We evaluated the utility of single nucleotide polymorphism (SNP) markers for parentage testing in Breton (BR) and Percheron (PR) horses in Japan using the proposed International Society for Animal Genetics (P-ISAG) 147 SNP panel and 414 autosomal SNPs. Genomic DNA was extracted from 98 horses of two breeds, BR (n = 47) and PR (n = 51), and sequenced using next-generation sequencing. The average minor allele frequencies for the P-ISAG panel for BR and PR were 0.306 and 0.301, respectively. The combined probabilities of exclusion (PEs) given two parents and one offspring: exclude a relationship (PE01) and given one parent and one offspring: exclude their relationship (PE02) were over 0.9999 for both breeds. Using the P-ISAG panel, no exclusion or doubtful cases were identified in 35 valid parent–offspring pairs, suggesting that the P-ISAG panel is helpful for parentage verification in both breeds. In contrast, as 0.18% of falsely accepted parentages were observed in the parentage discovery cases, additional markers such as the combination of the P-ISAG panel and 414 autosomal SNPs (561-SNP set) presented here should be used to identify valid parent–offspring pairs of horses with unknown parentage relationships.

1 INTRODUCTION

Genetic markers are an important part of molecular technologies for identifying individuals and verifying parentage. Short tandem repeats (STRs) are used in molecular analyses to perform parentage verification. Simple Mendelian inheritance principles between offspring and paternal and maternal candidates occur in many species (Binns et al., 1995; de Groot & van Haeringen, 2017; Heaton et al., 2002; Jones et al., 2010; Lipinski et al., 2007; Marklund et al., 1994; van Asch et al., 2009). For horses, the International Society for Animal Genetics (ISAG) runs regular comparison tests to certify that labs performing genetic analyses of animal DNA samples maintain high comparative standards. Parentage verification for the registration of equine pedigrees requires efficient processing of large samples and accurate allele identification. However, STR genotyping is not well automated, and it can be difficult to identify alleles because of issues such as stutter and plus-A peaks (Perlin et al., 1995). Correct genotyping involves recognizing the pattern of errors and noise, and an experienced specialist should manually check automated calling.

The transition from STRs to single nucleotide polymorphisms (SNPs) for parentage verification and identification for farm animals has attracted increasing interest (Heaton et al., 2002; Holl et al., 2017; Wu et al., 2019) and is ongoing for many livestock species. SNPs offer several advantages over STRs, such as abundance in the genome and low mutation rates, compared with STRs (10⁻⁸ vs. 10⁻³) (Morin et al., 2004; Väli et al., 2008). Moreover, recent sequencing technology and cost reduction improvements have increased the advantages of using SNPs. Hundreds of SNPs can now be genotyped at a cost similar to that of STR genotyping (Tokarska et al., 2009). SNP genotyping is more conducive to robotic automation than STR genotyping, thereby increasing testing (de Groot et al., 2021). Millions of SNPs have been identified for horses (Tozaki et al., 2021; Wade et al., 2009), and Hirota et al. (2010) and Holl et al. (2017) developed parentage testing panels based on these SNPs. Based on their reports, ISAG performed three pilot equine SNP comparison tests (2017, 2019, and 2021 SNP horse comparison tests [SNP-HCTs]), which applied the proposed 154 SNP panel (this was reduced to 147 SNPs for the 2021 SNP-HCT: https://www.isag.us/Docs/ISAG_Horse_SNP_Panels_2020.xlsx). For example, for SNP analysis in horses, MassARRAY (Hirota et al., 2010), microarray (Holl et al., 2017; Schaefer et al., 2017), and genotyping by sequencing (GBS; Flynn et al., 2021) have been performed. Holl et al. (2017) and Flynn et al. (2021) also investigated the utility of analyzing parentage testing using SNP genotyping in many horse breeds.

Flynn et al. (2021) and ISAG investigated the usefulness of the proposed International Society for Animal Genetics (P-ISAG) 147 SNP panel in thoroughbred horses. After light-breed horses, draft horses are the most common horses in Japan; in 2018, 43,210 light-breed horses and 4978 heavy horses, including draft horses, were raised in Japan, according to a report published by the Japan Equine Affairs Association (https://www.bajikyo.or.jp/). Since the 19th century, Percheron (PR), Breton (BR), and other draft horses were introduced for service and farming (Ishii, 2012). In addition to purebreds, crossbreds are also active in Ban'ei horse racing (Aoki et al., 2020). There are no reports of SNP diversity for the Japanese heavy-draft horse population. Therefore, the aim of this study was to evaluate the usefulness of the P-ISAG panel and determine whether SNP parentage testing can be applied to the BR and PR population in Japan using an equine SNP kit with over 600 SNPs that included a P-ISAG panel.

