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Leveraging an existing whole genome resequencing population dataset to characterize toll‐like receptor gene diversity in a threatened bird



Species recovery programs are increasingly using genomic data to measure neutral genetic diversity and calculate metrics like relatedness. While these measures can inform conservation management, determining the mechanisms underlying inbreeding depression requires information about functional genes associated with adaptive or maladaptive traits. Toll‐like receptors (TLRs) are one family of functional genes, which play a crucial role in recognition of pathogens and activation of the immune system. Previously, these genes have been analyzed using species‐specific primers and PCR. Here, we leverage an existing short‐read reference genome, whole‐genome resequencing population dataset, and bioinformatic tools to characterize TLR gene diversity in captive and wild tchūriwat'/tūturuatu/shore plover (Thinornis novaeseelandiae), a threatened bird endemic to Aotearoa New Zealand. Our results show that TLR gene diversity in tchūriwat'/tūturuatu is low, and forms two distinct captive and wild genetic clusters. The bioinformatic approach presented here has broad applicability to other threatened species with existing genomic resources in Aotearoa New Zealand and beyond.
Mol Ecol Resour. 2022;00:1–16.
Advances in high- throughput sequencing technologies and bio-
informatic tools are enabling scientists to generate and analyse
whole- genome resequencing data (Auwera et al., 2013 ; Ekblom &
Wolf, 2 014). This progress has largely increased the measurement
of neutral genetic diversity in nonmodel organisms often to inform
the conservation management of threatened species (Allendorf
et al., 2010; Angeloni et al., 2012; Primmer, 2009). However,
studies suggest that neutral genetic diversity may be a poor prox y
for functional diversity (Grueber et al., 2015; Marsden et al., 2013;
Sommer, 2005). Further, whereas neutral genetic diversity can be
used to measure inbreeding and assess inbreeding depression, de-
termining the genetic mechanisms that underlie inbreeding depres-
sion requires information about specific functional loci associated
with adaptive or maladaptive traits (Kohn et al., 2006; Mable, 2019;
Ouborg et al., 2010; Won et al., 2021). When whole- genome rese-
quencing data and a reference genome for a population of interest
Received: 10 January 2022 
Revised: 29 Ap ril 2022 
Accepted: 26 May 2022
DOI : 10.1111/1755-0998.13 656
1School of Biologica l Sciences, Universit y
of Canterbury, Chris tchurch, New Zealand
2Tea Break Bioi nformatic s, Ltd,
Palmerston North, New Zealand
3Department of Conservation,
Biodive rsity Group, Auckland, New
4Instit ute of Veterinar y, Animal, and
Biomedical Sciences, Wildbase, Massey
University, Palmerston North, New
Molly Magid, School of Biological
Science s, University of Cante rbury, 20
Kirkwood Avenue, Upper Riccarton,
Christchurch NZ 8140, New Zealand.
New Zealand Department of
Conser vation; New Zealan d Ministr y of
Business, Innovation and Employment
Endeavour Fund, Grant/Award Number:
UOCX1602; Fulbright New Zealand;
Ministry of Busine ss, Innovat ion and
Employment; University of Canterbury
 Paul A. Hohenlohe
Species recovery programs are increasingly using genomic data to measure neutral
genetic diversity and calculate metrics like relatedness. While these measures can
inform conservation management, determining the mechanisms underlying inbreed-
ing depression requires information about functional genes associated with adaptive
or maladaptive traits. Toll- like receptors (TLRs) are one family of functional genes,
which play a crucial role in recognition of pathogens and activation of the immune
system. Previously, these genes have been analysed using species- specific primers
and PCR. Here, we leverage an existing short- read reference genome, whole- genome
resequencing population data set, and bioinformatic tools to characterize TLR gene
diversity in captive and wild tchūriwat’/tūturuatu/shore plover (Thinornis novaeseelan-
diae), a threatened bird endemic to Aotearoa New Zealand. Our results show that TLR
gene diversity in tchūriwat’/tūturuatu is low, and forms two distinct captive and wild
genetic clusters. The bioinformatic approach presented here has broad applicability to
other threatened species with existing genomic resources in Aotearoa New Zealand
and beyond.
conservation genomics, immune genes, shore plover, toll- like receptors, whole- genome
This is an op en access ar ticle under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction
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© 2022 The Authors. Molecular Ecology Resources published by John Wiley & Sons Ltd.
 MAGID et Al.
already exists, bioinformatic tools may be used to analyse these data
to characterize functional genetic diversity (Hoelzel et al., 2 019;
Kohn et al., 2006).
There is a growing interest in characterizing the functional di-
versity of immune genes within intensively managed species to im-
prove outcomes and reduce the burden of wildlife disease (Morris
et al., 2015; Zhu et al., 2020). Low immune gene diversity increases
susceptibility to a variety of pathogens (Spielman et al., 2004).
Inbreeding leads to an overall decrease in genetic diversity at im-
mune genes, loss of rare and potentially advantageous alleles, and
a decreased ability to adapt to novel or rapidly evolving pathogens
(Altizer et al., 2003; Spielman et al., 200 4). Research from both
wild and laboratory populations show that inbreeding contributes
to an increased parasite load, a greater susceptibility to pathogens,
a higher likelihood that individuals will act as disease reservoirs,
and ultimately, higher rates of mortalit y due to disease (Acevedo-
Whitehouse et al., 2003; Ross- Gillespie et al., 2007; Whitehorn
et al., 2011; Whiteman et al., 2005). Further, increased immune gene
diversit y may help populations respond more rapidly to pathogens
(Bonneaud et al., 2012; Davies et al., 2021).
Toll- like receptor (TLR) genes are an innate immune gene family
involved in pathogen recognition and immune response (Acevedo-
Whitehouse & Cunningham, 2006; Grueber et al., 2012; V inkler &
Albrecht, 2009). TLRs are an ancient part of the immune system,
present in nearly all multicellular organisms (Singh et al., 2003).
They are located either on the cell membrane or intracellularly on
the membrane of lysosomes or endosomes within innate immune
or somatic cells (Behzadi et al., 2021; Takeda & Akira, 2005). TLRs
recognize conserved pat terns of pathogens by binding residues,
known as antigens, from pathogens that enter the body (Singh
et al., 2003). Pathogen recognition by TLRs is necessary for the
proper activation and direction of the adaptive immune response
(Chen et al., 2021; Clark & Kupper, 2005; Kawasaki & Kawai, 2014;
Pasare & Medzhitov, 2005; Xia et al., 2021). Studies have found that
TLR gene diversity tends to be low in small, highly threatened pop-
ulations compared to large populations of species of least concern
(Alcaide & Edwards, 2 011; Dalton et al., 2016; Knafler et al., 2017;
Morrison et al., 2020). In previous studies, having higher TLR gene
diversit y has been associated with greater disease resistance, sur-
vival, and reproduction (Antonides et al., 2019; Bateson et al., 2016;
Davies et al., 2021; Heng et al., 2011; Podlaszczuk et al., 2021;
Quéméré et al., 2021). Having a robust TLR gene diversity within a
population allows for selection of the most beneficial variant(s) to
effectively deal with pathogens. Also, immune gene diversity may
not correlate with neutral measures of genome- wide diversity, so
directly characterizing TLR genes will explicitly reveal how diver-
sity at innate immune genes are affected in highly threatened avian
populations (Grueber et al., 2015; Hartmann et al., 2014; Marsden
et al., 2013).
