Molecular Ecology (2011) 20, 4408–4420
doi: 10.1111/j.1365-294X.2011.05292.x
Disentangling the roles of natural selection and genetic
drift in shaping variation at MHC immunity genes
J O L E N E T . S U T T O N , 1 S H I N I C H I N A K A G A W A , 1 B R U C E C . R O B E R T S O N and I A N G . J A M I E S O N
Department of Zoology, University of Otago, PO Box 56, Dunedin 9054, New Zealand
Abstract
The major histocompatibility complex (MHC) forms an integral component of the
vertebrate immune response and, due to strong selection pressures, is one of the most
polymorphic regions of the entire genome. Despite over 15 years of research, empirical
studies offer highly contradictory explanations of the relative roles of different
evolutionary forces, selection and genetic drift, acting on MHC genes during population
bottlenecks. Here, we take a meta-analytical approach to quantify the results of studies
into the effects of bottlenecks on MHC polymorphism. We show that the consequences
of selection acting on MHC loci prior to a bottleneck event, combined with drift during
the bottleneck, will result in overall loss of MHC polymorphism that is 15% greater
than loss of neutral genetic diversity. These results are counter to general expectations
that selection should maintain MHC polymorphism, but do agree with the results of
recent simulation models and at least two empirical studies. Notably, our results suggest
that negative frequency-dependent selection could be more important than overdominance for maintaining high MHC polymorphism in pre-bottlenecked populations.
Keywords: adaptive variation, balancing selection, major histocompatibility complex, meta-analysis, negative frequency dependence, overdominance, population bottleneck, publication bias
Received 28 June 2011; revision received 15 August 2011; accepted 25 August 2011
Introduction
One of the most polymorphic regions in the vertebrate
genome, the major histocompatibility complex (MHC),
encodes proteins that initiate cell-mediated immune
responses (Piertney & Oliver 2006; Spurgin & Richardson 2010). Strong selection pressures on MHC genes are
ultimately responsible for the high levels of polymorphism and can lead to discrepancies between patterns
of MHC and neutral variation in natural populations
(Alcaide 2010). However, whether natural selection can
maintain MHC polymorphism following a population
bottleneck remains unclear (Alcaide 2010). Most empirical evidence suggests that bottlenecks render the variation at MHC loci effectively neutral, resulting in overall
loss of MHC polymorphism through genetic drift
(reviewed in Radwan et al. 2010). On the other hand,
MHC polymorphism has sometimes been maintained
even in the face of severe bottlenecks (Aguilar et al.
Correspondence: Jolene T. Sutton, Fax: 64 3 479 7584;
E-mail: [email protected]
1
J.T.S and S.N. contributed equally to this work.
2004; but see Hedrick 2004; van Oosterhout et al. 2006),
while a few studies have revealed evidence for both
drift and selection acting on MHC diversity in bottlenecked populations (Miller et al. 2010; Radwan et al.
2010). Recent simulation models suggest that loss of
adaptive polymorphism will actually be greater than
loss of neutral genetic diversity immediately following
bottleneck events (Ejsmond & Radwan 2011), and there
are at least two examples of empirical evidence for this
occurring at MHC loci (Alcaide et al. 2010; Eimes et al.
2011).
The roles of selection and genetic drift in shaping
MHC polymorphism
Co-evolution with pathogens is commonly invoked as
the main mechanism driving MHC polymorphism
(Spurgin & Richardson 2010). There are three main,
non-mutually exclusive theories of pathogen-driven
selection (2010): overdominance (Doherty & Zinkernagel
1975; Hughes & Nei 1988), negative frequency-dependent selection (Clarke & Kirby 1966; Slade & McCallum
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H O W S E L E C T I O N A N D D R I F T S H A P E M H C V A R I A T I O N 4409
1992) and diversifying selection (Hill 1991). Briefly, the
overdominance model predicts that heterozygous individuals will be favoured because they are able to present a wider range of antigens and are thus able to resist
a broader array of pathogens. Under negative frequency-dependent selection, pathogen antigenicity can
overcome the MHC-based immune response of the host
that is most common in the population. As the relative
fitness of the most common MHC genotypes decline,
selection will favour rarer genotypes. The time lag
between host and pathogen responses is expected to
cause cycling of fitness values for different genotypes,
resulting in high MHC diversity in host populations
(Bernatchez & Landry 2003). This principle of selection
pressures that vary among time or place is also the
basis of diversifying selection, which may contribute to
geographically local patterns of adaptation and MHC
polymorphism (Hedrick 2002; Bernatchez & Landry
2003; Loiseau et al. 2010). Other processes that are
believed to maintain MHC polymorphism include linkage disequilibrium and epistatic gene–gene interactions,
gene conversion and recombination, sexual selection,
and maternal–foetal interactions (Edwards & Hedrick
1998; Martinsohn et al. 1999; Penn & Potts 1999; van
Oosterhout 2009). Indirect evidence for selection is often
based on patterns of MHC polymorphism that differ
from neutral expectations. For example, higher polymorphism and lower differentiation at MHC loci may
be explained by overdominance, whereas higher MHC
differentiation could be the result of fluctuating selection pressures (Alcaide 2010).
