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\begin{document}

\title{Comparing genome scans among species of the stickleback order reveals
three different patterns of genetic diversity}



\author[1]{James Reeve}%
\author[1]{Qiushi Li}%
\author[1]{Dorothea Lindtke}%
\author[1]{Sam Yeaman}%
\affil[1]{University of Calgary}%


\vspace{-1em}



  
  \date{May 26, 2024}


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\selectlanguage{english}
\begin{abstract}
Comparing genome scans among species is a powerful approach for
investigating the patterns left by evolutionary processes. In
particular, this offers a way to detect candidate genes that drive
convergent evolution. We compared genome scan results to investigate if
patterns of genetic diversity and divergence are shared among divergent
species within the stickleback order (Gasterosteiformes): the threespine
stickleback (Gasterosteus aculeatus), ninespine stickleback (Pungitius
pungitus) and tubesnout (Aulorhynchus flavidus). Populations were
sampled from the southern and northern edges of each species' range, to
identify patterns associated with latitudinal changes in genetic
diversity. Weak correlations in genetic diversity (F\textsubscript{ST}
and expected heterozygosity) and three different patterns in the genomic
landscape were found among these species. Additionally, no candidate
genes for convergent evolution were detected. This is a counterexample
to the growing number of studies that have shown overlapping genetic
patterns, demonstrating that genome scan comparisons can be noisy due to
the effects of several interacting evolutionary forces.%
\end{abstract}%



\sloppy


\textbf{Introduction:}

Genome scans are useful tools for identifying the effects of
evolutionary processes on the genome of a species (Lotterhos and
Whitlock, 2015; Fraser and Whiting, 2019). In the past decade they have
been used to analyse genomic patterns in many wild species (Alves et
al., 2019; Dennenmoser, Vamosi, Nolte, \& Rogers, 2017; Jones et al.,
2012; Vijay et al., 2016; Westram et al., 2014), as they can provide
genetic information about evolution without requiring typically
impractical experimental setups. The growth of studies using genome
scans has provided a new opportunity to compare results among species to
identify common patterns of genetic variation, which may be imprinted on
different species through the same evolutionary processes. Ultimately,
comparisons of genome scans among species will help to assess the
generality of genetic patterns to learn how evolution shapes the genomes
of different species.

At the simplest level, genome scans are a comparison of genetic
diversity among different populations within a species. Genetic
diversity can be split into two main types; diversity within a
population and diversity among populations (referred to as genetic
divergence). Many statistics represent genetic diversity (e.g. \selectlanguage{greek}π,
\selectlanguage{english}H\textsubscript{E}, Tajima's D, and Fay \& Wu's H) or genetic divergence
(e.g. F\textsubscript{ST}, d\textsubscript{xy}), and different
interpretations of these scores have been discussed at length in other
papers (Burri et al., 2015; Ellegren et al., 2012; Reid et al., 2016;
Van Doren et al., 2017; Vijay et al., 2016, 2017). A genome scan moves
along the genome looking for extreme patterns of these statistics that
may be associated with local adaptation (Lotterhos \& Whitlock, 2015,
Fraser and Whiting 2019), but alternatively could be the product of
background selection (Charlesworth et al., 1993, Matthey-Doret \&
Whitlock, 2019) or demographic events such as range expansions,
population bottlenecks or inbreeding (Barton, 1998; Excoffier \& Ray
2008; Lotterhos \& Whitlock, 2014; Nielsen, Hellmann, Hubisz,
Bustamante, \& Clark, 2007). These extreme patterns can be identified
visually as ``peaks'' and ``troughs'' of genetic diversity or
divergence, from their distinctive shape on a Manhattan plot.
Statistical methods are used to determine which evolutionary processes
most likely generated these peaks and troughs, often as the first step
towards identifying candidate genes.

Comparison of genome scan results among species provides insight into
how shared ancestry, demography, and environmental conditions can affect
the similarity of patterns in their genomes. Commonly, genome scans are
compared to detect convergent evolution (Fraser and Whiting 2019), as
shared peaks or troughs have the potential to reveal genes that underpin
evolution to a shared environmental pressure in many species (Stern
2013). Examples of these convergently evolving genes have already been
found such as digestive proteins in primates (Stewart, Schilling, \&
Wilson, 1987), pigmentation in vertebrates (Gompel \& Prud'homme, 2009;
Hoekstra, 2006; Manceau et al., 2010) or anthocyanin proteins in
flowering plants (Kopp 2009). Outside of convergent evolution, comparing
genome scans can also show shared properties of the genome such as
recombination landscapes (Samuk \emph{et al.} , 2017) or ancestral
population structure (Vijay \emph{et al.} , 2017). On one hand, genomes
scans should not be used in isolation to detect convergent evolution, as
shared patterns can come from several sources. On the other hand, genome
scans offer a useful way to identify broad scale genetic similarities
among several species. By comparing patterns in diversity and divergence
across many species and environmental gradients, we can better
understand how evolutionary processes affect the genome.

