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\begin{document}

\title{Expanding the conservation genomics toolbox: incorporating structural
variants to enhance genomic studies for species of conservation concern}



\author[1]{Jana Wold}%
\author[2]{Klaus-Peter Koepfli}%
\author[1]{Stephanie Galla}%
\author[3]{Carolyn J. Hogg}%
\author[4]{David Eccles}%
\author[5]{Marissa Le Lec}%
\author[5]{Joseph Guhlin}%
\author[6]{Anna Santure}%
\author[1]{Tammy  Steeves}%
\affil[1]{University of Canterbury}%
\affil[2]{Smithsonian Conservat Biol Inst}%
\affil[3]{The University of Sydney}%
\affil[4]{Malaghan Institute of Medical Research}%
\affil[5]{University of Otago}%
\affil[6]{The University of Auckland}%


\vspace{-1em}



  
  \date{May 05, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Structural variants (SVs) are large rearrangements (\textgreater{} 50
bp) within the genome that impact gene function and the content and
structure of chromosomes. As a result, SVs are a significant source of
functional genomic variation, i.e. variation at genomic regions
underpinning phenotype differences, that can have large effects on
individual and population fitness. While there are increasing
opportunities to investigate functional genomic variation in threatened
species via single nucleotide polymorphism (SNP) datasets, SVs remain
understudied despite their potential influence on fitness traits of
conservation interest. In this future-focused Opinion, we contend that
characterizing SVs offers the conservation genomics community an
exciting opportunity to complement SNP-based approaches to enhance
species recovery. We also leverage the existing
literature--predominantly in human health, agriculture and
eco-evolutionary biology--to identify approaches for readily
characterizing SVs and consider how integrating these into the
conservation genomics toolbox may transform the way we manage some of
the world's most threatened species.%
\end{abstract}%



\sloppy


\textbf{Running Head:} Structural variants and conservation genomics

\section*{Structural variants: a new tool in the conservation genomics
toolbox}

{\label{structural-variants-a-new-tool-in-the-conservation-genomics-toolbox}}

Over the past 10 years, the field of conservation genomics has largely
focused on transitioning from a handful of microsatellite markers to
thousands of genome-wide single nucleotide polymorphisms (SNPs)
(Allendorf, Hohenlohe, \& Luikart, 2010; Mable, 2019). Whether
characterized using reduced-representation data or whole-genome sequence
(WGS) methods, SNP-based estimates of genome-wide variation are being
used to better inform conservation by delineating species, including
cryptic species (e.g., Binks, Steane, \& Byrne, 2021; Quattrini et al.,
2019\href{https://www.zotero.org/google-docs/?nK6Aq0}{;} but see Stanton
et al., 2019), detecting hybridization and introgression (Dufresnes \&
Dubey, 2020; Peters et al., 2016; but see Forsdick et al., 2021; Hauser,
Athrey, \& Leberg, 2021 preprint), identifying conservation units
(Liddell et al., 2020), informing conservation translocations (Dresser,
Ogle, \& Fitzpatrick, 2017; Glassock, Grueber, Belov, \& Hogg, 2021),
guiding conservation breeding programs (e.g., Galla et al., 2020; Wright
et al., 2020) and identifying the genomic basis of adaptive traits
(Duntsch et al., 2020). However, SNPs constitute only one component of
genome-wide variation (Ho, Urban, \& Mills, 2020). Structural variants
(SVs) are genomic rearrangements such as insertions, deletions,
duplications, inversions and translocations, and are generally defined
as [?]50 bp in length (Figure 1). Variation in chromosome structure was
one of the earliest types of genetic variation studied (e.g., McClung,
1905; Sturtevant, 1921), and a recent focus on the impacts of these
genomic rearrangements on sequence variation has found that SVs affect
more overall genome content and intersect with genes more often than
SNPs (Catanach et al., 2019; Chakraborty, Emerson, Macdonald, \& Long,
2019; Chiang et al., 2017; Frayling, 2014; Pang et al., 2010).

Structural variants can have significant evolutionary impacts on
species. These impacts include genomic incompatibilities between
ancestral and novel variants that may influence hybridization and
introgression (Weissensteiner et al., 2020) and may even lead to
speciation (Davey et al., 2016; Todesco et al., 2020). In addition,
emerging evidence indicates that SVs can underlay fine-scale population
structure, and may facilitate local adaptation (Cayuela et al., 2021;
Dorant et al., 2020; Tigano et al., 2021). These findings--which are
generally the result of integrating SNPs and SVs--highlight the
opportunity for both variant types to inform conservation. Nevertheless,
the broad application of SVs in conservation remains challenging in part
due to the uncertainty in how best to detect and genotype all SV types
at the population scale (M\selectlanguage{ngerman}érot, Oomen, Tigano, \& Wellenreuther, 2020).
Further, although SVs are more likely to impact fitness traits than SNPs
(Pang et al., 2010), the overall impacts of SVs on population
persistence is relatively unexplored (Oomen et al., 2020).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.88\columnwidth]{figures/Figure-1/Figure-1}
\caption{{Structural Variant (SV) types and how they differ from a reference
genome: A) deletion, where a segment of DNA is not present in an
individual, but present in the reference; B) duplication, a
rearrangement where there is more than one copy of a particular region
of the genome, often in tandem. These can be either intrachromosomal, as
shown here, or interchromosomal; C) insertion, a DNA sequence present in
an individual sample, but not present in the reference; D) inversion, a
segment of the chromosome that has had a double-stranded break at an
upstream and downstream location and become reversed in order; E)
translocation, a rearrangement where a portion of one chromosome has
been transposed onto another.
{\label{648595}}%
}}
\end{center}
\end{figure}

Here, we leverage our cross-sector expertise in conservation (AWS, CJH,
JG, JRW, KPK, MLL, SJG, TES), human health (DE), agriculture (JG),
quantitative genetics (AWS) and bioinformatics (DE, JG, JRW, MLL) to
consider the significance of SVs in population persistence and species
extinction risk, and discuss opportunities and challenges for relating
SVs to fitness traits in small populations. We identify approaches for
reliably characterizing SVs with an emphasis on leveraging new and
existing genomic resources for species of conservation concern. We also
describe how the incorporation of genome assemblies for multiple
reference individuals can be used to improve SV discovery and
genotyping, even for large and/or complex SVs (Alonge et al., 2020;
Eizenga et al., 2020; Gao et al., 2019; Golicz, Batley, \& Edwards,
2016; Tettelin et al., 2005). We contend that the study of SVs has the
potential to improve conservation outcomes by enhancing our
understanding of genome-wide variation and traits related to fitness.

