Genetic relationship and admixture in four Tunisian sheep breeds revealed by microsatellite markers

Accepted Manuscript Title: Genetic relationship and admixture in four Tunisian sheep breeds revealed by microsatellite markers Author: S. Kdidi J.H. Calvo L. Gonz´alez-Calvo M. Ben Sassi T. Khorchani M.H. Yahyaoui PII: DOI: Reference:

S0921-4488(15)30048-1 http://dx.doi.org/doi:10.1016/j.smallrumres.2015.08.012 RUMIN 5017

To appear in:

Small Ruminant Research

Received date: Revised date: Accepted date:

3-10-2013 15-7-2015 18-8-2015

Please cite this article as: Kdidi, S., Calvo, J.H., Gonz´alez-Calvo, L., Sassi, M.Ben, Khorchani, T., Yahyaoui, M.H., Genetic relationship and admixture in four Tunisian sheep breeds revealed by microsatellite markers.Small Ruminant Research http://dx.doi.org/10.1016/j.smallrumres.2015.08.012 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Genetic relationship and admixture in four Tunisian sheep breeds revealed by microsatellite markers S. Kdidi1,2, J.H. Calvo4,5, L. González-Calvo4, M. Ben Sassi3, T. Khorchani1, M.H. Yahyaoui1* 1

Livestock and Wildlife Laboratory, Arid Lands Institute, Route Djorf km 22- 4119 Medenine Tunisia

2

Laboratory of Genetics, Immunology and Human Pathology, Faculty of Sciences, Tunis-El Manar

University, Tunisia Office de l’Elevage et des Pâturages 30, rue Alain Savary 1002 Tunis Tunisia 4

CITA, 50059 Zaragoza, Spain

5

ARAID, 50004 Zaragoza, Spain

*

Correspondence: Livestock and Wildlife Laboratory, Arid Lands Institute, Route Djorf km 22- 4119

Medenine Tunisia. Tel.: +21675633005, Fax: +21675633006. e-mail: [email protected]

Highlights ► There was a relatively high level of genetic diversity among populations. ► Significant levels of inbreeding within the four breeds as indicated by Fis values. ► High level of gene flow was observed between breeds and absence of genetic structure. ► Uncontrolled crossbreeding was the main cause of this genetic admixture. ► ►

Abstract The aim of this study was to assess the genetic diversity and differentiation patterns of four Tunisian sheep breeds. A total of 186 animals belonging to Barbarin (n=59), Western Thin Tail (n=46), Black Thibar (n=40), and Sicilo Sarde (n=41) breeds were genotyped for a panel of 30 microsatellite markers. In addition, a sample of 29 Spanish Merino sheep breed was used as an outgroup for tree topology. All markers were highly informative with PIC values ranging from 0.588 to 0.885 and a mean number of private alleles of 0.016. In all breeds, inbreeding was indicated by heterozygosity

deficiency with estimated FIS values varying from 0.134 (Barbarin) to 0.165 (Western Thin Tail), and a highly significant departure (p<0.01) from Hardy–Weinberg proportions was observed. Wright’s FST index indicated low genetic differentiation between Tunisian breeds (0.017). The lack of clear genetic differentiation was also evidenced by cluster analysis using STRUCTURE as well as by the significant gene flow observed between breeds, especially Barbarin and Western Thin Tail, on the basis of the estimated Reynolds’ genetic distances (DR) and the number of migrants (Nm). The highest DR values were observed between Sicilo Sarde and the three other breeds, reflecting different phylogenetic origins of this breed. The study represents a first step towards further genetic characterization of Tunisian sheep breeds and results presented here are useful for better management and conservation of sheep resources.

Keywords: genetic diversity, genetic structure, microsatellites, sheep, Tunisia.

Introduction The North African region, which includes Morocco, Algeria, Tunisia, Libya and Egypt, raises more than 100 million sheep (FAOstat, 2012), and is ranked among the top sheep production areas in the world (FAO, 1981). These sheep are mainly reared for meat and leather production, and to a small extent for wool and milk. In Tunisia, sheep farming is an important economic and social activity contributing to 39 % of the total red meat production (OEP, 2014). More than 6.8 million sheep heads are raised in Tunisia, including about 3.9 million ewes (ONAGRI, 2012) that belong to four different breeds: Barbarin (60.3 %), Western Thin Tail (34.6 %), Black Thibar (2.1 %) and Sicilo Sarde (0.7 %). The fat-tailed Barbarin (BB) is the most representative of Tunisian sheep and is commonly found throughout the country. It is a rustic breed well adapted to the arid and semi-arid environments characterised by the prevalence of low input production systems (Djemali, 2000). Western Thin Tail

