation from neutral expectations. They are not final proof of selection as long
alternative scenarios are not accounted for (e.g. population history; Table 1), and as long as fitness
correlates favouring particular phenotypes are not
demonstrated (Fig. 1). However, they could serve as
a starting point for further studies, and Schlo¨tterer
(2002b) reviewed evidence of reliability of such
‘genome scans’ to identify loci targeted by natural
selection into various species, including both animals
such as Drosophila, and plants such as maize.
2.2. DNA sequences
Tests for selection using DNA sequences have
been reviewed several times in the recent years (e.g.
Kreitman, 2000; Skibinski, 2000; Nielsen, 2001;
Ford, 2002; Schlo¨tterer, 2002b). Methods suppose
the repeated action through time of selective pressures
through time (Fig. 1). Through the accumulation of
B. Guinand et al. / Journal of Sea Research 51 (2004) 167–182
substitutions along the molecule, a statistically significant pattern can emerge. The time scale on which
selection has to operate to be detectable is clearly not
that of a few generations only, as considered in most
ecological and stock management questions. This is
outside the scope of the present review, so we have
limited ourselves to a brief presentation of selected
tests. Skibinski (2000) also reviewed inferences that
can be drawn by comparing levels of genetic variation
in distinct classes of genetic markers for marine
organisms. We do not expand upon this topic.
The family of tests derived after the Tajima’s D test
(Tajima, 1989) deserves a special mention here, because it encompasses both long-term (mutational) and
short-term (drift) effects. This test was designed to
determine whether the frequency spectrum of sequence polymorphisms observed in within-species
data sets violates neutral expectation (Table 1). This
test compares the differences between two estimators
of neutral parameters (S, the number of segregating
sites, and k, the average number of pairwise differences in the number of nucleotides). The time scale of
the events likely to imprint the relative distribution of
sequence polymorphisms may be anything between
one generation and the inverse of the effective population size (1/Ne; a result classically drawn from
theoretical population genetics). Significant positive
and negative values of the test correspond to departures of equilibrium neutral expectations in the direction of having data skewed towards too many
intermediate-frequency polymorphisms [one index of
balancing selection; typical selection operating at
genes of (histo)compatibility systems, for instance]
or too many low-frequency polymorphisms [positive
selection; one allele is over-represented in samples],
respectively. Unfortunately, interpretation of this test
is highly sensitive to population history (Slatkin and
Hudson, 1991; Simonsen et al., 1995). One derivative
( F; Table 1) proposed by Fu (1996) of the original D
test has been shown to be fairly robust to the influence
of past population growth on distribution of polymorphism (Fu, 1997; Ramos-Onsins and Rozas, 2002).
Pogson (2001) found differences between D and F in
an empirical study of the pantophysin gene in cod
(Gadus morhua). Chikhi (1995) reported Tajima’s D
test in marine fish (Sardinella spp.) using mitochondrial DNA, but he interpreted significant results as
bias due to population processes, rather than by
175
selection. Fauvelot et al. (2003) reached a similar
conclusion for several coral reef species.
In conclusion, possibilities to detect selection at the
molecular level have greatly improved in the past few
years. Tajima’s tests and relatives are also based on
analyses of frequencies. Those methods are still poorly used, and reported examples are scarce.
