Development of 16 novel EST-SSR markers for species identification and cross-genus amplification in sambar, sika, and red deer.

Deer genera around the globe are threatened by anthropogenic interference. The translocation of alien species and their subsequent genetic introgression into indigenous deer populations is particularly harmful to the species of greatest conservation concern. Products derived from deer, including venison and antler velvet, are also at risk of fraudulent labeling. The current molecular markers used to genetically identify deer species were developed from genome sequences and have limited applicability for cross-species amplification. The absence of efficacious diagnostic techniques for identifying deer species has hampered conservation and wildlife crime investigation efforts. Expressed sequence tag-simple sequence repeat (EST-SSR) markers are reliable tools for individual and species identification, especially in terms of cross-species genotyping. We conducted transcriptome sequencing of sambar (Rusa unicolor) antler velvet and acquired 11,190 EST-SSRs from 65,074 newly assembled unigenes. We identified a total of 55 unambiguous amplicons in sambar (n = 45), which were selected as markers to evaluate cross-species genotyping in sika deer (Cervus nippon, n = 30) and red deer (Cervus elaphus, n = 46), resulting in cross-species amplification rates of 94.5% and 89.1%, respectively. Based on polymorphic information content (>0.25) and genotyping fidelity, we selected 16 of these EST-SSRs for species identification. This marker set revealed significant genetic differentiation based on the fixation index and genetic distance values. Principal coordinate analysis and STRUCTURE analysis revealed distinct clusters of species and clearly identified red-sika hybrids. These markers showed applicability across different genera and proved suitable for identification and phylogenetic analyses across deer species.

Introduction

After Bovidae, the family Cervidae is the most diverse of the order Artiodactyla/Cetartiodactyla. Fifty-five species in this group have been recognized, which are broadly distributed throughout the Northern Hemisphere [1]. Regrettably, half of these species are listed as vulnerable, endangered, critically endangered or extinct in the wild by the International Union for Conservation of Nature (IUCN). Several wild species are suffering increased risks due to anthropogenic interference [2]. For instance, human-mediated translocation has given rise to allopatric hybridization across different species, such as the interbreeding of introduced Japanese sika deer (Cervus nippon) to Scotland with indigenous red deer (Cervus elaphus) [3].

Deer across Asia, Europe, and Oceania are often managed as economically important animals that are farmed for their antler velvet and meat (venison) [4]. Species hybridization can enhance antler velvet growth via a heterotic effect. This practice has resulted in the allopatric hybridization of deer species worldwide. Red deer are commonly crossed with sika deer to improve antler growth. In New Zealand, wapiti (Cervus canadensis) have also been crossed with red deer, resulting in the introgression of wapiti genes into farmed red deer populations [5]. This process can harm the perceived quality and integrity of the farming industry [5,6]. As a traditional Chinese medicine, the market price for antler velvet can vary depending on the source species [7]. Premium pricing and production demands can promote the adulteration of antler velvet [8]. Thus, this industry is at risk of fraud resulting from procuring velvet from hybrid deer. Consequently, there is an evident need for better tracing of sources of velvet to validate product provenance. Significantly, fine-scale genetic analyses of deer populations would benefit both conservation research and deer farming in Asia, since sika deer and sambar occur as both farmed and wild species on the continent.

Detection technologies for identifying adulterated deer products have been developed using polymerase chain reaction (PCR)-based mitochondrial D-loop sequencing [7] to genotype antlers. However, mitochondrial sequencing analysis solely reveals the maternal lineage, so it is not suitable for detecting hybrids. Ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) based on enzyme-digested peptides has been applied to venison [9], and real-time polymerase chain reaction (RT–PCR) of the kappa-casein precursor gene has been employed to detect different deer species in the venison industry [10,11]. These technologies can only resolve species among specific samples, and they have not yet been proven capable of detecting hybrids.

Interspecific hybridization across members of Cervidae is common in both wild and domesticated populations, and such matings can produce fertile offspring, as observed in the crossbreeding of red deer with sika deer in Scotland [3]. The confident identification of hybrid deer using morphological traits alone is complicated. Their coloration and antler shape are not always intermediate between parental phenotypes [12]. For instance, in the case of red-sika hybridizations, hybrid deer display phenotypic variations related to carcass weight, jaw length, and incisor arcade breadth that reveal substantial additive genetic variation of these quantitative traits, hampering morphological-based identification [13]. Cross-genus hybridizations have also been reported, for example, between male sambar and female red deer [14]. The premise of hybrid detection is that markers should be able to clearly genotype species to reveal their ancestry. Previous published markers, such as species-diagnostic single nucleotide polymorphisms (SNPs), have been developed to detect hybridization events and genetic introgression only within a given genus [12,15]. Considering these studies together reveals a gap in our ability to diagnose source species and to identify cases of counterfeit deer products.

Simple sequence repeats (SSRs) are typically highly variable and, consequently, can be used for individual identification, paternity analysis, and pedigree construction, with applications in monitoring wildlife [16-18] and livestock breeding programs [19-21]. SSRs can be categorized into genomic-derived SSRs (gSSRs) or expressed sequence tag-derived SSRs (EST-SSRs). gSSRs display limited utility when performing cross-species amplification among different taxa [22,23]. Since EST-SSRs present better cross-species applicability, these markers have been developed for diverse animal taxa, including birds [22,24], domestic pigs (Sus scrofa) [25], buffalo (Bubalis bubalis) [21], giant pandas (Ailuropoda melanoleuca) [26], and cattle (Bos taurus) [27]. EST-SSRs are derived from untranslated regions (UTRs) or coding regions (CDSs) in the genome, providing conserved flanking regions that enable primer pairs to be designed for genotyping [28,29]. Cross-species genotyping is critical for diagnosing fraudulent labeling and for identifying hybrid individuals. gSSRs have been used previously in studies of sika deer, red deer, and their hybrid offspring [3,13], but their effectiveness has only been assessed within the genus Cervus, and they have not been tested against different genera.

Transcriptome sequencing represents an effective approach for generating multiple EST sequences from nonmodel organisms [30,31]. This approach has been applied to sika deer [31,32], but EST-SSRs are still lacking for sambar, and no EST-SSR panel for deer has yet been subjected to cross-species amplification.

