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

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23 November 2023

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24 November 2023

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Abstract
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) maintained of India (not necessarily indigenous breeds). 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. The mean number of alleles per locus ranged from 5 to 29 and the effective number of alleles 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, as the samples were collected from owners and dog breeders belonging to various states, including Punjab, Haryana, Himachal Pradesh, and Karnataka. 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 results indicated that the selected 12 markers are effective enough to determine the parentage of the dogs.
Keywords: 
Subject: Biology and Life Sciences  -   Animal Science, Veterinary Science and Zoology

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 (Wayne and Von 2012; Larson et al., 2012; Pedersen et al., 2015). 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. 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). Parentage determination using microsatellite and SNP marker panels (Kalbfleisch et al., 2014; Heaton et al., 2014; Yu et al., 2015; Flanagan et al., 2019) have been reported for different species. Relevant literature reports the associated prospects and challenges with parentage determination in humans and animals (Stark et al., 2014; Chan et al., 2014; Goswami, 2015). Very limited works have reported on applications of SSR markers for parentage determination in dogs (Hollinshead et al., 2020), especially in India. 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 (Singh et al 2022; Mukhopadhyay and Singh 2021). The goal of this work is 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 method (PCI) method (with modification of Sambrook et al., 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.

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 various literature (Mellersh et al 1997, Neff et al 1998, Sargan 2007, Coutt 2009, David Parra et al 2009, Whiteside 2011). The primers were custom synthesized and the SSR-length polymorphism was done from Biologia Research India Pvt. Ltd, Karnal, India.

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 (Nei, 1987). 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 PICi =1−ΣP2i − (ΣP2i) 2 + ΣP4i
Where, n: number of alleles; pi & pj = allele frequencies in population i and j, respectively (Botstein et al., 1980)
Heterozygosity (He) H = 1− Σ h= Σ2pq
Where, h: Homozygosity; p & q: frequencies of two alleles of a locus
Homozygosity (Ho), h = Σi (pi)2
Where, pi: frequency of ith allele of a locus
Probability of exclusion (PE) PE = h2 (1−2hH2)
Where, h: frequency of heterozygotes, H: frequency of homozygotes
Likelihood ratio for parentage assignment L(H1,H2)|=P(D|H1)/P(D|H2)
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 and
D: data in the form of offspring and parental genotypes

Results and Discussions

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.)

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.
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).

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 observed F for the markers lies between the upper and the lower limit of 95% which depicted that 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 I dogs
Table 7. Ewens-Watterson Test for Neutrality for the markets.
Table 7. Ewens-Watterson Test for Neutrality for the markets.
Locus n k Obs.F Min F Max F Mean* SE* L95* U95*
NPPM30 114 9 0.178 0.111 0.870 0.310 0.013 0.169 0.617
NPPM769 114 20 0.074 0.050 0.722 0.131 0.002 0.082 0.237
PEZ11 114 25 0.073 0.040 0.668 0.100 0.001 0.066 0.168
NPPM905 114 10 0.236 0.100 0.855 0.279 0.010 0.156 0.541
NPPM930 114 13 0.137 0.077 0.812 0.215 0.006 0.127 0.422
NPPM10 114 5 0.277 0.200 0.932 0.507 0.028 0.265 0.866
PEZ12 114 29 0.097 0.035 0.629 0.082 0.001 0.056 0.140
PEZ17 114 10 0.181 0.100 0.855 0.277 0.009 0.161 0.519
PEZ16 114 28 0.066 0.036 0.639 0.086 0.001 0.058 0.145
UOR4107 104 11 0.235 0.091 0.826 0.251 0.007 0.143 0.471
NPPM244 114 17 0.129 0.059 0.759 0.157 0.003 0.097 0.291
NPPM858 114 8 0.227 0.125 0.885 0.346 0.014 0.188 0.648

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 studies 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 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 (2.82E-12) 0.99999995.
Table 8. PIC and PE of the markers.
Table 8. PIC and PE of the markers.
SN Marker Polymorphism information Content Exclusion Probability
1 PEZ16 0.932 0.972
2 NPPM769 0.926 0.966
3 PEZ11 0.916 0.959
4 PEZ12 0.893 0.944
5 NPPM930 0.854 0.895
6 NPPM244 0.831 0.872
7 PEZ17 0.811 0.841
8 NPPM30 0.797 0.824
9 NPPM858 0.765 0.794
10 UOR4107 0.754 0.777
11 NPPM905 0.753 0.782
12 NPPM10 0.673 0.658
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.
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.

Future Prospectives

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

YS: Did the lab work and sample collection; BPK: manuscript writing; MPK: Data analysis; YHM: Sample collection from Karnataka; CSM: Designed the project and was the Principal Investigator, proofreading. All authors contributed to the manuscript revision, and read, and approved the present version.

