Indian Journal of Animal Research

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Microsatellite DNA Analysis of Genetic Diversity and Parentage Testing in Popular Dog Breeds in India

Yogeshwar Sandhu1, Bhawanpreet Kaur1, Manpreet Kaur1, H.M. Yathish2, C.S. Mukhopadhyay1,*
1Department of Bioinformatics, College of Animal Biotechnology, Guru Angad Dev Veterinary and Animal Sciences University, Ludhiana-141 004, Punjab, India.
2Department of Animal Genetics and Breeding, College of Veterinary Science, Karnataka Veterinary Animal and Fisheries Sciences University, Bangalore-560 024, Karnataka, India.

ABSTRACT

Background: Microsatellite DNA sequencing has emerged as an important method for determining genetic variety and parentage in domesticated species, including popular dog breeds. The role of microsatellite markers helps in understanding the genetic landscape of dog breeds in India, where the growing popularity of particular breeds has generated worries about inbreeding and loss of genetic variety.

Methods: For the parentage testing in canine microsatellite length, polymorphism markers were used to check the efficacy of the markers. In the current study 5 fluorescently labeled 12 SSR markers were used to check the use of the markers in popular owned-dog breeds (Labrador, German Shepherd, Pug, Mudhol Hound, Tibetan Mastiff, Gaddi dog, Beagle, Belgian Malinois, Pointer, and Cane Corso). The number of alleles, heterozygosity, polymorphism information content and probability of exclusion were determined for all the markers to check the effectiveness of the markers.

Result: The analysis utilized a panel of microsatellite markers to evaluate genetic variation among several breeds including Punjab, Haryana, Himachal Pradesh and Karnataka. The mean number of alleles per locus ranged from 5 to 29 and the effective number ranged from 3.6 to 15.2. The expected heterozygosity was greater than 0.73. The population inbreeding coefficient (FIS) demonstrated that there was no inbreeding in the breeds studied. The polymorphism information content and the probability of the exclusion values were greater than 0.65. The combined probability of exclusion for all the breeds was (2.82E-12) 0.99999995. The findings revealed that the 12 particular microsatellite markers chosen for paternity testing demonstrated substantial exclusion probability for determining parentage.

INTRODUCTION

Dogs have coexisted with humans for thousands of years and have been used as guard animals to herd livestock, hunt and protect homes, as well as companion animals (Pedersen et al., 2015; Wayne and von Holdt, 2012; Larson et al., 2012). Canis lupus, the wolf, is estimated to have given rise to dogs roughly 100,000 years ago. Since prehistoric times, the dog species has evolved through stringent selection (for desirable traits) which has ultimately led to the evolution of more than 350 distinct breeds of dogs worldwide. Scientific breeding of dogs is now a popular practice to entice dog owners and buyers by stamping on desirable traits of purebred dogs (Haq et al., 2024). This although increases the price, also necessitates molecular testing of parentage to verify the claim of parentage of the animals.
       
Molecular markers can identify the degree of genetic relatedness between animals, making parentage and individual identification easier. Microsatellites are tracts composed of short tandem repeats (STRs) or simple sequence repeats (SSRs) of DNA patterns ranging between one to six nucleotides, with repeats of 5 to 50 times (Vieira et al., 2016). Repeat sequences are distributed ubiquitously in the genome, highly variable and have been demonstrated to be effective tools in genome mapping (Oudet et al., 1991). Microsatellites have been effectively used to determine the molecular signatures or DNA fingerprints of individuals (humans and animals), to determine parentage, to build pedigree, to select animals through marker-assisted selection for genetic improvement through selective breeding, etc. The use of microsatellites as molecular markers for animal identification and parentage verification generated highly accurate and effective results (Linacre et al., 2011).
       