\section*{Small population paradigm and structural
variants}

{\label{structural-variant-discovery-and-genotyping}}

Like SNPs, SVs vary within and among individuals, and SV variation is
subject to genetic drift, selection, mutation and gene flow, and is
impacted by demography, particularly effective population size (Ne).
Species and/or populations with large Ne should harbor a higher number
of SVs, whereas populations with small Ne should harbor less (e.g.,
fewer SV counts in the critically endangered Hawaiian crow versus
non-threatened conspecifics; Weissensteiner et al., 2020). Although also
influencing the mutation rates and genomic locations of SNP variants,
the genome composition and architecture (e.g., overall chromosome repeat
content) has a much larger impact on the number, type, location and
mutation rate of SVs (Collins et al., 2020). Further, while both SNPs
and SVs can be neutral, their size relative to single base substitutions
mean that SVs are more likely to overlap gene and regulatory regions and
impact fitness. The evolutionary dynamics of SVs in large populations
will therefore be determined by the interplay between the functional
effects of these SVs, if any, and variation in mutation rates of
different SV sizes and types, which may lead to enrichment of SVs of
particular types and/or in particular genomic regions (Conrad \& Hurles,
2007).

In contrast, populations of conservation concern are likely to be
fragmented or chronically small, and the evolutionary dynamics of both
SNP and SV variation will be dominated by genetic drift and founder
effects that interplay with the small Ne to both decrease the overall
level genetic variation and reduce the efficacy of natural (purifying)
selection (Henn, Botigu\selectlanguage{ngerman}é, Bustamante, Clark, \& Gravel, 2015).
Decreasing genetic variation leads to further reductions in Ne which
increases the probability of extinction through both the loss of
adaptive variants and the increase in frequency of deleterious variants.
This can lead to a downward spiral of further loss of diversity and
increasing extinction risk, a process termed an `extinction vortex'
(Gilpin \& Soule 1986). Further, inbreeding is unavoidable in small
populations, leading to higher frequencies of deleterious recessive
individuals in the population, with this inbreeding depression leading
to a further fitness burden on the threatened species (Kardos, Taylor,
Ellegren, Luikart, \& Allendorf, 2016; Stoffel, Johnston, Pilkington, \&
Pemberton, 2021).

While both SNPs and SVs can have deleterious fitness effects, their size
and potential to overlap gene regions mean that SVs may be enriched in
the pool of deleterious variants that are maintained in small
populations. While SVs of very large deleterious effect may be purged
from the population, small populations may selectively accumulate
slightly deleterious SVs. This enrichment indicates that SVs likely
underpin some negative fitness traits associated with inbreeding
depression. Although there are some clues as to the evolutionary
dynamics of SVs in small populations, further study is critical for
resolving the significance of SVs in population persistence and species
extinction risk.

\section*{Relating structural variants to fitness
traits}

Advancements in WGS technologies coupled with increased computational
capacities, have renewed interest in the role of SVs in determining
trait differences (Chiang et al., 2017; Pang et al., 2010; Sadowski et
al., 2019; Yi \& Ju, 2018). For example, characterizing SVs in
agricultural species has led to the identification of variants
associated with economically significant traits in crops like grapevine
(Zhou et al., 2019), maize (Yang et al., 2019), soybean (Liu et al.,
2020) and tomato (Alonge et al., 2020), and identified specific genes
and gene regions associated with domestication in vertebrates
(Bertolotti et al., 2020; Cagan \& Blass, 2016; vonHoldt et al., 2017).
The impact of these SVs on trait variation may be direct or indirect.
For example, SVs can lead to direct changes to gene coding regions and
gene regulation that change the function or expression of proteins
(Derek M. Bickhart \& Liu, 2014; Collins et al., 2020). In contrast, in
some cases where a particular SV is associated with a trait, the SV may
not be the causal variant, but rather increase the likelihood of
\emph{de novo} causal variants nearby. This is the case for a relatively
common 1.3Mb inversion on the human Y-chromosome where microdeletions
accrue at inversion breakpoints, which can result in profound impacts on
male fertility (Hallast et al., 2021). There is also growing evidence
that SVs can further impact the gene regulatory landscape by altering
the formation of topologically associating domains (TADs), genomic
regions that physically interact with themselves more frequently than
with regions elsewhere (Sadowski et al., 2019; Shanta et al., 2020).

There is growing evidence that SVs--in particular, the suppression of
recombination and subsequent evolution of `supergenes', or tightly
linked co-adapted alleles--can impact fitness traits in natural
populations (Huynh et al., 2011; Jay et al., 2018; K.-W. Kim et al.,
2017). For example, sperm swimming speed in zebra finch is determined by
inversion haplotypes, with heterokaryotypic males producing faster sperm
than homokaryotypic males (K.-W. Kim et al., 2017; Knief et al., 2017).
In addition, a supergene resulting from an inversion has been found to
determine mating strategy and morphology in the Eurasian ruff (Küpper et
al., 2016). Moreover, it is notable that this inversion is likely a
lethal recessive variant (Lamichhaney et al., 2016). Similarly, a large
inversion resulting in a supergene underlies significant morphological
and behavioral differences among white-striped and tan-striped morphs in
White-throated sparrows, with aggressiveness being monogenic in the
white-striped morph (Merritt et al., 2020). Although many fitness traits
are likely to be polygenic, including traits of conservation interest
such as disease susceptibility, reduced fertility and developmental
abnormalities (e.g., Moran et al., 2021; Murchison et al., 2012; Roelke,
Martenson, \& O'Brien, 1993; Savage, Crane, Team, \& Hemmings, 2020),
those impacted by supergenes are likely to have relatively simple
inheritance patterns which will enable their characterization and
management.

