Molecular Biology Reports

, Volume 41, Issue 2, pp 957–965 | Cite as

Signatures of selection in five Italian cattle breeds detected by a 54K SNP panel

  • Giordano Mancini
  • Maria Gargani
  • Giovanni Chillemi
  • Ezequiel Luis Nicolazzi
  • Paolo Ajmone Marsan
  • Alessio Valentini
  • Lorraine Pariset
Open Access


In this study we used a medium density panel of SNP markers to perform population genetic analysis in five Italian cattle breeds. The BovineSNP50 BeadChip was used to genotype a total of 2,935 bulls of Piedmontese, Marchigiana, Italian Holstein, Italian Brown and Italian Pezzata Rossa breeds. To determine a genome-wide pattern of positive selection we mapped the F st values against genome location. The highest F st peaks were obtained on BTA6 and BTA13 where some candidate genes are located. We identified selection signatures peculiar of each breed which suggest selection for genes involved in milk or meat traits. The genetic structure was investigated by using a multidimensional scaling of the genetic distance matrix and a Bayesian approach implemented in the STRUCTURE software. The genotyping data showed a clear partitioning of the cattle genetic diversity into distinct breeds if a number of clusters equal to the number of populations were given. Assuming a lower number of clusters beef breeds group together. Both methods showed all five breeds separated in well defined clusters and the Bayesian approach assigned individuals to the breed of origin. The work is of interest not only because it enriches the knowledge on the process of evolution but also because the results generated could have implications for selective breeding programs.


Selection signatures SNP Cattle breeds MDS Bayesian assignment 


Present day cattle breeds are the result of years of human selection, adaptation to different environments and demographic effects as domestication, migration and selection, all contributing to the actual patterns of genetic diversity [1, 2]. During the domestication process, breeds were selected for productivity traits as, for example, milk yield [3]. Moreover, animal and semen exchange, carried out to improve production characteristics, have affected the genetic features of the breeds. This anthropic selection has influenced the genetic structure of cattle breeds, therefore a high percentage of loci purposely chosen for influencing potentially selected traits could result under selection [4, 5].

Recently, the availability of high density SNP panels has given the possibility of performing population genetic studies in cattle populations using thousands of markers distributed across the entire genome. Medium density SNP panels have been used for example to analyze the genetic structure of cattle populations [6, 7, 8] to study past effective population size [9], to detect selection signatures [10], and to discover copy number variation (CNV) suitable for understanding genetic features and accelerating genetic improvement for complex traits [11].

We considered a total of 2,935 bulls belonging to two dairy (Italian Brown and Italian Holstein), two beef (Piedmontese and Marchigiana) and one double purpose (Italian Pezzata Rossa) breeds. The Italian Holstein derives from Dutch and North America Holstein breeds imported in Italy in the late XX century and it is currently the most common dairy breed. Piedmontese is mainly located in Northern Italy and it was in the past a dual purpose breed, while today it is selected for beef traits mainly exploiting a private myostatin mutation [12]. Marchigiana is a beef breed from central Italy derived from very ancient breeds like Chianina and Romagnola breeds. The Italian Brown was originally a multi purpose breed reared in the Alps; it was selected from 1950 as a dairy breed by importing Swiss Brown bulls from the U.S. Pezzata Rossa, Simmental, is a beef/dairy breed imported from Swiss/Austria and herded mostly in North East Italy.

The aim of this study was to identify genomic regions potentially under selection in the above five Italian cattle breeds using a 54K medium-density SNP panel. Our results could have implications for selective breeding programs by identifying signatures of artificial selection in gene involved in milk, meat or functional traits. We analyzed also the genetic structure of the breeds by classical multidimensional scaling and by Bayesian inference methods.

Materials and methods

Samples and high throughput genotyping

The initial sample was formed by 2,935 bulls: 761 Italian Brown, 899 Italian Holstein, 323 Piedmontese, 464 Marchigiana and 488 Italian Pezzata Rossa bulls, respectively. The sample represents almost all the bulls available in Italy for all breeds but Holstein, where the bulls analyzed correspond to slightly less than a half of the available ones. Genomic DNA was extracted from semen using the NucleoSpin Tissue kit (Macherey-Nagel) according to manufacturer’s instruction. DNA was checked for quality on agarose gel and quantified using a DTX microplate reader (Beckman Coulter) after staining with Picogreen (Invitrogen). Samples were genotyped using BovineSNP50 Genotyping BeadChips (Illumina, San Diego, CA, USA). Genotyping was outsourced to Geneseek ( The 50K SNP array contains 54,001 SNPs distributed across the entire genome, with an average SNP spacing of 51 Kb and a proportion of known chromosome positions of about 97 %; SNP positions within each chromosome were based on the Bos taurus genome assembly Btau_4.0 [13].