3. If selection, what kind of genes?
3.1. What kind of genes?
Ford (2002) recently proposed one interesting list
of nuclear genes (n = 119) probably affected by
selection across both animals and plants. Ford
(2002) reported four distinct class of genes: (1)
genes involved in host-parasite interactions sensu
lato (n = 47), including host immune response genes
such as genes of the major histocompatibility complex; (2) genes involved in sexual reproduction
(n = 35); (3) genes involved in energy metabolism
(n = 15) (see also Eanes, 1999; note that Akey et al.,
2002, provided a specific review for humans suggesting a larger role for genes involved in energy
metabolism); (4) miscellaneous genes that did not
fall into a clear functional category (e.g., odour
receptors genes, genes with unknown function)
(n = 22). However, results concerning marine organisms are few (n = 7, Table 2). All results on marine
organisms are based on DNA sequence analyses,
Table 2
Summary of known genes undergoing selection in marine
organisms (based on the review by Ford, 2002)
Genes
involved in
Nature
of genes
Sexual
bindin
reproduction
sperm
proteins
Metabolism
Unknown
function
Organism Examples
of references
sea
urchins
abalone
sea snail
egg-laying
sea snail
hormone
lactatekillifish
dehydrogenase
haemoglobin Antarctic
fish
pantophysin
cod
gene
Palumbi (1999),
Debenham et al. (2000)
Vacquier et al. (1997)
Hellberg et al. (2000)
Endo et al. (1996)
Schulte (2000)
Bargelloni et al. (1998)
Pogson (2001)
176
B. Guinand et al. / Journal of Sea Research 51 (2004) 167–182
and not allele frequency tests (Table 1). They are
certainly poorly relevant for selection acting at
ecological time scales. Mitochondrial genes experiencing selection should, however, certainly be added
to this review of nuclear genes, but evidence for
marine fish or even marine invertebrates are still rare
(review in Gerber et al., 2001). We just focus here
on more relevant studies, stressing points of particular interests about footprints of selection.
3.2. Nuclear gene: case studies
Geographical variation in selective pressures on
nuclear genes has only been investigated for killifish
and cod. Focal study of the Pan-1 gene reported by
Pogson (2001) emerged from former ones that
looked at classical (‘neutral’) studies searching for
genetic structure of cod in the northern Atlantic (e.g.
Pogson et al., 1995, 2001). This particular locus was
unusual in not showing a relationship between inferred levels of gene flow and geographic distance as
other loci did, in indicating high population differentiation that contrasted with other loci (Pogson et
al., 2001), and in exhibiting high linkage disequilibrium among three restriction site polymorphisms in
the Pan-1 gene (Pogson and Fevolden, 1998). This
gene was represented by two main distinct alleles
that may coexist in natural populations and differed
by two stretches of DNA showing evidence of
selection (one is the first intron of the Pan-1 gene,
the other one in the fourth exon of this gene).
Because of the observed frequency of each allele
in particular areas, Pogson (2001) suggested that one
allele probably originated in the western Atlantic
(Nova Scotia) and spread eastward, whereas the
second allele probably originated in the Barents
Sea and made the reverse migration. This scenario
is based on directional selection (see Pogson, 2001,
for further details and rejection of alternative scenarios). In our opinion, the paper by Pogson (2001) is
particularly relevant on two points, one positive and
one negative. The good point is that his study has
clearly shown how one selected gene may disrupt
conclusions based on the neutral approach. Including
the Pan-1 gene in former studies (Pogson et al.,
2001) completely disrupted the isolation by distance
mechanism, and critical evaluation of each locus
separately illustrated that data sets may contain both
information about selection, and about pattern of
migration of cod during their history. The ‘bad’
point is that Pogson (2001) was unable to link
selection at the Pan-1 gene with any environmental
factor, because little is known of the function of
pantophysin in fishes (Pogson, 2001). If efforts
should be made to identify more accurately the role
and importance of pantophysin in the cell (e.g.,
comparing in situ levels and distribution of pantophysin in various tissues), the impossibility to track a
correlation between environment and presence/fitness
of each allele or genotype suggests that future
studies should maybe focus on particular genes that
are known to present metabolic efficiency (group 3
of Ford, 2002, see above). Killifish, F. heteroclitus,
offers a way to illustrate this point, both on one
nuclear gene implied in energy pathways and on
major histocompatibility loci.
Since earlier studies using allozymes (Mitton and
Koehn, 1975), killifish has become a model organism to investigate selection at the molecular level
(e.g. Schulte, 2000). Killifish are almost continuously distributed along east coast of North America
from Newfoundland to Florida, being particularly
abundant in salt marsh flats and estuaries. Northern
populations may encounter ice formation during
winter, whereas southern populations may experience summer temperatures higher than 40 jC.
