Genetic variability of dairy cattle breeds in Lithuania

INTRODUCTION

Structured cattle development in Lithuania is observed since the  beginning of the  twentieth century (Skinkytė  et  al., 2005). Lithuanian Red, Lithuanian Red and White, Lithuanian Black and White are the main cattle breeds raised in Lithuania. To increase the  phenotypic and genotypic parameters, they are bred with foreign species (Šveistienė, Jatkauskienė, 2008). The  breed of the  Lithuanian Black and White cattle was obtained by crossbreeding local cattle with Dutch Belted, German Black and White, and Swedish Black and White. It is important to mention that Lithuanian cattle are bred with Holstein cattle breed to increase the productivity (LBWCBA, 2011). The Lithuanian Red and Lithuanian Red and White cattle breeds were obtained by crossbreeding local cattle with Danish Red, Anglers, Swiss and Latvian Brown, Swedish Red and White, and sometimes with Simmentals. It is estimated that by increasing the percentage of the Lithuanian Red genotype in the individual, a  negative effect on milk productivity is observed but it increases the  lifetime of the  individual (Japertienė  et  al., 2016). To increase the lifetime, crossbreeding with Anglers, Swedish Red and White, and Swiss Red is also suggested. To improve milk quality, crossbreeding with Ayrshire is advised, and to improve physical properties, Red and White Holstein, Danish Red are advised (Ministry of Agriculture of the Republic of Lithuania, 2003). The aim of the  current study was to evaluate the  genetic diversity among Lithuanian Black and White, Red and White, and Red cattle breeds in order to provide information for future breeding programmes.

MATERIALS AND METHODS

Lithuanian Black and White and Lithuanian Red Cattle Improvers’ associations provided 192 ear tissue samples. DNA extraction was performed using Genomic DNA Purification Kit (Thermo Fisher Scientific, Lithuania) according to the  manufacturer’s recommendations and its concentration (ng/μl) was assessed using NanoDrop™ 2000 (Thermo Fisher Scientific™) spectrophotometer. DNA samples were stored at –20°C until use. A set of 11 microsatellite loci (BTJAB1, BOVIRBP, BM6438, BM2830, BM1225, BM1818, TGLA122, ETH10, HEL9, CSSM66, TGLA227) used in this study were chosen from part of a  panel markers recommended by the International Society of Animal Genetics (ISAG/FAO, 2004).

The final volume of the PCR mix was 15 μL, which contained 7.5 μL of 2X PCR buffer, 2 μL of the  target DNA, 1  μL of each primer pair, and ddH₂O, the volume of which was based on the  missing final volume of the  reaction mix. The forward primers for the screened polymorphic loci were labelled with fluorescent dyes (5-CY3 or 5-FAM) and their PCR products were separated by capillary electrophoresis using a SeqStudio Genetic Analyzer Sequencer (Applied Biosystems^®).

The allele size was determined using GeneMapper™ 6.0 Software (Thermo Fisher Scientific, No. 4475073). The mean number of alleles per locus (Na), expected heterozygosity (H_E), and observed heterozygosity (H_O) were calculated using GeneAlEx v. 6.5 (Peakall, Smouse, 2012). The Markov chain algorithm was used to calculate the exact P value where the following parameters were used: 10,000 dememorization, 100 batches, and 1000 replications. Hierarchical molecular variance analysis (AMOVA) was performed using Arlequin v. 3.5.2.2 (Excoffier et  al., 2010). The  distribution of total genetic diversity between sample groups and individuals (excluding repetitive genotypes) was assessed using the FST (IAM) model. Groups of genetic clusters were identified by Bayesian cluster analysis according to the  Markov chain algorithm (MCMC) for 200,000 iterations with 100,000 replications; each was run ten times with STRUCTURE 2.3.4 software (Pritchard et al., 2000). To summarise the obtained results, the  CLUMPAK system package (Kopelman et al., 2015) analysis based on the formed Q – matrix was performed.

RESULTS AND DISCUSSION

A total of 88 different alleles were found in the  group of Lithuanian Black and White (LBW) cattle breed, 98 in the Lithuanian Red (LR) cattle breed, and 106 in the  Lithuanian Red and White (LRW) cattle breed. The  observed heterozygosity (H_O) and the expected heterozygosity (H_E) ranged from 0.067 to 1.000 and from 0.065 to 0.887, respectively. BM2830 locus had the highest H_E and H_O in all three cattle breeds, except the CSSM66 locus, which had the highest expected heterozygosity rates in the LRW cattle breed. On the other hand, BOVIRBP had the  lowest rates (0.067 and 0.065, respectively) in LBW, LR, and LRW cattle breeds (Table 1).

Table 1. Values of the individuals studied at the locus (N), the number of different alleles (Na), the number of effective alleles (Ne), the observed heterozygosity (HO), and the expected heterozygosity (H_E)

Thirty-two private alleles were detected in all cattle breeds (15 private alleles in LRW, nine private alleles in LBW, eight private alleles in LR). Private alleles were not detected in the  HEL9 loci (Table  2). Private alleles usually appear in big populations due to random mutations, gene drift, or individuals’ migration between populations. These alleles are specific and may have a crucial role in future breeding (Stolpovsky et al., 2020).

There is a  clear tendency to form three groups of subpopulations based on allelic frequencies (Fig. 1). However, after executing Principal coordinates analysis (PCoA), which is based on genetic distances, no clear clustering was observed (Fig. 2).