Identifying patterns of selection and genetic drift
from bottlenecked populations
As argued above, current empirical data provide conflicting conclusions about the loss or maintenance of
MHC diversity following a bottleneck. Consequently, a
general understanding of the evolutionary forces that
affect variation at MHC loci is unattainable without
combining the results from multiple study systems
(Alcaide 2010). Here, we take a unique meta-analytical
approach to assess the roles of selection and genetic
drift in shaping post-bottleneck MHC polymorphism.
Meta-analysis provides a quantitative method for synthesizing previous research findings and assessing the
plausible causes of variation associated with the results
(Chapman et al. 2009). In particular, we test three predictions. First, selection will maintain MHC diversity
(i.e. post-bottleneck MHC diversity may be depleted,
but not to the same extent as neutral genetic diversity).
This prediction follows expectations that selection will
maintain MHC polymorphism, even in the face of bottlenecks. Second, drift will outweigh selection (i.e. MHC
2011 Blackwell Publishing Ltd
diversity will be depleted alongside neutral genetic
diversity). This prediction follows the general current
consensus based on the majority of conclusions from
empirical data (Radwan et al. 2010). Third, reductions
in MHC diversity will be greater than loss of neutral
genetic diversity. This prediction stems from recent evidence suggesting that bottlenecks result in accelerated
decreases of adaptive MHC variation (Alcaide et al.
2010; Eimes et al. 2011; Ejsmond & Radwan 2011; for
further details, see Discussion).
Methods
Data collection
In June 2010, we conducted a keyword search of the
terms ‘MHC’ and ‘bottleneck’ in the Web of Knowledge
(http://apps.isiknowledge.com). We also obtained any
articles cited in Table 1 of Radwan et al. (2010) that
were missed in our original search, and we checked the
cited references of all relevant articles to locate studies
that we might have previously overlooked. We determined the initial relevance of each study by reading the
title and abstract. For all studies that were likely candidates for inclusion, we read the full text to ensure they
satisfied our criteria. To be included in our meta-analysis, studies must have been conducted on multiple populations. At least one of the study populations had to
have experienced a bottleneck and was compared to at
least one other population that was a ‘control’, or which
was the source for the bottlenecked population. Finally,
we only included studies for which data were available
for individual populations, rather than those that
pooled data across sampling locations. We assigned bottleneck status based on demographic history provided
in the text; however, our assignments did not necessarily maintain the population comparisons made in the
original study, and we did not necessarily include every
population. For example, for Hedrick et al. (2001) we
omitted populations from Stewart Mountain and New
Mexico. Although the authors treated these as non-bottlenecked, the former was established from translocations, and the latter was a captive population. Another
reference, Hess et al. (2007), included two bottleneck
scenarios — a translocation followed by a disease outbreak. To avoid including the same population as both
bottlenecked (post-translocation) and also as a control
(post-translocation; pre-disease outbreak), all of our
population comparisons were made using pre-disease
data.
We only included a study if (i) the bottleneck histories of the populations were based on information other
than genetic data; (ii) genetic diversity estimates were
provided for all populations that were compared; and
4410 J . T . S U T T O N E T A L .
Table 1 Summary of included studies with their data contributions
Species included in meta-analysis
Amphibians
Great crested newt (Triturus cristatus)
Birds
Galapagos penguin (Spheniscus mendiculus) and
Megellanic penguin (S. magellanicus)
House finch (Carpodacus mexicanus)
New Zealand South Island robin
(Petroica a. australis)
Reed warblers (Acrocephalus arundinaceus,
A. griseldis, and A. sechellensis)
Fish
Atlantic salmon (Salmo salar)
Brook trout (Salvelinus fontinalis)
Brown trout (Salmo trutta)
Gila trout (Oncorhynchus g. gilae)
Guppy (Poecilia reticulata)
Mammals
Beaver (Castor fiber)
Black-footed rock wallaby (Petrogale lateralis
lateralis)
Desert bighorn sheep (Ovis canadensis mexicana)
Island fox (Urocyon littoralis)
Spotted suslik (Spermophilus suslicus)
Reptiles
Sand lizard (Lacerta agilis) and adder
(Vipera berus)
Tuatara (Sphenodon guntheri and S. punctatus)
Main source
[supplementary
sources]
No. of
population
comparisons
included
Neutral markers
Major
histocompatibility
complex class
Babik et al. (2009)
11
Microsatellite
2
Bollmer et al.
(2007) [Akst et al.
2002]
Hess et al. (2007)
Miller & Lambert
(2004) [Ardern &
Lambert 1997;
Ardern et al. 1997]
Hansson &
Richardson (2005)
1
Microsatellite
2
2
2
Anonymous loci
Minisatellite
2
2
1
Microsatellite
1
12
Microsatellite
2
1
12
Microsatellite
Microsatellite
1
2
2
Microsatellite
2
1
Microsatellite
2
Ellegren et al.
(1993)
Mason et al. (2010)
2
Minisatellite
1
4
Microsatellite
2
Hedrick et al.
(2001)
Aguilar et al. (2004)
[Gilbert et al. 1990;
Goldstein et al.
1999; Wayne et al.
1991]
Biedrzycka &
Radwan (2008)
2
Microsatellite
2
9
Microsatellite;
minisatellite
2
40
Microsatellite
2
6
Microsatellite;
minisatellite
Microsatellite
1
Landry &
Bernatchez (2001)
Hansen et al. (2007)
Campos et al.
(2006)
Peters & Turner
(2008)
van Oosterhout
et al. (2006)
Madsen et al.