Data editing and genome-wide analysis

Data were initially filtered using the GenABEL R package (, Only autosomal markers were used and SNP with complete map information were used. Sires and markers with a call rate under 99 % were discarded as well as SNPs having a minor allele frequency (MAF) <5 % according to the currently employed thresholds [14]. Sires were checked for abnormally high autosomal heterozygosity and discarded when showing a false discovery rate (FDR) <1 % [15]. Then, sires of each breed were separated and Hardy–Weinberg equilibrium (HWE) was checked within each breed setting a threshold of P < 0.01 in the filtered data set [16]. Finally, data were pooled again and filtering criteria explained above were applied once more. Kinship among sires was estimated directly from genomic data as proposed by Astle and Balding [17].

To determine a genome-wide pattern of positive selection, the F st at each locus was calculated [18]. The loci under selection are expected to show an allele frequency that deviate from that of neutral loci, leading to an increased level of genetic differentiation. F st values were then plotted against genome location. Signatures of selection can be recognized when adjacent SNPs in a region show high F st [19] thus we used a sliding window approach, with a window of eight SNPs. A region with high F st implies divergent selection between breeds, whereas low F st imply balancing selection between breeds. Fixation index was calculated using the method proposed by Nei and Chesser [20] using in-house written R codes; this method was chosen because the sample includes (almost) all sires in the national herdbooks for three breeds (Marchigiana, Piedmontese and Pezzata Rossa) and thus fixed effects errors of sampling, i.e. effects unbounded by a prior distribution, seemed more important. Graphs were obtained using matplotlib (

Genetic distance between breeds and sires was estimated calculating the matrix d ij  = (0.5 − k ij ) where d is the distance and k is genetic kinship coefficient for sires i and j and then applying classical multidimensional scaling to the complete matrix. STRUCTURE software v. [21] was used to analyse population structure. A total of 15,000 Markov chain Monte Carlo (MCMC) iterations (5,000 burn-in and 10,000 sampling) were performed for each tested K using the admixture model, considering allele frequencies correlated among populations and including no informative prior about individual membership; K values from 2 to 5 were used. Five independent runs for each tested K value were performed. The number of steps was chosen following [22], although for each K a single run of 50,000 iterations to test the effects of longer runs was performed. Evanno et al. [23] reported that in most cases, the estimated ‘log probability of data’ did not provide a correct estimation of cluster number (K value), and argued that an ad hoc statistic ΔK based on the rate of change in the log probability of data between successive K values could accurately detect true K. The suggested statistics was:
$$\varDelta K = m(|L\left( {K + 1} \right) - 2L\left( K \right) + L\left( {K - 1} \right)|)/s\left[ {L\left( K \right)} \right],$$
where L(K) represents the Kth LnP(D), m is to the mean of 10 runs and s their standard deviation. We used the method of Evanno et al. [23] to estimate the number of populations. Graphical visualization of STRUCTURE results was performed by means of the DISTRUCT package [24].


Data editing

Elimination of markers on the X chromosome or with incomplete map information left 51,515 SNPs out of the 54,001. A total of 138 sires and 12,388 markers showing a call rate <99 % were excluded. Additional 8,874 SNPs with a MAF <5 % were discarded. Then, a total of 1,443 markers were discarded because they were out of HWE in at least one breed. No sires or markers were excluded after the second check on call rates and allele frequencies. The final complete dataset was thus formed by 2,797 sires (755 Italian Brown, 861 Italian Holstein, 483 Italian Pezzata Rossa, 317 Piedmontese and 381 Marchigiana bulls) and 29,848 SNPs.