To date, there are generally two approaches to investigate the genomic
basis of traits, both of which require clearly defined traits and
well-curated data sets and high-quality, well annotated, reference
genomes: 1) Comparative genomics, where well-characterized groups
(individuals, populations or species) are used to identify highly
differentiated genomic regions (Alonge et al., 2020; McHale et al.,
2012; vonHoldt et al., 2017; Weissensteiner et al., 2020); and 2)
Association studies, where associations between specific markers (ie.,
SNPs and/or SVs) and traits are assessed (Chakraborty et al., 2019). For
example, association studies and/or comparative approaches using SVs
have revealed signatures of domestication (e.g., aquaculture salmon and
dogs; Bertolotti et al., 2020; Cagan \& Blass, 2016; vonHoldt et al.,
2017), and the relative contribution of SVs to species evolution~ (e.g.,
Atlantic cod, Corvids, \emph{Heliconius} butterflies, Sunflowers; Berg
et al., 2017; Joron et al., 2011, 2006; Rieseberg, Whitton, \& Gardner,
1999; Weissensteiner et al., 2020).

Whole-genome SNP-based association studies have been extensively applied
to model organisms, agriculturally significant species and humans,
although they are increasingly applied to wild and non-model species
(Santure \& Garant, 2018). However, for many SNP-based association
studies, significantly associated loci do not explain the vast majority
of known trait heritability (i.e., the proportion of trait variation
that is due to genetic rather than environmental differences between
individuals; Clarke \& Cooper, 2010; Eichler et al., 2010; Manolio et
al., 2009). This ``missing heritability'' is often attributed to genetic
variation not explained by genotyped loci, that is to say that causal
variants may remain uncharacterized (Manolio et al., 2009). Another
hypothesis to explain this missing heritability is that SVs are a
significant source of trait variation (Eichler et al., 2010; Frazer,
Murray, Schork, \& Topol, 2009). Further, although a given SV is likely
to be linked to a nearby SNP (Wilder, Palumbi, Conover, \& Therkildsen,
2020), they may not be in strong linkage disequilibrium with each other,
so that the SNP will not capture the impact of the SV on the trait (Pang
et al., 2010). Structural variants may also lead to challenges in
aligning reads and calling SNPs in a region, in turn preventing the
identification of SVs from sequence data (Pang et al., 2010). The
inclusion of SVs into association studies is therefore likely to lead to
better power to detect causative variants, particularly given advances
in GWAS methods, including machine-learning approaches, that can more
effectively prioritize variants in a region and account for non-additive
interactions (Brieuc, Ono, Drinan, \& Naish, 2015; Ramzan, Gültas,
Bertram, Cavero, \& Schmitt, 2020). This is particularly promising as
evidence suggests that SVs have larger phenotypic effects and may be
deleterious more often than SNPs (Chakraborty et al., 2019; Conrad et
al., 2010; Cridland, Macdonald, Long, \& Thornton, 2013; Emerson,
Cardoso-Moreira, Borevitz, \& Long, 2008; Rogers et al., 2015). For
example, a quantitative trait locus (QTL) study for~\emph{Drosophila
melanogaster} found that about half of all candidate genes underpinning
mapped QTL were impacted by SVs, and that a large proportion of genes
contained multiple rare SVs (Chakraborty et al., 2019).

\section*{Structural variant discovery and genotyping in threatened
species}

\subsection*{A high-quality reference genome is an invaluable
investment}

{\label{478643}}

While linkage mapping and cytological techniques can be used to
characterize SVs (Deakin et al., 2019), here we focus on the approaches
most likely to be accessible to the conservation genetics community.
Partnerships with global genome consortia (e.g., Vertebrate Genomes
Project, Rhie et al., 2021; Earth BioGenome Project, Lewin et al., 2018;
Nature and Zoonomia Consortium, Genereux et al., 2020) are providing
increased accessibility to highly contiguous, well-annotated genome
assemblies generated from both short and long read data and long-range
scaffolding approaches for many species, including those of conservation
concern and their close relatives (Whibley, Kelley, \& Narum, 2020). As
costs of generating and analyzing WGS data continue to drop, a growing
number of conservation genomicists working on species beyond global
genome consortia are investing in the assembly and annotation of
high-quality reference genomes. We readily recognize that not all
conservation programs are able to access the resources or sample quality
to generate a high-quality, well annotated reference genome, nor do we
recommend this action for all threatened species. Where there is a clear
hypothesis that SVs may be impacting a particular gene or region,
comparative approaches (Alonge et al., 2020; Weissensteiner et al.,
2020), and target capture methods (vonHoldt et al., 2017) may be the
most cost-effective, especially if a reference genome from a closely
related species can be leveraged instead. However, as described below,
investment in a high-quality reference genome is a near necessity for
accurate and precise SV discovery and genotyping, particularly if the
goal is to characterize a broad range of SV types for downstream
functional analyses (e.g., investigating the genomic basis of traits
linked to fitness). Further, if the goal is to characterize a large
proportion of SVs--including large and/or complex SVs--across the
genome, integration of multiple reference genomes provide a powerful
tool for accurately characterizing genome-wide variation and may
facilitate genotyping across multiple variant classes (i.e., both SNPs
and SVs; Figure 2). ~\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=1.00\columnwidth]{figures/Figure-21/Figure-2}
\caption{{Decision tree for characterizing structural variants (SVs), including
large and/or complex SVs. See text for details. Chromatin conformation
capture methods include Hi-C, Omni-C, and Pore-C. PacBio = Pacific
Biosciences; ONT = Oxford Nanopore Technologies.~\textsuperscript{1}Choi
et al., (2009);~\textsuperscript{2}Iannucci et al., (2021).
{\label{428891}}%
}}
\end{center}
\end{figure}