To date, TLR gene diversity has been mostly characterized using
species- specific TLR primers and PCR protocols to perform SNP
genotyping of individuals in a population (Allendorf et al., 2010;
Dalton et al., 2016; Grueber et al., 2015; Lara et al., 2020; Morrison
et al., 2020). However, these approaches can be expensive and time-
consuming. When whole- genome resequencing data and a refer-
ence genome for the species of interest already exists, researchers
can use bioinformatic tools to analyse these existing resources to
identify TLR genes and characterize TLR gene diversity (Hoelzel
et al., 2019; Wang et al., 20 05).
TLR genes may be more suited for identification using bioinfor-
matic tools, in comparison to other immune genes like MHC, be-
cause they are relatively conserved and gene duplications are rare
and well- defined (Gr ueber et al., 2015). The TLR protein is composed
of three protein domains: the extracellular binding domain, trans-
membrane protein, and intracellular toll/interleukin 1 (TIR) signalling
domain (Yilmaz et al., 2005). There is mostly conserved evolution
and synonymous substitutions within TLR sequences, and the ma-
jority of variation is within the region coding for the extracellular
binding protein, which is what comes into contact with antigens
(Werling et al., 2009). The binding domain has a pattern of leucine-
rich repeats, and so is also known as the leucine- rich repeat (LRR)
domain (Alcaide & Edwards, 2011). Inserts of lecuines within the LRR
domain may impact pathogen recognition (Offord et al., 2010), and
single nucleotide polymorphisms (SNPs) within this region can also
affect the binding affinity of TLRs (Keestra et al., 2008; Matsushima
et al., 20 07). In contrast, the TIR signalling domain is largely con-
served across the TLR family, and phylogenetically among related
species (Beutler & Rehli, 2002; Narayanan & Park, 2015; Yilmaz
et al., 2005). The implications of these protein structures are two-
fold: (1) the pattern of repeats within the LRR domain and conserved
TIR domain are ideal for bioinformatic identification due to the simi-
larity of sequences within taxonomically related species, and (2) the
sequences within the LRR region are variable and may contain SNPs
that are adaptive for the recognition of par ticular pathogens.
Advances in bioinformatic tools utilizing comparative genom-
ics and sequence similarity comparison and the growth of online
repositories and databases like NCBI with extensive genomic data
that includes functional genes from a diversity of species, together
facilitate the identification of functional genes within the genomes
of nonmodel organisms (Feng et al., 2020; Grueber, 2015; Ren
et al., 2021). However only a few studies to date have used bioinfor-
matic tools to identify functional genes and characterize functional
genetic diversity within a whole- genome resequencing population
data set (Brandies et al., 2020; Zhang et al., 2 014). Previous research
on avian TLRs have used tools such as NCBI Basic Local Alignment
Search Tool (blast) to design primer sequences to amplify these genes
(Chávez- Treviño, 2017; Grueber et al., 2015; Morrison et al., 2020)
and to compare sequences among closely- related species (Raven
et al., 2017), so using established methods for primer design may
also help in the identification of TLR genes (Yilmaz et al., 2005). A
study on immune gene diversity in Tasmanian devils used bioinfor-
matics to characterize diversity of several immune genes previously
identified through a combination of transcriptome analysis and
comparative genomes (Morris et al., 2015). Research using genomes
from the B10K consortium highlights the efficacy of comparative
genomics to identify orthologues and conser ved regions using
MAGID et A l.
bird species in the same taxonomic class (Feng et al., 2020; Zhang
et al., 2014). Increasingly, immune gene databases are available to
identif y and annotate immune genes within specific taxonomic
groups (Grueber, 2015; Mueller et al., 2020; Wong et al., 2 011).
These resources provide an avenue for characterizing immune gene
diversity in nonmodel organisms.
Tchūriwat’/tūturuatu/shore plover (Thinornis novaeseelandiae)
is a threatened shorebird endemic to Aotearoa New Zealand
(Robertson et al., 2021). Tchūriwat’/tūturuatu is thought to have
been widespread on the coastal areas of mainland Aotearoa,
but populations decreased dramatically during the 19th cen-
tury because of introduced mammalian predators (Davis, 1994).
Tchūriwat’/tūturuatu was eventually extirpated from the main-
land and the remaining wild population is now restricted to the
Chatham Islands archipelago (Davis, 1994). The current wild popu-
lation totals 234 birds and is mostly confined to a single predator-
free island, Hokorereoro/Rangatira (Department of Conservation,
Shore Plover Recovery Group herein DOC SPRG). A conservation
breeding program was established between 1991– 1996 with eggs
brought from Hokorereoro/Rangatira and was augmented in 2003
by a single adult male from Western Reef (Dowding et al., 2005).
At the time of this study, the captive population had not been aug-
mented since 2003 and the number of individuals in the captive
population was 41. Based on the pedigree of the captive popula-
tion, individuals were highly related to one another (average mean
kinship = 0.119) and mean inbreeding was relatively high (0.0 48)
(SPRG, unpublished data).
Captive birds are also highly vulnerable to contracting avian
pox (DOC SPRG). Avian pox is caused by avipoxvirus (APV), a large
dsDNA virus with hundreds of strains that can infect a wide range
of bird species (Bolte et al., 1999; Boyle, 20 07). Infection with APV
causes lesions on the body at the site where it enters the skin and
on feather- free areas of the body. APV is generally transmitted
through insect vectors, but if there is a break in epithelial integ-
rity, it can also spread through contact with an infected bird or via
contact with shared objects like feeders used by infected birds
(Hansen, 1999). In rare instances, APV can also be transmitted
through the inhalation of viral particles (Hansen, 1999). Aspects
of captive breeding programmes like shared aviaries and resources
make them particularly vulnerable for the spread of infection (van
Riper & Forrester 2007).
In addition to being highly transmissible within a captive setting,
avian pox infections in captive birds are often severe, can last for
several months, and may never resolve (DOC SPRG). In contrast,
wild tchūriwat’/tūturuatu contract mild avian pox infections which
are most often cleared in 1– 2 weeks (DOC SPRG). Longer infection
periods for captive birds allow pox lesions more time to develop,
making it more likely that birds will contract secondar y bacterial in-
fections which increase the severit y of and rate of mor tality from
APV (Alley & Gartrell, 2019; Gartrell et al., 2002; Hansen, 1999; Weli
& Tryland, 2011). While death from the virus remains low, the sever-
ity of infection is high. Also, chicks and juveniles are more likely to
contract APV (DOC SPRG), and without a fully developed adaptive
immune system, the innate immune system may be especially im-
portant for young birds in fighting off poxvirus infections (Fellah
et al., 2008; Palacios et al., 2009). Protecting juveniles from infec-
tion is important for the timely release of young birds into the wild
(Dowding, 2013, DOC SPRG).
Ongoing research in an aligned project based on approximately
50 K single nucleotide polymorphisms (SNPs) generated using a
reduced- representation sequencing approach shows that wild and
captive tchūriwat’/tūturuatu populations are genetically distinct (I.
