Evaluating the Relationship between Evolutionary Divergenceand Phylogenetic Accuracy in AFLP Data Sets
Marı´a Jesu´s Garcı´a-Pereira, Armando Caballero,* and Humberto Quesada
Departamento de Bioquı´mica, Gene´tica e Inmunologı´a, Facultad de Biologı´a, Universidade de Vigo, Vigo, Spain
*Corresponding author: E-mail: [email protected]
Associate editor: Dan Graur
Using in silico amplified fragment length polymorphism (AFLP) fingerprints, we explore the relationship between sequencesimilarity and phylogeny accuracy to test when, in terms of genetic divergence, the quality of AFLP data becomes too lowto be informative for a reliable phylogenetic reconstruction. We generated DNA sequences with known phylogenies using
balanced and unbalanced trees with recent, uniform and ancient radiations, and average branch lengths (from the mostinternal node to the tip) ranging from 0.02 to 0.4 substitutions per site. The resulting sequences were used to emulate theAFLP procedure. Trees were estimated by maximum parsimony (MP), neighbor-joining (NJ), and minimum evolution (ME)methods from both DNA sequences and virtual AFLP fingerprints. The estimated trees were compared with the referencetrees using a score that measures overall differences in both topology and relative branch length. As expected, the accuracyof AFLP-based phylogenies decreased dramatically in the more divergent data sets. Above a divergence of approximately
0.05, AFLP-based phylogenies were largely inaccurate irrespective of the distinct topology, radiation model, or phylogeneticmethod used. This value represents an upper bound of expected tree accuracy for data sets with a simple divergencehistory; AFLP data sets with a similar divergence but with unbalanced topologies and short ancestral branches producedmuch less accurate trees. The lack of homology of AFLP bands quickly increases with divergence and reaches its maximumvalue (100%) at a divergence of only 0.4. Low guanine-cytosine (GC) contents increase the number of nonhomologousbands in AFLP data sets and lead to less reliable trees. However, the effect of the lack of band homology on tree accuracy issurprisingly small relative to the negative impact due to the low information content of AFLP characters. Tree-buildingmethods based on genetic distance displayed similar trends and outperformed parsimony at low but not at highdivergences. However, the impact of using alternative phylogenetic methods on tree accuracy was generally small relativeto the uncertainty arising from factors such as divergence, nonhomology of bands, or the low information content of AFLPcharacters. Nevertheless, our data suggest that under certain circumstances, AFLPs may be suitable to reconstruct deeperphylogenies than usually accepted.
Key words: AFLP, phylogenetic inference, homoplasy, computer simulation, accuracy, phylogeny.
2005) and Bayesian analysis (Brouat et al. 2004; Luoet al. 2007).
Amplified fragment length polymorphism (AFLP; Vos et al.
The appropriateness of AFLP data for phylogenic recon-
1995) is becoming an extensively used DNA fingerprinting
struction is however compromised by several properties of
method for many studies on plants, animals, and microor-
AFLP markers that have led many authors to question their
ganisms (Mueller and Wolfenbarger 1999; Meudt and
utility as phylogenetic characters (e.g., Hollingsworth and
Clarke 2007). The discovery that many AFLP data sets con-
Ennos 2004; Kosman and Leonard 2005). One of the most
tain phylogenetic signal (Giannasi et al. 2001; Despres et al.
2003; Koopman 2005) has stimulated its use as a source of
important drawbacks of the AFLP technique is that comi-
genetic information for phylogenetic inference, particularly
grating bands of the same length may not be homologous
among closely related genera or species (Meudt and Clarke
with one another, a phenomenon termed ‘‘fragment size
2007). The technique is based on the selective amplification
homoplasy'' (Vekemans et al. 2002). This implies that
of DNA fragments from digested total genomic DNA, gen-
within an individual, a band may contain fragments from
erating reproducible fingerprints that are usually recorded
different regions of the genome and that, in comparisons
as a 1/0 band presence–absence binary matrix. It is partic-
among taxa, fragments of equal size may also not come
ularly suitable for nonmodel organisms for which no prior
from the same locus (Gort et al. 2006; Althoff et al.
DNA sequence is available, producing a large number of
2007). In addition, the generally anonymous nature of
genomewide informative markers per assay unit at a rela-
bands may lead to the inadvertent presence of contami-
tively low cost (Bensch and A
˚ kesson 2005). Phylogenetic
nant DNA from parasites, symbionts, or hosts (Karp
relationships are usually inferred from AFLP data convert-
et al. 1996). The absence of bands is also susceptible to
ing the binary matrix into a distance matrix using dissim-
the lack of homology because they are treated as homol-
ilarity measures, or using the binary matrix directly for
ogous despite the multiple ways in which a fragment can be
character-based methods such as parsimony (Koopman
lost (Simmons et al. 2007).
