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Microsatellite marker analysis of Tswana cattle kept under in situ conservation at Botswana University of Agriculture and Natural Resources

Tirelo Bakae, Phetogo Ineeleng Monau, Patrick Kgwatalala and Shalaulani James Nsoso

Botswana University of Agriculture and Natural Resources, Faculty of Animal and Veterinary Science, Department of Animal Sciences, Private Bag 0027, Gaborone, Botswana
pmonau@buan.ac.bw

Abstract

The study was carried out to assess genetic diversity of Tswana cattle conserved at Botswana University of Agriculture and Natural Resources (BUAN) farm. Twelve microsatellite markers recommended by the International Society for Animal Genetics were used for assessment of genetic diversity on twenty-six Tswana cattle. A total of seventy-five alleles were distinguished across all loci with the mean value of 6.25±2.60. The markers TGLA227, BM2113, ETH10, TGLA122, ETH3, ETH225, CMSS60, CMSS66, and ILST006 were highly informative. The polymorphic information content ranged from 0.38 (BM1818) to 0.84 (ETH225) with an overall mean of 0.67. Two microsatellite markers; CMSS60 and CMSS66 deviated significantly from Hardy-Weinberg Equilibrium. The level of gene diversity (HE) across all loci was 0.79±0.04 with moderate inbreeding coefficient (F IS) of 20%. The results have provided insights on the genetic diversity of Tswana cattle that is relevant for decision making on the population herd structure and other research work. Maintenance of genetic diversity of Tswana cattle is recommended as a fundamental component in long-term management strategies for conservation programme. The level of inbreeding in the herd should be managed through introduction of new breeding bulls to counter effect genetic erosion.

Key words: genetic diversity, heterozygosity, inbreeding coefficient, indigenous cattle


Introduction

The Tswana cattle breed is indigenous to Botswana and constitute about 42% of the cattle population kept under communal management system (Statistics Botswana 2019). The breed is an important genetic resource possessing valuable adaptive qualities such as tolerance to local diseases, high heat, poor quality feeding, insufficient water and ability to produce under low input resources (Mapiye et al 2019). The other attributes include high fertility, good maternal qualities, longevity and good carcass traits (Buck et al 1982). The Tswana cattle breed contributes significantly to the households of resource-poor farmers as a source food in the form of milk and meat, income through animal sales and insurance in case of crop failure (Mapiye et al 2019). The communal farming set up however, is renowned for uncontrolled mating and unorganised crossbreeding that threatens the genetic integrity and predisposes the indigenous Tswana cattle breed to risk of extinction (Senyatso and Masilo 1996; Nsoso and Morake 1999; Podisi, 2011). Consequently, indigenous Tswana population have declined at a startling rate and about 53% of non-descript crossbreds are dominating the communal farming sector which is unfortunate as they are less productive and have low survivability (Statistics Botswana 2019). Conservation of indigenous Tswana breed is very important for food security and nutrition, sustainable development of the agricultural sector, breeding programmes and future unpredictable global environmental changes such as climate change.

Food Agricultural Organization (FAO) initiated the “Global Plan of Action for Animal Genetic Resources” to facilitate the characterisation and conservation of indigenous livestock breeds and avoid genetic erosion of well adapted breeds that might be relevant in future breeding programmes (FAO 2007). In Botswana, the Department of Agriculture Research under the Ministry of Agricultural Development and Food Security have the mandate to conserve the indigenous livestock breeds. The ministry has since implemented the in situ conservation programme of Tswana cattle in 1989 (APRU 1989). A representative of Tswana cattle has been conserved in different government institutions including Botswana University of Agriculture and Natural Resources (BUAN). However, genetic characteristics and diversity of the conserved Tswana cattle population has never been examined. Genetic diversity is essential for assessing the genetic integrity and inbreeding of livestock, for effective utilization and sustainable genetic improvements (Notter 1999). Monitoring the evolutionary dynamics of genetic diversity is also important for a conserved population (Boettcher et al 2010).

