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Characterization of single nucleotide polymorphisms (SNPs) in sheep and
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their variation as an evidence of selection
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L. Pariset1, I. Cappuccio1, M.S. D’Andrea2, D. Marletta3, P. Ajmone Marsan4, A.
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Valentini1 and ECONOGENE Consortium5
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Dipartimento di Produzioni Animali, Università della Tuscia, Viterbo, Italy;
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Dipartimento di Scienze Animali Vegetali e dell'Ambiente, Università degli Studi del
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Molise, Campobasso, Italy;
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Università di Catania, Catania, Italy;
Dipartimento di Scienze Agronomiche, Agrochimiche e delle Produzioni Animali,
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Istituto di Zootecnica, Università Cattolica del Sacro Cuore, Piacenza, Italy;
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http://lasig.epfl.ch/projets/econogene/
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Correspondence: Lorraine Pariset. Dipartimento di Produzioni Animali, Facoltà di Agraria,
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Università della Tuscia, via S. Camillo de Lellis, 01100 Viterbo, Italy - Tel. +39 0761
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357447- Fax: +39 0761 357434 - Email:pariset@unitus.it
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SUMMARY
Single nucleotide polymorphisms (SNPs), already used in commercial tasks like
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traceability, paternity testing and selection for suitable genotypes, are recently applied also in
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evolutionary, ecological and conservation studies.
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SNP discovery and characterization in sheep, was performed in 8 different European
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breeds. Eleven SNPs were characterised and genotyped on about 1700 individuals belonging
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to 57 breeds and basic population parameters were computed. A method for the identification
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of loci that are likely to be subject to selection (outliers) was applied and three of them were
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identified.
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INTRODUCTION
Single nucleotide polymorphisms (SNPs) spread across the genome and have the
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potential to become the marker of choice in ecological and conservation studies (Morin et al.,
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2004; Seddon et al., 2005). Moreover, some studies have evaluated the utility of SNPs for
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estimating parameters such as population history and inference of relationships (Kuhner et al.,
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2000; Glaubitz et al., 2003). SNPs are less variable than microsatellites, but offer a vast
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number of potentially available loci (Brumfield et al., 2003). Despite the difficulties in SNPs
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identification, the use of those markers leads to a rapid, large scale and cost effective
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genotyping (Syvanen et al., 2001; Vitalis et al., 2001; Vignal et al., 2002; Schlotterer, 2004).
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Loci that show unusually low or high levels of genetic differentiation are often assumed to be
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subject to selection (Bowcock et al., 1991). A method for identify such loci was described by
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Beaumont and Nichols (1996).
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Signatures of natural selection can be detected quantifying the variation of SNP allele
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frequencies between populations by the statistic Fst (Lewontin and Krakauer, 1973;
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Beaumont and Nichols, 1996). Under selective neutrality, Fst is determined by genetic drift,
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which will affect all loci across the genome in a similar fashion. On the other hand, selection
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is a locus-specific force that can cause deviation in Fst valued in a selected gene and close
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genetic markers.
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A list of genes involved in key metabolic pathways influencing production, disease
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resistance and morphological traits have been selected for SNP discovery under the EU
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Econogene project (http://lasig.epfl.ch/projets/econogene/). All these genes are candidate to
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have high adaptive value and to be under strong natural or artificial selection.
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Here, we show the potential of SNPs as genotypic markers for assessing genetic
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differentiation and heterozygosity in sheep. We therefore applied a method to identify loci
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behaving differently from neutral models and potentially under natural or artificial selection.
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MATERIAL AND METHODS
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Samples
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Blood samples of a total of 1748 sheep, about one third males, belonging to 57 breeds,
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52 of which are autochthonous, were sampled and DNA was extracted with conventional
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methods (tab. 1). Whenever possible, in each breed 3 samples per farm were collected in 11
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farms spread over the traditional rearing area of the breed.
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For polymorphisms screening and SNPs characterization, a panel of 16 unrelated
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sheep belonging to 8 different European breeds was used as representing the most of the
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variation across a wide geographic area (fig. 1).
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SNP discovery
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SNP discovery was set up on the previously described panel and polimorphysms have
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been searched for in exon and intron sequences following direct sequencing and DHPLC
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analyses. Eleven genes carrying at least one SNP were discovered from sixteen genes screened.
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PCR amplifications
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Pairs of PCR primers were designed on genomic sequences published in Genebank
belonging either to Ovis aries or to the most genetically related species available.