The Author 2009. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, pleasee-mail: [email protected]
Mol. Biol. Evol. 27(5):988–1000. 2010
Advance Access publication December 21, 2009
Phylogenetic Accuracy of AFLPs · doi:10.1093/molbev/msp315
Empirical and theoretical studies that have tested the
Computer simulation provides an efficient alternative to
homology of comigrating bands have demonstrated that
experimental approaches because it can mimic the AFLP
the lack of homology is a real and prevalent component
technique producing virtual AFLP fingerprints that enable
of AFLP data sets (e.g., Rouppe van der Voort et al.
the identification of fragments based on their length and
1997; El-Rabey et al. 2002; Althoff et al. 2007). The propor-
DNA sequence (Rombauts et al. 2003). This approach al-
tion of nonhomologous bands ranged from about 10%
lows for the generation of data sets that evolve following
within closely related genotypes (Rouppee van der Voort
a given model tree, constructing phylogenetic trees from
et al. 1997; Hansen et al. 1999) to as high as 100% for dis-
that data set, and then comparing the inferred trees with
tantly related taxa (O'Hanlon and Peakall 2000). Therefore,
the model tree to determine the accuracy of phylogenetic
nonhomology rapidly increases among AFLP profiles from
estimation. In silico, AFLP has already been applied to
increasingly divergent taxa (Althoff et al. 2007) and with
quantify the effect of using presence–absence characters
the number of bands per assay unit (Koopman and Gort
in AFLP data relative to standard multistate characters
2004). The artefactual similarities among taxa originated by
(Simmons et al. 2007), to test band homology (Koopman
the lack of band homology may contribute to spurious
and Gort 2004; Althoff et al. 2007), to select restriction en-
phylogenetic relationships, and on average, this should
zymes (Peters et al. 2001), or to assess the effect of homo-
be more of a problem when the studied species are dis-
plasy on the estimation of population genetic parameters
tantly related (Bensch and A
˚ kesson 2005).
and the detection of natural selection in AFLP data sets
Concerns other than lack of homology have also been
(Caballero et al. 2008).
expressed that may potentially limit the usefulness of AFLP
In this study, we take a bioinformatics approach to
data for phylogenetic analyses (e.g., Bensch and A
assess the accuracy of AFLPs for phylogenetic reconstruc-
2005; Koopman 2005; Meudt and Clarke 2007). One impor-
tion. Using in silico AFLP fingerprints, we explore the rela-
tant limitation is that AFLP bands are usually scored as
tionship between sequence similarity and phylogeny
dominant markers (Simmons et al. 2007). Therefore, bands
accuracy over a wide range of tree topologies, phylogenetic
that do not comigrate are not necessarily independent of
methods, and evolutionary divergences to test when, in
one another even though they are scored as different pres-
terms of genetic divergence, the quality of AFLP data be-
ence–absence characters (Koopman 2005). There is also an
comes too low to be informative for a reliable phylogenetic
asymmetry in the probability of losing or gaining fragments,
because it is more likely destroy rather than generate newrestriction sites (Luo et al. 2007). These features would
Materials and Methods
increase the amount of stochastic noise in the data,making them less likely to recover the correct phylogenetic
Simulation of DNA Data Sets
relationships (Koopman 2005; Simmons et al. 2007).
Alignments of DNA sequences were generated with Seq-
Given the popularity of AFLP markers for phylogenetic
Gen (Rambaut and Grassly 1997), a program that evolves
inference, it is surprising that relatively little has been done
sequences along a specific tree. The input of the program is
to investigate the actual reliability of AFLPs. Some authors
a tree (hereafter referred to as the reference tree) in which
have tried to determine the accuracy of AFLP-based trees by
the user specifies its topology, number of sequences to be
comparing them with trees obtained using an independent
simulated, and the average length of the branches on the
source of data (e.g., Marhold et al. 2004; Sullivan et al. 2004;
tree. We used the Jukes and Cantor (1969) substitution
¨ller et al. 2007; Pellmyr et al. 2007; Dasmahapatra
model in Seq-Gen, which assumes equal base frequencies
et al. 2009). Koopman (2005) compared AFLP and internal
and equal mutation rates. The length of the simulated
transcriber spacer (ITS) data sets over a wide variety of taxa
sequences was limited to 40,000 nt in order to get 90–
(plant, fungi, and bacteria) and found that AFLP-based phy-
100 bands per AFLP profile, as recommended for
logenies were largely consistent with those derived from se-
experimental studies (Vos et al. 1995).
quence data. Approaches have also been developed aimed at
Simulations with Seq-Gen were performed along phy-
testing the significance of a given pairwise similarity among
logenetic trees with two different topologies, a symmetric
distinct AFLP fingerprints, enabling the removal of distantly
tree and an asymmetric tree. These known trees (refer-
related genotypes contributing to noise in AFLP data sets
ence trees) were manually constructed. We sought a range
(Koopman and Gort 2004). However, the comparison of
of evolutionary divergences comparable with those likely
fragment homology among taxa suggested that the phylo-
to be encountered when estimating phylogenies from ex-
genetic usefulness of AFLPs varies greatly depending on the
perimental data sets. The minimum and maximum
time since divergence and the specific genomic features of
lengths from the most internal node to the tips were
the compared taxa (Althoff et al. 2007). Although it is gen-
0.02 and 0.40 substitutions per position, respectively.
erally accepted that AFLPs may not provide an accurate es-
The branch lengths of the trees in figure 1 were multiplied
timate of species phylogeny when genetic divergence is too
by factors of 0.2, 0.5, 1, 2, 3, and 4, so that we used in total 6
high, it is still a matter of debate where this limit is (Meudt
different sets of evolutionary divergences. Saturation of
and Clarke 2007). Experimental studies aimed at assessing
simulated DNA data sets was tested by plotting pairwise
this question are, however, resource intensive and hampered
transition and transversion sequence differences against
by the fact that we rarely know the true tree.