(WTT) is a common breed in Tunisia and Algeria -where it is known as “Ouled Jellal” (Iñiguez, 2006) and is mainly found in the steppes of central Tunisia. The breed shows a relatively good adaptation to harsh and dry conditions but it is more sensitive to high temperatures than the Barbarin breed (Djemali, 2000). The Black Thibar (BT) breed is a composite black coat sheep found in the sub-humid northern region of Tunisia with meat production vocation. Its roots can be traced from the beginning of the 20th century where native Algerian thin tail sheep were crossed with the French Merinos d'Arles (Chalh et al., 2007) with an aim to obtain uniformly black animals. The breed was rescued in mid 1980s by introducing Brown Swiss rams after appearance of white fleeces and fertility problems due to an increasing level of inbreeding (Chalh et al., 2007). The Sicilo-Sarde (SS) is the only dairy sheep breed native to North Africa. SS is derived from a cross between two imported Italian dairy breeds (Sarde and Sicilian) from Sicily and is currently restricted to limited areas of the Northwestern Tunisia (Djemali, 2000). Genetic characterisation of livestock species provides useful information about the magnitude of genetic structure and relationships between populations as well as their diversity and conservation status (Arranz et al., 1998, 2001; Pariset et al., 2003; Alvarez et al., 2004; Uzun et al., 2006; Peter et al., 2007; Ligda et al., 2009; Tapio et al., 2010; Calvo et al., 2011, Tolone et al., 2012, Ciani et al., 2013). Genetic variability and relationships among Tunisian sheep breeds using only 17 microsatellites has been published (Ben Sassi-Zaidy et al., 2014). The aim of our study is to investigate the genetic diversity and admixture in four major breeds of sheep raised in Tunisia by use 30 microsatellites. Materials and methods Blood samples A total of 186 blood samples were collected from the four Tunisian sheep breeds (Barbarin (n=59), Western Thin Tail (n=46), Black Thibar (n=40) and Sicilo Sarde (n=41). Sampling was carried out in 2011, and was obtained from different flocks located in the north, center and south, and

representing all geographic regions of the country. A total of 23 and 17 flocks were sampled from the northern parts of the country for the Black Thibar and Sicilo Sarde breeds, respectively. Barbarin and Western Thin Tail are reared throughout the whole country, and then, 49 and 29 flocks were sampled, respectively (Fig. S1, Table S0). Information about relatedness between animals was obtained from farmers when pedigree data is not recorded and a maximum of three samples from unrelated animals were taken per flock. DNA extraction was performed from blood according to the standard phenol-chloroform protocol (Sambrook et al., 1989). Genotypic data Thirty ovine microsatellites were analysed in all the individuals. Microsatellites used in this study were selected from the list recommended by the FAO-ISAG group for biodiversity studies (www.fao.org/docrep/meeting/021/j1998e.pdf). Details of markers and PCR conditions are given in supporting information (tables S1 and S2). Fragments were detected using an ABI Prism 310 DNA Sequencer (Applied Biosystems, Spain) and the accompanying GeneScan software, v.3.1 for the determination of allele sizes. Genotyping data of Spanish Merino sheep -used here as an out group- was previously reported (Calvo et al., 2011). Statistical analysis Cervus v. 3.0.3 (Marshall et al., 1998) software was used to analyse the number of alleles, observed and expected heterozygosity (corrected for sampling bias) and polymorphic information content (PIC). The effective number of alleles and the frequencies of private alleles were calculated using Genetic Analysis in Excel (GenAlex) version 6.501 (Peakall and Smouse, 2006). Genepop v.4 (Raymond and Rousset, 1995) software was utilised to calculate the exact test for Hardy–Weinberg equilibrium, the linkage disequilibrium test between markers and the gene flow value (Nm). We used Bottleneck 1.2.02 (Cornuet and Luikart, 1996) to test significant deviation in allelic diversity and

