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\begin{document}

\title{Genome-wide target capture baits for endangered shark species}



\author[1]{Clara Isabel Wagner}%
\author[2]{Marvin Choquet}%
\author[3]{Irina Smolina}%
\author[4]{Cathy S Jones}%
\author[1]{Leslie Noble}%
\author[3]{Galice Hoarau}%
\affil[1]{Nord universitet}%
\affil[2]{University of Oslo Faculty of Mathematics and Natural Sciences}%
\affil[3]{Nord Universitetet}%
\affil[4]{University of Aberdeen}%


\vspace{-1em}



  
  \date{May 21, 2024}


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\selectlanguage{english}
\begin{abstract}
More than 30\% of extant shark species are classified as threatened with
extinction, yet reliable population-level data is often rare, frequently
due to a lack of genomic resources. Here we present a new genome-wide
marker set for endangered shark species. We developed a target gene
capture bait set based on the transcriptome and genome of a Lamnid
shark, and tested it on 36 shark specimens, representing seven species
from three orders. Illumina read mapping and calling of Single
Nucleotide Polymorphisms (SNPs) showed high target recovery rates,
especially in the order Lamniformes, providing several thousand detected
biallelic SNPs in each species tested. Our results show this marker set
can be used for SNP-calling in a broad range of shark species, enabling
detailed population assessments and other ecological and evolutionary
studies.%
\end{abstract}%



\sloppy


C. Isabel Wagner\textsuperscript{1}*, Marvin Choquet\textsuperscript{2},
Irina Smolina\textsuperscript{1}, Catherine S. Jones\textsuperscript{3},
Leslie R. Noble\textsuperscript{1} \& Galice Hoarau\textsuperscript{1}*

Author affiliations:

\textsuperscript{1}Faculty of Biosciences and Aquaculture, Nord
University, Norway; \textsuperscript{2}Natural History Museum of Oslo,
Norway; \textsuperscript{3}School of Biological Sciences, University of
Aberdeen, United Kingdom

*Corresponding authors:

C. Isabel Wagner, Nord University, Universitetsall\selectlanguage{ngerman}éen 11, 8026 Bodø,
Norway, isabel.wagner@nord.no

Galice Hoarau, Nord University, Universitetsalléen 11, 8026 Bodø,
Norway, galice.g.hoarau@nord.no

\section*{Abstract}

{\label{abstract}}

More than 30\% of extant shark species are classified as threatened with
extinction, yet reliable population-level data is often rare, frequently
due to a lack of genomic resources. Here we present a new genome-wide
marker set for endangered shark species. We developed a target gene
capture bait set based on the transcriptome and genome of a Lamnid
shark, and tested it on 36 shark specimens, representing seven species
from three orders. Illumina read mapping and calling of Single
Nucleotide Polymorphisms (SNPs) showed high target recovery rates,
especially in the order Lamniformes, providing several thousand detected
biallelic SNPs in each species tested. Our results show this marker set
can be used for SNP-calling in a broad range of shark species, enabling
detailed population assessments and other ecological and evolutionary
studies.

\section*{Keywords}

{\label{keywords}}

Target gene capture, sharks, elasmobranchs, conservation genomics,
population genomics

\section*{Introduction}

{\label{introduction}}

More than 30\% of the over 500 extant shark species are currently
classified globally as Vulnerable, Endangered, or Critically Endangered
(Dulvy et al. 2021; Fricke et al. 2023). Moreover, shark conservation
often suffers from inaccurate assessments, mostly coming from
non-systematic observations such as sightings per unit effort, among
others (\emph{e.g.} Pacoureau et al. 2021). Detailed molecular
assessments can contribute to shark conservation by assessing
population-level characteristics such as population structuring,
population sizes, local genetic, and adaptive potential to environmental
change (reviewed in Hohenlohe et al. 2021). However, in the case of
molecular studies relevant to shark conservation, research for many
species still relies on small parts of the mitochondrial genome, and/or
nuclear microsatellite DNA (\emph{e.g.} Hoelzel 2001; Hoelzel et al.
2006; Veríssimo et al. 2010; Thorburn et al. 2018; González et al. 2020;
Lieber et al. 2020). Both types of marker have limitations, both
biological and technical (see \emph{e.g.} Teske et al. 2018; Choquet et
al. 2023).

