\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[T2A]{fontenc}
\usepackage[polish,ngerman,english]{babel}



\usepackage{float}








\begin{document}

\title{The promise and challenges of characterising genome-wide structural
variants: A case study in a critically endangered parrot}



\author[1]{Jana Wold}%
\author[2]{Joseph Guhlin}%
\author[2]{Peter Dearden}%
\author[3]{Anna Santure}%
\author[1]{Tammy  Steeves}%
\affil[1]{University of Canterbury}%
\affil[2]{University of Otago}%
\affil[3]{University of Auckland}%


\vspace{-1em}



  
  \date{May 24, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
There is growing interest in the role of structural variants (SVs) as
drivers of local adaptation and speciation. From a biodiversity genomics
perspective, the characterisation of genome-wide SVs provides an
exciting opportunity to complement single nucleotide polymorphisms
(SNPs). However, little is known about the impacts of SV discovery and
genotyping strategies on the characterisation of genome-wide SV
diversity within and among populations. Here, we explore a near
whole-species resequence dataset, and long-read sequence data for a
subset of highly represented individuals in the critically endangered
k\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō \selectlanguage{english}(\emph{Strigops habroptilus}). We demonstrate that even when
using a highly contiguous reference genome, different discovery and
genotyping strategies can significantly impact the type, size and
location of SVs characterised genome-wide. Further, we found that the
mean number of SVs in each of two k\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō \selectlanguage{english}lineages differed both within
and across generations. These combined results suggest that genome-wide
characterisation of SVs remains challenging at the population-scale. We
are optimistic that increased accessibility to long-read sequencing and
advancements in bioinformatic approaches including multi-reference
approaches like genome graphs will alleviate at least some of the
challenges associated with resolving SV characteristics below the
species level. In the meantime, we address caveats, highlight
considerations, and provide recommendations for the characterization of
genome-wide SVs in biodiversity genomic research.%
\end{abstract}%



\sloppy


\textbf{Hosted file}

\verb`Wold_et_al_MER-Supplemental.docx`  available at \url{https://authorea.com/users/391738/articles/614029-the-promise-and-challenges-of-characterising-genome-wide-structural-variants-a-case-study-in-a-critically-endangered-parrot}

\section*{Introduction}

{\label{320182}}

The increased accessibility of whole-genome sequencing (WGS) technology
has revolutionised population genetic/genomic studies in non-model
species, and continues to provide valuable insights into the mechanisms
underpinning genome divergence during speciation as well as the
interplay between mutation, genetic drift, selection, and gene flow in
the context of population demography (Campbell et al., 2018; Chueca et
al., 2021; Cruickshank \& Hahn, 2014; Formenti et al., 2022; Lado et
al., 2020; Mathur \& DeWoody, 2021). To date, the vast majority of these
studies use single nucleotide polymorphisms (SNPs) to investigate these
processes, yet there is a growing interest in the evolutionary and
adaptive significance of structural variants (SVs), which are genomic
rearrangements that include deletions, duplications, insertions,
translocations, and inversions (M\selectlanguage{ngerman}érot et al., 2020; Wellenreuther \&
Bernatchez, 2018). SVs have been shown to influence the evolutionary
trajectory of populations by determining traits associated with
reproductive strategies (Huynh et al., 2011; Küpper et al., 2016), local
adaptation and adaptive potential (Berdan et al., 2021; Cayuela et al.,
2021; Dorant et al., 2020; Huang et al., 2020; Kess et al., 2021; Tigano
et al., 2021). There is also growing evidence that SVs may lead to
speciation (Davey et al., 2011; Funk et al., 2021; Todesco et al.,
2020).

Previous studies exploring SV diversity in natural populations have
generally combined multiple sequencing technologies (e.g., short- and
long-read sequencing, optical mapping) and large sample sizes (reviewed
in Wold et al., 2021). Further, many studies to date have aimed to
identify SVs in close association with specific traits of interest and
subsequently validate them with more traditional approaches (e.g.,
vonHoldt et al., 2017). There is ample opportunity to develop `good'
practice to reliably investigate population-level differences in SV
frequency, location or size distributions in non-model species. However,
agricultural and human genomics studies have identified caveats to
consider before using short-read sequence data to call SVs. For example,
we expect to observe a high false-positive rate and biases in the type
and size range of SVs detected (Cameron et al., 2019; English et al.,
2015; Ho et al., 2020; Mahmoud et al., 2019). This is in part because SV
discovery tools commonly use discordant reads (i.e., those that are
improperly aligned and/or depart from expected and observed insert
lengths) and read depth to identify putative variants (Alkan et al.,
2011; Cameron et al., 2017; X. Chen et al., 2016; Layer et al., 2014;
Rausch et al., 2012). Although discordant reads do occur as a result of
`true' SVs, they may also arise as the result of mapping/sequencing
error or reference error (Bayer et al., 2020; Hurgobin \& Edwards,
2017).

Distinguishing between the underlying sources of discordant read mapping
generally requires independent data, such as extensive long-read
sequencing, PCR amplification and Sanger sequencing, or Optical mapping
(Ho et al., 2020). Such resource intensive approaches may not be
feasible for many non-model species, especially those of conservation
concern. Given that long-read sequences have been shown to outperform
short-read data for SV discovery (Alkan et al., 2011; Chaisson et al.,
2019; Mahmoud et al., 2019), researchers may choose to use a strategic
approach that combines long-read sequencing for SV discovery and
short-read sequencing for population-scale genotyping (e.g., Chander et
al., 2019; Huddleston et al., 2017; Jun et al., 2021). Guidelines around
the application of genotyping SVs with short-read data in non-model
species remain somewhat unclear (e.g., target sequence depth, ideal read
insert size distribution, considerations for polyploids). This is in
large part due to the lack of datasets--excluding human genomic
datasets--suitable for benchmarking SV discovery and genotyping
strategies (e.g., Cameron et al., 2019; Kosugi et al., 2019).

The critically endangered k\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō \selectlanguage{english}is a nocturnal ground parrot endemic to
Aotearoa New Zealand. Once widely distributed throughout the North and
South Islands of Aotearoa, k\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō \selectlanguage{english}populations rapidly declined as a
result of anthropogenic disturbances and introduced mammalian predators
(Lloyd \& Powlesland, 1994; Veltman, 1996; Williams, 1956). Populations
continued to decline across the mainland and are believed to have gone
extinct on the North Island in the 1930's. The last known South Island
population was lost in the 1980's (Lloyd \& Powlesland, 1994). A relict
population was discovered on Rakiura (Stewart Island) in 1977 and a
translocation of a small handful of k\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō \selectlanguage{english}found in Fiordland National
Park on the West Coast of the South Island was attempted (Best \&
Powlesland, 1985; Lloyd \& Powlesland, 1994). However, only one
individual from Fiordland successfully bred with individuals from
Rakiura. After intensive conservation management interventions, the
k\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō \selectlanguage{english}population has grown from a record low of 51 individuals in 1995
to \textasciitilde{}200 individuals as of the 2021/2022 breeding season
(K\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō \selectlanguage{english}Recovery Group, 2017; K\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō \selectlanguage{english}Recovery Team \emph{personal
communications}). In fact, of the \textasciitilde{}200 birds discovered
on Rakiura and in Fiordland National Park, the extant k\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō \selectlanguage{english}population
can be traced back to only 35 founding individuals (K\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō \selectlanguage{english}Recovery Team
\emph{personal communications}). In an effort to mitigate the effects of
small population size and inbreeding in k\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō, \selectlanguage{english}island translocations
are partially informed by pedigree data and more recently, genomic
estimates of relatedness as a result of the K\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō\selectlanguage{english}125+ consortium
(Guhlin et al., 2022 preprint). Briefly, as described in Guhlin et al.
(2022), to inform k\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō \selectlanguage{english}conservation efforts, the K\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō\selectlanguage{english}125+ project
was initiated in 2015 to sequence all 125 living k\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō \selectlanguage{english}at the time.
Between 2015 and 2018, whole-genome short-read sequence data for these
125 individuals, and an additional 44 deceased adults and chicks, were
generated for a total of 169 sequenced individuals. The K\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō\selectlanguage{english}125+
project has established a near-whole species high-quality variant
dataset for a species of conservation concern and presents an exciting
opportunity to explore strategies for SV discovery and genotyping in a
non-model species. Here, we combine these data with long-read sequence
data for a subset of individuals highly represented in the k\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō
\selectlanguage{english}pedigree, a highly contiguous reference genome (Rhie et al., 2021), and
extensive life history data for all individuals, including verified
pedigree relationships (Bergner et al., 2014; Guhlin et al., 2022
preprint) to compare four short-read and two long-read SV discovery and
genotyping strategies to assess how each impacts inferences about SV
frequency and size distributions in k\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō. \selectlanguage{english}This study represents a
critical first step towards our understanding the eco-evolutionary
dynamics of SVs in small populations (Wold et al., 2021).

\par\null

\section*{Materials and Methods}

{\label{598438}}

All details regarding read processing, variant discovery, genotyping and
analyses may be found in the following GitHub repository:
\url{https://github.com/janawold1/2022\_MER\_Submission}.

\subsection*{Read processing and
alignment}

{\label{195690}}

A highly contiguous reference genome, assembled by the Vertebrate Genome
Project (VGP), is available for a single female k\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō, \selectlanguage{english}`Jane' (Rhie et
al., 2021). As part of the K\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō\selectlanguage{english}125+ project, paired-end sequence
libraries for 94 males and 75 females were sequenced to a target depth
of 30x coverage on multiple Illumina platforms, including MiSeq2500,
TruSeq Nano, and HiSeqX. Read lengths varied from 125 - 150bp. All
preprocessing of raw sequence data was conducted by JG to maintain
consistency across K\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō\selectlanguage{english}125+ subprojects. Briefly, reads were trimmed,
adaptor content removed, and overlapped reads were collapsed into a
single read using the default quality thresholds (minimum quality of 2)
for fastp v0.20.0 (S. Chen et al., 2018) and AdapterRemoval v2.2.4
(Schubert et al., 2016). These processed reads were aligned to the
reference genome and a machine learning program, DeepVariant (Poplin et
al., 2018), employed to generate high quality SNPs for downstream
analyses led by the K\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō\selectlanguage{english}125+ consortium (Guhlin \emph{et al.} 2022
preprint). For short-read based SV discovery, reads were aligned to the
reference genome using Burrows-Wheeler Aligner v0.7.17 (BWA; Li \&
Durbin, 2009).

In addition to the near-whole species resequence data, ten individuals
highly represented in the extant population (5 male, 5 female), were
targeted for long-read sequencing on the Oxford Nanopore Technologies
platform. All individuals were sequenced on a MinION using R9 flow cells
using the PCR-free LSK-110 ligation sequencing kit. Basecalling was
performed using Guppy v6.3.7 using the `super' accuracy model
(dna\_r9.4.1\_450bps\_sup). Adapters were trimmed using Porechop v0.2.4
(Wick, 2017/2022), lambda DNA removed using NanoLyse v1.2.0 (De Coster
et al., 2018) and reads were filtered for a minimum Q-score of 10 and
read length of 3kb using NanoFilt v2.8.0 (De Coster et al., 2018). Both
the raw and filtered long-read quality were visualised using NanoPlot
v1.39.0 (De Coster et al., 2018). For long-read based SV discovery,
reads were aligned to the reference genome using Winnowmap v2.03 (Jain
et al., 2020). Read mapping quality was assessed for both short- and
long-read alignments using Mosdepth v0.3.3 (Pedersen \& Quinlan, 2018)
and qualimap v2.2.2 (Garc\selectlanguage{ngerman}ía-Alcalde et al., 2012), with summaries of
outputs from these tools visualised using MultiQC v1.13 (Ewels et al.,
2016). A minimum alignment depth of 4x was required for inclusion in
long-read-based SV discovery.

