# Evidence for GC-biased gene conversion as a driver of between-lineage differences in avian base composition

Claudia C. Weber, Bastien Boussau, Jonathan Romiguier, Erich D. Jarvis, H.

Ellegren

► To cite this version:

Claudia C. Weber, Bastien Boussau, Jonathan Romiguier, Erich D. Jarvis, H. Ellegren. Evidence for GC-biased gene conversion as a driver of between-lineage differences in avian base composition. Genome Biology, 2014, 15, pp.549. 10.1186/s13059-014-0549-1. hal-02045537

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RESEARCH

Open Access

# Evidence for GC-biased gene conversion as a driver of between-lineage differences in avian base composition

Claudia C Weber¹, Bastien Boussau², Jonathan Romiguier³, Erich D Jarvis⁴ and Hans Ellegren¹*

## Abstract

**Background:** While effective population size ($N_e$) and life history traits such as generation time are known to impact substitution rates, their potential effects on base composition evolution are less well understood. GC content increases with decreasing body mass in mammals, consistent with recombination-associated GC biased gene conversion (gBGC) more strongly impacting these lineages. However, shifts in chromosomal architecture and recombination landscapes between species may complicate the interpretation of these results. In birds, interchromosomal rearrangements are rare and the recombination landscape is conserved, suggesting that this group is well suited to assess the impact of life history on base composition.

**Results:** Employing data from 45 newly and 3 previously sequenced avian genomes covering a broad range of taxa, we found that lineages with large populations and short generations exhibit higher GC content. The effect extends to both coding and non-coding sites, indicating that it is not due to selection on codon usage. Consistent with recombination driving base composition, GC content and heterogeneity were positively correlated with the rate of recombination. Moreover, we observed ongoing increases in GC in the majority of lineages.

**Conclusions:** Our results provide evidence that gBGC may drive patterns of nucleotide composition in avian genomes and are consistent with more effective gBGC in large populations and a greater number of meioses per unit time; that is, a shorter generation time. Thus, in accord with theoretical predictions, base composition evolution is substantially modulated by species life history.

## Background

Life history traits (LHTs) and, by extension, effective population size ($N_e$) have long been connected to patterns of sequence evolution. Lower body mass and shorter generation time predict rapid molecular evolution [1-5], while small-bodied animals with putatively large populations tend to show overall decreases in the $d_N/d_S$ ratio [6,7], reflecting variation in both substitution rates and selection efficiency between lineages. Meanwhile, the connection between population size, generation time, and base composition is less well understood. In principle, any life history-related trait affecting substitution patterns should also impact on the evolution and the dynamics of base composition. GC-biased gene conversion (gBGC)

associated with meiotic recombination leads to the preferential fixation of GC in AT/GC heterozygotes and is a major determinant of base composition. Direct experimental evidence is currently limited to S. cerevisiae, with a significant 1.3% excess of transmitted GC alleles thought to result from a bias in the mismatch repair machinery [8-11]. However, evidence for its effects is observed across a wide range of taxa [12-16], leading to a widespread association between GC content and crossover rates [8,13,17-20].

Importantly, it has been suggested that LHTs predict how strong the effects of gBGC on compositional evolution are [21]. Much as recombination locally modulates $N_e$ [22], $N_e$ is in turn predicted to increase the efficacy of gBGC in the same manner that it increases the efficacy of selection. This is because GC alleles behave as though they were positively selected when their fixation is favored and the effect of drift will decrease with

<footnote>* Correspondence: Hans.Ellegren@ebc.uu.se
¹Department of Evolutionary Biology, Evolutionary Biology Centre, Uppsala University, Norbyvägen 18D, SE-752 36 Uppsala, Sweden
Full list of author information is available at the end of the article</footnote>
increasing N<sub>e</sub> [15,23]. Species with short generation
times additionally experience a greater number of mei-
oses per unit time, and therefore more frequent gBGC.
In agreement with this idea, Romiguier et al. [21]
observed that mammalian lineages show negative cor-
relations between both body mass, expected to be
negatively associated with N<sub>e</sub>, and generation time and
GC. Subsequent studies on mammals have reinforced
these findings [24,25].

The interpretation of these results may however be
complicated by differences in chromosomal architecture
between species under comparison. Large-scale chromo-
somal rearrangements may obscure the impact of LHTs
on base composition by shifting the recombination land-
scape [26,27]. Rodents, which exhibit weaker gBGC than
primates despite having larger populations, are a striking
example. This is thought to be a consequence of having
lengthened chromosome arms and reduced crossover
rates due to a shift to acrocentric centromeres [28]. Add-
itional studies in different clades are therefore necessary
to disentangle the effects of changes in the recombination
map and changes in population size on GC content evolu-
tion [25].

Here, we explore the idea that some of the caveats as-
sociated with changes in chromosomal architecture
might be avoided by studying birds. The avian karyotype
comprises a large number of chromosomes (haploid
count = 39 for chicken, typical for most birds) with a re-
markably low rate of interchromosomal rearrangement
between species [29-32]. For instance, despite a split
time of 84 to 94 million years (My, reported in our com-
panion phylogenomic study [33]), the karyotypes of
chicken and zebra finch differ merely by one fission and
one fusion event [34]. Accordingly, we expect the stabil-
ity of the avian karyotype to translate to greater stability
in broad-scale recombination landscapes over time. This
is empirically supported by a correlation in the rate of
recombination in 1 Mb windows between homologous
regions of chicken and zebra finch chromosomes [19].
Additionally, birds lack a functional copy of *PRDM9*
[35], which is expected to reduce shifts in the recombin-
ation landscape associated with rapid hotspot turnover
[36,37]. As a consequence, between-lineage variation in
composition should be owing to differences in LHTs ra-
ther than genome architecture. Meanwhile the effects of
recombination on a given sequence will have remained
consistent throughout its history, and are therefore ex-
pected to leave clear signatures [36,38].