Garcı´a-Pereira et al. · doi:10.1093/molbev/msp315
enzymes EcoRI and MseI, which are the typical enzymesused in AFLP studies. The program searches for all the re-striction sites and returns all fragments that would resultfrom the digestions. The output is the list of fragmentssorted by their length, as it would appear in a real exper-iment. The in silico AFLP profiles were generated withoutselective nucleotides to obtain the highest possible numberof AFLP fragments. Only fragment sizes between 40 and 440nt were considered in the subsequent analyses, which cor-respond to polymerase chain reaction (PCR) fragments be-tween 72 and 472, because the typical EcoRI and MseIprimers contain a 16-bp long sequence when excludingthe selective nucleotides. This set of fragment sizes is equiv-alent to the range of fragment sizes normally scored inAFLP studies (Meudt and Clarke 2007). This yielded 90–100 bands per AFLP profile, as recommended for experi-mental data sets (Vos et al. 1995; Vekemans et al. 2002).
Data from different profiles were converted into a binary1/0 presence–absence matrix.
True Fragments versus Experimental BandsBecause all selected fragments were individually identified,this allowed us to count the total number of fragments(hereafter referred as ‘‘true fragments''), the number ofbands in a putative electrophoretic setting (hereafter re-ferred as ‘‘experimental bands''), and the proportion ofthese that are nonhomologous.
A comparison between the phylogenies inferred from
true fragments and experimental bands allowed us to as-sess the impact of the lack of band homology on phyloge-netic accuracy in AFLP data sets. To this aim, two differentbinary matrices were generated from each simulated data
FIG. 1. Reference tree topologies. Alignments of DNA sequences
set, a matrix including the true fragments, and a matrix
were generated with Seq-Gen along symmetric and asymmetric
including the experimental bands.
trees with ancestral, uniform, and recent radiation. The scale bar, insubstitutions/position, corresponds to the trees with a divergence ofx1. All outgroup sequences were pruned for presentation.
Phylogenetic AnalysesAFLP-based phylogenies were estimated with PAUP*
For each tree topology, branch lengths were specified
(Swofford 2002) using the three most widely used methods
using three radiation models: uniform, recent, and ancient.
in AFLP data sets (Koopman 2005): 1) neighbor-joining
In uniform radiation, daughter branches were as long as the
(NJ), 2) minimum evolution (ME), and 3) maximum par-
parent branch. In recent radiation, daughter branches were
simony (MP). For distance-based trees (NJ and ME), each
half the evolutionary distance of the parent branch,
binary file was used to make a distance matrix using the Nei
whereas for ancient radiation, daughter branches were
and Li (1979) method. This distance method uses shared
twice as long as the parent branch. These trees had several
presences and is less susceptible to lack of homology than
short internal branches that made them difficult to resolve.
methods based on shared absences and presences (e.g., Eu-
Thus, they are trees where the interplay between sequence
clidean distance; Kosman and Leonard 2005). The charac-
similarity and tree topology will differentially impact the
ter-based MP method used the Wagner parsimony
phylogenetic accuracy. An outgroup was included in the
criterion directly on the binary matrix. Although this crite-
reference trees used as input in Seq-Gen to root the trees.
rion assumes equal loss–gain probabilities of restriction
This outgroup could be easily identified as the first se-
sites, it better fits the AFLP data than methods assuming
quence of the DNA alignment generated by SeqGen. For
unequal probabilities such as Dollo parsimony (Koopman
each tree created, 1,000 independent replicate data sets
2005). MP and ME were conducted using heuristic searches
were generated by simulating evolutionary change along
(10 replicates) with random addition using tree bisection
and reconnection swapping. When several equally optimaltrees were found (for MP and ME), only the first one was
Simulation of AFLP Fingerprints
used. Given that the primary objective of this study is to
A computer program written in C was used to simulate the
assess the accuracy of both branch length and topology
cutting of the generated DNA sequences with restriction
estimation for AFLP-based trees, we were forced to select
Phylogenetic Accuracy of AFLPs · doi:10.1093/molbev/msp315
a single optimal tree rather than working with a strict con-sensus to measure performance. NJ, ME, and MP trees werealso directly estimated from DNA sequences for the sake ofcomparison. This allowed us to quantify how much less ac-curate AFLP-based trees are with respect to the trees esti-mated from the corresponding source DNA sequences. Wedid not perform Bayesian trees because of the enormouscomputational time required for doing enough numberof replicates of all simulations performed.
Tree EvaluationEstimated trees were compared with their correspondingreference trees by the program Ktreedist (Soria-Carrascoet al. 2007), which takes into account both topologyand branch length information of a phylogenetic tree. Thisprogram computes a K-score that measures overall differ-ences in the relative branch length and topology of twophylogenetic trees after scaling one of the trees to havea global divergence as similar as possible to the other tree.
This allows for the comparison among trees differing inevolutionary rates and/or divergence scales (Soria-Carrascoet al. 2007). Trees compared with Ktreedist must be allrooted or unrooted. To this end, trees were saved with es-timated branch lengths, the outgroups were pruned, andunrooted reference and estimated trees were comparedwith Ktreedist. High K-scores indicate a poor match be-tween the estimated tree and the reference tree. An aver-
FIG. 2. Relationship between sequence divergence and the pro-
age K-score was computed for each set of 1,000 replicates,
portion of nonhomologous bands. Simulations were conducted
except for DNA-based trees, in which only 100 replicates
using asymmetric (a) and symmetric (b) reference trees with recent,
were used because of computational demands.
uniform, and ancestral radiation. Divergence values represent the
For some data sets, we also compared reference and es-
divergence in substitutions/position between the most internal
timated trees using the symmetric difference or Robinson–
node and the tip in the reference tree.