The highly contiguous VGP reference genome assembly represents a female
k\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō \selectlanguage{english}and thus includes both the Z and W sex chromosomes. This may be
problematic for SV discovery as the W sex chromosome contains highly
repetitive content homologous with content throughout the genome (Rhie
et al., 2021). A preliminary analysis of SNPs indicated that this
homology resulted in sufficient numbers of reads mapping to the W
chromosome that erroneous heterozygous SNP calls were produced in both
females and males (data not shown). Given that males are the homogametic
sex (ZZ) and females are heterogametic (ZW), heterozygous SNP calls on
the W for either sex indicate mis-mapping. To address these challenges,
reads were realigned for all individuals excluding single-end reads and
excluding the W chromosome from male alignments. Alignment for females
also excluded single-end reads, but included the W chromosome scaffold
to ensure that reads belonging to the W did not interfere with SV
discovery on other chromosomes. For joint analyses of the k\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō
\selectlanguage{english}population, the Z and W chromosomes and all unplaced scaffolds were
excluded from downstream analyses due to low confidence in variant
discovery for these scaffolds.

\subsection*{Structural variant discovery and
genotyping}

{\label{583101}}

Short-read structural variant discovery was conducted with Delly v0.8.7
(Rausch et al., 2012), Manta v1.6.0 (X. Chen et al., 2016) and the
wrapper programme Smoove v0.2.8 (Pedersen et al., 2020), which
implements Lumpy-sv v0.2.13 for SV discovery (Layer et al., 2014),
annotates variants with Duphold v0.2.1 (Pedersen \& Quinlan, 2019) and
genotypes SVs with SVTyper v0.7.0 (Chiang et al., 2015). Long-read SV
discovery was conducted using CuteSV v1.0.11 (Jiang et al., 2020) and
Sniffles v2.0.7 (Sedlazeck et al., 2018), and raw individual calls were
refined for population genotyping using Jasmine v1.1.5 (Kirsche et al.,
2023).

Each SV discovery tool differs in approach. For the short-read based
discovery strategies, both Delly and Smoove (i.e., Lumpy-sv) implement
two algorithms (paired-end and split-read), while Manta implements three
(paired-end, split-read and assembly-based). The short-read tools also
differ in the suggested strategy for population-level SV discovery. Both
Delly and Smoove iterate through individual samples and subsequently
merge SV calls for individual genotyping, whereas Manta recommends
conducting SV discovery with all available samples at once to increase
power (X. Chen et al., 2016). However, due to the assembly-based
algorithm, Manta is computationally resource-heavy, and running
\textgreater{}10 individuals at \textasciitilde{}30x sequence coverage
set can often exceed 125 Gb RAM (as observed in the Kak\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō\selectlanguage{english}125+ data). In
instances where computational resources are limited, samples may be run
in batches or individually, although this is not recommended due to the
loss of power to resolve SVs and the challenges associated with merging
variants called in different sample batches (Github, 2016a; Github,
2016b).

To assess the impacts of using a batched vs. joint calling strategy for
SV discovery, Manta was run in two ways: 1) a batched approach where
samples were divided into 14 batches (7 male batches and 7 female
batches) with an average of 11 individuals per batch (Manta-Batch); and
2) a joint approach where all males were run together and all females
were run together. For both datasets, male and female SV discovery was
conducted separately due to the inclusion of the W chromosome in female
alignments (Manta-Joint). In both cases, variants were merged into
`batched' and `joint' datasets with BCFtools v1.12 (Danecek et al.,
2021) with the merge -m all flag.

Long-read SV discovery approaches must incorporate methods to account
for the low accuracy associated with long-read sequence data (Jiang et
al., 2020; Sedlazeck et al., 2018). The two tools included here (CuteSV
and Sniffles) also attempt to address two challenges associated with
alignment-based SV discovery. For example, CuteSV uses multiple
signature extraction methods to distinguish SVs from the background
noise of long-read data, then implements clustering and refinement
approaches to increase sensitivity and identify the signature of
heterozygous SVs (Jiang et al., 2020). Sniffles similarly identifies the
signature of different SV classes but implements additional methods to
resolve nested SVs (Sedlazeck et al., 2018). SV discovery for both tools
is performed on an individual-basis. Jasmine, which implements a
modified minimum spanning forest algorithm, was used to merge SVs
detected in individual k\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō \selectlanguage{english}in each call set in preparation for
population-scale genotyping with the available short-read data (Kirsche
et al., 2023).

Regardless of discovery strategy, nominal genotype outputs from SV
discovery tools are generally regarded as unreliable (Chander et al.,
2019). To address this, both Delly and Smoove include genotyping
programs (delly genotype, and SVTyper respectively), yet Manta, CuteSV
and Sniffles do not. To genotype these call sets at the
population-scale, SVs were filtered (as described below) and genotyped
using the aligned k\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō\selectlanguage{english}125+ short-reads with the genotyping tool
BayesTyper v1.5 (Sibbesen et al., 2018). BayesTyper uses alignments of
k-mers to a variant graph and reference genome, then implements a
probabilistic model of k-mer counts to genotype individuals. BayesTyper
has the benefit of being able to genotype a wide range of genomic
variants (e.g., SNPs, small INDELs and SVs), in fact the inclusion of
SNP data is recommended as it aids in matching relevant k-mers to
sequence reads (Github, 2019.). Each VCF output from Manta was processed
with the program BayesTyperTools convertAllele to convert symbolic
allele notations to REF and ALT sequences. This step was not necessary
for the long-read based call sets as they already provided REF and ALT
sequences. For both Manta call sets (batch and joint), CuteSV and
Sniffles, a SNP call set generated with DeepVariant (Guhlin et al., 2022
preprint) was used to aid SV genotyping. All VCFs were normalised,
variants left-aligned and any multiallelic sites split with BCFtools
norm prior to merging variants with BayesTyperTools combine. Finally,
BayesTyper requires the generation of large intermediate files
(\textgreater{}2Tb for this dataset) with the tool KMC (Kokot et al.,
2017). As recommended, KMC v3.1.1 was run with k=55 and singleton k-mers
included (-ci1) and a k-mer bloom filter for each individual was
generated with BayesTyperTools makeBloom. Since BayesTyper cannot
genotype more than 30 individuals at once, samples were batched into 5
groups of 30 and 1 group of 19 individuals prior to identifying variant
clusters with BayesTyper cluster and genotyping with BayesTyper genotype
under default settings.

\subsection*{Filtering parameters}

{\label{273597}}

Once SV discovery and genotyping were complete, filtering for each SV
dataset was conducted in two stages for: 1) SV call quality; and 2)
individual genotype quality. The outputs from SV call quality filters
were used for comparisons of SV type frequency, size distributions and
location (i.e., frequency per chromosome) between tools (described
further in the \emph{Structural variant analyses analyses} section
below). For comparisons of genotype consistency and variability among
individual kak\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō, \selectlanguage{english}the outputs from genotype quality filters were used
(see \emph{Structural variant analyses} below).

Upon completion of SV discovery, removal of SVs marked as low quality,
and additional recommended filtering parameters specific to each tool,
were implemented using BCFtools. A standardised filtering approach was
not applied to outputs from all three short-read tools since each
program recommends different statistics to assess the quality of SVs and
genotypes. Structural variant filtering for all short-read tools
excluded all breakend calls, and SVs [?]50kb in length as these likely
represent unresolved complex variants, mapping error, and/or reference
bias. Additional filtering for Delly excluded duplications and
inversions \textless{}300bp, and deletions \textless{}50bp using the
delly merge -m option. All remaining SVs that did not pass all variant
call quality filters were removed with BCFtools (i.e., INFO/FILTER =
``PASS'''). This excludes all SVs where paired-end support was
\textless{}3 and a MAPQ score \textless{}20 (Rausch, 2014). Finally,
genotype filtering for Delly SVs excluded all sites where
\textless{}80\% of variable genotypes passed all genotype filters with
BCFtools (i.e., FMT/FT=''PASS'').

For Smoove, the lumpy\_filter program identifies and discards
interchromosomal read pair mismatches \textgreater{}3, and those with
alternative matches, unless the identified split matches the location of
the mate pair. This inbuilt filtering programme also removes reads where
the depth is greater than 1,000x, as well as any orphaned reads.
Variants are then genotyped and ready for annotation with the Smoove
annotate programme. Once thesteps were complete, all breakends,
deletions that did not have a depth fold-change relative to flanking
regions (FORMAT/DHFFC) \textless{} 0.7, and duplications that did not
have a depth fold-change relative to bins in the genome with similar
GC-content (INFO/DHBFC) \textgreater{} 1.3 were excluded using BCFtools.
or genotype filtering, an overall Mean Smoove Het Quality (INFO/MSHQ) [?]
3 was implemented with BCFtools. The Smoove Het Quality (INFO/SHQ)
metric scores confidence in individual heterozygous genotypes where 1 is
a low confidence call and 4 is highest, with MSHQ representing the mean
score for all heterozygous genotypes (Pedersen, 2018/2022).

Variants for both the Batch and Joint Manta outputs were filtered using
BCFtools to exclude all variants \textless{}50bp in length, all breakend
calls and all variants that did not pass all record-level filters (i.e.
INFO/FILTER=PASS). Specifically, this excluded: all sites with a QUAL
score \textless{}20; deletions and duplications not consistent with
diploid expectations; SVs with breakpoint depths \textgreater{}3x the
median chromosome depth; SVs \textless{}1kb in size where
\textgreater{}40\% of samples contained a MAPQ score of 0 around either
breakend; all SVs that were significantly larger than the paired-read
fragment size and did not have paired-read support for the alternate
allele in any individual; and finally, SVs where no sample passed all
sample-level filters.

CuteSV and Sniffles call sets were refined using Jasmine, followed by
the manual removal of all imprecise sites including breakend calls.
However, it is notable that while the CuteSV dataset had sufficient read
depth to filter for SV specificity (i.e., INFO/IS\_SPECIFIC=1), Sniffles
did not retain any SVs once this metric was implemented. As a result,
the Sniffles call set was not filtered on SV specificity in this study.

The SV call sets for both Manta datasets, CuteSV and Sniffles were
genotyped using BayesTyper, which implements four `hard' genotype
filtering parameters by default. This includes the exclusion of variants
with fixed heterozygous genotypes, alleles with \textless{}1 sampled
k-mer, genotypes with a posterior probability \textless{}0.99, and
alleles with k-mer coverage that fall below
1-e\textsuperscript{-0.275x}. Here, x represents the mean of the
negative binomial distribution for k-mer coverage for a specific sample
(Sibbesen, 2018 Github). All variants with \textgreater{}20\% genotypes
missing and variants where \textless{}80\% of genotypes passed all four
BayesTyper quality metrics were excluded. Although BayesTyper ships with
a programme for converting allele sequences to symbolic alleles
(bayesTyperTools convertSeqToAlleleID), we found It challenging to
resolve the class of all genotyped variants with this approach (i.e.,
insertions are incompatible and additional SV classes were changed or
remained unresolved). To relate genotyping results back to the called SV
class, BCFtools was used to identify the chromosomal positions of the
genotyped variants and compared with the locations of SVs prior to file
conversion with bayesTyperTools convertAllele.

\subsection*{Structural variant
analyses}

{\label{880505}}

Structural variants were counted for each SV discovery tool prior to and
after filtering. To explore the level of call consensus between these
outputs, the number of overlapping SVs were identified using SURVIVOR
v1.0.7 (Jeffares et al., 2017) in 1kb, 500bp, 50bp windows and for exact
overlaps. To count as a consensus call, SV type and strand were required
to match and a minimum variant length of 50bp were required. To assess
whether some chromosomes carried more SVs relative to their size, we
estimated the number of SVs per chromosome and the proportion of
base-pairs of each chromosome within an SV (i.e., the sum of all SV
lengths for a given chromosome / chromosome size).