In this study, we aimed to develop a polymorphic EST-SSR panel for species identification across deer species from different genera. Based on the EST-derived character of this marker set, we hypothesized that it would show good transferability, allow reliable genotype interpretation, and could be employed to evaluate hybrid status among different members of the Cervus genus after capillary electrophoresis. Finally, we selected 16 polymorphic markers that revealed clear clustering across the three target deer species and identified two red-sika hybrids. Our EST-SSR panel can be used in cross-genus species identification and should prove helpful in wild deer conservation efforts, domestic breeding management, and the detection of product fraud.

Materials & methods

DNA sample collection

All animal experiments were approved by IACUC of National Taiwan University (Permit number: NTU107-EL-00234) and followed its Guidelines for Animal Experimentation.

Since red deer are not endemic to Taiwan and have been introduced mostly from New Zealand, we sampled red deer (n = 46) from commercial deer farms. An additional two sika-red deer hybrids (n = 2) were also sampled from commercial farms, and their ancestry was verified by the sequencing of mitochondrial DNA and the zinc finger Y-linked (ZFY) locus. We collected fresh antler velvet tissue slices when farmers were harvesting deer antlers. The deer were carefully restrained, and their antler velvet was cut with a saw. Slices of tissue were sampled with a sterile blade from the velvet tip of the antler and stored in 99% ethanol. Sambar samples (n = 45) were mostly collected from the Kaohsiung Animal Propagation Station, Pingdong, Taiwan (n = 30), and an additional 15 individuals were collected from deer farms. We obtained 30 sika deer samples from Shedding Nature Park, Pingdong, Taiwan. Blood samples from both sambar and sika deer were collected when the deer were undergoing veterinary health checks. EDTA-containing tubes were used for blood collection. DNA was extracted and purified from the antler velvet and blood samples using a Wizard Genomic Purification kit (Promega, WI) according to the manufacturer’s procedure.

One sambar deer from a deer farm was selected to collect fresh antler velvet tissue for transcriptome sequencing. The tissue was stored in liquid nitrogen until RNA purification. Total RNA was extracted by using TRIzol (Invitrogen, CA) following the manufacturer’s instructions. A total of 46.17 μg of RNA was collected for cDNA library construction.

SSR mining and SSR marker development

Transcriptome sequencing from the velvet tip of the antler generated 67,054 unigenes. These unigenes were downloaded into MIcroSAtellite (MISA), software to identify microsatellites in the nucleotide sequences [33], and 11,190 SSRs were identified. Mononucleotides and short SSRs (total length <15 base pairs (bp), i.e., SSRs with a minimum of 7 repetitions for dinucleotides, 4 repetitions for trinucleotides, 3 repetitions for tetranucleotides, 2 repetitions for pentanucleotides, or 2 repetitions for hexanucleotides) were excluded to retain SSRs with repeat motifs offering greater potential polymorphism. We obtained 2,179 SSRs that fit these criteria. Next, we culled SSRs that were allocated at the beginning or the end of a unigene because it was difficult to design primers for these loci in flanking regions. To avoid linkage disequilibrium, we ruled out SSRs allocated to the same unigene. In clusters containing unigenes with highly similar sequences (e.g., more than 70% sequence identity), the unigenes may come from the same gene or homologous genes. SSRs within the same cluster were also avoided. After screening based on the above criteria, we finally designed 103 primer pairs by using Primer 3–2.3.4 software [34,35].

Detection of polymorphic SSRs

We conducted temperature gradient tests on 103 primer pairs to optimize annealing in PCR, and primer pairs that failed to produce specific PCR products were excluded. Fifty-five primer pairs were selected based on their clear and specific results in agarose gel electrophoresis. To screen for loci displaying high polymorphism, these 55 primer pairs were used for PCR in 45 sambar deer samples, and to determine their transferability, we conducted cross-species amplification in sika deer and red deer samples. The information of twenty-six polymorphic markers based on our sambar transcriptome sequences is listed in Table 1, and the allele frequencies of these markers in each species are shown in S1 Table.

Table 1

Primer information on 26 polymorphic microsatellite markers according to the sambar transcriptome sequences.

Locus	Primer sequence (5’->3’)	Repeat unit	No. of repeat units	TA (°C)	PCR product length	Fluorescent dye
Locus_3	F: TCTCTGAAGAGACAGAGTCCTGC	TGC	6	61	130	6-FAM
	R: AAAGAATGGCCCTCCCAAC
Locus_4	F: AGTTGCAGTTGAAGAAAGGACAG	GTG	6	58	127	6-FAM
	R: GAATCAGTCAAACAAAGTGGGAG
Locus_7	F: CCTTTCAGGTCTCTCTGGAGG	GGA	7	61	138	PET
	R: AGCTGGCAAAGTCGGCTAC
Locus_8	F: GTACCCTAGAAATCCCACCTGAC	CAGA	6	58	155	PET
	R: ACTGCCGAGTCACTCAAAGG
Locus_10	F: GATGTATTCTCCCAGCCGTTAC	GCA	7	58	136	PET
	R: CTGATACATTGTGGTCTGCTGG
Locus_14	F: TGTCTCCCTTCTCTCATCTCATC	TC	10	58	138	6-FAM
	R: CTTCCAAGCCAGGATATGTTATG
Locus_15	F: GCCATCTCTCCTCCCTTACTTAG	AC	9	61	138	PET
	R: GCAGAACCTTATCTGTTGGTGTC
Locus_16	F: AAGTCACTAAATCCTCCCTCCTG	TG	9	61	141	PET
	R: AACAACATGAGTGCTTATGCTCC
Locus_20	F: GTTCTCTGTCGTCTGGTGTGAG	GAT	6	58	114	6-FAM
	R: AGAGTCGGACACGACTGAAGTG
Locus_21	F: AGATGACACTCAGGAGGATGGT	ACCCTG	3	58	151	PET
	R: CACATCCTATCCCAGGAGCTA
Locus_25	F: GAGCTCCTGAGGTTTACAGGTG	GACA	5	58	147	6-FAM
	R: ACAGATGAGGAAACTGAGGTGTG
Locus_26	F: GTGCAGGAGGTGCTTGATGT	GCT	6	58	105	6-FAM
	R: CAGCAGGAGAACAAGAGCAAC
Locus_32	F: ATCAACTGTGAGGATCAGCGTAG	TGTTT	3	58	145	6-FAM
	R: TACCACTAAGTTATCCCTTGCCC
Locus_34	F: TATCAGCTAGTGAGTGGAAGC	TGG	6	56	156	VIC
	R: CTGTTCACAGCTTTGGTGTT
Locus_37	F: CTGTGACCATCTCTCCCTCCT	GTCTCC	4	58	146	6-FAM
	R: GCAGTTTCTACCAGAGACCACAG
Locus_39	F: AGGGAACACAGCATGAAGATG	GAA	6	58	145	VIC
	R: CTTCAACTCTGACTGGCTTCTTT
Locus_40	F: AGCTTCCCAGTCTCTGACTTTCT	TC	12	58	153	VIC
	R: AGGATTTGGAGGGAGTGATATGT
Locus_41	F: GTAGTTTCTCCTTAGGCGTGGAT	TGAG	5	58	135	VIC
	R: CCACTGGAATCACAAAGTGTTCT
Locus_42a	F: TGGCCTTTGATATGATACTGGAG	GTTT	5	58	112	VIC
	R: CGCACAACACATTATCTCAGAAC
Locus_43	F: CTTGCACTCTCAACCTACCTTGT	AC	7	58	146	VIC
	R: ACTCATTTCCAGAGCATCACAGT
Locus_44b	F: TCAGTGACAATACACACTCGGTT	GT	10	58	139	VIC
	R: CCAGTTAACAGTGCAGATCCATT
Locus_46	F: CAGCACAGCAGATTCCCAG	CCTGC	5	61	147	6-FAM
	R: TAAGTAAAGCAGCTGGGAGGAG
Locus_48	F: TTGTAACCAACACATAGCACACG	ACC	4	58	144	6-FAM
	R: TCACCTCTGGGCTAATTGTAGAC
Locus_49	F: AGACCACATGTAAAACTGGCTGT	AC	8	58	120	6-FAM
	R: CATACGTTTCTAGCCTGTTGCTT
Locus_50	F: ACCTATATGTTCTTCGGCTCCAT	GT	9	58	137	6-FAM
	R: CTTTGGAACACTTGAGGAGACAT
Locus_52	F: GAACAACTGGATGCTGTG	GCC	6	58	211	6-FAM
	R: GTTGAGTTGAGGCTGAGAAT
Locus_53b	F: GTTGCAGGCCTTCTTTATC	TA	10	58	162	6-FAM
	R: CAGATTCAAGGCTGTAGCA
Locus_54	F: GTGTTTCCTGAATCCAGATG	GCTGGG	3	58	285	6-FAM
	R: GTGTTCTGTCCGTGCAAA
Locus_55	F: CTGGTTAACCTCTGAGAATCC	CCCCAT	3	58	164	6-FAM
	R: GGAGTCAGAGTCACAGAGAAA