Selection signatures

Figure 1 shows F st values calculated for all autosomal SNPs and averaged over a 8-wide SNP window within each chromosome. Results including all five populations, only dairy breeds and only beef breeds are displayed in panels A, B and C, respectively. Fixation index values containing the 98 % of point are also indicated with green lines. In panel A, F st ranges from 0.04 to 0.30, with an average value of 0.077 ± 0.058, with the exception of a single F st = 0 signal on BTA 19. SNP clusters with F st values above 0.0175 (the 99 % threshold) were located on chromosomes 3–6, 13, 14, 16, 18, 26 and 29. In most cases F st peaks are formed by only one or two groups of 8 markers, therefore spanning about 4 Mb. More consistent signals are present on BTA13 and on BTA6, where peaks are formed by 11 and 4 groups of markers above the 99 % quantile value. A similar pattern is shown in Panels B and C, where F st values are calculated for dairy and beef breeds separately; the strongest signals are still observed on BTA6. F st obtained in dairy breeds are higher than those observed for beef breeds. The peak identified in dairy breeds spans from 72.35 to 72.47 Mb on BTA6 (Figs. 1b, 2b). Three of the four SNPs identified in this region are located in the platelet-derived growth factor receptor, alpha polypeptide (PDGFRA) gene involved in the reproduction process and in the regulation of calcium level and near the KIT (gene v-kit Hardy–Zuckerman 4 feline sarcoma viral oncogene homolog) gene expressed in the lactating bovine mammary gland and implicated in determining coat colour [5]. In dairy breeds a weaker peak, formed by four markers, is also observable around 38 Mb on BTA6 where ATP-binding cassette, sub-family G (ABCG2) and polycystic kidney disease 2 (PKD2) genes are located. The two genes play a role in the regulation of bovine lactation [25] and in calcium homeostasis, respectively [26].
Fig. 1

Genome wide Manhattan plots of fixation index (F st ) calculated for all polymorphisms averaged over a window of 8 SNPs within each chromosome and calculated for all five breeds (a), dairy breeds (b) and beef breeds (c). F st for odd and even chromosomes are represented with blue circles and red squares, respectively. The values corresponding to the lowest 1 and 99 % quantiles are represented with green dashed lines. (Color figure online)

Fig. 2

Plot of fixation index (F st ) calculated for all markers on BTA6 averaged over a 3 SNP window within all breeds (a), dairy breeds (b) and beef breeds (c). The values corresponding to the lowest 1 and 99 % quantiles are represented with green dashed lines. (Color figure online)

In beef breeds the peak on BTA6 consists of six SNPs spanning from 37.32 to 38.76 Mb (Figs. 1c, 2c). This interval contains 15 genes including LAP3 (leucine aminopeptidase 3) non-SMC condensin I complex subunit G (NCAPG) and ligand-dependent nuclear receptor corepressor-like protein (LCORL), genes involved in calving ease [27].

Figure 2a shows F st values for markers on BTA6 only, without any averaging. Several markers that have F st above the 99 % quantile can be observed at 18 Mb, between 36 and 39 Mb and at 95 Mb, but the strongest signal (considering either the number of SNPs with high F st or the maximum value) can be observed at ~38 Mb.

Moreover we calculated the F st values for BTA6 comparing each breed against the remaining four (Fig. 3). In Italian Brown breed we can observe a signals at 72 and 37 Mb, in Italian Holstein and Pezzata Rossa breeds the strongest signal can be observed at 72 Mb, while in Marchigiana a peak is located around 37 Mb. The Piedmontese breed showed two signals one around 18 Mb and the other around 68 Mb.
Fig. 3

Plot of F st values calculated on BTA6 for each breed versus all others. The values corresponding to the 99 % quantiles are represented with red lines. (Color figure online)

The F st calculated only for dairy breeds (Fig. 1b) shows a peak formed by four SNPs spanning from 47.23 to 48.30 Mb on BTA13. In this region we identified 14 genes among which CDS2 (CDP–diacylglycerol synthase) could affect milk fat composition. Four significant SNPs on chromosome 13 at positions 67197635, 67464116, 67490718, 67766784 were identified in beef breeds (Fig. 1c). Of the 8 genes in this region, SRC (v-src sarcoma viral oncogene homolog avian) and CTNNBL1 (catenin, beta like 1) may be related to muscle formation and to body weight, respectively.