\subsection*{Alignment-based SV discovery
methods}

{\label{alignment-based-sv-discovery-methods}}

Once a high-quality reference genome has been assembled for a species of
interest, SVs can be detected using alignment-based methods where short-
and/or long read sequence data for multiple individuals are aligned to
the reference without prior assembly. These methods rely on identifying
patterns in read mapping between a sample and the reference genome.
Common algorithms include read-pair, read depth, split-reads
and~\emph{de novo} or local assembly (Hajirasouliha et al., 2010; Korbel
et al., 2007; Yoon, Xuan, Makarov, Ye, \& Sebat, 2009). No single
algorithm is well suited to detecting all SV types. For example, read
pair-based algorithms, where the orientation and distance between paired
ends are assessed, are suitable for detecting deletions, duplications,
and inversions. In the case of read depth approaches, deletions and
duplications are identified through variation in mapping depth.
Split-reads identify regions where alignments map across a breakpoint,
which is best suited for discerning the ends of an inversion or
translocation (Figure 3). Finally,~\emph{de novo} and local assembly
algorithms are best suited to identifying variants not present in the
reference (e.g., insertions) as reads aligned to contigs may be
reassembled alongside unmapped reads for pairwise comparison to a
reference~(Mahmoud et al., 2019). Early implementation of SV discovery
programs typically relied on a single approach~(e.g., BreakDancer,
Pindel; Chen et al., 2009; Ye, Schulz, Long, Apweiler, \& Ning, 2009).
Although these programs are less computationally intensive, they are
limited to calling only a few SV types and tend to underperform against
generalist programs that incorporate at least three algorithms for SV
detection~(e.g., Delly, Lumpy, Manta, SvABA; Chen et al., 2016; Layer,
Chiang, Quinlan, \& Hall, 2014; Rausch et al., 2012; Wala et al., 2018).
~

Because of the challenges of identifying and classifying SVs, many SV
discovery approaches have a high (and systematic) false positive rate
(e.g., Cameron et al., 2019), especially when data is derived from
short-read sequencing methods (Figure 3; see more below). In order to
address this error, an ensemble approach for SV characterization has
been extensively applied in human data and in domesticates (Du et al.,
2021; Ho et al., 2020; Zhou et al., 2019). With an ensemble approach,
multiple SV callers are integrated into a single pipeline as a means to
create more certainty around SV discovery, with only variants that
intersect multiple SV callers retained (Becker et al., 2018; Mohiyuddin
et al., 2015; Zarate et al., 2018 preprint). Further, because validated
SV call sets have been developed for humans, it is straightforward to
benchmark appropriate program combinations for SV discovery (Collins et
al., 2020; Ho et al., 2020; Parikh et al., 2016; Zook et al., 2020).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.84\columnwidth]{figures/Figure-31/Figure-3}
\caption{{Structural Variant (SV) types and common problems associated with
short-read sequence data: A) deletion, called when reads do not map to,
and/or are split across, a given region on the reference genome.
Generally the most straightforward SV to detect with short-read data,
but complex rearrangements may preclude mapping and result in a false
call; B) duplication, typically identified by an increase in read depth,
however reference error may result in a false call, preclude assessments
of copy number variation, or miss sequence variation due to unmapped
reads; C) insertion, short-reads may be mapped if the majority of each
read aligns to the reference genome, but reads composed mostly of the
insertion sequence may remain unmapped; D) inversion, breakpoints (i.e.,
exact positions of double-stranded DNA breaks) are difficult to resolve
as they typically occur in highly repetitive regions; E) translocation,
breakpoints difficult to resolve as they commonly occur in highly
repetitive regions, reads may also incorrectly map to the `original'
chromosome from which the translocation arose. Further, it is common for
more than one SV or different SV types to occur in close proximity.
Resolving these complex SVs is particularly challenging with short-read
sequence data as multiple mapping signals may contradict one another
(e.g., multiple deletions in close proximity, deletions that overlap
with inversion/translocation breakpoints), unless large samples sizes
are used (Collins et al., 2020).
{\label{527968}}%
}}
\end{center}
\end{figure}

When validated SV call sets are lacking, we recommend using a generalist
SV discovery program that combines multiple SV discovery algorithms to
target a range of SVs at once. These generalist programs enable a
combination of assembly-based methods alongside read depth, read pairs
and/or split-read approaches (i.e., GRIDSS, Cameron et al., 2017; Manta,
Chen et al., 2016). These programs not only perform well on their own,
but have the added benefit of performing well over a range of SV types
(Cameron et al., 2019; Kosugi et al., 2019). Using a generalist program
has an additional benefit that it overcomes the challenge faced by many
ensemble methods, where distinguishing between true variants and false
positives is difficult due to the significant overlap in false positives
across methods (Cameron et al., 2019). However, conservation geneticists
should be aware that the computational resources required to
characterize SVs at the population scale using generalist SV discovery
programs are substantial (e.g., for a diploid 1.15Gb genome with 170
individuals sequenced to [?]25x coverage, 72 physical cores, 460 Gb RAM,
\textgreater{}3 Tb storage was required to implement paired-, split-read
and assembly-based SV discovery algorithms; JRW personal observation).