2 MATERIALS AND METHODS

2.1 Draft horses

All procedures involving animals in the present study were performed under the sampling protocol approved by the Committee for Animal Research and Welfare of the Laboratory of Racing Chemistry (20–04) and were performed using the Animal Research: Reporting of In Vivo Experiments (ARRIVE) guidelines (https://arriveguidelines.org/arrive-guidelines). The blood samples were obtained from 98 draft hoses of two pure breeds (47 BR and 51 PR) maintained in the National Livestock Breeding Center Tokachi Station, Hokkaido, Japan. These samples included 35 parent–offspring pairs, including three complete trios. These samples also included two full siblings and 306 half siblings.

2.2 Nucleic acid isolation

Genomic DNA was isolated from peripheral blood cells using a DNeasy Blood & Tissue Kit (QIAGEN, Hilden, Germany) according to the manufacturer's protocol. The quantity and quality of the genomic DNA were assessed using a Qubit photometer (Thermo Fisher Scientific, Waltham, MA, USA) and a Qubit dsDNA HS (High Sensitivity) Assay Kit (Thermo Fisher Scientific) according to the manufacturer's instructions. The genomic DNA samples were diluted to 10 ng/μL in a Tris-EDTA buffer.

2.3 GBS SNP panel performance and informativeness

Library preparation using the AgriSeqTM HTS Library Kit (A34143-Applied Biosystems; Thermo Fisher Scientific) and AgriSeq Custom Equine Parentage and ID Plus Traits and Disorders Panel (A50141-Applied Biosystems; Thermo Fisher Scientific), next-generation sequencing using the Ion S5XL system (Ion Torrent; Thermo Fisher Scientific), and genotyping using Torrent Variant Caller (TVC; https://github.com/iontorrent/Torrent-Variant-Caller-stable) are described in Section 1 in Supporting Information. The AgriSeq Custom Equine Parentage and ID Plus Traits and Disorders Panel comprise a P-ISAG panel (147 SNPs; autosomal chromosome), autosomal SNPs (417 SNPs), Y chromosome SNPs (1 SNP), and trait and disorder markers (40 markers, including three X chromosome markers). The obtained sequences were deposited in the DNA Data Bank of Japan Sequence Read Archive under accession number DRA014008.

The traits and disorder markers were removed before further analysis. We analyzed a set of 561 SNPs (147 P-ISAG SNPs and 414 autosomal SNPs) excluding 40 trait SNPs, one Y chromosome SNP, and three SNPs showing tri-alleles following analysis. Genotype quality control metrics were applied at the sample and SNP levels. Samples with a call rate greater than 90% were applied to the present analysis. SNPs indicating the following qualities were removed from further analysis: (1) coverage less than 10× and (2) call rate lower than 90%.

Minor allele frequency (MAF), polymorphic information content (PIC), observed heterozygosity (Ho), and expected heterozygosity (He) were directly calculated using CERVUS 3.0 (Field Genetics Ltd, London, UK; Kalinowski et al., 2007) based on the allelic frequencies of the remaining SNPs. Then, each breed's average PIC, Ho, He, and inbreeding coefficient (FIS) were estimated. Furthermore, PI, which is the probability that two animals would share the same genotype by chance, was calculated as described by Heaton et al. (2014). The parent exclusion probabilities for an individual SNP locus when (i) given two parents and one offspring: exclude a relationship (PE01) and (ii) given one parent and one offspring: exclude their relationship (PE02) were calculated as described by Jamieson and Taylor (1997). Finally, the respective breeds' combined PE01 and PE02 values were estimated.