Foulds (R–F) distance (Robinson and Foulds 1981), whichonly takes into account the topology of a phylogenetic tree.
0.0003). Thus, as the evolutionary divergence among
This allowed us to evaluate the relative contribution of tree
AFLP profiles increased, many of the bands became unique
topology and branch length to tree accuracy.
to a taxon, only a few bands were shared among taxa, andan increasing amount of these became nonhomologous.
The proportion of nonhomologous bands was smaller in
recent than in uniform radiations, which in turn had
We generated phylogenies from two tree topologies (sym-
a smaller overall number of nonhomologous bands than
metric and asymmetric), three radiation models, three phy-
did ancestral radiations. Therefore, the impact of the lack
logenetic methods, and six evolutionary divergences. The
of homology on recent radiations with respect to that
symmetric and asymmetric tree topologies included 16
observed for ancient and uniform radiations was relatively
and 8 taxa, respectively (to preserve the distribution of in-
smaller in asymmetric than in symmetric trees.
ternode distances, the asymmetric trees had fewer taxa).
Figure 3 shows the quality of phylogenetic reconstruc-
The comparison of the number of base changes due to
tions in simulated data sets by comparing the reference
transitions and transversions as a function of the diver-
tree to the AFLP-based trees obtained with the three phy-
gence among paired sequences fitted a linear regression
logenetic methods. The two distance-based methods (NJ
on each simulated data set (r2 5 0.99; P , 0.001), thus
and ME) showed almost identical performance for any tree
not suggesting saturation.
topology and evolutionary divergence; because the two
The percentage of nonhomologous experimental bands
distance-based methods performed almost identically,
increased (i.e., homology decreased) with increasing evolu-
the ME method alone is presented hereafter. The relative
tionary divergence, reaching the value of 100% at a diver-
efficiencies of MP with the distance-based methods in ob-
gence of 0.4 (fig. 2). This increase is entirely explained by the
taining the correct topology and in estimating the branch
decreasing homology among comigrating bands from
lengths were critically affected by the tree topology. For
different taxa, because the proportion of bands containing
asymmetric trees, the distance-based methods were more
nonhomologous fragments within an individual remained ef-
efficient than the character-based MP method (fig. 3a). The
fectively invariable in all the simulated data sets (average 5
superiority of distance-based trees disappeared for the
Garcı´a-Pereira et al. · doi:10.1093/molbev/msp315
FIG. 3. Relative accuracy of phylogenetic methods for reconstructing AFLP-based trees. Simulations were conducted using asymmetric (a) andsymmetric (b) reference trees with ancestral, uniform, and recent radiation. K-scores were calculated comparing the estimated tree with thetrue tree. High K-scores indicate a poor match with the reference tree. Divergence values represent the divergence in substitutions/positionbetween the most internal node and the tip in the reference tree. The data points of ME and NJ are almost completely overlapping. Trees wereinferred from experimental bands.
symmetric tree topology, for which MP and distance-based
long sequence (Vos et al. 1995). This analysis, however, did
methods had similar accuracy results, with MP slightly in-
not have an appreciable effect on the reliability of the newly
ferior in the ancestral radiation model at large evolutionary
reconstructed AFLP trees, yielding nearly identical results
divergences (.0.10) (fig. 3b).
to those displayed in figure 4.
The overall success of phylogenetic reconstruction based
Because researchers are often interested on the accuracy
on characters that were simulated on symmetrical and
of tree topology, we evaluated this feature directly using R–
asymmetrical trees using true fragments, experimental
F topological distance. Figure 5 shows the average R–F
bands, and aligned DNA sequences, are presented in
distance to the reference tree for the same trees estimated
figure 4. As expected, DNA sequence-based phylogenies
in figure 4. In general, R–F distances displayed a similar pat-
produced much more reliable estimates of the true phylog-
tern to that shown by K-scores, with low evolutionary di-
eny than AFLP-based phylogenies under all the radiation
vergences producing more reliable trees. However, in terms
models and evolutionary divergences. More closely related
of topology, the likelihood of estimating the correct tree is
sequences produced more accurate trees, and accuracy
most dependent on whether radiation was ancient or re-
steadily decreased with evolutionary divergence. Thus,
cent. Remarkably, the likelihood of recovering the correct
for a given number of taxa, the K-score increased as the
tree topology was outstandingly higher for recent radia-
average branch length increased, and this increase occurred
tions, which displayed relatively low R–F distances even
much more rapidly for AFLP-based trees than for DNA-
at large evolutionary divergences. This pattern was not ob-
based trees. For divergences below approximately 0.05,
served for uniform and ancestral radiation models, in which
phylogenies from ancestral radiations were more difficult
the likelihood of recovering the correct topology rapidly
to reconstruct accurately than those from uniform radia-
decreased with evolutionary divergence. Consequently, un-
tions, which, in turn, were more difficult to reconstruct
reliable branch length estimation appears to play a major
than recent radiations (not easily appreciated in fig. 4 be-
role on the magnitude of K-score for recent radiations.