Such limitations can be overcome by use of genome-wide markers such as
Single Nucleotide Polymorphisms (SNPs, \emph{e.g.} Cairns et al. 2023;
Choquet et al. 2023). Genome wide SNPs can be derived in various ways,
such as from whole genome resequencing (\emph{e.g.} Foote et al. 2016;
Lamichhaney et al. 2017), providing the widest range of genomic
information possible, or from reduced representation methods
(\emph{e.g.} Baird et al. 2008). The latter is ideally suited in cases
where a high-quality genome is not available, the genome of interest is
too large for cost effective re-sequencing at sufficient depth for the
required number of individuals, or when computational resources are
limited. Only thirteen nuclear shark genomes have been published to date
(Read et al. 2017; Hara et al. 2018; Marra et al. 2019; Weber et al.
2020; Zhang et al. 2020; Rhie et al. 2021; Nishimura et al. 2022; Sayers
et al. 2022; Stanhope et al. 2023; Wagner et al. 2023) and are in
general large, with assembly sizes from 2.8~to~5.0~Gb; hence the
relevance of reduced representation methods.

Reduced-representation sequencing approaches popular in conservation
studies include, but are not limited to, restriction site associated DNA
(RAD) sequencing (Miller et al. 2007) and target gene capture (TGC,
Gnirke et al. 2009). Methods like RAD sequencing have been applied to
several shark species (\emph{e.g.} Devloo-Delva et al. 2019; Domingues
et al. 2022; Nikolic et al. 2023). However, while the TGC approach
requires a closely related reference genome, it has the advantages of
higher quality data, potentially larger number of SNPs, and enriched
sequencing data for known genomic regions, which can be used to address
additional questions related to selection and adaptation (Barto\selectlanguage{polish}š \selectlanguage{english}et al.
2023). TGC approaches are particulary suited for species with
large/complex genomes for which RAD-seq are often not cost efficient
(\emph{e.g.} Choquet et al. 2019; Domingues et al. 2022). Furthermore,
TGC approaches show potential when used on museum specimens
(\emph{e.g.}Kollias et al. 2015; Agne et al. 2022).

To improve the availability of molecular resources for multiple shark
species, we present a new genomic TGC marker set, derived from nuclear
coding regions of the white shark genome (\emph{Carcharodon carcharias}
, Marra et al. 2019; Rhie et al. 2021), and show its utility in six
additional species (bull shark, \emph{Carcharhinus leucas} ,
tope,\emph{Galeorhinus galeus} , basking shark, \emph{Cetorhinus
maximus} , porbeagle, \emph{Lamna nasus} , shortfin mako, \emph{Isurus
oxyrinchus} , and spurdog, \emph{Squalus acanthias} ). These species
represent three different shark orders (Carcharhiniformes, Lamniformes
and Squaliformes), and are all considered threatened (Rigby et al.
2019a, b, 2021a, b, 2022; Finucci et al. 2020; Walker et al. 2020).



\section*{Material \& Methods}

{\label{material-methods}}

\subsection*{Bait development}

{\label{bait-development}}

For an overview over the whole bait development process, see Figure 1.

\subsubsection*{}

{\label{section-1}}

\subsubsection*{Transcriptome assemblies}

{\label{transcriptome-assemblies}}

To target expressed genomic regions relevant for investigating signature
of selection, genome-guided transcriptome assemblies were used for bait
design. Raw Illumina RNA-Seq data from blood, epidermis, subdermis,
muscle and heart tissue of the white shark (\emph{Carcharodon
carcharias} ) were obtained from Richards et al. (2013), Marra et al.
(2017) and Marra et al. (2019). The data was quality checked with FastQC
(v.~0.11.5, Babraham Bioinformatics 2010) and detected adapters trimmed
via Trimmomatic (v. 0.39, Bolger et al. 2014). Paired end (PE) data were
filtered for TruSeq3-PE-2 adapters in palindrome mode, minimum adapter
length of 1 bp and retention of both reads, with the addition of
TruSeq2-SE-adapter clipping for data obtained from Marra et al. (2019).
Single end (SE) data were filtered for TruSeq3-SE adapters.