Acknowledgments

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 DoGerman 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.

Competing Interests

The authors declare no competing interests.

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

References

  1. American Kennel Club. Available online: https://www.akc.org/press-center/articles-resources/facts-and-stats/breeds-year-recognize (accessed on 1 January 2021).
  2. Botstein, D., R. L. White, M. Skolnick, and R.W. Davis,1980, Construction of a genetic linkage map in man using restriction fragment length polymorphism. Am. J. Human Genet 32:314–331.
  3. Chan TK, Hui E, Chung B. A child born with Edward's syndrome: the legal and moral duty to accede to the request for parentage determination. J Med Ethics. 2014 Jun;40(6):383-6. [CrossRef]
  4. Flanagan SP, Jones AG. The future of parentage analysis: From microsatellites to SNPs and beyond. Mol Ecol. 2019 Feb;28(3):544-567. [CrossRef]
  5. Goswami GK. The genetic truth of surrogate parentage. Med Leg J. 2015 Dec;83(4):188-93. [CrossRef]
  6. Hamilton MG. Maximum likelihood parentage assignment using quantitative genotypes. Heredity (Edinb). 2021 Jun;126(6):884-895. [CrossRef]
  7. Heaton MP, Leymaster KA, Kalbfleisch TS, Kijas JW, Clarke SM, McEwan J, Maddox JF, Basnayake V, Petrik DT, Simpson B, Smith TP, Chitko-McKown CG; International Sheep Genomics Consortium. SNPs for parentage testing and traceability in globally diverse breeds of sheep. PLoS One. 2014 Apr 16;9(4):e94851. [CrossRef]
  8. Hollinshead FK, Ontiveros M, Burns JG, Magee C, Hanlon DW. Factors influencing parentage ratio in canine dual-sired litters. Theriogenology. 2020 Dec;158:24-30. [CrossRef]
  9. Kalbfleisch TS, Murdoch BM, Smith TPL, Murdoch JD, Heaton MP, McKay SD. A SNP resource for studying North American moose. F1000Res. 2018 Jan 10;7:40. [CrossRef]
  10. Kirtypal Singh. 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.
  11. 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. Rethinking dog domestication by integrating genetics, archeology, and biogeography. Proc. Natl. Acad. Sci. USA 2012, 109, 8878–8883. [CrossRef]
  12. Levene H. 1949 On a matching problem arising in genetics. Ann. Math. Stat. 20, 91–94.
  13. Linacre, A., Gusmão, L., Hecht, W., Hellmann, A. P., Mayr, W. R., Parson, W.,... & 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. [CrossRef]
  14. Mukhopadhyay CS and Singh KP. 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 & Animal Husbandry, DUVASU, Mathura, Uttar Pradesh.
  15. Nei M. 1973 Analysis of gene diversity in subdivided populations Proc. Natl. Acad. Sci. USA 70, 3321–3323. [CrossRef]
  16. Oudet C., Heilig R., Hanauer A. & 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.
  17. Peakall, R., Smouse, P.E., 2012. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research--An update. 28, 2537–2539. [CrossRef]
  18. Pedersen, N.C.; Liu, H.; Leonard, A.; Griffioen, L. 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. 2015, 2, 17. [CrossRef]
  19. Raymond M, Rousset F. GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism. J Hered 1995;86:248-9. a111573. [CrossRef]
  20. Sambrook JF and Russell DW. “Molecular Cloning: A Laboratory Manual (3-Volume Set) Edition”. 3Publisher: Cold Spring Harbor Laboratory Press.