cause of the scale). In all cases, the accuracy of AFLP-based
In contrast, inaccurate tree topology estimation has
trees decreased dramatically at greater divergences, with no
a key contribution to K-score for uniform and ancestral
substantial differences in performance among the different
radiation models. Because fragment homology is increased
AFLP-based trees conducted on experimental bands had
by excluding smaller fragments (Althoff et al. 2007), we
a generally lower accuracy than AFLP-based trees con-
tested the effect of fragment size on tree accuracy using
ducted on true fragments (figs. 4 and 5). In some ME trees,
only large fragments. We only used fragments longer than
the reliability of trees inferred from experimental bands
100 nt, which correspond to PCR fragments of 132 bp, be-
strikingly outperformed that of trees inferred from true
cause the typical EcoRI and MseI adaptors contain a 16-bp
fragments at large evolutionary divergences (.0.20)
Phylogenetic Accuracy of AFLPs · doi:10.1093/molbev/msp315
FIG. 4. Relationship between sequence divergence and tree accuracy. Trees were estimated using both MP (a) and ME (b) methods. Simulationswere conducted using asymmetric and symmetric reference trees with ancestral, uniform, and recent radiation. K-scores were calculatedcomparing the estimated tree with the true tree. High K-scores indicate a poor match with the reference tree. Divergence values represent thedivergence in substitutions/position between the most internal node and the tip in the reference tree. AFLP-based trees were inferred fromtrue fragments (i.e., fragments individually identified irrespective of their size) and experimental bands (i.e., bands observed in a putativeelectrophoretic setting, which could include one or more fragments of a given size). NJ yielded similar results to those of ME and is notpresented.
despite the presumably negative impact of nonhomolo-
Because the number of nonhomologous experimental
gous bands (figs. 4b and 5b). This unexpected observation
bands is a function of the number of fragments produced,
is the consequence of an excessively large divergence in
which in turn depends on the GC content (GC 5 0.5 in the
some pairwise comparisons among AFLP profiles, for which
results shown so far), we evaluated the impact of varying
the Nei and Li distance is undefined. In these cases, the
the GC content on the overall success for phylogenetic re-
program PAUP*4 arbitrarily sets the values of these un-
construction (fig. 7). For these simulations, we used the
defined distances as twice the distance of the largest de-
HKY model (Hasegawa et al. 1985) in Seq-Gen assuming
fined distance in the distance matrix. To avoid the
equal transition and transversion rates. The relationship
undesirable effects of these undefined distances on tree
between phylogenetic accuracy and genome GC content
accuracy, we tried a value of 1.0 as the default upper
yielded contrasting results depending on the set of markers
bound divergence in the matrix generated by PAUP*4.
used to infer the phylogeny. There is a negative relationship
This resulted in a consistently lower efficiency of
between phylogenetic accuracy and GC content for AFLP-
experimental bands than true fragments for inferring
based trees inferred from experimental bands. In contrast,
AFLP-based trees across the entire set of evolutionary
this relationship turns out to be positive for AFLP-based
divergences (fig. 6), as expected.
trees inferred from true fragments. The topology or
Garcı´a-Pereira et al. · doi:10.1093/molbev/msp315
FIG. 5. Testing the effect of tree topology on phylogenetic accuracy. Average R–F topological distances to the reference tree for the same treesestimated in figure 4. High R–F scores indicate a poor match with the reference tree.
phylogenetic method used had a small effect on this rela-
To better visualize the relationship between K-score and
tionship, which was more apparent in asymmetric than in
tree accuracy, we plotted the reference tree and the DNA-
symmetric trees. No appreciable effect of GC content on
and AFLP-based trees estimated from one of the replicate
phylogenetic accuracy was observed for DNA-based trees.
data sets. Seq-Gen introduces random variation into the
FIG. 6. Testing the effect of undefined distances on tree accuracy. A value of 1.0 was used as the default upper bound divergence in the distancematrix generated by PAUP*4. Trees are estimated using ME for the three cases displaying an anomalous pattern in Figure 4b. K-scores werecalculated comparing the estimated tree with the true tree. High K-scores indicate a poor match with the reference tree. Divergence valuesrepresent the divergence in substitutions/position between the most internal node and the tip in the reference tree. AFLP-based trees wereinferred from experimental bands and true fragments.
Phylogenetic Accuracy of AFLPs · doi:10.1093/molbev/msp315
FIG. 7. Relationship between tree accuracy and GC content. Simulations were conducted using asymmetric (a) and symmetric (b) referencetrees. The divergence between the most recent common ancestor of the ingroup and the tip in the reference tree was set to 0.1 substitutions/position. K-scores were calculated comparing the estimated tree using MP or ME with the reference tree. High K-scores indicate a poor matchwith the reference tree. To make fair comparisons among trees estimated from different GC contents, sequence lengths ranged from 20 to 40kb to get 90–100 experimental bands per AFLP profile.
lengths of the trees it generates with respect to the refer-
As expected, the accuracy of AFLP-based phylogenies was
ence tree that may obscure the evolutionary model when
the greatest in the least divergent data sets (Althoff et al.
only one replicate is tested. However, for a given evolution-
2007). However, the efficiency of phylogenetic reconstruc-
ary model, K-scores from independent replicates were
tion decreased dramatically in more divergent data sets.