Genome-guided transcriptome assemblies were constructed via Scallop
(Shao and Kingsford 2017) and StringTie (Pertea et al. 2015; Kovaka et
al. 2019). For assembly, RNA reads were aligned to the white shark
reference genome (Marra et al. 2019) in four data sets:
Specimen-specific and data type-specific (SE vs. PE), via the
splice-aware mapper STAR (v. 2.7.5a\_2020-06-29, Dobin et al. 2013).
Genome indices were generated with 150 bp splice junction overhang and
usage of the parent GFF attribute to assign the exons to the
transcripts. Sorted bam-files were then generated in basic two-pass
mode, including the XS field. Splice junction overhang was again 150 bp
for all data sets and the parent GFF attribute was used.

Transcripts of the individual, aligned data sets were assembled in
StringTie (v. 2.1.4, Pertea et al. 2015; Kovaka et al. 2019), and the
results were merged via StringTie's merge mode. Furthermore, Scallop (v.
0.10.5, Shao and Kingsford 2017) was used to assemble transcripts of
each aligned data set, using default options for unstranded library
types. The resulting four Scallop-assemblies were then merged via TACO
(v. 0.7.3, Niknafs et al. 2017), with no minimum length or expression
filtering applied.

Expressed exon sequences were extracted from gtf to fasta-format via the
software gffread (v. 0.12.1, Pertea and Pertea 2020). Assemblies were
then compared for quality via the tool rnaQUAST (v. 2.1.0, Bushmanova et
al. 2016), using the PE read data. Estimation of the presence of
Benchmarking Universal Single-Copy Orthologs (BUSCO) for vertebrates
(Seppey et al. 2019) were included in the analysis. Genes and
transcripts were not inferred in the process, as provided via the genome
annotation. After analysis, the Scallop/TACO-derived assembly was chosen
for further downstream analysis because the assembly covered more
annotated genes as well as vertebrate BUSCOs than the StringTie-derived
assembly.

\subsubsection*{Sequence selection for probe set
design}

{\label{sequence-selection-for-probe-set-design}}

To reduce the probability of inclusion of paralogues into the bait
design, multiple filtering steps were applied to the assembled
transcriptomes. For this, transcripts were aligned to the genome and to
themselves with minimap2 (v. 2.17-r941, Li 2018). First, the transcripts
were aligned to the genome in splice-aware mode, with parameters that in
theory allow for up to 33\%, but in practice for up to 15\%, sequence
divergence (personal communication by the software's authors).
Paralogous genes with 15\% divergence or less should therefore be
covered by alignments from the same transcript. Up to 1 Mio secondary
alignments to the genome were included to allow for hits to putative
paralogous genomic loci. In a second step, all transcripts were aligned
to each other, not allowing for splicing but instead for up to 20\% of
sequence divergence, and again including up to 1 Mio secondary hits to
catch as many paralogues as possible.

Continuing from the alignment output, sequences for the target capture
probe design were selected via a custom-written Python 3 script (v.
3.8.5, Rossum and Boer 1991). In a first step, potential paralogous
sequences were detected, to be later excluded from the probe set. First,
all transcripts that aligned to more than one locus in the genome were
marked as possible paralogues. Furthermore, two transcripts mapping to
each other were also marked as possible paralogues if the two
transcripts did not overlap in their mapping locus in the genome. If
one, or both of two transcripts mapping to each other, did not map to
the genome at all, both were declared as possible paralogues. Finally,
all transcripts that mapped to a potential paralog were themselves
marked as potential paralogues in a last, repetitive step.

Annotated genes were then selected to be included in the probe design if
they corresponded to a transcript, therefore being acknowledged as
expressed, but excluded if they corresponded to a transcript previously
identified as a potential paralogue, therefore excluding putative
paralogue gene copies. Next, to avoid genomic linkage between loci
included in our design, all selected genes from the same scaffold were
filtered for being located at least 250,000 bp apart. Then, sequences
were cut to their first 200 bases and sequences with two or more
consecutive missing nucleotides were discarded. 49 additional genes
annotated as part of a major histocompatibility complex (MHC) were
included in the bait set with their whole assembled nucleotide sequence
(Marra et al. 2019). Another four full genes suggested to be under
positive selection in some shark species were included, two of them
possibly involved in reproduction (\emph{VAMP4} and \emph{TCTEX1D2} )
and two in brain development (\emph{YWHAE} and \emph{ARL6IP5} ) (Swift
et al. 2016).