(2000)
Miller et al. (2008)
[MacAvoy et al.
2007]
(iii) genetic diversity estimates were provided for MHC
and neutral markers, or references to additional sources
for this information were provided. As ‘neutral diversity data’, we included anonymous loci, microsatellites
and minisatellites. For MHC data, we recorded both
class I and class II, and included both locus-specific and
multilocus-based data. We recorded intra-population
data for allelic richness ⁄ fixation, average percent differ-
1
1
ences, heterozygosity, haplotype diversity, band sharing, number of polymorphic loci and number of
variable sites. When a study reported a combination of
the total number of alleles, the number of alleles
adjusted to a particular sample size and ⁄ or allelic richness for the same marker, we included only one of
these variables in our data. In such cases, we prioritized
for allelic richness followed by the number of
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H O W S E L E C T I O N A N D D R I F T S H A P E M H C V A R I A T I O N 4411
standardized alleles. Similarly, if both expected and
observed heterozygosity were reported for the same
genetic marker, we only included observed heterozygosity in the analysis. We were able to include different
genetic metrics because our analysis focused on the
standardized, relative change between the control and
the bottlenecked population at each metric (see Effect
size calculation).
During data collection, it was possible to record multiple genetic diversity estimates (e.g. heterozygosity,
allelic richness) from multiple genetic markers for each
population comparison. In such cases, we recorded each
estimate separately, noting whether it represented MHC
or neutral genetic diversity, and we also recorded
whether the estimate originated from count- or percentbased metrics (e.g. number of alleles vs. heterozygosity;
Step 1 in Fig. S1, Supporting information). We distinguished count- from percent-based metrics because we
assumed that these two types of data would have different underlying distributions and, therefore, different
associated variances, which we needed to address during effect size calculations (see Effect size calculation).
In addition to the genetic diversity estimates and
whether these represented MHC or neutral variation,
we also recorded the following fixed factors: (i) Years
since bottleneck (categorical with four levels: ‘<50’, ‘50–
99’, ‘100–199’ and ‘‡200’). We included years since bottleneck because simulation models suggest that while
MHC polymorphism is initially lost following a bottleneck, post-bottleneck selection should act to eventually
increase MHC diversity faster than neutral genetic
diversity (Ejsmond & Radwan 2011). The increments of
time that we used reflected the data available from the
studies we included. (ii) Whether the control population
had also experienced some level of bottleneck (binary:
yes ⁄ no). We included this factor because populations
that have already lost most of their original genetic
diversity may be unlikely to lose more during subsequent bottlenecks if they are already genetically depauperate (Taylor & Jamieson 2008). (iii) The cause of the
bottleneck (categorical with three levels: ‘natural’,
‘exploitation ⁄ habitat alteration’ and ‘translocation’; note
that examples of bottlenecks due to epizootic events
occurred too infrequently to include in our study). We
included this factor to test whether different categories
of bottlenecks may be more severe than others. (iv) Bottleneck duration (categorical with two levels: ‘prolonged’ or ‘rapid recovery’). We included this factor
with the expectation that prolonged bottlenecks would
be more severe and, therefore, lead to greater loss of
diversity compared to bottlenecks from which populations were able to recover quickly. We assigned the
level of bottleneck duration qualitatively, based on
descriptions from the published studies. For example, a
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population formed by translocating a small number of
bighorn sheep to Tiburon Island was described as
increasing rapidly (Hedrick et al. 2001), and hence, we
categorized the bottleneck duration as ‘rapid recovery’.
In contrast, an island population of adders was
described as likely remaining small and isolated for
several thousand years (Madsen et al. 2000), which we
recorded as ‘prolonged’. We also recorded the identities
of publications (record) and of population comparisons
to incorporate as random factors in our models (see
Meta-analytic procedures). We attempted to test for differences among vertebrate taxonomic groups, but there
was a paucity of studies for particular groups (see
Table 1), and models that included ‘vertebrate group’
as a random factor failed to converge.
Effect size calculation
We used modified versions of the effect size statistic,
Hedges’ d (Hedges & Olkin 1985). Hedges’ d is widely
used in meta-analysis studies that focus on the differences between groups (in our case, the control and bottlenecked populations). The formula for Hedge’s d
(eqn 1) divides the raw difference in the variable of
interest between the groups (e.g. the change in heterozygosity) by the pooled standard deviation for the
groups (eqn 2), thus standardizing the differences and
making them comparable across studies (Hedges & Olkin 1985). More detailed descriptions of the use of
Hedge’s d can be found in Hedges & Olkin (1985), and
Borenstein et al. (2009).
Hedges’d and its standard error (SE) can be defined
as follows (Hedges & Olkin 1985):
d¼
m1 m2
3
;
1
spooled
4ðn1 þ n2 2Þ 1
spooled
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ðn1 1Þs21 þ ðn2 1Þs22
¼
;
n1 þ n2 2
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
n1 þ n2
d2
SEd ¼
;
þ
n1 n2
2ðn1 þ n2 2Þ
ðeqn 1Þ
ðeqn 2Þ
ðeqn 3Þ
where m1 and m2 are mean (estimated) values of two
groups, s21 and s22 are corresponding variance values,
and n1 and n2 are corresponding sample sizes. We
faced two problems when we applied the above formulae to genetic metric data: (i) m is expected to be
normally distributed and (ii) s is required to estimate
both d and SEd, but variances (s2) associated with
genetic metric data are not normally provided because
genetic metrics are almost always calculated at the
group level rather than at the individual level. To
circumvent these two problems, we assumed that
4412 J . T . S U T T O N E T A L .
percent-based metrics (e.g. heterozygosity) followed
binomial distributions, whereas count-based metrics
(e.g. number of alleles) followed Poisson distributions.