Amplification conditions were optimised to produce single amplicons of the predicted
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length. Each polymerase chain reaction (PCR) was performed in volume of 30 l containing
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30 ng of genomic DNA, 1.6 pMol of each primer (MWG Biotech), 200 mM dNTPs
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(Promega), 1X PCR buffer and 0.2 unit Taq DNA polymerase (Amersham Pharmacia
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Biotech) on a PCR Express cycler (Hybaid). After initial denaturation at 94°C for 5 min, 35
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cycles of amplification with denaturation at 94°C for 30 sec, annealing for 30 sec at the
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temperature optimised for each primer pair and extension at 72°C for 1 min, followed by a
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final extension step at 72°C for 10 min, were performed.
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Sequence analysis
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Polymerase chain reaction products were purified through Sephadex G-50 (Amersham
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Pharmacia Biotech) to remove residual primers and dNTPs and used as templates for two
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sequencing reactions. Sequencing was performed using a CEQ8800 sequencer using DTCS
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QuickStart Kit (Beckman Coulter) according to manufacturer’s instructions. The sequence
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data were analysed and aligned with Bioedit software (Hall, 1999) to ascertain
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polymorphisms and characterize SNPs.
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DHPLC analysis
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Seven gene fragments have been subjected to DHPLC scanning on an automated
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HPLC instrument (Transgenomic WAVETM DNA Fragment Analysis System) for SNPs
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discovery. PCR products, loaded on a DNASep analytical column (Transgenomic), were
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eluted from the column with a binary gradient of 0.1 M triethylammonium acetate (TEAA,
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pH 7.0) and 0.1 M TEAA with 25% of acetonitrile (pH 7.0) at a flow rate of 1.5 ml/min.
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Elution gradients and analysis temperatures are automatically predicted from the target
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sequences by WAVEMaker 4.1 software (Transgenomic) and eventually adjusted manually
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according to results, and the eluted products detected by UV analysis at 260 nm. Polymorphic
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individuals have been sequenced to confirm and characterize each SNP.
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SNP analysis
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Genotyping was performed by K Biosciences (www.Kbioscience.com), using
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Amplifluor™ (Serologicals™) and Taqman™ (Applied Biosystems™) chemistries.
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Generally, accuracy greater than 99% was achieved. Quality control criteria were performed
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(Water as negative control, inter plate duplicate testing of a known DNA, intra plate testing of
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a known DNA).
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Data analysis
Heterozygosis and Fst were calculated with Fdist software and used to quantify the
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variation of SNP allele frequencies between populations for detecting signatures of selection
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(Beaumont and Nichols, 1996). Population datasets were built by bootstrapping 200000
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replications on real data using the coalescent model. An upper and lower confidence limit of
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95% quantiles was assumed for conditional joint distribution of Fst versus mean
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heterozygosity.
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Significant differences in allele frequencies of outlier genes in sheep breeds with
different productive attitude were assessed using a t-student test.
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RESULTS AND DISCUSSION
Using primers designed on sequences of Ovis aries or of the most related species
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available, 16 fragments of genes with different functions were identified as polymorphic by
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DHPLC analysis or direct sequencing in a panel of 8 sheep breeds.
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Sequence data obtained with primers designed for this study were deposited into
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GenBank nucleotide sequence database, under the accession numbers AY292283 (GHR),
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AY292289 (GHRHR), AY737509 (IGF1), AY737517 (MYH1), AY737511 (TYRP1),
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AY787745 (ITGB1), AY787747 (CTSB).
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Twelve SNPs were identified on a total of 6686 bp sequenced, giving an average
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density of one SNP every 557 bases. Successful genotyping was performed on 11 SNPs, 6 of
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which were transitions (4 G<->A and 2 T<->G) and 5 were T<->G transversions.
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The frequencies of the alleles are reported in table 2. Frequencies < 0.5 % were
considered as monomorphic.
Heterozigosity of the loci determined from SNP frequencies (tab. 3) ranged from
0.063 to 0.497, with a mean of 0.28.
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The Fst among different sheep breeds determined from SNPs frequencies (tab. 3)
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shows a low level differentiation, although the mean heterozygosity of the loci is 0.28, a value
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not particularly low for these type of markers, and the sample included a large amount of
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populations spanning a considerable geographic area. A possible explanation is that the
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easiness of transportation even in ancient times shuffled the genetic pool and did not permit
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stratification through genetic drift.