\subsection*{Multiple reference genomes improve genome-wide structural
variant discovery and
genotyping}\label{multiple-reference-genomes-improve-genome-wide-structural-variant-discovery-and-genotyping}

A pangenome is the aggregate characterization of genomic variation
present in a group of interest, including species and populations (e.g.,
variation between strains of tomato, Alonge et al., 2020). Pangenomes
offer a straightforward solution to address the challenges associated
with SV discovery and genotyping with short-read sequence data. Although
originally developed to characterize variation in bacteria (Tettelin et
al., 2005), they are commonly used in studies of trait diversity in
humans (Pang et al., 2010) and agriculturally significant species (e.g.,
cattle, goats, soybean, and maize; D. M. Bickhart et al., 2020; Della
Coletta, Qiu, Ou, Hufford, \& Hirsch, 2021; Golicz et al., 2016; Liu et
al., 2020; Low et al., 2020; McHale et al., 2012; Yang et al., 2019).
There are two components of a pangenome: the `core' genomic regions that
do not vary among individuals, and `accessory' genomic regions that vary
among individuals (Bayer, Golicz, Scheben, Batley, \& Edwards, 2020;
Golicz, Batley, \& Edwards, 2016; Hurgobin \& Edwards, 2017; Figure 4).
In a pangenomic approach, genomes of multiple individuals are assembled
\emph{de novo} using multiple platforms (e.g., long reads, Hi-C, Optical
mapping; Song et al., 2020; Soto et al., 2020; Weissensteiner et al.,
2020; Zhou et al., 2019), followed by pairwise comparisons of
whole-genome alignments for SNP and SV discovery (e.g., Cortex, MUMmer,
Minimap2; Delcher et al., 1999; Iqbal, Caccamo, Turner, Flicek, \&
McVean, 2012; H. Li, 2018). Once variant discovery is complete, genome
graphs representing the variation in the pangenome may be constructed to
efficiently represent `core' and `accessory' regions (Eizenga et al.,
2020; Li, 2018; Rakocevic et al., 2019; Tettelin et al., 2005) that are
encompassed in analyses of copy number variation (CNV) and
presence-absence variation (PAV) (Della Coletta et al., 2021). Genome
graphs are a powerful method for population-level genotyping and
consistently outperform alignment-based genotyping (e.g., Ebler et al.,
2020 preprint; Eggertsson, 2017; Iqbal et al., 2012; D. Kim, Paggi,
Park, Bennett, \& Salzberg, 2019; H. Li, 2018). As a result, pangenomic
approaches can resolve complex variants (i.e., multiple overlapping
events) that may otherwise go undetected in alignment-based approaches,
hampering the discovery of causal variants (Alonge et al., 2020; McHale
et al., 2012).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.84\columnwidth]{figures/Figure-41/Figure-4}
\caption{{Characterization of SVs using assembled whole-genomes and genome graphs
using multiple reference genomes: (a) Schematic of structural variant
(SV) discovery using alignment of assembled whole genomes. (b) Schematic
of genome graphs, which can then be used to characterize a pangenome and
facilitate rapid genotyping of individuals, as `core' regions (genomic
regions that do not vary among individuals) are readily distinguished
from `accessory' regions (genomic regions that do vary among
individuals). Genome graphs can also facilitate accurate genotyping as
more than one allele may be considered at once. The use of multiple
reference genomes can reduce reference bias and are better able to
capture insertions, inversion haplotypes and complex SVs (i.e., genomic
regions where multiple SVs occur in close proximity).
{\label{846099}}%
}}
\end{center}
\end{figure}

For species of conservation concern, individuals selected for reference
genomes may not be the most representative in that some are chosen to
showcase individuals known to the public (e.g., animal ambassadors) or
are the result of opportunistic sampling. Regardless, in most cases, it
is unlikely that a single individual accurately represents the genomic
variation within the species as a whole, even in highly inbred species
(Gao et al., 2019; McHale et al., 2012). The primary advantage of a
pangenomic approach is that multiple alternative alleles are assessed
while read mapping, effectively removing reference bias (Paten, Novak,
Eizenga, \& Garrison, 2017). In other words, sequence reads are not
precluded from mapping if they are not represented in the primary
reference (e.g. insertions, Figure 3). The promise of pangenomes is
exemplified in crop species such as soy, wheat and \emph{Brassica} spp.
where many more SVs are resolved, leading to a paradigm shift in crop
improvement (Della Coletta et al., 2021; Golicz et al., 2016; Liu et
al., 2020; Montenegro et al., 2017; Song et al., 2020). However, the
ability of conservation programs to establish pangenomes largely depends
on the availability of existing genomic resources and the ability to
secure substantive funding. In lieu of a conventional pangenome, whereby
all genomic variation is captured, the targeted sequencing and assembly
of multiple representative individuals to construct genome graphs may
provide significant improvements over the use of a single reference
genome of comparable assembly quality (Figure 2). For a related approach
see Tigano et al., (2021).