Multidimensional scaling

Figure 4 shows the first three components of the multidimensional scaling decomposition of the genetic distance matrix (calculated as 0.5-kinship). Relative distances between selected clusters are shown in the graph and range from 0.195 (Piedmontese–Pezzata Rossa) to 0.291 (Marchigiana–Holstein). The five breeds form compact clusters separated from each other with the partial exception of a small group of outlying Brown bulls located near the “centre of mass” of clusters (represented with a diamond). The first component (C1 hereafter) separates Brown and Holstein from Piedmontese, Marchigiana; Pezzata Rossa is located between these two breeds; Brown and Holstein are both located at comparable distance from the centre on the negative half of component two (C2 hereafter) and separated from the other breeds; both clusters form approximate ellipsoids with the major axis along different diagonals in the plane of the first two components while component three (C3 hereafter) contribution is very small. The opposite is true for the relative position of beef breeds and Pezzata Rossa for which the greatest differentiation is due to C3. Piedmontese forms the most compact cluster and is closer to the centre. Also, it must be observed that, although Pezzata Rossa is a double purpose breed, its cluster is actually more distant from dairy breeds as compared to Marchigiana and Piedmontese and that Piedmontese is more distant from Marchigiana than from Pezzata Rossa. Figures S1 and S2 in the Supplementary materials show analogous calculations restricted to either dairy or beef breeds. In both cases two breeds (Brown and Pezzata Rossa in Fig. S1, Marchigiana and Piedmontese in Fig. S2) are located in the C1–C2 plane while the remaining population (Holstein in Fig. S1 and Pezzata Rossa in Fig. S2) is located in a different quadrant in the C1–C2 plane and is highly dispersed along C3.
Fig. 4

Classical multidimensional scaling plot of genomic distance calculated as 0.5—genomic kinship for all five breeds. The first three components are shown as C1, C2 and C3, respectively. Subjects are depicted as grey squares (Italian Brown), black circles (Italian Holstein), red hexagons (Italian Pezzata Rossa), blue triangles (Marchigiana), green pentagons (Piedmontese), respectively. The centre of mass of the complete distribution is represented as a magenta diamond and relative distances between each cluster centre or between any cluster and the general centre is indicated by cyan arrows and annotated in the figure. (Color figure online)

Bayesian inference

To estimate the number of genetic clusters within the 2,797 cattle samples and 29,848 SNPs, a parametric genetic mixture analysis implemented in the STRUCTURE software was performed. Between 2 and 5 clusters (K values) were tested using the admixture model, considering allele frequencies correlated. Consistent results across runs were obtained and a clear clustering of breeds was observed for any K tested (Fig. 5). With K = 2 Brown and Holstein individuals are assigned to different clusters while sires of the other three breeds belong to each cluster with probabilities near 50 %. The separation becomes sharper when three clusters are hypothesized: in this case Marchigiana, Piedmontese and Pezzata Rossa sires are assigned to a third cluster different from Brown and Holstein (although some noise is present for Piedmontese. With K = 4 Marchigiana also forms a distinct cluster while Piedmontese is assigned with comparable probability to either Marchigiana or Pezzata Rossa and more unlikely to Brown or Holstein (the noise present for K = 3 is basically conserved). Finally, with K = 5 Piedmontese is assigned to a distinct cluster. Under the ad hoc criterion of Evanno et al. [23] K = 5 was the most likely number of genetic groups.
Fig. 5

Summary plot of Q estimates (estimated membership coefficients for each individual, in each cluster) for K = 2, 3, 4, 5 obtained with a 5,000 burn-in, and 35,000 MCMC, under the admixture model, for the breeds analyzed. Individuals are represented in breed groups separated by vertical lines. Breed is indicated above while the breeds’ attitude is indicated under each group. Each individual is represented by a single vertical line broken into K coloured segments, with lengths proportional to each of the K inferred clusters. Each colour represents the proportion of membership (M) of each individual (represented by a vertical line) to the K clusters. (Color figure online)


Recently many innovative tools, such as medium or high density SNP chips, have been developed for various domesticated species. In this study we presented two applications of population genetic analysis in five Italian cattle breeds using 50K bovine SNP chips. We first investigated the potential of SNP markers to identify selection signatures peculiar of each breed and then we analyzed the genetic structure in the same samples.