Baits were designed as a myBaits Custom DNA-Seq kit by Arbor Biosciences
(Ann Arbor, USA) with 80-mer probes and a tiling density of 3x, as
recommended for fresh as well as degraded DNA.

\subsection*{Capture probe testing}

{\label{capture-probe-testing}}

\subsubsection*{DNA extraction \& library
preparation}

{\label{dna-extraction-library-preparation}}

To test the efficiency of the designed baits, three individuals each of
bull sharks (\emph{Carcharhinus leucas} ), tope (\emph{Galeorhinus
galeus} ), porbeagle (\emph{Lamna nasus} ), shortfin mako (\emph{Isurus
oxyrinchus} ) and spurdog (\emph{Squalus acanthias} ), as well as nine
basking sharks (\emph{Cetorhinus maximus} ) and twelve white sharks
(\emph{Carcharodon carcharias} , Table 1 and Supplementary File~1) were
used.

All sharks were sampled either via muscle plugs or fin clips, except for
the basking sharks from which skin mucus swabs were taken. Genomic DNA
was extracted following a standard phenol-chloroform extraction approach
(Sambrook et al. 1989) or with the DNeasy Blood \& Tissue Kit (QIAGEN
N.V., Venlo, The Netherlands), including an over-night incubation step.
In the following, DNA was fragmented via sonication with a M220
Focused-ultrasonicator (Covaris, LLC, Woburn, USA) to a target peak
length of 350 bp and libraries were prepared using the NEBNext\selectlanguage{ngerman}® Ultra\selectlanguage{english}
II DNA Library Prep Kit for Illumina (New England Biolabs, Inc.,
Ipswich, USA) and 8 nucleotide long NEBNext(r) Multiplex Oligos for
Illumina (New England Biolabs, Inc., Ipswich, USA) with 530 ng as a
starting amount. Library quality and quantity was checked on an Agilent
Tape Station 4150 (Agilent Technologies, Inc., Santa Clara, USA). Three
target gene capture reactions were performed according to manufacturer's
high sensitivity hybridization protocol v. 5.01, by pooling twelve
individual per capture reaction and using equal starting amount of each
individual library within each capture reaction (namely 500 ng, 400 ng,
and 300 ng). The white shark samples were pooled in one single pool,
while the individuals of the other species were randomly mixed between
two pools, so that each reaction contained representatives of each of
the six species. Hybridization and wash temperatures were 60 degC to
account for bait- and target sequence divergence, and hybridization was
performed over 72 hours. The captured libraries were sequenced together
on an Illumina NextSeq 500 platform (Illumina, Inc., San Diego, USA)
with a 2x150 bp mid-output NextSeq 500/550 v. 2.5. kit (Illumina, Inc.,
San Diego, USA). After sequencing, reads were demultiplexed with
bcl2fastq (v. 2.20.0.422, Illumina, Inc., San Diego, USA).

\subsubsection*{Data analysis}

{\label{data-analysis}}

Sequencing data was analyzed for technical artefacts with FastQC (v.
0.11.9, Babraham Bioinformatics 2010). Poly-G tails were then trimmed
with cutadapt (v. 3.5, Martin 2011) via the option ``nextseq-trim'', and
a phred threshold of 20. Trimmomatic (v. 0.39, Bolger et al. 2014) was
then used in palindrome paired end mode to remove internally provided
TruSeq3-PE-2 adapters and low-quality base calls, with a maximum seed
mismatch count of 2, a palindrome clip threshold of 30, a simple clip
threshold of 10, and minimum detected adapter length threshold of 1.
Both reads were retained after adapter clipping. Reads were then cleaned
of low quality, with a phred threshold of 20 for clipping bases at both
read ends, and for trimming within a sliding window of 4 bp. All reads
with a minimum length of 36~bp were retained.