Home ranges are small (estimated to 30 m within
a season; Brown and Chapman, 1991), and gene
flow is likely to be extremely limited across the
geographical range. In fact, studies revealed a zone
of admixture at intermediate latitudes (Bernardi et
al., 1993). Extensive works by Powers and coworkers (review in Powers and Schulte, 1998)
indicated that there are indeed genetic differences
between northern and southern populations of F.
heteroclitus, both in gene sequence and gene expression, that have a substantial impact on fitness
correlates (e.g. DiMichele and Powers, 1982). Selection experiments indicated high selection coefficients associated with the glycolytic lactatedehydrogenase-B (LDH-B) (DiMichele and Powers,
1991). Crawford and Powers (1992) demonstrated a
twofold difference in LDH-B specific activity in
liver between northern and southern populations,
paralleled by twofold differences in protein and
mRNA amounts and in transcription rate, as mea-
B. Guinand et al. / Journal of Sea Research 51 (2004) 167–182
sured by in-vitro run-on assays. Such a transcriptional difference – the level of activity closer of the
target DNA sequence – between populations may
lie in two places: variation in protein transcription
factor, or directly in the DNA sequences to which
they bind (or both; Carey and Smale, 2000). Segal
et al. (1996) cloned the complete Ldh-B gene of
northern and southern individuals of killifish, also
indicating high-level of variation in the non-coding
5Vflanking sequence of the gene. Schulte et al.
(1997) have demonstrated that there were two
distinct genotypes present in F. heteroclitus, with
extreme northern populations containing only one
genotype, while extreme southern populations presented the other. Schulte et al. (2000) reported that
sequence variation was itself responsible of between-population differences in Ldh-B gene transcription. They identified specific mutations
located near the Ldh-B transcription start site playing a complex role for gene regulation between the
two environments. Mutations in the 5V regulatory
sequence lowered the ability of the gene to respond
to stress hormones, then to regulate Ldh-B activity
in a similar way for each genotype (Schulte et al.,
2000). Experiments briefly described here indicate
that changes in gene expression are components of
adaptation to distinct environments, and the killifish
is certainly the organism where the link between
genotypes (DNA sequences) and phenotypes (distribution of genotypes in different thermal environments) is the best known. Using DNA microarray
procedures to study as many as 907 genes, Oleksiak
et al. (2002) recently reported substantial variation
in gene expression within and among three populations of Fundulus (two F. heteroclitus, one F.
grandis). The expressions of fifteen genes were
significantly different among populations with more
differences between northern and southern F. heteroclitus populations than between F. heteroclitus and
F. grandis populations living in the same southern
environment. However, this study did not demonstrate that such variations related to distinct polymorphisms at the DNA level as demonstrated for
Ldh-B. Results have also shown that some genes
presented unexpected patterns of changes in gene
expression unrelated to evolutionary distance. Such
quantitative variation in gene expression may reveal
specific, local adaptation of populations to their
177
environment, and such a variation may provide
raw material for evolution (Fig. 1) that still needs
to be investigated.
Nacci et al. (1999) reported strong, probably
heritable, differences in survival rates of killifish
to dioxin-like compounds. Cohen (2002) then used
the killifish to investigate response of Mhc at class
II loci to acute stress due to environmental contaminants and parasites as Mhc variation may reflect
pattern of antigenic stressors in the local environment (e.g. Bernatchez and Landry, 2003). Cohen
(2002) reported selection and population specific
localisation of amino-acid substitutions in different
functional parts of the peptide-binding region (i.e.
the region directly coding antigen-binding codons
implied in the immunological process) between
clean and contaminated populations. Cohen (2002)
also demonstrated that adaptation to the environment should be more local than previously reported
for the Ldh-B gene in killifish, and therefore that
selection is acting at different scales reflecting
different features of the environment (changes in
thermal environment for Ldh-B, change in stressors
[parasite, heavy metals] for Mhc variation). To date,
studies of Mhc variation at the population level are
scarce for marine organisms (beluga: Murray et al.,
1999; killifish: Cohen, 2002), compared to freshwater fish (e.g. Miller et al., 1997; Kim et al., 1999).