The advances in genomics such as molecular markers have been applied to assess and monitor genetic diversity in livestock (Silva et al 2012). Microsatellite markers have been dominating in the past decades due to their lower prices, however, their use could be limited to newer technologies such as single nucleotide polymorphism (SNPs). In southern Africa, microsatellite markers have been extensively used to investigate genetic diversity in different indigenous cattle breeds such as Mozambican cattle (Bessa et al 2009), Nguni cattle (Saranana et al 2016) and Zimbabwean Sanga cattle (Gororo et al 2018). Microsatellite markers have also been used to establish a relationship between cattle genetic diversity and resistance to infectious diseases such as bovine tuberculosis highlighting the importance of cattle diversity in adaptation (Kadarmideen et al 2011). The indigenous Tswana cattle has never been genetically characterized and there is no baseline data that exists for future monitoring of genetic trends as well as guidance on utilization and conservation programmes. The purpose of this study was to assess the genetic diversity of Tswana cattle population kept under conservation at BUAN farm using 12 microsatellite markers.


Materials and methods

Experimental Animals

The base population of the Tswana Cattle Conservation Project was assembled using different strains of Tswana cattle from Kgalagadi, Kweneng, Southern and Central regions of Botswana (APRU 1989). The base population was separated into selection lines for growth and reproductive performance and the unselected control line. Botswana University of Agriculture and Natural Resources (BUAN) received Tswana cattle from the unselected control line in 1989 and the herd has since remained closed with no artificial selection. In order to keep BUAN Tswana cattle herd as a purebred and manage inbreeding, the Department of Agricultural Research supplied and rotated breeding Tswana bulls. The closed populations of purebred Tswana cattle at BUAN are representative of the indigenous Tswana cattle breed in terms of both genetic purity and composition (sampled from various parts of the country). There are about 70 animals kept under extensive management system with little feed supplementation during dry season and water provided ad libitum. The geographical location and vegetation characteristics of the area are detailed by Madibela et al (2002).

Sample Collection and DNA Extraction

An amount of 5ml whole blood was collected from the jugular vein in EDTA tubes from 26 randomly selected and unrelated purebred Tswana cattle kept at BUAN farm. Pedigree records ascertained that sampled animals were unrelated. DNA was extracted from whole blood samples using Quick gDNA blood kit (Zymo research, USA) following the manufacturer’s protocol. The concentration of gDNA was measured using a spectrophotometer (Nanodrop 2000) and the purity of the extracted gDNA was calculated by the 260/280 absorbance ratio (Thermo Fisher Scientific Inc., Waltham, MA, USA).

Polymerase Chain Reaction (PCR)

A panel of twelve bovine microsatellite markers endorsed for estimating genetic diversity by the International Society for Animal Genetics (ISAG) and Food and Agricultural Organization (FAO) advisory board (FAO, 2011) was used to amplify specific regions of gDNA. All these markers were used to study genetic diversity in Tswana cattle including inbreeding coefficient (FIS). The microsatellite markers, their chromosomal positions, size range and primers used for their amplification are shown in Table 1. All the 12 markers were amplified in a single multiplex polymerase chain reaction using fluorescence-labelled primers at Agricultural Research Council (ARC), molecular genetics laboratory (Irene, Pretoria, South Africa). A 15 µl reaction was prepared with deionized water, 10 x PCR buffer optimized with 50 mM Mgcl2 and 100 mM deoxynucleotides triphosphates, 5U DNA taq polymerase (Bioline USA, Inc.), 0.3 µl of 10 mol/µl primers (Applied Biosystems, Foster city, CA, USA) and 5 µl of 50 ng of gDNA. DNA amplification of the 12 marker loci was achieved using GeneAmp PCR System® 9700 gold thermal cycler (Applied Biosystems, Foster city, CA, USA). Amplification of the markers was achieved using the following PCR conditions- initial denaturation at 98°C for 60 seconds, followed by 30 cycles of 98 for 20s, annealing temperature of 60 for 75s and DNA extension at 72 for 30s, followed by final extension step at 72 for 5minutes.

Table 1. Microsatellite markers employed in the characterization of Tswana cattle

Locus

Chr

Allele range

Primer sequences

Dye

Reference

BM1824

1

182-196

GAGCAAGGTGTTTTTCCAATC
CATTCTCCAACTGCTTCCTTG

PET

Barendse et al (1994)

BM2113

2

120-144

GCTGCCTTCTACCAAATACCC
CTTAGACAACAGGGGTTTGG

PET

Sunden et al (1993)

INRA023

3

183-217

GAGTAGAGCTACAAGATAAACTTC
TAACTACAGGGTGTTAGATGAACTCA

NED

Varinman et al (1994)

ETH10

5

207-223

GTTCAGGACTGGCCCTGCTAACA
CCTCCAGCCCACTTTCTCTTCTC

6 FAM

Toldo et al (1993)