By mapping the F st values against genome location we identified genes showing signatures of positive selection involved in biological processes such as reproduction, metabolism of lipids, calving ease. A strong selection signal was observed on BTA6 when considering F st across all cattle breeds. Interestingly, the F st calculated only in dairy breeds revealed the evidence for selection in the region located at 72.45 Mb on chromosome 6, far from the caseins cluster, for which selection is carried out in some breeds. The peak is located near the PDGFRA gene which is associated with β-estradiol and implicated in the reproduction process and in the regulation of calcium level and near the KIT gene expressed in the lactating bovine mammary gland and implicated also in determining coat colour. These results are consistent with those obtained by Flori et al. [28] and Stella et al. [5] who found a positive selection signature in the same region in dairy cattle breeds. Flori et al. [28] used the F st approach to detect the selection signatures in three French dairy cattle breeds and highlighted 13 significant signatures including the PDGFRA gene which is proposed as candidate gene. Stella et al. [5] reported the largest composite log likelihood (CLL) in the same location on BTA6 within the KIT gene which is responsible for the piebald phenotype in four of the five dairy breeds analyzed. The peak around 38 Mb falls near the ABCG2 and PKD2 genes. Several studies identified a QTL affecting milk yield and milk composition on chromosome 6 in a region around 38 Mb containing ABCG2 gene [25, 29, 30]

Recently the results obtained by Wei et al. [25] suggested that ABCG2 plays a role in mammary epithelial cell proliferation and that the polymorphisms in this gene may influence milk production.

The other interesting gene in the region is PKD2 gene that could be related with the content of water in the milk since it is involved in calcium homeostasis [26].

The peak located at ~38 Mb on BTA6 in beef breeds is near LAP3, NCAPG and LCORL. LAP3 encodes for a leucine aminopeptidase, which is responsible of the oxytocin hydrolysis [31]. Recent studies demonstrated the role of LAP3 in calving ease in Norwegian Red cattle [32]. Moreover, Bongiorni et al. [27] found a strong association between LAP3, NCAPG, LCORL and calving ease trait in Piedmontese.

These results are in agreement with the F st values calculated for each breed against the other in BTA6.

The signal in dairy breeds is due mainly by Bruna, Holstein and Pezzata Rossa, while the signal around 37 Mb identified in beef breeds is due mainly to Marchigiana breed (Fig. 3). It is worth to notice that the Piedmontese breed shows a peak spanning between 17 and 18 Mb on BTA6. This region contains five genes including COL25A1 collagen gene.

A cluster of signals reflecting strong evidence of selection was observed also in BTA13. When we analyzed separately the F st for beef breeds (Fig. 1c) a strong peak at position 67 Mb could be observed. Two interesting genes are located in this region: SRC and CTNNBL1. SRC is involved in the regulation of actin cytoskeleton and in the focal adhesion pathway [33]. Some studies reported the role of focal adhesion pathway for muscle formation in cattle [34] and muscle strength and integrity in racing horses [35]. The other gene, CTNNBL1, is associated with body weight and height [36]. In human it has been showed to be involved in the Wnt/beta-catenin-signaling pathway and associated with obesity [36]. In dairy breeds we observed a peak spanning 47–48 Mb on BTA13 near the CDS2 gene which could influence milk fat composition; the gene is in fact involved in the phospolipid biosynthetic process. The Table 1 shows a list of candidate genes for genomic regions presenting the most extreme peaks in dairy and beef breeds.
Table 1

List of candidate genes for genomic regions presenting the most extreme peaks in dairy and beef breeds


Start in Mb

End in Mb

Candidate gene

Gene name and function






ATP-binding cassette, sub-family G


Polycystic kidney disease 2





Platelet-derived growth factor receptor, alpha polypeptide


v-kit Hardy–Zuckerman 4 feline sarcoma viral oncogene homolog





CDP–diacylglycerol synthase






Leucine aminopeptidase 3


Non-SMC condensin I complex subunit G


Ligand-dependent nuclear receptor corepressor-like protein





v-src sarcoma viral oncogene homolog avian


Catenin, beta like 1

No F st peaks have been detected on or near the few genes today known to influence dairy or beef traits, like DGAT1, caseins, myostatin, leptin. This may be due to the large genetic network that influences the complex traits under selection, as well as the changes of the selection policies cross time: i.e. at least in Italy and many European countries, in the early days, the main selection objective was milk yield, afterwards it was protein and fat percentage, now sustainability traits are included in the selection index.