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Three loci out of 11 (MYH1, MEG3 and CTSB) were found to lie outside the 95%
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confidence region of the conditional joint distribution of Fst and mean heterozygosity (fig.2),
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resulting potentially under selection. All of them are related to meat production and they can
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potentially produce a morphology modification. Since this trait is a major (and most effective)
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selection target in sheep, it is not unreasonable that we found under selection such a large
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percentage of genes related to meat production. However, the t-student test revealed that the
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allele frequency of these three genes is not significantly correlated with the productive attitude
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of the breeds.
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A survey on the allele frequencies in each breed revealed that MYH1 behave according
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to a neutral model, ranging from 0.48 to 0.52, in all the four Hungarian breeds analysed.
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Further studies will be necessary to assess what caused this difference in this particular area.
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Detecting evidence for selection is challenging because the effect of selection on the
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distribution of genetic variation can be mimicked by population demographic history (Ackey
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et al., 2002). The recent use of SNPs as genetic markers provides the opportunity to identify
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genome-wide signature of selection, as shown in humans (Sunyaev et al., 2000; Fay et al.,
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2001). Identifying loci that have been targets of selection is a challenging area of research in
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animal genetics. Calculating Fst and mean heterozygosity as a measure of genetic
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differentiation for each locus, we have identified three candidate genes in sheep, the
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distribution of genetic variation of which indicates that they have been targets of selection.
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ACKNOWLEDGMENTS
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This work has been partially supported by the EU Econogene contract QLK5-CT-2001-
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02461. The content of the publication does not represent necessarily the views of the
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Commission or its services.
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REFERENCES
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Akey, J. M., Zhang, G., Zhang, K., Jin L., Shriver, M. D. (2002) Interrogating a High-Density
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SNP Map for Signatures of Natural Selection. Genome Research 12, 1805-1814.
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Beaumont, M.A., Nichols, R.A. (1996) Evaluating loci for use in the genetic analysis of
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population structure. Proc. Roy. Soc. Lond. B. 263, 1619-1626.
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Bowcock, A. M., Kidd J. R., Mountain J. L., Hebert J. M., Carotenuto L., Kidd K. K.,
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Cavalli-Sforza L. L. (1991) Drift, admixture, and selection in human evolution, a study with
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DNA polymorphisms. Genetics 88, 839–843.
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Brumfield, R. T., Beerli, P., Nickerson, D. A., Edwards, S. V. (2003) The utility of single
16
nucleotide polymorphisms in inferences of population history. Trends in Ecology and
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Evolution 18, 249–256.
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Fay, J.C., Wyckoff, G.J., Wu, C.I. (2001) Positive and negative selection on the human
19
genome. Genetics 158, 1227–1234.
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Glaubitz, J. C., Rhodes, O. E. Jr, Dewoody, J. A. (2003) Prospects for inferring pairwise
21
relationships with single nucleotide polymorphisms. Molecular Ecology 12, 1039–1047.
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Hall, T.A. (1999) BioEdit, a user-friendly biological sequence alignment editor and analysis
23
program for Windows 95/98/NT. Nucl. Acids. Symp. Ser. 41, 95-98.
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1
Kuhner, M. K., Beerli, P., Yamato, J., Felsenstein, J. (2000) Usefulness of single nucleotide
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polymorphism data for estimating population parameters. Genetics 156, 439–447.
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Lewontin, R.C., Krakauer, J. (1973) Distribution of gene frequency as a test of the theory of
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the selective neutrality of polymorphisms. Genetics 74, 175–195.
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Morin, P. A., Luikart, G., Wayne, R. K. (2004) SNP workshop Group SNPs in ecology,
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evolution and conservation. Trends in Ecology and Evolution 19, 208–216.
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Schlötterer, C. (2004) The evolution of molecular markers — just a matter of fashion? Nature
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Reviews Genetics 5, 63–69.
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Seddon, J. M., Parker, H. G., Ostrander, E. A., Ellegren H. (2005) SNPs in ecological and
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conservation studies, a test in the Scandinavian wolf population. Molecular Ecology 14, 503–
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511.
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Sunyaev, S.R., Lathe III, W.C., Ramensky, V.E., Bork, P. (2000) SNP frequencies in human
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genes an excess of rare alleles and differing modes of selection. Trends Genet. 16, 335–337.
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Syvanen, A.C. (2001) Accessing genetic variation: genotyping single nucleotide
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polymorphisms. Nature Genetics Reviews 2, 930-942.
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Vignal, A., Milan, D., San Cristobal M., Eggen A. (2002) A review on SNP and other types of
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305.