Avian genomes show considerable variation in chromo-
some size, with the majority of chromosomes being
small micro-chromosomes. Given the requirement for
at least one crossover per chromosome [39], this results
in high crossover rates [40-42]. Signatures of gBGC
ought to be most readily detected in lineages with more

fragmented karyotypes, that is, many small chromo-
somes [16,24]. Indeed, the continuing reinforcement of
intragenomic heterogeneity in GC content appears to be
particularly pronounced in chicken [43], unlike in some
mammals where erosion of GC-rich regions has oc-
curred [15,21,28,44-46]; these studies have excluded
CpG sites in the analyses so there is a remaining issue
how such sites influence the evolution of base compos-
ition. Finally, base composition varies greatly between
different bird lineages [47].

Thus, birds have several features that make them espe-
cially interesting for investigating the interactions between
recombination, selection, base composition and substitu-
tion rates. A recent effort that sequenced 45 whole bird
genomes along with three previously published ones
(48 total, see Additional file 1) covering all major avian
orders now provides the opportunity to investigate these
questions [48]. The availability of orthologous coding and
intronic sequences from these species allows us to exam-
ine trends that may be less apparent with fewer sequences
or taxa. Here, we focus on the impact of between-lineage
differences in effective population size and time-scaled
recombination rates on base composition in birds, and
test whether gBGC might explain the substantial vari-
ation in GC content observed. We first ask whether
there is a significant negative association between LHTs
and GC content, and find this to be the case. We also
test how robust this result is by employing alternative
proxies of N<sub>e</sub> based on phylogenetic discord among
gene trees. We then consider the degree to which differ-
ent classes of sites are affected, how it corresponds to
recombination rate estimates, and whether the impact
of gBGC on the base composition of avian genomes is
ongoing.

Results

Correlation between GC3 and life history traits is
consistent with stronger gBGC in large populations with
short generation times

Given the substantial heterogeneity in GC3 content (the
proportion of GC at third codon positions) between avian
species [33,47] (Figure 1), we asked whether there is evi-
dence that third codon sites, which should be the least
constrained coding positions, might be subject to the in-
fluence of recombination-associated gBGC. Species with
smaller body mass are expected to have both shorter gen-
eration times and larger effective population sizes, increas-
ing both the number of meioses per unit time and the
efficacy of gBGC [21,23]. If gBGC is a factor in determin-
ing GC, small-bodied species ought then to have elevated
GC. This is indeed what we observed, with species with
greater body mass exhibiting lower GC3 than species with
smaller body mass (Spearman's rho = -0.5866, P = 6.2e-05,
n = 42; see Figure 2). Despite the limited number of
<figure><img src="image_2.png" /><figcaption>**Figure 1** GC3 content varies substantially between different avian lineages. Total evidence nucleotide tree [33] showing differences in GC3 content between lineages. Ancestral GC3 was estimated by ML using contMap from R phytools for illustration. See Additional file 1 for species names.</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>**Figure 2** GC3 content is significantly negatively correlated with body mass. Small-bodied birds have elevated GC3 content compared to larger-bodied species.</figcaption></figure>
species for which data are available, maximum longevity (rho = -0.3645, *P* = 0.0616, *n* = 27) and age of first female sexual maturity (rho = -0.5957, *P* = 0.0071, *n* = 19) showed similar trends, consistent with the possibility that short generation times lead to an increase in GC3 assuming equilibrium has not yet been reached. In the following we only examine body mass, as this maximizes the number of species we can consider.

Nucleotide composition at both coding and non-coding
sites is predicted by body mass

Selective constraint and mutational and neutral forces
acting on base composition interact with each other and
modulate to what extent the composition at a given class
of site varies. In species with large effective population
sizes, selection against weakly deleterious mutations is
more efficient, which can lead to synonymous sites being
constrained, for instance due to selection on transla-
tional efficacy [49]. According to theory, this trend
would be further exacerbated by locally increased N_e in
regions of high recombination, owing to the increased
efficacy of selection [22]. However, support for increased
levels of codon usage bias in highly expressed genes is
thus far absent in birds [50,51] and weak if at all present
in mammals [51-55].

To nevertheless rule out the possibility that our findings
can be accounted for by selection on synonymous sites,
we assessed whether intronic sequences are similarly

negatively correlated with body mass and found this to be
the case (rho = -0.4411, P = 0.0038). Selection on mRNA
folding tends to be enhanced by high GC content and is
thus suggested to constrain sequence evolution [56]. How-
ever, as this is hypothesized to relate to translational effi-
ciency [56,57] only mature mRNA structure is relevant.
Thus, intronic GC content ought not to be subject to con-
straint in this respect, and gBGC is a plausible explanation
for the pattern observed.

As it is well-established that gBGC influences both
synonymous and non-synonymous coding positions
[58], we next tested whether the relationship between
body mass and GC3 can be generalized to first and sec-
ond codon positions. As expected, GC1 (rho = -0.5631,
*P* = 0.0001) and GC2 (rho = -0.5639, *P* = 0.0001) show
significant negative correlations with body mass that
are of a similar magnitude to that observed for GC3.
However, as one would predict if first and second cod-
ing positions are under stronger selective constraint
than third positions, the range of GC values is substan-
tially narrower (sd = 0.003 for GC1, sd = 0.0015 for
GC2, sd = 0.0189 for GC3; see Figure 3). Owing to the
structure of the genetic code, differences in GC2 between
species ought to be associated with slight changes in
amino acid usage between lineages. This is indeed ob-
served (Additional file 2) and in agreement with previ-
ous observations that amino acid usage correlates with
base composition [59].

<figure><img src="image_4.png" /><figcaption>**Figure 3** GC3 is more heterogeneous between species than GC1 and GC2. GC content for first, second, and third codon positions and introns (GCi). Species were ordered from left to right by ascending GC3 content. See Additional file 1 for species names.</figcaption></figure>
The fact that first, second, and third coding sites as well as intronic sites exhibit correlations with body mass is consistent with a pervasive influence of gBGC on base composition, while the strength of the effect on a given class of site appears to be modulated by the degree of selective constraint. These relationships are not explained by phylogenetic inertia, as controlling associations between body mass and GC at different classes of sites for phylogeny did not render the correlations non-significant (Additional file 3).