Regarding the assessment of the genetic structure we used Bayesian and multidimensional scaling approaches. Multidimensional scaling separated each breed in five well defined clusters. Piedmontese formed the most compact cluster indicating that the breeding policy in this breed tends towards a narrower genetic basis. It has to be noted that in the past 50 years the selection has been oriented to strongly select for the double muscling trait and culling all non carrier subjects. It is worth to observe that the double purpose Pezzata Rossa is more distant from dairy breeds than beef breeds, suggesting in this breed a different management of selection and lack of admixture with other dairy breeds. Interesting, the Italian Brown showed a small group of outlying bulls in the MDS plot suggesting a potential substructure maybe due to the double type of exploitation of this breed both in high producing farms in the valleys and in harsher conditions in the mountains.

The genetic isolation and lack of admixture among the two dairy breeds are confirmed by Bayesian analysis in which the breeds do not cluster on the basis of their purpose even for K = 2 (Fig. 5). This means that the differentiation pre-dates the selection for different purposes. Beef and dual purpose breeds tend to cluster together up to K = 3 and at K = 4 Piedmontese is assigned almost equally to the other two beef breeds. We hypothesize in this case a possible convergent artificial selection for beef breeds.



The authors wish to thank Gabriella Porcai for technical assistance and Cesare Gruber for computer support. This work has been supported by the SelMol ( and Innovagen projects financed by MIPAAF and by the CASPUR standard HPC grant 2010 “Utilizzo di panel da 54K SNPs per identificare regioni cromosomiche ad alta differenziazione intraspecifica o sotto selezione per la scoperta di geni implicati nella variazione fenotipica”. GM PhD fellowship was granted by CASPUR.

Supplementary material

11033_2013_2940_MOESM1_ESM.doc (228 kb)
Supplementary material 1 (DOC 228 kb)


