CC BY
50
Ukrainian Journal of Ecology
Ukrainian Journal ofEcology, 2018, 8(2), 12-17 doi: 10.15421/2018_303
ORIGINAL ARTICLE
UDC 636.4.082.25:575.22
Genetic diversity and bottleneck analysis of the Red Steppe cattle based on microsatellite markers
A.S. Kramarenko1, E.A. Gladyr2, S.S. Kramarenko1, T.V. Pidpala1, L.A. Strikha1, N.A. Zinovieva2
Mykolayiv National Agrarian University, Georgiy Gongadze Str., 9, Mykolayiv, 54020, Ukraine
E-mail: kssnail0108@gmail. com 2Federal Science Center for Animal Husbandry named after Academy Member L.K. Ernst, Dubrovitsy, 60, Podolsk
Municipal District, Moscow Region, 142132, Russia Received: 12.02.2018Accepted:25.03.2018
Thirty-nine dairy cows representing the Red Steppe (RS) cattle breed (the State Enterprise "Pedigree Reproducers "Stepove" Mykolayiv region, Ukraine) were included in the study. A set of 11 microsatellite markers recommended by International Society of Animal Genetics (ISAG) for cattle was used to study genetic diversity in the RS cattle population. All of the studied loci were highly informative and polymorphic. In total, 71 alleles were detected at 11 microsatellite loci, from which 16 (22.5%) had frequency lower than 5%. The number of detected alleles per locus (TNA) ranged from four to ten, with a mean value of 6.45±0.51. The mean effective number of alleles (Ae) was 3.77±0.37. The allele frequencies ranged from 0.013 to 0.714. The average values for observed (Ho) and expected (He) heterozygosities were 0.607±0.085 and 0.703±0.034, respectively. The within breed estimate Fis indicates heterozygosity shortage of 0.179. The Hardy-Weinberg equilibrium test revealed that 2 out of 11 loci deviated from equilibrium. The RS cattle population is non-bottlenecked, i.e., it has not undergone any recent reduction in the effective population size and remained at mutation-drift equilibrium. The estimated mean Ne for the RS cattle population was 23.3 (95% CIs = 11 -74) individuals. These low values emphasize the need of controlling the rate of increase of inbreeding in the RS cattle herds.
Key words: genetic diversity, allele pool, bottleneck-effect, microsatellites DNA, Red Steppe cattle, dairy cow
Introduction
Cattle is an important livestock species that have played a special role in the human history and culture, and had a considerable impact on human society. The worldwide population of cattle is estimated to 1.4 billion animals, of which 159 million (11%) are found in Europe and Central Asia (Felius et al., 2011).
Dairy industry requires the development of very standardized cattle herds to fulfill their commercial needs that reflects on selection practices in breeding programs. The genetic characterization of populations, breeds and species allows evaluation of genetic variability, a basic element in working out breeding strategies and genetic conservation plans. Molecular markers have revolutionized our ability to characterize genetic variation and rationalize genetic selection (Goddard and Hayes, 2007; Hayes et al., 2009). Microsatellites (highly polymorphic simple sequence repeats) are still remained the popular molecular markers, essentially owing to the option of blending their analysis with use of the polymerase chain reaction (PCR). The employment of microsatellite markers is one of the powerful means for studying the genetic diversity, calculation of genetic distances, detection of bottlenecks and admixture because of high degree of polymorphism, random distribution across the genome, co-dominance and neutrality with respect to selection (Putman and Carbone, 2014).
The Red Steppe (RS) cattle breed was created in the Ukraine and southern European Russia by crossing of Red East Friesian and Angeln breeds with Ukrainian Grey and later with Swiss Brown and East Friesian breeds during the time from 1789 to 1824 by Mennonites. The RS breed was the most widespread breed found in the former U.S.S.R., which was characterized by the highest milk yield comparing to other breeds used fir milk production in the country (Mason, 1996).
Considering the importance of cattle in Ukrainian agriculture, few efforts have been made to evaluate the genetic diversity and relationship in Ukrainian cattle breeds using microsatellite markers (Shkavro et al., 2014: Shelyov, 2015; Kramarenko et al., 2015; Shelyov et al., 2017). Thus, a deeper knowledge of the genetic diversity and population structure of Ukrainian cattle breeds can provide a rational basis for the need of conservation and possible use of native breeds as genetic resources to meet potential future demand of adaptation to changing environment or production needs.