ILST006

7

282-302

TGTCTGTATTTCTGCTGTGG
ACACGGAAGCGATCTAAACG

VIC

Brezinsky et al (1993)

ETH225

9

137-159

GATCACCTTGCCACTATTTCCT
ACATGACAGCCAGCTGCTACT

VIC

Steffen et al (1993)

CSRM60

10

92-120

AAGATGTGATCCAAGAGAGAGGCA
AGGACCAGATCGTGAAAGGCATAG

PET

BCM (2006)

CSSM66

14

179-199

ACACAAATCCTTTCTGCCAGCTGA
AATTTAATGCACTGAGGAGCTTGG

PET

Barendse et al (1994)

TGLA227

18

79-99

CGAATTCCAAATCTGTTAATTTGCT
ACAGACAGAAACTCAATGAAAGCA

6 FAM

Georges and Massey (1992)

ETH03

19

113-125

GAACCTGCCTCTCCTGCATTGG
ACTCTGCCTGTGGCCAAGTAGG

PET

Toldo et al (1993)

TGLA122

21

135-163

CCCTCCTCCAGGTAAATCAGC
AATCACATGGCAAATAAGTACATAC

6 FAM

Georges and Massey (1992)

BM1818

23

255-269

AGCTGGGAATATAACCAAAGG
AGTGCTTTCAAGGTCCATGC

NED

Bishop et al (1994)

Chr= chromosome, BCM = Baylor College of Medicine

Genotyping

1.5µl of Polymerase Chain Reaction (PCR) products were then mixed with 11µl of deionised formamide and 0.3µl of GeneScan 500 LIZ size standard and denatured by heating at for 3mins followed by cooling on ice. The PCR products were then separated using capillary electrophoresis ABI Prism 310 Genetic Analyzer (Applied Biosystems, Foster city, CA, USA).

Statistical analysis of data

The MS toolkit software (Kim et al 2002) was used to determine the number of alleles per locus, allele frequencies, mean number of alleles per locus, observed and expected heterozygosity and the polymorphic information content (PIC) for each locus. The inbreeding coefficient (FIS) for each locus was computed using the program FSTAT (Goudet, 2001). The probability test approach (Guo and Thomson, 1992) implemented in the GENEPOP software (Raymond and Rousset, 1995) was used to test each locus for Hardy-Weinberg equilibrium. Data on fragment size were analysed automatically using Genescan Analysis Software v.3.1 which provided information on allele size.


Results and discussion

All the 12 microsatellite markers used for genetic diversity characterisation of Tswana cattle population were polymorphic and yielded 75 alleles (Table 2). The number of alleles per marker ranged from 2 (BM1818) to 10 (TGLA227) with a mean value of 6.25±2.6. The most polymorphic markers were TGLA227, ETH225, ETH10 and BM2113 with each having nine to ten alleles per marker (Table 2). The allelic diversity of Tswana cattle was comparable to the South African Nguni cattle (MNA=6.5) and South-Western European bovine breeds (MNA=6.5) (Saranana et al 2016; Beja-Pereira et al 2003). Tswana cattle had higher allelic diversity than five native Indonesian cattle breeds with mean number alleles (MNA) per marker of 4.2 (Setyawan and Lymbery 2015) and Ongole and Deoni Indian cattle breeds with MNA per marker of 4.5 and 4.1, respectively (Metta et al 2004). All the markers used in the current study except for BM1818 and ILST0076 exhibited sufficient allelic polymorphism in Tswana cattle and are suitable for breed characterization and estimation of genetic differences within the population. The high allelic diversity could be due to lack of artificial selection as well as broad genetic base resulting from the admixture of Tswana cattle strains from different regions of Botswana during assembly of the base population in 1989. Nevertheless, the observed allelic richness of the conserved Tswana cattle must be maintained for adaptation to future environmental changes, since this diversity is the raw material for evolution through natural selection.

A total of six private alleles (bolded in Table 2) were detected in Tswana cattle. These private alleles indicate genetic uniqueness of indigenous Tswana from other cattle breeds based on the 12 microsatellite markers (Erhardt and Weinmann 2007). However, a broader investigative study should be carried out where Tswana population is compared with other cattle populations or breeds. Private alleles are common in diversity studies of indigenous livestock and are often used as a tool for the measurement of the genetic distinctiveness of a population (Szpiech and Rosenburg 2011; Ema et al 2014). In a study conducted by Gororo et al (2018) thirty four out of 119 alleles were unique to specific Zimbabwean cattle breeds: Mashona (12), Nkone (7) and Tuli (17).