heterozygosity from mutation-drift equilibrium predictions. Arlequin 3.5.1.2 (Excoffier et al., 2005) was used to determinate the FST values for pairwise comparisons of the breeds, and for the analysis of molecular variance (AMOVA). Parameters of genetic differentiation (F statistics), mean number of alleles across populations, observed, average expected (non-biased) and average observed heterozygosities were calculated using Genetix 4.05.2 software (Belkhir and Borsa, 1998). Rarefaction approach as implemented in HP-RARE (Kalinowski, 2005) was used for allelic and private allelic richness estimation. The population structure was analysed by cluster techniques with the software STRUCTURE 2.3.4 (Pritchard et al., 2000) with K ranging from 2 to 10 (the real number of breeds plus 6). The true K was determined using Structure Harvester Web version 0.6.93 (Earl, 2012). Reynolds Genetic distances (DR) (Reynolds et al., 1983) were estimated using Populations v 1.2.32 (Langella, 1999) and dendrograms were constructed according to the neighbour-joining algorithm, with Spanish Merino sheep used as outgroup (Calvo et al., 2011). Tree topology was constructed using Populations v 1.2.32, and the reliability of each node was estimated by 1000 resampling of the data. The Tree view program 1.6.6 (Page, 1996) was used for tree drawing. Results and discussion Microsatellite analysis A total of 428 alleles were detected in the 30 loci studied. The number of alleles per locus ranged from 6 (BM1824) to 24 (HUJ616) with a mean number of alleles per locus of 14.27. These results are similar to those observed in several European sheep breeds (Pariset et al., 2003; Alvarez et al., 2004; Uzun et al., 2006; Peter et al., 2007; d’Angelo et al., 2009; Ligda et al., 2009 ), but higher than those detected in African sheep (Wafula et al., 2005; Muigai et al., 2009). Descriptive statistics of the 30 microsatellite loci in the overall population are shown in Table 1. All markers were highly informative with PIC values ranging from 0.588 (OARAE129) to 0.885 (INRA063), indicating that all the microsatellite loci were sufficiently polymorphic and thus suitable for diversity analysis. Similar PIC values were given by Ben Sassi-Zaidy et al. (2014) for the same breeds. Analysis of within-breed

genetic diversity showed that all Tunisian breeds had a large mean number of alleles per breed, ranging from 9.23 for Sicilo Sarde to 11.1 for Barbarin (Table 2). On the other hand, allelic richness (AR) varied from 7.27 (Sicilo Sarde) to 7.84 (Western Thin Tail). These values were lower than those reported by Ben Sassi-Zaidy et al. (2014). All breeds showed substantial reduction in allelic richness when using the rarefaction approach, most notably for the Barbarin and Western Thin Tail, yielding private allele estimates that were less than one per breed varying between 0.65 and 0.96 for Sicilo Sarde and Western Thin Tail, respectively (Table 2). However, the lowest (0.27) and the highest (0.67) frequency of private alleles found by Ben Sassi-Zaidy et al. (2014) were detected for the Black Thibar and the Sicilo Sarde, respectively. Sassi-Zaidy et al. (2014) used 17 microsatellite markers while in the present study the animals were genotyped for 30 microsatellites. Overall, the mean frequency of private alleles and the number of migrants (Nm) after correction for size were 0.016 and 10.389 respectively, indicating a relatively high gene flow among Tunisian breeds. Twelve microsatellites showed significant (p<0.05) departures from the Hardy-Weinberg proportions in the whole population, however, when considering breeds separately, only one marker per breed was in Hardy–Weinberg disequilibrium (p < 0.05): OARFCB226, MAF214, MAF65, and ILSTS11 microsatellites for Barbarin, Black Thibar, Sicilo Sarde, and Western Thin Tail breeds, respectively. The four breeds showed a strongly significant departure (p<0.01) from Hardy–Weinberg proportions when considering all loci, which might be explained by the uncontrolled mating in history of the breeds. This deviation might also be caused by the presence of null alleles, however no pedigree was available to be used in the estimation of null alleles as well as for an unbiased estimation of inbreeding (identity by descent rather than identity by state). Average observed heterozygosity (HO) over loci was less than expected in all breeds (Table 2), the lowest value (0.644) being detected in Sicilo Sarde, similar to that for the Sarda breed (0.641) reported by Lawson Handley et al. (2008). Barbarin and Western Thin Tail showed higher values of HO when compared to Black Thibar and Sicilo Sarde.