The aim of the current study was to evaluate the genetic diversity among the Red Steppe (RS) cattle breed, in order to provide information for future breeding programmes and conservation management strategy of the breed.
Methods
Thirty-nine dairy cows representing the Red Steppe cattle breed (the State Enterprise "Pedigree Reproducers "Stepove" Mykolayiv region, Ukraine) were included in the study. The animals were unrelated and were randomly selected from herd. Genomic DNA was extracted from tissue samples using Nexttec column (Nexttec Biotechnology GmbH, Germany) following the manufacturer's instructions. The DNA concentration was estimated by measuring the absorbance at 260 nm and the DNA quality was checked by separation on agarose gels.
Eleven microsatellite markers (BM1818, BM1824, BM2113, ETH3, ETH10, INRA023, TGLA53, TGLA122, TGLA126, TGLA227 and SPS115) were analyzed to estimate various parameters of genetic diversity. Microsatellites were amplified in two multiplex reactions. Electrophoresis was carried out using an ABI 3130xl Genetic Analyzer (Applied Biosystems, USA). Allele sizes of each microsatellite were determined using GeneMapper ver. 4.0 (Applied Biosystems).
GenAIEx v.6.5 software (Peakall and Smouse, 2012) was used to estimate basic population genetic descriptive statistics for each marker: allelic frequencies, observed total number of alleles (TNA), effective number of alleles (Ae), observed (Ho) and expected heterozygosity (He). The effective allele number (Ae) for each locus was calculated using the following formula: Ae = 1 / (1 - He), where He corresponds to the expected heterozygosity. Allelic richness (Ar) for the RS cattle population (for 14 diploid individuals) was calculated to correct distortion by sample size difference using FSTAT v. 2.9.3.2. (Goudet, 2002).
Deviations from Hardy-Weinberg equilibrium (HWE) were tested for each locus using the Markov chain method implemented by Guo and Thompson (1992), using the software GENEPOP v.4.2 (Rousset, 2008) using Markov chain algorithm implemented according to authors recommendation (with 10,000 dememorizations, 200 batches and 5,000 interactions per batch). The BOTTLENECK v.1.2.03 (Cornuet and Luikart, 1996) analysis was performed to find out whether this cattle population was exhibiting a significant number of loci with the excess of heterozygosity.
Genetic diversity was assessed by effective population size (Ne) and it was calculated by linkage disequilibrium (LD) method, as implemented in the software package NeESTIMATOR v. 2.01 (Do et al., 2014).
Results and discussion
All bovine microsatellite loci were highly polymorphic for the RS cattle population. The alleles, which were observed only once, were excluded for the analysis. The allelic frequencies of the 11 microsatellite loci in the RS cattle population are presented in Table 1.
Table 1. The allelic frequencies of the 11 microsatellite loci in the RS cattle population
Locus Allele Freq. Locus Allele Freq. Locus Allele Freq.
TGLA227 77 0.064 ETH10 213 0.077 TGLA126 115 0.432
79 0.013 215 0.077 117 0.189
81 0.282 217 0.346 119 0.108
83 0.141 219 0.231 121 0.027
89 0.179 221 0.167 123 0.081
91 0.154 223 0.026 125 0.149
93 0.026 225 0.077 127 0.014
95 0.038 SPS115 248 0.628 BM1818 258 0.103
97 0.090 252 0.064 260 0.013
103 0.013 254 0.077 262 0.269
BM2113 125 0.038 256 0.077 264 0.064
127 0.179 258 0.013 266 0.449
131 0.013 260 0.141 268 0.103
133 0.038 TGLA122 133 0.103 ETH3 109 0.097
135 0.179 137 0.167 117 0.375
137 0.295 139 0.064 119 0.167
139 0.256 141 0.231 121 0.097
TGLA53 160 0.241 143 0.321 123 0.028
166 0.537 145 0.115 125 0.097
168 0.111 INRA23 206 0.071 127 0.125
170 0.037 208 0.143 129 0.014
172 0.056 212 0.714 BM1824 178 0.186
174 0.019 214 0.071 180 0.086
182 0.471 188 0.257
In total, 71 alleles were detected, from which 16 (22.5%) had frequency lower than 5%. FAO has specified a minimum of four distinct alleles per locus for evaluation of genetic differences among domestic livestock breeds. By this criterion, all of 11 microsatellites applied in this study showed ample polymorphism for assessment genetic variation within the RS cattle population.
