Genetic components of grey cattle in Estonia as revealed by microsatellite analysis using two Bayesian clustering methods
© Li et al; licensee BioMed Central Ltd. 2011
Received: 6 July 2010
Accepted: 11 February 2011
Published: 11 February 2011
It was recently postulated that a few individual grey cattle still found in Estonia might be a relict of the old native cattle stock. Genotypes at 17 microsatellite loci from a total of 243 cattle from North European breeds and 11 grey cattle in Estonia were used in an attempt to clarify the genetic composition of the grey cattle.
We characterize the genetic components of 11 examples of the grey cattle in Estonia at the population and individual levels. Our results show that the grey cattle in Estonia are most genetically similar to the Holstein-Friesian breed and secondarily to the Estonian Red cattle.
Both Bayesian approaches gave similar results in terms of the identification of numbers of clusters and the estimation of proportions of genetic components. This study suggested that the Estonian grey cattle included in the analysis are a genetic composite resulting from cross-breeding of European dairy breeds.
Conservation of farm animal genetic resources is of great value to the agricultural, economic, social and cultural sectors . This is particularly true for native farm animals because the specific genes and gene combinations they carry may be useful, for example to cope with the challenge of global climate change (see ).
So far the genetic composition of grey cattle relative to other existing breeds in Estonia is still unknown. In this study we use a panel of 17 microsatellite loci and Bayesian-based assignment techniques to evaluate the relationship of Estonian grey cattle to other breeds occurring in North Europe.
Cattle samples and microsatellite data
Data for the 11 grey cattle analysed in Estonia
dark grey (blackish)
Eleven Estonian grey cattle individuals from different stocks were blood-sampled. Particular efforts were made in all cases, using both the limited pedigree information (e.g. mostly only parent-offspring and full-sibling relationships) available and the knowledge of local herdsmen (e.g. the farm or village where the cattle originate from and the previous owners) via the interview questionnaire, to ensure that the animals were unrelated and had characteristics typical of the population . Genomic DNA was extracted using a standard phenol/chloroform protocol . PCRs were carried out following the protocols available at the Cattle Diversity Database http://www.projects.roslin.ac.uk/cdiv/markers.html. The size characterization of PCR products was done on a MegaBACE™ 500 capillary sequencer (GE Healthcare Life Sciences, Little Chalfont, UK) using the Fragment Profiler program ver. 1.2 (GE Healthcare Life Sciences). International control samples were also genotyped in order to standardize the size of allele fragments. Blood sampling of the 11 Grey cattle in Estonia was taken by a veterinarian in a procedure according to the Estonian Veterinary and Food Board and satisfied all ethical concerns.
Tests for genotypic linkage disequilibrium (LD) for each locus pair and tests for deviation from Hardy-Weinberg equilibrium (HWE) were analysed in GENEPOP version 3.4 . The global and pairwise genetic differentiation were determined as unbiased estimates of FST using FSTAT version 184.108.40.206 . Significance of the results was established by applying sequential Bonferroni corrections (see ).
A Bayesian clustering method was first employed to assess population structure using the program STRUCTURE version 2.2 . We performed 10 runs for each K value at 2 - 10 and ran the program assuming a model of admixture and correlated allele frequencies. We did not use any prior information about the population origin of the animals. A burn-in period of 200 000 generations and MCMC simulations of 500 000 iterations were used in all the above runs. The values of Ln P(D) (the log probability of data) were estimated assigning a prior from 2 to 10 and the optimal K was chosen based on the delta K (ΔK) value. This criterion was originally described in Evanno et al. and was shown to be effective in later studies [1, 13]. We then evaluated the population and individual membership coefficients (Q) of the 11 grey cattle in Estonia to the K inferred clusters.
BAPS version 5.4  was run setting the maximum number of clusters at 20. Results were based on 50 simulations from the posterior allele frequencies. Since the mode of the posterior distribution of K almost always provided an overestimate of K, we used the number of clusters containing more than 3 individuals as a point estimate of K, as recommended by Tang et al.. For runs in which K was correctly estimated, we calculated the average probability (q) of assignment to the 'correct' cluster ('correct' defined as q > 0.9 in the correct cluster). Individuals with a likelihood admixture ratio greater than 3.0 were considered to be significantly admixed.