The estimated FIS was significant in all studied breeds ranging from 0.134 (Barbarin) to 0.165 (Western Thin Tail). All the FIS found in this study were higher than those reported by Ben Sassi-Zaidy et al. (2014). The FIS value for the Barbarin was less than that observed in Tunis breed of USA (Blackburn et al., 2011a; 2011b) that was derived from Barbarin. The high FIS values found in the Tunisian sheep are more likely overestimated, especially for established Barbarin and Western Thin Tail breeds with high number of animals and wide geographic distribution, as a result of Wahlund effect due to the sampling strategy. Nevertheless, based on the estimated FIS and previously reported problems of inbreeding (Chalh et al., 2007), Black Thibar and Sicilo Sarde, that are breeds with small number of animals and confined to limited geographic areas, should be monitored for their conservation status. This genetic situation imperils especially the Sicilo Sarde, which represents the sole dairy sheep breed in the North of Africa and suffered severe reduction in population size in the last decades (Mohamed et al., 2008). Genetic differentiation. The global FIT and its confidence interval at 95% after 1000 bootstraps was 0.166 (0.119 - 0.215). In the overall population, the homozygote excess (FIT) was caused mainly by a within-breeds homozygote excess (FIS = 0.151, 0.101 - 0.204) and partially by the low genetic differentiation among breeds (FST = 0.017, 0.009 - 0.029). A higher Wright’s statistics values across populations were observed in Egyptian (El Nahas et al., 2008), Nigerian (Olufunmilayo et al., 2004) and Chinese (Jia et al., 2003) sheep breeds than in our study. The FST in the present work was lower than that observed in other studies which ranged between 0.03 and 0.11 (Arranz et al., 1998; 2001; Alvarez et al., 2004; Rendo et al., 2004; Mukesh et al., 2006; Peter et al., 2007; Santos-Silva et al., 2008; Ligda et al., 2009; Calvo et al., 2011; Pons et al., 2015; Gaouar et al., 2015). The AMOVA results revealed that the greatest variation (83.36%) is within the individual, 14.89% among individuals within populations and 1.75% among populations which are consistent with FST results.

Pairwise FST-values were calculated (after 5000 permutations) between these breeds, all values were significant (p<0.05) with the lowest FST observed between BB and WT and the highest between BT and SS. Similar trends were observed for the number of migrants (Nm). The results illustrated by the table 3 reflect a low gene flow between the Merino and the Tunisian sheep breeds whereas a significant gene flow was registered among the Tunisian sheep breeds. The uncontrolled reproduction and the absence of breed development programs might be the main explanation of this process. The largest genetic distances DR (0.141–0.148, Table 3) were between Tunisian sheep breeds and the Spanish Merino included here as an outgroup. The highest number of migrants (Nm) and the lowest Reynold’s genetic distance DR were found for the pair Barbarin and Western Thin Tail (40.88), this result reflects the degree of crossbreeding between these two breeds. The Barbarin, fat tailed breed, was known by its perfect adaptation in different conditions and the superiority of its meat quality trait (Bedhiaf-Romdhani et al., 2008). The fat of its tail can represent up to 15% of the carcass weight (Bedhiaf-Romdhani et al., 2008) hence, the market demand for thin tail breeds and their crosses. These facts constrain breeders to shift to the Western Thin Tail crosses. Considering only Tunisian Sheep breeds, the highest values of pairwise DR and the lowest values of Nm registered for Sicilo Sarde may be explained by the dairy vocation. The phylogenetic tree constructed using the Reynold’s genetic distance (Fig. 1) distinguished two groups. The first group consisted of the Merino (used as outgroup) and the black Thibar identified with a bootstrap value (47%). The second group could be correctly identified with a high bootstrap value of 86% and was formed by Barbarin and Western Thin Tail. The Sicilo Sarde breed was identified between these two groups. The outgroup breed was located far from the Tunisian sheep breeds gene pool. The clustering results based on genetic distances also suggest that Merino has a unique allelic distribution of microsatellites that greatly differ from Tunisian sheep gene pool, also evident were high levels of differentiation between Merino and Tunisian sheep breeds. Moreover, our results indicate a closer relationship and higher degree of admixing between Barbarin and Western Thin Tail, resulting from the different management practices in the country.

Results from the STRUCTURE analysis revealed that the true K was K=3. Table 4 shows the membership percentage for each pre-defined breed in the three clusters. The structure results showed that these breeds were not very well differentiated and an admixture process between breeds had occurred, pointed out the lack of a clear genetic differences between populations (Fig. S2). Conclusion This study presents valuable insight into the genetic structure and diversity of the four Tunisian sheep breeds using 30 microsatellite markers. Although the Tunisian sheep breeds are diverse, our data suggest low level of differentiation as well as high genetic flow between them as indicated by the tests of genetic differentiation and assignment of individuals to populations. The observed high positive FIS values might be explained mainly by the occurrence of uncontrolled reproduction. The findings of this study may be used when drawing different breeding and genetic management programs. Such programs should take into consideration (1) the high gene flow between breeds, (2) the genetic erosion of the Barbarin, and (3) the inbreeding problem observed in Black Thibar and Sicilo Sarde breeds. Acknowledgments This work was supported by the Ministry of Higher Education and Scientific Research (MESRS). The authors thank sheep owners and OEP (central office and regional directions) in Tunisia for providing blood samples. Kdidi S. was supported by a scholarship from MESRS. We thank Andrew Gitahi MARETE for English assistance.