Allele ranges, number of alleles, heterozygosity per locus are summarized in Table 2. The TNA per locus ranged from four (INRA023 and BM1824) to 10 (TGLA227), with a mean value of 6.45±0.51 alleles. The mean value of Ho across loci was 0.607±0.085, with estimates per locus ranged from 0.000 (INRA023) to 0.872 (TGLA227). For He, the mean value for all loci was 0.703±0.034 with variation between 0.459 (INRA023) and 0.830 (TGLA227). In general, genetic variation of this population is high according to the allele numbers and heterozygosity values of the microsatellite loci (Table 2). The mean observed number of alleles across all the microsatellite loci was lower than other Ukrainian local dairy cattle breeds - Ukrainian Black Pied (mean of TNA across loci was 9.2 with estimates per locus ranging from 6 to 14 alleles per locus) and Ukrainian Red-and-White (mean of TNA across loci was 9.5 with estimates per locus ranging from 7 to 13 alleles per locus) (Shelyov, 2015). Lower allelic diversity than studied populations has been reported in indigenous cattle - Ukrainian Grey breed (the mean value of TNA across loci was 5.1 with estimates per locus ranging from 3 to 8 alleles) (Shkavro et al., 2010).
Table 2. Measures of genetic variation in the RS cattle population
Locus Size range, Parameters
bp TNA Ae Ar Ho He Fis HWE
TGLA227 77-103 10 5.88 7.91 0.872 0.830 -0.050 ns
BM2113 125-139 7 4.54 5.84 0.769 0.780 0.013 ns
TGLA53 160-174 6 2.75 5.18 0.185 0.636 0.709 *
ETH10 213-225 7 4.56 6.41 0.846 0.781 -0.084 ns
SPS115 248-260 6 2.32 5.13 0.462 0.569 0.189 ns
TGLA122 133-145 6 4.72 5.86 0.769 0.788 0.024 ns
INRA023 106-214 4 1.85 4.00 0.000 0.459 1.000 *
TGLA126 115-127 7 3.79 5.92 0.649 0.736 0.119 ns
BM1818 258-268 6 3.34 5.21 0.692 0.701 0.012 ns
ETH3 109-129 8 4.69 6.93 0.806 0.787 -0.024 ns
BM1824 178-188 4 3.03 3.96 0.629 0.670 0.062 ns
Mean 6.45 3.77 5.67 0.607 0.703 0.179
SE 0.51 0.37 0.35 0.085 0.034 0.105
bp - base pair; TNA - total number of alleles; Ae - effective number of alleles; AR - allelic richness (for 14 diploid individuals); Ho - observed heterozygosity; He - expected heterozygosity; FIS - heterozygote deficiency; HWE - Hardy-Weinberg equilibrium; ns - non significant p-value; * - p < 0.05; SE- standard error.
The RS cattle seem to harbour a good amount of genetic variation. The average observed heterozygosity in this study is lower than that shown in Ukrainian Black Pied (0.821) and Ukrainian Red-and-White breeds (0.784) (Shelyov, 2015). Overall heterozygosity values were comparable with those estimated in Ukrainian Grey cattle (0.680) (Shkavro et al., 2010). The RS cattle population displayed considerable levels of genetic diversity as estimated by expected heterozygosity (He = 0.703±0.034). Shelyov (2015) had found a value of He of 0.819 in Ukrainian Black Pied and 0.884 in Ukrainian Red-and-White breeds. The high values of allelic diversity, gene diversity and expected heterozygosity obtained in this study well confirm that RS cattle breed represent an important reservoir of genetic variability and it reflects the absence of selection or organized breeding programs for Ukrainian dairy cattle.