When binomially distributed data are logit-transformed
(normalizing transformation), their variance is p2 ⁄ 3. In
a similar manner, when Poisson-distributed data are
ln-transformed (natural logarithm, normalizing transformation), their variance (s2) can be approximated by
following:
1
s ¼ ln þ 1 ;
b
2
ðeqn 4Þ
where b is a count value (e.g. the number of alleles).
We applied the above formulae (eqns 1–3) to countand percent-based genetic metrics by using appropriate
normalizing transformations and corresponding variance values. We note the following limitations of our
assumptions: (i) count data in practice are almost
always overdispersed (meaning that their true variance
will exceed variance values from assumed Poisson distributions (Gelman and Hill 2007) and (ii) it is uncertain
that percent-based metrics, such as heterozygosity, actually follow assumed binomial distributions, but this
was not testable.
We calculated d for each genetic metric presented in
the original studies (Step 2, Fig. S1, Supporting information). Most data were provided as metrics whereby
higher values equated to higher genetic diversity (e.g.
heterozygosity). For others, higher values suggested
lower genetic diversity (e.g. percent band sharing in a
population). For the latter, we multiplied d by )1 so
that all d estimates could be compared. After these
adjustments, all positive d estimates indicated loss of
genetic diversity.
We then calculated weighted mean d for MHC and
neutral diversity data for each population comparison,
and for each data type (count vs. percent; i.e. each population comparison could have up to two mean MHC d
estimates and two mean neutral diversity d estimates;
Step 3, Fig. S1, Supporting information). Importantly,
before calculating mean d, we ensured that MHC vs.
neutral, and count vs. percent were evenly balanced for
each population comparison. In this way, a count- or
percent-based mean d estimate for MHC diversity corresponded to a count- or percent-based mean d estimate
for neutral genetic diversity (Step 3, Fig. S1, Supporting
information). When calculating the weighted mean d
estimates, we used the associated ‘variance of the mean
of several correlated variables’ (Borenstein et al. 2009,
Box 24.1, p. 228) as the weight (eqn 5). We calculated
mean values for each population comparison, keeping
MHC and neutral diversity d estimates separate, as well
as keeping count- and percent-based d estimates sepa-
rate. The formula for calculating the associated variance
is:
m
1X
var
½di m i¼1
!
0
1
2 X
X pffiffiffiffiffiqffiffiffiffiffi
1 @ m
¼
Vi þ
rij Vi Vj A; :
m
i6¼j
i¼1
ðeqn 5Þ
where Vi and Vj are the ith and jth variance associated
with the ith and jth d estimate (i and j = 1,…, m, but i is
not equal to j) and rij is a correlation between di and dj.
We set r = 1 so that the estimate of combined variance
would be the most conservative (if one sets r = 0, variance estimates will be much smaller and liberal).
Meta-analytic procedures
We implemented all models in R version 12.1.3 (R
Development Core Team 2011) using the package
MCMCglmm (Hadfield 2010). First, we estimated the
correlation between MHC and neutral diversity d estimates to test whether the overall effects of bottlenecks
were similar (i.e. to see whether the direction of change
in genetic diversity between the control and the bottlenecked populations was the same for MHC and neutral
data). To estimate the correlation, we used a bivariateresponse Bayesian generalized linear mixed-effects
model with Markov chain Monte Carlo (MCMC; Hadfield 2010; Hadfield & Nakagawa 2010), which we
referred to as Bayesian mixed-effects meta-analysis
(hereafter, bivariate-response BMM). We then used
standard meta-analytic techniques (Nakagawa & Cuthill
2007; Hadfield & Nakagawa 2010) implemented in univariate-response BMM models to test for differences
between MHC and neutral diversity d estimates while
controlling for our fixed and random factors. This
allowed us to test our main predictions while also
examining factors (e.g. bottleneck duration) that consistently influenced d estimates among studies. Finally, we
assessed heterogeneity (Higgins & Thompson 2002) in
our models and tested for possible publication bias
(Roberts & Stanley 2005).
In our bivariate-response BMM model, we used an
inverse Wishart prior with V = diag(2), nu = 2 (in
MCMCglmm, V stands for variance and nu is the degree
of belief for V; diag(2) represents a 2 by 2 identity
matrix) for the random effects (record and populations
compared) and the error term. Using this bivariate
framework, we modelled the correlation between MHC
d estimates and neutral diversity d estimates at the
residual level only (the use of idh argument in the
MCMCglmm function).