References

Alvarez, I., Royo, L. J., Fernandez, I., Gutiérrez, J. P., Gómez, E., Goyache, F., 2004. Genetic relationships and admixture among sheep breeds from Northern Spain assessed using microsatellites. J. Anim. Sci. 82(8), 2246–2252.

Arranz, J.J., Bayón, Y., San Primitivo, F., 1998. Genetic relationships among Spanish sheep using microsatellites. Anim. Genet. 29 (6), 435–440.

Arranz, J.J., Bayón, Y., San Primitivo, F., 2001. Differentiation among Spanish sheep breeds using microsatellites. Genet. Sel. Evol. 33 (5), 529–542.

Bedhiaf-Romdhani, S., Djemali, M., Zaklouta, M., Iniguez, L., 2008. Monitoring crossbreeding trends in native Tunisian sheep breeds. Small Rumin. Res., 74, 274-278.

Belkhir, K., Borsa, P., 1998. GENETIX, logiciel sous WindowsTM pour la génétique des populations. Laboratoire Génome, Populations, Interactions CNRS UMR 5000, Université de Montpellier II, Montpellier (France). In: http://www.univ-montp2.fr/∼genetix/genetix.htm.

Ben Sassi-Zaidy, Y., Maretto, F., Charfi-Cheikrouha, F., Cassandro, M., 2014 Genetic diversity, structure, and breed relationships in Tunisian sheep. Small Rumin. Res., 119, 52-56.

Blackburn, H. D. , Paiva, S. R., Wildeus, S., Getz, W., Waldron, D., Stobart, R., Bixby, D., Purdy, P. H., Welsh, C., Spiller, S., Brown, M. 2011a. Genetic structure and diversity among sheep breeds in the United States: Identification of the major gene pools. J. Anim. Sci. 89:2336–2348.

Blackburn, H. D., Toishibekov, Y., Toishibekov, M., Welsh, C. S., Spiller, S. F., Brown, M., Paiva, S. R. 2011b. Genetic diversity of Ovis aries populations near domestication centers and in the New World. Genetica 139(9), 1169-1178.

Calvo, J.H., Alvarez-Rodriguez, J., Marcos-Carcavilla, A., Serrano, M., Sanz, A., 2011. Genetic diversity in the Churra tensina and Churra lebrijana endangered Spanish sheep breeds and relationship with other Churra group breeds and Spanish mouflon. Small Rumin. Res.95, 34–39.

Chalh, A., El Gazzah, M., Djemali, M., Chalbi, N., 2007. Genetic and Phenotypic Characterization of the Tunisian Noire De Thibar Lambs on Their Growth Traits. J. Biol. Sc. 7, 1347–1353.

Ciani, E., Ciampolini, R., D’Andrea, M., Castellana, E., Cecchi, F., Incoronato, C., d’Angelo, F., Albenzio, M., Pilla, F., Matassino, D., Cianci, D. 2013. Analysis of genetic variability within and among Italian sheep breeds reveals population stratification and suggests the presence of a phylogeographic gradient. Small. Rumin. Res. 112, 21–27.

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.

D’Angelo, F., Albenzio, M., Sevi, A., Ciampolini, R., Cecchi, F., Ciani, E., Muscio, A., 2009. Genetic variability of the Gentile di Puglia sheep breed based on microsatellite polymorphism. J. Anim. Sci. 87(4), 1205–1209.

Djemali, M. (2000) Genetic improvement objectives of sheep and goats in Tunisia. Lessons learned. In: Proceedings of the Meeting of the Sub-Network on Genetic Resources of the FAO-CIHEAM InterRegional Cooperative Research and Development Network on Sheep and Goats. Zaragoza (Spain), 1820 November 1999. Options Mediterraneennes. Serie A: Seminaires Mediterraneens (France), no. 43 Gabina, D. (ed.) / FAO, Rome (Italy). Animal Production and Health Division, p. 121-127

Earl, D. A., 2012. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4(2), 359–361.