Two loci in the RS cattle breed (TGLA53 and INRA023) have shown deviation from HWE. Thus, our results differed from those of Kramarenko et al. (2015) where the Southern Meat cattle breed gave a significant deviation from HWE for TGLA227, TGLA53, ETH3, ETH225 and SPS115 loci. This deficiency of heterozygotes among populations is an indicator of inbreeding among cattle breeds or the occurrence of population substructure.
The values of Fis were positive at 8 loci that indicates the within population heterozygotes deficiency, whereas three loci were characterized by negative Fis values. The Fis estimates ranged between -0.084 (ETH10) and 1.000 (INRA023), with average value of 0.179±0.105. Thus, the studied RS population was characterized a substantial heterozygote deficiency (17.9%). Only two microsatellite loci (TGLA53 and INRA023) significantly contributed to observed heterozygote deficiency in the RS cattle population.
Microsatellite data were also subjected to statistical analysis to test whether the population exhibits a significant number of loci with gene diversity excess. Three mutation models - the Infinite Allele Model (IAM), Two Phase Model of mutation (TPM) and Stepwise Mutation Model (SMM) - were selected for running the BOTTLENECK program to test for population bottlenecks. The probability values obtained under these three models using three different statistical tests are depicted in Table 3. The expected numbers of loci with heterozygosity excess were 6.49, 6.52 and 6.56 in IAM, TPM and SMM, respectively. The null hypothesis was not rejected using the Sign test and the Wilcoxon test and not indicated a recent genetic bottleneck event. In case of standardized difference test, the hypothesis of mutation-drift equilibrium was rejected for TPM (p = 0.015) and SMM (p < 0.001) models; under IAM, the results displayed no genetic bottleneck effect. Mode-shift indicator test as a second test for potential bottleneck was used. The microsatellite alleles were organized into 8 frequency classes, which permit checking whether the distribution followed the normal L-shaped form, where alleles with low frequencies (0.01-0.10) are the most numerous (Figure 1).
Table 3. Heterozygosity excess/deficiency under different mutation models (Heterozygosity Method) in the RS cattle population
Models Sign test Standardized differences test Wilcoxon test
IAM Hee =6.49 T2 = -0.111 P (one tail for H deficiency): 0.768
Hd = 3 p = 0.456 (ns) P (one tail for H excess): 0.260
He = 8 P (two tails for H excess and deficiency): 0.520
p = 0.274 (ns)
TPM Hee = 6.52 T2 = -2.168 P (one tail for H deficiency): 0.483
Hd = 4 p = 0.015 P (one tail for H excess): 0.551
He = 7 P (two tails for H excess and deficiency): 0.966
p = 0.513 (ns)
SMM Hee = 6.56 T2 = -5.847 P (one tail for H deficiency): 0.139
Hd = 7 p < 0.001 P (one tail for H excess): 0.880
He = 4 P (two tails for H excess and deficiency): 0.278
p = 0.104 (ns)
IAM - Infinite allele model; TPM - Two phase model; SMM - Stepwise mutation model. Parameters for T.P.M: Variance = 30.00. Proportion of SMM in TPM = 70.00%; Estimation based on 1,000 replications. Hee - heterozygosity excess expected; Hd - heterozygosity deficiency; He -heterozygosity excess; P - probability; ns - non significant p-value.
The observed distribution suggests that the RS cattle breed did not encounter a genetic bottleneck in the recent past. According to the Bottleneck analysis, Turkish native cattle breeds were revealed a normal L-shaped distribution indicating that these populations did not experience any recent potential risk of extinction (Oz§ensoy and Kurar, 2014). The qualitative test of mode shift analysis supported the conservative SMM model which indicated absence of genetic bottleneck in the recent past in Senegalese cattle populations (Ndiaye et al., 2015). Bottleneck was examined assuming all three mutation models which showed that the population has not experienced bottleneck in recent past for the Kherigarh cattle also (Pandey et al., 2006). On other hand, bottleneck has been reported in two sub strains of Japanese black cattle by Sasazaki et al. (2004).