Read mapping, cleaning and Single Nucleotide Polymorphisms (SNPs)
calling was performed following a pipeline developed elsewhere (Choquet
et al. 2019; Choo et al. 2020), which was adapted to the needs of this
study. Reads were mapped to the white shark reference genome (NCBI
accession number GCF\_017639515.1, Rhie et al. 2021; Sayers et al. 2022)
using BWA-MEM (v. 0.7.17-r1188, Li 2013). Reads with ambiguous
alignments were then removed via SAMtools (v. 1.10, Danecek et al.
2021), with filtering for reads with multiple mapping loci (XA flag),
reads that produced chimeric alignments (SA flag), non-mapping reads,
reads with secondary or supplementary alignments, and duplicated reads
(flag 3332, ``UNMAP,SECONDARY,DUP,SUPPLEMENTARY''). In addition,
duplicate reads were removed and all remaining reads grouped into one
single read group via Picard (v. 2.26.6, Broad Institute 2019).

SNPs were then called and processed using the Genome Analysis Toolkit
(GATK, v. 4.2.3.0, McKenna et al. 2010), following the project's best
practice recommendations (Van der Auwera and O'Connor 2020) and sped up
using GNU Parallel (v. 20161222, Tange 2011). Variants were called for
each individual with HaplotypeCaller and afterwards combined for each
species separately with CombineGVCFs. Species-specific joined genotyping
was performed using the GenotypeGVCFs tool, allowing for a maximum of 18
alternative alleles. SNPs were then extracted with SelectVariants, and,
after visualization of quality metrics via the VariantsToTable tool and
R (v. 4.1.2, R Core Team 2019), hard filtered with VariantFiltration.
Lenient filtering thresholds were used, with one filter expression of
QualByDepth (QD) \textless{} 2.0, FisherStrand (FS) \textgreater{} 60.0,
RMSMappingQuality (MQ) \textless{} 50.0, MappingQualityRankSumTest
(MQRankSum) \textless{} -5.0 or ReadPosRankSumTest (ReadPosRankSum)
\textless{} -5.0, in accordance with parameters of previous studies by
our group (Choo et al., 2020; Choquet et al., 2019).

Afterwards, SNPs were filtered to retain only biallelic SNPs, using a
custom Python3 script (Supplementary File 2, Python3 v. 3.8.5, van
Rossum and de Boer 1991) and PLINK2 (v.~2.00a3, Chang et al. 2015) for
allele frequency computation. In addition, only SNPs present in at least
80\% of the genotypes were retained, as well as SNPs with a minimum read
coverage of~5, using VCFtools (v. 0.1.16, Danecek et al. 2011).

Furthermore, to allow for better comparison between data sets, SNP
numbers were calculated for target regions only. For this, the bait
sequences were aligned to the white shark reference genome from NCBI
(accession number GCF\_017639515.1, Rhie et al. 2021; Sayers et al.
2022) with Bowtie 2 (v. 2.4.5, Langmead and Salzberg 2012) in end-to-end
mode. All target loci with multiple alignments were then excluded
(``XS:i'' flag). The resulting coordinate set, produced with BEDTools
(v. 2.27.1, Quinlan and Hall 2010), was used in the filtering steps
described above to keep the detection of SNPs within the actual target
regions.

\section*{Results \& Discussion}

{\label{results-discussion}}

Our design approach resulted in a final bait set including 9,424 x 200bp
sequences as well as 53 full genes, 49 of them immuno-related and
annotated as part of a major histocompatibility complex (MHC, Marra et
al. 2019), two possibly involved in reproduction (\emph{VAMP4}
and\emph{TCTEX1D2} ) and two in brain development (\emph{YWHAE}
and\emph{ARL6IP5} ) (Swift et al. 2016). The cumulative length of the
bait set was 2,629,938 bp.

The baits were then tested on seven shark species (white
shark,\emph{Carcharodon carcharias} , bull shark\emph{, Carcharhinus
leucas} , tope \emph{Galeorhinus galeus} , basking shark,
\emph{Cetorhinus maximus} , porbeagle, \emph{Lamna nasus} , shortfin
mako, \emph{Isurus oxyrinchus} , and spurdog, \emph{Squalus acanthias}
). All 36 samples produced sequence data, however, one white shark
sample (``elasm-11022'') produced less than 100,000 reads (Supplementary
File 1 and 3) and was therefore excluded from further analysis. This was
one of the older, but not the oldest, sample included, however, the
tissue was from a dried specimen, lacquered with an unknown preservative
and afterwards exposed to direct sunlight for many years.