Threespine stickleback (\emph{Gasterosteus aculateus} ) is a good system
for comparative genome scans, as several regions of the genome have been
identified that are strongly associated with local adaptation in this
species (Colosimo et al., 2005; Hohenlohe et al., 2010; Jones et al.,
2012; Schluter \& Conte, 2009). Several closely related fish species
live in overlapping niches allowing their genomic landscape to be
compared to the threespine stickleback's to learn how evolution shapes
patterns in their respective genomes. This study aims to compare
patterns of genetic diversity and divergence in the threespine
stickleback with both the ninespine stickleback (\emph{Pungitus
pungitus} ; for simplicity the stickleback species will be referred to
as threespines and ninespines) and tubesnout (\emph{Alurhychous
flavidus} ), as an example of how comparisons of genome scan results can
identify common genetic patterns.

Ninespines and threespines diverged 26mya (Varadharajan et al., n.d.)
and have already been subjected to comparative genetic studies
(Varadharajan \emph{et al.} , no date; Shikano \emph{et al.} , 2013;
Nelson and Cresko, 2018), in part because both species have colonised
freshwater lakes in similar regions. Interestingly, while targeted
genetic studies support convergent evolution to freshwater
(Shikano\emph{et al.} , 2013), whole genome data found no genetic
signatures of convergent evolution (Raeymaekers \emph{et al.} , 2017).
The extent of similarity in genetic patterns among these sticklebacks is
still an open question.

We are only beginning to compare the genomes of the threespine and
tubesnout (Li et al. in review) and have yet to explore the patterns of
genetic diversity. These species diverged approximately 50mya
(Betancur\emph{et al.} , 2013), which is a timeframe similar to a study
in birds which found similar patterns of genetic diversity maintained
across 55 million years (Vijay \emph{et al.} , 2017). In contrast to the
ninespine-threespine comparison, tubesnouts are an exclusively marine
species that overlaps with the marine threespine along most of its range
in the Pacific. Marine threespines are known to have genetic structure
along the North American West coast (Morris \emph{et al.} , 2018), which
may be the result of gene flow from locally-adapted freshwater
populations (Nelson and Cresko, 2018). Thus, we may expect to find
patterns in the threespine genome that differ from the tubesnout's, due
to differences in their demographic history, selection, and ancestral
variation.

Here, we compare patterns of population genomic diversity and divergence
in these species to assess how such patterns vary across the stickleback
order. Specifically, we study patterns in F\textsubscript{ST} and
genetic diversity from populations at each end of a latitudinal gradient
and compare these patterns among species-pairs at a whole-genome and a
gene-by-gene level to assess their similarity and test for signatures of
convergent evolution. We focus on latitude-related effects (e.g.
adaptation in traits related to body size, growth rate, changing
breeding times or oxygen binding {[}Andersen et al., 2009; Bell \&
Foster, 1994, pp. 155--157; Blanck \& Lamouroux, 2007{]}) instead of the
patterns of salinity-driven adaptation more commonly investigated in
threespine and ninespine, as the tubesnout has not evolved to live in
freshwater systems. By studying broad-scale patterns that covary with
the selection pressures associated with latitude, we aim to detect
whether patterns of genetic diversity are shared among these species, to
learn how evolution may have shaped such patterns.

\textbf{Method:}

\emph{Sampling}

Tubesnout and threespine samples were collected between May - August
2017 from the West Coast of North America using dip netting and minnow
traps. These fish were euthanised in the field using a mixture of 0.5g/L
MS222 (Ethyl 3-aminobenzoate methanesulfonate) in sea water, the
carcases were then preserved in 95\% ethanol which was replaced after 24
hours. The northern populations of both species and all ninespine
samples were donated by collectors. Between 30-52 fish were collected
per population (Table 1), the specific details of sampling locations are
included in Table S1, and population labels are described in Figure 1.

\emph{DNA extractions and sequencing}

DNA was extracted from a \textasciitilde{}2mm clip of the pectoral fin
of each fish using a Qiagen DNeasy Blood and Tissue Kit. The protocol
was modified slightly to increase yield by washing the fins in
dH\textsubscript{2}O before lysis and by repeating the elution step
twice using half the volume of buffer. The DNA samples were checked for
fragmentation using gel electrophoresis, quality tested with an Implen
N60 Nanophotometer, and concentration was measured using a Qubit 3.0
with three replicates per sample. Samples with low quality (A260/A280
\textless{} 1.8; A260/A230 \textless{} 2.0) or low quantity
(concentration \textless{} 8ng/\selectlanguage{greek}μ\selectlanguage{english}L) were re-extracted. Any sample that
failed three re-extractions was removed. This quality check was repeated
after pooling DNA samples (see below).

Individual DNA samples were pooled together by population before library
preparation (see Table S2 for quality scores of pools). The DNA pools
were sent to Genome Qu\selectlanguage{ngerman}ébec (McGill University and Génome Québec
Innovation Centre, Montréal, Canada) for library preparation and
paired-end whole genome shotgun sequencing on their Illumina HiSeqX
platform. The estimated coverage of each pool was set as double the
number of individuals in the sample (2Nx), so that ideally each
chromosome of each individual was sequenced once. A PCR step was
performed, even though it is not advised for Pool-seq protocols
(Schlötterer et al. 2014), because the mass of DNA in the pools did not
meet Genome Québec's minimum threshold for PCR-free sequencing.