The FST analysis across breeds showed that 5.6% of the total genetic variation could be explained by the difference among populations. A low level of genetic differentiation was found between the grey cattle in Estonia and Finnish Holstein-Friesian (FST = 5.2%; results not shown) as well as between the grey cattle in Estonia and the Estonia Red cattle (FST = 5.6%; results not shown). Neither of the values was statistically significant at the 0.05 level (P > 0.05). No specific locus pairs showed a consistent deviation from LE that would have been in each, or even in most, of the populations. Deviations from HWE across the loci were present in the population of grey cattle in Estonia, which is most probably due to the small population size. However, no evidence for significant deviation from HWE was detected when a test was performed across all loci for all populations.
Membership proportions (Q) of the 11 grey cattle in Estonia for the 6 genetic clusters
(Latvian Danish Red)
On-average we found higher proportions of membership for Finnish Holstein-Friesian and Estonian Red cattle in the grey cattle. The grey cattle represent a composite of North European cattle.
The composite genetic components may explain their distinctive grey colour, which is a mixture of colours. This finding is also evidenced by the fact that a grey cow sometimes has grey and/or black-and-white calves in the same birth. Although the grey cattle are characterized as having most of their genetic components from the black-and-white dairy cattle (i.e. Holstein-Friesian) or Estonian Red, they can be valuable in the investigation of the genetics of the colour genes.
Both STRUCTURE and BAPS correctly inferred the number of clusters in a dataset when genetic differentiation among populations was low. However, it seems that the proportions of individual membership in the clusters estimated by the program STRUCTURE are more consistent with the breeding history for the populations. For example, Latvian Danish Red, Estonian Red and Latvian Brown are the local derived populations from the Anglen and Danish Red cattle. This shared ancestry is reflected in the results of STRUCTURE, but not of BAPS. For the 11 grey cattle in Estonia, both programs gave comparable results for proportions of individual membership. To secure high confidence in results, we advocate using both programs for inferring the number of clusters and assignment of individuals to clusters, particularly when the level of genetic differentiation among populations is low.
Finally, a growing number of domestic animal populations are genotyped for the same panel of microsatellites (see ), for example the markers recommended by the FAO (Food and Agriculture Organization of the United Nations). This can help address similar kinds of questions on genetic components and the nature of native animal stocks because more data for potential reference and parental populations are available. The livestock populations for which there is a high priority for conservation, in terms of proportions of their native genetic components (e.g. [16–18]), can be identified and, thus, need to be included in conservation programmes in the near future.
In conclusion, given the low levels of genetic differentiation among the populations investigated, both Bayesian approaches gave similar results in terms of identification of the numbers of clusters and the estimation of proportions of genetic components. Our study shows that the Estonian grey cattle analysed were a genetically admixed population, most influenced by the Holstein-Friesian and Estonian Red cattle.
We wish to thank Kaia Lepik from the Estonian Fund for Nature for her generous help and professor Haldja Viinalass at the Estonian University of Life Sciences (Tartu, Estonia) for the comments. This study was supported by a grant from the Estonian Science Foundation, target financing project SF0180122s08 from the Estonian Ministry of Education and Sciences; and from the European Union through the European Regional Development Fund (Centre of Excellence FIBIR).