TA (°C): Optimized annealing temperature; a: Failed to be amplified in sika deer; b: Failed to be amplified in red deer.

PCR amplification was conducted using the Blend Taq Plus system (TOYOBO, Japan). We adopted a modified protocol involving reaction mixtures of 10 μL containing 6.5 μL of ddH2O, 1.0 μL of 10X PCR buffer for Blend Taq, 1.0 μL of each dNTP (2.0 mM), 0.5 μL of the forward and reverse primers (10 μM), 1.0 μL of the DNA template (50 ng/μL), and 0.25 μL of Blend Taq Plus (2.5 U/μL). Reactions were conducted in an ABI PCR machine under the following conditions: 5 min at 94°C, followed by 40 cycles of 30 sec at 94°C, 30 sec at 58–61°C, 30 sec at 72°C, and a final elongation step of 10 min at 72°C. The sizes of the PCR amplicons were measured via ABI 3730 capillary electrophoresis (Applied Biosystems, CA) at the National Center for Genome Medicine (NCGM), Taiwan.

SSR sequence validation

To confirm the EST-SSR sequences of the amplicons, we sequenced all distinct alleles from each of the 29 polymorphic loci, and alleles that were only detected in heterozygous individuals were cloned by purifying the respective PCR products and introducing them into the pGEM-T easy vector (Promega, WI) before transforming them into competent DH10B cells. The clones with inserted EST-SSRs were selected and sequenced.

Data analysis

The ABI 3730 outputs were read using Peak scanner version 1.0 software (Applied Biosystems, CA). MICROCHECKER [36] was applied to detect genotyping errors, including null alleles and allele dropout in each species. The number of alleles (NA), observed heterozygosity (Ho), expected heterozygosity (He), polymorphic information content (PIC), and probability of identity (PID) were calculated using Cervus version 3.0.3 [37]. The inbreeding coefficient (FIS) was measured using Genetix v4.05 [38] with 10,000 permutations. The probability of exclusion (PE) was calculated in GenALEx 6.5 [39].

To achieve efficiency in species identification and phylogenetic analyses, we selected sixteen loci with a PIC>0.25, which were moderately informative (0.2540] and Genepop 4.7 [41]. Principal coordinate analysis (PCoA) was conducted in GenALEx. We used STRUCTURE 2.3.4 [42] for assignment tests. This software models genetic structure by probabilistically assigning individuals to certain populations or to more than one population if the individuals are hybrids. The software generates estimates of the proportion of admixture (termed Q) for individuals in a sample set [42]. The test was performed with 10 iterations for each of three populations (K) with the Markov Chain Monte Carlo (MCMC) algorithm running for 500,000 generations, with an initial burn-in of 10,000 generations.

Results

De novo assembly of EST-SSRs from sambar antler velvet

We performed transcriptome sequencing to acquire EST-SSR sequences from an RNA sample taken from one sambar antler tip. The transcriptome assembly yielded 65,074 unigenes (mean length 1,131 bp), and 11,190 SSRs were identified by using MIcroSAtellite (MISA) software. After selection process described in the methods, primer pairs for PCR amplification were designed for the obtained set of 103 EST-SSR loci. We ruled out primer pairs that failed to yield specific PCR products from the sambar DNA template. Finally, 55 primer pairs were deemed suitable for further study and were labeled with fluorescent dyes to test their genotyping efficiency and polymorphism.