\emph{Bioinformatics}

Unless otherwise mentioned, the default parameter settings were used for
all software mentioned. Sequenced reads were trimmed of adaptors with
Trimmomatic (v0.38; Bolger, Lohse, \& Usadel, 2014), using the
paired-end mode `PE' and with a minimum length set to 120bp. Further
trimming was deemed unnecessary after inspecting read quality with
FastQC (v0.11.7;
https://www.bioinformatics.babraham.ac.uk/projects/fastqc). Trimmed
reads were mapped onto each species genome (\textbf{threespine} :
Peichel, Sullivan, Liachko, \& White, 2017; \textbf{tubesnout} : Li \&
Yeaman, n.d.; \textbf{ninespine} : Nelson \& Cresko, 2018) with BWA- MEM
(v0.7.12; Li \& Durbin, 2009). PCR-duplicates were flagged using
Picard-MarkDuplicates (v2.18.7; http://broadinstitute.github.io/picard).
As a prerequisite before running MarkDuplicates, the reads were sorted
and read group information was added with Picard -
AddOrReplaceReadGroups. Reads were realigned around indels to adjust
quality scores for sites surrounding indels using GATK3 --
IndelRealigner (v3.8-1-0; McKenna et al., 2010). Before indel
realignment the ninespine reads files, which were sequenced on separate
lanes, were combined into a single file per population using samtools --
merge (v1.9; Li, 2011; Li et al., 2009). After indel realignment
samtools -- mpileup was used to combine reads from all populations
within a species. Any reads flagged as duplicates were ignored by
samtools. VarScan (v2.3.9; Koboldt et al., 2012) was used to call SNPs
for each species. The ploidy for each sample was set as double the
number of individuals in the pool (2N). Thresholds were set to filter
out multiallelic SNPs, low coverage (cov \textless{} 50), quality (qual
\textless{} 20), minor alternative allele frequency (maf \textless{}
0.01), and SNPs with less than two reads for the minor allele
(min-read-count \textless{} 2). The coverage filter was set to ensure
that each individual in a sample was represented at least once, assuming
DNA pooling was balanced.

\emph{Genetic diversity calculations}

Genetic diversity was measured as two different values, the genetic
diversity within a population (\({\overset{\overline{}}{H}}_{E}\)) and the genetic
divergence (F\textsubscript{ST}). F\textsubscript{ST} was calculated for
each species using the R package \emph{poolFstat} (Hivert\emph{et al.} ,
2018). \({\overset{\overline{}}{H}}_{E}\) was estimated per population from the average
expected heterozygosity of all SNPs within a 50,000bp window, including
invariant sites as 0s in the calculation. This approach was relatively
unbiased by depth of coverage, as \({\overset{\overline{}}{H}}_{E}\) did not correlate
with average window coverage (Fig. S2). \({\overset{\overline{}}{H}}_{E}\ \)was calculated
directly from the VCFs using a custom R script (GitHub:
ja-Reeve/CompGenoScan/R\_scripts/Heterozygosity).

\emph{Identifying signatures of local adaptation (within species)}

Genes showing signatures of differentiation across the latitudinal
gradient were identified for each species using a top-candidate approach
(Yeaman \emph{et al.} , 2016). Initially, F\textsubscript{ST} outliers
were identified as any SNPs with scores in the top
999\textsuperscript{th} quantile. Then, the number of F\textsubscript{ST
}outliers within each gene was compared to the expected number that
could have arisen by chance, which was estimated from a binomial
distribution with a probability of success of 0.001 (i.e. the
probability of being an outlier). Any gene that had more observed
F\textsubscript{ST} outliers than the 999\textsuperscript{th} quantile
of this binomial distribution was considered a top candidate for local
adaptation (using qbinom in R).

\emph{Determining orthologs:} \emph{comparing patterns between species
pairs}

To assess patterns consistent with convergent evolution between species
pairs, candidate genes were matched to orthologs in the other species.
Orthologs were identified between threespines and tubesnouts using a
table compiled by Li et al. (in review) using OMA (v2.3.0; Altenhoff et
al., 2018; Glover, Altenhoff, \& Dessimoz, 2019). As the two stickleback
species are more closely related and share higher sequence identity, a
gapped-alignment program (GMAP; v2017-06-20; Wu \& Watanabe, 2005) was
used to identify orthologs between threespine and ninespine. For this,
any alignments with a mapping quality of \textless{} 80 or a percentage
identity \textless{} 90\% were filtered out. Additionally, any genes
with multiple matches (1:many \& many:many orthologs) or overlapping
positions within a species were removed.

To compare population divergence among species, the average
F\textsubscript{ST} score was calculated per gene. A similar approach
could not be used to compare \({\overset{\overline{}}{H}}_{E}\) because larger windows
were required to obtain sufficiently precise estimates, and multiple
genes could be present within a single window. Instead, the score for
the whole window was applied to each gene and if a gene's location
spanned two windows then it was assigned the score of the window where
most of that gene was located. This approach produces some
pseudoreplication in the data as a given gene will be present in several
neighbouring windows, but this should have only a minor effect, causing
an overestimation of the significance of any true correlation. Given
that we found less correlation in these metrics than previous studies
(see Discussion), this should be a conservative approach.