Cubrinovska, unpublished data). These SNPs reflect genome- wide
diversit y, but may also be indicative of differences at the gene level,
including functional genes like TLRs that contribute to the immune
response to poxvirus. If the wild population– which appears to be
less susceptible to avian pox (DOC SPRG)– has higher immune gene
diversity, then genetic rescue of the captive population through
augmentation with wild individuals may improve future disease
Sourcing individuals from genetically diverse or different pop-
ulations is recognized as a way to improve both genome- wide di-
versity and immune gene diversity (Glassock et al., 2021; Grueber
et al., 2017; Heber et al., 2013; McLennan et al., 2020). Leveraging
existing genomic resources and bioinformatic tools, we identify
TLR genes and characterize TLR gene diversity in captive and wild
tchūriwat’/tūturuatu. In addition to informing future research to de-
termine whether a recent translocation of tchūriwat’/tūturuatu from
the wild to captivity has increased TLR gene diversit y and an asso-
ciated immune response to the avipox infection, the bioinformatic
approach presented here is broadly applicable to threatened birds
with existing genomic resources.
 | 
Originally generated for an aligned project (see Benefit- sharing
statement), we used short- read sequence data for 66 tchūriwat’/
tūturuatu (39 captive, 27 wild), including a wild male named Maui
that was chosen for the tchūriwat’/tūturuatu reference genome.
Blood samples for captive birds were collected during routine
health checks by Brett Gartrell and Isaac Conservation and Wildlife
Trust staff. Blood samples for wild birds were collected as part of
the 2018/2019 breeding season juvenile banding trip to Maung’ Rē/
Mangere and Hokorereoro/Rangatira in the Chathams Island ar-
chipelago. Blood was taken from juveniles and adults and stored in
lysis buffer for shipment. All samples were subsequently stored at
−0°C until extraction. High quantity and quality DNA was extracted
using a tailored lithium chloride extraction method (Galla, 2019).
Extractions were assessed for quality by running 2 μl of DNA on a
2% agarose gel. A Qubit 2.0 fluorometer (Fisher Scientific) was used
for DNA quantification. Blood samples and DNA extractions are
stored at the University of Canterbury on behalf of the Shore Plover
Recovery Programme.
 MAGID et Al.
Libraries were prepared with the Nextera DNA Flex Library
Prep Kit according to the manufacturer's specifications and se-
quenced across one lane of an Illumina Novaseq 6000 to achieve
an average coverage depth of approximately 10× (Galla et al.,
2020), excluding the individual used for the reference genome
which was sequenced to an average depth of approximately 100×.
Libraries for 21 individuals that had low depth of coverage were
subsequently resequenced as above to achieve an average cover-
age depth of at least 10×.
 | 
fastqc version 0.11.8 (FastQC, 2015) was used to evaluate the qualit y
of the raw Illumina data and assess potential sample contamination.
Initial read trimming was performed using trimgalore version 0.6.6
(Kreuger, 2021), using pair- end mode, a minimum length of 54 bp, and
with the - - nextseq two- colour chemistry option. A kmer abundance
plot was created using jellyfish 2.3.0 (Marçais & Kingsford, 2011)
with a kmer length of 31 prior to assembly to assess heterozygo-
sity and contamination. The genome was assembled in two stages
using two assemblers, masurca version 3.3.4 (Zimin et al., 2013), and
meraculous version 2.2.6 (Goltsman et al., 2017 ). While the assem-
blies with MaSuRCA give the best contiguity, the assembler can have
problems with two- colour chemistry Illumina data when finding runs
of low- quality bases or reads with a high proportion of “G” bases (De-
Kayne et al., 2020). In those cases, the assembler tends to create
regions where the consensus is simply a string of “G” bases and/or
unknown “N” bases, as a result of both the error- correction module
and the superread creation during the assembly process. Meraculous
is much less prone to these artefact s due to their agnostic approach
to error correction, which relies on coverage and bubble resolution
in de Bruijn graphs, at the expense of contiguity when compared to
MaSuRCA. Thus, both assemblers were combined.
In the first stage, MaSuRCA was used to create the main as-
sembly, using the trimmed reads padded with low quality “N”
bases to a uniform length of 150 bp. Parameter adjustments in-
clude a grid batch size of 500,000,0 00, a Jellyfish hash size of
8,00 0,000,000, use of the Celera assembler for the final step,
and the inclusion of scaffold gap closing. All other parameters
were set to default for nonbacterial Illumina assemblies. For
Meraculous, a kmer size of 61 with a minimum depth cutoff of five
was selec ted, and the assembly was run on Diploid mode 1 (to
create a single haploid reference). Finally the Meraculous assem-
bly was used to correct the ambiguities and gaps in the MaSuRCA
assembly by first aligning both genomes using Last version 980
(Kielbasa et al., 2011), filtering for contigs with low quality regions
(due to the presence of poly- C/poly- G regions right next to a po-
ly- N region) using awk, then using the Perl
script available at en/SemHe lpers. Th e
hardware used for the assembly was a 12- core, 24- thread work-
station with 128 Gb of RAM. Assembly took 5 days for MaSuRCA
and 10 h for Meraculous.
 | 
Figure 1 provides an overview of the methods used for TLR iden-
tification and characterization. We used a virtual machine (VM;
16 vCPUs, 9 TB of memory, 128 GB of R AM) in the host Research
Compute Cluster (RCC) at the University of Canterbur y.
 | 
2.4.1  |  blast alignment
To identify TLR genes, we used NCBI Basic Local Alignment
Search Tool (blast), given its prior use to design primer se-
quences that amplify TLR genes (Chávez- Treviño et al., 2017;
Yilmaz et al., 2005) and previous comparisons of TLR sequences
among closely- related avian species (Mueller et al., 2020; Raven
et al., 2017). We used bird TLR nucleotide (BLASTn) and protein
(tBLASTn) sequences from close relatives and/or species with
high quality genome assemblies to search for similar nucleotide
sequences within the reference genome for tchūriwat’/tūturuatu
(Madden, 2013). Unlike other analyses that focused solely on the
most variable LRR binding region, we chose to identify the en-
tire sequence for each TLR, because it provided additional con-
firmation that the whole TLR was captured. For inclusion in our
search through the tchūriwat’/tūturuatu reference genome, TLR
sequences were either characterized for other species in the labo-
ratory through targeted amplification and sequencing, or identi-
fied using the NCBI genome annotation pipeline (Table 1).
BLAST uses an algorithm to map input query sequences to the
most similar region within the tchūriwat’/tūturuatu genome. If
BLAST is able to map the input quer y sequence to a region or re-
gions within the database, it outputs the region(s) as a list. The list
is ordered by the expect value (e- value) of each alignment, which is
the number of alignments with a similar score that are expected by
chance. The lower the e- value of the alignment, the less likely that
the alignment is due to chance and the more likely it is that the
alignment reflects a biological similarity between the sequences
(NCBI, 2020). In addition to the e- value, the percent quer y cover
and percent identity of the alignment help the user to judge the
quality of each alignment. The percent query cover is percent of
the quer y sequence that is aligned to a database sequence, out of
the whole length of the query sequence, regardless of the iden-
tity of the bases within a sequence. The percent identity is how
many of the bases within the query sequence alignment match the
bases within the database sequence, out of the whole length of
the query sequence. The closer both of these percentages are to
100%, the greater the likelihood that the alignment is biologically
To further ensure that agreement between the query se-
quence and individual target sequences was not due to chance,
two strategies were implemented: (1) a comparative approach
MAGID et A l.