Table 2. Alleles of 12 microsatellite markers found in Tswana cattle at BUAN farm

Locus

Observed No.
of allele

Alleles sizes(bps) and their frequencies

BM1818

2

262

264

ILST006

3

286

294

296

INRA23

4

196

198

208

214

ETH3

5

115

117

125

127

129

CSSM66

5

179

181

183

187

195

BM1824

6

146

176

178

180

182

195

CSRM60

6

92

96

100

102

110

114

TGLA122

7

137

143

151

161

179

181

183

BM2113

9

121

125

127

133

135

137

139

141

143

ETH10

9

206

213

214

215

217

218

219

221

225

ETH225

9

137

140

144

146

150

154

159

176

180

TGLA227

10

77

79

81

83

87

89

97

99

101

103

Total

75

Mean (MNA)

6.25±2.6

Private alleles bolded in Table 2

Expected heterozygosity (HE), polymorphic information content (PIC), inbreeding coefficient (FIS) and Hardy-Weinberg Equilibrium (HWE) of Tswana cattle are reported in Table 3. HE varied from 0.45(INTRA23) to 1 (BM1818) with mean values across all loci of 0.79±0.04. The observed heterozygosity indicates high levels of genetic variability in BUAN Tswana cattle conservation herd. The average HE of Tswana cattle is comparable to the Hallikar breed of India (0.79) and Pakistani cattle breeds (0.82) (Kumar et al 2006; Hussain et al 2016). However, the average HE values were higher than HO in Tswana cattle (0.79 vs. 0.63) which is worrisome as it indicates signs of deficiency of heterozygotes in the studied population (Pandey et al 2006). This could be due to small effective population size, pronounced effect of random drift and natural selection. It will be of interest to evaluate the effective population size (Ne) of the conserved Tswana cattle. An efficient in situ conservation scheme relies on an effective population size, as well as an effective selection and mating strategy (Mtileni et al 2016).

The average polymorphic information content (PIC) for all the 12 loci was 0.67 (Table 3). Most of the markers were highly informative (PIC >0.5) except INTRA23 and BM1818 which had moderate value of 0.39 and 0.38, respectively. This indicate that the used microsatellite markers were suitable for assessment of genetic diversity in Tswana cattle population and for other molecular applications like forensics (individual identification), parentage verification or segregation analysis in Tswana cattle. The results are comparable to South African Nguni cattle (0.66) and Sudanese Zebu cattle breeds (0.63) (Sanarana et al 2016; Hussein et al 2016).

The inbreeding coefficient (FIS) values per locus ranged between -0.04 (BM2113) and 0.77 (CSSM66). The average inbreeding coefficient across all loci was 0.20, indicating moderate levels of inbreeding in BUAN Tswana cattle herd (Table 3). All the microsatellite markers except for TGLA227, BM2113, BM1818 and ILST006 contributed to the observed 20% heterozygote deficit. The contribution of most markers to the inbreeding coefficient of the whole population suggests inbreeding as the likely cause of the deficiency of heterozygotes in BUAN Tswana cattle population. This might be due to small number of breeding bulls or repetition of similar bulls used for breeding over the years at BUAN conservation farm. According to Nei (1972), inbreeding, genetic hitchhiking, null alleles and the occurrence of population substructure are some of the established reasons for heterozygote deficiencies. In order to reduce the level of inbreeding, new breeding bulls can be outsourced from communal farmers or cows can be serviced through artificial insemination using Tswana semen. This will further increase the effective population size of the conservation herd and counter the effects of genetic erosion.