\emph{Identifying signatures of convergent evolution}

The simplest approach for detecting of patterns of convergent evolution
is to look for genes that are F\textsubscript{ST} outliers in multiple
species, however this approach may miss some true signals as it is very
stringent (Storey and Tibshirani, 2003; Fraser and Whiting, 2019). As a
more sensitive test, the Null-W approach (Yeaman \emph{et al.} , 2016)
was used to detect signatures of convergent evolution, by identifying
top candidate genes in one species, and then comparing the
F\textsubscript{ST} scores of orthologs to the top candidate genes to
a null distribution of randomly chosen genes from the genome. This was
done using a standard set of 10,000 randomly chosen control SNPs and
comparing both the orthologs and the null distribution genes to the
control SNPs using Wilcoxon ranked sum test W-scores (Wilcoxon, 1945;
for more details see Reeve, 2019 or Supp. Mat. of Yeaman et al., 2016).
These W-scores were normalized into Z-scores using a formula from
Whitlock and Schluter (2009, p. 342), and empirical P-values for the
orthologs were calculated based on their position in the null
distribution using the \emph{empPvals} function of the \emph{qvalue} R
package (Storey \emph{et al.} , 2015). Empirical P-values were corrected
to reduce false discoveries using a Bonferroni correction. Any gene
pairs that remained significant were considered signatures of convergent
evolution.

\textbf{Results:}

Whole genome sequencing yielded 3.9 million threespine SNPs and 3.5
million tubesnout SNPs with consistent coverage and quality after
filtering (see Table S3 for summary statistics and Figure S1 for
distributions). Only 0.7 million ninespine SNPs were detected after
filtering, likely as the result of the low depth of coverage for one
ninespine population (NsABm).

\emph{Comparison of genome-wide patterns}

On a genome-wide level, average intraspecific F\textsubscript{ST}
and\({\overset{\overline{}}{H}}_{E}\) were found to be relatively similar between the
threespine and tubesnout (Table 1), but ninespine\({\overset{\overline{}}{H}}_{E}\) was
tenfold lower and F\textsubscript{ST} was almost four times higher
(Table 1). Patterns of variation in these summary statistics involved
longer ``genomic islands'' with elevated F\textsubscript{ST} and
lower\({\overset{\overline{}}{H}}_{E}\) in the threespine compared to the tubesnout
(Fig. 2; 99\textsuperscript{th} F\textsubscript{ST} quantile threespine
= 0.67, tubesnout = 0.55; 99\textsuperscript{th}\({\overset{\overline{}}{H}}_{E}\)
quantile threespine = 0.0070, tubesnout = 0.0056). Patterns of
F\textsubscript{ST} in ninespines were extremely heterogenous to the
point that no peaks could be identified, and \({\overset{\overline{}}{H}}_{E}\) was
noticeably lower than the other two species (Fig. 2) with the exception
of the sex chromosome (i.e. chr12; Shapiro et al., 2009; Shikano, Laine,
Herczeg, Vilkki, \& Merilä, 2013).

\emph{Comparison of gene-by-gene level patterns}

At a gene-by-gene level, there was no clear relationship among average
F\textsubscript{ST} and \({\overset{\overline{}}{H}}_{E}\) for orthologous genes for
any species pair (Fig. 3). Average F\textsubscript{ST} per gene was
weakly correlated among all species pairs, with tubesnouts and
ninespines having a negative albeit non-significant correlation (Table
2). A lack of similarity was also observed with \({\overset{\overline{}}{H}}_{E}\)
scores, with a slightly stronger negative correlation between
threespines and ninespines (Table 2). Additionally, pairwise comparisons
between populations showed less similarity in \({\overset{\overline{}}{H}}_{E}\) for
among-species comparisons (\selectlanguage{greek}ρ \selectlanguage{english}\textless{} 0.2) than within-species
comparisons (\selectlanguage{greek}ρ \selectlanguage{english}\textgreater{} 0.4; Fig. 3B). No clear visual pattern
exists in\({\overset{\overline{}}{H}}_{E}\) (Fig. 3A) or F\textsubscript{ST}(Fig. 3C),
with the exception of a flattening of\({\overset{\overline{}}{H}}_{E}\) and elongation
of F\textsubscript{ST}towards the ninespine axes. Overall, these
patterns show broad-scale similarity between threespines and tubesnouts,
which does not extend to the local gene level, or overlap with
ninespines.

\emph{Testing for signatures of convergent evolution}

Northern and southern populations of each species were analysed for
genetic patterns driven by adaptation to some unmeasured factor related
to latitude, by searching for genes with abnormally high patterns of
F\textsubscript{ST}. Using the top candidate approach (Yeaman \emph{et
al.} , 2016) 73 genes had extreme values of F\textsubscript{ST} in
threespines compared with 65 genes in tubesnouts (Table S4; Fig. 3A).
None of the top candidates were directly shared between these species,
but a pair of candidate genes encoding proteins in the forkhead box
family were detected (Ts: \emph{foxo3b} ; Tu: \emph{foxb2} ; Table S4).
No signatures of high F\textsubscript{ST} could be detected in
ninespines because too many scores were close to F\textsubscript{ST} = 1
to identify meaningful outliers. Additionally, comparing all species,
only three \({\overset{\overline{}}{H}}_{E}\) scores overlapped in the upper 95\% of the
distribution (Fig. 3A). The Null-W test identified five possible
signatures of convergent evolution between threespines and tubesnouts
(Fig. 4B), but after adjusting for false discoveries these signatures
lost significance (Table S5). The Null-W test did not identify any
forkhead box genes as candidates.