Orthologs with high between-species GC heterogeneity show a stronger effect of gBGC on base composition

In addition to treating different classes of sites separately, we can distinguish between orthologs that show high or low levels of compositional heterogeneity (that is, variance in GC) among species when addressing the relationship between gBGC and LHTs. Such heterogeneity is expected to be most pronounced in sequences that are differentially affected by recombination-associated GC fixation bias due to differences in generation time and $N_e$; that is, sequences in highly recombining regions. Conversely, sequences experiencing little recombination overall should be more homogenous between species. When only genes whose third sites do not strongly reject the homogenous TN93 + GAMMA model in favor of the non-stationary model of Galtier and Gouy [60], hereafter referred to as 'homogenous' genes, were considered (n = 310) the correlation between body mass and GC3 became modestly weaker (rho = -0.4563, P = 0.0026) than when considering 'non-homogenous' (n = 1,470) genes (rho = -0.5887, P = 5.7e-05).

The difference in the strength of the correlation is not owing to sample size, as none of 10,000 randomizations where 310 non-homogenous genes were randomly sampled gave a correlation between GC3 and body mass that was equal to or weaker (that is, less negative) than that observed for the homogenous set. However, given that both the standard deviation and mean of GC3 are lower for homogenous genes (sd = 0.0084, mean = 0.4565) than for non-homogenous genes (sd = 0.0201, mean = 0.4991), the smaller correlation coefficient is expected, particularly if a low and less heterogeneous GC3 is indicative of gBGC having a weaker influence on these sequences. That there is nevertheless a relationship between GC3 and body mass for homogenous genes, albeit with a shallower slope (see Figure 4), cautions against the assumption that gBGC has no influence at all where the homogenous model is not rejected.

Additionally, we compared correlations between the 830 orthologs with the highest and lowest variance in GC3 [46], respectively, and obtained similar results. GC12 (rho = -0.6604, P = 2.8e-06), GC123 (rho = -0.6965, P = 6.3e-07), and GC3 (rho = -0.7057, P = 4.4e-07) were significantly negatively correlated with body mass for high-variance orthologs. The somewhat stronger correlation for GC3 is expected given the above definition of 'high variance'. Low-variance orthologs showed a similar but weaker pattern. GC3 had the weakest correlation for the low-variance set (rho = -0.3138, P = 0.0409), as expected given its reduced heterogeneity between species (Figure 5). GC12 (rho = -0.3935, P = 0.0095) and GC123 (rho = -0.357, P = 0.0193) were somewhat more strongly correlated, but less so than for high-variance genes (Figure 5).

<figure><img src="image_5.png" /><figcaption>Figure 4 GC3 is more strongly associated with body mass for genes with non-homogenous between-species composition.</figcaption></figure>
<figure><img src="image_6.png" /></figure>

Introns associated with the high variance orthologs showed significant correlations between GC content and body mass (rho = -0.6451, *P* = 5.3e-06; Figure 5), whereas those associated with low-variance orthologs showed no significant correlation (*P* = 0.4378). These trends are consistent with base composition of introns of high- and low-variance genes evolving in a manner similar to the associated coding sequences, but with a weaker impact on the non-coding sequences.

High between-species variance is driven by GC evolution
in small-bodied birds

In order to test if high GC3 variance orthologs were pro-
duced by increases of GC3 in small-bodied species or
decreases of GC3 in large-bodied species, we computed
a time-corrected index of GC3 conservation for 19 pairs
of species (following [61], see Materials and Methods).
We again retrieved strong correlations with body mass
when we considered this measure of GC3 dynamics in-
stead of average GC3. GC3 conservation was higher
between pairs of large-bodied species than pairs of
small-bodied species (rho = 0.72, P = 8.2e-04; Figure 6)
in agreement with predictions and with previous results in
mammals [61]. GC-content heterogeneity between species
is presumably due to increased gBGC in small-bodied spe-
cies, whereas the GC-content of a gene tends to remain
similar when evolving in large-bodied lineages.

GC correlates with multiple proxies of population size

While body mass is a convenient measure to consider in
absence of data on actual effective population sizes,

correspondence between the two may not necessarily be
strong in birds [62]. To ensure that our findings are ro-
bust, we considered an additional approach. Shared ances-
tral polymorphisms can lead to disagreement between
gene and species trees for closely related lineages, particu-
larly when N<sub>e</sub> is large. We may therefore estimate popula-
tion size by using information on discordance between
gene and species trees to compute coalescent-based in-
ternal branch lengths representing ‘the quotient of the
number of generations that elapsed between the more
ancient divergence and the more recent divergence, and
the haploid population size N’ [63] (see Materials and
Methods).

We examined correlations between the average GC3
for the descendants of a given branch and inferred popula-
tion size for said branch. Only branches giving rise to two
terminal branches were considered, as effective population
sizes cannot be determined for terminal branches, and are
unreliable for deeper branches, which tend to be very
short in this data set. If large ancestral population size
is predictive of higher GC in the descendants, we ex-
pect a positive correlation between N and GC3. This is
indeed what we observe regardless of the method of
branch length estimation (rho = 0.3041, P = 0.0856 for
our method; rho = 0.3471, P = 0.0522 for MP-EST).
These correlations contain several outliers with ex-
tremely large inferred population sizes, which may be
explained by errors in the topologies of the gene trees,
due to low signal in gene sequences. Accordingly, when
we restrict our analysis to branches with a length of ≥0.1
coalescent units the positive correlation becomes
<figure><img src="image_7.png" /></figure>

**Figure 6** GC3 is more conserved between pairs of large-bodied species. Each point on the plot represents one species pair. GC3 is less conserved between genes evolving in small-bodied species pairs (see Additional file 6). The hummingbird/swift (CALAN/CHAPE) pair appears to be an outlier with high conservation (approximately 4.75), possibly owing to hummingbird having a low, and therefore more conserved, GC for its size (GC: 0.4929, body mass 4.25 g).

more robust (rho = 0.4963, *P* = 0.0092 for our method; rho = 0.5233, *P* = 0.0048 for MP-EST; Figure 7). Similar results were obtained when only branches where boot-strap support values for the associated nodes are ≥50 were considered (rho = 0.5583, *P* = 0.0064). Given the long time period that has elapsed since the putative ancestral populations were alive, descendant GC3 may not adequately reflect their base composition. We therefore also examined the correlation between the mean of the reconstructed equilibrium GC (GC3*) values at both ends of a given branch and *N*, which yielded a similar positive correlation (rho = 0.4726, *P* = 0.0277 for branches with coalescent length ≥0.1 and bootstrap ≥50).