Table 3. Heterozygosity (He), Polymorphic information content (PIC), Inbreeding coefficient (FIS)and Hardy-Weinberg Equilibrium (HWE) for each locus in Tswana cattle

Locus

HE

PIC

FIS

HWE p-value

TGLA227

0.87

0.83

-0.02

0.66

BM2113

0.84

0.79

-0.04

0.27

ETH10

0.90

0.82

0.18

0.06

TGLA122

0.76

0.70

0.21

0.13

INRA23

0.46

0.39

0.27

0.13

BM1818

1.00

0.38

0.00

1.00

ETH3

0.70

0.62

0.28

0.33

ETH225

0.89

0.84

0.23

0.06

BM1324

0.68

0.62

0.17

0.14

CSSM60

0.77

0.71

0.38

0.04

CSSM66

0.87

0.75

0.77

0.00

ILST006

0.75

0.58

0.00

0.47

Mean

0.79±0.04

0.67±0.02

0.20±0.02

0.66±0.00

Ten out of 12 microsatellite markers used in the current study were in Hardy-Weinberg Equilibrium (HWE) and only two markers CSSM60 and CSSM66 deviated from HWE (Table 3). This indicates high genetic stability of BUAN Tswana cattle population. Significant deviation from HWE for markers CSRM60 and CSSM66 could be due to non-random mating with respect to the two markers or due to genetic drift, population subdivision or natural selection with respect to the two markers. Quantitative trait loci (QTL) for milk production, milk composition and growth traits (post weaning average daily weight and back-fat thickness) and functional genes underlying those QTL have been mapped to BTA14 (Wibowo et. al 2008) where microsatellite marker CSSM66 is also found. Natural selection for low milk production and slow growth rate in Tswana cattle over centuries might have fixed alleles for those traits, which might be in linkage disequilibrium with alleles at CSSM66 locus resulting in significant departure of CSSM66 marker from HWE. A QTL for tick resistance/susceptibility has been mapped to BTA10 (Machado et al 2010) where the microsatellite marker CSRM60 is also mapped. Natural selection for tick resistance in Tswana cattle over centuries might have fixed the tick resistance alleles which might be in linkage disequilibrium with alleles at CSRM60 locus (tick resistance alleles and marker alleles always inherited together) resulting significant deviation of CSRM60 marker from HWE.


Conclusions


Authors’ declaration

Authors declares no conflict of interest


Acknowledgements

The authors would like to thank Botswana University of Agriculture and Natural Resources for funding the study and Agricultural Research Council-Animal Production Institute (ARC-API) for availing their laboratory (equipment and reagents) for microsatellite marker amplification and analysis and for further assisting with data analysis using their various molecular software.


References

APRU 1989 Selection of Tswana cattle for Beef production. Livestock and Range Research in Botswana. Animal Production and Range Research Unit. Ministry of Agriculture. Gaborone. Botswana. pp 93-95.

Barendse W, Armitage S M, Kossarek L M, Shalom A, Kirkpatrick B W, Ryan A M, Clayton D, Li L, Neibergs H L, Zhang N and Grosse W M 1994 A genetic linkage map of the bovine genome. Nature genetics 6(3):227-35 https://doi.org/10.1038/ng0394-227

Baylor College of Medicine Human Genome Sequencing Centre 2006 Bovine Whole Genome Assembly release Btau-3.1 http/www/hgsc.tmc.edu./project/bovine/

Beja-Pereira A, Caramelli D, Lalueza-Fox C, Vernesi C, Ferrand N, Casoli A, Goyache F, Royo L J, Conti S, Lari M and Martini A 2006 The origin of European cattle: evidence from modern and ancient DNA. Proceedings of the National Academy of Sciences 103(21):8113-8, https://doi.org/10.1073/pnas.0509210103

Bessa I, Pinheiro I, Matola M, Dzama K, Rocha A and Alexandrino P 2009 Genetic diversity and relationships among indigenous Mozambican cattle breeds. South African Journal of Animal Science 39(1), https://doi.org/10.4314/sajas.v39i1.43548

Bishop M D, Kappes S M, Keele J W, Stone R T, Sunden S L, Hawkins G A, Toldo S S, Fries R, Grosz M D and Yoo J 1994 A genetic linkage map for cattle 136(2):619-39.

Boettcher P J, Tixier‐Boichard M, Toro M A, Simianer H, Eding H, Gandini G, Joost S, Garcia D, Colli LI, Ajmone‐Marsan P A and Globaldiv Consortium 2010 Objectives, criteria and methods for using molecular genetic data in priority setting for conservation of animal genetic resources. Animal Genetics 41:64-77, https://doi.org/10.1111/j.1365-2052.2010.02050.x

Brezinsky L, Kemp S J and Teale A J 1993 ILSTS005: a polymorphic bovine microsatellite. Animal Genetics 24(1):73-.14. https://doi.org/10.1111/j.1365-2052.1993.tb00932.x

Buck N, Light D and Lethola L 1982 Beef cattle breeding systems in Botswana; the use of indigenous breeds. World Animal Review (FAO) 43, 12-16,

Ema P N, Manjeli Y, Meutchieyié F, Keambou C, Wanjala B, Desta A F, Ommeh S, Skilton R and Djikeng A 2014 Genetic diversity of four Cameroonian indigenous cattle using microsatellite markers. Journal of Livestock Science 5:9-17.