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Vitalis, R., Dawson, K., Boursot, P. (2001) Interpretation of Variation Across Marker Loci as
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Evidence of Selection. Genetics 158, 1811–1823.
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Table 1. Breeds analysed, country of origin and number of genotyped samples.
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* breeds included in the SNPs detection panels.
Breed
Origin
Animals analysed
Akkaraman*
Altamurana
Turkey
Italy
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31
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Anogeiano
Bardhoka
Bergamasca*
Churra
Cikta
Colmenareña
Cypriot fat-tailed sheep
Dagliç
Delle Langhe
Exmoor Horn
Gentile di Puglia
German Grey Heath
German Merino
Heri
Hungarian Merino
Hungarian Tsiagia
Kalarritiko
Kamieniec
Karagouniko*
Karakul
Karayaka
Kefallinias
Kymi
Laticauda
Lesvos
Manchega
Morkaraman
Naemi
Najdi
Orino
Ossimi
Piliorritiko
Polish Merino
Polish Mountain (Owza gorska)
Pomorska
Racka
Rhönsheep*
Romanian Merino
Romanian Tsigaia
Rubia del Molar*
Ruda
Scottish Blackface
Segureña
Sfakia
Shkodrane
Skopelos
Spanish Merino I
Spanish Merino II
Swaledale
Thône et Martod
Turcana*
Welsh Mountain*
White and Brown Mountain Sheep
Wrzosowka
Zelazna*
Greece
Albania
Italy
Portugal
Hungary
Spain
Cyprus
Turkey
Italy
Great Britain
Italy
Germany
Germany
Saudi-Arabia
Hungary
Hungary
Greece
Poland
Greece
Romania
Turkey
Greece
Greece
Italy
Greece
Spain
Turkey
Saudi-Arabia
Saudi-Arabia
Greece
Egypt
Greece
Poland
Poland
Poland
Hungary
Germany
Romania
Romania
Spain
Albania
Great Britain
Spain
Greece
Albania
Greece
Spain
Spain
Great Britain
France
Romania
Great Britain
Germany
Poland
Poland
31
31
31
30
31
31
32
31
31
31
31
31
31
29
31
29
31
31
31
31
31
31
31
31
31
31
31
29
31
31
31
31
31
31
31
31
31
31
31
31
31
31
31
31
31
31
31
32
31
31
31
31
31
31
31
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Table 2. Locus, gene name, characterization and allele frequencies for each genotyped SNP.
Locus
Gene name
Allele1 Allele2 %Ho1 %He %Ho2
SERPINA3 alpha1-antichymotrypsin
A
G
1.1
16.5
82.4
MEG3
callipyge
T
G
13.3
42.3
44.4
CTSB
cathepsin B
A
G
1.3
14.5
84.1
GHRHR
G
T
5.5
31.8
62.7
GHR
growth hormone releasing hormone
receptor
growth hormone receptor
G
A
30.2
46.0
23.8
IGF1
insulin like growth factor 1
T
C
38.8
44.1
17.1
ITGB1
integrin B1
T
C
1.4
15.1
83.5
GDF8
myostatin
T
C
0.5
5.3
94.2
TYRP1
tyrosinase-related protein 1
C
T
2.8
23.4
73.9
ZP2
zona pellucida2
T
C
4.8
27.7
67.5
MYH1
myosin
G
A
84.4
15.0
0.6
2
3
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Table 3. Heterozygosity and Fst calculated for each locus
Locus
Sample
heterozygosity
Sample
Fst
Test P (simulated Fst
statistic <Sample Fst)
SERPINA3
0.170
0.039
-1.407
0.094
MEG3
0.451
0.030
-2.555
0.007
CTSB
0.169
0.022
-2.647
0.005
GHRHR
0.338
0.054
-0.736
0.256
GHR
0.497
0.085
0.864
0.218
IGF1
0.466
0.046
-1.303
0.113
ITGB1
0.166
0.068
0.115
0.463
GDF8
0.063
0.055
-0.181
0.441
TYRP1
0.254
0.057
-0.480
0.339
ZP2
0.308
0.087
0.873
0.215
MYH1
0.169
0.196
3.353
0.001
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Figure 1. Distribution of the 8 European breeds used for SNPs characterization.
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Figure 2. Plot of Fst against heterozigosity for the 11 SNPs analysed. ▲: upper and lower
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confidential limits of 95% quantiles. ▬: median of 200000 replications of expected Fst and
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heterozygosity using coalescent model.
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