  21. Serrote, C. M. L., Reiniger, L. R. S., Silva, K. B., dos Santos Rabaiolli, S. M., & Stefanel, C. M. (2020). Determining the Polymorphism Information Content of a molecular marker. Gene, 726, 144175. [CrossRef]
  22. Singh K., Mukhopadhyay CS*, Kaur S and Arora JS. 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. (url: https://actascientific.com/ASVS/ASVS-04-0505.php). [CrossRef]
  23. Stark Z, Delatycki MB. Parentage determination: a medical responsibility? J Med Ethics. 2014 Jun;40(6):387-8. [CrossRef]
  24. Vieira, M. L. C., Santini, L., Diniz, A. L., & Munhoz, C. D. F. (2016). Microsatellite markers: what they mean and why they are so useful. Genetics and molecular biology, 39, 312-328. [CrossRef]
  25. Wayne, R.K.; von Holdt, B.M. Evolutionary genomics of dog domestication. Mamm. Genome 2012, 23, 3–18. [CrossRef]
  26. Whalen A, Gorjanc G, Hickey JM. Parentage assignment with genotyping-by-sequencing data. J Anim Breed Genet. 2019 Mar;136(2):102-112. [CrossRef]
  27. Yu GC, Tang QZ, Long KR, Che TD, Li MZ, Shuai SR. Effectiveness of microsatellite and single nucleotide polymorphism markers for parentage analysis in European domestic pigs. Genet Mol Res. 2015 Feb 13;14(1):1362-70. [CrossRef]
  28. Zhou M, Zhang HQ, Wang J. [Formula Derivation and Validation of Probability of Exclusion in the Cases of Standard Triplet Parentage Testing]. Fa Yi Xue Za Zhi. 2017 Aug;33(4):363-367. Chinese.
Table 1. Family orientation (Sire/Dam/Offspring) and breed detail of the experimental animals.
Table 1. Family orientation (Sire/Dam/Offspring) and breed detail of the experimental animals.
SN Sire Dam Offspring Trio-Id Duo-Id Breed
1 1 2 3 T1 -- Labrador
2 7 8 9 T2 -- Labrador
3 16 17 18 T3 -- German Shepherd
4 25 26 27 T4 -- German Shepherd
5 40 41 42 T5 -- Pug
6 47 48 46 T6 -- Mudhol Hound
7 50 51 49 T7 -- Mudhol Hound
8 54 53 52 T8 -- Mudhol Hound
9 57 56 55 T9 -- Mudhol Hound
10 60 59 58 T10 -- Mudhol Hound
11 64 65 66 T11 -- Tibetan Mastiff
12 67 68 69 T12 -- Gaddi
13 67 68 70 T13 -- Gaddi
14 67 68 71 T14 -- Gaddi
15 82 83 84 T15 -- Belgian Malinois
16 93 94 95 T16 -- Cane Corso
17 13 NA 15 -- D1 Pug
18 22 NA 24 -- D2 German Shepherd
19 31 NA 33 -- D3 Pug
20 NA 85 86 -- D5 Beagle
21 NA 87 88 -- D6 Pointer
22 NA 87 89 -- D7 Pointer
23 61 62 NA -- D4 Gaddi*
* for parentage assignment testing.
Table 2. Detail of the 5' labeled simple sequence repeat primers.
Table 2. Detail of the 5' labeled simple sequence repeat primers.
SN Loci Forward_Primer (5’ to 3’) sequence (and length) FReverese_Primer (5’ to 3’) sequence (and length) Allele_S ize TM Dye
NPPM10 GTGGACCATGTGACTCTTGA (20) TTTGTGTGATGCCACTACAGTAAG (24) 176-182 58 6-FAM
NPPM244 GTCACTTAATAGGATGATTTCTTGG (25) CTAAAACCTGGATTGTCTAATTTGT (25) 315-338 58 6-FAM
NPPM30 AGGACTATTTCACGCCTTGTTG (22) ATTCCCACCTCAGTGATTACAG (22) 276-286 58 HEX
NPPM769 TGGTAGCCACAGAAGCATTG (20) TTGGATTAAGTGTGTAGTCCTGAGC (25) 218-238 58 TAMRA
NPPM855* TGAGTTTTTGGTCCCCTCCA (20) CTCTGGTCCAGCAGTTGAAAC (21) 226-238 58 TAMRA
NPPM858 CAGTTTGCTACCTTTTGTGTAATCA (25) CTCACCCATTGTAGTCTCTGTCTTC (25) 187-204 58 HEX
NPPM905 TCCAGAGTCACAACTTCAGAAAC (23) GCTAGATTGCTGCCCTTTACTC (22) 201-221 58 HEX
NPPM930 TCTTTACCCTTCTGGAAAATGAG (23) GTGATTGAACACGCAAGGGAT (21) 247-262 58 TAMRA
NPPM981* GAACATCTTCCTTCTTCCACTG (22) TCCTAGAGACC TGGGATGAAGT (22) 318-328 58 HEX
PEZ11 ATTCTCTGCCTCTCCCTTTG (20) TGTGGATAATCTCTTCTGTC (20) 121-173 55 6-FAM