2.4 Parentage analysis

Two types of parentage analyses were performed. The first was used for true parent–offspring pairs. There are no criteria for using SNPs to determine horse relationships. Relationships were categorized by referring to ISAG guidelines: (1) Case with offspring and one parent tested. The minimum number of common SNPs in the verification of offspring using the P-ISAG panel and the 561-SNP set was over 133 (90% of 147 SNPs) and 505 (90% of 561 SNPs), respectively. If the mismatches using the P-ISAG panel and the 561-SNP set were 0–1 and 0–5, respectively, it was recognized as parentage accepted. If the mismatches using the P-ISAG panel and the 561-SNP set were 2–3 and 6–10, respectively, it was considered parentage doubtful. If the mismatches using the P-ISAG panel and the 561-SNP set were ≥4 and ≥11, respectively, it was recognized as parentage excluded. (2) Case with offspring and both parents tested. The minimum number of common SNPs in the verification of offspring using the P-ISAG panel and the 561-SNP set was over 125 (85% of 147 SNPs) and 477 (85% of 561 SNPs), respectively. If the mismatches using the P-ISAG panel and the 561-SNP set were 0–2 and 0–8, it was recognized as parentage accepted. If the mismatches using the P-ISAG panel and the 561-SNP set were 3–4 and 9–15, respectively, it was considered as parentage doubtful. If the mismatches using the P-ISAG panel and the 561-SNP set were ≥5 and ≥16, respectively, it was recognized as parentage excluded.

In the second analysis (parentage discovery cases), we assumed that the parent–offspring relationship between the samples was unknown; therefore, we performed parentage analysis using a total combination of samples within the breeds. Relationships were categorized using cases in which offspring and one tested parent were true parent–offspring pairs.

The false positive rate was calculated as the ratio between falsely assumed relationships and the total number of unrelated pairs. In contrast, the false negative rate was calculated as the ratio between falsely excluded relationships and all true parent–offspring pairs (Holl et al., 2017).

3 RESULTS

3.1 Allele counts and heterozygosity

After GBS, we obtained approximately 78 million reads for 98 samples (Table S1). Sample call rates were in the ranges of 63.65%–99.82% (mean = 97.79%) for 47 BR and 97.16%–99.65% (mean = 98.89%) for 51 PR samples. In total, the 45 BR and 51 PR samples were analyzed for all SNPs of the P-ISAG panel; two BR samples with a call rate of <0.90 were excluded (Table 1). The BIEC349712, BIEC884767, and AX-104808710 markers of both breeds were tri-allelic and were thus removed from further analysis, as described earlier (Table S2). The number of SNP markers in the BR and PR samples was reduced to 545 and 551, respectively, after a quality control procedure (call rate > 90%), and these SNPs were used in further analysis (Table 1). The MAF was calculated using 45 BR and 51 PR samples across the P-ISAG panel and the 561-SNP set. The P-ISAG panel for the BR and PR samples displayed an average MAF of 0.306 and 0.301, compared with 0.308 and 0.312 across the 561-SNP set (Table 1), respectively. The number of SNPs in the P-ISAG panel with an MAF of <0.05 in BR and PR samples was 11 and 15, respectively (Table S3), in comparison with 24 and 27 for the 561-SNP set, respectively (Table S3). The number of SNPs in the P-ISAG panel with an MAF of 0 for the BR and PR samples was 4 and 4, respectively, compared with 5 and 7 for the 561-SNP set, respectively (Table S2).

TABLE 1. Observed heterozygosity (Ho), expected heterozygosity (He), fixation index (FIS), polymorphism information content (PIC), minor allele frequency, probability of identity (PI), and probability of exclusion (PE01^a and PE02^b) for Breton and Percheron breeds.

Breed	n	Number of SNP	Sample call rate > 0.9	Marker call rate > 0.9	Ho	He	FIS	PIC	MAF	PI	PE01	PE02
Breton	47	147	45	147	0.408	0.393	−0.039	0.304	0.306	1.90E-48	>0.9999	>0.9999
Breton	47	561	45	545	0.417	0.396	−0.052	0.308	0.308	1.01E-178	>0.9999	>0.9999
Percheron	51	147	51	147	0.394	0.381	−0.034	0.296	0.301	3.74E-47	>0.9999	>0.9999
Percheron	51	561	51	551	0.415	0.397	−0.044	0.308	0.312	8.53E-183	>0.9999	>0.9999

Note: PE01 score and PE02 score are different. Each PE01 score is higher than each PE02 score. The P-ISAG panel of the combined PE01 and PE02 in both populations was lower than that of the 561 SNPs set.
^a Given two parents and one offspring; exclude a relationship.
^b Given one parent and one offspring; exclude their relationship.