Following SV discovery across the six strategies, all individuals were
genotyped using the aligned k\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō\selectlanguage{english}125+ short-read dataset. The genotype
filtered SV data for all six variant call sets were used to compare
individual and generational variability. Due to population structure
previously identified using SNPs (Guhlin et al., 2022 preprint) and
demonstrated here using SVs (Supplemental Figure 1), generational
variability was assessed for Fiordland and Rakiura separately. When
reporting the number of SVs per individual and number of SVs among
k\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō \selectlanguage{english}generations, we use presence or absence of SVs per individual.
That is, we consider genotypes as evidence of whether or not the
individual carries the SV (0/1 \& 1/1 = carrier; 0/0 = non-carrier).
Both Fiordland- and Rakiura-derived birds were used for comparisons
across three generations (n = 1, 3, 4 for Fiordland F0, F1 and F2 and n
= 40, 59, 10 for Rakiura F0, F1 and F2 respectively). Due to the lek
mating system and a relatively long lifespan, the k\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō \selectlanguage{english}population has
had significant backcrossing through the generations. Therefore, the F1
and F2 generations represented here excluded all individuals with
backcrossed lineages, as this may bias true generational patterns in SVs
carried by individuals. Due to the high variability in the number of
individuals representing each generation for both lineages, and the
number of SVs carried by individuals, individual SV counts were
transformed using the natural log and the mean estimated for each
generation. Trends between generations, lineages and data sets were then
plotted for comparison.

In the absence of a previously validated catalogue of SVs, neither a
`true' nor `false' positive rate of detection could be assessed. Despite
not being able to estimate the precision and accuracy of SVs called in
our data, we aimed to test the consistency of genotyping results using
Mendelian Inheritance tests with parent-offspring trios. Although this
does not eliminate the possibility of systematic error, nor does it
provide an indication of the precision or accuracy of SV detection,
departures from Mendelian Inheritance may indicate inconsistency of
genotyping within a given SV call. Genotyping consistency is an
important consideration for population studies as patterns of population
structure or inferences about local adaptation may be impacted by
inconsistencies.

To identify SVs that violate Mendelian Inheritance patterns, the
BCFtools +mendelian plugin was used. Pedigree data provided by the New
Zealand Department of Conservation identified 120 parent-offspring trios
consisting of 158 unique individuals in the K\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō\selectlanguage{english}125+ sequence data. We
tested SV genotypes by calculating the proportion of Mendelian
Inheritance errors relative to the number of non-missing genotypes
(i.e., GT != ``mis''). Four thresholds were tested where adherence to
Mendelian Inheritance expectations were either 100\%, [?]95\%, [?]90\% and
[?]80\% of genotypes passed. It is important to note that not all 169
sequenced individuals were represented in pedigree trios, as they may
not have descendants or antecedents represented in the short-read data
analysed here. In addition, some individuals are represented multiple
times in different family groups. This bias towards highly represented
individuals in the k\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō \selectlanguage{english}breeding population may not adequately capture
all SVs called within the population. As such, we did not filter SVs
using Mendelian Inheritance errors for downstream analysis. Rather,
these tests may provide some insights into the relative performance of
genotyping strategies among the pipelines used here.

\section*{Results}

{\label{812040}}

The mean individual mapping depth of short-reads for chromosomes was
\textasciitilde{}18x, and ranged from \textasciitilde{}9x to
\textasciitilde{}38x. Of the 10 individuals sequenced using long-reads,
seven met the minimum depth threshold of 4x coverage for long-read SV
discovery. The mean individual mapping depth of long-reads for
chromosomes was \textasciitilde{}10x, and ranged from
\textasciitilde{}4x to \textasciitilde{}16x. There was considerable
variability in the number of SVs initially detected by each of the six
strategies (herein datasets), with the most being the Manta-Batch and
fewest being the CuteSV dataset (Table 1). In addition, inversions were
the most common SV type detected in short-read discovery tools, while
deletions were more common in long-read SV discovery tools. This pattern
was consistent across call quality and genotype filtering thresholds
(Table 1). The proportion of SVs passing call quality thresholds also
varied, with Delly retaining the lowest proportion of SVs
(\textasciitilde{}4\%). Both the Manta-Batch and -Joint call quality
filters retained roughly 26\% of variants, whereas 27\% of CuteSV and
32\% of Sniffles variants were retained. The Smoove call set retained
the highest proportion of SVs with \textasciitilde{}68\% passing call
quality thresholds (Table 1). It is notable that the size distribution
of filtered SVs varied between each of the SV discovery tools and that
all reported insertions in Delly were below the minimum size threshold
of 50bp (Table 2).\selectlanguage{english}
\begin{table}[H]
\centering
\normalsize\begin{tabulary}{1.0\textwidth}{CCCCC}
 &  & Unfiltered & Call Quality Filters & Genotype Filters \\
Delly & Breakends & 9672 & 0 & 0 \\
 & Deletions & 5167 & 696 & 57 \\
 & Duplications & 2099 & 73 & 12 \\
 & Insertions & 473 & 441 & 228 \\
 & Inversions & 35397 & 753 & 437 \\
 & Total & 52808 & 1963 & 734 \\
Manta - Batch\textsuperscript{\textbf{1}}
 & Breakends & 71872 & 0 & 0 \\
 & Deletions & 4236 & 1614 & 515 \\
 & Duplications & 1907 & 510 & 70 \\
 & Insertions & 1803 & 749 & 177 \\
 & Inversions & 60434 & 32959 & 342 \\
 & Total & 140252 & 35832 & 1104 \\
Manta - Joint\textsuperscript{\textbf{2}}
 & Breakends & 63740 & 0 & 0 \\
 & Deletions & 2915 & 1194 & 495 \\
 & Duplications & 1246 & 294 & 73 \\
 & Insertions & 1538 & 221 & 74 \\
 & Inversions & 58393 & 30363 & 301 \\
 & Total & 127832 & 32072 & 943 \\
Smoove & Breakends & 4635 & 0 & 0 \\
 & Deletions & 1899 & 1505 & 1023 \\
 & Duplications & 973 & 435 & 183 \\
 & Insertions & N/A & N/A & N/A \\
 & Inversions & 10068 & 10037 & 2825 \\
 & Total & 17575 & 11977 & 4031 \\
CuteSV & Breakends & 1048 & 0 & 0 \\
 & Deletions & 3864 & 1209 & 72 \\
 & Duplications & 254 & 138 & 0 \\
 & Insertions & 2972 & 879 & 6 \\
 & Inversions & 18 & 12 & 0 \\
 & Total & 8156 & 2238 & 78 \\
Sniffles & Breakends & 5068 & 0 & 0 \\
 & Deletions & 2624 & 2734 & 87 \\
 & Duplications & 99 & 61 & 0 \\
 & Insertions & 3893 & 2339 & 39 \\
 & Inversions & 253 & 95 & 0 \\
 & Total & 11937 & 5229 & 126 \\
\textsuperscript{1}Samples divided into 14 batches (7 male batches and 7
female batches) for SV discovery
 &  &  &  &  \\
\textsuperscript{2}Samples divided into a male specific and female
specific batch for SV discovery
 &  &  &  &  \\
\end{tabulary}
\caption{\selectlanguage{polish}{Counts of kākāpō structural variants (SVs) by type for unfiltered
variants, those retained after SV quality filters and after genotype
quality filters specific to each of the structural variant discovery
tools Delly, Manta, Smoove, CuteSV and Sniffles. Delly, Manta and Smoove
are short-read based, and CuteSV and Sniffles are long-read based,
variant discovery tools.
{\label{510918}}%
}\selectlanguage{english}}
\end{table}\selectlanguage{english}
\begin{table}[H]
\centering
\normalsize\begin{tabulary}{1.0\textwidth}{CCCCCC}
Data & Structural Variant Type & Count & Size Range (bp) & Median Size (bp) & Mean Size (bp) \\
Delly & Deletions & 696 & 49 - 26273 & 922 & 2181 \\
 & Duplications & 73 & 355 - 34273 & 3592 & 6476 \\
 & Insertions & 441 & 22 - 46 & 29 & 30 \\
 & Inversions & 753 & 300 - 48626 & 369 & 1088 \\
Manta - Batch & Deletions & 1614 & 50 - 47230 & 623 & 3216 \\
 & Duplications & 510 & 52 - 40508 & 1976 & 5919 \\
 & Insertions & 749 & 51 - 1704 & 575 & 461 \\
 & Inversions & 32959 & 51 - 49035 & 202 & 458 \\
Manta - Joint & Deletions & 1194 & 50 - 47230 & 329 & 1773 \\
 & Duplications & 294 & 52 - 44414 & 307 & 4588 \\
 & Insertions & 221 & 56 - 888 & 315 & 341 \\
 & Inversions & 30363 & 51 - 49035 & 192 & 383 \\
Smoove & Deletions & 1505 & 53 - 47780 & 696 & 3123 \\
 & Duplications & 435 & 148 - 47433 & 4108 & 8873 \\
 & Insertions & N/A & N/A & N/A & N/A \\
 & Inversions & 10037 & 76 - 45629 & 686 & 1039 \\
CuteSV & Deletions & 1209 & 39 - 47874 & 170 & 847 \\
 & Duplications & 150 & 198 - 97051 & 9420 & 12380 \\
 & Insertions & 879 & 36 - 32549 & 151 & 578 \\
 & Inversions & 12 & 258 - 31628 & 1350 & 6190 \\
Sniffles & Deletions & 2734 & 49 - 47873 & 135 & 690 \\
 & Duplications & 61 & 211 - 87106 & 9118 & 14928 \\
 & Insertions & 2339 & 45 - 24610 & 130 & 526 \\
 & Inversions & 96 & 50 - 67769 & 208 & 6452 \\
\end{tabulary}
\caption{\selectlanguage{polish}{Summary of kākāpō structural variant size characteristics for Delly,
Manta, Smoove, CuteSV and Sniffles data sets filtered for SV call
quality. Delly, Manta and Smoove are short-read based, and CuteSV and
Sniffles are long-read based, variant discovery tools.
{\label{742793}}%
}\selectlanguage{english}}
\end{table}Consensus between the six call quality filtered datasets was relatively
low, except when considering the two Manta datasets
(\textasciitilde{}76\%, n = 29,219 SVs). The next two tools with the
highest proportion of agreement were the two long-read based call sets
for CuteSV and Sniffles (\textasciitilde{}17 - 49\% agreement, n = 1,099
SVs). The overall agreement between datasets tends to decrease as more
tools are included in comparisons, leaving only 94 SVs (90 deletions, 4
duplications) overlapping in all six datasets (Figure 1). These SVs,
ranging in size from 314bp to more than 20kb, were challenging to
consistently genotype. Few passed genotype thresholds in each dataset,
this included twelve deletions and two duplications in both Manta
datasets, five deletions in the Smoove dataset and one deletion in the
CuteSV dataset. It is challenging to glean a pattern in the overall
agreement between datasets given the variability in the number of SVs
passing call quality thresholds. For example, Sniffles tended to have
more calls overlap with short-read based call sets than CuteSV. However,
the filtered Sniffles call set was more than twice the size of the
filtered CuteSV call set.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-1-2/Figure-1}
\caption{\selectlanguage{polish}{Counts of consensus calls between SV type and strand within a 50 bp
window and the number of overlapping calls among the six datasets used
for kākāpō. Delly, Manta and Smoove are short-read based, and CuteSV and
Sniffles are long-read based, variant discovery tools. The coloured bar
chart on the upper left represents the number of SVs passing
call-quality thresholds in each of the six datasets. Dark green circles
with lines between denote which datasets have consensus SV calls. Bars
to the right represent the number of SVs overlapping between these
datasets. See Supplemental Table 1 for a summary of the number and type
of overlapping SVs.
{\label{239247}}%
}\selectlanguage{english}}
\end{center}
\end{figure}

The number of SVs found on each chromosome correlated with chromosome
size in all six datasets (Figure 2a). This pattern was consistent when
considering the proportion of chromosome base pairs impacted by SVs.
However, there appeared to be variability in the type of SV impacting
these chromosomes with inversions tending to impact the largest
proportion of base pairs in the short-read datasets. In contrast,
duplications tended to affect the largest proportion of the smaller
chromosomes in the long-read datasets. Further, there was some
variability in which of the smaller chromosomes were most impacted
(Figure 2b). ~\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-2/Figure-2}
\caption{\selectlanguage{polish}{Structural variant (SV) counts per kākāpō chromosome as called using
short-read, (Delly, Manta-Batch, Manta-Joint, Smoove), and long-read
(CuteSV, Sniffles) based tools. For each, call sets were filtered for SV
quality and the number of SVs per chromosome (A), and the proportion of
chromosome base-pairs impacted by structural variants (B) were
estimated. Chromosomes are ordered left to right by size (1-23), as per
the naming convention for the kākāpo reference genome, excluding the Z
and W sex chromosomes. The largest chromosome, chromosome 1,
consistently carried the highest number of SVs detected in all six
datasets. However, the smallest chromosomes consistently had the highest
proportion of base pairs impacted by SVs (i.e., sum of all SV lengths /
chromosome size) in all six datasets.
{\label{967331}}%
}\selectlanguage{english}}
\end{center}
\end{figure}