 Work flow schematic to show how bioinformatic tools and genomic resources are used to identify and characterize TLR genes
in threatened birds. See text for details. bcf, BIM collaboration format; LRR, leucine rich repeat; ORF, open reading frame; SNP, single
nucleotide polymorphism; TLR, toll- like receptor. All remaining abbreviations refer to names of bioinformatic tools utilized
TLR Identification
Genomic Resources Bioinformatic tools Workflow
Short-read reference
genome, reference
species TLR sequences,
reference species
2. ORF finder
3. LRRsearch
4. SMART protein
TLR Characterisation
Genomic Resources Bioinformatic tools Workflow
Short-read reference
genome, short-read
population resequencing
5. FastQC
6. TrimGalore
7. Burrows-Wheeler
aligner (bwa)
8. SAMtools
9. BCFtools
10. VCFtools
11. Geneious
12. Beagle
13. DNAsp
14. PopART
15. Adegenet
To analyse protein sequence and visualise protein structure,
use LRR finder and SMART protein visualiser (3, 4)
To identify preliminary TLR sequences, align reference
species TLR sequences to reference genome, use BLASTn
and tBLASTn (1)
To perform reciprocal best hit test, use (1) to align
preliminary TLR sequences against well-annotated reference
species genomes
To identify cross-alignment and false positives, use (1) to
align preliminary TLR sequences against short-read
reference genome
To find open reading frames and transcribe species TLR
sequences, use ORF Finder (2)
To phase SNPs for haplotype analysis, use Beagle (12) and
to produce consensus files for each allele within an
individual, use (9, 10)
To concatenate individual bcf files into a population vcf file
use (9) and to filter SNPs for the file, use BCFtools (9) and
VCFtools (10)
To trim raw resequencing reads, use TrimGalore (6), and to
analyse quality of trimmed reads, use (5)
To align trimmed resequencing reads to the reference
genome use bwa (7), and to produce alignment files for
downstream analyses, use SAMtools (8)
To run mpilutp call SNPs at the population level through
comparison to the reference genome, use BCFtools (9)
To analyse synonymous and non-synonymous SNPs,
use Geneious (11)
To calculate haplotypes statistics, use DNAsp (13), to create
haplotype networks use PopART (14), and to create a PCA
use Adegenet (15)
To analyse quality of resequencing files, use FastQC (5)
 MAGID et Al.
seeking agreement among multiple species' TLR sequences align-
ing to a sp ecific reg ion in th e tch ūriwat’/tū tur uat u genome; an d (2)
the reciprocal best hit (RBH) test. In the first instance, if the same
region in the tchūriwat’/tūturuatu genome was being mapped to
with the same type of TLR gene from different bird species, there
was greater confidence in that region. In the second, the RBH test
is an approach that has been used within comparative genomics to
confirm the identity of orthologs in nonmodel species using well-
annotated reference genomes (Kristensen et al., 2011). This limited
the test to just chicken and zebra finch genomes, since both are
high- qualit y genome assemblies and are well- annotated. This test
involves taking the “best hit” region identified within tchūriwat’/
tūturuatu using the TLR sequence from a reference species and
BLASTing it against the genome of that reference species (Irizarry
et al., 2016). If the “best hit” of this BLAST search matches the
original reference TLR, it provides greater support that the genes
are orthologs of one another (Kristensen et al., 2011). Each pre-
liminary TLR sequence identified in tchūriwat’/tūturuatu passed
the RBH test, mapping to the original chicken and zebra finch ref-
erence TLR sequences within each genome. This helped to con-
firm not only that a TLR gene was likely identified, but also that
the specific TLR gene of interest had been located (i.e., a BLAST
search with preliminar y TLR3 sequence brought up only the TLR3
sequence in reference species).
After conducting these tests of gene alignment, each pre-
liminar y TLR sequence was BLASTed against the remainder of
the tchūriwat’/tūturuatu genome to find whether there was
alignment of this sequence to other regions within the genome.
This test was conducted to determine whether there were
nontarget sequences within the tchūriwat’/tūturuatu genome
that may align to these TLR sequences during the process of
whole- genome alignment. There was little cross- alignment of
one type of TLR mapping to other TLR genes (e.g., TLR1A refer-
ence aligning to the TLR3 region). When this did happen, it was
often within pairs of duplicated TLRs ( TLR1A & TLR1B, TLR2A
& TLR2B), and it was only a partial alignment (at most 50%),
so these cross alignment s were easy to distinguish from the
true alignment. False positives were also investigated within
the genome, to find out whether there were TLR sequences
aligning to other, non- TLR regions. There were no false positive
sequences within the tchūriwat’/tūturuatu genome. Based on
these tests, the Burrows- Wheeler Aligner version 0.7.17 (bwa)
(Li & Durbin, 2009) with a default maximum mismatch value
of 4% for read alignments (allowing only 4% of bases to differ
in identity within sequence alignments) would be sufficient to
prevent the cross- alignment of duplicated TLRs.
2.4.2  |  Protein analysis
Once the preliminary TLR regions passed these quality control
measures, each sequence was entered into the NCBI Open Reading
Frame (ORF) finder ( nder). This
tool searches for reading frames within the query DNA sequence
and transcribes it into a protein sequence with each possible reading
frame. The result is a graphic showing the protein sequence result-
ing from each reading frame that is used for transcription (Wheeler
et al., 2003). Then we used BL ASTp searches with the result ant pro-
tein as the input sequence and the BLA ST protein database as com-
parison sequences. When the correct reading frame for the protein
was identified, the search would bring up the reference TLR protein
sequences that we had originally aligned with.
Two additional web applications were used to examine TLR
protein sequences and investigate the protein products of TLR
genes. The first was LRRfinder (Of ford et al., 2010), which uses a
database of toll- like receptor TLR sequences acquired from NCBI
to identif y the LRR regions within a TLR (LRRs, LRRNT, LRRCT,
transmembrane protein, TIR signalling domain). The search tool
focuses on predicting potential LRR regions, because this region
is the most variable and may affect the binding specificity and af-
finity of TLRs (Keestra et al., 2008; Matsushima et al., 2007). All
searches revealed multiple LRR regions within the sequences, a
transmembrane protein region in almost all sequences, and a TIR
domain in all sequences. Also the LRR identifications matched the
   
Killdeer (Charadrius vociferus)Family Charadriidae NCBI genome annotationa
Black- headed Gull
(Chroicocephalus ridibundus)
Order Charadriiformes Targeted amplification and
Chicken (Gallus gallus)Class Aves Targeted amplification and
Zebra Finch (Taeniopygia
Class Aves NCBI genome annotation
excluding targeted
amplification and
sequencing of TLR4d
aThe NCBI Eukaryotic Genome Annotation Pipeline, e/annot
ation_euk/proce ss/.
bPodlaszc zuk et al. (2021).
cYilmaz et al. (2005).
dVinkler et al. (2009).
 Reference species for toll- like
receptor ( TLR) identification in tchūriwat’/
tūturuatu, listed alongside the taxonomic
group shared with tchūriwat’/tūturuatu.