Cross-genus amplification and characterization of polymorphic markers

We evaluated the polymorphism and cross-species transferability of the 55 candidate EST-SSRs across the sambar (n = 45), sika (n = 30), and red deer (n = 46) antler tip samples. A summary of the cross-species amplification of all 55 loci is shown in Table 2. The successful amplification rate was 94.5% (52/55) in sika deer and 89.1% (49/55) in red deer, and 21, 11 and 21 of the loci displayed polymorphism in sambar, sika deer and red deer, respectively. No signal of allele dropout or null alleles was detected among these polymorphic markers in any of the species with the sole exception of Locus_15, at which a null allele was detected in red deer. The average Ho and expected He ranged from 0.2804 to 0.4574 and 0.2991 to 0.3855, respectively, across the three deer species. The mean PIC of all three species was >0.25. PID ranged from 1.69E-05 to 6.42E-09, and PE ranged from 0.970 to 1 across the three species. The overall FIS ranged from -0.233 to 0.063 and showed no significant deviation from zero, indicating that our tested samples were not closely related. Among the 55 markers, three markers showed polymorphism in one species but produced nonspecific amplicons in other species. Specifically, Locus_42 was polymorphic in Sambar and red deer but failed to be amplified in sika deer. Locus 44 and Locus_53 were polymorphic in Sambar and sika deer but produced nonspecific results in red deer. Overall, a total of 29 EST-SSRs displayed polymorphism in all three deer species (see S1 and S2 Tables for details).

Table 2

Cross-species amplification of 55 EST-SSR loci in the sambar, sika deer and red deer samples.

	No. of genotyped individuals	No. of successfully genotyped markers	No. of polymorphic loci	NA	Ho	He	PIC	FIS	PID	PE
Sambar	45	55	21	2.81	0.3489	0.3523	0.3382	0.034	6.42E-09	0.998
Sika deer	30	52	11	2.31	0.4574	0.3855	0.3151	-0.223	1.69E-05	0.970
Red deer	46	49	21	3.48	0.2804	0.2991	0.2761	0.063	6.82E-08	1

NA: Number of different alleles; Ho: Observed heterozygosity; He: Expected heterozygosity; PIC: Polymorphic information content; PID: Multilocus probability that two matching genotypes taken at random come from the same individual; PE: Probability of exclusion in parentage analysis.

To increase efficiency and informativeness, we excluded markers with a PIC<0.25. Finally, 16 EST-SSR markers were chosen for phylogenetic analysis and were further characterized (Table 3). In this set of 16 markers, the number of alleles per locus ranged from 2 to 9, Ho ranged from 0.008 to 0.689 (mean = 0.2951), and He ranged from 0.329 to 0.769 (mean = 0.5416). Although only one individual showed heterozygosity at Locus_26 and Locus_46, they still revealed a reasonable PIC across the 121 deer samples because different alleles had become fixed in different deer species. For the 16-marker set, PIC ranged from 0.307 to 0.729 (mean = 0.4829), and PID ranged from 0.102 to 0.494 (Combined = 2.62E-10). A summary flowchart of the process of marker selection for species identification is shown in Fig 1.

Table 3

Summary data of 16 polymorphic EST-SSRs in 45 sambar, 30 sika deer, and 46 red deer.

Locus	k	Ho	He	PIC	PID	FST
Locus_7	6	0.588	0.708	0.667	0.126	0.211
Locus_8	4	0.353	0.715	0.659	0.137	0.647
Locus_14	6	0.184	0.578	0.486	0.270	0.760
Locus_15	8	0.353	0.623	0.576	0.186	0.384
Locus_16	5	0.252	0.387	0.336	0.438	0.327
Locus_20	5	0.223	0.329	0.307	0.468	0.155
Locus_25	3	0.198	0.452	0.384	0.370	0.698
Locus_26	3	0.008	0.381	0.310	0.449	0.986
Locus_34	4	0.421	0.652	0.576	0.194	0.645
Locus_40	5	0.504	0.675	0.612	0.171	0.443
Locus_41	8	0.292	0.426	0.410	0.343	0.261
Locus_43	6	0.689	0.769	0.729	0.092	0.162
Locus_46	2	0.008	0.476	0.362	0.389	0.988
Locus_49	2	0.269	0.333	0.277	0.494	0.262
Locus_50	9	0.271	0.748	0.708	0.102	0.678
Locus_52	2	0.109	0.413	0.327	0.432	0.742
Mean	4.9	0.2951	0.5416	0.4829		0.5464
Total					2.62E-10

Ho: Observed heterozygosity; He: Expected heterozygosity; PIC: Polymorphic information content; PID: Multilocus probability that two matching genotypes taken at random come from the same individual; FST: Fxation index.

Fig 1

Flowchart of marker development for 16 cross-genus EST-SSRs.

Evaluation of the genetic differentiation capability of the 16-marker set for species identification

We used the set of 16 EST-SSR markers to determine its species identification power across sambar, sika deer and red deer. For this purpose, we calculated genetic distance (Nei’s DA) and FST using 10,000 permutations of allele frequencies (Table 4). We observed a shorter Nei’s DA (0.2663) between sika and red deer than between sika deer and sambar (0.5806) or red deer and sambar (0.4858). The pairwise FST results mirrored those of Nei’s DA, with the FST value between red and sika deer (0.4148) being lower than that between red deer and sambar (0.6472) or sika and sambar (0.5260). Pairwise FST analysis across all samples revealed significant differentiation (P value<0.001) among the three deer species.

Table 4

Nei’s DA (below the diagonal) and pairwise FST (above the diagonal) for 16 EST-SSR markers among the three deer species.

Species	red deer	sika deer	sambar
Red deer		0.4148	0.6472
Sika deer	0.2663		0.5659
Sambar	0.4858	0.5806

To better illustrate genetic separation among the three species, we conducted PCoA in GenALEx (Fig 2). Two known red-sika hybrid deer were used as controls. Our results show that the three species were clearly distinguishable, with explanatory scores for Coordinate 1 and Coordinate 2 of 44.07% and 15.13%, respectively. As anticipated, the two known hybrid individuals, HY01 and HY02 (purple circles), were positioned between the sika and red deer clusters.

Fig 2

Principal coordinate analysis of three deer species and two hybrids (HY01 and HY02) using the 16-marker set.

To analyze the genetic structure of 123 deer individuals, we performed assignment testing in STRUCTURE software. All but two individual samples were unambiguously assigned to one of three clusters (K = 3), which represented sika deer (blue cluster), red deer (red cluster), and sambar deer (green cluster) (Fig 3). The two deviant samples, representing the hybrid individuals HY01 and HY02, were split between the sika deer and red deer clusters as expected. For HY01, the Q value was 0.459 for sika deer and 0.539 for red deer. For HY02, the Q value was 0.331 for sika deer and 0.664 for red deer.

Fig 3

Assignment test in STRUCTURE for 123 deer samples based on 16 EST-SSR markers.