The Coevol approach [64], which we used to estimate
ancestral generation time for the above calculations and
which makes use of associations between substitution
patterns and life history traits (see Materials and Methods),
also allows correlations between trait data and base
composition through time to be examined. Results for
10 concatenated alignment blocks were qualitatively
consistent with the above, namely a negative correlation
between age of first female sexual maturity (used to ap-
proximate generation time) and GC, although not all
reconstructions reached significant posterior probabil-
ities (Additional file 4).

Recombination rates in chicken and zebra finch correlate
with heterogeneity in GC

The above results indicate that base composition is ro-
bustly associated with life history traits, and presumably
population size, in agreement with the idea that the im-
pact of gBGC is most pronounced in species with high
N<sub>e</sub> and short generation times. However, to establish a
crossover-related process as a driver of between-species
variation in GC content, we need to assess how com-
positional differences between lineages relate to meiotic
recombination. If gBGC is a major driver of base com-
position, GC should be overall higher in regions of high
recombination, which has indeed been established in
multiple species [8,13,17-20]. In these regions, we might
also expect GC to increase more rapidly in species
where recombination occurs more frequently per unit
time than in those with long generation times, resulting
in greater heterogeneity between orthologs of the same
sequence. It has recently been suggested that the avian
ancestor was large compared to extant species [7], per-
haps indicative of a lower baseline GC content. However,
even if the ancestral genome was not GC-poor, high N<sub>e</sub>
ought, in theory, to lead to more efficient gBGC in
small-bodied species counteracting the effects of the AT-
biased mutation process on sequence composition [65].
In contrast, GC would decline in species with reduced
<figure><img src="image_8.png" /><figcaption>**Figure 7** Ancestral population size predicts descendant GC3. Reconstructed population sizes for internal branches with two descendant lineages are positively correlated with descendant GC3 content. Population size was inferred from relationships between ancestral generation time, split dates, and coalescent branch lengths computed from the degree of gene tree discordance. Note that the population sizes here are expected to be overestimates, as age of first female sexual maturity was used as a proxy for generation time (see Materials and Methods), and are intended to be interpreted in terms of rank order.</figcaption></figure>

N<sub>e</sub>. Under both scenarios, composition will be heteroge-
neous between species in highly recombining regions,
while composition in regions of low recombination should
be dominated by other forces such as mutation bias.

The absence of recombination maps for the majority
of the 48 bird species limits our ability to test whether
sequences located in regions of relatively high recom-
bination show more pronounced between-species het-
erogeneity in GC content. We can, however, consider
recombination maps from chicken [20] and zebra finch
[19], two distantly related lineages, across 1 Mb windows
and test for a relationship between standard deviation in

GC3 and crossover rate. In accord with recombination
driving increased heterogeneity in GC3, we find that both
chicken and zebra finch recombination rates show a posi-
tive relationship with the between-species standard devi-
ation in GC3 for each of 1,780 orthologs found across all
species (Table 1). This cannot simply be owing to the
mean and variance being coupled, which we have no *a
priori* reason to expect, as regions where AT is high will
have reduced rather than increased heterogeneity in AT
between species.

As a consequence of the correlation between recom-
bination and variance in GC, the orthologs from the

<table><caption>Table 1 Correlations between standard deviation in GC and median GC and recombination</caption>
  <thead>
    <tr>
      <th>Correlation with recombination</th>
      <th>Standard deviation of GC</th>
      <th>Mean GC</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Chicken</td>
      <td></td>
      <td></td>
    </tr>
    <tr>
      <td>GC3</td>
      <td>rho = 0.2432</td>
      <td>P = 2.3e-09</td>
    </tr>
    <tr>
      <td>GC3 'homogenous'</td>
      <td>rho = 0.2136</td>
      <td>P = 0.0016</td>
    </tr>
    <tr>
      <td>GC i</td>
      <td>rho = 0.2371</td>
      <td>P = 2.0e-05</td>
    </tr>
    <tr>
      <td>Zebra finch</td>
      <td></td>
      <td></td>
    </tr>
    <tr>
      <td>GC3</td>
      <td>rho = 0.2915</td>
      <td>P = 4.7e-11</td>
    </tr>
    <tr>
      <td>GC3 'homogenous'</td>
      <td>rho = 0.2009</td>
      <td>P = 0.0054</td>
    </tr>
    <tr>
      <td>GC i</td>
      <td>rho = 0.1663</td>
      <td>P = 0.0121</td>
    </tr>
  </tbody>
</table>

Intronic GC was calculated only for windows with a minimum of 10 introns present to avoid noise owing to low numbers of sites.
previously considered high-variance gene set showed higher recombination and variance in recombination in chicken and zebra finch than the low-variance gene set (Wilcoxon test for median rate <2.2e-16; see Table 2). Considering only ‘homogenous’ orthologs yielded similar but modestly weaker correlations (see Table 1), consistent with our above observation that the association between body mass and GC3 extends to these loci. Moreover, intronic GC content and standard deviation also correlated positively with recombination rates (see Table 1).