Allele frequency class
Figure 1. L-shaped mode-shift graph showing lack of recent genetic bottleneck in the RS cattle population
The estimated mean Ne for the RS cattle population was 23.3 (95% CIs = 11-74) individuals. Table 4 gives the estimates of effective population size for certain dairy cattle breeds. The effective population size obtained for the RS cattle in this study were in agreement with those reported data for the Reyna Creole cattle in Nicaragua (Corrales et al., 2010), Montbeliarde and Normande breeds in France (Leroy et al., 2013), Holstein in the USA (Weigel, 2001), Guernsey in South Africa, the USA and Canada (Melka et al., 2013). Generally, estimates of Ne in some modern breeds of dairy cattle (Ayrshire, Holstein, Jersey, etc.) are of the order of 100 or more.
From a consideration of the net genetic response in economic merit in dairy cattle breeding, Goddard and Smith (1990) suggested 40 as a minimum effective size. Another approach toward defining minimum effective size was considered by Meuwissen and Woolliams (1994), which balanced inbreeding depression and gain in fitness through natural selection. This resulted in recommendations of the order of 30 to 250. The current effective size of the RS cattle population is smaller than these critical values. Thus, the small Ne found in the RS cattle reflects the fact that breeding strategies followed in this breed have implied a very heavy use of few top sires.
Table 4. The estimates of effective population size (Ne) for certain dairy cattle breeds
Breed Country Ne (min-max) Reference
Ayrshire South Africa 148 Maiwashe et al., 2006
Brown Swiss France 80 (55-98) Leroy et al., 2013
Brown Swiss South Africa 45-132 de Ponte Bouwer et al., 2013
Guernsey South Africa 165 Maiwashe et al., 2006
Guernsey South Africa 57 Melka et al., 2013
Guernsey Canada 46 Melka et al., 2013
Guernsey USA 46 Melka et al., 2013
Holstein France 74 (49-93) Leroy et al., 2013
Holstein Spain 66-79 Rodriguez-Ramilo et al., 2015
Holstein South Africa 137 Maiwashe et al., 2006
Holstein Germany 103 Qanbari et al., 2010
Holstein Canada 77 (33-114) Stachowicz et al., 2011
Holstein USA 39 Weigel, 2001
Holstein Australian 150 Hayes et al., 2003
Holstein Korea 122 Shin et al., 2013
Icelandic cattle Iceland 111 (100-127) Asbjarnardottir et al., 2010
Jersey South Africa 108 Maiwashe et al., 2006
Jersey Canada 114 (54-153) Stachowicz et al., 2011
Montbeliarde France 57 (30-82) Leroy et al., 2013
Normande France 64 (37-93) Leroy et al., 2013
Red Steppe Ukraine 23 (11 -74) present study
Reyna Creole cattle Nicaragua 28-46 Corrales et al., 2010
Sahiwal Kenya 270 (4-576) Kamiti et al., 2016
Ukrainian Black Pied Ukraine 397 Shelyov et al., 2017
Ukrainian Red-and-
White Ukraine 555 Shelyov et al., 2017
Conclusions
The study reports a first genetic within breed diversity estimate of the RS cattle population through microsatellite markers recommended by the ISAG. The TNA, Ho and He values observed in the present study is indicative of the fact that the markers used are highly informative for genetic characterization of the RS cattle and give reliable information on genetic diversity and population structure. Only two loci in the RS cattle breed (TGLA53 and INRA023) have shown deviation from HWE. Our data suggest that the RS cattle population has not undergone any reduction at least in the recent past. Estimates of effective population size were ranged from about 11 to 74. These low values emphasize the need of controlling the rate of increase of inbreeding in the RS cattle herds.
Acknowledgements
Financial support was received from the Ministry of Education and Science of Ukraine (state registration number 0117U000485) to A.S.Kramarenko.
References
Asbjarnardottir, M.G., Kristjansson, T., Jonsson, M.B., & Hallsson, J.H. (2010). Analysis of genetic diversity and population structure within the Icelandic cattle breed using molecular markers. Acta Agriculturae Scand. Section A, 60(4), 203-210. doi: 10.1080/09064702.2010.538714
Cornuet, J.M., & Luikart, G. (1996). Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics, 144(4), 2001-2014.