Identification of breed-specific molecular signatures benefits dog owners and breeders and helps characterize the dog germplasm maintained in India (both foreign and indigenous) (Singh 2022; Raja et al., 2017). Parentage determination using microsatellite and SNP marker panels (Kalbfleisch et al., 2018; Heaton et al., 2014; Yu et al., 2015; Flanagan and Jones, 2019) have been reported for different species. Relevant literature reports the associated prospects and challenges with parentage determination in humans and animals (Stark and Delatycki 2014; Chan et al., 2014; Goswami, 2015). Minimal works have reported on applications of SSR markers for parentage determination in dogs (Kaur et al., 2025; Hollinshead et al., 2020), especially in India (Sowmyashree et al., 2022). The present research has been designed to investigate the informative microsatellite markers for parentage testing in canines. A Ph.D. thesis has been submitted from our lab on parentage determination in cattle and buffalo using microsatellites as well as SNP markers (Singh 2021) and relevant literature was published and presented (Rana et al., 2025; Singh 2022; Mukhopadhyay and Singh 2021). This work aims to create and standardize a set of SSR primers to validate and verify parentages in dogs using the most popular dog breeds maintained in India.

MATERIALS AND METHODS

Experimental animal selection and DNA extraction
 
The experimental animals were selected based on trio and duo relationship to assess the informativeness of the markers for parentage determination, belonging to ten divergent germplasm, namely, (Labrador (Abbreviated as Lab), German Shepherd (GS), Pug, Mudhol Hound (MH), Tibetan Mastiff (TMS), Beagle, Belgian Malinois (BM), Pointer and Cane Corso (CC)) breeds and Gaddi dogs. The animals were available from dog owners and breeders belonging to four Indian states: Punjab, Himachal Pradesh, Haryana and Maharastra (Table 1). Two ml of peripheral blood was collected aseptically with an anticoagulant (0.5 M EDTA). Genomic DNA was extracted using the commercially available kit and Phenol:Chloroform: Isoamyl alcohol (PCI) method with modification of (Sambrook and Russel 2001). Samples collected from distant places were stored at -20° and transported to the lab maintaining a cold chain. The quality and quantity of the extracted DNA were then measured with a NanoDrop (Thermo Scientific, Waltham, MA, USA) and agarose gel electrophoresis, respectively.

Table 1: Family orientation (Sire/Dam/Offspring) and breed detail of the experimental animals.


 
SSR-marker selection
 
Initially, 15 microsatellite markers (5¢ fluorescent labeled with FAM, HEX, or TAMRA) (Table 2) were selected based on the higher polymorphism information content (PIC) and observed heterozygosity (He) from reported primers (Yuzbasiyan-Gurkan  et al., 1997; Kukekova et al., 2004). The primers were custom synthesized and the SSR length polymorphism was done from Biologia Research India.  Pvt. Ltd, Karnal, India.

​

Table 2: Detail of the 5' labeled simple sequence repeat primers.


 
Analysis of SSR-length polymorphism results
 
The genotypic data were first manually checked for inconsistencies using Microsoft Office Excel 2007. The Peak Scanner™ Software v1.0 and GeneMapper® Software were used to perform the analysis of *.fsa files. The Windows OS-based stand-alone Peak Scanner™ Software (v1.0) (https://peak-scanner-software.software.informer.com/1.0/) was used to accurately identify the correct peaks and fragment sizes vis-s-via functional annotation (viz. labeling, merging and splitting) of peaks and further the peak data was feed in Microsoft Excel for the genetic analysis parameters. The descriptive statistics based on genotyping data were obtained using the Genetic Analysis in Excel (GenAlEx) tool v. 6.5 (Peakall and Smouse, 2012). The number of alleles per locus (Na), the effective number of alleles (Ne) and the fixation index (F) expected homozygosity and heterozygosity (Levene 1949) and expected heterozygosity (Nei 1973).
       
The Hardy-weinberg equilibrium test was carried out with the help of the POPGENE computer program (Raymond and Rousset, 1995), which was used to estimate F-statistics (the global mean inbreeding coefficient [FIT], the average inbreeding coefficient of an individual concerning the local subpopulation [FIS] and the average inbreeding coefficient of subpopulations relative to the total population [FST]) for each locus, the pairwise FST Allelic occurrence, Genic Variation Statistics for All Locations Molecular Evolutionary Genetics, Summary of Heterozygosity Statistics. The exclusion probability (Jamieson and Taylor 1997) and the polymorphism information content (Botstein et al., 1980) were calculated by using PARFEX v1.0 EXCEL™ tool and Cervus 3.0.7 software (https://cervus.software.informer.com/download/). The probability of exclusion or power of exclusion (PE) is a priori statistic that determines the likelihood for a sample to be representative of a population (Zhou et al., 2017).
       