For analysis of bait efficiency and SNP calling, the target sequences
were reduced to single-copy genomic loci, amounting to 9128 bait
sequences. Coverage of the target region showed varying results across
the species analyzed (Figure 2). In the Lamniformes,\emph{C.~carcharias}
, \emph{L. nasus, I. oxyrinchus} and \emph{C. maximus} more than two
thirds of the target regions were recovered at 100\% (5x coverage,
averaged across samples, Table 2). These four species showed the highest
number of covered target regions compared to the rest of the species,
which showed a recovery rate of only \textasciitilde{} 1/3 of the target
regions (again 5x coverage, averaged across samples). Sequence
divergence between target genes in these species, or their
present/absence, has likely caused the varying amounts of capture
success. However, proportion of on-target read bases varied only
relatively slightly between clades (61.37\% - 65.97\% vs. 51.57\% \&
52.15\% -- 54.75\%, Table 2), indicating that though the individual bait
sequences show variation in their efficiency, overall the method works
successfully for all species included.

Indeed, SNP calling resulted in several tens of thousands of biallelic
markers per species (Table~3, whole genome lenient). The basking shark
reached the highest number with over 130,000 biallelic SNPs, whereas the
lowest number of SNPs \textless{} 36,000 recovered was in the tope. The
white shark ranged at the lower end of numbers of biallelic SNPs, with
40,243 SNPs detected. For all species, filtering SNPs for missing
genotype data as well as minimum read coverage reduced the SNPs numbers
to less than half their initial numbers. The white shark had the lowest
number of SNPs surviving this filtering process, with \textless{} 9,000
SNPs. The low SNP numbers in the white shark hint to a generally low
genetic diversity in this species, supporting recent findings (Stanhope
et al. 2023).

Excluding samples with low read numbers (see Table 3 for details) in
most cases resulted in lower biallelic SNP numbers for both the basking
shark and the white shark, however, excluding the failed white shark
sample ``elasm-11022'' resulted in an increase of SNP numbers for the
white shark after subsequent filtering steps, likely due to low read
coverage of detected SNPs in this sample.

In general, our SNP numbers are comparable, if not higher than in other
target gene capture approaches from our group (Choquet et al. 2019; Choo
et al. 2020, but see Choquet et al. 2023). Correlating the SNP numbers
with the genomic distance to the white shark reference genome, we see
that, if we exclude the white shark, SNP numbers are constant in a range
of 0.01 to 0.045, and drop slightly thereafter (Figure 3). However,
relative reduction in SNP numbers between species and between higher
level taxa were relatively low when compared to other target gene
capture experiments (Choo et al. 2020). This could be due to the fact
that the genomic distance of shark species included, even if only
roughly estimated here, is low. The three non-Lamniformes species show a
genetic distance of 5 -- 6\% from the white shark. Though this estimate
has to be accepted cautiously, as our approach favours genomic regions
of higher similarity to the reference genome, our finding shows that
genomic distance between the shark species investigated here is
extremely low compared to for example congeneric planktonic species
(Choo et al. 2020; Choquet et al. 2023).

We conclude that the marker set presented here can be used for all
species included, for studies of evolution, ecology, and conservation.
The markers' functionality across orders, together with the low
molecular evolutionary rates characteristic of sharks (Hara et al.
2018), suggest that they could also be used for a larger range of shark
species. Given the scarce genomic resources for the Selachii, it is
anticipated that such a ``universal'' shark genotyping method will be
extremely useful in future research efforts.

\section*{Acknowledgements}

{\label{acknowledgements}}

The authors would like to thank Klas Sundelin, for his support with
sampling as well as social outreach connected to this project.
Furthermore, we thank Ed Heist, Malcolm Francis, Matt Witt, James
Thorburn, Aletta van der Merwe, Simon Gulak, Hua-Hsun Hsu, Enrico
Gennari, Amanda Pardini, and Barry Bruce for providing samples. For the
purpose of open access, the author has applied a Creative Commons
Attribution (CC BY) licence to any Author Accepted Manuscript version
arising from this submission.

\section*{Funding}

{\label{funding}}

This project was funded by the Faculty of Biosciences and Aquaculture,
Nord University.

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\section*{Data accessibility and benefit
sharing}

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

The raw sequencing data for this project are deposited to NCBI, under
BioProject PRJNA990973.