Erhardt G and Weimann C 2007 Use of molecular markers for evaluation of genetic diversity and in animal production. Archivos Latinoamericanos de Producción Animal 15(5); 63-66. https://ojs.alpa.uy/index.php/ojs_files/article/view/2717

FAO 2007 The Global Plan of Action for Animal Genetic Resources and the Interlaken Declaration on Animal Genetic Resources. FAO, Rome, Italy. http://www.fao.org/home/en/

FAO 2011 Molecular Genetics Characterization of animal genetic resources. FAO Animal production and Health guidelines. No.9. Rome, Italy http://www.fao.org/home/en/

Georges M and Messey J 1992 Polymophic DNA markers in Bovine. World Intellectual Property Organization Geneva Pontent Application WO PUBL No 92/13102.

Gororo E, Makuza S M, Chatiza F P, Chidzwondo F and Sanyika T W 2018 Genetic diversity in Zimbabwean Sanga cattle breeds using microsatellite markers. South African Journal of Animal Science 48(1);128-141. https://hdl.handle.net/10520/EJC-e4553e87f

Goudet, J 2002 Fstat version 2.9.3. Lausame (Switzerland). Institute of Ecology. http://www2.uni/ch/izea/softwares/fstat.html.

Guo SW and Thompson EA 1992 Performing the exact test of Hardy-Weinberg proportion for multiple alleles. Biometrics 1:361-72. https://doi.org/10.2307/2532296

Hussain T, Babar M E, Peters S O, Wajid A, Ali A, Azam A, Ahmad Z, Wasim M, Ali A, Kizilkaya K and De Donato M 2016 Microsatellite Markers Based Genetic Evaluation of Pakistani Cattle Breeds. Pakistan Journal of Zoology 1;48(6).

Kadarmideen H N, Ali AA, Thomson PC, Müller B and Zinsstag J 2011 Polymorphisms of the SLC11A1 gene and resistance to bovine tuberculosis in African Zebu cattle. Animal genetics 42(6):656-8. https://doi.org/10.1111/j.1365-2052.2011.02203.x

Kim K S, Yeo J S and Choi C B 2002 Genetic diversity of north‐east Asian cattle based on microsatellite data. Animal genetics 33(3):201-4. https://doi.org/10.1046/j.1365-2052.2002.00848.x

Kumar P, Freeman A R, Loftus R T, Gaillard C, Fuller D Q and Bradley D G 2003 Admixture analysis of South Asian cattle. Heredity 91(1):43-50. https://doi.org/10.1038/sj.hdy.6800277

Machado M A, Azevedo A L, Teodoro R L, Pires M A, Peixoto M G, de Freitas C, Prata M C, Furlong J, da Silva M V, Guimarăes S E and Regitano L C 2010 Genome wide scan for quantitative trait loci affecting tick resistance in cattle (Bos taurus× Bos indicus). BMC genomics 11(1):1-1. https://doi.org/10.1186/1471-2164-11-280

Madibela O R, Letso M, Boitumelo W S, Masedi M and Alton K 2002 Chemical composition of four parasitic plants harvested over a period of 6 months from two sites in Botswana. Animal feed science and technology 25;95(3-4):159-67. https://doi.org/10.1016/S0377-8401(01)00320-0

Mapiye C, Chikwanha OC, Chimonyo M and Dzama K 2019 Strategies for sustainable use of indigenous cattle genetic resources in Southern Africa. Diversity 11(11):214.