We may note that study of Mhc variation in marine
fish could take advantage of the large population
sizes classically encountered for those organisms.
Cohen (2002) indicated that results on killifish were
not obscured by the occurrence of bottlenecks, and
that signature of selection was certainly more easily
detected in species with local population sizes
estimated to >10 000 individuals per estuary.
Investigating crosses between laboratory-reared tilapia species (salt-adapted Oreochromis mossambicus,
and freshwater-adapted O. niloticus), Streelman and
Kocher (2002) have recently shown that polymorphism
of a microsatellite in the promoter of the prolactin 1 (prl
1) gene was associated with differences in prl 1 gene
expression and the growth response of salt-challenged
fishes. Prolactin has a ‘freshwater adapting’ role increasing plasma osmolality by changing Na+-K+ATPase activity in teleost fish (McCormick, 2001;
Manzon, 2002). Streelman and Kocher (2002) have
shown that individuals homozygous for microsatellite
178
B. Guinand et al. / Journal of Sea Research 51 (2004) 167–182
alleles with larger number of repeats expressed less prl
1 in freshwater, but more prl 1 in seawater or a mixture
of the two than fishes with other genotypes (especially
homozygotes with alleles having lower numbers of
repeats), that they grew best at different salinity treatments, and that mean growth of both homozygotes was
inversely correlated with prl 1 expression. Study did
not firmly prove that growth rate would lead to fitness
differences of homozygotes in distinct environments,
but growth rate is a common fitness correlate in fish
(e.g. Danzmann and Ferguson, 1995).
We do not report numerous works trying to link
allozyme polymorphism to fitness-related traits (backgrounds and reviews in Nevo et al., 1984; Depledge,
1996; Mitton, 1997). Such studies are common in
flatfishes (e.g. flounder: Laroche et al., 2002; Marchand et al., 2003) and other marine fishes (e.g. Huang
et al., 2001; Roy et al., 1995) living in highly polluted
habitats. In flounder, Marchand et al. (2003) reported
that some alleles observed at peculiar allozyme loci
were more frequent in three contaminated estuarine
sites than in a clean reference site. Authors also
demonstrated that individuals carrying those peculiar
alleles displayed good fitness (as measured by DNA
integrity by flow cytometry). Selection certainly acted
on local estuarine populations, enabling changes in
allele frequencies to occur from one generation to the
following (Fig. 1).
Hence, recent studies encourage investigations of
small-scale processes (salinity differences between
open-sea and estuaries or lagoons, differences in
concentration of heavy metals) that may affect genetic variation and phenotypic distributions of peculiar populations or cohorts of marine fishes,
including flatfishes.
3.3. A role for mitochondrial genomes: lines of
evidence
Selection on mitochondrial genes revealed distinct
lines of evidence that have been recently reviewed for
instance by Blier et al. (2001), Gerber et al. (2001). In
some cases, cyto-nuclear interactions have been shown
to affect performance of organisms. For instance,
Burton et al. (1999) have examined the interaction
between mitochondrial cytochrome oxidase genes
(COI, COII) and nuclear c sequences in the copepod
Tigriopus californicus to demonstrate outbreeding de-
pression. Co-adapted genes, including mitochondrial
ones, provided better physiological ability to populations in their natural environment than in other environments. Crosses between different locally adapted
natural populations of copepod disrupted adaptation
and indicated potential barriers to free gene flow
between marine populations (Burton et al., 1999;
Rawson and Burton, 2002). T. californicus offers a rare
example where co-adapted mitochondrial and nuclear
genes differing by few amino acid substitutions have
been selected in natural environments to confer specificity to each population. Generally, such co-adaptations (i.e. epistatic interactions between genes) are still
largely unknown (Rawson and Burton, 2002).