nearly identical to each other (standard error across
Above a divergence of approximately 0.05, AFLP-based
1,000 replicates ranged from 0.00005 to 0.002), indicating
phylogenies were largely inaccurate irrespective of the dis-
a strong correlation between the reference tree and the
tinct topologies, radiation models, or phylogenetic meth-
resulting simulated trees. Figure 8a and b shows the results
ods used (fig. 4). For these divergences, AFLP profiles
corresponding to a symmetric topology under a uniform
from different taxa shared so few fragments that trees in-
radiation model. At small evolutionary divergences typical
ferred from experimental bands were almost effectively
of very closely related genotypes, haplotypes were correctly
random with respect to the reference tree (fig. 8b). For di-
grouped within the AFLP-based trees but estimated branch
vergences below 0.05, generally the likelihood of estimating
lengths were very inaccurate when compared with those
an accurate AFLP-based tree was most dependent on tree
obtained in the DNA-based tree (fig. 8a). However,
topology or on whether radiation was ancient or recent. At
AFLP-based trees produced vastly incorrect topologies
this divergence level, simple symmetric topologies were re-
and branch lengths for large evolutionary divergences typ-
markably more likely to be accurately reconstructed than
ically above the species rank (fig. 8b). The performance of
the more complex asymmetric topologies (fig. 8a and c).
AFLP-based trees was drastically reduced for asymmetric
Similarly, the efficiency in recovering the correct tree
topologies even at small divergences (fig. 8c)
was sensibly superior for recent than for uniform radiations,which in turn had a better performance than ancient ra-
diations (figs. 4 and 5), as expected from their increasinglyshorter ancestral branches (Cantarel et al. 2006). Conse-
Relationship between Sequence Divergence and
quently, AFLP markers appear to be appropriate for phy-
logenetic inference as long as divergence is small, the
We have evaluated the limits of AFLP markers for phylo-
topology of the reference tree is symmetric, and not very
genetic reconstruction by assessing the accuracy of recov-
short ancestral branches exist.
ering the correct tree for diverse data sets evolving under
It is not feasible to precisely define the taxonomic rank
different tree topologies, radiation models and evolution-
that would correspond to the divergences assayed in this
ary divergences. Our results demonstrate that phylogenetic
study. Levels of genetic divergence between congeneric
accuracy, as measured in both topology and branch length,
species and confamilial genera vary among taxa as a result
is seriously compromised by sequence divergence and, to a
of differences in age, rate of molecular evolution, or biolog-
lower extent, by tree topology and radiation pattern (fig. 4).
ical features (Johns and Avise 1998; Sites and Marshall
Garcı´a-Pereira et al. · doi:10.1093/molbev/msp315
FIG. 8. Phylogenetic accuracy with respect to evolutionary divergence. (a) Symmetric trees. The divergence between the most recent commonancestor of the ingroup and the tip in the reference tree is of 0.02 substitutions/position. (b) Symmetric trees. The divergence between themost internal node and the tip in the reference tree is of 0.20 substitutions/position. (c) Asymmetric trees. The divergence between the mostinternal node and the tip in the reference tree is of 0.02 substitutions/position. MP trees estimated from one randomly chosen replicate dataset. The scale bar of MP trees corresponds to the number of steps.
2003). An extensive literature review revealed a rough to-
and Peakall 2000; Bussel et al. 2005; Koopman 2005; Althoff
pological congruence of AFLP and ITS trees (Koopman
et al. 2007). For example, human and chimpanzee have ac-
2005), suggesting that AFLP markers are informative at
cumulated a genomic divergence of 1.24% after they di-
somewhat lower taxonomic levels than ITS sequences. If
verged from each other 6 Ma (Chen and Li 2001). This
we use congruence with ITS as a proxy for determining tax-
implies that sequences evolving under the scenario repre-
onomic rank, AFLP markers should be useful as a source of
sented in figure 8a would produce reliable AFLP-based tree
phylogenetic information at a level generally not exceeding
topologies among species that diverged 19 Ma, provided an
closely related congeneric species (Savelkoul et al. 1999)
evolutionary rate similar to that of hominids.
and would be quite informative in determining phylogeo-
Because simulation studies are performed under well-
graphic patterns. Our data, however, suggest that under
defined conditions, our results almost certainly overesti-
certain circumstances, AFLPs may also be suitable to recon-
mate our ability to reconstruct accurate phylogenies
struct deeper phylogenies than usually accepted (O'Hanlon
from empirical data. For example, a substantial source of
Phylogenetic Accuracy of AFLPs · doi:10.1093/molbev/msp315
uncertainty in AFLP data not included in our simulations is
simulations show this comparison and indicate that the
that due to an insufficient fragment mobility resolution or
lack of band of homology among taxa quickly increases
an incorrect scoring of bands (Pompanon et al. 2005).