For each referece species, TLR sequences
used for comparison were either identfied
through the use of the NCBI annotation
pipelinea or through amplification and
sequencing of targeted TLRs in the
MAGID et A l.
type of TLR gene they were from (i.e., all LRRs for TLR3 matched
TLR3 LRRs in reference species).
To further visualize these protein products, we used the sim-
ple modular architecture research tool (smart version 9.0) (Letunic
et al., 2021; Schult z et al., 1998). SMART contains a protein data-
base that uses markov modes to identify protein domains within an
input protein sequence by calculating the expected value (e- value)
SWise score, which is the output of the established SWise protein
search algorithm (Birney et al., 1996), for each alignment between
the quer y sequence and sequences in the SMART database (Letunic
et al., 2021; Schult z et al., 1998). The protein domains assigned
with low e- values are less likely to be assigned by chance, and at
a predetermined e- value threshold, the protein domain will appear
as a visual block on a 2D schematic of the protein. This visualiza-
tion revealed whether we had captured all expected protein do-
mains within the TLR protein sequences and had not missed parts
of the sequence. This also facilitated a visual comparison between
the tchūriwat’/tūturuatu TLR protein schematics to the TLR SMART
protein schematics in chicken (Temperley et al., 2008).
 | 
2.5.1  |  Population resequencing
alignment and analysis
Once TLR regions were identified within t he genome, whole- genome
alignment of population resequencing data to the tchūriwat’/
tūturuatu reference genome was performed to characterize TLR SNP
diversit y. To assess read quality, fastqc version 0.11.9 (FastQC, 2015)
was run to identify low quality regions and baseline quality scores,
and then trim galore version 0.6.5 (Kreuger, 2021) was used to trim
Illumina paired end 150 bp reads and remove low quality reads. The
two- colour chemistr y option was chosen, which supports trimming
and removal of low- quality sequences of non- G bases and improves
the removal of low quality G's by removing them regardless of their
quality (Kreuger, 2021).
Trimmed reads were aligned to the reference genome with
bwa version 0.7.17 (Li & Durbin, 2009). samtools version 1.10. (Li
et al., 2009) was used to sort BAM files prior to SNP discovery.
A custom perl script (“”) (Moraga, 2018)
was used to split the bam files into chunks that could be pro-
cessed more quickly with bcftools version 1.11 (Li et al., 2009).
bcftools mpileup was run with annotations GT, PL, DP, SP, ADF,
ADR, AD to allow for downstream filtering. vcftools version 0.1.16
(Danecek et al., 2011) was used to filter the data set as follows:
minor allele frequency (maf ) ≥0.05, Phred- score (quality) >20,
max- missingness = 0.90, minimum depth >5, minGQ >10, and
maximum depth <200. b cftools was also used to filter the vcf for
strand bias using the parameter of strand- bias adjusted Phred-
score <60. Hardy– Weinberg equilibrium filtering was not applied
because two assumptions are likely to be violated in this study
(no selection and random mating). In addition, vcfto ols was used
to examine the site and individual depth and missingness and to
remove individuals with low depth and high missingness from fur-
ther analysis (n = 1 captive individual). Final SNPs were analysed in
Geneious Prime 2020 (https://www.genei to determine
whether they made synonymous or nonsynonymous changes to
TLR protein products. Nonsynonymous SNPs were further inves-
tigated in Geneious to analyse the amino acid change that resulted
from each SNP. Then, a preliminary analysis of nonsynonymous
SNPs was conducted to determine which physiochemical changes
that might occur due to the change in amino acid identity, using the
well- defined physicochemical attributes of charge and polarity.
beagle version 5.2 (Browning et al., 2021) was used with default
settings to phase haplotypes for both populations. Each TLR gene
was phased separately by phasing along the whole contig that con-
tained that TLR gene. For each individual, genotype sequences were
generated using bcftools. To increase quality and decrease errors
within haplotypes, sites with low depth and high strand bias were
set to missing using bcftools +setgt with options - i ‘FORMAT/DP<5’
and ‘FORMAT/SP>60’. Then, the filtered VCF was used to produce
TLR consensus sequences for each individual in the population. sam-
tools faidx was used to target each TLR region in the genome, and
bcftools consensus was used with parameters - M to output any miss-
ing genotypes as “N”, and - H 1pIu and 2pIu to produce both phased
haplotypes for an individual. If individuals had any sites within the
TLR sequence where they were missing a genot ype, they were not
included in the haplotype analysis for that TLR gene. While the
accepted standard for SNP analysis is that nonsynonymous SNPs
have significant influence on the functionality of a protein, synon-
ymous SNPs may also change protein function (Sauna & Kimchi-
Sarf aty, 2011), and within TLR genes, synonymous SNPs have been
associated with disease resistance and immune response (Cho
et al., 2013; Junjie et al., 2012). Given this, both synonymous and
nonsynonymous SNPs were used in the construction of haplotypes.
The resulting TLR consensus sequence for each individual was
output into a population mega file for either the captive or wild
population, and the process was repeated for every polymorphic
TLR gene. The resulting mega files were imported into dnasp v. 6.12
(Rozas, 2017) and were analysed to calculate haplotype diversity,
nucleotide diversity (𝛑), and Tajima's D. Haplotype nexus files for
each TLR were also created in DNAsp and imported into popart
(Leigh & Br yant, 2015) to construct minimum- joining haplotype net-
works. We also visualized differences in TLR haplotypes between
populations with a PCA. Adegenet (Jombart, 2008) was used to con-
struct the PCA with scaled mean haplotype frequencies for all TLR
genes, using code adapted from the introductory tutorial to ade-
genet (Jombart, 2015).
Nine TLRs were identified within the tchūriwat’/tūturuatu genome:
TLR1A, TLR1B, TLR2A, TLR2B, TLR3, TLR4, TLR5, TLR7, and TLR21.
A partial sequence of the TLR15 gene was identified within the
 MAGID et Al.
genome, but was not included in the analysis. The protein schematic
structure for each TLR identified is shown in Figure 2.