Discussion

In the present study, we used de novo assembly to establish a panel of EST-SSR loci to confidently identify species of Cervidae. Our main goal was to develop a set of genetically based markers allowing the identification of deer species. In recent years, many studies have focused on comparing the utility of microsatellite SSRs and SNPs. Ross et al. [43] estimated relatedness in Chinese rhesus macaques (Macaca mulatta) and suggested that SSRs offer more precise predictive power than SNPs for establishing how individuals are related. Fernández et al. [44] examined the effectiveness of SSRs and SNPs in a consanguineous Angus cattle herd (Bos taurus) and found that twice as many SNP markers as SSRs were required to achieve the same effectiveness in individual identification and parentage analysis. Sorkheh et al. [45] similarly showed that SSRs present higher PIC than SNPs. Furthermore, SSR markers have been successfully used to genotype samples with low DNA quality, exhibiting greater efficacy in such samples than SNPs and genotyping-by-sequencing methods [22]. SSRs have been successfully and widely applied for cross-species amplification in diverse vertebrates, including cetaceans, birds, and frogs [46]. Mitrus et al. [47] used gSSR markers developed from Ficedula hypoleuca and found that 13 of the 24 tested primer pairs (54%) could be used in Ficedula parva. Tardy et al. [48] assessed the amplification of 32 markers derived from Balaenoptera physalus in four additional cetacean species, revealing transferability >72%. Maduna et al. [49] tested the cross-amplification of 11 gSSRs across six shark species, demonstrating genotyping success rates of 72%-100%. However, SSRs developed in certain taxa, such as fishes [50], display a low probability of cross-amplification or shared polymorphism. To the best of our knowledge, no other assessment of EST-SSR marker cross-amplification has been reported in Cervidae. Our novel panel of 16 EST-SSRs shows high transferability and provides a reasonably high information content (0.5>PIC>0.25 and PIC>0.25, Table 2) [51]. Markers developed from ESTs show slightly lower polymorphism [31,52]. While markers with a reasonably high PIC are less informative, they can be combined with other high-information-content markers for phylogenetic analysis [53], as reported in the unweighted pair group method with arithmetic mean (UPGMA) analysis of domestic breeds [54] and the analysis of molecular variance (AMOVA) and discriminant analysis of principal components (DAPC) of papaya [55]. In addition to their phylogenetic applicability, we selected these reasonably high-PIC markers because they showed private alleles or the fixation of a certain allele in one of the species (S1 Table). For example, Locus_26 was fixed in sika deer, with a private allele size = 97 base pairs, while its PIC value was 0.310 across the three species. This marker played an important role in indicating the sika deer origin of a species-unknown individual. To include these markers in our species identification marker set, we set the criterion of 0.25

The wild sika deer population in Kenting National Park, Taiwan, is derived from a small population comprising 5 males and 17 females that were reintroduced to the park from the Taipei Zoo in 1994. Thus, this founder event may explain the low polymorphism displayed by our sika samples. In contrast, the high polymorphism observed in our red deer samples may be due to the diverse origins of farmed red deer. Furthermore, farmed red deer are often mated with wapiti, resulting in higher genetic diversity in red deer populations. The combined PID values were 6.42E-9, 1.69E-5, and 2.90E-7 for our sambar, sika and red deer sample sets, respectively, demonstrating that our EST-SSR panel competently achieved individual identification, requiring a PID<0.01 [56,57]. This panel also displayed utility for pedigree establishment in parentage analysis, with mean PE values ranging from 97.0% - 99.9%. Accordingly, our marker set should assist in the breeding management of farmed deer.

The refined set of 16 polymorphic markers revealed the lowest pairwise genetic distance (mean Nei’s DA = 0.2663) and pairwise FST (mean = 0.4148) between red and sika deer samples, corroborating a previous mitochondrial DNA study [58] and the current taxonomy [59,60]. FST values revealed that this set of markers could distinguish all three tested species. PCoA validated that outcome, showing that, apart from the two hybrid samples, all individuals were divided into three distinct clusters corresponding to the three species (Fig 3). The first two axes of our PCoA accounted for 59.2% of the variation in the dataset, which is higher than the percentages of variation explained by markers developed for species identification in other studies, such as studies of zebra [61] and Elasmobranchii [62]. Assignment analysis in STRUCTURE showed that all 123 samples could be sorted into the three a priori clusters (K = 3), with HY01 and HY02 again showing admixed ancestry. Based on these results, we propose that our panel of 16 EST-SSRs can be used to accurately discriminate the three deer species from the Cervus and Rusa genera, which should prove useful in detecting counterfeit deer products and in breeding programs.

Conclusions

We successfully performed the cross-species amplification of 55 EST-SSR loci in three related species belonging to two genera in the family Cervidae. We selected a panel of 16 EST-SSRs that displayed unambiguous genotyping and informativeness for population genetics and phylogenetic analyses. This panel can be used for species identification and hybrid detection across deer genera.

Allele frequency of 29 EST-SSR markers among sambar, sika deer, and red deer.

(XLSX)

Click here for additional data file.

Marker polymorphism information of 29 EST-SSRs among sambar, sika deer and red deer.

k, number of alleles; PIC, polymorphism information content; P(ID), HWE, deviation from Hardy-Weinberg equilibrium (NS not significant, ND not done, *P<0.05). LD, linkage disequilibrium. Microchecker null, MICROCHECKER results of tests for evidence of null allele. Microchecker dropout, MICROCHEKER results of tests for evidence of allele dropout.

(XLSX)

Click here for additional data file.

Pairwise Queller and Goodnight relatedness (above the diagonal) and relatedness based on maximum likelihood method (below the diagonal) of each individual in sambar, red deer, and sika deer by polymorphic EST-SSRs.

Pairwise Queller and Goodnight relatedness was calculated by GenALEx 6.5. Relatedness based on maximum likelihood method was calculated by ML-Relate (https://www.montana.edu/kalinowski/software/ml-relate/index.html).

(XLSX)

Click here for additional data file.

19 Mar 2021

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

12 Nov 2021

Development of 16 novel EST-SSR markers for species identification and cross-genus amplification in sambar, sika, and red deer

PLOS ONE

Dear Dr. Ju,

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Mukesh Thakur, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (if provided):

I now checked the manuscript, entitled "De novo characterization of the sambar antler velvet transcriptome and EST-SSR development for cross-genus species-identification" . I found it significant as the developed new EST-SSR markers can be applicable for population genetic studies of deer species. Based on two reviewer's comments and my own evaluation of the contents, I would recommend manuscript as "Major Revision" and would advise reviewer to estimate the genotyping errors for the markers prioritized as the significant occurrence of allele drop out, null alleles, false alleles may affect the overall explanation/outcome of the results. I suggest authors to consider the reviewer's comments and revise the manuscript accordingly.