Chicken and zebra finch recombination rates for 1 Mb windows overlapping the 1,780 orthologs were positively correlated (rho = 0.3846, *P* <2.2e-16), indicating a degree of conservation of recombination rates for our set in line with previously reported estimates [19]. As there is no perfect correspondence between rates, the above correlations are likely to be weaker than if we were able to include only loci whose recombination rates have remained constant across all species. For instance, if a previously highly recombining sequence moved to a region of low recombination and experienced amelioration of GC, the strength of the relationship between chicken chromosomal location and heterogeneity would be reduced. There is indeed evidence that chromosomal inversions are associated with altered recombination rates [26,27].

### Chromosome size predicts GC content and heterogeneity

As interchromosomal rearrangements are rare in birds, we can further employ chromosomal class as a proxy for ‘very broad scale’ recombination rates. Given the requirement for at least one crossover per chromosome [39], small chromosomes have higher recombination rates [19,20,41]. Therefore higher heterogeneity in GC3 compared to larger chromosomes is expected, along with the higher median GC3. Indeed, orthologs on the smaller chicken chromosomes 10-32 showed a higher median standard deviation in GC3 than the larger chromosomes 1-9 (see Table 3). Zebra finch exhibits the same pattern, which is not surprising given the high degree of karyotypic conservation. Similar results were obtained for intronic GC content, with both the chicken and zebra finch median and standard deviation for GC<sub>i</sub>

<table><caption>**Table 2** Recombination rates differ between high- and low-variance orthologs</caption><thead><tr><th></th><th></th><th>Recombination rate (cM/Mb)</th><th>Standard deviation</th></tr></thead><tbody><tr><td rowspan="2">Chicken</td><td>Low-variance</td><td>2.035</td><td>1.756</td></tr><tr><td>High-variance</td><td>4.347</td><td>5.609</td></tr><tr><td rowspan="2">Zebra finch</td><td>Low-variance</td><td>0.1035</td><td>1.18</td></tr><tr><td>High-variance</td><td>3.189</td><td>3.599</td></tr></tbody></table>

being higher for orthologs on small chromosomes (Table 3).

### Avian base composition is not at equilibrium

Previous work on birds has shown that GC content is increasing in a subset of avian lineages [43,47], coinciding with a reinforcement of isochore structure, whereas in other lineages GC has declined. In mammals it was originally assumed that GC was becoming eroded and homogenized based on observations in rodents and primates. A more comprehensive analysis including additional species showed that these examples were in fact exceptions [21]. We therefore ask whether there is evidence for a general trend in GC evolution across all major avian orders. This is typically done by examining the relationship between weak (W: GC → AT) and strong (S: AT → GC) substitutions. Calculating (W → S)/(W → S + S → W) for summed substitution counts obtained by mapping W → S and S → W counts onto the branches of the avian tree using mapNH and a homogenous T92 model yields an approximate GC3* value for each species [66]. This estimate represents the GC3 composition that would be expected for an infinitely long branch.

At equilibrium, we would expect GC3 and GC3* to be approximately equal. However, in the majority of cases we observed that GC3* was in fact greater than current GC3, with the slope between the two measures being 2.85 (Figure 8), indicating that GC has increased [43]. These equilibrium frequencies should be interpreted with a degree of caution, as they are based on observations of substitutions along branches of finite length and changes in base composition will ultimately lead to a shift in the balance of forces acting on a sequence. The effect of distorted segregation favoring W → S changes on composition ought to become less pronounced as the frequency of unfixed GC variants declines, while the proportion of targets susceptible to C → T transitions increases. Very high GC contents could in principle also be selected against in certain sequence contexts in sufficiently large populations, as GC content is a major predictor of nucleosome occupancy and therefore DNA accessibility (see, for example, [67]). As such, the notion that high N<sub>e</sub> will accelerate gBGC [23] may not hold in all scenarios. Given that we do not control for the effects of CpG hypermutability on substitutions in our model, it is possible that we overestimate the true equilibrium frequencies here to an extent. Nevertheless, our data provide evidence that, if anything, GC3 has undergone recent increases in most species surveyed. The overall trend for GC3* to be higher than current GC3 is consistent with the idea that, on average, bird body masses have decreased throughout their evolution [7].
<table><caption>Table 3 Median and standard deviation (sd) of GC for orthologs to chicken and zebra finch genes located on large (chromosomes 1-9) and small chromosomes (chromosome 10-) for third codon position and introns</caption><thead><tr><th></th><th>GC small chrom.</th><th>GC large chrom.</th><th>Wilcoxon test</th><th>sd GC small</th><th>sd GC large</th><th>Wilcoxon test</th></tr></thead><tbody><tr><td>Chicken third sites</td><td>0.495</td><td>0.458</td><td>P = 2.0e-11</td><td>0.0457</td><td>0.0349</td><td>P &lt;2.2e-16</td></tr><tr><td>Chicken intronic</td><td>0.493</td><td>0.449</td><td>P = 0.0020</td><td>0.0432</td><td>0.0353</td><td>P = 6.7e-07</td></tr><tr><td>Zebra finch third sites</td><td>0.497</td><td>0.457</td><td>P = 1.5e-13</td><td>0.0456</td><td>0.0348</td><td>P &lt;2.2e-16</td></tr><tr><td>Zebra finch intronic</td><td>0.500</td><td>0.447</td><td>P = 0.00038</td><td>0.0418</td><td>0.0352</td><td>P = 4.3e-05</td></tr></tbody></table>

Although we cannot conclusively rule out the possibility that the equilibrium GC frequencies we estimated are greater than the frequencies that would be reached in the future given sufficient time and constant population sizes, past work on primates has shown that a model where the efficiency of gBGC depends on recombination and effective population size fits the observed data well [14]. The primate results also suggest that the time required to halve the difference between current and equilibrium composition is of the order of hundreds of millions of years outside of hotspot regions [14]. If we assume a range of 100,000 to 1,000,000 for avian effective population sizes, we can demonstrate using a model similar to that of Duret and Arndt [14] that the number of generations required to halve the difference between GC3* and GC is large - between 124,218,180 and 565,739,002 generations - relative to the time scales we investigate here (see Additional file 5). We might therefore plausibly expect GC3* to exceed current GC3.