There were 28 SNPs in the TLR genes of the captive tchūri-
wat’/tūturuatu population (n = 38) ( Table 2). The SNPs were
unevenly distributed among TLR genes, with two that were mono-
morphic (TLR2B, TLR 21), three with one SNP (TLR1A , TLR1B,
TLR2A), one with two SNPS (TLR4), one with three SNPs (TLR3),
one with 7 SNPs (TLR5), and one with 13 SNPs (TLR7). Out of the
 SMART protein schematics for each tchūriwat /tūturuatu toll- like receptor identified (available via licence: CC BY 2.0). Key
shows the visual representation for each protein domain. The pink boxes are areas of low compositional complexity
 Tchūriwat’/tūturuatu toll- like receptor gene diversit y statistics and comparisons for captive (n = 38) and wild (n = 26)
tchūriwat’/tūturuatu populations
 
    𝛑 
              
TLR1A 2289 (761) 1 (0:1) 3 (2:1) 1 (0:1) 1 (0:1) 3 (2:1) 1 (0:1) 2 (35) 3 (25) 1 0.00011 0.00042 0. 248 0.483 0 .244 03 0.91554
TLR1B 1857 (592) 1 (1:0) 1 (1: 0) 1 (1:0) 1 (1:0) 1 (1:0) 1 (1:0) 2 (35) 2 (25) 2 0.00024 0.00023 0.437 0.429 1.37017 1.23557
TLR2A 2423 (806) 1 (1:0) 0 0 1 (1:0) 0 0 2 (37) 1 (26) 10.00018 00.425 01.4606 0
TLR2B 2156 (717) 01 (1 :0) 0 0 0 0 1 (38) 2 (26) 1 0 0.0001 00.216 00.0466
TLR3 2367 (783) 3 (3:0) 2 (2:0) 2 (2:0) 1 (1 :0) 1 (1: 0) 1 (1: 0) 4 (38) 3 (26) 20.00045 0.00029 0.478 0.521 0.79912 0.46712
TLR4 22 59 (752) 2 (1:1) 1 (1:0 ) 1 (0:1) 1 (0:1) 0 0 3 (38) 2 (24) 20.00027 0.00013 0.537 0.284 0. 7749 6 0.35171
TLR5 2585 (860) 7 (4:3) 8 (4:4) 7 (4:3) 4 (2:2) 4 (2:2) 4 (2:2) 3 (35) 4 (23) 3 0.00126 0.00146 0. 551 0. 673 3.08884* 2.8 9296*
TLR7 3141 (1045 ) 13 (4:9) 13 (4:9) 13 (4:9) 13 (4:9) 13 (4:9) 13 (4:9) 6 (38) 6 (26) 60.000206 0.00206 0.665 0.6 81 4.0516 4* 3.69 096*
TLR21 16 49 (548) 0 0 0 0 0 0 1 (38) 1 (26) 1 0 0 0 0 0 0
Total Private 3 (2:1) 4 (3:1) 2 (1 :1) 2 (2:0) 5 5
Tot al 28 (14:14) 29 (15:14) 25 (11:14) 22 (9:13) 22 (10:12 ) 20 (8 :12) 24 24 19
Note: As terisks denote signific ance at p < .01.
Abbreviations: bp(aa), base pairs (amino acids); syn:nsyn, ratio of synonymous to nonsynonymous SNPs.
MAGID et A l.
total SNPs, half (14) were synonymous, and the remaining half (14)
were nonsynonymous. The majority of SNPs (22) were within the
LRR binding domain. The majority of nonsynonymous SNPs (12)
were also located in the LRR region and half of them (7) caused a
physicochemical change due to the change in identity of the amino
acid (Table 3). A total of 24 haplot ypes were observed across all
TLR genes in the captive population, with the TLR genes that
have higher SNP diversity also having a higher number of inferred
haplotypes (Table 2). Tajima's D was nonsignificant for most loci,
except for TLR5 and TLR7, which were both significantly positive
(p< .01).
A total of 29 SNPs were observed in the TLR genes of the wild
tchūriwat’/tūturuatu population (n = 26) (Table 2). There were two
monomorphic genes (TLR2B, TLR 21), three with one SNP each
(TLR1B, TLR2A, TLR4), one with two SNPS (TLR3), one with three
SNPs (TLR1A), one with eight SNPs (TLR5), and one with 13 SNPs
(TLR7). Out of the total SNPs, slightly more than half (15) were syn-
onymous, and the remaining 14 were nonsynonymous. The majority
of SNPs (22) were within the LRR binding domain. The majorit y of
nonsynonymous SNPs (12) were also located in the LRR region and
half of them (7) caused a physicochemical change due to the change
in identit y of the amino acid (Table 3). A total of 24 haplotypes
were obser ved across all TLR genes in the wild population (Table 2).
Tajima's D was nonsignificant for most loci, except for TLR5 and
TLR7, which were both significantly positive (p< .01).
The wild and captive populations of tchūriwat/tūturuatu both
had low overall TLR SNP and haplotype diversity. Each had a similar
ratio of nonsynonymous to synonymous SNPs. Across all loci, the
populations shared 25 SNPs and 19 haplotypes (Table 2). The cap-
tive population had three private SNPs and five private haplotypes
in four genes (TLR1A, TLR2A, TLR3, TLR4). The wild population had
four private SNPs and five private haplot ypes in four genes (TLR1A,
TLR2B, TLR4, TLR5). Haplot ype networks reveal there are closely
related haplotypes for each TLR gene, and that many of the TLR hap-
lotypes shared between captive and wild tchūriwat’/tūturuatu occur
at different frequencies with each population (Figure S1). The PC A
reveals two relatively distinct genetic clusters corresponding to wild
and captive populations (Figure 3).
The bioinformatic approach developed here was successful for
identif ying TLR genes and characterizing TLR gene diversity in
tchūriwat’/tūturuatu. This research provides a critical first step to-
wards using TLR gene diversity to inform conservation action for a
threatened Aotearoa New Zealand endemic bird. We were able to
identif y and charac terize the same number of or more TLR genes
as compared to similar studies done using amplicon sequencing
of TLRs within threatened species (Dalton et al., 2016; Grueber
et al., 2015; Morrison et al., 2020). Fur ther, as the generation of
whole- genome resequencing data for threatened species becomes
routine (Allendor f et al., 2010 ; Forcina & Leonard, 2020), identify-
ing and characterizing TLRs using existing genomic resources will
be more cost effective and more efficient than traditional amplicon
All complete TLRs identified display the structure of TLR pro-
teins in other species: a region of leucine rich repeats (the bind-
ing domain), then a transmembrane protein (carboxyl- terminal
tail) where the TLR sits within the cell or lysosome membrane,
and then the TIR (toll- like/interleukin receptor) signalling domain
(Kannaki et al., 2010; Yilmaz et al., 2005). While LRRs vary in num-
ber between TLR sequences, the overall structure and approximate
amount of LRRs within each type of TLR is similar to what is seen in
chicken TLRs (Temperley et al., 2008). A partial sequence of TLR 15
was identified, but this sequence did not include the TIR signalling
 Tchūriwat/tūturuatu toll- like receptor gene diversit y statistics and comparisons for captive (n = 38) and wild (n = 26)
tchūriwat/tūturuatu populations
 
    𝛑 
              
TLR1A 2289 (761) 1 (0:1) 3 ( 2:1) 1 (0:1) 1 (0 :1) 3 (2:1) 1 (0 :1) 2 (35) 3 (25) 1 0.00011 0.00042 0.248 0.483 0 .24403 0.91 554
TLR1B 1857 (592) 1 (1:0) 1 (1:0) 1 (1:0) 1 (1:0) 1 (1:0) 1 (1:0) 2 (35) 2 (25) 2 0.0 0024 0.00023 0.437 0.429 1.370 17 1.23557
TLR2A 2423 (806) 1 (1:0) 0 0 1 (1:0) 0 0 2 (37 ) 1 (26) 10.00018 00.425 01.4606 0
TLR2B 2156 (717) 01 (1 :0) 0 0 0 0 1 (38) 2 (26) 1 0 0.0001 00.216 0−0.0466
TLR3 2367 (783) 3 (3:0) 2 (2:0) 2 (2:0) 1 (1 :0) 1 (1: 0) 1 (1: 0) 4 (38) 3 (26) 20.00045 0.00029 0.478 0.521 0.79912 0.46712
TLR4 22 59 (752) 2 (1:1) 1 (1:0 ) 1 (0:1) 1 (0:1) 0 0 3 (38) 2 (24) 20.00027 0.00013 0.537 0.284 0. 7749 6 0.35171
TLR5 2585 (860) 7 (4:3) 8 (4:4) 7 (4:3) 4 (2:2) 4 (2:2) 4 (2:2) 3 (35) 4 (23) 3 0.00126 0.00146 0. 551 0. 673 3.08884* 2.8 9296*
TLR7 3141 (1045 ) 13 (4:9) 13 (4:9) 13 (4:9) 13 (4:9) 13 (4:9) 13 (4:9) 6 (38) 6 (26) 60.000206 0.00206 0.665 0.6 81 4.0516 4* 3.69 096*
TLR21 16 49 (548) 0 0 0 0 0 0 1 (38) 1 (26) 1 0 0 0 0 0 0
Total Private 3 (2:1) 4 (3:1) 2 (1 :1) 2 (2:0) 5 5
Tot al 28 (14:14) 29 (15:14) 25 (11:14) 22 (9:13) 22 (10:12 ) 20 (8 :12) 24 24 19
Note: As terisks denote signific ance at p < .01.