The results reported thus far have focused on the SVs retained after
overall `call quality filtering', or those SVs that passed quality
thresholds irrespective of individual genotype quality. Figure 3
summarises the results of SVs that passed both call quality thresholds
and genotype quality thresholds.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-3/Figure-3}
\caption{\selectlanguage{polish}{Overview of structural variant (SV) discovery and genotyping strategies
using short-read, (Delly, Manta-Batch, Manta-Joint, Smoove), and
long-read (CuteSV, Sniffles) based tools for kākāpō. Delly and Smoove
each have their own in-built genotyping programs, while Manta, CuteSV
and Sniffles do not. Variants called by Manta, CuteSV and Sniffles were
genotyped using short-read data as input for the BayesTyper genotyping
software package. Data were analysed in two steps: 1) An initial
filtering threshold(s) for call quality step used for comparisons of SV
type, size distributions and overlaps (in gold); and 2) genotype quality
threshold(s) used to explore variability in number of SVs carried by
individuals and genotype consistency among tools (in green). The
proportion of SVs passing Mendelian Inheritance were estimated across a
range of thresholds (Table 3). ~
{\label{906166}}%
}\selectlanguage{english}}
\end{center}
\end{figure}

Overall, the relative proportion of SV classes that pass genotype
filtering thresholds followed a similar pattern to those that passed SV
call quality thresholds with the most SVs being retained on the largest
chromosomes. While the size distribution of SVs was somewhat similar to
those filtered for call quality (Supplemental Figure 2 and Supplemental
Table 2), the proportion of individual chromosomes impacted was no
longer consistent among tools and did not follow a clear pattern
(Supplemental Figure 3). Of the genotyped filtered datasets tested for
Mendelian Inheritance, Sniffles had the highest proportion of genotypes
pass all parent-offspring trios tested for Mendelian Inheritance while
the Delly dataset had the lowest proportion of SV pass at this threshold
(Table 3). As the stringency of the Mendelian Inheritance tests were
relaxed, the proportion of passing SVs increased for all datasets (Table
3).\selectlanguage{english}
\begin{table}[H]
\centering
\normalsize\begin{tabulary}{1.0\textwidth}{CCCCCC}
 & Deletions & Duplications & Insertions & Inversions & Total \\
Delly Genotype Filtered Counts & 57 & 12 & 228 & 437 & 734 \\
100\% trios pass & 16 & 2 & 15 & 1 & 34 \\
[?]95\% trios pass & 48 & 6 & 25 & 420 & 499 \\
[?]90\% trios pass & 54 & 8 & 27 & 436 & 525 \\
[?]80\% trios pass & 56 & 10 & 28 & 437 & 531 \\
Manta / BayesTyper - Batch Genotype Filtered Counts & 515 & 70 & 177 & 342 & 1104 \\
100\% trios pass & 320 & 30 & 122 & 190 & 662 \\
[?]95\% trios pass & 513 & 50 & 177 & 335 & 1075 \\
[?]90\% trios pass & 515 & 56 & 177 & 341 & 1089 \\
[?]80\% trios pass & 515 & 62 & 177 & 342 & 1096 \\
Manta / BayesTyper - Joint Genotype Filtered Counts & 495 & 73 & 74 & 301 & 943 \\
100\% trios pass & 311 & 33 & 64 & 159 & 567 \\
[?]95\% trios pass & 490 & 54 & 74 & 289 & 907 \\
[?]90\% trios pass & 494 & 57 & 74 & 300 & 925 \\
[?]80\% trios pass & 495 & 63 & 74 & 301 & 933 \\
Smoove Genotype Filtered Counts & 1023 & 183 & N/A & 2825 & 4031 \\
100\% trios pass & 347 & 44 & N/A & 56 & 447 \\
[?]95\% trios pass & 772 & 90 & N/A & 2556 & 3418 \\
[?]90\% trios pass & 894 & 115 & N/A & 2700 & 3709 \\
[?]80\% trios pass & 965 & 148 & N/A & 2800 & 3913 \\
CuteSV / BayesTyper - Genotype Filtered Counts & 72 & 0 & 6 & 0 & 78 \\
100\% trios pass & 36 & 0 & 3 & 0 & 39 \\
[?]95\% trios pass & 71 & 0 & 6 & 0 & 77 \\
[?]90\% trios pass & 72 & 0 & 6 & 0 & 78 \\
[?]80\% trios pass & 72 & 0 & 6 & 0 & 78 \\
Sniffles / BayesTyper - Genotype Filtered Counts & 87 & 0 & 39 & 0 & 126 \\
100\% trios pass & 57 & 0 & 25 & 0 & 82 \\
[?]95\% trios pass & 87 & 0 & 39 & 0 & 126 \\
[?]90\% trios pass & 87 & 0 & 39 & 0 & 126 \\
[?]80\% trios pass & 87 & 0 & 39 & 0 & 126 \\
\end{tabulary}
\caption{\selectlanguage{polish}{Number of kākāpō SVs by type adhering to Mendelian Inheritance
expectations in 100\%, 95\%, 90\% and 80\% of trios tested. Conversion
of BayesTyper genotypes from long sequence format to symbolic alleles
could only resolve a subset of all genotypes reported. As such, the
exact proportion of these SVs exhibiting Mendelian Inheritance patterns
could not be reported. Smoove does not call or genotype insertions.
Delly, Manta and Smoove are short-read based, and CuteSV and Sniffles
are long-read based, variant discovery tools.
{\label{925567}}%
}\selectlanguage{english}}
\end{table}In general, the individual k\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō \selectlanguage{english}that carried the highest number of SVs
in one dataset also appeared to carry a relatively high number of SVs in
other datasets (Figure 4). For example, the individual carrying the
highest number of SVs in the Delly dataset (A), is the same individual
carrying the second highest number of SVs in the Smoove dataset (D).
Upon closer inspection we found that the three individuals that
consistently carried the most SVs in the Delly and Smoove datasets were
not read mapping outliers (22.8x, 23.12x and 26.5x). In addition, there
appeared to be either high variability in the number of SVs per
individual (Delly \& Smoove), or relatively little variability (both
Manta datasets, CuteSV and Sniffles). Another interesting note is
variability in SV type underlying these individual differences.
Specifically, inversions are the dominant SV type among individuals
carrying the most SVs in the Delly datasets, whereas deletions dominate
in both Manta datasets, CuteSV and Sniffles. For the Smoove data,
inversions are the most common SV type in individuals carrying the most
SVs, despite deletions being more consistently observed across the
population.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-4/Figure-4}
\caption{\selectlanguage{polish}{Counts of SV types carried by individual kākāpō (vertical bars, n = 169)
for each of the six genotype filtered datasets presented in Table 1.
{\label{304267}}%
}\selectlanguage{english}}
\end{center}
\end{figure}

Within each generation for both k\selectlanguage{polish}ā\selectlanguage{english}kap\selectlanguage{polish}ō \selectlanguage{english}lineages, the mean number of SVs
observed among the six datasets differs (Figure 5, Supplemental Figure
4). With the exception of F0 for the Fiordland lineage, Smoove
consistently detects the most SVs, followed by Manta - Batch, Manta -
Joint, Sniffles, CuteSV, and Delly in that order. Generational trends
also vary. For example, the mean number of SVs per generation for the
Fiordland lineage slightly increase in the Smoove dataset, remain
consistent in the CuteSV dataset, and decrease in the Delly and both
Manta datasets. For the Rakiura lineage birds, the mean number of SVs is
largely consistent in the CuteSV, Sniffles and both Manta datasets, but
there is an upward trend in the Delly and Smoove datasets.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-5/Figure-5}
\caption{\selectlanguage{polish}{Mean number of structural variants (SVs) per generation in two kākāpō
lineages among each of the six genotype filtered datasets presented in
Table 1.
{\label{709051}}%
}\selectlanguage{english}}
\end{center}
\end{figure}

\section*{Disucussion}

{\label{518821}}

We explored six strategies for SV discovery and genotyping with short-
and long-read data in the critically endangered k\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō. \selectlanguage{english}We found that
the choice of SV discovery tool heavily impacted the overall count,
location, and size distribution of SV types characterised. Further, the
proportion of SVs retained after filtering for SV call quality and
genotype quality varied across all six datasets. Finally, after
leveraging a meticulously curated pedigree, we also found that each
genotyping approach had variable success in consistently genotyping high
quality SVs. As a result, the number and type of SVs carried by
individual k\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō \selectlanguage{english}also differed. Nevertheless, there was some agreement
between datasets as to which individuals carried a relatively high
number of SVs. On the other hand, for the six genotyped filtered
datasets, the mean number of SVs in each of two k\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō \selectlanguage{english}lineages differed
both within and across generations. Our combined results highlight the
challenges associated with the discovery and genotyping of SVs
genome-wide. Despite this, we address caveats and highlight
considerations and provide recommendations to encourage the pursuit of
genome-wide SV characterization in biodiversity genomic research.

\subsection*{Challenges associated with resolving
SVs}

{\label{832217}}

The SV discovery tools used here vary in the overall number of SVs
detected, and the type, size and location of SVs, which suggests each
may be sensitive to different mapping characteristics. For instance, the
overall number of SVs initially detected, and those that passed call
quality filters, varied greatly among the six datasets. Although all
tools examined implement similar algorithms for SV discovery, the
priority and sensitivity thresholds for each algorithm, as well as
approaches for merging calls across individuals, are unique to each
tool. Methods used to estimate SV call quality metrics also vary between
tools, ranging in complexity from the number of supporting reads (e.g.,
Delly, Sniffles) to Bayesian inference of allele likelihoods (e.g.,
Manta). Further, it can be challenging to determine appropriate metric
thresholds, in part because these are generally optimised for model
species.~

Variability in the number of each SV type detected was observed in all
six datasets, especially when comparing long- and short-read based
tools. All four short-read call quality filtered datasets had a very
high prevalence of inversions. Both the individual-based strategy
implemented by Delly and Smoove, as well as the multi-sample approach
implemented by Manta, likely over-represented the number of inversions
relative to other SV types. This is not surprising given the challenges
associated with resolving inversion breakpoints, even after the merging
of a consensus call set (Ho et al., 2020; Mahmoud et al., 2019). In
addition, no clear filtering approach for consistently resolving
well-supported inversion breakpoints emerged for the tools used here.
While inversions are likely overrepresented in the short-read call set
(e.g., Hallast et al., 2021; Kim et al., 2017; Knief et al., 2017),
long-read based discovery strategies retained a relatively higher number
of insertions than short-read discovery tools. This is also not
surprising given the known limitations of short-read data when
characterising insertions (Delage et al., 2020).

All six datasets had a wide size distribution across all SV types. It is
challenging to determine whether the largest of these SVs are real.
There is no clear consensus regarding the largest SV size short-read
data can accurately detect (Ho et al., 2020; Mahmoud et al., 2019). It
is also unclear what the ideal depth, read length and quality scores are
required for long-reads to consistently resolve large SVs (but see
below). For example, duplications were, on average, the largest across
the six datasets, which could reflect highly repetitive regions and/or
mapping error.