\textbf{Discussion:}

Comparing the results of three genome scans we found few similarities in
genomic patterns among species. Only the tubesnout and threespine had
similar genome-wide average F\textsubscript{ST}
and\({\overset{\overline{}}{H}}_{E}\)scores, but these similarities did not extend to
gene-level patterns, while comparison to the ninespine found no
similarities at the genome-wide or gene levels.. Additionally, we found
contrasting genome scan patterns for each species and no strong evidence
to support convergent evolution. This study highlights how the
complexities of evolutionary histories, such as genetic bottlenecks or
gene flow from unsampled habitats, can complicate the comparisons of
genome scans.

\emph{Genetic patterns within each species}

The patterns of genetic diversity along the threespine genome have
previously been described in studies of divergence between marine and
freshwater threespine population pairs (Hohenlohe et al. 2010, Chan et
al. 2010, Jones et al. 2013, Roesti et al. 2015).
F\textsubscript{ST}scores typically cluster in several broad peaks in
comparisons among freshwater and marine environments, with pronounced
peaks around the\emph{Eda} locus (chr4; Hohenlohe et al. 2010) and the
\emph{Pitx1}locus (chr7; Chan et al. 2010), which are involved in
freshwater adaptation. Additionally, broad peaks found at three
inversions (chr1, 11 \& 21) have also been associated with freshwater
adaptation (Jones et al. 2012; Roesti et al. 2015). Unexpectedly, as we
compared two marine populations, we identified some of these
characteristic patterns of marine-freshwater divergence in this study
(Fig. S3). A possible explanation is that the northern and southern
populations differ in the degree to which they receive gene flow from
freshwater populations. In the south, threespines were sampled from an
isolated stream that drained directly into the ocean, while the northern
threespines were sampled from a lake connected to an estuary (Tables
S1). Counterintuitively, the patterns we found probably came from
freshwater alleles in the southern population, as a previous study of
the lake in the north found no evidence of hybridization between
`anadromous' and freshwater populations (Drevecky, Falco and Aguirre,
2013), and a study of marine populations in the North-West Pacific found
a higher frequency of freshwater associated alleles at the \emph{EDA}
locus in Oregon than Alaska (Morris \emph{et al.} , 2018). However, to
test such hypotheses about introgression, we would have to look at the
frequency of the low-plate \emph{EDA} allele and the frequencies of
inversions in Oregon and Alaska and contrast this with nearby freshwater
populations. An alternative explanation is that the some of the patterns
of marine-freshwater adaptation may also be pleiotropically connected to
thermal regulation, as has been suggested for the \emph{EDA} locus
(Morris \emph{et al.} , 2018). Whether it is differential gene-flow or
pleiotropic adaption, we have found that the genomic landscape of
geographically diverse marine threespines is strikingly similar to the
marine-freshwater landscape.

In contrast to the patterns found in threespines, no large peaks of
F\textsubscript{ST} were present along the tubesnout genome (Fig. 2).
Instead, there were several small and narrow F\textsubscript{ST} peaks
suggesting that the tubesnout genome has been shaped by processes that
do not leave strong genetic signals, such as genetic drift or polygenic
adaptation (Rockman 2012, Stinchcombe and Hoekstra 2008, Yeaman 2015).
As the Null-W test is designed to detect linked clusters of
F\textsubscript{ST} outliers, this also explains the lack of any
signatures of convergent evolution. Since the patterns of
F\textsubscript{ST} were not strongly heterogeneous in tubesnout, it is
unsurprising that no significant matches to threespine were found.

The genetic patterns present in the ninespine stickleback were likely
the result of a strong genetic bottleneck and isolation between the
northern and southern populations, as on average, genetic divergence was
high and genetic diversity was low in all four populations (Table 1,
Fig. 2). Southern populations were sampled from two prairie lakes, which
were formed when a larger post-glacial lake dried up, isolating these
ninespine populations and presumably causing a genetic bottleneck
(Tufts, 2018), similar to the founder-effect observed in Nordic
populations (Shikano \emph{et al.} , 2010). In contrast, the northern
populations were sampled from lakes close to the sea, which potentially
has provided several opportunities for gene flow from the marine
populations. A phylogeographic study separated ninespine populations
from the Atlantic coast and Great Lakes regions into two post-glacial
lineages, with evidence suggesting that the divergence time among these
lineages may be much older than the last glacial maximum
(Aldenhoven\emph{et al.} , 2010). Presumably, the prairie lake
populations are part of this Great Lakes lineage (Tufts, 2018) and
therefore should be highly diverged from the Northern populations. The
extreme genetic divergence among these populations is likely to be the
result of long-term genetic isolation combined with a strong genetic
bottleneck in the southern populations, not adaptation to latitude.