The genetic parameters were obtained using the following formula:
 
Polymorphism informative content (PIC) for co-dominant markers =
 

 
 
Where,

n = Number of alleles.
pi and pj = Allele frequencies in population i and j respectively (Botstein  et al., 1980).
 
 
Where,

h = Homozygosity.
p and q = Frequencies of two alleles of a locus.

 
 
Where,

pi = Frequency of ith allele of a locus.
 
 
Where,

h = Frequency of heterozygotes.
H = Frequency of homozygotes.

 Likelihood ratio for parentage assignment =  
              
 
Where,
H1 = The first hypothesis stating the agreement that the  candidate parental pair is the true parental pair.
H2 = Hypothesis stating that the alternative candidate parental pair is the true parental pair.
D = Data in the form of offspring and parental genotypes.

RESULTS AND DISCUSSION

Genetic diversity of microsatellites
 
Out of 15 SSR markers used 12 markers were amplified for the samples under study. The results obtained have been presented based on the output of these 12 markers. Table 3 shows the sample size, observed number of alleles, effective number of alleles and microsatellite loci of the experimental samples. The number of alleles per SSR locus (Na) ranged from 5 (NPPM10) to 29 (PEZ12), with a mean of 15.4167 (±8.2402 s.e.). The number of effective alleles per locus (Ne) varied from 3.6140 (NPPM10) to 15.2178 (PEZ16), with a mean value of 7.9664 (±4.2066 s.e.). The mean value of Shannon’s Information index (I) was 2.1804 (± 0.5581 s.e.).

​

Table 3: Genetic parameters of the 12 microsatellite loci obtained from dog populations.


       
Measures of heterozygosity statistics
 
The average expected heterozygosity (Ave_Exp_Het) across all loci was 0.85 (Table 4). The observed heterozygosity (Obs_Het) average was 0.80. The expected heterozygosity was found to be relatively high for all the markers as all the markers have heterozygosity of more than 0.5. All 12 markers were found to be highly polymorphic and can be used for the genetic studies of the dogs.

​

Table 4: Measures of genetic variation (heterozygosity statistics for all loci) in dog population.


       
Therefore, the Mean (± SEM) observed heterozygosity, averaged over loci, was 0.8020 ± 0.1345, which was lower than the expected heterozygosity.
       
The population inbreeding coefficient (FIS) ranged from -0.1400 (NPPM10) to 0.2274 (NPPM930). The Fis value was positive in a few markers, indicating the in-breeding of the population. The FST values of all the loci was 0.0000 which indicated there was no genetic subdivision. The genetic variation existed within dogs (Table 5).

​

Table 5: Summary of the F-statistics and gene flow among dog populations.


       
Hardy-weinberg test
 
The results of HWE tests of the 12 microsatellite loci indicated UOR4107 shows significant differences (P>0.05) and NPPM30, NPPM769, NPPM905, NPPM930, PEZ16, NPPM244 are statistically significant (P>0.001) (Table 6). The deviation from the Hardy Weinberg can be due to the non-random mating or due to some evolutionary processes. The Ewens-Watterson test of neutrality differentiates between the observed and expected hemizygosities in a sample under a neutral model (Table 7). The mean values, corresponding standard errors of the means, lower and upper limits at 95% level of confidence have been adumbrated for each SSR markers.

​

Table 6: Summary of the chi-square and hardy weinberg test.



​

Table 7: Ewens-Watterson Test for Neutrality for the simple sequence repeat markers.


       
The observed F for the markers lies between the upper and the lower limit of 95% which depicted those markers were not under any selection pressure or associated with any of the quantitative traits. Thus, these markers can be used for the parentage identification in dogs.
 
Polymorphism information content and probability of exclusion
 
Polymorphism information content (PIC) and the probability of exclusion are indeed important measures in genetic research of microsatellites. PIC and probability of exclusion were used to assess the informativeness of a genetic marker. High PIC values suggest that a marker is highly informative and can discriminate well between alleles, making it useful for various applications such as genetic diversity and parentage studies (Serrote et al., 2020). The use of quantitative genotypes for statistical assignment of parentage has been discussed by Hamilton (2021). Parentage assignment using genotyping by sequencing data has been recently reported by Whalen  et al. (2019) All the markers in the study were highly polymorphic, as all had a PIC value of more than 0.673. Probability of exclusion represents the marker’s average capability to eliminate one parent when the genotype of that parent is unknown, to confirm the parent’s contribution to the offspring’s genotype when the offspring’s genotype is either known or unknown, or to exclude both potential parent pairs when determining offspring parentage. The exclusion probability (Table 8) for all the markers values greater than 0.658 which depicts that all the markers were highly informative which can help to achieve the 99.9% success rate for the parentage studies as the combined exclusion probability (CPE) values of 0.99999995.