Consensus on the location of SVs across the six datasets was limited,
and was largely dependent on the number of tools being compared. The
lack of complete overlap in the location of SVs between the two Manta
datasets is interesting given the overall similarity in the number of
SVs per chromosome and the overall counts of each SV type. The
strategies used to call SVs with Manta differ only in the way that
individuals were grouped during the initial SV discovery (i.e., samples
divided into 14 batches, versus all males analysed jointly together and
all females analysed jointly). Given that Manta incorporates local
assembly of reads when detecting SVs, it is possible that different read
sets have therefore led to differences in both the power and precision
to accurately locate SVs in these analyses. Randomisation of sample
batches would have aided in resolving this, however, this was not
possible due to computational resource limitation. It is notable that
although the two long-read based tools had a high level of agreement
with one another, CuteSV had fewer overlapping calls with the short-read
based tools. Whether this can be attributed to the smaller number of SVs
in the CuteSV dataset and/or the relative accuracy of each tool is
unknown.

\subsection*{Challenges associated with genotyping
SVs}

{\label{923704}}

Accurately genotyping SVs is in some ways more challenging than SV
discovery (see Alkan et al., 2011; Antaki et al., 2018; Jiang et al.,
2023 preprint; Nguyen et al., 2023). This is in part because the
approaches implemented for SV discovery are not immediately
transferrable to genotyping individuals (Alkan et al., 2011). Although
some discovery tools provide complementary genotyping packages (e.g.,
Delly, Smoove), many do not (e.g., CuteSV, Manta, Sniffles). Like SV
discovery tools, genotyping tools may implement different approaches for
different SV types and scoring qualities (S. Chen et al., 2019; Jiang et
al., 2023 preprint; Nguyen et al., 2023; Sibbesen et al., 2017). This in
turn makes it challenging to identify appropriate thresholds across a
range of tools. In addition, there is growing evidence that short-read
data is simply insufficient to accurately genotype some SV types (e.g.,
Sibbesen et al., 2018). For example, relatively few duplications and
inversions passed genotyping quality thresholds in all six datasets,
including the long-read based datasets which were genotyped using
short-read data.

Assessing the performance of genotyping tools and approaches is
challenging for most non-model species as there is a paucity of data for
independently validating SVs (i.e.,~ truth call sets; Cameron et al.,
2019; Kosugi et al., 2019). Because it is next to impossible to estimate
false discovery rates or verify genotypes without a truth call set (but
see Belyeu et al., 2021), we leveraged a well curated pedigree to assess
the proportion of SVs among each of the genotype filtered datasets that
adhered to Mendelian inheritance in parent-offspring trios. Although
concordance across all (100\%) trios was low for some tools, it is
promising to note that call and genotype-filtered SVs had between
72-100\% concordance in at least 80\% of trios. Where robust pedigree
data is available, this additional filtering step is likely to enrich
datasets for true positives and subsequent validation.

\subsection*{Challenges associated with characterising SV
diversity}

{\label{863664}}

The challenges with accurately detecting and genotyping SVs had
implications for assessments of individual SV diversity. Although there
was some concordance between Delly and Smoove when identifying three
individuals with the highest number of SVs, the SV type largely driving
this pattern are inversions. As noted previously, inversions are
particularly difficult to resolve using short-read data, making it
difficult to determine whether these variants are indicative of a true
biological signal or reflect systematic error in short-read mapping. The
significantly lower frequency of inversions in both long-read datasets
is notable as long-read data should better resolve more complex variants
like duplications, insertions and inversions (Alkan et al., 2011;
Chaisson et al., 2019; Mahmoud et al., 2019; M\selectlanguage{ngerman}érot et al., 2022).
Further work is needed to determine whether the small sample size and
relatively low sequence depth for the long-read data impeded discovery
of inversions, or whether these calls are largely false-positives in the
short-read based datasets.

Although the three individuals consistently carrying the most SVs in
Delly and Smoove were not depth outliers, there was a trend for
individuals with higher sequence depths to retain more SVs after
genotype quality filtering for both datasets (Supplemental Figure 5).
This has implications for the number of SVs detected in each generation
and observed trends across datasets within each lineage. Despite some
consistency in the datasets with the highest and lowest mean number of
SVs in each generation, where Smoove had the most SVs followed by Manta
-- Batch, Manta -- Joint, Sniffles, CuteSV and/or Delly, there were
different overall trends between the two lineages. However, interpreting
these results is challenging as sample sizes representing generations of
Fiordland lineage are small (F0 = 1, F1 = 3, F2 = 4). In addition, there
is some indication that a higher mean sequence depth of samples
sequenced in later Rakiura generations is driving the higher observed
mean in later generations (e.g., mean Rakiura F0 \textasciitilde{}15x
coverage vs Rakiura F2 \textasciitilde{}24x coverage). Although the
effect of mean sequence depth was not as strong in the datasets
genotyped using BayesTyper (i.e., Manta -- Batch, Manta -- Joint,
CuteSV, Sniffles), these results indicate the importance of accounting
for sequencing batch effects when interpreting results.

\subsection*{Caveats and
considerations}

{\label{411042}}

\textbf{\emph{Caveats --}} Benchmarking studies of the tools examined
here demonstrate that SV discovery is a complex endeavour--even in well
studied model species like humans--that can be influenced by sequence
type, depth and coverage, as well as the characteristics of the SVs
themselves (e.g., Cameron \emph{et al}., 2019; Kosugi et al. 2019).
Another important consideration is that many SV tools are developed and
benchmarked with human clinical applications in mind (e.g., Cameron et
al., 2019; X. Chen et al., 2016; Jiang et al., 2016; Jiang et al., 2020;
Kosugi et al., 2019; Pedersen et al., 2019; Rausch et al., 2012), and
come with the implicit assumption that researchers have access to
high-quality, chromosomally assembled reference genomes. All of these
reasons combined make it challenging to retrofit these tools for
population-scale studies in non-model species (e.g., GRIDSS; Cameron et
al. 2017).

An inherent problem with linear reference genomes is the potential of
reference bias (Theissinger et al., 2023; Wold et al. 2021). In this
study, the reference genome was assembled using an individual of pure
Rakiura lineage. This is significant since the Fiordland founder is the
only individual without direct relation to the Rakiura lineage. The
tendency to observe more SVs in birds of this lineage may be
attributable to the comparison of groups of more- and less- related
birds against a single reference. It is likely that the Fiordland
founder carried more genetic differences in comparison to the reference
genome, and these differences are likely to be inherited by his
descendants. However, the small sample size representing the Fiordland
lineage makes comparisons between the two difficult.

Below we highlight strategies to facilitate the characterisation of
genome-wide SVs in biodiversity genomic research, with an emphasis on SV
discovery given that SV genotyping is an especially active area of
research (e.g., Nguyen et al., 2023). Here we acknowledge the extensive
resources that made this study feasible including a relatively small,
high-quality diploid reference genome, a near whole-species
high-coverage short-read dataset, and a meticulously curated pedigree
for a genetically depauperate species (Guhlin et al. 2022 preprint).
Further, we acknowledge that there are many other tools and approaches
for SV discovery and genotyping not outlined in this study and encourage
researchers to explore both established and developing approaches.

\textbf{\emph{Considerations --}} ~Two key considerations for research
that seeks to characterise genome-wide SVs within and among populations
are data characteristics (e.g., read length, sequence depth) and
available resources (e.g., financial, computational). We frame our
recommendations below with these considerations in mind.

Best case scenario, we recommend generating long-read sequence data for
a target sequence depth of \selectlanguage{english}[?]20x for consistent performance across a
broad range of SV types (Sedlazeck et al., 2018; Jiang et al., 2020).
However, the size profile of reads can influence the sequence depth
required (i.e., 20kb reads at \selectlanguage{english}[?]10x coverage for Sniffles; Sedlazeck et
al., 2018). This was potentially demonstrated when all Sniffles calls
failed filtering thresholds for specificity as the sequence depth and
read length profile may be lower than recommended. The low number of SVs
retained in the CuteSV dataset after filtering is also likely due to
this constraint as this tool performs better when sequence coverage \selectlanguage{english}[?]20x
(Jiang et al., 2020). Nevertheless, the future promise of long-read
approaches and their ability to outperform most short-read SV discovery
tools make these strategies our recommended investment for the
characterisation of SVs. In our experience, alignment tools for
long-read data and SV discovery did not exceed our computational
resources (128Gb of RAM, 1Tb storage). However, we note that preparation
of whole-genome long-read sequence data can be computationally intensive
(e.g., basecalling of ONT data) and that neither CuteSV or Sniffles
provide recommended tools for population-scale genotyping.

When long-read data is not practical, but substantial computational
resources are available, we recommend using Manta and a target sequence
depth \selectlanguage{english}[?]20x coverage (e.g., Supplementary Figure S13, Kosugi et al.,
2019). We acknowledge that jointly calling samples can be challenging as
Manta was by far the most computationally intensive programme used in
this study. We did not track our final usage, but to call each of the
Joint and Batch data sets, we increased our RAM allocation to 460Gb.
However, caution is advised when implementing a batched approach given
that Manta has more power to resolve SV breakpoints when all samples are
pooled together. Another drawback is that, like both CuteSV and
Sniffles, Manta does not provide a recommended programme for
population-wide genotyping and interpreting the raw outputs is not
recommended.

When financial and computational resources are limited, we suggest that
Delly and Smoove are relatively interchangeable. Both tools call SVs on
an individual sample prior to merging into a raw call set. As a result,
they require significantly less computational resources than Manta and
use similar computational resources to either CuteSV or Sniffles for SV
discovery. They have the additional benefit of including their own
genotyping methods, which were relatively efficient compared to
BayesTyper (required \textgreater{}3Tb of storage and was run using 16
cores each with 8Gb of RAM over several days). It is notable that the
number of genotyped SVs increased for individuals with higher depth in
Delly and Smoove suggesting that consistency of sequence depth across
samples may be an important consideration for these tools (Supplemental
Figure 5).

\section*{Concluding remarks}

{\label{910387}}

As SV-based studies become more ubiquitous over time, we anticipate many
of the caveats and trade-offs discussed above will be better resolved
and we are excited to see the wide application of SVs in the
biodiversity genomic literature. In particular, multi-reference
approaches, like genome graphs, promise to alleviate many of the
challenges associated with genome-wide SV characterisation (Bayer et
al., 2020; Ebler et al., 2020 preprint; Eizenga et al., 2020; Hurgobin
\& Edwards, 2017). For example, for SV studies in species with robust
pedigrees like k\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō, \selectlanguage{english}it may be more economical to target a subset of
highly represented individuals for long-read sequencing and the
construction of genome graphs. Similar approaches for SV discovery are
underway to better inform breeding and selection in agriculturally
significant species such as cattle, soybean and tomato (Alonge et al.,
2020; Cappetta et al., 2020; Liu et al., 2020; Talenti et al., 2022).
Individual- and population-scale genotyping may then be possible with
short-read data and assessments of genome-wide SV diversity.

\section*{Data accessibility and benefits
sharing}

{\label{418075}}

This research was undertaken as part of the K\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō\selectlanguage{english}125+ Project that
includes research partnerships between the University of Canterbury's
Conservation, Systematics and Evolutionary Research Team (ConSERT,
including JRW, TES), Genomics Aotearoa (including AWS, JGG, PKD, TES),
New Zealand Department of Conservation (DOC) and Te R\selectlanguage{polish}ū\selectlanguage{english}nanga o Ng\selectlanguage{polish}ā\selectlanguage{english}i Tahu
(TRONT). The goal of the K\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō\selectlanguage{english}125+ Project is to facilitate the
development and implementation of conservation management strategies to
enhance the recovery of this critically endangered taonga, or treasured,
species. Approval to access the K\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō\selectlanguage{english}125+ short-read data used in this
study was granted to TES and her research team by DOC and TRONT. The
K\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō\selectlanguage{english}125+ Project short-read data is stored in the Aotearoa Genomic
Data Repository (AGDR): \url{https://data.agdr.org.nz/} and is subject
to the K\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō\selectlanguage{english}125+ Genomics Data Sharing Terms and Conditions described
here:
\url{https://www.doc.govt.nz/our-work/kakapo-recovery/what-we-do/research-for-the-future/kakapo125-gene-sequencing/request-kakapo125-data/}.
The generation of the long-read data was conducted under DOC
authorisation (authorisation number: 97814-FAU) and enabled by High
Quality Genomes and Population Genomics at Genomics Aotearoa. In
accordance with FAIR and CARE data principles (Carroll et al., 2020,
2021; Mc Cartney et al., 2022), the long-read data is also stored in the
AGDR and data sharing subject to approval by DOC and TRONT.