  1. 1.
    Bruford MW, Bradley DG, Luikart G (2003) DNA markers reveal the complexity of livestock domestication. Nat Rev Genet 4:900–910CrossRefPubMedGoogle Scholar
  2. 2.
    Laloë D, Moazami-Goudarzi K, Lenstra J, Ajmone-Marsan P, Azor P, Baumung R, Bradley D, Bruford MW, Bruford MW, Cañón J, Dolf G, Dunner S, Erhardt G, Hewitt G, Kantanen J, Obexer-Ruff G, Olsaker I, Rodellar C, Valentini A, Wiener P, European Cattle Genetic Diversity Consortium, Econogene Consortium (2010) Spatial trends of genetic variation of domestic ruminants in Europe. Diversity 2(6):932–945CrossRefGoogle Scholar
  3. 3.
    Taberlet P, Coissac E, Pansu J, Pompanon F (2011) Conservation genetics of cattle, sheep, and goats. C R Biol 334(3):247–254CrossRefPubMedGoogle Scholar
  4. 4.
    Pariset L, Mariotti M, Nardone A, Soysal MI, Ozkan E, Williams JL, Dunner S, Leveziel H, Maroti-Agots A, Bodo I, Valentini A (2010) Relationships between Podolic cattle breeds assessed by single nucleotide polymorphisms (SNPs) genotyping. J Anim Breed Genet 127:481–488CrossRefPubMedGoogle Scholar
  5. 5.
    Stella A, Ajmone-Marsan P, Lazzari B, Boettcher P (2010) Identification of selection signatures in cattle breeds selected for dairy production. Genetics 185:1451–1461CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    McKay SD, Schnabel RD, Murdoch BM, Matukumalli LK, Aerts J, Coppetiers W, Crews D, Dias Neto E, Gill CA, Gao C, Mannen H, Wang Z, Van Tassel CP, Williams JL, Taylor JF, Moore SS (2008) An assessment of population structure in eight breeds of cattle using a whole genome SNP panel. BMC Genet 9:37CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Gautier M, Laloe D, Moazami-Goudarzi K (2010) Insights into the genetic history of French cattle from dense SNP data on 47 worldwide breeds. PLoS One 5:e13038. doi: 10.1371/journal.pone.0013038 CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Lewis J, Abas Z, Dadousis C, Lykidis D, Paschou P, Drineas P (2011) Tracing cattle breeds with principal components analysis ancestry informative SNPs. PLoS One 6:e18007CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Kim JJ, Kim KS, Sonstegard TS, Van Tassell CP, Neibergs HL, McEwan JC, Brauning R, Coutinho LL, Babar ME, Wilson GA, McClure MC, Rolf MM, Kim J, Schnabel RD, Taylor JF (2009) Resolving the evolution of extant and extinct ruminants with high-throughput phylogenomics. Proc Natl Acad Sci USA 1061:8644–8649Google Scholar
  10. 10.
    Hayes BJ, Chamberlain AJ, Maceachern S, Savin K, McPart-lan H, MacLeod I, Sethuraman L, Goddard ME (2009) A genome map of divergent artificial selection between Bos taurus dairy cattle and Bos taurus beef cattle. Anim Genet 40:176–184CrossRefPubMedGoogle Scholar
  11. 11.
    Hou Y, Liu GE, Bickhart DM, Cardone MF, Wang K, Kim ES, Matukumalli LK, Ventura M, Song J, VanRaden PM, Sonstegard TS, Van Tassell CP (2011) Genomic characteristics of cattle copy number variations. BMC Genomics 12:127CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Kambadur R, Sharma M, Smith TPL, Bass JJ (1997) Mutations in myostatin (GDF8) in double-muscled Belgian Blue and Piedmontese cattle. Genome Res 7:910–915PubMedGoogle Scholar
  13. 13.
    Liu Y, Qin X, Song X-ZH, Jiang H, Shen Y, Durbin KJ, Lien S, Kent MP, Sodeland M, Ren Y, Zhang L, Sodergren E, Havlak P, Worley KC, Weinstock GM, Gibbs RA (2009) Bos taurus genome assembly. BMC Genomics 10:180CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Panagiotou OA, Evangelou E, Ioannidis JPA (2010) Genome-wide significant associations for variants with minor allele frequency of 5 % or less—an overview: a HuGE review. Am J Epidemiol 172(8):869–889CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Pearson TA, Manolio TA (2008) How to interpret a genome-wide association study. J Am Med Assoc 299:1335–1344CrossRefGoogle Scholar
  16. 16.
    Wigginton JE, Cutler DJ, Abecasis GR (2005) A note on exact tests of Hardy–Weinberg equilibrium. Am J Hum Genet 76:887–893CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Astle W, Balding DJ (2009) Population structure and cryptic relatedness in genetic association studies. Stat Sci 24:451–471CrossRefGoogle Scholar
  18. 18.
    Wright S (1931) Evolution in Mendelian populations. Genetics 16:97–159PubMedPubMedCentralGoogle Scholar
  19. 19.
    Weir BS, Cardon LR, Anderson AD, Nielsen DM, Hill WG (2005) Measures of human population structure show heterogeneity among genomic regions. Genome Res 15:1468–1476CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Nei M, Chesser RK (1983) Estimation of fixation indices and gene diversities. Ann Hum Genet 47:253–259CrossRefPubMedGoogle Scholar
  21. 21.
    Pritchard J, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959PubMedPubMedCentralGoogle Scholar