The research described in this publication complies with relevant
national laws implementing the Convention on Biological Diversity and
Nagoya Protocol agreements.

\section*{Author contributions}

{\label{author-contributions}}

C.I.W., M.C., C.S.J., L.R.N. and G.H. designed the study, C.I.W. and
M.C. designed the baits, I.S. conducted the laboratory work, C.I.W.
analysed the data, C.I.W. drafted the manuscript, and all authors
contributed to the final manuscript version.

\section*{Tables and figures}

{\label{tables-and-figures}}\par\null

\textbf{Table 1} Sampling details and DNA sequencing output for seven
shark species. For sample details, see Supplementary File 1, for details
on sequencing output see Supplementary File 3. Shown are average numbers
per species and standard deviation. Initial reads were 151-bp paired-end
reads. Reads and bases that survived cleaning and filtering prior to and
after mapping (see Material and Methods) were taken as high quality
(``HQ''). Genome sizes were taken from Stingo et al. (1989), Hardie and
Hebert (2003), Marra et al. (2019) and Wagner et al. (2023). Genome
assembly size for Isurus oxyrinchus from Stanhope et al. (2023) was not
included in coverage calculations.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\textbf{Order} & \textbf{Species} & \textbf{Sample number} &
\textbf{Average read number +/- SD} & \textbf{Average HQ mapping reads
+/- SD} & \textbf{Average HQ mapping bases {[}bp{]} +/- SD} &
\textbf{Genome coverage (min. - max.)}\tabularnewline
\midrule
\endhead
Carcharhiniformes & \emph{Carcharhinus leucas} & 3 & 2,824,313 +/- &
1,089,363 +/- & 155,087,622 +/- &\tabularnewline
& & & 527,989.0 & 369,798.9 & 51,840,356.2 &\tabularnewline
Carcharhiniformes & \emph{Galeorhinus galeus} & 3 & 2,284,935 +/- &
880,157 +/- & 125,312,919 +/- &\tabularnewline
& & & 778,238.4 & 360,831.9 & 50,291,885.9 &\tabularnewline
Lamniformes & \emph{Cetorhinus maximus} & 9 & 11,213,894 +/- & 6,519,443
+/- & 917,639,540 +/- &\tabularnewline
& & & 3,592,108.4 & 2,948,894.6 & 413,378,751.8 &\tabularnewline
Lamniformes & \emph{Carcharodon carcharias} & 12 & 7,249,733 +/- &
3,862,613 +/- & 538,500,622 +/- & 0.003 - 0.403\tabularnewline
& & & 3,337,699.3 & 2,079,505.3 & 290,521,937.3 &\tabularnewline
Lamniformes & \emph{Lamna nasus} & 3 & 15,046,285 +/- & 7,965,032 +/- &
1,117,881,354 +/- &\tabularnewline
& & & 4,194,491.8 & 1,135,395.0 & 161,856,527.1 &\tabularnewline
Lamniformes & \emph{Isurus oxyrinchus} & 3 & 18,765,002 +/- & 9,730,458
+/- & 1,375,289,438 +/- & 0.273 - 0.957\tabularnewline
& & & 8,660,199.4 & 3,629,234.1 & 516,678,719.2 &\tabularnewline
Squaliformes & \emph{Squalus acanthias} & 3 & 2,577,878 +/- & 898,613
+/- & 126,639,857 +/- & 0.086 - 0.105\tabularnewline
& & & 227,339.0 & 164,917.4 & 23,445,855.6 &\tabularnewline
\bottomrule
\end{longtable}