Similarly, in our univariate-response BMM models,
we used an inverse Wishart prior with V = 10, nu = )2
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71.3
8.0
35.2
Record +
populations
compared
3
)451.2
63.3
77.5
6.2
16.1
)423.5
7
71.2
47.5
17.3
2.5
462.8
1
Model 3
Model 2
Neutral vs.
major histocompatibility
complex (MHC)
Neutral vs. MHC + count vs.
percent data + bottleneck
duration + whether the control
population was bottlenecked
+ time since bottleneck + cause
of bottleneck
Neutral vs. MHC + count vs.
percent data + bottleneck
duration
DIC
Record +
populations
compared
Record +
populations
compared
30.2
Total
heterogeneity
%
Heterogeneity
(VarPop) %
Heterogeneity
(VarRec) %
Range of DIC among
three MCMC chains
No. of
fixed effects
Model 1
where r2P is the population-level variance, r2R is the
record-level variance, r2E is the error variance and r2M is
what is referred to as the ‘typical’ measurement error
variance, which can be defined as (Higgins & Thompson 2002):
Fixed effects
ðeqn 6Þ
Model
r2T ¼ r2P þ r2R þ r2E þ r2M
Random
effects
(representing non-informative prior) for the two random effects (record, and populations compared), and
the error term. We set the estimates calculated from eqn
5 with r = 1 as the vector of measurement error variances. For all models, we ran three MCMC chains (i.e.
the three independent runs of MCMCglmm models). For
each bivariate-response BMM chain, we ran 13 000 iterations with the thinning of 10 after a ‘burn-in’ of 3000.
For each univariate-response BMM chain, we ran 350
million iterations with the thinning of 1000 after a
‘burn-in’ of 349 million. We checked models for convergence and mixing by examining the Gelman–Rubin
statistic (Gelman & Rubin 1992; the potential scale
reduction factor <1.1 for all parameters including fixed
and random factors) among the three chains, and autocorrelation within chains (Hadfield 2010). For each
chain of each model, we checked the deviance information criterion (DIC; Spiegelhalter et al. 2002), whose
value can be used for model selection (i.e. the smaller,
the better). The parameters we considered were posterior modes, means (with standard deviation, SD) and
95% credible intervals (CIs; Table S1, Supporting information). We deemed parameters as statistically significant if associated CIs did not span across zero. We used
posterior means for point estimates of fixed effects and
for point estimates throughout this paper (note that
additional data are available in Table S1, Supporting
information). We structured our univariate-response
BMM models as follows (Table 2). Model 1 included a
single fixed factor (neutral vs. MHC data) to test
whether d estimates differed between MHC and neutral
genetic diversity. For model 2, we included all six fixed
factors. For model 3, we included only those fixed
factors that were deemed statistically significant in
model 2.
To estimate variation in d across studies and populations, we calculated heterogeneity as the percentage of
variance at each random effect in relation to the sum of
all variance components (Higgins & Thompson 2002;
Table 2). More traditional measures of heterogeneity
such as Q and I2, both of which were developed for
medical ⁄ social sciences (Higgins et al. 2003), were not
easily calculable for our meta-analytic models with multiple random factors. If we set r2T as the total sum of
the variance components in our typical univariateresponse BMM, r2T can be written as:
Table 2 Univariate-response BMM models and their deviance information criterion (DIC) values, variance and heterogeneity. The model with the smallest DIC was chosen as
the best model. Heterogeneity (i.e. % variance at a particular level in relation to the total variance) is presented at the level of the study (i.e. publication record) (VarRec), and at
the level of the populations compared (VarPop). Total heterogeneity was calculated as VarRec + VarPop
H O W S E L E C T I O N A N D D R I F T S H A P E M H C V A R I A T I O N 4413
4414 J . T . S U T T O N E T A L .
where wi is the inverse of the ith measurement error
variance associated with the ith d estimates (i = 1,…, k).
Thus, the proportion of the population-level variance in
relation to the total sum of variance components is
r2P ⁄ r2T .
Testing for publication bias
A main concern with meta-analysis is the possibility of
publication bias, which refers to the likelihood that
studies reporting statistically significant effects will be
more likely published than studies with non-significant
effects (Borenstein et al. 2009). To test for publication
bias, we applied Egger’s regression (Egger et al. 1997)
to the residuals (the error term and measurement error
term in this case) of our best model, accompanied with
their original measurement error variance (Roberts &
Stanley 2005; Table 2). These ‘residual’ points can be
considered to be independent from each other because
dependence resulting from population and record identities had been removed. If the slope of Egger’s regression is not significantly different from zero, one can
conclude there is little evidence for publication bias (Egger et al. 1997). We implemented the Egger’s regression
test in MCMCglmm and tested for model convergence
as described above.
greater are considered ‘large’ (Cohen 1992), which suggests that the effects of bottlenecks on overall genetic
diversity are strong.
Loss of neutral vs. adaptive MHC genetic diversity
Although bottlenecks resulted in overall losses for both
MHC and neutral genetic diversity, our univariateresponse BMM models demonstrated that this effect
was stronger for MHC d estimates, indicating even
greater losses of adaptive MHC diversity compared to
neutral genetic diversity. The univariate-response BMM
model 3 had the smallest DIC (Table 2), and here we
present the results of this model (for results of all models, see Table S1, Supporting information). Model 3
indicated that reductions in MHC polymorphism
between control and bottlenecked populations were
15% greater compared to neutral genetic diversity
(Fig. 2; model 3 in Table S1, Supporting information;
univariate-response BMM: b[MHC vs. neutral difference] =
)0.242, 95% CI = )0.360 to )0.125). Additionally,
prolonged bottlenecks resulted in more marked reductions in both MHC and neutral genetic diversity (i.e.
larger d estimates; Fig. 2; model 3 in Table S1,
Supporting information; univariate-response BMM:
b[prolonged vs. rapid recovery difference] = )1.28, 95% CI =
)1.730 to )0.812). This result is in agreement with previous observations that severe bottlenecks deplete
genetic diversity to a greater degree than moderate
3
Neutral marker d
0
We calculated Hedges’ d for MHC and neutral diversity
data for 109 population comparisons derived from a
total of 17 studies (Table 1) that matched our selection
criteria. Seventy-six percent of the neutral diversity data
originated from microsatellites. Ninety-four percent of
MHC data were based on class II polymorphism. Note
that these data summaries are not based on the number
of studies, but rather on the number of total data points
(n = 378).