\section*{Acknowledgements}

{\label{667034}}

We are grateful to the K\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō\selectlanguage{english}125+ Project led by the New Zealand
Department of Conservation (DOC) in partnership with Te R\selectlanguage{polish}ū\selectlanguage{english}nanga o Ng\selectlanguage{polish}ā\selectlanguage{english}i
Tahu (TRONT). The generation and availability of the short-read data
used in this study is owed to the following additional parties: Genetic
Rescue Foundation, University of Otago, New Zealand Genomics Limited,
Rockefeller Institute, Duke University, Science Exchange and
Experiment.com. We also thank DOC and TRONT for providing the
opportunity to explore structural variants in k\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō \selectlanguage{english}using both short-
and long-read data. JRW and TES are grateful to the Shorebirds Trust,
DOC, Genomics Aotearoa, and the University of Canterbury (UC) for their
financial support and also extend their thanks to the UC Conservation,
Systematics and Evolution Research Team (ConSERT), the UC Research
Computational Cluster, especially Francois Bissey, and the New Zealand
eScience Infrastructure for support in accessing the computational
resources necessary for generating this manuscript. We are also grateful
for three anonymous reviewers for their constructive feedback on earlier
versions of this work.

\textbf{Hosted file}

\verb`Wold_et_al_MER-Supplemental1.docx`  available at \url{https://authorea.com/users/391738/articles/614029-the-promise-and-challenges-of-characterising-genome-wide-structural-variants-a-case-study-in-a-critically-endangered-parrot}

\section*{References}

{\label{487875}}

Alkan, C., Coe, B. P., \& Eichler, E. E. (2011). Genome structural
variation discovery and genotyping. \emph{Nature Reviews Genetics},
\emph{12}(5), Article 5. https://doi.org/10.1038/nrg2958

Alonge, M., Wang, X., Benoit, M., Soyk, S., Pereira, L., Zhang, L.,
Suresh, H., Ramakrishnan, S., Maumus, F., Ciren, D., Levy, Y., Harel, T.
H., Shalev-Schlosser, G., Amsellem, Z., Razifard, H., Caicedo, A. L.,
Tieman, D. M., Klee, H., Kirsche, M., \ldots{} Lippman, Z. B. (2020).
Major impacts of widespread structural variation on gene expression and
crop improvement in tomato. \emph{Cell}, \emph{182}(1), 145-161.e23.
https://doi.org/10.1016/j.cell.2020.05.021

Antaki, D., Brandler, W. M., \& Sebat, J. (2018). SV2: accurate
structural variation genotyping and de novo mutation~ detection from
whole genomes. \emph{Bioinformatics, 34(10),} 1774-1777.
https://doi.org/10.1093/bioinformatics/btx813

Bayer, P. E., Golicz, A. A., Scheben, A., Batley, J., \& Edwards, D.
(2020). Plant pan-genomes are the new reference. \emph{Nature Plants},
\emph{6}(8), 914--920. https://doi.org/10.1038/s41477-020-0733-0

Belyeu, J.R., Chowdhury, M., Brown, J., Pedersen, B. S., Cormier, M. J.,
Quinlan, A. R. \& Layer, R. M.~ (2021). Samplot: a platform for
structural variant visual validation and automated filtering.
\emph{Genome Biology, 22}, 161.
https://doi.org/10.1186/s13059-021-02380-5{}

Berdan, E. L., M\selectlanguage{ngerman}érot, C., Pavia, H., Johannesson, K., Wellenreuther, M.,
\& Butlin, R. K. (2021). A large chromosomal inversion shapes gene
expression in seaweed flies (Coelopa frigida). \emph{Evolution Letters},
\emph{5}(6), 607--624. https://doi.org/10.1002/evl3.260

Bergner, L. M., Jamieson, I. G., \& Robertson, B. C. (2014). Combining
genetic data to identify relatedness among founders in a genetically
depauperate parrot, the Kakapo (Strigops habroptilus).
\emph{Conservation Genetics}, \emph{15}(5), 1013--1020.
https://doi.org/10.1007/s10592-014-0595-y

Best, H. A., \& Powlesland, R. (1985). \emph{Kakapo}. New Zealand
Wildlife Service.

Cameron, D. L., Di Stefano, L., \& Papenfuss, A. T. (2019).
Comprehensive evaluation and characterisation of short read
general-purpose structural variant calling software. \emph{Nature
Communications}, \emph{10}(1), Article 1.
https://doi.org/10.1038/s41467-019-11146-4

Cameron, D. L., Schröder, J., Penington, J. S., Do, H., Molania, R.,
Dobrovic, A., Speed, T. P., \& Papenfuss, A. T. (2017). GRIDSS:
Sensitive and specific genomic rearrangement detection using positional
de Bruijn graph assembly. \emph{Genome Research}.
https://doi.org/10.1101/gr.222109.117

Campbell, C. R., Poelstra, J. W., \& Yoder, A. D. (2018). What is
Speciation Genomics? The roles of ecology, gene flow, and genomic
architecture in the formation of species. \emph{Biological Journal of
the Linnean Society}, \emph{124}(4), 561--583.
https://doi.org/10.1093/biolinnean/bly063

Cappetta, E., Andolfo, G., Di Matteo, A., Barone, A., Frusciante, L., \&
Ercolano, M. R. (2020). Accelerating Tomato Breeding by Exploiting
Genomic Selection Approaches. \emph{Plants}, \emph{9}(9), Article 9.
https://doi.org/10.3390/plants9091236

Carroll, S. R., Garba, I., Figueroa-Rodríguez, O. L., Holbrook, J.,
Lovett, R., Materechera, S., Parsons, M., Raseroka, K.,
Rodriguez-Lonebear, D., Rowe, R., Sara, R., Walker, J. D., Anderson, J.,
\& Hudson, M. (2020). The CARE Principles for Indigenous Data
Governance. \emph{Data Science Journal}, \emph{19}(1), Article 1.
https://doi.org/10.5334/dsj-2020-043

Carroll, S. R., Herczog, E., Hudson, M., Russell, K., \& Stall, S.
(2021). Operationalizing the CARE and FAIR Principles for Indigenous
data futures. \emph{Scientific Data}, \emph{8}(1), Article 1.
https://doi.org/10.1038/s41597-021-00892-0

Cayuela, H., Dorant, Y., Mérot, C., Laporte, M., Normandeau, E.,
Gagnon-Harvey, S., Clément, M., Sirois, P., \& Bernatchez, L. (2021).
Thermal adaptation rather than demographic history drives genetic
structure inferred by copy number variants in a marine fish.
\emph{Molecular Ecology}, \emph{30}(7), 1624--1641.
https://doi.org/10.1111/mec.15835

Chaisson, M. J. P., Sanders, A. D., Zhao, X., Malhotra, A., Porubsky,
D., Rausch, T., Gardner, E. J., Rodriguez, O. L., Guo, L., Collins, R.
L., Fan, X., Wen, J., Handsaker, R. E., Fairley, S., Kronenberg, Z. N.,
Kong, X., Hormozdiari, F., Lee, D., Wenger, A. M., \ldots{} Lee, C.
(2019). Multi-platform discovery of haplotype-resolved structural
variation in human genomes. \emph{Nature Communications}, \emph{10},
1784. https://doi.org/10.1038/s41467-018-08148-z

Chander, V., Gibbs, R. A., \& Sedlazeck, F. J. (2019). Evaluation of
computational genotyping of structural variation for clinical diagnoses.
\emph{GigaScience}, \emph{8}(9).
https://doi.org/10.1093/gigascience/giz110

Chen, S., Zhou, Y., Chen, Y., \& Gu, J. (2018a). fastp: An ultra-fast
all-in-one FASTQ preprocessor. \emph{Bioinformatics}, \emph{34}(17),
i884--i890. https://doi.org/10.1093/bioinformatics/bty560

Chen, S., Krusche, P., Dolzhenko, E., Sherman, R. M., Petrovski, R.,
Schlesinger, F., Kirsche, M., Bentley, D. R., Schatz, M. C., Sedlazeck,
F. J., \& Eberle, M. A. (2019). Paragraph: a graph-based structural
variant genotyper for short-read sequence data. \emph{Genome Biology,
20(1),} 291. https://doi.org/10.1186/s13059-019-1909-7

Chen, X., Schulz-Trieglaff, O., Shaw, R., Barnes, B., Schlesinger, F.,
Källberg, M., Cox, A. J., Kruglyak, S., \& Saunders, C. T. (2016).
Manta: Rapid detection of structural variants and indels for germline
and cancer sequencing applications. \emph{Bioinformatics}, \emph{32}(8),
1220--1222. https://doi.org/10.1093/bioinformatics/btv710

Chiang, C., Layer, R. M., Faust, G. G., Lindberg, M. R., Rose, D. B.,
Garrison, E. P., Marth, G. T., Quinlan, A. R., \& Hall, I. M. (2015).
SpeedSeq: Ultra-fast personal genome analysis and interpretation.
\emph{Nature Methods}, \emph{12}(10), 966--968.
https://doi.org/10.1038/nmeth.3505

Chueca, L. J., Schell, T., \& Pfenninger, M. (2021). Whole-genome
re-sequencing data to infer historical demography and speciation
processes in land snails: The study of two Candidula sister species.
\emph{Philosophical Transactions of the Royal Society B: Biological
Sciences}, \emph{376}(1825), 20200156.
https://doi.org/10.1098/rstb.2020.0156

Cleal, K., \& Baird, D. M., (2022). Dysgu: efficient structural variant
calling using short or long reads. \emph{Nucleic Acids Research, 50(9)},
e53. https://doi.org/10.1093/nar/gkac039

Cresswell, M. (1996). \selectlanguage{polish}\emph{Kākāpō Recovery Plan 1996-2005}\selectlanguage{english} (No. 21;
Threatened Species Recovery Plan). Department of Conservation.

Cruickshank, T. E., \& Hahn, M. W. (2014). Reanalysis suggests that
genomic islands of speciation are due to reduced diversity, not reduced
gene flow. \emph{Molecular Ecology}, \emph{23}(13), 3133--3157.
https://doi.org/10.1111/mec.12796

Danecek, P., Bonfield, J. K., Liddle, J., Marshall, J., Ohan, V.,
Pollard, M. O., Whitwham, A., Keane, T., McCarthy, S. A., Davies, R. M.,
\& Li, H. (2021). Twelve years of SAMtools and BCFtools.
\emph{GigaScience}, \emph{10}(2).
https://doi.org/10.1093/gigascience/giab008

Davey, J. W., Hohenlohe, P. A., Etter, P. D., Boone, J. Q., Catchen, J.
M., \& Blaxter, M. L. (2011). Genome-wide genetic marker discovery and
genotyping using next-generation sequencing. \emph{Nature Reviews
Genetics}, \emph{12}(7), Article 7. https://doi.org/10.1038/nrg3012

De Coster, W., D'Hert, S., Schultz, D. T., Cruts, M., \& Van
Broeckhoven, C. (2018). NanoPack: Visualizing and processing long-read
sequencing data. \emph{Bioinformatics}, \emph{34}(15), 2666--2669.
https://doi.org/10.1093/bioinformatics/bty149

Delage, W. J., Thevenon, J., \& Lemaitre, C. (2020). Towards a better
understanding of the low recall of insertion variants with short-read
based variant callers. \emph{BMC Genomics}, \emph{21}, 762.
https://doi.org/10.1186/s12864-020-07125-5

Dorant, Y., Cayuela, H., Wellband, K., Laporte, M., Rougemont, Q.,
M\selectlanguage{ngerman}érot, C., Normandeau, E., Rochette, R., \& Bernatchez, L. (2020). Copy
number variants outperform SNPs to reveal genotype--temperature
association in a marine species. \emph{Molecular Ecology},
\emph{29}(24), 4765--4782. https://doi.org/10.1111/mec.15565

Ebler, J., Clarke, W. E., Rausch, T., Audano, P. A., Houwaart, T.,
Korbel, J., Zody, M. C., Dilthey, A. T., \& Marschall, T. (2020).
Pangenome-based genome inference. \emph{BioRxiv}, 37.