Comparing the genome scans of all species reveals three distinct
patterns, suggesting that the balance between the evolutionary processes
has differed among these species. The F\textsubscript{ST} Manhattan
plots (Fig. 2A) show different patterns, which can be interpreted as the
result of three distinct evolutionary scenarios: local adaptation
(threespine), genetic bottlenecks (ninespine) and a weak or polygenic
selection and/or drift (tubesnout). This does not imply that the
ninespine has not experienced selection or that the threespine has not
been affected by drift, just that the patterns of diversity in the
genome have been more strongly affected by different processes in each
species.

A major caveat to these results is that very few populations were
sampled per species. Pool-seq mixes alleles across a population, which
means that the basic sampling unit is a population, in effect each
species had only 2-4 data points. The comparisons made in this study may
have been underpowered to detect any shared genetic patterns. However,
the presence of threespine peaks in previously identified regions
undergoing adaptation (Fig. S3) shows that strong genetic patterns were
detectable, thus only subtle patterns of genetic diversity were lost.
The lack of this pattern in tubesnout may be due to the lack of an
evolutionary history of repeated colonization followed by gene-flow from
freshwater populations, which can lead to complex genomic architecture
for adaptive traits (Tigano and Friesen, 2016; Faria \emph{et al.} ,
2019). All things considered; this study demonstrates the diversity of
genetic patterns that can be identified from genome scans of wild
species, even with a limited number of populations.

\emph{Comparative genome scans in a broader context}

In many cases, similarity in patterns revealed by genome scans among
species decreases with phylogenetic distance. Divergent populations of
the same species, and sister species that have recently diverged, often
have more strongly shared genetic patterns (Fischer \emph{et al.} ,
2013; Renaut \emph{et al.} , 2013; Westram \emph{et al.} , 2014; Burri
\emph{et al.} , 2015; Ravinet \emph{et al.} , 2016; Vijay \emph{et al.}
, 2016). At greater phylogenetic distances, species that diverged long
ago often show less similarity in their genetic patterns, with most of
the residual patterns being attributed to convergent evolution (Alan Le
Moan, Gaggiotti, Henriques, \& Martinez, n.d.; Raeymaekers et al., 2017;
Vijay et al., 2017) . Henderson and Brelsford (2020) studied this
contrast explicitly in three hummingbird species-pairs, showing that
more distantly related species pairs had reduced correlations in genetic
diversity and increased F\textsubscript{ST} across the genome.
Similarly, a meta-analysis (Conte \emph{et al.} , 2012) demonstrated a
negative relationship between the proportion of shared signatures of
trait variation and the time since divergence of both species and
population pairs. Shared patterns of genome scan variation is not a
universal outcome, as R\selectlanguage{ngerman}æymaekers et al. (2017) showed no shared genetic
patterns among species despite significant phenotypic sharing. Our study
fits in with this latter category, without any signatures of convergent
evolution and widespread differences in genetic patterns along the
genome.

An interesting contrast to the results of this study is Vijay's (et al.
2017) study of the long-term conservation of genomic patterns among
three species of birds. They compared species that had similar
generation and divergence times to the fishes used in this study (Bird
clades in Vijay et al. = 23-55mya; threespine to ninespine = 26mya
{[}Varadharajan et al., n.d.{]}; threespine to tubesnout = 50mya
{[}Betancur et al., 2013{]}); suggesting that patterns of genetic
diversity are conserved long past speciation. Vijay found stronger
correlations in genetic diversity among their species pairs (range of
Pearson's r = 0.08-0.27) than were found in this study (range Spearman's
\selectlanguage{greek}ρ \selectlanguage{english}= -0.07 -- 0.09). However, Manhattan plots of F\textsubscript{ST} and
genetic diversity also did not show any clear overlapping peaks or
troughs (Fig. 2). Other studies looking at fewer genetic markers have
also identified more conserved levels of genetic diversity in birds than
fishes (Johns and Avise, 1998; Adams and Hadly, 2013), possibly as the
result of a faster genome-averaged mutation rate, which has been
observed between teleosts and mammals (Ravi and Venkatesh, 2008).
Alternatively, fish genomes may evolve faster than birds due to
differences in their recombination map or gene densities. Investigating
the differences in the rates of evolution among broad taxonomic groups
is an interesting question, which is now possible with the increase in
publicly available whole genome data.

\emph{Conclusion:}

In some ways the lack of shared genetic patterns among species is not
surprising, as evolution is a balance of several forces that leave a
complex mosaic of patterns in the genome. Finding any common patterns
among species would require very strong evolutionary forces to
consistently shift this balance in the same way for every species. When
comparing genome scans divergence in such patterns may be the norm and
conservation may be a comparatively rare exception. Our results
demonstrate that genome scans can be noisy, due to the effects of
demographic shifts, genomic architecture or selective sweeps. Yet these
noisy results help in the development of a general theory on how
evolutionary forces shape the genome, by showing when similarities do
not arise and some of the oddities that one may see when performing a
genome scan.