PEZ12 GTAGATTAGATCTCAGGCAG (20) TAGGTCCTGGTAGGGTGTGG (20) 266-313 58 TAMRA
PEZ16 GCTCTTTGTAAAATGACCTG (20) GTGGGAATCGTCCTAAAACCC (21) 281-317 58 6-FAM
PEZ17 CTAAGGGACTGAACTTCTCC (20) GTGGAACCTGCTTAAGATTC (20) 199-227 58 HEX
PEZ22* TGGGGAGATCTACAGACCAC (20) CTAATGTGTCTCTCAAGCCG (20) 171-189 55 6-FAM
UOR4107 TGACCCTTCTACAACTCGGG (20) TGTGACCAGTCACTGCTTCC (20) 220-232 58 TARMA
*Results were obtained for 12 primer pairs .
Table 3. Genetic parameters of the 12 microsatellite loci obtained from dog populations.
Table 3. Genetic parameters of the 12 microsatellite loci obtained from dog populations.
Locus Sample Size na* ne* I*
NPPM30 114 9 5.61 1.86
NPPM769 114 20 13.59 2.76
PEZll 114 25 13.74 2.85
NPPM905 114 10 4.25 1.69
NPPM930 114 13 7.29 2.16
NPPM10 114 5 3.61 1.36
PEZ12 114 29 10.36 2.80
PEZ17 114 10 5.51 1.88
PEZ16 114 28 15.22 3.02
UOR4107 104 11 4.26 1.73
NPPM244 114 17 7.75 2.34
NPPM858 114 8 4.40 1.71
Mean 113 15.42 7.97 2.18
St . Dev 8.24 4.21 0.56
Note: Na, number of alleles; Ne, number of effective alleles; I = Shannon's Information index.
Table 4. Measures of genetic variation (Heterozygosity Statistics for All Loci) in dog population.
Table 4. Measures of genetic variation (Heterozygosity Statistics for All Loci) in dog population.
Locus Sample Size Obs_Hom Obs_Het Exp_Hom* Exp_Het* Nei** Avg_Het
NPPM30 114 0.12 0.88 0.17 0.83 0.82 0.82
NPPM769 114 0.04 0.96 0.07 0.93 0.93 0.93
PEZll 114 0.00 1.00 0.06 0.94 0.93 0.93
NPPM905 114 0.26 0.74 0.23 0.77 0.76 0.76
NPPM930 114 0.33 0.67 0.13 0.87 0.86 0.86
NPPM10 114 0.18 0.82 0.27 0.73 0.72 0.72
PEZ12 114 0.04 0.96 0.09 0.91 0.90 0.90
PEZ17 114 0.23 0.77 0.17 0.83 0.82 0.82
PEZ16 114 0.25 0.75 0.06 0.94 0.93 0.93
UOR4107 104 0.29 0.71 0.23 0.77 0.77 0.77
NPPM244 114 0.46 0.54 0.12 0.88 0.87 0.87
NPPM858 114 0.19 0.81 0.22 0.78 0.77 0.77
Mean 113 0.20 0.80 0.15 0.85 0.84 0.84
St . Dev 0.13 0.13 0.07 0.07 0.07 0.07
Note: Obs_Hom is the Observed Homozygosity; Exp_Hom is the Expected Homozygosity; Obs_Het is the Observed Heterozygosity; Exp_Het is the Expected Heterozygosity.
Table 5. Summary of the F-Statistics and gene flow among dog populations.
Table 5. Summary of the F-Statistics and gene flow among dog populations.
Locus Sample Size Fis Fit Fst Nm*
NPPM769 114 -0.042 -0.042 0.000 ****
PEZll 114 -0.079 -0.079 0.000 ****
NPPM905 114 0.036 0.036 0.000 ****
NPPM930 114 0.227 0.227 0.000 ****
NPPMlO 114 -0.140 -0.140 0.000 ****
PEZ12 114 -0.068 -0.068 0.000 ****
PEZ17 114 0.057 0.057 0.000 ****
PEZ16 114 0.193 0.193 0.000 ****
UOR4107 104 0.070 0.070 0.000 ****
NPPM244 114 0.376 0.376 0.000 ****
NPPM858 114 -0.044 -0.044 0.000 ****
Mean 113 0.046 0.050 0.000 1000
Nm = Gene flow estimated from FST = 0.25 (1 - FST)/FST.
Table 6. Summary of the chi-square and Hardy Weinberg test.
Table 6. Summary of the chi-square and Hardy Weinberg test.
Locus DF ChiSq Probability Significance
NPPM30 36 142.027 0.000 ***
NPPM769 190 324.874 0.000 ***
PEZ11 300 368.802 0.004 ***
NPPM905 45 162.047 0.000 ***
NPPM930 78 165.012 0.000 ***
NPPM10 10 29.441 0.001 ***
PEZ12 406 399.108 0.587 ns
PEZ17 45 60.104 0.065 ns
PEZ16 378 565.516 0.000 ***
UOR4107 55 80.343 0.015 *
NPPM244 136 274.682 0.000 ***
NPPM858 28 34.536 0.184 ns
ns=not significant, * P<0.05, ** P<0.01, *** P<0.001.
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