\subsection*{SV discovery and genotyping with short-read sequence
data}

{\label{sv-discovery-and-genotyping-with-short-read-sequence-data}}

Moderate coverage (e.g., \textasciitilde{}10x) short-read sequencing is
becoming the predominant approach in conservation genomics (Cam et al.,
2020; Galla et al., 2020; Lado et al., 2020; Lew et al., 2015; Lujan,
Weir, Noonan, Lovejoy, \& Mandrak, 2020; Oyler-McCance, Cornman, Jones,
\& Fike, 2015, p.; Robinson et al., 2016). Moderate coverage datasets
are generally cost effective and appropriate for characterizing SNPs
using a single reference genome, whereas a minimum of 30x coverage is
often recommended for de novo SV discovery and genotyping (Ahn et al.,
2009; Kosugi et al., 2019; Sims, Sudbery, Ilott, Heger, \& Ponting,
2014; Wang et al., 2008). This is because at low coverage it is
challenging to determine whether a detected variant is an artefact of
sequencing/mapping error or a `true' variant (Figure 3). For example,
mapping errors may occur when the relative size of a SV spans a large
portion or the entire length of a read, or in the case of a complex
rearrangement, prevents mapping altogether (Sedlazeck et al., 2018; Yi
\& Ju, 2018). In addition, read insert size (400 - 600 bp) may bias the
discovery of insertions and deletions in some pipelines (Kosugi et al.,
2019). By increasing average read depth to 30x, the likelihood of
distinguishing between sequencing error, read mapping errors and true
genomic variation increases as well. However, due to the increased
costs, such high coverage short-read datasets may be challenging to
generate for many species of conservation concern. The question then
becomes how best to fully utilize moderate coverage short-read datasets
to investigate SVs. Recent studies demonstrate that an average read
depth of 10x is sufficient for population-scale comparisons, provided
sampling is representative and a high-quality reference genome is
available (Collins et al., 2020; Du et al., 2021; Zhou et al., 2019).
Regardless of coverage, it is important to note that genotypes called in
short-read SV discovery programs are prone to high error rates (Chander,
Gibbs, \& Sedlazeck, 2019). One strategy to alleviate this is to conduct
SV discovery across all individuals to establish a SV call set and then
use dedicated programs for SV genotyping (e.g., BayesTyper, SVTyper,
Paragraph)(Chander et al., 2019). In addition, SV genotyping programs
have variable performance across a range of SV sizes and types. As such,
many programs are unable to genotype across the whole range of SVs that
may have been called during discovery. A final consideration is that
genotyping programs are still an area of active development. Although
they may alleviate some of the false discovery rates prevalent in SV
discovery programs, they do not provide a definitive solution (Chander
et al., 2019). As a result, putative SVs identified using short-read
data alone should be treated as preliminary. One method that may aid in
removing false variants is a trio-binning approach, whereby SVs that
fail to abide by Mendelian inheritance patterns (either due to
challenges with accurate genotyping or incorrect variant calls) are
removed from the SV call set (e.g., Patel et al., 2014; Pilipenko et
al., 2014). This is particularly relevant for intensively managed
threatened species, as pedigree relationships are often known, and
suitable trios can be identified for sequencing. Alternatively, if
sufficient resources are available, the sensitivity of long-read
sequence data may be leveraged multiple reference genomes approach for
SV discovery and may facilitate the use of lower coverage short-read
resequencing data for SV genotyping, but this largely remains untested.
Target-capture methods for genes or regulatory regions of interest can
also provide an affordable alternative to WGS for species with large
genomes and projects with tight budgets (Andermann et al., 2020). For
example, vonHoldt et al., (2017) used a targeted-sequencing approach
combined with PCR validation to characterize a region on chromosome 6
under positive selection in domestic dog breeds, which significantly
reduced sequence coverage requirements.

\subsection*{SV discovery and genotyping beyond short-read sequence
data}

{\label{187428}}

When characterizing genomic features, especially SVs, there are many
sequencing platforms and approaches to choose from, and although they
may perform well when addressing specific challenges, each has its own
caveats (Table 1). Two providers prominently feature in long-read
sequencing: Pacific Biosciences (PacBio) and Oxford Nanopore
Technologies (ONT). Since the launch of these technologies in 2011 and
2014, the long-read sequencing space has been characterized by
fast-paced progress and innovation as demonstrated by the first telomere
to telomere assembly of the human X chromosome achieved with
ultra-long-read sequencing (Miga et al., 2020). The precise error rates
between these two technologies remain somewhat contentious (Dohm,
Peters, Stralis-Pavese, \& Himmelbauer, 2020; Lang et al., 2020
preprint), but as a general rule, ONT currently provides longer average
read lengths than PacBio overall (Logsdon, Vollger, \& Eichler, 2020) at
the cost of higher sequence error rates. Further, when PacBio sequencing
providers are overseas, ONT offers the advantage that data can be
generated in-house, which may, for example, be more responsive to the
needs and aspirations of Indigenous Peoples (Collier-Robinson, Rayne,
Rupene, Thoms, \& Steeves, 2019; Galla et al., 2016) and/or alleviate
the need for export permits. Despite the challenges, the power of
long-read sequencing technologies to span a significant portion, if not
the entire length, of complex regions of the genome in a single read
provides a powerful tool for SV discovery and population-level
genotyping. When used in conjunction with a high-quality, well annotated
reference genome, this improves confidence in read mapping across the
genome (Amarasinghe et al., 2020 for review), and substantially
increases precision (the proportion of variant calls that are `true')
and recall (the proportion of `true' SVs detected) rates for both SNPs
and SVs (Wenger et al., 2019). In addition, platforms that directly
sequence native DNA remove the amplification bias common in many
short-read sequencing approaches (Depledge et al., 2019). Furthermore,
there are emerging `adaptive' sequencing approaches that have the
potential to selectively sequence specific regions of the genome (Payne
et al., 2020 preprint).

Structural variants significantly alter genome topology and impact the
gene regulatory landscape (Sadowski et al., 2019; Shanta et al., 2020).
In light of these impacts, the hierarchical organization of DNA within
the nucleus is of particular interest when investigating the
relationship of transcriptional regulation mechanisms. Chromatin
conformation capture (3C) based sequencing approaches enable the
investigation of the organization of chromatin across genomes (Kong \&
Zhang, 2019 for review) and have identified the chromatin signature in
gene expression (Lieberman-Aiden et al., 2009; Lupi\selectlanguage{ngerman}áñez et al., 2015;
Shanta et al., 2020). In addition, there are emerging advancements in
Nanopore sequencing methods to integrate chromatin conformation capture
with long-read sequencing (i.e., Pore-C; Ulahannan et al., 2019
preprint). Rather than the amplification bias introduced by preparing a
short read library, long-read sequencing provides data on chromatin at a
range of distances along the linear genome and enables contacts to be
sequenced without amplification. This information is particularly
beneficial for `non-model' species where gene annotations, regulatory
element annotations and gene regulatory networks are un- or poorly-
characterized, leading to challenges in predicting the impacts of SVs on
gene expression. For example, 3C-based approaches combined with SV
detection and analysis have revealed the molecular basis for human
developmental diseases previously diagnosed through karyotypes (Melo et
al., 2020) and resolved the mechanisms underlying reproductive disorders
in goats (Guang-Xin et al., 2021).~