Table 2. Distribution of private alleles in the  populations of Lithuanian Black and White (LBW), Lithuanian Red (LR), and Lithuanian Red and White (LRW) cattle

Model-based clustering analysis revealed that Lithuanian dairy cattle had the highest ΔK when K was set to 3; that provided an explanation for the  genetic structure and levels of admixture for the populations. By completing CLUMPAK analysis, results were presented in the dispersed bar (Fig. 3).

To identify genetic differentiation between different cattle breeds, Nei genetic distances and FST values were obtained. Both parameters revealed low genetic differentiation between cattle breeds (the total mean value of FST was 0.019). Also, FST and Nei values were calculated for breed groups, where the highest value was observed between LBW and LRW (FST = 0.027; Nei = 0.084) (Table 3).

Moreover, the  analysis of molecular variance (AMOVA) was performed, which confirmed low genetic variability between sub-populations; however, it showed high genetic differentiation between individuals in subpopulations (Table 4).

Table 3. Values of F_ST (below diagonal) and Nei (above diagonal) genetic distance between the sub-populations dairy cattle studied in Lithuania. Maximum values are highlighted

Table 4. Values of degrees of freedom (d.f.), sum of squares (SS), and variance components expressed as a percentage of variability

DISCUSSION

Investigation into genetic variability of Lithuanian cattle breeds using microsatellite primers is very important for the preservation and improvement of their phenotypic and genotypic properties. After studying 192 individuals, from 88 (N = 95) to 106 (N = 48) different alleles were found in subpopulations (292 being the total number of different alleles). The study conducted by Svishcheva et al. (2020) focused on cattle breeds in the Eurasian area, where it was found that the number of different alleles in the  breeds of the  European region ranged from 79 (N = 49) to 113 (N = 48). Ozsensoy and Kurar (2014) determined that an average of 13.45 different alleles are found per locus; in the study of Snegin et al. (2019), it ranged between 6.5 and 13.6 alleles per population, whereas in our study this value was 26.55. These differences are due to the number of individuals in different populations (subpopulations), the values of the range of the markers used, and differences in breeds.

The H_O values analysed in this study ranged from 0.705 ± 0.056 to 0.743 ± 0.080 and the H_E values from 0.669  ±  0.067 to 0.732  ±  0.065. The  values of the  observed heterozygosity in the  Swiss Brown breed (the breed used for Lithuanian Red crossbreeding) studied by Svishcheva et al. (2020) were 0.72 and 0.71, which fully correlates with the  data obtained in our study into the  subpopulation of Lithuanian Black and White cattle (0.74 and 0.67).

During the  analysis of private alleles, we found as many as 15 private alleles in our study into the subpopulation of the Lithuanian Red cattle breed (N = 48). Van der Westhuizen et al. (2020) conducted a study with a larger popula tion sample (N = 550) and found 2–9 private alleles. Snegin  et  al. (2019) also found a  lower number of private alleles (1–5 alleles per breed) in his study (N  =  752). In our study, most of the  private alleles (6) were found in the  TGLA122 locus. In Snegin  et  al. (2019), the majority of private alleles were also found in the TGLA122 locus (4), and Svishcheva et al. (2020) found the largest number of private alleles in the  TGLA227 locus (3) (N  =  1168), whereas in our study, two (one in LJ and one in LJM subpopulations) were found (N = 192). A review of the overall trend and a comparison of the number of private alleles and the number of individuals tested suggest that the primers selected in this study were suitable for the identification of private alleles for the selected breeds.

The results of this study showed that the variation is 2.85% between subpopulations and 97.15% within a  population. Ozsensoy and Kurar (2014) examined native Turkish varieties and obtained a  2% variation between populations and 98% within a  population. Meanwhile, Prusak et al. (2015) showed a 26.5% variation between populations and 73.5% within the population when studying local Polish cattle breeds.

The FST analysis among dairy cattle breeds (LBW, LR, LRW) resulted in overall FST = 0.019. Among bovine subpopulations, the  highest FST and Nei values (FST = 0.027; Nei = 0.084) were low compared to the  results obtained from different studies. In a  study conducted in Poland among the Black and White, Polish Black-Backed, and Polish Red populations, FST ranged from 0.247 to 0.941 (Prusak et al., 2015). According to Zatoń-Dobrowolska et al. (2007), FST values varied between 0.173 and 0.197 in Red cattle breeds; Nei values were 0.112–0.157. Meanwhile, Svishcheva  et  al. (2020) obtained the  FST value of 0.153 value. In Snegin  et  al. (2019), the  highest FST value among breeds was 0.469 and Nei 0.679.

CONCLUSIONS

Our results provide new information about the genetic variability of the Lithuanian cattle breeds: Lithuanian Red, Lithuanian Red and White, Lithuanian Black and White. The results of the  present study demonstrate low genetic variability between subpopulations; however, it showed high genetic differentiation between individuals within subpopulations. It is recommended to use these results for further Lithuanian cattle breeding to maintain high phenotypic and genotypic performance.

ACKNOWLEDGEMENTS

The authors are grateful for financial support from the National Paying Agency under the Ministry of Agriculture of the Republic of Lithuania via the Project of Genetic Analysis of Lithuanian Black and White, Holsteins, Lithuanian Red, and Lithuanian Red and White cattle breeds (grant No.  MT-19-16). We would also like to thank Virgilijus Urbonavičius and Juozas Darbutas, the leaders of the Lithuanian Black and White and Lithuanian Red Cattle Improvers’ Association, for providing tissue samples.

References

Romualdas Lapickis, Loreta Griciuvienė, Asta Aleksandravičienė, Indrė Lipatova, Algimantas Paulauskas

PIENINIŲ GALVIJŲ VEISLIŲ GENETINIS KINTAMUMAS LIETUVOJE