In the case of the P-ISAG panel for BR and PR samples, the average Ho was 0.408 and 0.394, the average He was 0.393 and 0.381, the PIC was 0.304 and 0.296, FIS was −0.039 and −0.034, and the PI values were 1.90 × 10⁻⁴⁸ and 3.74 × 10⁻⁴⁷, respectively (Table 1). For the 561-SNP set for BR and PR samples, the average Ho was 0.417 and 0.415, the average He was 0.396 and 0.397, PIC was 0.308 and 0.308, FIS was −0.052 and −0.044, and the PI values were 1.01 × 10⁻¹⁷⁸ and 8.53 × 10⁻¹⁸³, respectively (Table 1). The combined PE01 and PE02 scores for the P-ISAG panel and the 561-SNP set in BR and PR samples were >0.9999 (Table 1). Additionally, each PE01 score was higher than each PE02 score. The combined PE01 and PE02 in both populations based on the P-ISAG panel was lower than that based on the 561-SNP set.

3.2 Parentage analysis

Initially, the P-ISAG panel was used for parentage verification in the analyzed populations. In the 30 known dam–foal pairs, 24 pairs were perfect matches, and six pairs showed one mismatch with the P-ISAG panel. These mismatch markers were observed for the BIEC2-158202 and BIEC2-721061 markers. In the five known sire–foal pairs, these pairs were perfect matches with the P-ISAG panel. One trio in the three complete parent–offspring trios demonstrated a perfect match, and two trios demonstrated one mismatch with the P-ISAG panel. Then, the 561-SNP set was used for parentage verification in the analyzed populations. In the 30 known dam–foal pairs, 13 pairs indicated perfect matches, and the other pairs showed one or two mismatches with the 561-SNP set. These mismatches were observed for the BIEC2-158202, BIEC2-721061, AX-103056213, AX-103111201, AX-103402969, AX-103432095, AX-103502204, AX-103658688, AX-103890108, AX-104255078, AX-104438376, and AX-104591457 markers. One pair in the five known sire–foal pairs was a perfect match, and the other pairs showed one mismatch marker with the 561-SNP set. One trio in the three complete parent–offspring trios was a perfect match, and two trios demonstrated two or three mismatches with the 561-SNP set.

The P-ISAG panel was used for parentage verification in the parentage discovery cases. The 30 known dam–foal pairs and five known sire–foal pairs were deemed as accepted. However, four half-sibling pairs were deemed accepted. Additionally, one full-sibling pair, 25 half-sibling pairs, and 13 unrelated pairs were deemed doubtful. The number of mismatch markers of the four half-sibling pairs using the P-ISAG panel was 0–1. The number of mismatch markers of the one full-sibling pair, 25 half-sibling pairs, and 13 unrelated pairs using the P-ISAG panel was 2–3. There was a 0.18% false positive rate overall using the P-ISAG panel (Table 2). Although the 30 known dam–foal pairs and five known sire–foal pairs were qualified as accepted using the 561-SNP set, the others were qualified as excluded. The 561-SNP set did not exhibit any false positives. The numbers of mismatch markers of the four half-sibling pairs, which were qualified as accepted using the P-ISAG panel, were 15–17 using the 561-SNP set. The numbers of mismatch markers of the 1 full-sibling pair, 25 half-sibling pairs, and 13 unrelated pairs that were qualified as doubtful using the P-ISAG panel were 11–36 using the 561-SNP set.

TABLE 2. Performance of SNP panel on known families.

P-ISAG panel^a	Identical	Accepted	Doubtful	Excluded	Total	561 SNPs set^b	Identical	Accepted	Excluded	Total
Parent–offspring	0	35	0	0	35	Parent–offspring	0	35	0	35
Full siblings	0	0	1	1	2	Full siblings	0	0	2	2
Half siblings	0	4	25	277	306	Half siblings	0	0	306	306
Unrelated	0	0	13	1909	1922	Unrelated	0	0	1922	1922
FP (all)					0.18%	FP (all)				0%
FP (family)					1.30%	FP (family)				0%
FN					0%	FN				0%
Accuracy					98.10%	Accuracy				100%

Note: Pairwise relationships in the proposed ISAG SNP panel are classified as accepted if there were 0–1 excluding SNPs, doubtful for 2–3 excluding SNPs, and missed for 4+ exclusions.
Abbreviations: FN, false negative; FP, false positive.
^a P-ISAG panel is the proposed International Society for Animal Genetics 147 SNP panel.
^b 561 SNPs set is the combination of the P-ISAG panel and autosomal 414 SNPs (total of 561 SNPs).