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3. In your Methods, please describe the exact protocol used for the collection of the antler velvet samples.

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[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Comments:

The manuscript “Development of 16 novel EST-SSR markers for species identification and cross-genus amplification in sambar, sika, and red deer.” by Hsiao et al. developed new EST-SSR markers for genetic identification of deer species. These markers demonstrate applicability across different genera and proved suitable for identification and phylogenetic analyses across deer species. The manuscripts is well written and developed SSR markers to resolve different issue related to the deer species conservation. Although the use of the methodology and analytical approaches seem appropriate, but there are some flaws in presentation of results and it is advised to authors to incorporate suggestions:

1. Give reference in line Number 51.

2. Among the 16 SSR markers selected for the species identifications, eight markers showed lower PIC (polymorphism Information Content) value i.e. <0.5, which is not acceptable for Species, Individual and population Genetic structure Identification. Lower PIC value may reduce the discriminatory power of theses markers may reduce. Authors need to Clarify that why they are selecting these markers.

3. Authors did nice effort to screen SSR markers and demonstrated their use in the species identification but I could not see any effort to estimate the genotyping error. Genotyping error is inherent problem with SSR markers. Especially with degraded samples (like Antlers, Hair and Fecal samples). Presence of genotyping error (allele dropout and null allele) may mislead the shorting of population/species based on the allele frequency clustering. Therefore, authors need to address the Genotyping error issue for all the 16 selected markers.

4. Relatedness assessment is need to justify that the tested samples did not include only closely related individuals are not present in the data.

Reviewer #2: Reviewer comment:

In recent times, the population status of many deer species are declining with few species on the verge of extinction. Most of the published population genetic and hybridization studies on deer species have used cross species amplification microsatellites. The current study entitled "De novo characterization of the sambar antler velvet transcriptome and EST-SSR development for cross-genus species-identification" tries to fill the lacunae by designing microsatellites for Sambar and tested their efficacy in other two deer species.

The study holds significance. The authors tried to answer majority of the earlier reviewer’s comments. But still few sections are confusing and require changes to be made especially the marker designing, selection and annotation sections for better understanding for readers. Moreover, I suggest the authors to remove transcriptome and annotation part in the results and discussion sections as it has no significance in this study.

Few suggestions and questions :

Introduction: Hypothesis is not well established in the last paragraph of Introduction.

Line 80-81: Give citation for the sentence

Line 156-162: How many SSR’s were retained finally for primer designing?

Line 163-165: Elaborate the methodology a bit by including the wet lab work and any other software used in selecting 55 primers from a total of thousands of SSR’s

Line 168-169: I believe “ Information on twenty-six polymorphic markers based

on our sambar transcriptome sequence is listed in Table 1” should be moved to results. Also there is no proper justification for reporting only 26 primers out of 55 in table 1.

Line 170: Report if you have information on chromosome number in Table-1 from which the loci is picked. I believe the chromosome number is important in population genetic analysis to avoid any linkage disequilibrium.

Line 187-188: Reason for selecting 16 primers out of 55 selected and 26 in Table 1 is missing. It’s too confusing, please clarify wherever you are reducing the number of microsatellites.

Line 194: SSR sequence validation- I believe this section should go before finalizing the SSR’s for further analysis. Also mention how many microsatellites were used for validation.

Line 215-236: Though most of the above questions were answered here in these paragraphs, it’s too confusing and I believe it should be in methodology section for better understanding for readers.

Line 237: Report results of HWE and LD tests too for each species in Table 2 as they are crucial for population genetic analysis.

Line 237-238: I suggest you to merge both Tables 2 and 3 and report single table with basic diversity estimates for all three species.

Line 253-254: Overall and individual species wise PIC values were very less. Can you comment on the reliability of selected 16 markers for its use in other population genetic and hybridization studies?

Line 272: Remove asterisk marks on the FST p-values as all are significant.

Line 282-286: Move to methodology section.

Line 351: Replace ‘they’ with ‘the’.

Line 426-427: Mention population genetics and hybridization studies in the final sentence of conclusion section.

S1 Table: I suggest S1 table should be moved to methodology section as it more clear than writing part.

**********

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Reviewer #2: No

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18 Jan 2022

Revise to reviewers

Additional Editor Comments:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

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Response:

We have checked the format. We corrected the symbol of the corresponding author. We modified figures by PACE, according to journal requirements.

2. We suggest you thoroughly copyedit your manuscript for language usage, spelling, and grammar. If you do not know anyone who can help you do this, you may wish to consider employing a professional scientific editing service.

Whilst you may use any professional scientific editing service of your choice, PLOS has partnered with both American Journal Experts (AJE) and Editage to provide discounted services to PLOS authors. Both organizations have experience helping authors meet PLOS guidelines and can provide language editing, translation, manuscript formatting, and figure formatting to ensure your manuscript meets our submission guidelines. To take advantage of our partnership with AJE, visit the AJE website (http://learn.aje.com/plos/) for a 15% discount off AJE services. To take advantage of our partnership with Editage, visit the Editage website (www.editage.com) and enter referral code PLOSEDIT for a 15% discount off Editage services. If the PLOS editorial team finds any language issues in text that either AJE or Editage has edited, the service provider will re-edit the text for free.

Upon resubmission, please provide the following:

The name of the colleague or the details of the professional service that edited your manuscript

A copy of your manuscript showing your changes by either highlighting them or using track changes (uploaded as a *supporting information* file)

A clean copy of the edited manuscript (uploaded as the new *manuscript* file).

Response:

We have asked the edition service from AJE. We acknowledged the editor of AJE and another English editor for their English correction in Acknowledgements. We also prepared a supporting information file named “manuscript with track change by AJE”, This file only showed track changes by AJE, comparing to “Revised Manuscript with Track Changes”, which remaining all changes during the revision.

3. In your Methods, please describe the exact protocol used for the collection of the antler velvet samples.

Response:

We described the protocol used for antler velvet collection at Line 139-143.

4. PLOS requires an ORCID iD for the corresponding author in Editorial Manager on papers submitted after December 6th, 2016. Please ensure that you have an ORCID iD and that it is validated in Editorial Manager. To do this, go to ‘Update my Information’ (in the upper left-hand corner of the main menu), and click on the Fetch/Validate link next to the ORCID field. This will take you to the ORCID site and allow you to create a new iD or authenticate a pre-existing iD in Editorial Manager. Please see the following video for instructions on linking an ORCID iD to your Editorial Manager account: https://www.youtube.com/watch?v=_xcclfuvtxQ.