with divergence, thus rapidly compromising the phyloge-
Holland et al. (2008) found that the choice of parameter
netic usefulness of AFLP data sets (fig. 2). The upper limit
settings in automated scoring software notoriously affects
to fragment homology is reached at a divergence threshold
phylogenetic resolution. Similarly, the data sets generated
of only 0.4, in which the few bands that are still shared
in this study were based on the simulation of random DNA
among taxa are all nonhomologous. Consequently,
sequences, but in fact DNA sequences of living organisms
data sets containing highly diverged genotypes, typical
follow specific nonrandom patterns (Karlin and Burge
of ancient radiations, are consistently more affected by
1995). These compositional heterogeneities have an impor-
the lack of band homology than data sets displaying more
tant impact on the amount of nonhomologous bands,
closely related genotypes, as those seen in recent radiations
genomic coverage, and information content of AFLP data
sets (Young et al. 1999; Peng et al. 2000; Campbell and
One of the primary motivations of this study was to
Bernatchez 2004; Caballero and Quesada 2010). Thus,
quantify the impact of the lack of homology among AFLP
our estimates of reconstruction accuracy must be seen
bands on phylogenetic accuracy. To this end, we compared
as best case values.
the relative accuracies of trees based on experimental
We note that although our in silico simulations did not
bands (susceptible to be nonhomologous) and trees based
use selective nucleotides, in practice, selective nucleotides
on the corresponding true fragments (where nonhomology
are always employed in experimental AFLP studies. How-
is nonexistent) (figs. 4 and 5). As expected, AFLP-based
ever, the role of selective nucleotides is just to sample a ran-
trees inferred from experimental bands had a generally
dom subset of restriction fragments to get a reduced
lower accuracy than AFLP-based trees inferred from true
number of bands (90–100) per AFLP profile (Vos et al.
fragments. However, the difference in efficiency due to
1995). The number of bands containing nonhomologous
the lack of band homology was surprisingly small compared
comigrating fragments within an AFLP profile increases
with the much more reliable trees obtained when using
rapidly with the number of bands (Gort et al. 2006) and
is independent of the genome size for a given GC content
The dramatically lower performance of AFLP-based trees
(Caballero and Quesada 2010). This implies that for a similar
with respect to DNA-based trees is likely the consequence
in silico setting, results with and without selective nucleo-
of the much higher information content and lower nucle-
tides would be similar provided that the number of bands
otide sampling error of DNA sequences (Kumar and
per AFLP profile is the same. This was corroborated by per-
Gadagkar 2000). Indeed, increasing the number of AFLP
forming additional in silico AFLP runs using one combina-
markers would still result in a poor performance, as indi-
tion of selective nucleotides (A for both restriction sites)
cated by the more than 1,000 true fragments scored at a di-
under different radiation models, tree topologies, and evo-
vergence threshold of 0.4 (fig. 4). The difference in
lutionary divergences. For fair comparisons, the genome
performance between DNA sequences and AFLPs did
size in these simulations was increased to 12 Mb in order
not change substantially even when the length of DNA se-
to get a number of bands (90–100) per AFLP profile similar
quences used to construct sequence-based trees was set to
to that observed without selective nucleotides. As ex-
a number of nucleotides (700) similar to the number of
pected, the accuracy of phylogenetic reconstruction using
AFLP fragments scored across each evolutionary divergence
selective nucleotides was very similar to that without selec-
(not shown). This indicates that the poorer performance of
tive nucleotides Supplementary
AFLP-based trees is not the result of sampling a much lower
number of informative AFLP fragments than nucleotides.
Other commonly invoked limitations of AFLP data sets,
Impact of the Lack of Band Homology on
such as the asymmetry of loosing and gaining fragments,
the dominant nature of AFLP characters, or the possible
One basic assumption of AFLP-based phylogenies is that all
nonindependence of fragments, do not appear to have
comigrating bands are homologous or that only a very
an effect as dramatic on phylogenetic accuracy as that seen
small proportion of bands are nonhomologous (Koopman
here (Koopman 2005; Althoff et al. 2007; Simmons et al.
2005). In the latter case, it is assumed that the phylogenetic
2007). Thus, the lower information content of AFLP data
signal from homologous bands would overcome the phy-
sets is a factor with a much higher negative impact on phy-
logenetic noise from nonhomologous bands (Bussel et al.
logenetic accuracy than the lack of band homology itself.
2005). Several studies, however, have pointed out that the
Consequently, even in experiments where all fragments
lack of band homology could be more prevalent than orig-
could be individually characterized by sequencing, it would
inally thought and that it is especially problematic for dis-
not be possible to substantially improve the reliability of
tantly related taxa (O'Hanlon and Peakall 2000; Vekemans
AFLP-based trees. This is a challenging finding with respect
et al. 2002; Mechanda et al. 2004; Althoff et al. 2007;
to the traditional view that the lack of band homology is
Caballero and Quesada 2010). Nevertheless, because taxo-
the most important drawback of AFLP markers regarding
nomic rank is not comparable among taxa, it is difficult
phylogenetic accuracy (O'Hanlon and Peakall 2000; Bussel
to interpret the general relevance of these results. Our
et al. 2005; Althoff et al. 2007).
