Microsatellite-based genetic diversity and population structure of domestic sheep in northern Eurasia

Abstract

Background: Identification of global livestock diversity hotspots and their importance in diversity maintenance is essential for making global conservation efforts. We screened 52 sheep breeds from the Eurasian subcontinent with 20 microsatellite markers. By estimating and weighting differently within- and between-breed genetic variation our aims were to identify genetic diversity hotspots and prioritize the importance of each breed for conservation, respectively. In addition we estimated how important within-species diversity hotspots are in livestock conservation.

Results: Bayesian clustering analysis revealed three genetic clusters, termed Nordic, Composite and Fat-tailed. Southern breeds from close to the region of sheep domestication were more variable, but less genetically differentiated compared with more northern populations. Decreasing weight for within-breed diversity component led to very high representation of genetic clusters or regions containing more diverged breeds, but did not increase phenotypic diversity among the high ranked breeds. Sampling populations throughout 14 regional groups was suggested for maximized total genetic diversity.

Conclusions: During initial steps of establishing a livestock conservation program populations from the diversity hot-spot area are the most important ones, but for the full design our results suggested that approximately equal population presentation across environments should be considered. Even in this case, higher per population emphasis in areas of high diversity is appropriate. The analysis was based on neutral data, but we have no reason to think the general trend is limited to this type of data. However, a comprehensive valuation of populations should balance production systems, phenotypic traits and available genetic information, and include consideration of probability of success.

Figure 1

Clustering of 52 sheep breeds. Individuals are presented as vertical lines divided into K colors, representing constructed populations. The lowest row represents further clustering of 3 groups, identified at K = 3, separately. The Nordic group is divided into 7 subclusters, while the Composite (in the middle) and the Fat-tailed groups each split into 3 subclusters.

Figure 2

Principal coordinate plot of breeds based on Chord distance. Axis I explains 48% of the variation, axis II explains 7% of the variation. Breeds from Nordic cluster (based on STRUCTURE) are marked with red, breeds from Fat-tailed cluster are marker with yellow and breeds from the Composite cluster are marked with black circles.

Figure 3

Distribution of three inferred genetic clusters in the study regions. Slices in the pie diagrams represent Fat-tailed (yellow), Composite (black) and Nordic (red) clusters. The Caucasus area is represented by four regions: South Caucasus (1), North Caucasus (2), Stavropol (3) and the Caspian depression (4). The Asian region is represented by three regions: Kazakhstan and east of the Caspian Sea region (5), the Altai region (6) and the Buryatia region (7). The remaining groups belong to eastern fringe of Europe: the Volga region (8), West Russia (9), Ukraine (10), Southeast Europe (11) Poland (12), Finland (13), Scandinavia (14), Denmark (15) and Iceland and the Faeroe Islands (16).

Figure 4

Contour synthetic map of total genetic diversity (H_T) calculated for triplets of neighboring breeds. Darker shading indicates higher levels of diversity.