El Nahas, S. M., Hassan, A. A., Mossallam, A. A. A., Mahfouz, E. R., Bibars, M. A., Oraby, A. S., de Hondt, H. A., 2008. Analysis of genetic variation in different sheep breeds using microsatellites. Afr. J. Biotechnol. 7 (8), 1060–1068.

Excoffier, L., Laval, G., Schneider, S., 2005. Arlequin ver. 3.5.1.2: an integrated software package for population genetics data analysis. Evol. Bioinform. Online 1, 47–50.

FAO, 1981. Production Yearbook. UN Food and Agriculture Organisation, Rome.

FAOSTAT, 2012. FAOSTAT Statistics Database. UN Food and Agriculture Organisation, Rome.

Gaouar, S.B.S., Da Silva, A., Ciani, E., Kdidi, S., Aouissat, M., Dhimi, L., Lafri, M., Maftah, A., Mehtar, N, 2015. Admixture and Local Breed Marginalization Threaten Algerian Sheep Diversity. PLoS one, 10.

Iñiguez, L. (2006) Characterization of small ruminant genetic resources in Central Asia, the Caucasus, West Asia and North Africa. In: International Workshop “Options and Strategies for the Conservation of Farm Animal Genetic Resources”. AGROPOLIS, Montpellier (France), 7-10 November 2005, p. 3439.

Jia, B., Chen, J., Zhao, R., Luo, Q., Yan, G., 2003.Microsatellite analysis of genetic diversity and phylogenetic relationship of eight sheep breeds in Xinjiang. Yi Chuan Xue Ba. 30, 847–854.

Kalinowski, S.T., 2005. HP-Rare: A computer program for performing rarefaction on measures of allelic diversity. Mol. Ecol. Notes 5, 187–189.

Langella,

O.

1999.

Populations,

1.2.32.

Available

via

http://bioinformatics.org/~tryphon/populations/, Accessed Avril 20, 2013.

Lawson Handley, L. J., Byrne, K., Santucci, F., Townsend, S., Taylor, M., Bruford, M.W., Hewitt, G. M., 2008. Genetic structure of European sheep breeds. Heredity.99, 620–631.

Ligda, C. H., Altarayrah, J., Georgoudis, A., 2009. Genetic analysis of Greek sheep breeds using microsatellite markers for setting conservation priorities. Small Rumin. Res. 83, 42–48.

Marshall, T.C., Slate, J., Kruuk, L., Pemberton, J.M., 1998. Statistical confidence for likelihood-based paternity inference in natural populations. Mol. Ecol. 7, 639–655.

Mohamed, A., Khaldi, S., Rekik, B., Khaldi, G., 2008. Normal and Residual Milk Yields in Sicilo - Sarde Ewes: Effects of Litter Size and the Weaning Age of Lambs. Research J. Anim. Sci. 2, 144 –148

Muigai, A. W. T., Okeyo, A. M., Kwallah, A. K., Mburu, D., Hanotte, O., 2009. Characterization of sheep populations of Kenya using microsatellite markers: Implications for conservation and management of indigenous sheep populations. S. Afr. J. anim. sci., 39(1) 93–96.

Mukesh, M., Sodhi, M., Bhatia, S., 2006. Microsatellite-based diversity analysis and genetic relationships of three Indian sheep breeds. J. Anim. Breed. Genet. 123 (4), 258–264.

OEP, 2014. http://www.oep.nat.tn/index.php/fr/donnees-sectorielles/41-productions. Accessed June 25, 2014.

Olufunmilayo, A., Williams, J. L., Blott, S., Urquhart, B. 2004. Genetic relationships between native sheep breeds in Nigeria based on microsatellite DNA polymorphisms. Anim. Genet. Res. Inform. Bull. 34, 27–39.

ONAGRI (Observatoire National de l'Agriculture en Tunisie), 2012. La production animale en 2012. http://www.onagri.tn/statistiques. Accessed June 25, 2015.

Page, R.D., 1996. Tree view: an application to displayphylogenetics trees on personal computers. Comput. Appl. Biosci. 12, 357–358.

Pariset, L., Savarese, M. C., Cappuccio, I., and Valentini, A., 2003. Use of microsatellites for genetic variation and inbreeding analysis in Sarda sheep flocks of central Italy. J. Anim. Breed. Genet. 120(6), 425–432.

Peakall, R, Smouse P. E., 2006. GENALEX 6.501 genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 6:288-295.

Peter, C., Bruford, M., Perez, T., Dalamitra, S., Hewitt, G., and Erhardt, G., 2007. Genetic diversity and subdivision of 57 European and Middle‐Eastern sheep breeds. Anim. Genet. 38(1), 37–44.