de Ponte Bouwer, P., Visser, C., & Mostert, B.E. (2013). Analysis of inbreeding of the South African Dairy Swiss breed. South African Journal of Animal Science, 43(1), 38-43. doi: 10.4314/sajas.v43i1.4
Corrales, R., Näsholm, A., Malmfors, B., & Philipsson, J. (2010). Population structure of Reyna Creole cattle in Nicaragua. Tropical Animal Health and Production, 42(7), 1427-1434. doi: 10.1007/s11250-010-9571-9
Do, C., Waples, R.S., Peel, D., Macbeth, G.M., Tillett, B.J., & Ovenden, J.R. (2014). NeEstimator v2: re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Molecular Ecology Resources, 14(1), 209-214. doi: 10.1111/1755-0998.12157
Felius, M., Koolmees, P.A., Theunissen, B., Lenstra, J.A., & European Cattle Genetic Diversity Consortium. (2011). On the breeds of
cattle - historic and current classifications. Diversity, 3(4), 660-692. doi: 10.3390/d3040660
Goddard, M.E., & Hayes, B.J. (2007). Genomic selection. Journal of Animal Breeding and Genetics, 124(6), 323-330.
doi: 10.1111/j.1439-0388.2007.00702.x
Goddard, M.G., & Smith, C. (1990). Optimum number of bull sires in dairy cattle breeding. Journal of Dairy Science, 73(4), 1113-1122. doi: 10.3168/jds.S0022-0302(90)78771 -1
Goudet, J. (2002). FSTAT, a program to estimate and test gene diversities and fixation indices, version 2.9.3.
Guo, S.W., & Thompson, E.A. (1992). Performing the exact test of Hardy-Weinberg proportion for multiple alleles. Biometrics,
361-372. doi: 10.2307/2532296
Hayes, B.J., Bowman, P.J., Chamberlain, A.J., & Goddard, M.E. (2009). Invited review: Genomic selection in dairy cattle: Progress and challenges. Journal of Dairy Science, 92(2), 433-443. doi: 10.3168/jds.2008-1646
Hayes, B.J., Visscher, P.M., McPartlan, H.C., & Goddard, M.E. (2003). Novel multilocus measure of linkage disequilibrium to estimate past effective population size. Genome Research, 13(4), 635-643. doi: 10.1101/gr.387103
Kamiti, D., Ilatsia, E., Bett, R., & Kahi, A. (2016). Population structure and demographic trends of the registered Sahiwal cattle in Kenya. Tropical Animal Health and Production, 48(5), 1029-1036. doi: 10.1007/s11250-016-1055-0
Kramarenko, O., Hladyr, H., Naydyonova, V., Dubinsky, O. & Zinov'eva, N. (2015). Analysis of the microsatellite loci polymorphism of the Southern Meat cattle breed. Collection of Scientific Works of the Podilsky State Agricultural and Technical University: Agricultural Sciences, 23: 382-390 (In Ukrainian).
Leroy, G., Mary-Huard, T., Verrier, E., Danvy, S., Charvolin, E., & Danchin-Burge, C. (2013). Methods to estimate effective population
size using pedigree data: examples in dog, sheep, cattle and horse. Genetics Selection Evolution, 45(1), 1.
Maiwashe, A., Nephawe, K.A., van der Westhuizen, R.R., Mostert, B.E., & Theron, H.E. (2006). Rate of inbreeding and effective
population size in four major South African dairy cattle breeds. South African Journal of Animal Science, 36(1), 50-57.
Mason, I.L. (1996). A World Dictionary of Livestock Breeds, Types and Varieties. Fourth Edition. CAB International.