4
Results
2
ðeqn 7Þ
1
Pk
wi ðk 1Þ
r2M ¼ P i¼1 2 P
k
k
2
i¼1 w i¼1 wi
Mean d estimates were positive, indicating overall loss
of genetic diversity during bottlenecks, and the bivariate-response BMM showed a strong positive correlation
between MHC and neutral diversity data (Fig. 1; posterior mean correlation, r = 0.50, 95% CI = 0.32–0.68;
MHC meta-analytic mean d = 0.91, 95% CI = 0.40–1.37;
neutral data meta-analytic mean d = 0.85, 95%
CI = 0.43–1.26). In meta-analysis, d estimates of 0.80 or
–1
Bottleneck effects on overall genetic diversity
–1
0
1
2
3
4
MHC d
Fig. 1 Correlation between MHC and neutral marker mean d
estimates. The bivariate-response BMM indicated that the correlation coefficient was 0.50, 95% CI = 0.32–0.68). Dotted lines
represent posterior means and thick, grey lines indicate 95%
credible intervals.
2011 Blackwell Publishing Ltd
H O W S E L E C T I O N A N D D R I F T S H A P E M H C V A R I A T I O N 4415
Fig. 2 A forest plot of posterior mean d estimates for the relationship between MHC vs. neutral data and bottleneck exposure (from
model 3). Dots represent the overall meta-analytic posterior means with 95% credible intervals (CIs) represented by the horizontal
lines. Positive values indicate loss of genetic diversity from pre-bottlenecked ⁄ control to bottlenecked populations. Grey panel: When
populations are exposed to prolonged or ongoing bottlenecks, d values are consistently positive (95% CIs do not cross zero), indicating that both neutral and MHC polymorphism are lost. When populations experience rapid recovery, 95% CIs cross zero, indicating
that bottleneck effects are not consistent. White panel: under each bottleneck scenario, d estimates are larger for MHC than for neutral marker data, and this difference is consistent (as shown by the pairwise comparison between MHC and neutral d estimates, for
which CIs do not cross zero). Additional information is available from Table S1 (Supporting information).
bottlenecks and may have greater impacts on fitness
(England et al. 2003; Briskie & Mackintosh 2004).
Heterogeneity was high in all the models (Table 2),
indicating that bottleneck effects on genetic diversity
varied among published studies and among populations compared within studies. In model 3, heterogeneity at the level of the study was 63.3%, and at the level
of the populations compared was 8.0% (total heterogeneity, of 71.3%, Table 2). However, despite high levels
of heterogeneity, our overall results were still robust
after testing for publication bias.
Testing for publication bias
The slope from Egger’s regression was statistically significant (b = )0.360, 95% CI = )0.578 to )0.172; Fig. S2).
This result can be interpreted in two ways: (i) publication bias existed in our data set and our meta-analytic
estimates should be corrected for, and ⁄ or (ii) heterogeneity existed among populations and ⁄ or studies in our
data set, and this was caused by unmeasured variables
(Egger et al. 1997). The latter is not testable, but the former can be achieved by using the ‘trim and fill’ test
(Duval & Tweedie 2000). The trim and fill test is a
method for estimating the number of studies missing
2011 Blackwell Publishing Ltd
from a meta-analysis, and also the effect that these
studies might have had on the outcome (Duval & Tweedie 2000). By adjusting for missing studies, the point
estimate of the overall effect size (e.g. Hedges’ d) is
approximately corrected (Duval & Tweedie 2000). We
used a trim and fill method implemented in the R package meta (Schwarzer 2010) and applied this method to
the residual points with their corresponding measurement error variance. From a random-effects meta-analytic procedure, our trim and fill test added 77 data
points to the original 378 data points and provided the
estimate of )0.167, which we used to adjust our original
estimates (Fig. S2). Importantly, adjusting our mean
estimates by )0.167 did not change the significance of
the factors in Fig. 2 and Table S1 (Supporting information) (i.e. it did not change whether the 95% confidence
intervals cross zero) and, therefore, did not alter our
main conclusions.
Discussion
Our results, based on empirical data from multiple
study systems, clearly show that selection cannot be
expected to maintain MHC polymorphism in the face of
a bottleneck and that both MHC and neutral genetic
4416 J . T . S U T T O N E T A L .
diversity are generally lost during these events.
Although several empirical studies have reported loss
of both MHC and neutral genetic diversity following a
bottleneck event (reviewed in Radwan et al. 2010), few
have reported that MHC polymorphism is lost to a
greater degree than neutral genetic diversity (but see
Alcaide et al. 2010; Eimes et al. 2011), and ours is the
first comprehensive support for this phenomenon. From
the studies we examined, five articles (Landry & Bernatchez 2001; Hansson & Richardson 2005; Campos
et al. 2006; Biedrzycka & Konopiński 2008; Babik et al.