Piry, S., Luikart, G., Cornuet, J.M., 1999. BOTTLENECK: a computer program for detecting recent reductions in the effective population size using allele frequency data. J. Hered. 90 (4), 502–503.

Pons, A. L., Landi, V., Martinez, A., Delgado, J. V., 2015. The biodiversity and genetic structure of Balearic sheep breeds. J. Anim. Breed. Genet., 132, 268-276.

Pritchard, J.K., Stephens, M., Donnelly, P., 2000. Inference of population structure using multilocus genotype data. Genetics 155, 945–959.

Raymond, M., Rousset, F., 1995. GENEPOP (version 4): population genetics software for exact tests and ecumenicism. J. Hered. 86 (3), 248–249.

Rendo, F., Iriondo, M., Jugo, B.M., Mazon, L.I., Aguirre, A., Vicario, A., Estonba, A., 2004. Tracking diversity and differentiation in six sheep breeds from the North Iberian Peninsula through DNA variation. Small Rumin. Res. 52 (3), 195–202.

Reynolds, J., Weir, B.S., Cockerham, C.C., 1983. Estimation of the coancestry coefficient: basis for a short-term genetic distance. Genetics 105 (3), 767–779.

Sambrook J., Fritsch E.F., Maniatis T., 1989. Molecular Cloning: a laboratory manual, pp. 10.32– 10.33. Cold Spring Harbour Press, NY.

Santos-Silva, F., Ivo, R.S., Sousa, M.C.O., Carolino, M.I., Ginja, C., Gama, L.T., 2008. Assessing genetic diversity and differentiation in Portuguese coarse-wool sheep breeds with microsatellite markers. Small Rumin. Res. 78 (1–3), 32–40.

Tapio, M., Ozerov, M., Tapio, I., Toro, M. A., Marzanov, N., Ćinkulov, M., Goncharenko, G., Kiselyova,T., Murawski, M., Kantanen, J., 2010. Microsatellite-based genetic diversity and population structure of domestic sheep in northern Eurasia. BMC Genet. 11(1), 76.

Tolone, M., Mastrangelo, S., Rosa, A. J. M., Portolano, B., 2012. Genetic diversity and population structure of Sicilian sheep breeds using microsatellite markers. Small. Rumin. Res., 102(1), 18–25.

Uzun, M., Gutiérrez-Gil, B., Arranz, J. J., San Primitivo, F., Saatci, M., Kaya, M., Bayón, Y., 2006. Genetic relationships among Turkish sheep. Genet. Select. Evol. 38(5), 513–524.

Wafula, P.O., Jianlin, H., Sangare, N., Sowe, J.M., Coly, R., Diallo, B., Hanotte, O., 2005. Genetic characterization of West African Djallonke sheep using microsatellite markers. International workshop “The role of biotechnology for the characterization and conservation of crop, forestry, animal and fieshery genetic resources”; Villa Gualino, Turin. 177–178.

Figure Captions Fig. 1. Genetic relationship among the 5 sheep breeds using DR genetic distance according to the neighbour-joining algorithm. The values at the forks indicate the number of replicates occurrence in a bootstrap sampling of 1000 trees. The Spanish Merino was used as outgroup. Tables

Table 1 Genetic variability measures at the 30 microsatellites loci analysed across the four Tunisian breeds Microsatellites

Na

Nab

MNAc

MAF65

171

11

8.500

179

17

156

18

165

18

162

16

164

21

155

13

148

6

154

23

183

9

7.750

154

17

9.000

134

11

7.250

OARFCB193

OARFCB304

OARJMP29

OARJMP58

MAF70

MAF209

BM1824

INRA063

BM8125

MAF214

ILSTS005

Ned

4.11 0

10.50

3.98

0

0

15.25

7.38

0

5

12.50

5.10

0

8

12.00

4.95

0

9

15.50

7.64

0

5

10.00

4.65

0

0

5.000

3.34 7

15.50

8.58

0

9 3.81 3 2.96 0 3.97 8

Size range

Hoe

Hef

PICg

HWh

168-178

0.678

0.763

0.727

NS

108-153

0.676

0.765

0.738

NS

117-141

0.891

0.877

0.863

NS

170-102

0.770

0.810

0.786

NS

150-176

0.790

0.813

0.794

NS

136-169

0.677

0.889

0.877

*

107-135

0.774

0.800

0.775

NS

148-192

0.588

0.716

0.661

**

154-279

0.786

0.897

0.885

NS

181-263

0.628

0.747

0.710

NS

107-133

0.481

0.673

0.636

***

114-152

0.366

0.750

0.709

***

(bp)

