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. π,
HE, Tajima’s D, and Fay & Wu’s H) or genetic divergence
(e.g. FST, dxy), 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 et al. , 2017) or ancestral
population structure (Vijay 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 (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 (Pungitus
pungitus ; for simplicity the stickleback species will be referred to as
threespines and ninespines) and tubesnout (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 et al. , no date; Shikano 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 (Shikanoet al. , 2013), whole genome data found no genetic signatures of
convergent evolution (Raeymaekers 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 (Betancuret 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 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 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 FST 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.