Optical mapping approaches are a useful complement to long-read
sequencing approaches, and have enhanced genome assembly outcomes by
providing insights into the `big picture' of large-scale genomic
variants (as per Weissensteiner et al., 2020). Optical mapping utilizes
a technique based on light-microscopy to identify specific sequence
motifs (such as restriction enzyme cut sites), which are then used to
generate images of fluorescently-labeled DNA molecules (Schwartz et al.,
1993), enabling the characterization of large, complex rearrangements
missed by long-reads alone (Yuan, Chung, \& Chan, 2020). On average,
optical maps span \textasciitilde{}225 kb, providing information on the
physical distance and relationship among genomic features. Besides being
used to improve the scaffolding of genome assemblies (Howe \& Wood,
2015; Zhang, 2015), including those of endangered species (Rhie et al.,
2021), optical mapping methods directly enable the identification of
both intraspecific and interspecific SVs (Levy-Sakin et al., 2019;
Zhihai et al., 2016). The primary current commercial provider of optical
mapping technology is Bionano Genomics and their Saphyr instrument,
which uses a nano-channel microfluidic chip to linearise and capture
images of fluorescently-labeled ultra-long DNA fragments to generate
optical maps at a resolution of 500bp (Yuan et al., 2020). While optical
maps provide information on the physical topology of chromosomes, they
do not provide sequence information on an allele. Because long-reads and
optical maps complement each other, the ideal data set for SV discovery
would include both data types (e.g., Soto et al., 2020; Weissensteiner
et al., 2020).\selectlanguage{english}
\begin{table}[H]
\centering
\normalsize\begin{tabulary}{1.0\textwidth}{CCCCC}
Feature & Short-reads & Long-reads & Multiple reference genomes & Optical mapping \\
Type of SV that can be assessed & Deletions, Insertions, Inversions, Translocations & Deletions, Insertions, Inversions, Translocations, Duplications, Complex SVs & Deletions, Insertions, Inversions, Translocations, Duplications, Complex SVs & Deletions, Insertions, Inversions, Translocations, Duplications \\
High-quality reference genome & Required & Required & Required (multiple) & Optional \\
Technology used to generate data for SV discovery and genotyping & Illumina HiSeq, NovaSeq, NextSeq & Pacific Biosciences (PacBio) Sequel; & 1.\selectlanguage{ngerman}     Multiple reference genomes generated using a combination of short-, long-, linked-reads (e.g., Hi-C, Pore-C) for representative individuals & Bionano Genomics Saphyr \\
 &  & Oxford Nanopore Technologies (ONT) & 2.\selectlanguage{ngerman}     Short- and/or long-reads generated for population-level genotyping &  \\
DNA quality & Moderate to high molecular weight & High to ultra-high molecular weight\textsuperscript{a}
 & High to ultra-high molecular weight\textsuperscript{a}
 & Ultra-high molecular weight\textsuperscript{a}
 \\
Length of SVs detected & 50 bp to < 1 Mb & 50 bp to > 1 Mb & 50 bp to > 1 Mb & 500 bp to > 1 Mb \\
Sequence coverage required & [?] 10x & [?] 10x & 1.\selectlanguage{ngerman}     \selectlanguage{english}[?] 10x to generate multiple reference genomes & > 30x \\
 &  &  & 2.\selectlanguage{ngerman}     \selectlanguage{english}[?] 10x for population-level genotyping &  \\
Method of SV discovery & Alignment-based, including read-pair, read depth, split-reads and local assembly & Alignment-based with local assembly & 1.~~~~ Alignment of multiple \emph{de novo} assembled whole reference
genomes;
 & Alignment-based, either to reference genome or to optical maps from different samples \\
 &  &  & 2.\selectlanguage{ngerman}     Genome graphs &  \\
SV genotype evidence & Multiple sources of evidence required depending on SV type (read pair, read depth, split read) & Single source of evidence (long continuous reads) usually sufficient & Two sources of evidence required (multiple references and population-level sampling) & Order, position, and orientation of fluorescently-labeled sequence motifs \\
 &  &  &  &  \\
Example algorithms/programs & BreakDancer; LUMPY; Manta & SMRT-SV (PacBio); & Minimap2; MuMmer; & Bionano SVCaller \\
 &  & NanoVar (ONT) & Graphtyper2 &  \\
Proportion of SVs discovered across the genome & Moderate & High & High & High \\
Recall rate (proportion of 'true' variants that are detected) & Low & High & High & High \\
False positive rate for detection & High & Low & Low to moderate & Low \\
SV discovery challenges & 1.\selectlanguage{ngerman}     Choice of method influences number and types of SVs detected; & Sequencing error rate can complicate read mapping and SV detection. & 1.\selectlanguage{ngerman}     Total number and type of SVs discovered dependent on number of genomes sequenced and compared (sample size); & 1.\selectlanguage{ngerman}     Sequence motif used for fluorescent labeling is highly limited and thus may reduce application to some species with complex genomes; \\
 & 2.\selectlanguage{ngerman}     Benchmark data mostly lacking for non-model species to verify SV accuracy; &  & 2.\selectlanguage{ngerman}     Precision affected by complexity of SV type. & 2.\selectlanguage{ngerman}     Absence of nucleotide information may hinder detection of complex SVs. \\
 & 3.\selectlanguage{ngerman}     Reference genome bias; &  &  &  \\
 & 4.\selectlanguage{ngerman}     Low precision for detecting complex SVs; &  &  &  \\
 & 5.\selectlanguage{ngerman}     Low performance in low complexity regions, near SNPs and small indels. &  &  &  \\
Computational resources required & High & Moderate, depending on sample number & High, depending on sample number & Moderate, depending on sample number \\
Cost per sample & Low & High & Moderate to High & Moderate \\
aHigh molecular weight DNA [?] 20 Kb fragments; ultra-high molecular weight DNA [?] 100 Kb fragments &  &  &  &  \\
\end{tabulary}
\caption{{Comparison of short-read sequencing, long-read sequencing, multiple
reference genomes and optical mapping approaches for characterizing
structural variants (SVs).
{\label{566553}}%
}}
\end{table}\section*{Concluding remarks}