Abbreviations: bp(aa), base pairs (amino acids); syn:nsyn, ratio of synonymous to nonsynonymous SNPs.
 MAGID et Al.
domain. The sequence ended prematurely at the end of a contig, so
the scaf folding of the short- read genome may have prevented iden-
tification of the full gene. Morris et al. (2015) encountered a similar
problem identifying immune gene sequences when they were frag-
mented or split between contigs in the genome assembly.
In nonpasserine avian species, there are most often 10 avian TLRs,
eight of which are orthologous to other ver tebrate TLRs (TLR1A/B,
TLR2A/B, TLR3, TLR4, TLR5, TLR7), one that is orthologous to bony
fish and Xenopus (TLR21), and one that is unique to reptiles and birds
(TLR15) (Alcaide & Edwards, 2011; Grueber et al., 2014). Studies of
avian TLR evolution show a pattern of both gene loss and duplication
in these regions (Kannaki et al., 2010 ; Temperley et al., 2008; Velová
et al., 2018). Recent research suggests there is also a duplication of
TLR7 in some avian taxa. The duplicated TLR7 is thought to have
a similar function, though with slight difference, and this is an area
of ongoing research (Raven et al., 2017). To date, it has been found
in Charadriiformes, Cuculiformes, Mesiornithiformes, and some
Passeriiformes (Velová et al., 2018). Tūturuatu are within the order
Charad riiformes, and t his species is likely to h ave a duplication of TLR7
and thus have 11 TLRs total (Raven et al., 2017; Velová et al., 2018).
 Principal component
analysis genetic clustering of captive
(n = 38) and wild (n = 26) tchūriwat’/
tūturuatu. Produced using scaled mean
allele frequencies for each individual (see
text for details)
 Amino acid analysis of nonsynonymous SNPs within TLR genes of captive and wild tchūriwat’/tūturuatu populations
 
  
 
TLR 1A Bacteria, liproproteins 503 LRR As p/His Charge Captive, Wild
TLR 4 LPS, gram negative bacteria 372 LRR Asn/Asp Charge Captive
TLR 5 Flagellin 9LRR Phe/Leu None Captive, Wild
326 LRR Val/I le Charge Wild
664 TIR Hi s/Arg None Captive, Wild
843 TIR Lys/G lu Charge Captive, Wild
TLR7 ssRNA, virus 4LRR Ala/Pro None Captive, Wild
79 LRR Th r/Il e Polarity Captive, Wild
100 LRR Met /Leu None Captive, Wild
411 LRR Leu/Phe None Captive, Wild
459 LRR Gln/His Charge Captive, Wild
465 LRR Ala/Val None Captive, Wild
467 LRR Glu/Gly Charge, Polarity Captive, Wild
469 LRR Asn/Ser None Captive, Wild
755 LRR Gln/Lys Charge Captive, Wild
Abbreviations: AA, amino acid, standard abbreviations for amino acids used; LRR, leucine rich repeat binding domain; SNPs, single nucleotide
polymorphisms; TIR, TIR signalling domain; TLR- toll- like receptor.
aAlcaide and Edwards (2011).
MAGID et A l.
However, we did not find evidence of the duplication of TLR7
within tchūriwat’/tūturuatu. After identifying the first gene coding
for TLR7, this region was removed from the genome and the BLAST
search with reference TLR7 sequences was repeated with the re-
mainder of the genome. This second search was done to ensure
that the reference TLR sequence would not align to the previously
identified TLR7 region. There was no additional alignment to other
sequences in the genome. It is possible that the duplication of TLR7
within the reference genome is not fully resolved, meaning the du-
plicated genes are collapsed into one region. Alternatively, the du-
plicated region may be incomplete with gaps in the sequence or it
may be of low quality, so that it is either partially or fully cut out of
the assembly. In the case of a low quality or par tial assembly, the
duplicated region would not be easily identified through the use
of BLAST. In either case, only one TLR7 gene was identified. If the
duplication does exist and is of low quality within the tchūriwat’/
tūturuatu genome, this means the population resequencing data
may not properly align to the low- qualit y TLR7 gene and may instead
align to the other TLR7 gene. In this case, SNPs found within TLR7
may not be true SNPs, but instead may be artefacts of the duplicated
TLR7 reads misaligned to this region.
Although it was not possible to make direct comparisons to
all birds with TLR data, our findings are consistent with the lower
TLR gene diversity found within small, isolated populations of
other threatened species (African penguins (Spheniscus demer-
sus); Dalton et al., 2016; Mohua (Mohoua ochrocephala) Grueber
et al., 2015; Orange- bellied parrot (Neophema chrysogaster);
Morrison et al., 2020).
Comparing captive and wild tchūriwat’/tūturuatu reveals that
both populations have low TLR gene diversity. These findings are
consistent with Dalton et al. (2016) which show that both captive
and wild populations of African penguins (Spheniscus demersus) have
low levels of TLR SNP diversity. Unlike tchūriwat’/tūturuatu, captive
African penguin populations have a lower number of nonsynonymous
SNPs compared to wild populations (Dalton et al., 2016). However,
it is also important to note that there is a dif ference in sample size
between the captive and wild tchūriwat’/tūturuatu populations, be-
cause the captive population (n = 38) has more samples than the wild
population (n = 26). The inclusion of more wild individuals may re-
veal additional TLR diversity, especially if there are rare TLR SNPs or
haplotypes in the wild population. Both captive and wild populations
have nonsynonymous SNPs that mostly fall within the LRR binding
region, which may have an effe ct on binding aff inity and TLR response
(Keestra et al., 2008; Mat sushima et al., 2007). Additionally, some of
the nonsynonymous SNPs do cause physiochemical changes in the
amino acid sequence, but further research is needed to determine
how these SNPs may influence functionality of TLR proteins.
In captive and wild populations, genetic diversity is unevenly dis-
tributed across TLR genes. Most TLR genes have a low number of
SNPs and haplotypes. SNP diversity within TLR7 was the highest of
all TLR genes. However, this result may not reflect the true diversity
of TLR7 within each population, if there is a TLR7 duplication that
is not well resolved within the reference genome. Both captive and
wild tchūriwat’/tūturuatu populations also had two TLRs (TLR5 and
TLR7) with a value of Tajima's D that was significant and positive,
indicating that rare alleles are scarce. The positive, significant values
may reflect a rapid decrease in population size and/or balancing se-
lection (Tajima, 1989). Since this result was the same for both wild
and captive populations, it may reflect the overall history of species
decline, given that the size and distribution of the species decreased
dramatically in a shor t amount of time after mammalian predators
were introduced (Davis, 1994) and has remained small and restricted
for more than 100 years (DOC SPRG). Nevertheless, it is also possi-
ble that this result reflects balancing selection on one or both TLRs
(but see below).