\emph{}

\textbf{Table 2} Various efficiency metrics for a target capture
experiment in seven different shark species. For sample details see
Table 1 and Supplementary File 1.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\textbf{Order} & \textbf{Data set} & \textbf{Average \% on-target bases}
& \textbf{Average \% target regions recovered [?] 5x}\tabularnewline
\midrule
\endhead
Lamniformes & \emph{Carcharodon carcharias} & 65.92 &
90.01\tabularnewline
Lamniformes & \emph{Carcharodon carcharias} excl. elasm-11022 & 65.93 &
90.25\tabularnewline
Lamniformes & \emph{Carcharodon carcharias} excl. elasm-11022 \&
elasm-247 & 65.97 & 90.43\tabularnewline
Lamniformes & \emph{Isurus oxyrinchus} & 61.69 & 90.17\tabularnewline
Lamniformes & \emph{Lamna nasus} & 61.37 & 88.68\tabularnewline
Lamniformes & \emph{Cetorhinus maximus} & 61.63 & 85.81\tabularnewline
Lamniformes & \emph{Cetorhinus maximus} excl. elasm-1437 & 61.67 &
86.03\tabularnewline
Carcharhiniformes & \emph{Galeorhinus galeus} & 54.75 &
36.14\tabularnewline
Carcharhiniformes & \emph{Carcharhinus leucas} & 52.15 &
34.66\tabularnewline
Squaliformes & \emph{Squalus acanthias} & 51.57 & 33.43\tabularnewline
\bottomrule
\end{longtable}

\emph{}

\textbf{Table 3} Single Nucleotide Polymorphism (SNP) numbers for
several shark species (the white shark, Carcharodon carcharias, the bull
shark, Carcharhinus leucas, the tope, Galeorhinus galeus, the basking
shark, Cetorhinus maximus, the porbeagle, Lamna nasus, the shortfin
mako, Isurus oxyrinchus, and the spurdog, Squalus acanthias). SNPs were
called against the genome of C. carcharias, and filtered in three
subsequent steps. SNPs were counted either for the whole genome, or for
pre-selected target regions only. For sample information, see Table 1
and Supplementary File 1.

\textbf{Filtration step}

C. carcharias

\emph{C. carcharias} \textbf{excl.}

elasm-11022 \& elasm-247

\emph{C. Carcharias} \textbf{excl.}

elasm-11022

C. leucas

C. maximus

\emph{C. maximus} \textbf{excl.}

elasm-1437

G. galeus

I. oxyrinchus

L. nasus

S. acanthias

\textbf{Whole genome}

Biallelic SNPs

40,243

36,251

39,072

48,410

133,530

111,992

35,703

76,911

99,094

53,063

\textless{} 0.2 missing genotype

13,881

15,623

15,668

27,630

63,221

52,955

22,753

56,938

67,591

32,331

Minimum depth 5x

7,566

8,670

8,808

15,986

42,442

32,879

13,040

37,770

43,243

18,887

\textbf{Target regions only}

Biallelic SNPs

3,927

2,939

3,336

30,240

20,793

11,172

22,219

9,651

12,489

33,759

\textless{} 0.2 missing genotype

3,688

2,811

3,182

17,131

18,769

9,725

14,573

9,390

12,126

21,011

Minimum depth 5x

2,875

2,617

2,923

9,655

17,433

8,874

8,504

9,024

11,711

12,246

\subsubsection*{}

{\label{section-2}}

\textbf{Figure 1} Bait designing process for a target gene capture
project.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image15/image15}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image16/image16}

\end{center}
\end{figure}

\textbf{Figure 3} Numbers of genome-wide Single Nucleotide Polymorphisms
(SNPs) detected in seven shark species (white shark, Carcharodon
carcharias, bull shark, Carcharhinus leucas, tope, Galeorhinus galeus,
basking shark, Cetorhinus maximus, porbeagle, Lamna nasus, shortfin
mako, Isurus oxyrinchus, and spurdog, Squalus acanthias), visualized
against the species' genomic distance to the reference genome of C.
carcharias. Each data point represents one specimen (see Supplementary
File 1, but excluding sample ``elasm-11022''). Color according to
species' order (blue: Lamniformes; pink: Squaliformes; black:
Carcharhiniformes). Genomic distance was calculated via the cumulative
edit distance from the alignments used for SNP-calling, normalized for
the total alignment length. SNPs were biallelic, and filtered for
missing genotype data \textless{} 0.8 and a minimum depth of 5x.

\section*{Supplementary Materials}

{\label{supplementary-materials}}

\begin{itemize}
\tightlist
\item
  Supplementary File 1, Sample details.
  ``Supplementary\_File\_1\_Sample\_details.docx''
\item
  Supplementary File 2, Python3 script.
  ``Supplementary\_File\_2\_TGC\_SNPfiltering.py''
\item
  Supplementary File 3, Detailed information on DNA sequencing output.
  ``Supplementary\_File\_3\_Sequencing\_details.xlsx''
\end{itemize}

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