ILSTS11

ILSTS28

MCM140

MCM527

OARFCB226

OARFCB128

OARCP34

OAR AE129

BM1329

OARCP38

HUJ616

MAF33

OARHH47

OARVH72

148

12

9.000

179

17

176

15

134

17

180

19

167

12

8.500

171

10

7.250

169

8

5.500

168

12

8.500

149

8

6.500

179

24

162

16

155

18

161

15

5.71 4

11.75

5.86

0

4

11.25

5.58

0

9

11.00

6.46

0

5

14.75

6.70

0

5 4.44 3 4.52 5 2.81 8 2.99 3 3.21 5

12.75

4.43

0

8

10.75

5.62

0

1

12.00

6.41

0

6

11.50

4.25

155-275

0.595

0.841

0.819

***

167-285

0.821

0.849

0.830

NS

62-173

0.841

0.839

0.819

NS

119-134

0.619

0.864

0.846

***

168-178

0.428

0.867

0.854

***

92-125

0.760

0.784

0.750

NS

66-167

0.789

0.796

0.762

NS

115-169

0.533

0.649

0.588

***

163-201

0.613

0.672

0.640

NS

105-141

0.477

0.693

0.660

***

164-204

0.709

0.785

0.759

NS

118-146

0.623

0.842

0.821

NS

123-171

0.619

0.856

0.838

***

115-195

0.658

0.768

0.747

NS

0

SRCRSP01

SRCRSP05

SRCRS09

DYMS1

179

12

7.250

169

8

6.250

141

9

6.500

161

16

4 3.36

99-159

0.715

0.714

0.672

NS

155-171

0.568

0.710

0.659

*

107-165

0.468

0.660

0.628

***

112-128

0.776

0.850

0.831

NS

0 3.26 9 3.00 2

11.50

5.93

0

0

a

Number of individuals typed for each locus.

b

Number of alleles at each locus.

c

Mean number of allele

d

Effective allele number

e

Mean heterozygosity observed (direct count estimate).

f

Mean heterozygosity expected (unbiased estimate Nei, 1987).

g

Polymorphic Information Content

h

Hardy–Weinberg equilibrium; NS: No significant, *p < 0.05, **p < 0.01, ***p < 0.001. Significant

p-values means deviations from equilibrium.

Table 2 Genetic diversity measures in each breed, standard deviations are in brackets. Hea

Hob

MNAc

ARd

PAe

FIS (IC 95%)f

BB

0.7780 (0.0890)

0.6746 (0.1421)

11.1

7.74

0.87

0.13408 (0.09424 - 0.15246)*

WTT

0.7803 (0.0772)

0.6529 (0.1459)

10.5

7.84

0.96

0.16508 (0.11664 - 0.18807)*

BT

0.7631 (0.1114)

0.6483 (0.1654)

9.3

7.43

0.85

0.15261 (0.10038 - 0.17211)*

SS

0.7690 (0.1122)

0.6445 (0.1471)

9.23

7.27

0.65

0.16386 (0.11704 - 0.18180)*

a

Unbiased expected heterozygosity.

b

Mean observed heterozygosity.

c

Mean number of alleles per loci.

d

Allelic richness (rarefacted)

e

Frequency of Private alleles

f

10000 Bootstrap over FIS by population, IC 95% = confidence interval at 95%.

*

Significant p-values (p < 0.01).

Table 3 Reynolds distance (DR) estimates (below the diagonal) and Nm (above the diagonal) among pairs of breeds. (1)

(2)

(3)

(4)

(5)

1.47

1.49

1.50

1.47

40.88

15.52

10.50

15.51

10.40

Merino (Outgroup)

(1)

Barbarin

(2)

0.146

Western Thin Tail

(3)

0.143

0.009

Black Thibar

(4)

0.141

0.019

0.020

Sicilo Sarde

(5)

0.148

0.026

0.026

9.92 0.030

Table 4 Proportion of membership of each of the 5 sheep breeds in the three clusters (K =3) inferred using STRUCTURE software. Cluster

Sample size

Breed 1

2

3

Merino

0.011

0.987

0.002

29

Barbarin

0.182

0.002

0.816

59

Western Thin Tail

0.205

0.002

0.793

46

Black Thibar

0.156

0.002

0.842

40

Sicilo Sarde

0.159

0.002

0.839

41

gr1 .