Response:

We renewed the ORCID information of the corresponding author Yu-Ten Ju.

Reviewer1

1. Give reference in line Number 51.

Changes made:

We added the reference at Line 53. Hoffmann et al. (2015) stated that “Overexploitation of natural populations (illegal hunting, etc.) and habitat loss have led to the severe population reductions”. According to this statement and to prevent over-interpret the description from reference, we removed the wordings of “the risks of extinction”.

2. Among the 16 SSR markers selected for the species identifications, eight markers showed lower PIC (polymorphism Information Content) value i.e. <0.5, which is not acceptable for Species, Individual and population Genetic structure Identification. Lower PIC value may reduce the discriminatory power of theses markers may reduce. Authors need to Clarify that why they are selecting these markers.

Changes made:

Thanks to reviewer’s insightful comments, we put forward our explanation based on the reviewer’s comments, so that our article can be more clear and reasonable. We agree that PIC>0.5 would be a better choice for phylogenetic analysis. However, the relatively low level of polymorphic information content in EST-SSR markers may be due to the source sequence of these markers. EST are more conserved sequences comparing to genomic sequences, which are spread throughout the genome, and markers developed from EST would feature slightly lower polymorphism (Khimoun et al. 2017; Jia et al. 2019). In addition, markers with 0.25

3. Authors did nice effort to screen SSR markers and demonstrated their use in the species identification but I could not see any effort to estimate the genotyping error. Genotyping error is inherent problem with SSR markers. Especially with degraded samples (like Antlers, Hair and Fecal samples). Presence of genotyping error (allele dropout and null allele) may mislead the shorting of population/species based on the allele frequency clustering. Therefore, authors need to address the Genotyping error issue for all the 16 selected markers.

Changes made:

The reviewer did point out the weakness of our original manuscript in verifying the EST-SSR markers. According reviewer’s suggestion, we checked allele dropout and null allele in each species by MICROCHECKER and the result clearly showed no genotyping error was found. In null allele detection, we detected only one null allele i.e. Locus_15 at red deer. We added these descriptions of MICROCHECKER in Methods (Line 207, 208) and Results (Line 246-248).

4. Relatedness assessment is need to justify that the tested samples did not include only closely related individuals are not present in the data.

Changes made:

We added overall FIS of each species at Table 2. Data analysis methods were described in Methods (Line 211,212). FIS value and significant tests were described in Results (Line 252-254). We stated that “no significant deviation from zero, indicating that our tested samples were not closely related.” at Line 252-254.

Reviewer2

In recent times, the population status of many deer species are declining with few species on the verge of extinction. Most of the published population genetic and hybridization studies on deer species have used cross species amplification microsatellites. The current study entitled "De novo characterization of the sambar antler velvet transcriptome and EST-SSR development for cross-genus species-identification" tries to fill the lacunae by designing microsatellites for Sambar and tested their efficacy in other two deer species.

The study holds significance. The authors tried to answer majority of the earlier reviewer’s comments. But still few sections are confusing and require changes to be made especially the marker designing, selection and annotation sections for better understanding for readers. Moreover, I suggest the authors to remove transcriptome and annotation part in the results and discussion sections as it has no significance in this study.

Changes made:

We removed paragraph of unigene annotation of SSR loci from Result and Discussion sections. While partial description of transcriptome assembly was remained because we want to explain how we got these SSRs.

Few suggestions and questions :

Introduction: Hypothesis is not well established in the last paragraph of Introduction.

Changes made:

We put forward the hypothesis of this manuscript based on the reviewer to make the topic more clear. We proposed a hypothesis as following and wrote down at Line 120-123 in the section of Introduction:

“Based on the EST-derived character of this marker set, we hypothesized that it would show good transferability, allow reliable genotype interpretation, and could be employed to evaluate hybrid status among different members of the Cervus genus after capillary electrophoresis.”

Line 80-81: Give citation for the sentence

Changes made:

We gave the example of the hybridization of red deer and sika deer and cited the reference (Line 85).

Line 156-162: How many SSR’s were retained finally for primer designing?

Changes made:

We got 103 SSRs after the screening. We added the number of the retained SSRs in the Methods section (Line 170-172).

Line 163-165: Elaborate the methodology a bit by including the wet lab work and any other software used in selecting 55 primers from a total of thousands of SSR’s

Changes made:

We described the criteria in selecting proper SSR loci in detail (Line 160-177) and works on wet lab examination to select the 55 primers (Line 176, 177).

Line 168-169: I believe “ Information on twenty-six polymorphic markers based

on our sambar transcriptome sequence is listed in Table 1” should be moved to results. Also there is no proper justification for reporting only 26 primers out of 55 in table 1

Changes made:

Following reviewer’s later suggestion, we moved Table S1 to Methods. Since Table S1 has mentioned some marker information such as locus name, we think remaining Table 1 in Methods would make the manuscript more understandable. Hence, we decide to remain Table 1 in Methods. Besides, we explained the reason why we only showed 29 primer pairs here that, we selected 29 primers because their PIC value > 0.25. This description was added at Line 267-269. We increase three primer pairs here to make the manuscript more logical. These three primers, i.e. Locus_42, Locus_44, and Locus_53, while showed polymorphism, were not shown in the original manuscript because they failed to be amplified in one of the three species (see Table S2 and Line 254-259). However, to recall the number of polymorphic markers we showed in Table 2, we added these markers in Table 1 and noted that they could not be amplified in all three species and also added description at Line 254-259. For the above reasons, the number of primer changed from 26 to 29. Allele frequency for all markers in each species (Table S1) was also modified to 29 markers.

Line 170: Report if you have information on chromosome number in Table-1 from which the loci is picked. I believe the chromosome number is important in population genetic analysis to avoid any linkage disequilibrium.

Changes made:

No sambar whole genome sequencing with chromosome construction was available online. Hence, we used red deer whole genome chromosome assembly (Cervus elaphus mCerEla1.1; Bana et al., 2018) to refer the chromosome number of each locus. Chromosome number of each locus was showed at Table S2.