4 DISCUSSION

In this study, we obtained information on SNP diversity in BR and PR horses in Japan.

The P-ISAG panel for BR and PR displayed average MAFs of 0.306 and 0.301, respectively, compared with 0.308 and 0.312 across the 561-SNP set, respectively (Table 1). These values are comparable with those previously reported for the horse breeds (Hirota et al., 2010; Holl et al., 2017). A multi-breed MAF average of 0.33 for 371 SNPs provides initial indications of the panel's capability to effectively aid identity, parentage verification, and parent discovery for breeds other than thoroughbreds (Flynn et al., 2021). Such MAF estimates (i.e., >0.300) bode well in terms of potential parentage analysis capability (Flynn et al., 2021; Holl et al., 2017).

Both PE01 and PE02 in BR and PR (>0.9999) support the potential effectiveness of the P-ISAG panel for parentage testing. These PEs are better than previous multi-breed observations (Flynn et al., 2021; Hirota et al., 2010; Holl et al., 2017). Additionally, the PE01 and PE02 scores in BR and PR of the 561-SNP set are better than that of the P-ISAG panel, suggesting the potential validity of the 561-SNP set for parentage testing and indicating that the 561-SNP set was more robust than the P-ISAG panel.

All pairs among the known parent–offspring pairs qualified as accepted based on parentage testing using the P-ISAG panel. This suggested that the P-ISAG panel is robust enough for parentage verification of draft horses in Japan. In contrast, the BIEC2-158202 and BIEC2-721061 markers showed mismatches when known parent–offspring pairs were tested for parentage. Despite the MAF of these markers being ≥0.277 with adequate read coverage, the number of mismatch pairs for BIEC2-158202 and BIEC2-721061 was 5 and 1, respectively. ISAG reported that the BIEC2-158202 marker showed a discrepancy among the platforms (https://www.isag.us/Docs/EquineGenParentage2021.pdf). Therefore, ISAG suggested that this marker may be removed from SNP panels. The mismatch of BIEC2-721061 was found in only one parentage relationship; thus, these results suggested that this mismatch was caused by sequence error or gene mutation. All pairs were also accepted with the 561-SNP set. However, AX-103056213, AX-103111201, AX-103402969, AX-103432095, AX-103502204, AX-103658688, AX-103890108, AX-104255078, AX-104438376, and AX-104591457 markers showed mismatch when known parent–offspring pairs were tested for parentage. The number of mismatch pairs for AX-103056213, AX-103111201, AX-103402969, AX-103432095, AX-103502204, AX-103658688, AX-103890108, AX-104255078, AX-104438376, and AX-104591457 was 2, 1, 2, 1, 1, 3, 7, 2, 1, and 1, respectively. The MAF of these markers ranged from 0.045 to 0.500 with adequate read coverage. The mismatch of AX-103111201, AX-103432095, AX-103502204, AX-104438376, and AX-104591457 was observed in only one parentage relationship, that is, BIEC2-721061; thus, these results suggested that this mismatch was caused by sequence error or gene mutation. In contrast, a mismatch in other markers was observed in several parentage relationships, suggesting that these markers were miscalled because of sequence error and may not be suitable as SNP genotyping markers in BR and PR horses.

In the parentage analysis using the P-ISAG panel, no exclusion or doubtful cases were identified in all valid parentage pairs within the BR and PR samples (Table 2), suggesting that the P-ISAG panel is useful for parentage verification of both breeds. However, in the parentage discovery cases, the false positive rate was 0.18% using the P-ISAG panel. In contrast, no false positives were recorded with the 561-SNP set. Previous reports indicated that the difficulty of excluding close relatives as parents is a known problem in exclusion analysis (Holl et al., 2017; Lee et al., 2013). Therefore, we propose that additional markers involving the combination of the P-ISAG panel and autosomal 414 autosomal SNPs (the 561-SNP set) presented here should be used to identify valid parent–offspring pairs of horses with unknown parentage relationships.