​

Table 8: Polymorphism information content (PIC) and probability of exclusion (PE) of the markers.


               
A total of 12 microsatellite loci were found and analyzed after being combined into four multiplex PCR reaction systems and genotyped in two multiplex loading systems. Because of the high variability of these microsatellite loci, very precise genotyping panels could be utilized for individual genotyping, parentage verification and individual identification. The total diversity structure was found to be quite strong and it corresponded with the use of the varieties and the breeding program’s tactics based on parental group pairings. All of these findings highlight the significance and necessity of maintaining these genotypes in germplasm repositories.

CONCLUSION

In conclusion, the results of analyzing the dog populations in India using 12 new microsatellite markers revealed their average anticipated heterozygosity and observation heterozygosity. As a result, these microsatellite markers are highly applicable to the populations studied. These findings suggest that the microsatellite markers have acceptable resolution when used to detect variations between dog breeds. Furthermore, power exclusion will be employed as a strong tool for paternity testing.

ACKNOWLEDGEMENT

The authors thankfully acknowledge the funding provided by the Department of Biotechnology, Government of India, through the collaborative research project “Parentage Determination and Cytogenetic Profiling in Dogs German Shepherd (GS) (DBT-19I)”. A special note of thanks to Mr. Anil Jamwal, integrated farmer and T. Mastiff breeder, Palampur, Mr. Newton Sidhu, Director, PHG-CTBI, Mohali and different pet owners for providing samples of dogs.
 
Future perspectives
 
In the future, the microsatellites identified in this work could be used to assess dog population structure, history and diversity, hence assisting in the genetic improvement of Indian dog breeds. To overcome outstanding identifying issues, more phenotypic and passport data checks are required.
 
Author contributions
 
Ygeshwar Sandhu: Did the lab work and sample collection; Bhawanpreet Kaur: manuscript writing; Manpreet Kaur: Data analysis; Yatish H.M.: Sample collection from Karnataka; C.S. Mukhopadhyay:PI of the project, guidance, proofreading.
       
All authors contributed to the manuscript revision and read and approved the present version.
 
Ethics approval and consent to participate
 
Permission from the Institutional Animal Ethics Committee (IAEC) was obtained (IAEC/2020/200-219, 14.12.2020).
 
Consent for publication
 
Obtained from the Office of the Director of Research, Guru Angad Dev Veterinary and Animal Sciences University, Ludhiana.
 
Research involving human participants and/or animals
 
Animals with IAEC Permission from the Institutional Animal Ethics Committee (IAEC) was obtained (IAEC/2020/200-219, 14.12.2020).
 
Informed consent
 
NA.

CONFLICT OF INTEREST

The authors declare no conflict of interest.

REFERENCES


  1. Botstein, D., White, R.L., Skolnick, M. and Davis, R.W. (1980). Construction of a genetic linkage map in man using restriction fragment length polymorphism. Am. J. Human Genet. 32: 314-331.

  2. Chan, T.K., Hui, E. and Chung, B. (2014). A child born with Edward’s syndrome: The legal and moral duty to accede to the request for parentage determination. J. Med. Ethics. 40(6): 383-386. doi: 10.1136/medethics-2012-100793.

  3. Flanagan, S.P. and Jones, A.G. (2019). The future of parentage analysis: From microsatellites to SNPs and beyond. Mol. Ecol. 28(3): 544-567. doi: 10.1111/mec.14988.

  4. Goswami, G.K. (2015). The genetic truth of surrogate parentage. Med. Leg. J. 83(4): 188-193. doi: 10.1177/00258172155 76877.

  5. Hamilton, M.G. (2021). Maximum likelihood parentage assignment using quantitative genotypes. Heredity (Edinb). 126(6): 884-895. doi: 10.1038/s41437-021-00421-0.