Metta M, Kanginakudru S, Gudiseva N and Nagaraju J 2004 Genetic characterization of the Indian cattle breeds, Ongole and Deoni (Bos indicus), using microsatellite markers–a preliminary study. BMC genetics 5(1):1-5. https://doi.org/10.1186/1471-2156-5-16

Mtileni B, Dzama K, Nephawe K, Rhode C 2016 Estimates of effective population size and inbreeding in South African indigenous chicken populations: implications for the conservation of unique genetic resources. Tropical animal health and production 48(5):943-50 https://doi.org/10.1007/s11250-016-1030-9

Nei M 1972 Genetic distance between populations. The American Naturalist 106(949):283-92 https://doi.org/10.1086/282771

Notter D R 1999 The importance of genetic diversity in livestock populations of the future. Journal of Animal Science.77(1):61-9. https://doi.org/10.2527/1999.77161x

Nsoso S J and Morake T G 1999 A critical at the use of exotic bulls in traditional beef farming in Botswana. South African Journal of Animal Science 29 (2). https://doi.org/10.4314/sajas.v29i2.44214

Pandey A K, Sharma R, Singh Y, Prakash B B, Ahlawat S P 2006 Genetic diversity studies of Kherigarh cattle based on microsatellite markers. Journal of genetics 85(2):117-22. https://doi.org/10.1007/BF02729017

Podisi B 2011 Management of farm animal Genetic Resources in Botswana (ed. Chite S. M., Setshogo M.P. & Ben G.). In Proceedings of the second Botswana workshop on plant genetic resources. 20-23 June, Gaborone Botswana. pp50-59.

Raymond M and Rousset F 1995 Genepop (version 1.2) – Population Genetics data: New tools, old concept. Trands in Ecology and Evolution, 12, 313-317. http://dx.doi.org/10.1016/s0169-5347(97) 01104-x

Sanarana Y, Visser C, Bosman L, Nephawe K, Maiwashe A and van Marle-Köster E 2016 Genetic diversity in South African Nguni cattle ecotypes based on microsatellite markers. Tropical animal health and production. 48(2):379-85. https://doi.org/10.1007/s11250-015-0962-9

Senyatso E K and Masilo B S 1996. Animal genetic resources in Botswana. Animal Genetic Resources/Resources génétiques animales/Recursos genéticos animales. 17:51-60. https://doi.org/10.1017/S1014233900000572 .

Setyawan A D and Lymbery A J 2015 Genetic diversity of five Indonesian native cattle breeds at microsatellite loci. Asian Journal of Animal Sciences 9(2):57-64, http://dx.doi.org/10.3923/ajas.2015.57.64

Silva A C, Paiva S R, Albuquerque M S, do EGITO A A, Santos SA, Lima F C, Castro ST, MARIANTE A D, Correa P S and McManus C M 2012 Genetic variability in local Brazilian horse lines using microsatellite markers. Genetics and Molecular Research 11; 881-890. http://www.alice.cnptia.embrapa.br/alice/handle/doc/931531

Statistics Botswana 2019 Annual Agricultural Survey Report of 2019. Gaborone, Botswana. www.statsbots.org.bw

Steffen P, Eggen A, Stranzinger G, Fries R, Dietz A B and Womack J E 1993 Isolation and mapping of polymorphic microsatellites in cattle. Animal Genetics 24(2):121-4. https://doi.org/10.1111/j.1365-2052.1993.tb00252.x

Sunden L F, Stones R T, Kappes S M, Bishop M P and Grosz M D 1993 Relationship between repeat composition and mapping utility of bovine microsatellite markers. In Proceedings of 8th North American Colloquium on Domestic Animal Cytogenetics and Gene Mapping. University of Guelph Guelph, Ontario, Canada (Vol. 175).

Szpiech Z A and Rosenberg N A 2011 On the size distribution of private microsatellite alleles. Theoretical population biology 80(2):100-13. https://doi.org/10.1016/j.tpb.2011.03.006

Toldo S S, Fries R, Steffen P, Neiberg H L, Barendse W, Womack J E, Hetzel D J and Stranzinger G 1993 Physically mapped, cosmid-derived microsatellite markers as anchor loci on bovine chromosomes. Mammalian Genome 4(12):720-7. https://doi.org/10.1007/BF00357796

Vaiman D, Mercier D, Moazami-Goudarzi K, Eggen A, Ciampolini R, Lépingle A, Velmala R, Kaukinen J, Varvio SL, Martin P and Levéziel H 1994 A set of 99 cattle microsatellites: characterization, synteny mapping, and polymorphism. Mammalian Genome 5(5):288-97. https://doi.org/10.1007/BF00389543

Wibowo T A, Gaskins C T, Newberry RC, Thorgaard G H, Michal JJ and Jiang Z 2008 Genome assembly anchored QTL map of bovine chromosome 14. International Journal of Biological Sciences 4(6):406. https://dx.doi.org/10.7150%2Fijbs.4.406