\textbf{Acknowledgements:}

We thank all the organisations and people that have assisted in the
collection of samples. The Canadian Department of Fish and Wildlife and
Fish and Wildlife Departments of Alaska and Oregon assisted us by
providing information about sampling locations. In particular we thank
Dr. Darcie Neffs of the Alaskan Department of Fish and Wildlife for
collecting tubesnouts from Juneau (TuAK), Prof. Mike Bell for collecting
the Alaskan threespine stickleback (TsAK) and Ms. Treasa Tufts who
collected the ninespine sticklebacks. Dr. Sarah Smith-Wuitchik is
thanked for her advice for sampling fish. We also thank Dr. Jon Mee and
the members of the Rogers Lab at the University of Calgary who provided
advice on finetuning the DNA extraction protocol. Finally, we would like
to thank Ms. Hazel Cameron-Inglis for her assistance during a month and
a half of field work. Funding for this work was provided by Alberta
Innovates (20150252) and NSERC Discovery (RGPIN/03950-2017).

\textbf{Data Accessibility:}

The raw sequencing data for each population has been uploaded to GenBank
(DOI\ldots{}..) and all code used in the analysis is uploaded to GitHub:
ja-Reeve/CompGenoScan.

\textbf{Author Contributions:}

JR collected the data and with the help of SY designed the project and
wrote the manuscript. QL and DL provided advice on the analysis and
created the list of orthologs to compare species. All authors have read
and approved this paper.

\textbf{Competing Interests:}

None of the authors have competing interests.

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\textbf{Tables:}

\textbf{Table 1:} Average genetic diversity, standard error (SE) and
population size (N) per population. Genetic diversity is represented by
both the average F\textsubscript{ST} per SNP and
average\({\overset{\overline{}}{H}}_{E}\) per window for each population and each
species. The population labels are explained in the caption of Fig. 1.\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
Population & N & Number of windows & \({\overset{\overline{}}{\mathbf{H}}}_{\mathbf{E}}\) & SE & Number of
SNPs & F\textsubscript{ST} & SE\tabularnewline
\midrule
\endhead
\emph{Threespine stickleback} & \emph{Threespine stickleback} &
\emph{Threespine stickleback} & \emph{Threespine stickleback} &
\emph{Threespine stickleback} & & &\tabularnewline
TsAK & 52 & 8,764 & 2.65x10\textsuperscript{-3} &
1.52x10\textsuperscript{-5} & 3,928,772 & - & -\tabularnewline
TsOR & 51 & & 3.41x10\textsuperscript{-3} & 1.75x10\textsuperscript{-5}
& & &\tabularnewline
Species average & Species average & - & 3.03x10\textsuperscript{-3} &
1.43x10\textsuperscript{-5} & - & 0.14 &
9.98x10\textsuperscript{-4}\tabularnewline
\emph{Tubesnout} & \emph{Tubesnout} & \emph{Tubesnout} &
\emph{Tubesnout} & \emph{Tubesnout} & & &\tabularnewline
TuAK & 44 & 8,925 & 2.93x10\textsuperscript{-3} &
1.73x10\textsuperscript{-5} & 3,466,658 & - & -\tabularnewline
TuBC & 50 & & 2.86x10\textsuperscript{-3} & 1.21x10\textsuperscript{-5}
& & &\tabularnewline
Species average & Species average & - & 2.90x10\textsuperscript{-3} &
1.18x10\textsuperscript{-5} & - & 0.12 &
4.92x10\textsuperscript{-4}\tabularnewline
\emph{Ninespine stickleback} & \emph{Ninespine stickleback} &
\emph{Ninespine stickleback} & \emph{Ninespine stickleback} &
\emph{Ninespine stickleback} & & &\tabularnewline
NsNUn & 46 & 15,058 & 0.41x10\textsuperscript{-3} &
0.67x10\textsuperscript{-5} & 687,627 & F\textsubscript{ST} only based
on north-south population pairs & F\textsubscript{ST} only based on
north-south population pairs\tabularnewline
NsNUd & 42 & & 0.37x10\textsuperscript{-3} & 0.83x10\textsuperscript{-5}
& & &\tabularnewline
NsABk & 30 & & 0.18x10\textsuperscript{-3} & 0.44x10\textsuperscript{-5}
& & &\tabularnewline
NsABm & 41 & & 0.13x10\textsuperscript{-3} & 0.40x10\textsuperscript{-5}
& & &\tabularnewline
Species average & Species average & - & 0.27x10\textsuperscript{-3} &
0.57x10\textsuperscript{-5} & - & 0.49 &
1.41x10\textsuperscript{-3}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2:} Spearmen's \selectlanguage{greek}ρ \selectlanguage{english}correlations among species. Correlations
are made between the average F\textsubscript{ST} and\({\overset{\overline{}}{H}}_{E}\)
of interspecific gene pairs.\({\overset{\overline{}}{H}}_{E}\) scores are averaged
across all populations before comparing species. See Fig. 3B
for\({\overset{\overline{}}{H}}_{E}\) correlations among populations.\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
Comparison & F\textsubscript{ST} & F\textsubscript{ST} &
\({\overset{\overline{}}{\mathbf{H}}}_{\mathbf{E}}\) & \({\overset{\overline{}}{\mathbf{H}}}_{\mathbf{E}}\) & Number of genes\tabularnewline
\midrule
\endhead
& \selectlanguage{greek}\textbf{ρ} & \textbf{P-value} & \selectlanguage{greek}\textbf{ρ} & \textbf{P-value}
&\tabularnewline
Threespine vs Ninespine & 0.01 & 0.10 & -0.07 &
2.2x10\textsuperscript{-6} & 20,155\tabularnewline
Threespine vs Tubesnout & 0.02 & 0.04 & 0.09 &
2.2x10\textsuperscript{-6} & 9,155\tabularnewline
Ninespine vs Tubesnout & -0.04 & 2.5x10\textsuperscript{-4} & -0.02 &
0.08 & 8,086\tabularnewline
\bottomrule
\end{longtable}