2009) lead to MHC d estimates that were much greater
than neutral diversity d estimates (differences between
d values were >0.75). However, none of these studies
concluded that there was greater loss of MHC polymorphism. Instead, four of the studies reported that postbottleneck levels of MHC diversity were similar to neutral genetic diversity (Hansson & Richardson 2005;
Campos et al. 2006; Biedrzycka & Konopiński 2008;
Babik et al. 2009), and genetic drift was generally
inferred to be the predominant force acting on MHC
polymorphism. The fifth study concluded that drift and
migration were more important than selection in shaping recent MHC differentiation, but also noted that
strong discrepancies between patterns of MHC and
neutral differentiation across habitats provided support
for recent diversifying selection (Landry & Bernatchez
2001).
That few empirical studies report a greater loss of
within-population MHC polymorphism is not surprising for several reasons. First, the overall effect of loss of
genetic diversity is highly correlated between neutral
markers and MHC loci. Second, the difference we
observed between loss of MHC and neutral genetic
diversity, although significant, was moderate, and individual studies may not have enough statistical power to
detect it. This potential lack of power in individual
empirical studies highlights the importance of the metaanalytical approach taken here, which provided a
means of detecting important but subtle effects. Finally,
our observation of greater loss of MHC polymorphism
is counter-intuitive, because high MHC polymorphism
is believed to provide a selective advantage. Thus, the
possibility that MHC polymorphism might be lost from
a population at a greater rate than neutral genetic diversity may not necessarily be tested in empirical studies.
In accordance with this, previous hypotheses have
tended to suggest that MHC diversity will either be
maintained or lost during a population bottleneck. That
is, post-bottleneck MHC diversity will either be greater
than neutral diversity or similar. Only recently have
simulation models suggested that bottlenecks might
have stronger negative impacts on functional MHC loci
(Ejsmond & Radwan 2011).
Why is MHC diversity lost at a greater rate than
neutral genetic diversity?
There are several plausible explanations for why loss of
functional MHC diversity could be greater than loss of
neutral genetic diversity. Based on simulation models,
Ejsmond & Radwan (2011) proposed that the greater
initial decline of MHC diversity under selection and
drift compared to models with only drift is likely due
to selection continuing to act on allele frequencies after
the bottleneck event. Under this scenario, selection on
current pathogen-resistant genotypes could cause the
most efficient MHC alleles to become fixed, thereby
accelerating the loss of rare alleles due to drift (Ejsmond
& Radwan 2011). This explanation has been suggested
for patterns of genetic diversity in bottlenecked Eurasian kestrels (Alcaide et al. 2010). A second explanation is that multiple MHC loci can code for some of the
same alleles; thus, fixation of one allele during a bottleneck could result in several loci becoming monomorphic (so-called drift-across-loci; Eimes et al. 2011).
Third, copy number variation (CNV) among individuals
could be lost due to drift, similar to how allelic diversity can be lost at individual loci (reviewed in Eimes
et al. 2011). Briefly, CNV can be generated because of
high rates of gene duplication and deletion, which are
characteristic of the MHC (Nei et al. 1997; Eimes et al.
2011). If particular haplotypes with many gene copies
become lost due to stochastic events, then multiple
genes could be lost at once (Eimes et al. 2011).
Here, we suggest that greater population-level loss of
MHC diversity compared to neutral genetic diversity
can be explained by an additional, non-mutually exclusive hypothesis. We propose that negative frequencydependent selection is predominant in pre-bottlenecked
populations and that loss of MHC polymorphism is
accelerated because of uneven allele distributions. Based
on this hypothesis that the rate genetic diversity is lost
will be mainly influenced by prior allele distributions,
pre-bottlenecked MHC diversity should be characterized by high proportions of rare alleles that are easily
lost because of genetic drift. Such skewed MHC allele
frequencies can occur under negative frequency-dependent selection, or by a combination of different types of
selection that include negative frequency-dependence
(Ejsmond et al. 2010; Ekblom et al. 2010). Furthermore,
as some MHC loci typically have many alleles [some
human HLA-B loci have hundreds of alleles (http://
www.ebi.ac.uk/imgt/hla/stats.html)], it is likely that a
large proportion of these alleles could occur at very low
frequencies. Hedrick (1972) described how negative
frequency-dependent selection can be more effective
than overdominance at maintaining polymorphism, and
uneven allele distributions have been observed in sev 2011 Blackwell Publishing Ltd
H O W S E L E C T I O N A N D D R I F T S H A P E M H C V A R I A T I O N 4417
eral natural populations (e.g. Meyer & Thomson 2001;
Ekblom et al. 2010).
Our hypothesis requires that negative frequencydependent selection establishes skewed allele distributions before a bottleneck, but does not depend upon
any form of selection acting during or immediately following the bottleneck event. Unfortunately, testing for
negative frequency-dependent selection remains challenging owing to the fluctuating nature of the parasite–
host genotype interactions (Piertney & Oliver 2006; Ekblom et al. 2010). Under negative frequency-dependent
selection, new or rare alleles only become advantageous
when a novel pathogen strain arises, and the rare allele
conveys better resistance than a common allele (Piertney & Oliver 2006). At this point, the rare allele starts
to become more common in the population. As there is
no guarantee that in a given place or time a particular
allele will be beneficial because it is rare, or detrimental
because it is common, it is nearly impossible to empirically test for evidence of negative frequency-dependent
selection (Ekblom et al. 2010). With these considerations
in mind, careful study of how allele frequencies vary
across populations, particularly in bottleneck situations,
is warranted.