{\label{acknowledgements}}

Structural variants provide an exciting opportunity to complement
SNP-based approaches and expand our understanding of genome-wide
variation. However, there remains much to learn about the integration of
SVs in the small population paradigm. In the meantime, mounting evidence
indicates that SVs play an important role in determining fitness traits
for both model and non-model species. Whereas SNPs impact single
nucleotides, SVs can capture multiple genes and gene regions
simultaneously and are therefore more likely to impact fitness. Emerging
approaches for SV discovery and genotyping, and relating SVs to fitness
traits, are now in reach for threatened species, and we are confident
that discoveries related to the role of SVs in determining fitness
traits of conservation interest are imminent. Moreover, the development
of SV-specific tools for analyses of within and between population
variation will further enhance the utility of SVs in species of
conservation concern (e.g., SV-Pop; Ravenhall, Campino, \& Clark, 2019).

However, developing strategies to manage fitness traits will be
challenging, as selection for or against them can lead to the reduction
of genome-wide diversity (Kardos et al., 2021 preprint; Kardos \&
Shafer, 2018). Despite this, current management practice can include
such actions. For example, individuals that carry, or express, negative
fitness traits can be assessed in conservation breeding programs,
followed by appropriate actions or recommendations (e.g., Attwater's
prairie chicken; Grueber, Hogg, Ivy, \& Belov, 2015; Hammerly, Morrow,
\& Johnson, 2013). Alternatively, when inheritance of these traits are
known, individuals may be screened prior to selective pairing (e.g.,
California Condor; Moran et al., 2021). Regardless, as we learn more
about the mechanisms underlying fitness traits, especially traits
associated with negative fitness, there is a pressing need to establish
comprehensive evidence-based management strategies that mitigate the
impact of these traits while minimizing the loss of genome-wide
diversity. Where we see the most promise for this is the emerging
practice of integrating WGS data into individual-based models, which
will enable different management actions to be modelled and compared
(Seaborn et al., 2021). Such models will allow researchers to forecast
the consequences of selection-based management of fitness traits
underpinned by adaptive or deleterious SVs on genome-wide diversity,
thus better informing conservation management actions. To achieve this,
strong collaboration between conservation practitioners, population
ecologists (e.g., modelers), quantitative geneticists, and conservation
genomicists will be required to establish the most effective models
possible (Hohenlohe, Funk, \& Rajora, 2020).

\section*{Glossary}

{\label{551858}}

\textbf{Breakpoint}: A double-stranded break in the DNA, occurring on
either side of an inversion, or a translocation.

\textbf{Chromatin Conformation Capture (3C)}: A suite of molecular
biology techniques for capturing the spatial organization of chromatin
within the nucleus of the cell. Common iterations include Hi-C, Pore-C
and Omni-C.

\textbf{Complex structural variants}: An instance where multiple SV
types occur in close proximity, such as an inversion with different
deletions at breakpoints.

\textbf{Deletion}: A chromosomal rearrangement where a sequence of DNA
is missing in an individual but present in the reference. The inverse of
an insertion.

\textbf{Duplication}: A chromosomal rearrangement where there is more
than one copy of a particular region of the genome. This can be anywhere
from [?]50bp to whole chromosomes and even the whole genome (whole genome
duplication).

\textbf{Functional diversity}: Genomic variation in regions underpinning
phenotypic traits, may occur directly in genes or within regulatory
elements. Variation in these regions can have consequences for
individual fitness.

\textbf{Genome-wide diversity}: Genomic variation inclusive of
functional and neutral diversity. The sum of all variation within the
genome.

\textbf{Genome graph}: A representation of multiple possible sequences.

\textbf{Insertion}: A chromosomal rearrangement where a sequence of DNA
is `inserted' into the original sequence. May also be defined as a
sequence of DNA present in an individual that is missing in the
reference. The inverse of a deletion.

\textbf{Inversion}: A chromosomal rearrangement where a section of the
DNA is reversed in order in an individual compared to a reference.

\textbf{Pangenome}: A reference genome approach where the genomes of
multiple individuals are assembled and aligned to form genome graphs. A
pangenome is divided into variable `accessory' and invariable `core'
components, with the goal to characterize all the variation present
within a group of interest. Currently the scale of pangenome projects
range from populations/strains to subfamilies.

\textbf{Regulatory element}: Regions of non-coding DNA involved in the
regulation of gene expression, the two primary regulatory elements are
enhancers and promoters.

\textbf{Structural Variant (SV)}: A genomic rearrangement [?]50bp in
length. Common classes of structural variants include deletions,
duplication, insertions, inversions and translocations.

\textbf{Topologically Associated Domain (TAD)}: Region of the genome
that physically interacts with itself more frequently than with regions
outside of the TAD.

\textbf{Translocation}: A chromosomal rearrangement where a segment of
one nonhomologous chromosome is transferred to another.

\section*{Acknowledgements}

{\label{acknowledgements}}

We wish to thank the Guest Editors of the `Whole-genome sequences in
Molecular Ecology' Special Issue for the invitation to contribute this
Perspective. We are grateful to the K\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō\selectlanguage{english}125+ Consortium led by
Genomics Aotearoa for providing the opportunity to explore structural
variants in a conservation context. We extend our thanks to the
University of Canterbury's Conservation, Systematics and Evolution
Research Team (ConSERT), including Roger Moraga at Tea Break
Bioinformatics for robust dialogue--and Kylie Price and Joanna Roberts
for their support and guidance--on this topic. We acknowledge the NSF
Track 2 EPSCoR Program (award number OIA-1826801) for their support of
SJG. We also thank three reviewers for their insightful comments on a
previous version of the manuscript.

\section*{Data accessibility and benefit
sharing}

{\label{data-accessibility-and-benefit-sharing}}

Not applicable.

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