\textbf{Figures:}

\textbf{Figure 1:} The sampling location for each species of fish.
Threespine locations are represented by green triangles, ninespines by
blue squares and tubesnouts by orange circles. Labels for each
population are used consistently throughout this paper; the first half
of the label denotes the species (Ts = thresspine stickleback, Tu =
tubesnout and Ns = ninespine stickleback). The second half of the label
denotes the state or province where the population was collected (AK =
Alaska, USA; BC = British Columbia, Canada; OR = Oregon, USA; AB =
Alberta, Canada; NU = Nunavut, Canada). The two Albertan ninespine
populations are combined into a single point (NsABm \& NsABk) for visual
clarity. The base map is projected in Azimutahl equal distances (datum =
WGS84) orientated to centre on Canada (latitude = 90 \& longitude =
-98.4). Ocean water is coloured by the annual range in sea surface
temperature (\selectlanguage{ngerman}°C) taken from the Bio-ORACLE database
(https://www.bio-oracle.org/). Threespine and tubesnout photos were
taken by Hazel Cameron-Inglis and used with permission, the ninespine
photo was taken by Piet Spanns and used under an open license. The final
plot was compiled in R using the sf, ggplot, raster and grid packages.

\textbf{Figure 2:} Genome wide patterns of genetic diversity within the
threespine stickleback, ninespine stickleback and tubesnout. \textbf{A}
) F\textsubscript{ST} per SNP and \textbf{B} )\({\overset{\overline{}}{H}}_{E}\) per
50Kb window for each species, excluding windows in intergenic regions.
Ninespine scores were mapped onto their position on the threespine
genome. Threespine and tubesnout F\textsubscript{ST} was downsized by
sampling every 100\textsuperscript{th} SNP along the genome, and
approximately 70 windows were filtered out of the
\({\overset{\overline{}}{H}}_{E}\)plots for visual clarity. The red-dashed lines show
the 999\textsuperscript{th} F\textsubscript{ST} and
99\textsuperscript{th}\({\overset{\overline{}}{H}}_{E}\) quantiles. This plot was
generated in R using the ggplot and gridExtra packages.

\textbf{Figure 3:} Comparison of genomic patterns among
species.\textbf{A)} shows the relationship in average genetic diversity
(\({\overset{\overline{}}{H}}_{E}\)) among genes for each species pair. Each point is a
gene which is orthologous among the species. The dashed lines represent
the 95\textsuperscript{th} and 5\textsuperscript{th}quantile of
\({\overset{\overline{}}{H}}_{E}\) in each species. Any points on the bottom left or top
right segments of a panel are genes with extreme \({\overset{\overline{}}{H}}_{E}\) that
are shared among species. \textbf{B)} is a matrix of
\({\overset{\overline{}}{H}}_{E}\)Spearman's correlations among all population pairs,
where the colour represents Spearman's \selectlanguage{greek}ρ \selectlanguage{english}and the text shows the
significance level of a correlation test (\textbf{*} P \textless{} 0.05;
\textbf{**} P \textless{} 0.01; *** P \textless{} 0.001). \textbf{C)}
shows the relationship and between the average F\textsubscript{ST} per
gene for each species. Colored points are signatures of local adaptation
for each species; red for threespine sticklebacks and blue for
tubesnouts. Gray points are genes not associated with local adaptation;
they are partially transparant to show overlapping genes. No signatures
of selection overlaped among species.

\textbf{Figure 4:} Detecting genes with elevated divergence and testing
for signatures of convergent evolution. \textbf{A-B)} Show the
top-candidate approach where each point is a separate gene. The total
number of SNPs is compared to the number of SNP outliers in each gene,
with top candidates identified as those genes that exceed the number of
outliers expected under a binomial distribution, represented by the
jagged red line. \textbf{C-D)} Null-W test results between
\textbf{C)}tubesnout orthologs of threespine top candidates and
\textbf{D)}threespine orthologs of tubesnout top candidates. The grey
curve is the null-distribution of Z-scores from all orthologs of
candidate genes in the focal species (i.e. tubesnout orthologs in
\textbf{C} and threespine orthologs in \textbf{D} ). The blue points are
top-candidate-orthologs, whose values on the y-axis have been jittered
for visual clarity. The red dashed line is the 95th quantile of
Z-scores. FDR corrections are not shown.\selectlanguage{english}
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