Eizenga, J. M., Novak, A. M., Sibbesen, J. A., Heumos, S., Ghaffaari,
A., Hickey, G., Chang, X., Seaman, J. D., Rounthwaite, R., Ebler, J.,
Rautiainen, M., Garg, S., Paten, B., Marschall, T., Sirén, J., \&
Garrison, E. (2020). Pangenome Graphs. \emph{Annual Review of Genomics
and Human Genetics}, \emph{21}(1), 139--162.
https://doi.org/10.1146/annurev-genom-120219-080406

English, A. C., Salerno, W. J., Hampton, O. A., Gonzaga-Jauregui, C.,
Ambreth, S., Ritter, D. I., Beck, C. R., Davis, C. F., Dahdouli, M., Ma,
S., Carroll, A., Veeraraghavan, N., Bruestle, J., Drees, B., Hastie, A.,
Lam, E. T., White, S., Mishra, P., Wang, M., \ldots{} Gibbs, R. A.
(2015). Assessing structural variation in a personal genome---Towards a
human reference diploid genome. \emph{BMC Genomics}, \emph{16}(1), 286.
https://doi.org/10.1186/s12864-015-1479-3

Ewels, P., Magnusson, M., Lundin, S., \& Käller, M. (2016). MultiQC:
Summarize analysis results for multiple tools and samples in a single
report. \emph{Bioinformatics}, \emph{32}(19), 3047--3048.
https://doi.org/10.1093/bioinformatics/btw354

Formenti, G., Theissinger, K., Fernandes, C., Bista, I., Bombarely, A.,
Bleidorn, C., Ciofi, C., Crottini, A., Godoy, J. A., Höglund, J.,
Malukiewicz, J., Mouton, A., Oomen, R. A., Paez, S., Palsbøll, P. J.,
Pampoulie, C., Ruiz-López, M. J., Svardal, H., Theofanopoulou, C.,
\ldots{} Zammit, G. (2022). The era of reference genomes in conservation
genomics. \emph{Trends in Ecology \& Evolution}, \emph{37}(3), 197--202.
https://doi.org/10.1016/j.tree.2021.11.008

Funk, E. R., Mason, N. A., Pálsson, S., Albrecht, T., Johnson, J. A., \&
Taylor, S. A. (2021). A supergene underlies linked variation in color
and morphology in a Holarctic songbird. \emph{Nature Communications},
\emph{12}(1), Article 1. https://doi.org/10.1038/s41467-021-27173-z

Galla, S., Brown, L., Couch\selectlanguage{english}-Lewis, Y., Cubrinovska, I., Eason, D.,
Gooley, R., Hamilton, J., Heath, J., Hauser, S., Latch, E., Matocq, M.,
Richardson, A., Wold, J., Hogg, C., Santure, A., \& Steeves, T. (2021).
The relevance of pedigrees in the conservation genomics era.
\emph{Molecular Ecology}, \emph{31}. https://doi.org/10.1111/mec.16192

Garcia-Alcalde, F., Okonechnikov, K., Carbonell, J., Cruz, L. M., Gotz,
S., Tarazona, S., Dopazo, J., Meyer, T. F., \& Conesa, A. (2012).
Qualimap: Evaluating next-generation sequencing alignment data.
\emph{Bioinformatics}, \emph{28}(20), 2678--2679.
\url{https://doi.org/10.1093/bioinformatics/bts503}{}

\emph{Github a, Issue \#30 * Illumina/manta}. (2016, February 19).
GitHub. https://github.com/Illumina/manta/issues/30

\emph{Github b, Issue \#53 * Illumina/manta}. (2016, November 29).
GitHub. \url{https://github.com/Illumina/manta/issues/53}{}

\emph{Github ConvertAllele and Variant normalization * Issue \#19 *
bioinformatics-centre/BayesTyper}. (n.d.). GitHub. Retrieved February
22, 2023, from
https://github.com/bioinformatics-centre/BayesTyper/issues/19

Guhlin, J., Lec, M. F. L., Wold, J., Koot, E., Winter, D., Biggs, P.,
Galla, S. J., Urban, L., Foster, Y., Cox, M. P., Digby, A., Uddstrom,
L., Eason, D., Vercoe, D., Davis, T., K\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō \selectlanguage{english}Recovery Team, Howard, J.
T., Jarvis, E., Robertson, F. E., \ldots{} Dearden, P. K. (2022).
\selectlanguage{polish}\emph{Species-wide genomics of kākāpō provides transformational tools to
accelerate recovery}\selectlanguage{english} {[}Preprint{]}. Genomics.
https://doi.org/10.1101/2022.10.22.513130

Hallast, P., Kibena, L., Punab, M., Arciero, E., Rootsi, S., Grigorova,
M., Flores, R., Jobling, M. A., Poolamets, O., Pomm, K., Korrovits, P.,
Rull, K., Xue, Y., Tyler-Smith, C., \& Laan, M. (2021). A common 1.6 mb
Y-chromosomal inversion predisposes to subsequent deletions and severe
spermatogenic failure in humans. \emph{ELife}, \emph{10}, e65420.
https://doi.org/10.7554/eLife.65420

Ho, S. S., Urban, A. E., \& Mills, R. E. (2020). Structural Variation in
the Sequencing Era: Comprehensive Discovery and Integration.
\emph{Nature Reviews. Genetics}, \emph{21}(3), 171--189.
https://doi.org/10.1038/s41576-019-0180-9

Huang, K., Andrew, R. L., Owens, G. L., Ostevik, K. L., \& Rieseberg, L.
H. (2020). Multiple chromosomal inversions contribute to adaptive
divergence of a dune sunflower ecotype. \emph{Molecular Ecology},
\emph{29}(14), 2535--2549. https://doi.org/10.1111/mec.15428

Huddleston, J., Chaisson, M. J. P., Steinberg, K. M., Warren, W.,
Hoekzema, K., Gordon, D., Graves-Lindsay, T. A., Munson, K. M.,
Kronenberg, Z. N., Vives, L., Peluso, P., Boitano, M., Chin, C.-S.,
Korlach, J., Wilson, R. K., \& Eichler, E. E. (2017). Discovery and
genotyping of structural variation from long-read haploid genome
sequence data. \emph{Genome Research}, \emph{27}(5), 677--685.
https://doi.org/10.1101/gr.214007.116

Hurgobin, B., \& Edwards, D. (2017). SNP Discovery Using a Pangenome:
Has the Single Reference Approach Become Obsolete? \emph{Biology},
\emph{6}(1), Article 1. https://doi.org/10.3390/biology6010021

Huynh, L. Y., Maney, D. L., \& Thomas, J. W. (2011). Chromosome-wide
linkage disequilibrium caused by an inversion polymorphism in the
white-throated sparrow ( Zonotrichia albicollis ). \emph{Heredity},
\emph{106}(4), Article 4. https://doi.org/10.1038/hdy.2010.85

Jain, C., Rhie, A., Zhang, H., Chu, C., Walenz, B. P., Koren, S., \&
Phillippy, A. M. (2020). Weighted minimizer sampling improves long read
mapping. \emph{Bioinformatics}, \emph{36}(Supplement\_1), i111--i118.
https://doi.org/10.1093/bioinformatics/btaa435

Jeffares, D. C., Jolly, C., Hoti, M., Speed, D., Shaw, L., Rallis, C.,
Balloux, F., Dessimoz, C., B\selectlanguage{ngerman}ähler, J., \& Sedlazeck, F. J. (2017).
Transient structural variations have strong effects on quantitative
traits and reproductive isolation in fission yeast. \emph{Nature
Communications}, \emph{8}(1), 14061. https://doi.org/10.1038/ncomms14061

Jiang, T., Liu, Y., Jiang, Y., Li, J., Gao, Y., Cui, Z., Liu, Y., Liu,
B., \& Wang, Y. (2020). Long-read-based human genomic structural
variation detection with cuteSV. \emph{Genome Biology}, \emph{21}(1),
189. https://doi.org/10.1186/s13059-020-02107-y

Jiang, T., Cao, S., Liu, Y., Liu, S., Liu, B., Wang, G., \& Wang, Y.
(2023). Regenotyping structural variants through an accurate
force-calling method. \emph{bioRxiv.}

Jun, G., Sedlazeck, F., Zhu, Q., English, A., Metcalf, G., Kang, H. M.,
Human Genome Structural Variation Consortium (HGSVC), Lee, C., Gibbs,
R., \& Boerwinkle, E. (2021). muCNV: Genotyping structural variants for
population-level sequencing. \emph{Bioinformatics}, \emph{37}(14),
2055--2057. https://doi.org/10.1093/bioinformatics/btab199

K\selectlanguage{polish}ā\selectlanguage{english}k\selectlanguage{polish}ā\selectlanguage{english}p\selectlanguage{polish}ō \selectlanguage{english}Recovery Group. (2017). \selectlanguage{polish}\emph{A history of kākāpō}. \selectlanguage{english}Department of
Conservation.
https://www.doc.govt.nz/our-work/kakapo-recovery/what-we-do/history/

Kess, T., Brachmann, M., \& Boulding, E. G. (2021). Putative chromosomal
rearrangements are associated primarily with ecotype divergence rather
than geographic separation in an intertidal, poorly dispersing snail.
\emph{Journal of Evolutionary Biology}, \emph{34}(1), 193--207.
https://doi.org/10.1111/jeb.13724

Kim, K.-W., Bennison, C., Hemmings, N., Brookes, L., Hurley, L. L.,
Griffith, S. C., Burke, T., Birkhead, T. R., \& Slate, J. (2017). A
sex-linked supergene controls sperm morphology and swimming speed in a
songbird. \emph{Nature Ecology \& Evolution}, \emph{1}(8), Article 8.
https://doi.org/10.1038/s41559-017-0235-2

Kirsche, M., Prabhu, G., Sherman, R., Ni, B., Battle, A., Aganezov, S.,
\& Schatz, M. C. (2023). Jasmine and Iris: Population-scale structural
variant comparison and analysis. \emph{Nature Methods}, 1--10.
https://doi.org/10.1038/s41592-022-01753-3

Knief, U., Forstmeier, W., Pei, Y., Ihle, M., Wang, D., Martin, K.,
Opatov\selectlanguage{ngerman}á, P., Albrechtová, J., Wittig, M., Franke, A., Albrecht, T., \&
Kempenaers, B. (2017). A sex-chromosome inversion causes strong
overdominance for sperm traits that affect siring success. \emph{Nature
Ecology \& Evolution}, \emph{1}(8), Article 8.
https://doi.org/10.1038/s41559-017-0236-1

Kokot, M., D\selectlanguage{polish}ł\selectlanguage{english}ugosz, M., \& Deorowicz, S. (2017). KMC 3: Counting and
manipulating k-mer statistics. \emph{Bioinformatics}, \emph{33}(17),
2759--2761. https://doi.org/10.1093/bioinformatics/btx304

Kosugi, S., Momozawa, Y., Liu, X., Terao, C., Kubo, M., \& Kamatani, Y.
(2019). Comprehensive evaluation of structural variation detection
algorithms for whole genome sequencing. \emph{Genome Biology},
\emph{20}(1), 117. https://doi.org/10.1186/s13059-019-1720-5

K\selectlanguage{ngerman}üpper, C., Stocks, M., Risse, J. E., dos Remedios, N., Farrell, L. L.,
McRae, S. B., Morgan, T. C., Karlionova, N., Pinchuk, P., Verkuil, Y.
I., Kitaysky, A. S., Wingfield, J. C., Piersma, T., Zeng, K., Slate, J.,
Blaxter, M., Lank, D. B., \& Burke, T. (2016). A supergene determines
highly divergent male reproductive morphs in the ruff. \emph{Nature
Genetics}, \emph{48}(1), Article 1. https://doi.org/10.1038/ng.3443