Studies of association between mitochondrial DNA
(mtDNA) haplotypes and life history variables (generally size, growth rate or weight) that may enhance
performance in the natural environment yielded mixed
results (Danzmann and Ferguson, 1995; Ferguson and
Danzmann, 1999). However, Doiron et al. (2002)
recently reported evidence for selective pressures
acting on NADH mtDNA genes in specific populations of brook charr (Salvelinus fontinalis; Salmonidae). Doiron et al. (2002) demonstrated introgression
of Arctic charr (Salvelinus alpinus) mtDNA in brook
charr, and suggested this pattern might be explained
by distinct original habitat requirements for temperature: brook charr originally inhabits warmer lacustrine
waters whereas Arctic charr is associated with cold
water of Arctic environments and/or deep lakes.
Introgressed genotypes of brook charr in cold lakes
from Que´bec may have been selected for, taken into
account that mitochondrial metabolism is sensitive to
temperature changes (Blier and Lemieux, 2001). In
this case, introgressed populations of brook charr
possess enzymes encoded by their own nuclear
DNA, and by Arctic charr mtDNA. However, changes
in performance across environments still need to be
investigated more closely. We are not aware of similar
results implying selection on mtDNA for fish in the
marine environment.
4. Selection in marine fishes: future research areas
In the previous sections, we have discussed how to
detect selection at the genetic level using appropriate
statistics and/or methods, notwithstanding the paucity
B. Guinand et al. / Journal of Sea Research 51 (2004) 167–182
of reported cases. Then we illustrated what genes may
be of interest and provided examples showing that
wild populations may be adapted to their environment
at different scales. Here, we resume missing links to
detect selection in the marine environment, and especially for fish.
First, numerous results were demonstrated in the
laboratory. Tangible proofs of functional differences
upon which natural selection has acted in natural
populations are not straightforward and evidence
still needs to be accumulated (Schulte, 2000). Any
transfer of results to natural conditions should be
cautious. In our opinion, the study of killifish at
Ldh-B gene provides the best example proving that
DNA sequence variation translates to transcriptional
difference that lead to different Ldh-B gene expression. If studies of gene expression variation in
natural populations using DNA microarrays represent an interesting tool for the future (Oleksiak et
al., 2002; see also Cheung and Spielman, 2002),
they do not prove that observed individual variation
is undermined by changes at the DNA level (sequence variation leading to distinct alleles distributed within and among populations). In other words,
that it is heritable. This question is indeed a
common feature for any quantitative trait, whether
molecular or not. Studies should now be designed
to detect selection at the molecular level in the wild,
then to link patterns of selection at these loci to
environmental features and/or to performance in
distinct environments.
Second, it is also clear that most effort should be
made to distinguish what selective patterns observed
in molecular data are relevant at the ecological time
scale - and then more relevant for management
decisions - from patterns that represent ‘fossil evidence’ of selection at wider evolutionary time scales
(certainly most examples in Table 2). Studies of
distributions of polymorphisms in different environments may help to understand which loci are potentially directly or indirectly affected by selection, then
to figure out subtle differences in potentially important stocks (Table 1). Such an approach may help to
select loci for which specific investigations (sequencing, quantitative expression, transcription rate of
alleles) could be carried out.
Finally, we stress that our paper is based on two
very different approaches to the investigation of
179
selection and adaptation. One is statistical (Table
1), trying to find footprints of selection directly at
the DNA level through thorough genome scans of
targeted populations (Schlo¨tterer, 2002b), but usually
ignoring the roles of transcription and expression that
would also act to mould phenotypes. The second
largely favours post-DNA levels of analyses, and
possibly ignores that variation in gene expression is
a component of phenotypic plasticity that would not
influence per se distributions of genotypes to the
next generation, revealing nothing about selective
patterns acting on a particular cohort in a peculiar
environment. Both approaches should now be used
for better investigation of selection patterns in the
marine environment.
Acknowledgements
The authors wish to thank A.J. Geffen and R.D.M.
Nash for inviting this contribution at the Isle of Man
flatfish symposium, as well as Dr H.W. van der Veer,
guest editor of this special issue for his patience.
Thanks to A. Sole´-Cava, J.-F. Agne`se for critical
reading and suggestions. Comments by three anonymous reviewers greatly improved the paper.
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