  6. Haq A.U., Malik H.U., Randhawa S.S., El-Shazly M., Chandran D. (2024). Behavioural disorders in dogs and cats: A review. Agricultural Reviews. 45(3): 514-519. doi: 10.18805/ ag.R-2410.

  7. 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. and Chitko-McKown, C.G. (2014). International sheep genomics consortium. SNPs for parentage testing and traceability in globally diverse breeds of sheep. PLoS One. 9(4): e94851. doi: 10.1371/ journal.pone.0094851.

  8. Hollinshead, F.K., Ontiveros, M., Burns, J.G., Magee, C. and Hanlon, D.W. (2020). Factors influencing parentage ratio in canine dual-sired litters. Theriogenology. 158: 24-30. doi: 10. 1016/j.theriogenology.2020.08.030.

  9. Jamieson, A. and Taylor, S.C. (1997). Comparisons of three probability formulae for parentage exclusion. Anim Genet. 28(6): 397-400. doi: 10.1111/j.1365-2052.1997.00186.x. 

  10. Kalbfleisch, T.S., Murdoch, B.M., Smith, T.P.L., Murdoch, J.D., Heaton, M.P. and McKay, S.D. (2018). A SNP resource for studying North American moose. F1000Res. 7: 40. doi: 10.12688/ f1000research.13501.1.

  11. Kaur, B., Yathish, H.M., Kashyap, N. and Mukhopadhyay, C.S. (2025). Phylogeographic and genetic diversity analysis through genome-wide SNPs in indigenous and exotic canine breeds owned in India. Discov. Biotechnol. 2: 5. https:// doi.org/10.1007/s44340-025-00012-3.

  12. Kukekova, A.V., Trut, L.N., Oskina, I.N. et al., (2004). A marker set for construction of a genetic map of the silver fox (Vulpes vulpes) J. Hered. 95: 185-194. doi: 10.1093/jhered/esh033.

  13. Larson, G., Karlsson, E.K., Perri, A., Webster, M.T., Ho, S.Y.W., Peters, J., Stahl, P.W., Piper, P.J., Lingaas, F., Fredholm, M. et al. (2012). Rethinking dog domestication by integrating genetics, archaeology and biogeography. Proc. Natl. Acad. Sci. USA. 109, 8878-8883, doi:10.1073/pnas.120300 5109.

  14. Levene H. (1949). On a matching problem arising in genetics. Ann. Math. Stat. 20: 91-94.

  15. Linacre, A., Gusmão, L., Hecht, W., Hellmann, A.P., Mayr, W.R., Parson, W., Prinzg, M., Schneider P.M. and Morling, N. (2011). ISFG: Recommendations regarding the use of non-human (animal) DNA in forensic genetic investigations. Forensic Science International: Genetics. 5(5): 501-505. https://doi. org/10.1016/j.fsigen.2010.10.017. 

  16. Mukhopadhyay, C.S. and Singh, K.P. (2021). Accessing the Most Informative SNPs for Parentage Determination in Bovines: Haplotype and Tapered-end SNP Analysis. Invited Talk Presented in Vth Annual Convention of Society of Veterinary Biochemists and Biotechnologists of India (SVBBI) and National Symposium on Current Challenges for Animal Biochemists and Biotechnologists for Improving Animal Health and Production in Post COVID Scenario on March 24-25, 2021 Organized by College of Veterinary Science  and Animal Husbandry. DUVASU, Mathura, Uttar Pradesh.

  17. Nei, M. (1973). Analysis of gene diversity in subdivided populations Proc. Natl. Acad. Sci. USA. 70: 3321-3323.

  18. Oudet, C., Heilig, R., Hanauer, A. and Mandel, J-L. (1991). Non- radioactive assay for new microsatellite polymorphisms at the 5 end of the dystrophin gene and estimation of intragenic recombination. American Journal of Human Genetics. 49: 311-19. 

  19. Peakall, R. and Smouse, P.E. (2012). GenAlEx 6.5: genetic analysis in Excel. Population Genetic Software for Teaching and Research-An Update. 28: 2537-2539. https://doi.org/10. 1093/bioinformatics/bts460.

  20. Pedersen, N.C., Liu, H., Leonard, A. and Griffioen, L. (2015). A search for genetic diversity among Italian greyhounds from continental Europe and the USA and the effect of inbreeding on susceptibility to autoimmune disease. Canine Genet. Epidemiol. 2: 17. doi:10.1186/s40575015 00309.