Lado, S., Elbers, J. P., Doskocil, A., Scaglione, D., Trucchi, E.,
Banabazi, M. H., Almathen, F., Saitou, N., Ciani, E., \& Burger, P. A.
(2020). Genome-wide diversity and global migration patterns in
dromedaries follow ancient caravan routes. \emph{Communications
Biology}, \emph{3}(1), Article 1.
https://doi.org/10.1038/s42003-020-1098-7

Layer, R. M., Chiang, C., Quinlan, A. R., \& Hall, I. M. (2014). LUMPY:
A probabilistic framework for structural variant discovery. \emph{Genome
Biology}, \emph{15}(6), R84. https://doi.org/10.1186/gb-2014-15-6-r84

Li, H., \& Durbin, R. (2009). Fast and accurate short read alignment
with Burrows-Wheeler transform. \emph{Bioinformatics (Oxford, England)},
\emph{25}(14), 1754--1760. https://doi.org/10.1093/bioinformatics/btp324

Liu, Y., Du, H., Li, P., Shen, Y., Peng, H., Liu, S., Zhou, G.-A.,
Zhang, H., Liu, Z., Shi, M., Huang, X., Li, Y., Zhang, M., Wang, Z.,
Zhu, B., Han, B., Liang, C., \& Tian, Z. (2020). Pan-Genome of Wild and
Cultivated Soybeans. \emph{Cell}, \emph{182}(1), 162-176.e13.
https://doi.org/10.1016/j.cell.2020.05.023

Lloyd, B. D., \& Powlesland, R. G. (1994). The decline of kakapo
Strigops habroptilus and attempts at conservation by translocation.
\emph{Biological Conservation}, \emph{69}(1), 75--85.
https://doi.org/10.1016/0006-3207(94)90330-1

Mahmoud, M., Gobet, N., Cruz-Dávalos, D. I., Mounier, N., Dessimoz, C.,
\& Sedlazeck, F. J. (2019). Structural variant calling: The long and the
short of it. \emph{Genome Biology}, \emph{20}(1), 246.
https://doi.org/10.1186/s13059-019-1828-7

Mathur, S., \& DeWoody, J. A. (2021). Genetic load has potential in
large populations but is realized in small inbred populations.
\emph{Evolutionary Applications}, \emph{14}(6), 1540--1557.
https://doi.org/10.1111/eva.13216

Mc Cartney, A. M., Anderson, J., Liggins, L., Hudson, M. L., Anderson,
M. Z., TeAika, B., Geary, J., Cook-Deegan, R., Patel, H. R., \&
Phillippy, A. M. (2022). Balancing openness with Indigenous data
sovereignty: An opportunity to leave no one behind in the journey to
sequence all of life. \emph{Proceedings of the National Academy of
Sciences}, \emph{119}(4), e2115860119.
https://doi.org/10.1073/pnas.2115860119

Mérot, C., Oomen, R. A., Tigano, A., \& Wellenreuther, M. (2020). A
Roadmap for Understanding the Evolutionary Significance of Structural
Genomic Variation. \emph{Trends in Ecology \& Evolution}, \emph{35}(7),
561--572. https://doi.org/10.1016/j.tree.2020.03.002

Mérot, C., Stenløkk, K. S. R., Venney, C., Laporte, M., Moser, M.,
Normandeau, E., Árnyasi, M., Kent, M., Rougeux, C., Flynn, J. M., Lien,
S., \& Bernatchez, L. (2022). Genome assembly, structural variants, and
genetic differentiation between lake whitefish young species pairs
(Coregonus sp.) with long and short reads. \emph{Molecular Ecology},
\emph{00}. https://doi.org/10.1111/mec.16468

Nguyen, T. V., Vander Jagt, C. J., Wang, J., Daetwyler, H. D., Xiang,
R., Goddard, M., E., Nguyen, L. T., Ross, E. M., Hayes, B. J.,
Chamberlain, A. J., \& MacLeod, I. M. (2023). In it for the long run:
perspectives on exploiting long-read sequencing in livestock for
population scale studies of structural variants. \emph{Genetics
Selection Evolution, 55(1),} 9.
https://doi.org/10.1186/s12711-023-00783-5

Pedersen, B. S., Layer, R., \& Quinlan, A. R. (2020). \emph{Smoove:
Structural-variant calling and genotyping with existing tools} (0.2.8).
https://github.com/brentp/smoove

Pedersen, B. S., \& Quinlan, A. R. (2018). Mosdepth: Quick coverage
calculation for genomes and exomes. \emph{Bioinformatics}, \emph{34}(5),
867--868. https://doi.org/10.1093/bioinformatics/btx699

Pedersen, B. S., \& Quinlan, A. R. (2019). Duphold: Scalable,
depth-based annotation and curation of high-confidence structural
variant calls. \emph{GigaScience}, \emph{8}(4), giz040.
https://doi.org/10.1093/gigascience/giz040

Poplin, R., Chang, P.-C., Alexander, D., Schwartz, S., Colthurst, T.,
Ku, A., Newburger, D., Dijamco, J., Nguyen, N., Afshar, P. T., Gross, S.
S., Dorfman, L., McLean, C. Y., \& DePristo, M. A. (2018). A universal
SNP and small-indel variant caller using deep neural networks.
\emph{Nature Biotechnology}, \emph{36}(10), 983--987.
https://doi.org/10.1038/nbt.4235

Rausch, T., Zichner, T., Schlattl, A., Stütz, A. M., Benes, V., \&
Korbel, J. O. (2012). DELLY: Structural variant discovery by integrated
paired-end and split-read analysis. \emph{Bioinformatics},
\emph{28}(18), i333--i339. https://doi.org/10.1093/bioinformatics/bts378

Rausch, T. (2014, October 16). \emph{Vcf format question}.
https://groups.google.com/g/delly-users/c/8IUXv4YRN4o/m/bLmTWiIe0m4J{}

Rhie, A., McCarthy, S. A., Fedrigo, O., Damas, J., Formenti, G., Koren,
S., Uliano-Silva, M., Chow, W., Fungtammasan, A., Kim, J., Lee, C., Ko,
B. J., Chaisson, M., Gedman, G. L., Cantin, L. J., Thibaud-Nissen, F.,
Haggerty, L., Bista, I., Smith, M., \ldots{} Jarvis, E. D. (2021).
Towards complete and error-free genome assemblies of all vertebrate
species. \emph{Nature}, \emph{592}(7856), Article 7856.
https://doi.org/10.1038/s41586-021-03451-0

Schubert, M., Lindgreen, S., \& Orlando, L. (2016). AdapterRemoval v2:
Rapid adapter trimming, identification, and read merging. \emph{BMC
Research Notes}, \emph{9}(1), 88.
https://doi.org/10.1186/s13104-016-1900-2

Sedlazeck, F. J., Rescheneder, P., Smolka, M., Fang, H., Nattestad, M.,
von Haeseler, A., \& Schatz, M. C. (2018). Accurate detection of complex
structural variations using single-molecule sequencing. \emph{Nature
Methods}, \emph{15}(6), Article 6.
https://doi.org/10.1038/s41592-018-0001-7

Sibbesen, J. A. (2018, June 18). \emph{Filtering ·
bioinformatics-centre/BayesTyper Wiki}. GitHub.
https://github.com/bioinformatics-centre/BayesTyper

Sibbesen, J. A., Maretty, L., \& Krogh, A. (2018). Accurate genotyping
across variant classes and lengths using variant graphs. \emph{Nature
Genetics}, \emph{50}(7), 1054--1059.
https://doi.org/10.1038/s41588-018-0145-5

Talenti, A., Powell, J., Hemmink, J. D., Cook, E. a. J., Wragg, D.,
Jayaraman, S., Paxton, E., Ezeasor, C., Obishakin, E. T., Agusi, E. R.,
Tijjani, A., Marshall, K., Fisch, A., Ferreira, B. R., Qasim, A.,
Chaudhry, U., Wiener, P., Toye, P., Morrison, L. J., \ldots{}
Prendergast, J. G. D. (2022). A cattle graph genome incorporating global
breed diversity. \emph{Nature Communications}, \emph{13}(1), Article 1.
https://doi.org/10.1038/s41467-022-28605-0

Theissinger, K., Fernandes, C., Formenti, G., Bista, I., Berg, P. R.,
Bleidorn, C., Bombarely, A., Crottini, A., Gallo, G. R., Godoy, J. A.,
Jentoft, S., Malukiewicz, J., Mouton, A., Oomen, R. A., Paez, S.,
Palsbøll, P. J., Pampouile, C., Ruiz-López, M. J., Secomandi, S.,
Svardal, H., Theofanopoulou, C., Vries, J., Waldvogel, A., Zhang, G.,
Jarvis, E. D., Bálint, M., Ciofi, C., Waterhouse, R. M., Mazzoni, C. J.,
\& Höglund, J., (2023). How genomics can help biodiversity conservation.
\emph{Trends in Genetics,}. https://doi.org/10.1016/j.tig.2023.01.005

Tigano, A., Jacobs, A., Wilder, A. P., Nand, A., Zhan, Y., Dekker, J.,
\& Therkildsen, N. O. (2021). Chromosome-Level Assembly of the Atlantic
Silverside Genome Reveals Extreme Levels of Sequence Diversity and
Structural Genetic Variation. \emph{Genome Biology and Evolution},
\emph{13}(6). https://doi.org/10.1093/gbe/evab098

Todesco, M., Owens, G. L., Bercovich, N., Légaré, J.-S., Soudi, S.,
Burge, D. O., Huang, K., Ostevik, K. L., Drummond, E. B. M., Imerovski,
I., Lande, K., Pascual-Robles, M. A., Nanavati, M., Jahani, M., Cheung,
W., Staton, S. E., Muños, S., Nielsen, R., Donovan, L. A., \ldots{}
Rieseberg, L. H. (2020). Massive haplotypes underlie ecotypic
differentiation in sunflowers. \emph{Nature}, \emph{584}(7822),
602--607. https://doi.org/10.1038/s41586-020-2467-6

Veltman, C. J. (1996). Investigating causes of population decline in New
Zealand plants and animals: Introduction to Symposium. \emph{New Zealand
Journal of Ecology}, \emph{20}(1), 1--5.
https://www.jstor.org/stable/24053728

vonHoldt, B. M., Shuldiner, E., Koch, I. J., Kartzinel, R. Y., Hogan,
A., Brubaker, L., Wanser, S., Stahler, D., Wynne, C. D. L., Ostrander,
E. A., Sinsheimer, J. S., \& Udell, M. A. R. (2017). Structural variants
in genes associated with human Williams-Beuren syndrome underlie
stereotypical hypersociability in domestic dogs. \emph{Science
Advances}, \emph{3}(7), e1700398. https://doi.org/10.1126/sciadv.1700398

Weissensteiner, M. H., Bunikis, I., Catalán, A., Francoijs, K.-J.,
Knief, U., Heim, W., Peona, V., Pophaly, S. D., Sedlazeck, F. J., Suh,
A., Warmuth, V. M., \& Wolf, J. B. W. (2020). Discovery and population
genomics of structural variation in a songbird genus. \emph{Nature
Communications}, \emph{11}(1), Article 1.
https://doi.org/10.1038/s41467-020-17195-4

Wellenreuther, M., \& Bernatchez, L. (2018). Eco-Evolutionary Genomics
of Chromosomal Inversions. \emph{Trends in Ecology \& Evolution},
\emph{33}(6), 427--440. https://doi.org/10.1016/j.tree.2018.04.002

Wick, R. (2022). \emph{Porechop} (0.2.4) {[}C++; Ubuntu 18.04{]}.
https://github.com/rrwick/Porechop (Original work published 2017)

Williams, G. R. (1956). \emph{The Kakapo (Strigops habrotilus, Gray): A
review and appraisal of a near-extinct species}.

Wold, J., Koepfli, K.-P., Galla, S. J., Eccles, D., Hogg, C. J., Le Lec,
M. F., Guhlin, J., Santure, A. W., \& Steeves, T. E. (2021). Expanding
the conservation genomics toolbox: Incorporating structural variants to
enhance genomic studies for species of conservation concern.
\emph{Molecular Ecology}, \emph{30}(23), 5949--5965.
https://doi.org/10.1111/mec.16141

\selectlanguage{english}
\FloatBarrier
\end{document}