Bana NÁ, Nyiri A, Nagy J, Frank K, Nagy T, Stéger V, Schiller M, Lakatos P, Sugár L, Horn P, Barta E, Orosz L. The red deer Cervus elaphus genome CerEla1.0: sequencing, annotating, genes, and chromosomes. Mol Genet Genomics. 2018 Jun;293(3):665-684. doi: 10.1007/s00438-017-1412-3. Epub 2018 Jan 2. PMID: 29294181.

Line 187-188: Reason for selecting 16 primers out of 55 selected and 26 in Table 1 is missing. It’s too confusing, please clarify wherever you are reducing the number of microsatellites.

Changes made:

Thank reviewer for their suggestion. Our expression is not clear in the original manuscript. The criteria of how we selected the sixteen loci was PIC>0.25 . Regarding the selection criteria and details of these 16 pairs of primer pairs, we added these descriptions in Line 267-269.

Line 194: SSR sequence validation- I believe this section should go before finalizing the SSR’s for further analysis. Also mention how many microsatellites were used for validation.

Changes made:

Thanks reviewer's suggestion, we moved the paragraph of SSR sequence validation to data analysis according to the reviewer's suggestion to make the manuscript more fluent(Line 198-204), and we also added the number of microsatellites that we were used for validation at Line 200.

Line 215-236: Though most of the above questions were answered here in these paragraphs, it’s too confusing and I believe it should be in methodology section for better understanding for readers.

Changes made:

We reorganizes the confusing parts of our texts and we revised the context to make it more logical and avoid confusion. We added descriptions of details of how we selected 103 primer pairs (Line 164-172) and 55 primer pairs (Line 174-177). Criteria of the selection of 16 cross-genus markers were described at Line 214-216 in Methods, Line 267-268 in Results, and Line 339-341 in Discussion. Besides, we followed reviewer’s suggestion that we removed some descriptions about the transcriptome sequencing, and we rewrote the section of “De novo assembly of EST-SSRs from sambar antler velvet” and only remained the passage about unigene generation derived from transcriptome sequencing.

Line 237: Report results of HWE and LD tests too for each species in Table 2 as they are crucial for population genetic analysis.

Changes made:

We provided a new supplemental table (Table S2) for describing marker information for each species, including HWE, LD, and the number of potential chromosome location based on red deer genome.

Line 237-238: I suggest you to merge both Tables 2 and 3 and report single table with basic diversity estimates for all three species.

Changes made:

We remained the two tables because these tables represented different information of the marker set. Table 2 showed the polymorphism of markers in each species. This result showed information of transferability of markers in each species. Besides, NA, heterozygosity, and PIC represented the efficiency of our panel for selecting polymorphic markers from EST. In contrast, Table 3 showed polymorphism and FST of each marker, which provided information for users to decide which markers they would like to choose. According to these reasons, we decided to remain the two tables.

Line 253-254: Overall and individual species wise PIC values were very less. Can you comment on the reliability of selected 16 markers for its use in other population genetic and hybridization studies?

Changes made:

We added discussions (Line 339-355) to explain why we set a relatively low PIC value (>0.25) as our threshold for cross-genus genotyping. The explanation was as follow:

“Our novel panel of 16 EST-SSRs shows high transferability and provides a reasonably high information content (0.5>PIC>0.25 and PIC>0.5, Table 3) [51]. Markers developed from ESTs show slightly lower polymorphism [31, 52]. While markers with a reasonably-informative PIC are less informative, they can be combined with other high-information-content markers for phylogenetic analysis [53], as reported in the unweighted pair group method with arithmetic mean (UPGMA) analysis of domestic horse breeds [54] and the analysis of molecular variance (AMOVA) and discriminant analysis of principal components (DAPC) of papaya [55]. In addition to their phylogenetic applicability, we selected these reasonably informative PIC markers because they showed private alleles or the fixation of a certain allele in one of the species (S1 Table). For example, Locus_26 was fixed in sika deer, with a private allele size = 97 base pairs, while its PIC value was 0.310 across the three species. This marker played an important role in indicating the sika deer origin of a species- unknown individual. To include these markers in our species identification marker set, we set the criterion of PIC>0.25, and 16 markers were retained for further analysis.”

Also, we removed the statement of “highly informative” of our marker set.

Line 272: Remove asterisk marks on the FST p-values as all are significant.

Changes made:

We removed asterisk marks in Table 2.

Line 282-286: Move to methodology section.

Changes made:

We moved the passage of STRUCTURE methodology to Data analysis at Line 220-224 in Methods section.

Line 351: Replace ‘they’ with ‘the’.

Changes made:

We revised the wording at Line 359 “the high polymorphism”.

Line 426-427: Mention population genetics and hybridization studies in the final sentence of conclusion section.

Changes made:

We highlighted the power of population genetics and hybridization for our marker set at Line 389.

S1 Table: I suggest S1 table should be moved to methodology section as it more clear than writing part.

Changes made:

We agree reviewer’s opinion. We referred S1 Table at Line 182 in Methods.

Submitted filename: Revise to reviewers.docx

Click here for additional data file.

1 Mar 2022

Development of 16 novel EST-SSR markers for species identification and cross-genus amplification in sambar, sika, and red deer

PONE-D-21-07853R1

Dear Sir/Madam,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

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Kind regards,

Mukesh Thakur, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Pls include estimates of genotyping error and relatedness as supplementary file before processing the MS for formal acceptance.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: In the revised manuscript authors responded to all the questions except questions 3 and 4. Therefore I still have a concern about these two comments given below:

Comments 1: In my previous review, I suggested for estimation of Genotyping error (allele dropout and null allele). In the response, the author stated that they checked by Program micro checker and there was no sign of error. In this regard, the author needs to add a supplementary table for the output of Microchecker and add it to the revised manuscript.

Comments 2. Authors need to estimate pairwise relatedness using Queller and Goodnight relatedness estimators as implemented in GENALEX 6.41 and the maximum likelihood method as implemented in ML-RELATE. These estimates will give a better representation of relatedness levels in the population.

Reviewer #2: The authors had made all the necessary changes to the revised manuscript as suggested by the reviewers. The revised manuscript now looks more clear and can be published.

Even though, authors have explained well in detail regarding considering the loci with lower PIC values, I still believe the loci have limitation. Moreover, table 2 can be removed as the values are based on 55 loci which are not further used in any analysis.

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Reviewer #1: No

Reviewer #2: Yes: Yellapu Srinivas

25 Mar 2022

PONE-D-21-07853R1

Development of 16 novel EST-SSR markers for species identification and cross-genus amplification in sambar, sika, and red deer

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