In conclusion, the P-ISAG panel exhibited 100% accuracy in identifying valid parentage pairs of the BR and PR horses in Japan. Thus, SNP typing has proved useful for parentage testing of draft horses in Japan. Further, in the parentage discovery cases, there was a 0.18% false positive rate overall with the P-ISAG panel. However, no false positives were recorded with the 561-SNP set. The 561-SNP set can be incorporated into the P-ISAG panel to enhance the robustness of parentage testing. Therefore, adding the 414 SNPs to the P-ISAG panel would be desirable. Future studies should evaluate the usefulness of the 561-SNP set in several horse breeds.

ACKNOWLEDGMENTS

The authors thank the staff of the Laboratory of Racing Chemistry for technical assistance. This research did not receive any specific grants from funding agencies in the public, commercial, or not-for-profit sectors.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflict of interest for this article.

Supporting Information

REFERENCES

Aoki, T., Chiba, A., Itoh, M., Nambo, Y., Yamagishi, N., Shibano, K. I., & Cheong, S. H. (2020). Colostral and foal serum immunoglobulin G levels and associations with perinatal abnormalities in heavy draft horses in Japan. Journal of Equine Science, 31(2), 29–34. https://doi.org/10.1294/jes.31.29
10.1294/jes.31.29
PubMed Google Scholar
Binns, M. M., Holmes, N. G., Rolliman, A., & Scott, A. M. (1995). The identification of polymorphic microsatellite loci in the horse and their use in thoroughbred parentage testing. British Veterinary Journal, 151(1), 9–15. https://doi.org/10.1016/S0007-1935(05)80057-0
10.1016/S0007-1935(05)80057-0
CAS PubMed Web of Science® Google Scholar
de Groot, M., Anderson, H., Bauer, H., Bauguil, C., Bellone, R. R., Brugidou, R., Buckley, R. M., Dovč, P., Forman, O., Grahn, R. A., Kock, L., Longeri, M., Mouysset-Geniez, S., Qiu, J., Sofronidis, G., van der Goor, L. H. P., & Lyons, L. A. (2021). Standardization of a SNP panel for parentage verification and identification in the domestic cat (Felis silvestris catus). Animal Genetics, 52(5), 675–682. https://doi.org/10.1111/age.13100
10.1111/age.13100
PubMed Web of Science® Google Scholar
de Groot, M., & van Haeringen, W. A. (2017). An evaluation of the International Society for Animal Genetics recommended parentage and identification panel for the domestic pigeon (Columba livia domestica). Animal Genetics, 48(4), 431–435. https://doi.org/10.1111/age.12555
10.1111/age.12555
CAS PubMed Web of Science® Google Scholar
Flynn, P., Morrin-O'Donnell, R., Weld, R., Gargan, L. M., Carlsson, J., Daly, S., Suren, H., Siddavatam, P., & Gujjula, K. R. (2021). Comparative analysis of single nucleotide polymorphisms and microsatellite markers for parentage verification and discovery within the equine Thoroughbred breed. bioRxiv, 2021-07. https://doi.org/10.1101/2021.07.28.453868
10.1101/2021.07.28.453868
Google Scholar
Heaton, M. P., Harhay, G. P., Bennett, G. L., Stone, R. T., Grosse, W. M., Casas, E., Keele, J. W., Smith, T. P., Chitko-McKown, C. G., & Laegreid, W. W. (2002). Selection and use of SNP markers for animal identification and paternity analysis in US beef cattle. Mammalian Genome, 13(5), 272–281. https://doi.org/10.1007/s00335-001-2146-3
10.1007/s00335-001-2146-3
CAS PubMed Web of Science® Google Scholar
Heaton, M. P., Leymaster, K. A., Kalbfleisch, T. S., Kijas, J. W., Clarke, S. M., McEwan, J., Maddox, J. F., Basnayake, V., Petrik, D. T., Simpson, B., Smith, T. P., Chitko-McKown, C. G., & International Sheep Genomics Consortium. (2014). SNPs for parentage testing and traceability in globally diverse breeds of sheep. PLoS ONE, 9(4), e94851. https://doi.org/10.1371/journal.pone.0094851