Discussion

Multiple lines of circumstantial evidence described in this study support a role for recombination-associated gBGC in shaping avian base composition. First, lineages with putatively higher $N_e$ and shorter generation times show increased GC content at all classes of sites we examined (coding and non-coding), as well as greater heterogeneity in GC3. In addition to life history traits, estimated ancestral population size is a predictor of GC content. These results are consistent with both the effects of a greater number of recombination events occurring per unit time and gBGC being more efficient in large populations [23], and accord with previous reports from mammals [21]. That non-coding sites are also affected argues against the effect being explained by selection on coding sequences. On the other hand, the impact of gBGC is pervasive and appears strong enough to drive some modest differences in amino acid usage between lineages, and extends to loci with low heterogeneity

<figure><img src="image_9.png" /><figcaption>**Figure 8** GC3 is not at equilibrium in the majority of avian species. For the majority of lineages, GC3* exceeds GC3. As an exception the ostrich, *Struthio camelus*, shows no evidence of increasing GC content (GC3* = 0.4645; GC3 = 0.4666).</figcaption></figure>
in GC content. The positive correlation in both chicken and zebra finch between recombination and heterogeneity in GC3 as well as median GC3 is consistent with a recombination-associated process increasing GC content. We find that the process appears to be ongoing, with equilibrium GC composition being higher than present composition for the majority of species surveyed. Interestingly, the ostrich, *Struthio camelus*, shows no evidence of increasing GC content (GC3* = 0.4645; GC3 = 0.4666). A reduction in GC relative to the ancestral state has previously been described in emus, *Dromaius novaehollandiae* [47]. Meanwhile, the great tinamou, *Tinamus major*, although closely related to both the emu and ostrich, shows a particularly high GC3*. In fact, ostrich has recently been shown to be outgroup to tinamou and emu [68], further indicating that phylogeny does not account for our observations (Additional file 3). Work on primates shows that the gBGC model fits the observed equilibrium frequencies well [14]. Nevertheless, future work will be required to address the extent to which selection opposing very high GC content or CpG methylation might affect equilibrium composition in coding sequences.

The counterintuitive observation that the range of in-
tronic GC appears to be somewhat more restricted than
GC3 (sd = 0.0189 for GCᵢ) may be explained by selection
on intronic nucleotide composition to facilitate correct
intron recognition by the splicing machinery [69,70].
Repeat-masking intronic sequences had a negligible effect
on overall intronic GC content (data not shown), indicat-
ing that transposable elements do not play an important
role in intronic nucleotide composition in birds as op-
posed to mammals [71]. Alternatively, the weaker effect in
introns could be due to a tendency for GC-rich introns to
be deleted. Recombination is associated with genome con-
traction [72] and constraint to maintain intronic sequence
is expected to be weaker than for coding sequences with
introns having an overall deletion bias [73].

Despite our comprehensive data set across the avian
phylogeny and the advantages of the avian system, we
acknowledge that some caveats remain. Given that we
likely underestimate both generation times and coales-
cent branch lengths, our reconstructions of population
size are imperfect and expected to be larger than the true
ancestral population sizes (see Materials and Methods).
However, our main objective was to capture the ranks of
the ancestral population sizes relative to one another in
order to draw conclusions about their relationship with
base composition.

At present, the absence of recombination rate data for
the majority of our study taxa, as well as the lack of
knowledge of historical rates, limits our ability to draw
firm conclusions about fine-scale changes in recombin-
ation landscapes over time and how this may impact the

patterns of base composition we observe. Sex-specific
rates might also be expected to give different results in
some study organisms. The impact of male recombin-
ation on signatures of gBGC is stronger than for female
recombination in mammals [14,28,74-76], possibly owing
to interactions between replication-associated mutation
patterns and crossover [77]. However, no such difference
between the effects of male and female recombination on
nucleotide composition is reported in chicken [78], al-
though this may be a matter of resolution. Nevertheless, if
anything we would expect shifts in the crossover land-
scape to weaken correlations, making our analysis more
conservative.

Future analyses employing both detailed crossover
maps and rearrangement data will shed light on whether
the reinforcement of GC-rich isochores in birds is in-
deed related to a connection between the stable avian
karyotype and stable recombination landscapes. One ex-
planation that has been proposed for the long-lived re-
combination hotspots in dog is the loss of *PRDM9* in
canids, leading to pronounced signatures consistent with
long episodes of gBGC [36]. Like dogs, birds appear to
lack a functional copy of the gene [35], implying that
karyotypic stability may not be the only possible explan-
ation for what we observe in birds.

Characterizing the strength of gBGC using a model
such as that of Lartillot [24] will enable us to gather
more information about the longevity of avian recom-
bination hotspots in future studies. In mammals, a mere
20% of the strength of gBGC is accounted for by karyo-
type and body mass, presumably owing to population
size not being perfectly correlated with body mass, and
karyotype not accounting for all of the variation in re-
combination rates [24]. Levels of species inbreeding are
also expected to play a role [79]. Therefore, gathering
more information about how well body mass explains
traces left in the genome by gBGC relative to other
proxies such as gene tree discordance or levels of het-
erozygosity will be important. The increasing availability
of polymorphism data will also allow fixation biases to
be modeled [25], perhaps providing more direct evi-
dence for gBGC in the future.