Other factors to consider in models of post-bottleneck
diversity
One limitation associated with comparing neutral markers, particularly microsatellites, to MHC genes is that,
irrespective of natural selection, they behave differently.
For example, microsatellites may have mutation rates
that can be several orders of magnitude greater than
those of unique sequences, and which can vary widely
among loci depending upon the sequence and repeat
lengths (Ellegren 2000). Although it is possible that high
mutation rates might lead to the development of novel
microsatellite alleles following a bottleneck event more
quickly than for MHC sequences, this was not the case
in our study, as evidenced by no change in the difference between MHC and neutral diversity d estimates as
time since bottleneck increased.
Simulation models predict that lower adaptive genetic
diversity (in terms of number of alleles) will occur for
40 post-bottleneck generations before selection begins
to increase MHC diversity (Ejsmond & Radwan 2011).
We did not find evidence that time since bottleneck (in
terms of years rather than generations) influenced d
estimates over the timescale we were able to incorporate in our analyses, and the model with the smallest
DIC did not include time (Tables 2 and S1, Supporting
information). As d estimates did not approach zero over
our timescale (i.e. genetic diversity did not start to
recover within 200 years of a bottleneck), we suggest
2011 Blackwell Publishing Ltd
that from a practical conservation perspective, adaptive
polymorphism may be slow to recover in response to
selection.
Conservation implications
To date, most conservation genetics research has relied
on relatively inexpensive and putatively neutral markers, such as microsatellites, as proxies for genome-wide
diversity. However, our observation of 15% greater
loss of adaptive MHC compared to neutral diversity is
likely to be biologically significant and poses a concern
for management of small populations. There are
numerous examples of specific MHC genotypes associated with either disease resistance or susceptibility in
wild populations (Bonneaud et al. 2006; Dionne et al.
2009; reviewed in Sommer 2005; Westerdahl et al.
2005), and of low MHC diversity being associated with
increased disease susceptibility (Siddle et al. 2010).
However, we do not recommend drawing conclusions
about overall immune adaptability in bottlenecked
populations based on only one part of the immunogenetic complex (i.e. MHC; Acevedo-Whitehouse &
Cunningham 2006).
As a proxy for estimating genome-wide functional
diversity, we suggest that post-bottleneck losses of
MHC diversity are more relevant than those of putatively neutral genetic diversity, as the former reflect
both drift and selection whereas the latter only represent the effects of drift. Our results are important for
conservation managers wishing to maintain the adaptive potential of small, threatened populations, particularly where previous research has focused solely on
neutral markers. However, rather than viewing neutral
markers as potentially misleading proxies for assessing
loss of adaptive variation in bottlenecked populations,
we suggest that conservation managers could interpret
patterns of neutral genetic diversity as conservative estimates of the true loss of functional genetic diversity.
Acknowledgements
The authors wish to thank David Coltman and three anonymous reviewers for their very insightful comments. We also
acknowledge Graham Wallis, Catherine Grueber and Sheena
Townsend for valuable discussions. I. Jamieson’s research on
genetic diversity in bottlenecked populations is funded by
Landcare Research (contract no. C09X0503), University of Otago, and Marsden Fund Council administered by the Royal Society of New Zealand. S. Nakagawa is supported by the
Marsden Fund (UOO-0812). B. Robertson’s conservation genetics research is funded by the University of Otago, and the
New Zealand Department of Conservation. J. Sutton is supported by scholarships from NSERC and New Zealand Ministry of Education (NZIDRS).
4418 J . T . S U T T O N E T A L .
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J.T.S is a PhD candidate interested in the how different evolutionary forces shape population genetic variation, and how
genetic tools can be used to aid programmes that manage
small, threatened populations. S.N. is an evolutionary biologist,
interested in general biological patterns and in quantitative
methods to capture such generality. B.C.R, a Senior Lecturer of
Zoology, has interests in conservation genetics, behavioural
and molecular ecology, and wildlife management. I.G.J. is an
associate professor in behavioural ecology and conservation
biology and is interested in the effects of inbreeding and loss
of genetic diversity in re-introduced populations. Contributions
by each author to this work are as follows: J.T.S., I.G.J. and
B.C.R. thought of the original question and study design; J.T.S.
collated the data; S.N. developed analytical procedures; J.T.S.
and S.N. analysed the data; and all authors wrote the paper.
Data accessibility
Meta-analysis data are deposited at Dryad: doi:10.5061/
dryad.g12v1. R code is available from the corresponding
author upon request.
Supporting information
Additional supporting information may be found in the online
version of this article.
Table S1 Parameter estimates from the models. Lower and
upper credible intervals (CI) in bold indicate significant fixed
effects in the models.
Fig. S1 Steps in data collection and calculation ⁄ structuring for
analysis.
Fig. S2 Funnel plots and the ‘trim and fill’ test.
Please note: Wiley-Blackwell are not responsible for the content
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authors. Any queries (other than missing material) should be
directed to the corresponding author for the article.
2011 Blackwell Publishing Ltd