- Li MH, Kantanen J: Genetic structure of Eurasian cattle (Bos taurus) based on microsatellites: clarification for their breed classification. Anim Genet. 2010, 41: 150-158. 10.1111/j.1365-2052.2009.01980.x.PubMedView ArticleGoogle Scholar
- Hoffmann I: Climate change and the characterization, breeding and conservation of animal genetic resources. Anim Genet. 2010, 41 (Suppl 1): 32-46. 10.1111/j.1365-2052.2010.02043.x.PubMedView ArticleGoogle Scholar
- Dmitriev NG, Ernst LK: Animal Genetics Resources of the USSR. 1989, Rome (Italy): Food and Agriculture Organization of the United NationsGoogle Scholar
- Michelson A: Grey cattle in Estonia. 2010, Mustiala (Finland): Department of Natural Sciences and the Environment, HAMK University of Applied SciencesGoogle Scholar
- Oskar L: Endis-Eesti elu-olu. IV Lugemispalu Karjakasvataja Elust. 2001, Tartu (Estonia): Eesti KirjandusmuuseumGoogle Scholar
- Sambrook J, Fritsch EF, Maniatis T: Molecular Cloning: a Laboratory Manual. 1989, Cold Spring Harbor (NY, USA): Cold Spring Harbor Laboratory PressGoogle Scholar
- Raymond M, Rousset F: Genepop (version1.2): population genetics software for exact tests and ecumenicism. J Hered. 1995, 86: 248-249.Google Scholar
- Weir BS, Cockerham CC: Estimating F-statistics for the analysis of population structure. Evolution. 1984, 38: 1358-1370. 10.2307/2408641.View ArticleGoogle Scholar
- Goudet J: FSTAT: Computer package for PCs. 2002, Lausanne (Switzerland): Institute of Ecology, University of LausanneGoogle Scholar
- Rice WR: Analyzing tables of statistical tests. Evolution. 1989, 43: 223-225. 10.2307/2409177.View ArticleGoogle Scholar
- Pritchard JK, Stephens M, Donnelly P: Inference of population structure using multilocus genotype data. Genetics. 2000, 155: 945-959.PubMedPubMed CentralGoogle Scholar
- Evanno G, Regnaut S, Goudet J: Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol. 2005, 14: 261-220.Google Scholar
- Medugorac I, Medugorac A, Russ I, Veit-Kensch CE, Taberlet P, Luntz B, Mix HM, Förster M: Genetic diversity of European cattle breeds highlights the conservation value of traditional unselected breeds with high effective population size. Mol Ecol. 2009, 18: 3394-3410. 10.1111/j.1365-294X.2009.04286.x.PubMedView ArticleGoogle Scholar
- Tang J, Hanage WP, Fraser C, Corander J: Identifying currents in the gene pool for bacterial populations using an integrative approach. PLoS Comput Biol. 2009, 5: e1000455-10.1371/journal.pcbi.1000455.PubMedPubMed CentralView ArticleGoogle Scholar
- Ajmone-Marsan P, The GLOBALDIV Consortium: A global view of livestock biodiversity and conservation - GLOBALDIV. Anim Genet. 2010, 41 (Suppl 1): 1-5. 10.1111/j.1365-2052.2010.02036.x.PubMedView ArticleGoogle Scholar
- Li MH, Tapio I, Vilkki J, Ivanova Z, Kiselyova T, Marzanov N, Cinkulov M, Stojanović S, Ammosov I, Popov R, Kantanen J: The genetic structure of cattle populations (Bos taurus) in northern Eurasia and the neighbouring Near Eastern regions: implications for breeding strategies and conservation. Mol Ecol. 2007, 16: 3839-3853. 10.1111/j.1365-294X.2007.03437.x.PubMedView ArticleGoogle Scholar
- Fan B, Wang ZG, Li YJ, Zhao XL, Liu B, Zhao SH, Yu M, Li MH, Chen SL, Xiong TA, Li K: Genetic variation analysis within and among Chinese indigenous swine populations using microsatellite markers. Anim Genet. 2002, 33: 422-427. 10.1046/j.1365-2052.2002.00898.x.PubMedView ArticleGoogle Scholar
- Li MH, Zhao SH, Bian C, Wang HS, Wei H, Liu B, Yu M, Fan B, Chen SL, Zhu MJ, Li SJ, Xiong TA, Li K: Genetic relationships among twelve Chinese indigenous goat populations based on microsatellite analysis. Genet Sel Evol. 2002, 34: 729-744. 10.1186/1297-9686-34-6-729.PubMedPubMed CentralView ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.