Garcı´a-Pereira et al. · doi:10.1093/molbev/msp315
In silico AFLP studies have shown that the proportion of
the utility of AFLP markers for phylogenetic reconstruction
nonhomologous bands increases linearly with the total
under certain evolutionary scenarios, despite the consis-
number of fragments scored, which in turn rely on the
tently poorer information content of AFLP data sets with
GC content (Caballero and Quesada 2010). Here we show
respect to DNA sequences.
that the lack of homology determines a distinct impact ofGC content on the accuracy of trees inferred from exper-
Differences in Performance among Phylogenetic
imental bands and true fragments (fig. 7). As GC content
increases, EcoRI and MseI cuts are less frequent because of
We did not find clear-cut differences in the performance of
their AT-biased recognition motifs. This results in a lower
the three phylogenetic methods commonly used in AFLP
number of fragments and a lower probability of comigra-
data sets. Distance methods (NJ and ME) had similar recon-
tion (Vekemans et al. 2002), which increases the accuracy
struction efficiencies, likely as a consequence of NJ being
of trees inferred from experimental bands (fig. 7). In con-
a special case of ME (Rzhetsky and Nei 1992). When tree
trast, because true fragments are by definition always ho-
topologies are symmetrical, all tree-building methods pro-
mologous, increased GC content results in a decrease of
duced similar trends, with distance methods performing
the number of informative homologous characters. This
better with some radiation models at large distances
in turn leads to less reliable trees. These findings have
(fig. 3b). Distance methods outperform MP when tree to-
two significant implications. First, the proportion of ho-
pologies are asymmetrical (fig. 3a). However, undefined dis-
mologous bands and the concomitant phylogenetic ac-
tances may seriously decrease the relative efficiency of
curacy depends on a species-specific feature such as GC
distance versus MP methods. This occurs when the average
content, which is usually unknown in nonmodel organ-
evolutionary divergence is large (figs. 4b, 5b, and 6), or some
isms. Second, the comparison among species differing in
distantly related genotypes are present in the data set
GC content will determine differential lineage-specific
(Koopman and Gort 2004). This problem has a higher im-
impacts of the lack of band homology, which will
pact on true fragments than on experimental bands. This is
certainly compromise the accuracy of phylogenetic
because the comigration of fragments in experimental
bands increases the similarity among taxa (Althoff et al.
Experimental approaches have been developed to re-
2007), making less likely the generation of undefined dis-
duce the impact of the lack of band homology in AFLP-
tances. Our results show that when this problem occurs, it
based trees. Dasmahapatra et al. (2009) have recently
is useful to use parsimony (fig. 4a). This will usually exclude
proposed a protocol that minimizes the number of nonho-
problematic characters that are very variable among taxa
mologous AFLP bands in experimental studies by excluding
by using only those characters that are parsimoniously
those bands that are suspicious to be nonhomologous
informative (Lockhart et al. 1994).
across species. This approach increases the phylogenetic
Simmons et al. (2007) similarly noted that MP per-
signal of experimental AFLP data sets and provides results
formed comparably to ME in symmetric trees but that
similar to those simulated here for true fragments, in which
MP outperforms ME in asymmetric trees. Two reasons ex-
nonhomology is nonexistent. This allowed them to con-
plain this discrepancy with respect to our results in asym-
struct an AFLP-based phylogeny of seals in which AFLP
metric trees: 1) Their conclusions only apply to tree
fragments maintained phylogenetic signal over a timescale
topology and not to both topology and branch length
of ;15 my (Dasmahapatra et al. 2009). The AFLP tree con-
as is the case here and 2) the distribution of internode dis-
structed for seals resembles the most favorable scenario for
tances was not preserved by reducing the number of taxa in
AFLP-based trees in terms of tree topology, that is, a tree
asymmetric trees, thus making asymmetric trees compar-
with recent radiation. As shown in our Figure 5, under re-
atively much more difficult to reconstruct. Although we
cent radiation scenarios, the topology of AFLP trees is well
cannot overemphasize the lack of resolution provided
preserved, even for large divergences. This agrees with the
by undefined distances, these results do not permit making
fact that the AFLP phylogeny of seals displays a topology
any generalized statement about the relative accuracies of
closely resembling those based on both nuclear (Fulton and
character-based MP versus distance-based (ME and NJ)
Strobeck 2006) and mitochondrial DNA (mtDNA) sequen-
methods for building AFLP-based trees. However, the un-
ces (Arnason et al. 2006). Even so, there are still some dis-
certainty in deciding which phylogenetic method to use is
crepancies with respect to the interspecific relationships of
generally small relative to the uncertainty arising from
some taxa. Thus, up to three taxa (Callorhinus ursinus, Pho-
factors such as divergence, nonhomology of bands or
ca hispida, and Ommatophoca rossi) display an incorrect
the low information content of AFLP markers. Whatever
interspecific relationship with respect to the nuclear phy-
methods are used, robust interpretations of reconstructed
logeny. However, the relative branch lengths of the recon-
AFLP-based trees will require the confirmation by an
structed AFLP phylogeny of seals are only rarely
independent source of data.
concordant with those from the nuclear and mtDNA trees.
This agrees with our observation in Figure 4 that recon-
structions from AFLP trees are largely wrong in termsof branch lengths for recent radiation. The data from
is available at Molecular Biology and
Dasmahapatra et al. (2009), however, further emphasize
Phylogenetic Accuracy of AFLPs · doi:10.1093/molbev/msp315
Gort G, Koopman WJM, Stein A. 2006. Fragment length
distributions and collision probabilities for AFLP markers.
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ETUDE RETROSPECTIVE DES EXAMENS GASTROSCOPIQUES EFFECTUES A LA CLINIQUE DES BREVIAIRES DU 02/12/2002 AU 05/12/2005 Marc BURIN des ROZIERS Clinique Equine des Bréviaires, 4 route de Vilpert, 78610 LES BREVIAIRES, FRANCE Introduction : L'utilisation de la fibroscopie pour l'observation de l'estomac des équidés a révélé la prévalence
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