  22. 22.
    Vaysse A, Ratnakumar A, Derrien T, Axelsson E, Rosengren Pielberg G, Sigurdsson S, Fall T, Seppälä EH, Hansen MST, Lawley CT, Karlsson EK, The LUPA Consortium, Bannasch D, Vilá C, Lohi H, Galibert F, Fredholm M, Häggström J, Hedhammar Å, André C, Lindblad-Toh K, Hitte C, Webster MT (2011) Identification of genomic regions associated with phenotypic variation between dog breeds using selection mapping. PLoS Genet 7(10):e1002316CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software structure: a simulation study. Mol Ecol 14:2611–2620CrossRefPubMedGoogle Scholar
  24. 24.
    Rosenberg N (2004) DISTRUCT: a program for the graphical display of population structure. Mol Ecol Notes 4:137–138CrossRefGoogle Scholar
  25. 25.
    Wei J, Geale PF, Sheehy PA, Williamson P (2012) The impact of ABCG2 on bovine mammary epithelial cell proliferation. Anim Biotechnol 23(3):221–224CrossRefPubMedGoogle Scholar
  26. 26.
    Olsen HG, Nilsen H, Hayes B, Berg PR, Svendsen M, Lien S, Meuwissen T (2007) Genetic support for a quantitative trait nucleotide in the ABCG2 gene affecting milk composition of dairy cattle. BMC Genet 8:32CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Bongiorni S, Mancini G, Chillemi G, Quaglino A, Albera A, Pariset L, Valentini A (2012) Identification of a short region in chromosome 6 affecting calving traits in Piedmontese cattle by genome-wide association mapping. PLoS One 7(12):e50137CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Flori L, Fritz S, Jaffrézic F, Boussaha M, Gut I, Heath S, Foulley JL, Gautier M (2009) The genome response to artificial selection: a case study in dairy cattle. PLoS One 4:e6595. doi: 10.1371/journal.pone.0006595 CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Olsen HG, Lien S, Gautier M, Nilsen H, Roseth A, Berg PR, Sundsaasen KK, Svendsen M, Meuwissen THE (2005) Mapping of a milk production quantitative trait locus to a 420-kb region on bovine chromosome 6. Genetics 169:275–283CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Cohen-Zinder M, Seroussi E, Larkin DM, Loor JJ, Everts-van der Wind A, Lee JH, Drackley JK, Band MR, Hernandez AG, Shani M, Lewin HA, Weller JI, Ron M (2005) Identification of a missense mutation in the bovine ABCG2 gene with a major effect on the QTL on chromosome 6 affecting milk yield and composition in Holstein cattle. Genome Res 15:936–944CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Zheng X, Ju Z, Wang J, Li Q, Huang J, Zhang A, Zhong J, Wang C (2010) Single nucleotide polymorphisms, haplotypes and combined genotypes of LAP3 gene in bovine and their association with milk production traits. Mol Biol Rep 38(6):4053–4061CrossRefPubMedGoogle Scholar
  32. 32.
    Olsen HG, Meuwissen TH, Nilsen H, Svendsen M, Lien S (2008) Fine mapping of quantitative trait loci on bovine chromosome 6 affecting calving difficulty. J Dairy Sci 91:4312–4322CrossRefPubMedGoogle Scholar
  33. 33.
    Lee YH, Kayyali US, Sousa AM, Rajan T, Lechleider RJ, Day RM (2007) Transforming growth factor-beta1 effects on endothelial monolayer permeability involve focal adhesion kinase/Src. Am J Respir Cell Mol Biol 7(4):485–493CrossRefGoogle Scholar
  34. 34.
    Qanbari S, Gianola D, Hayes B, Schenkel F, Miller S, Moore S, Thaller G, Simianer H (2011) Application of site and haplotype-frequency based approaches for detecting selection signatures in cattle. BMC Genomics 12:318CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Gu J, Orr N, Park SD, Katz LM, Sulimova G, MacHugh DE, Hill EW (2009) A genome scan for positive selection in thoroughbred horses. PLoS One 4:e5767CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Andreasen CH, Mogensen MS, Borch-Johnsen K, Sandbæk A, Lauritzen T, Almind K, Hansen L, Jørgensen T, Pedersen O, Hansen T (2009) Studies of CTNNBL1 and FDFT1 variants and measures of obesity: analyses of quantitative traits and case–control studies in 18,014 Danes. BMC Med Genet 10:17CrossRefPubMedPubMedCentralGoogle Scholar

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© The Author(s) 2014

Open AccessThis article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

Authors and Affiliations

  • Giordano Mancini
    • 1
    • 2
  • Maria Gargani
    • 1
  • Giovanni Chillemi
    • 2
  • Ezequiel Luis Nicolazzi
    • 3
  • Paolo Ajmone Marsan
    • 3
  • Alessio Valentini
    • 1
  • Lorraine Pariset
    • 1
  1. 1.Department for Innovation in Biological, Agro-food and Forest SystemsUniversity of TusciaViterboItaly
  2. 2.CASPUR Inter-University Consortium for the Application of Super-Computing for Universities and ResearchRomeItaly
  3. 3.Institute of ZootechnicsSacro Cuore Catholic UniversityPiacenzaItaly

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