At present, we cannot fully distinguish between the
relative importance of the generation time and population
size effects on the strength of GC-biased gene conversion
because species with long generation times tend also to
have small populations, and our results suggest that GC
has not reached equilibrium in the majority of lineages.
As with recombination, N<sub>e</sub> varies both along the genome
and between genomes. Indeed recombination itself modu-
lates N<sub>e</sub> locally. This is however likely to be of limited im-
portance. Differences in N<sub>e</sub> between genes along the
chromosomes are small in the species thus far surveyed
[22] suggesting that the modestly increased N<sub>e</sub> of highly
recombining sequences might affect the efficacy of gBGC less than the overall species-specific N<sub>e</sub>. Whether these predictions also hold for birds, where one might expect to see more pronounced intragenomic variation in N<sub>e</sub> owing to heterogeneous recombination rates, is as of yet not clear. Moreover, the extent to which selection on syntenous or non-coding sites might modulate the impact of gBGC on composition remains to be explored.

More generally, our results show that the relationship between life history traits and base composition is not limited to mammalian species, as would be expected for a mechanism that is conserved across a wide range of eukaryotic species. Interestingly, the sea lamprey genome, which contains a large number of micro-chromosomes has an extremely high GC3 content, in principle consistent with recombination driving an increase in GC. Surprisingly, despite the connection between the strength of gBGC and chromosome number, no significant correlation between GC and GC3 was reported [80]. Given the abundance of repetitive sequence in the lamprey and the fact that unmasked sequences were assayed, this may however be a method artefact rather than evidence against gBGC as a driver of GC in this particular genome.

Conclusions

Here, we have demonstrated the pervasive impact of gBGC on avian base composition and shown that life history affects lineage-specific patterns of GC evolution. The observation that a species' GC content can be predicted from its life history for multiple taxa strengthens the plausibility of gBGC acting as a driver of between-lineage differences in composition in addition to its well-established role as a determinant of within-species variation. Our results are in line with the expectation that a mechanism that is conserved across multiple eukaryotic groups ought to leave similar traces in their genomes, as appears to be the case for mammals and birds.

Our findings are also of broad interest for phylogenetic
reconstruction, as there is increasing evidence that base
composition can substantially alter the topologies of the
trees inferred from different classes of sequence [33,47].
This is further elaborated on in our large-scale phyloge-
nomic analysis of the genome sequences used herein [33].
Beyond model misspecification related to base compos-
ition, high recombination could affect phylogenetic infer-
ence in several ways, for instance via its association with
incomplete lineage sorting or, in lineages with rapid turn-
over of hotspots, by frequent switches in the substitution
regime [81]. Gathering further information on the dynam-
ics of evolution of avian recombination landscapes in
the future will shed light on whether the latter mechanism
is likely to be of importance in birds. More generally,
the impact of a mechanism that drives alleles to fixation

in absence of positive selection on lineage-specific sub-
stitutions [10] may be of practical importance for the in-
terpretation of evolutionary rates.

**Materials and methods**

Sequence data

This work was a companion study to a recent initiative
to resolve the phylogeny of modern birds and coding
and intronic sequence alignments were obtained from
the Avian Phylogenomics Consortium [33,48], who pro-
vide a detailed description of how data were generated.
Briefly, this data set comprises 8,295 orthologous protein
coding sequences identified by propagating chicken and
zebra finch annotations to the remaining species and
classifying orthology by combining information from
alignment statistics, reciprocal best hits and syteny. In-
trons for these orthologs were defined by identifying
gene-models with conserved exon-intron boundaries.
Multiple sequence alignments were generated by run-
ning SATé + PRANK followed by SATé + MAFFT on
concatenated exon sequences and two rounds of SATé +
MAFFT on introns [33].

Nucleotide composition

GC content was calculated for first, second, and third
coding positions, as well as at intronic sites for each spe-
cies. Only those orthologs present in all species were
considered (n = 1,780) to ensure comparisons between
species were made between comparable coding se-
quences. Nucleotide content was tallied for all sites at a
given position before computing the proportion of GC
nucleotides, so that short sequences do not introduce
noise. Similarly, for introns only those associated with
genes found in the above set of orthologs were consid-
ered (n = 404). Additionally, nucleotide content was cal-
culated in the above manner for all 8,295 orthologs for
c123, c12, c3 and introns.

Life history traits

Body mass data were extracted from the *CRC Handbook of Avian Body Masses* [82], with only data for unambiguously named tip nodes being used. Where multiple entries for a given species were present, the mean value was calculated. We therefore averaged across males and females where these were not already pooled in the dataset. Data on maximum longevity and age of first female sexual maturity were extracted from build 11 of the AnAge database [83] for each available species.

Reconstructed ancestral generation times

To obtain N, we required estimates of ancestral gener-
ation time. Given the limited availability of generation
time data, ancestral age of first female sexual maturity
was reconstructed using Coevol [64] on 10 subsets of
10 kb of concatenated sequence drawn randomly from the 1,185 1:1 orthologs. This approach allows ancestral traits to be estimated by combining a Brownian diffusion process and correlations between substitution rates and life history traits. As species $d_N/d_S$ does not correlate with life history traits in the manner expected under nearly neutral theory in birds [84], we employed overall substitution rates for the reconstructions (that is, Coevol was run without the codon model option). The prior for the root was set to 100 My, which is close to the estimated age in the dated tree [33], with a standard deviation of 100 My, and a burn-in of 100 points was used to evaluate the chains. Reconstructed estimates were highly correlated between the 10 concatenated alignments (Additional file 6), indicating that 10 kb of sequence were sufficient to give reproducible results while being less computationally costly. We ran additional Coevol chains with the above parameters allowing for variation in equilibrium GC, which in turn allowed us to estimate GC3* for internal branches and examine the correlations between GC and age of first female sexual maturity through time.

Ancestral generation time $g$ is typically calculated by $g = a + [s/(1 - s)]$ where $s$ denotes the expected adult survival rate and $a$ is age of first female sexual maturity. As we lack a curated source for $s$ for our species, we considered whether approximating ancestral survival by fitting a regression model to data from 271 bird species for which both $g$ and $a$ are available [85] would improve our estimation of $N$. However, after performing these calculations ($s/(1 - s)$) tended to be inflated in late-reproducing birds with high survival rates, leading to inferred generation times that were greater than maximum longevity. We therefore opted to use age of first female maturity as a proxy for generation time.