10.1371/journal.pone.0094851
PubMed Web of Science® Google Scholar
Hirota, K., Kakoi, H., Gawahara, H., Hasegawa, T., & Tozaki, T. (2010). Construction and validation of parentage testing for thoroughbred horses by 53 single nucleotide polymorphisms. The Journal of Veterinary Medical Science, 72, 719–726. https://doi.org/10.1292/jvms.09-0486
10.1292/jvms.09-0486
CAS PubMed Web of Science® Google Scholar
Holl, H. M., Vanhnasy, J., Everts, R. E., Hoefs-Martin, K., Cook, D., Brooks, S. A., Carpenter, M. L., Bustamante, C. D., & Lafayette, C. (2017). Single nucleotide polymorphisms for DNA typing in the domestic horse. Animal Genetics, 48(6), 669–676. https://doi.org/10.1111/age.12608
10.1111/age.12608
CAS PubMed Web of Science® Google Scholar
Ishii, M. (2012). The history and production of heavy draft horses (in Japanese). The Hip, 49, 26–33. http://id.nii.ac.jp/1588/00000921/
Google Scholar
Jamieson, A., & Taylor, S. C. (1997). Comparisons of three probability formulae for parentage exclusion. Animal Genetics, 28(6), 397–400. https://doi.org/10.1111/j.1365-2052.1997.00186.x
10.1111/j.1365-2052.1997.00186.x
CAS PubMed Web of Science® Google Scholar
Jones, A. G., Small, C. M., Paczolt, K. A., & Ratterman, N. L. (2010). A practical guide to methods of parentage analysis. Molecular Ecology Resources, 10(1), 6–30. https://doi.org/10.1111/j.1755-0998.2009.02778.x
10.1111/j.1755-0998.2009.02778.x
PubMed Web of Science® Google Scholar
Kalinowski, S. T., Taper, M. L., & Marshall, T. C. (2007). Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Molecular Ecology, 16(5), 1099–1106. https://doi.org/10.1111/j.1365-2052.2007.01632.x
10.1111/j.1365-294X.2007.03089.x
PubMed Web of Science® Google Scholar
Lee, J. C., Tsai, L. C., Chu, P. C., Lin, Y. Y., Lin, C. Y., Huang, T. Y., Yu, Y. J., Linacre, A., & Hsieh, H. M. (2013). The risk of false inclusion of a relative parentage testing—An in silico population study. Croatian Medical Journal, 54(3), 257–262. https://doi.org/10.3325/cmj.2013.54.257
10.3325/cmj.2013.54.257
PubMed Web of Science® Google Scholar
Lipinski, M. J., Amigues, Y., Blasi, M., Broad, T. E., Cherbonnel, C., Cho, G. J., Corley, S., Daftari, P., Delattre, D. R., Dileanis, S., Flynn, J. M., Grattapaglia, D., Guthrie, A., Harper, C., Karttunen, P. L., Kimura, H., Lewis, G. M., Longeri, M., Meriaux, J. C., … Lyons, L. A. (2007). An international parentage and identification panel for the domestic cat (Felis catus). Animal Genetics, 38(4), 371–377. https://doi.org/10.1111/j.1365-2052.2007.01632.x
10.1111/j.1365-2052.2007.01632.x
CAS PubMed Web of Science® Google Scholar
Marklund, S., Ellegren, H., Eriksson, S., Sandberg, K., & Andersson, L. (1994). Parentage testing and linkage analysis in the horse using a set of highly polymorphic microsatellites. Animal Genetics, 25(1), 19–23. https://doi.org/10.1111/j.1365-2052.1994.tb00050.x
10.1111/j.1365-2052.1994.tb00050.x
CAS PubMed Web of Science® Google Scholar
Morin, P. A., Luikart, G., Wayne, R. K., & the SNP workshop group. (2004). SNPs in ecology, evolution and conservation. Trends in Ecology & Evolution., 19(4), 208–216. https://doi.org/10.1016/j.tree.2004.01.009
10.1016/j.tree.2004.01.009
Web of Science® Google Scholar
Perlin, M. W., Lancia, G., & Ng, S. K. (1995). Toward fully automated genotyping: Genotyping microsatellite markers by deconvolution. American Journal of Human Genetics, 57(5), 1199–1210.
CAS PubMed Web of Science® Google Scholar