The finding that captive and wild tchūriwat/tūturuatu fall into
two relatively distinct genetic clusters based on TLR haplotype di-
versity is consistent with a PCA based on 50 K SNPs (I. Cubrinovska,
unpublished data). While most alleles are shared bet ween captive
and wild populations, the frequencies of these alleles differ between
each population, and there are a small number of private alleles in
both populations. Given that there has been little to no genetic ex-
change between the populations for decades, these differences can
most likely be attributed to genetic drift. Previous research in other
bird species has also found genetic differentiation in both neutral
(microsatellites) and TLR loci, which generally indicates that genetic
drift may be stronger than selection (Gonzalez- Quevedo et al., 2015;
Grueber et al., 2013; Knafler et al., 2016). However, it is possible that
selection at TLR loci may contribute to the genetic differentiation be-
tween populations (Grueber et al., 2013; Knafler et al., 2016, 2017 ).
Further analysis is nece ssary to determine the impac ts of genetic drif t
and selection on TLR gene diversity in tchūriwat/tūturuatu. In the
meantime, low overall TLR gene diversity combined with the genetic
differentiation of captive and wild tchūriwat/tūturuatu lends support
to the recent decision to augment the captive population with birds
from the wild. Future research will investigate how this augmentation
affects TLR gene diversity in the captive population and whether it
leads to increased immune response in captive individuals.
Below we highlight a few technical considerations that will influence
the broad applicability of our bioinformatic approach to other bird
species. If the species reference genome is relatively incomplete,
it may not be suf ficient for identifying all TLR genes. Undertaking
strategies to improve completeness of the genome may help, includ-
ing alignment of multiple assemblies to fill in sequencing gaps. It may
also be necessary to undertake deliberate sequencing strategies
to maximize depth and minimize missingness in resequencing data.
For example, if preliminary screening of resequencing data shows
low depth and high missingness, it may be necessary to invest in
resequencing of relevant libraries. Also, if there are individuals who
are particularly important, it may be beneficial to (re)sequence them
to greater depth. Further, we acknowledge that our approach was
used to identify TLR genes and characterize TLR gene diversity in a
 MAGID et Al.
threatened bird species, so it is unknown whether it may be useful
in other species with more complex genomes or heterozygous popu-
lations. Nevertheless, advancing technologies such as long- read
sequencing may provide opportunities to apply our approach to spe-
cies with different levels of heterozygosity and genome complexity
(Bayer et al., 2020; Pollard et al., 2018; Tettelin et al., 20 05). Despite
these caveats, we reiterate that if a whole- genome resequencing
population data set is readily available for a species of interest, these
genomic resources can be used to provide a first look at TLR genes
and TLR gene diversity. Further, even if TLR gene identification and
characterization is incomplete or wholly unsuccessful, a bioinfor-
matic approach is likely to produce useful information about TLR
regions that can inform targeted sequencing of TLR genes (e.g., even
partial information about TLR sequences can inform primer design).
We acknowledge Hokotehi Moriori Trust and Ngāti Mutunga o
Wharekauri, especially Susan Thorpe and Gail Amaru, for their guid-
ance and support. We also thank the students of Kaingaroa and Te
One for selecting Maui as the individual used for the tchūriwat’/
tūturuatu reference genome. We are grateful to all members and
associates of the the New Zealand Department of Conser vation
Shore Plover Recovery Group (formerly Specialist Group), including
Troy Makan (DOC), Kate McInnes (DOC), Rose Collen (DOC), John
Dowding (DM Consultants), Anne Richardson, and Leigh Percasky
(Isaacs Conservation and Wildlife Trust), and Todd Jenkinson and
Mireille Hicks (Pūkaha National Wildlife Centre). Many thanks also to
Francois Bissey for access to the University of Canterbur y's Research
Compute Cluster, Natalie Forsdick and Levi Collier- Robinson for as-
sistance with bioinformatic scripts, and to Stephanie Galla for out-
reach support on Rēkohu/Wharekauri. This research was funded by
the New Zealand Ministr y of Business, Innovation and Employment
Endeavour Fund (UOCX1602 awarded to TES), the New Zealand
Department of Conservation (contract awarded to TES) and Fulbright
New Zealand (Fulbright US Graduate Award awarded to MM). Open
access publishing facilitated by University of Canterbury, as part of
the Wiley - University of C anterbur y agreement via the Council of
Australian University Librarians.
Author declare no conflicts of interest.
    
Scripts for TLR identification and resequencing pipeline have been
made available on GitHub ( ConSE RT/Magid_
et_al.git). Tchūriwat ’/tūturuatu are culturally significant to Moriori
and Māori (the Indigenous Peoples of the Chatham Islands archipel-
ago and Aotearoa New Zealand, respectively), all genomic data ob-
tained from species like tchūriwat’/tūturuatu is culturally significant
in its own right (Collier- Robinson et al., 2019). To ensure that the ac-
cessibility of these data are consistent with the CARE data principles
(Carroll et al., 2020, 2021), the short- read reference genome, unfil-
tered population vcf file, and associated metadata used in this study
will be made available from a local genome browser (http://www.
uccon on the recommendation of Hokotehi Moriori
Trust and Ngāti Mutunga o Wharekauri Iwi Trust.
Tchūriwat’/tūturuatu/shore plover is an endemic shorebird clas-
sified as Nationally Critical according to the New Zealand Threat
Classification System criteria (Robertson et al., 2021). This cocreated
research is part of a long- standing research par tnership between the
Conservation Systematics and Evolution Research Team (ConSERT,
including TES, MM, IC) and Aotearoa New Zealand's Department of
Conser vation Shore Plover Recovery Group, which is a team of con-
servation scientists and prac titioners (including DH, BG, TES) that
provide guidance regarding the development and execution of the
Shore Plover Recovery Plan to ensure the recovery of tchūriwat’/
tūturuatu in the wild. This research was also conducted in collabo-
ration with Hokotehi Moriori Trust, Ngāti Mūtunga o Wharekauri
Iwi Trust, and the local community on Rēkohu/Wharekauri in the
Chatham Island archipelago. For example, following engagement
with both trusts regarding the selection of an individual bird for
the tchūriwat’/tūturuatu reference genome, IC and Stephanie Galla
visited the two schools on Rēkohu/Wharekauri: They first visited
Kaingaroa School (one class, Years 1– 8) and asked students to name
both candidate birds for the reference genome. Then they visited
Te One School (three classes, Years 1– 2, Years 3– 5, Years 6– 8), and
asked students to vote on whether “Kina” or “Maui” should be used
for the reference genome, and Maui won. MM also developed an im-
mune system outreach module that highlights our ongoing research
with tchūriwat’/tūturuatu that TES subsequently shared with imi
and iwi, and the principals of both schools during a recent visit to
Molly Magid
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