## Ancestral population size

Gene tree topologies were compared to the species tree with a double-recursive tree traversal similar to that used in PHYLDOG [86]. Briefly, the nodes of the gene tree (MP-EST in [33]) were first mapped onto nodes of the species tree (TENT ExaML in [33]), and then the number of gene lineages at the beginning and at the end of each branch of the species tree are recorded. These numbers are computed for all gene trees. Then these numbers are used to estimate branch lengths in coalescent units (coalescent units correspond to the number of generations divided by the effective population size along a branch of the species tree) using the following formula, for the branch $i$:

$$ \text{length of branch } i = \log((n12 + nkk)/nkk) $$

where $n12$ is the number of times one gene was found at the beginning of branch $i$, and two genes were found

at the end of branch $i$ over all gene trees, and $nkk$ is the number of times $k$ genes were found at the beginning and the end of branch $i$ all gene trees, $k \neq 1$. This formula is an approximation based on equation (2) in reference [63], and was found to work well on simulated data (data not shown). Additionally, branch lengths were also calculated with MP-EST version 1.4 [87].

Thus, for a given branch the reconstructed population size is:

$$ N = \frac{\text{internodal time span}}{(2 * \text{coalescent branch length} * \text{reconstructed generation time})} $$

where reconstructed generation time is the mean of the values inferred by Coevol for the nodes at either end of the branch. Internodal time spans were obtained from reference [33]. Note that underestimated coalescent branch lengths will inflate estimates of $N$ by decreasing the divisor of the equation. This is expected to be particularly problematic for poorly resolved parts of the tree, where errors in the gene trees are most frequent. Noise due to lack of information may tend to homogenize the frequencies of the gene trees, leading branch lengths to be underestimated. High levels of recombination in avian genomes are expected to exacerbate this problem.

## Time corrected GC3 conservation

Following the method of Romiguier et al. [61], we computed a time corrected index of GC3-conservation for 19 independent pairs of modern bird species. This index is $\gamma = -t/\log(\tau)$, where $t$ is the divergence time of the species pair and $\tau$ the Kendall's correlation coefficient of gene GC3 in species 1 vs species 2 (830 ortholog families with the highest GC3 variance).

In order to have comparable body-mass in each pair, we chose species that maximized the number of closely related pairs (Additional file 7). We excluded the two Paleognathes (ostrich and great tinamou) because of their extreme contrast in body mass. Among the Neognathae, we chose the *Haliaeetus albicilla* (white-tailed eagle)/*Cathartes aura* (turkey vulture) pair over the intra-genus *Haliaeetus albicilla/Haliaeetus leucocephalus* pair (bald eagle) because of the extremely short divergence time of the latter. These 19 time-corrected measures of GC3-conservation were then correlated with the mean body mass of the corresponding species pair.

## Recombination rates

Recombination rates for 1 Mb windows were obtained for chicken [20] and zebra finch [19]. Orthologs were mapped to their corresponding 1 Mb windows and GC for the sequences of interest was then computed for each of these windows.
**Statistical analyses**

All statistics were calculated in R.

**Data availability**

The genome sequences used in this study are available
from GigaDB [88].

**Additional files**

**Additional file 1:** List of species abbreviations used in figures.

**Additional file 2:** Relationships between amino acid usage and body mass.

**Additional file 3:** Phylogenetic independent contrasts.

**Additional file 4:** Correlations though time between dS, age of female sexual maturity, and GC3.

**Additional file 5:** Estimates of equilibrium process half times.

**Additional file 6:** Correlations between Coevol reconstructed traits for 10 concatenated alignments.

**Additional file 7:** Species pairs used for GC3 conservation analysis.

**Abbreviations**

dN/dS: ratio of synonymous to non-synonymous substitutions; gBGC: GC-biased gene conversion; GC3: GC content at third codon positions; GC3*: equilibrium GC at third codon positions; LHTs: Life history traits; Mb: Megabase pair; N<sub>e</sub>: Effective population size; S: strong substitution; W: weak substitution.

**Competing interests**

The authors declare that they have no competing interests.

**Authors' contributions**

CCW conceived the study, designed and performed the analyses, and wrote the manuscript. BB calculated coalescent branch lengths and conceived the ancestral population size analysis. JR designed and performed the GC3 concordance analyses. EDJ helped develop the study and made genomes available. HE initiated and supervised the project, and helped write the manuscript. All authors read, edited, and approved the manuscript.

**Acknowledgments**

Computational analyses were performed using resources provided by the Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) of Uppsala University, supported by the Swedish National Infrastructure for Computing (SNIC). The study was funded by the European Research Council (AdG 249976), Knut and Alice Wallenberg Foundation (Wallenberg Scholar Grant and the Swedish Research Council (2010-5650; all to HE). JR received funding from ANR-10-BINF-01-02 'Ancestry' and European Research Council (AdG 232971; principal investigator Nicolas Galtier). EDJ received funding from the Howard Hughes Medical Institute. We would like to thank Laurence Hurst, Benoît Nabholz, Sankar Subramanian, and Nicolas Galtier for helpful discussions. We also thank two anonymous reviewers for their comments on the manuscript.

**Author details**

¹Department of Evolutionary Biology, Evolutionary Biology Centre, Uppsala University, Norbyvägen 18D, SE-752 36 Uppsala, Sweden. ²Laboratoire de Biométrie et Biologie Evolutive, Université de Lyon, Université Lyon 1, CNRS, UMR5558, Villeurbanne, France. ³CNRS, Université Montpellier 2, UMR 5554, ISEM, Montpellier, France. ⁴Department of Neurobiology, Howard Hughes Medical Institute, Duke University Medical Center, Durham, NC, USA.

Received: 8 April 2014 Accepted: 19 November 2014

Published online: 11 December 2014

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doi:10.1186/s13059-014-0549-1

Cite this article as: Weber et al.: Evidence for GC-biased gene conversion as a driver of between-lineage differences in avian base composition. *Genome Biology* 2014 **15**:549.

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