Melka, M.G., Sargolzaei, M., Miglior, F., & Schenkel, F. (2013). Genetic diversity of Guernsey population using pedigree data and gene-
dropping simulations. Animal, 7(2), 192-201. doi: 10.1017/S1751731112001723
Meuwissen, T.H.E., & Woolliams, J. A. (1994). Effective sizes of livestock populations to prevent a decline in fitness. Theoretical and Applied Genetics, 89(7-8), 1019-1026. doi: 10.1007/BF00224533
Ndiaye, N.P., Sow, A., Ndiaye, S., Sawadogo, G.J., & Sembene, M. (2015). Bottleneck and molecular variance analyses in Senegalese local cattle breeds using microsatellite markers. Research Opinions in Animal and Veterinary Sciences, 5(4), 158-164. Ózfensoy, Y., & Kurar, E. (2014). Genetic diversity of native Turkish cattle breeds: Mantel, AMOVA and bottleneck analysis. Journal of Advanced Veterinary and Animal Research, 1(3), 86-93. doi: 10.5455/javar.2014.a21
Pandey, A.K., Sharma, R., Singh, Y., Prakash, B.B., & Ahlawat, S.P.S. (2006). Genetic diversity studies of Kherigarh cattle based on microsatellite markers. Journal of Genetics, 85(2), 117-122. doi: 10.1007/BF02729017
Peakall, R., & Smouse, P.E. (2012). GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and researchd - an update. Bioinformatics, 28(19), 2537-2539. doi: 10.1093/bioinformatics/bts460
Putman, A.I., & Carbone, I. (2014). Challenges in analysis and interpretation of microsatellite data for population genetic studies. Ecology and Evolution, 4(22), 4399-4428. doi: 10.1002/ece3.1305
Qanbari, S., Pimentel, E.C.G., Tetens, J., Thaller, G., Lichtner, P., Sharifi, A.R., & Simianer, H. (2010). The pattern of linkage disequilibrium in German Holstein cattle. Animal Genetics, 41(4), 346-356. doi: 10.1111/j.1365-2052.2009.02011.x Rodríguez-Ramilo, S.T., Fernández, J., Toro, M.A., Hernández, D., & Villanueva, B. (2015). Genome-wide estimates of coancestry, inbreeding and effective population size in the Spanish Holstein population. PLoS One, 10(4), e0124157. doi:10.1371/journal.pone.0124157
Rousset, F. (2008). genepop'007: a complete re-implementation of the genepop software for Windows and Linux. Molecular Ecology Resources, 8(1), 103-106. doi: 10.1111 /j.1471-8286.2007.01931 .x
Sasazaki, S., Honda, T., Fukushima, M., Oyama, K., Mannen, H., Mukai, F., & Tsuji, S. (2004). Genealogical relationship between pedigree and microsatellite information and analysis of genetic structure of a highly inbred Japanese black cattle strain. Asian Australasian Journal of Animal Sciences, 17(10), 1355-1359.
Shelyov, A.V. (2015). Polymorphism of microsatellite DNA loci in different species of farm animals. Animal Breeding and Genetics, 50, 183-189 (In Ukrainian).
Shelyov, A.V., Kopylov, K.V., Kramarenko, S.S., and Kramarenko, O.S. (2017). Analysis of population-genetic processes in different cattle breeds by microsatellite loci of DNA. Agricultural Science and Practice, 4(1), 74-78. doi: 10.15407/agrisp4.01.074 Shin, D.H., Cho, K.H., Park, K.D., Lee, H.J., & Kim, H. (2013). Accurate estimation of effective population size in the Korean dairy cattle based on linkage disequilibrium corrected by genomic relationship matrix. Asian-Australasian Journal of Animal Sciences, 26(12), 1672-1679. doi: 10.5713/ajas.2013.13320
Shkavro N.N., Radko, A., Slota, E., & Rossokha, V.I. (2010). Microsatellite DNA loci polymorphism of two cattle breeds. The Journal of V.N. Karazin Kharkiv National University. Series: biology, 11(905), 120-126 (In Ukrainian).
Stachowicz, K., Sargolzaei, M., Miglior, F., & Schenkel, F.S. (2011). Rates of inbreeding and genetic diversity in Canadian Holstein and Jersey cattle. Journal of Dairy Science, 94(10), 5160-5175. doi: 10.3168/jds.2010-3308
Weigel, K. A. (2001). Controlling inbreeding in modern breeding programs. Journal of Dairy Science, 84, E177-E184. doi: 10.3168/jds.S0022-0302(01 )70213-5
Citation:
Kramarenko, A.S., Gladyr, E.A., Kramarenko, S.S., Pidpala, T.V., Strikha, L.A., Zinovieva, N.A. (2018). Genetic diversity and bottleneck analysis of the Red Steppe cattle based on microsatellite markers. Ukrainian Journal of Ecology, 8(2), 12-17. I ("OE^^^MlThk work is licensed under a Creative Commons Attribution 4.0. License