  21. Raja, K.N., Saravanan, R., Devendran, P., Singh, K.P., Mishra, K.A., Ganguly, I. (2017). Cytogenetic profile of rajapalayam dog breed of Southern India. Indian Journal of Animal Research. 52(9): 1243-1247. doi: DOI:10.18805/ijar.v0iOF.9132.

  22. Rana, K., Randhawa, S.S., Mahindroo, J., Sethi, R.S. and Mukhopadhyay, C.S. (2025). Biocomputational identification of microRNAs from indigenous Gaddi dog genome. Gene Reports. 102167. https://doi.org/10.1016/j.genrep.2025.102167.

  23. Raymond, M. and Rousset, F. (1995). GENEPOP (version 1.2): Population genetics software for exact tests and ecumenicism. J. Hered. 86: 248-9. a111573.

  24. Sambrook, J.F. and Russell, D.W. (2001). Molecular Cloning: A Laboratory Manual (3-Volume Set) Edition. 3. Cold Spring Harbor Laboratory Press.

  25. Serrote, C.M.L., Reiniger, L.R.S., Silva, K.B., dos Santos Rabaiolli, S.M. and Stefanel, C.M. (2020). Determining the polymorphism information content of a molecular marker. Gene. 726: 144175.

  26. Singh, K. (2021). Identification of microsatellite and SNP markers for parentage determination in bovines. Ph.D. thesis submitted to the Guru Angad Dev Veterinary and Animal Sciences University, Ludhiana.

  27. Singh, K., Mukhopadhyay, C.S., Kaur, S. and Arora, J.S. (2022). Identification of microsatellite-based markers and breed- specific single nucleotide polymorphism panels for parentage assignment in bovines. Acta Scientific Veterinary Sciences. 4(9): 115-128. DOI: 10.31080/ASVS.2022.04.0505.

  28. Singh, P.A. (2022).  Labrador retriever a perfect apartment dog: A review. Bhartiya Krishi Anusandhan Patrika. 36(4): 353- 354. doi: 10.18805/BKAP325.

  29. Sowmyashree B.L., Jayashree, R., Kumar, N.S., Nagaraja, R., Nagaraja, C.S., Rajeshwari, Y.B. and Banuprakash A.R. (2022). Microsatellite DNA polymorphism studies in mudhol hound dog native of India. Indian Journal of Animal Research. 56(7): 848-853. doi: 10.18805/IJAR.B-4230.

  30. Stark, Z. and Delatycki, M.B. (2014). Parentage determination: A medical responsibility? J. Med Ethics. 40(6): 387-8. doi: 10.1136/medethics-2012-101158.

  31. Vieira, M.L.C., Santini, L., Diniz, A.L. and Munhoz, C.D.F. (2016). Microsatellite markers: What they mean and why they are so useful. Genetics and Molecular Biology. 39: 312-328.

  32. Wayne, R.K. and von Holdt, B.M. (2012). Evolutionary genomics of dog domestication. Mamm. Genome. 23: 3-18. doi: 10. 1007/s00335 011 9386 7.

  33. Whalen, A., Gorjanc, G. and Hickey, J.M. (2019). Parentage assignment with genotyping-by-sequencing data. J. Anim. Breed Genet. 136(2): 102-112. doi: 10.1111/jbg.12370.

  34. Yu, G.C., Tang, Q.Z., Long, K.R., Che, T.D., Li, M.Z. and Shuai, S.R. (2015). Effectiveness of microsatellite and single nucleotide polymorphism markers for parentage analysis in European domestic pigs. Genet Mol. Res.13. 14(1): 1362-1370. doi: 10.4238/2015.

  35. Yuzbasiyan-Gurkan, V., Blanton, S.H., Cao, Y., Ferguson, P., Li, J., Venta, P.J. and Brewer, G.J. (1997). Linkage of a microsatellite marker to the canine copper toxicosis locus in Bedlington terriers. Am. J. Vet. Res. 58: 23-27.

  36. Zhou, M., Zhang, H.Q. and Wang, J. (2017). Formula derivation and validation of probability of exclusion in the cases of standard triplet parentage testing. Fa Yi Xue Za Zhi. 33(4): 363-367. Chinese. doi: 10.3969/j.issn.1004-5619. 2017. 04.006.
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