Schaefer, R. J., Schubert, M., Bailey, E., Bannasch, D. L., Barrey, E., Bar-Gal, G. K., Brem, G., Brooks, S. A., Distl, O., Fries, R., Finno, C. J., Gerber, V., Haase, B., Jagannathan, V., Kalbfleisch, T., Leeb, T., Lindgren, G., Lopes, M. S., Mach, N., … McCue, M. E. (2017). Developing a 670k genotyping array to tag~ 2M SNPs across 24 horse breeds. BMC Genomics, 18(1), 565. https://doi.org/10.1186/s12864-017-3943-8
10.1186/s12864-017-3943-8
PubMed Web of Science® Google Scholar
Tokarska, M., Marshall, T., Kowalczyk, R., Wójcik, J. M., Pertoldi, C., Kristensen, T. N., Loeschcke, V., Gregersen, V. R., & Bendixen, C. (2009). Effectiveness of microsatellite and SNP markers for parentage and identity analysis in species with low genetic diversity: The case of European bison. Heredity, 103(4), 326–332. https://doi.org/10.1038/hdy.2009.73
10.1038/hdy.2009.73
CAS PubMed Web of Science® Google Scholar
Tozaki, T., Ohnuma, A., Kikuchi, M., Ishige, T., Kakoi, H., Hirota, K., Kusano, K., & Nagata, S. (2021). Rare and common variant discovery by whole-genome sequencing of 101 thoroughbred racehorses. Scientific Reports, 11(1), 16057. https://doi.org/10.1038/s41598-021-95669-1
10.1038/s41598-021-95669-1
CAS PubMed Web of Science® Google Scholar
Väli, U., Einarsson, A., Waits, L., & Ellegren, H. (2008). To what extent do microsatellite markers reflect genome-wide genetic diversity in natural populations? Molecular Ecology, 17(17), 3808–3817. https://doi.org/10.1111/j.1365-294X.2008.03876.x
10.1111/j.1365-294X.2008.03876.x
CAS PubMed Web of Science® Google Scholar
van Asch, B., Alves, C., Gusmão, L., Pereira, V., Pereira, F., & Amorim, A. (2009). A new autosomal STR nineplex for canine identification and parentage testing. Electrophoresis, 30(2), 417–423. https://doi.org/10.1002/elps.200800307
10.1002/elps.200800307
PubMed Web of Science® Google Scholar
Wade, C. M., Giulotto, E., Sigurdsson, S., Zoli, M., Gnerre, S., Imsland, F., Lear, T. L., Adelson, D. L., Bailey, E., Bellone, R. R., Blöcker, H., Distl, O., Edgar, R. C., Garber, M., Leeb, T., Mauceli, E., MacLeod, J. N., Penedo, M. C., Raison, J. M., … Lindblad-Toh, K. (2009). Genome sequence, comparative analysis, and population genetics of the domestic horse. Science, 326(5954), 865–867. https://doi.org/10.1126/science.1178158
10.1126/science.1178158
CAS PubMed Web of Science® Google Scholar
Wu, X.-L., Xu, J., Li, H., Ferretti, R., He, J., Qiu, J., Xiao, Q., Simpson, B., Michell, T., Kachman, S. D., Tait, R. G. J., & Bauk, S. (2019). Evaluation of genotyping concordance for commercial bovine SNP arrays using quality-assurance samples. Animal Genetics, 50(4), 367–371. https://doi.org/10.1111/age.12800
10.1111/age.12800
CAS PubMed Web of Science® Google Scholar

Volume94, Issue1

January/December 2023

e13854

Evaluation of parentage testing using single nucleotide polymorphism markers for draft horses in Japan

Abstract

1 INTRODUCTION

2 MATERIALS AND METHODS

2.1 Draft horses

2.2 Nucleic acid isolation

2.3 GBS SNP panel performance and informativeness

2.4 Parentage analysis

3 RESULTS

3.1 Allele counts and heterozygosity

3.2 Parentage analysis

4 DISCUSSION

ACKNOWLEDGMENTS

CONFLICT OF INTEREST STATEMENT

Supporting Information

REFERENCES

References

Information

About Wiley Online Library

Help & Support

Opportunities

Connect with Wiley

Evaluation of parentage testing using single nucleotide polymorphism markers for draft horses in Japan

Abstract

1 INTRODUCTION

2 MATERIALS AND METHODS

2.1 Draft horses

2.2 Nucleic acid isolation

2.3 GBS SNP panel performance and informativeness

2.4 Parentage analysis

3 RESULTS

3.1 Allele counts and heterozygosity

3.2 Parentage analysis

4 DISCUSSION

ACKNOWLEDGMENTS

CONFLICT OF INTEREST STATEMENT

Supporting Information

REFERENCES

References

Related

Information