Li et al. Genetics Selection Evolution 2010, 42:32 http://www.gsejournal.org/content/42/1/32 Genotypes of the 51 microsatellites were used (for details on the microsatellites, see [33-35]) among which data of the 30 markers from the panel of loci recom- mended for genetic diversity studies in cattle http:// www.projects.roslin.ac.uk/cdiv/markers.html were taken from the literature [2]. The 23 microsatellites (21 new ones and two from the recommended panel) on BTA1 and BTA20 were chosen on the basis of their vicinity to genes and QTL, which could be considered as candidate loci for selection because of their assumed involvement in the polled/horned phenotype [22] and in milk yield and body composition [35]. Details of the primers and microsatellite analysis protocols can be found in CaD- Base http://www.projects.roslin.ac.uk/cdiv/markers.html and [34]. In this study, GHRJA.UP, 5′- GGTTCGTTATGGAGGCAATG-3′, and GHRJA.DN, 5′-GTCACCGCTGGCAGTAGAT-3′ primers were designed based on the sequence of the promoter region of the growth hormone receptor gene [35] containing microsatellite GHRJA. Danish Jersey animals were ana- lyzed only at 41 loci (see Table 1). A full list of the loci studied and their chromosomal and genomic locations, as well as population and basic statistics, are available in Table 1. Microsatellite variability measures and test for linkage disequilibrium Microsatellite variability, expected heterozygosity (HEXP), allelic richness (AR), and Weir and Cockerham’s FST [36], were estimated with the FSTAT program, version 2.9.3.2 [37]. The D′ metric used to estimate the LD was calculated using Multiallelic Interallelic Disequilibrium Analysis Software (MIDAS; [38]). Values of D′ were calculated for all syntenic marker pairs on BTA1 and BTA20 across the populations. A more detailed description of the estimation of D′ can be found in [39]. The statistical significance of the observed association between pairs of alleles under the hypothesis of random allelic assortment was tested using a Monte-Carlo approxima- tion of Fisher’s exact test as implemented in the soft- ware ARLEQUIN [40] using a Markov chain extension to Fisher’s exact test for R × C contingency tables [41]. A total of 100 000 alternative tables were explored with the Markov chain and probabilities were typically esti- mated with a standard error of < 0.001. Estimation of the D′ metric for LD and tests for their significance were conducted only in three Finnish native breeds, i.e. Northern Finncattle, Eastern Finncattle and Western Finncattle. The graphic summary of the significance of LD determinations was displayed using the HaploView program, version 4.0 [42]. Fisher’s exact tests in the GENEPOP v 4.0 [43] were applied to assess LD determi- nations between all locus pairs across the sample. Page 3 of 14 Tests to detect loci under selection across populations Possible departures from the standard neutral model of molecular evolution - potentially revealing demographic events or the existence of selective effects at certain loci - were examined for each locus using the Ewens- Watterson test [44,45] and the Beaumont and Nichols’s modified frequentist method [9], as well as a more robust Bayesian test [12]. The Ewens-Watterson test of neutrality was per- formed with the ARLEQUIN program [40] assuming an infinite allele mutation model. To obtain sufficient precision with this test, the probability was recorded as the mean of 20 independent repeats of 1,000 simula- tions. The frequentist method used was that proposed by [9], further developed by [12], and implemented in the FDIST2 program http://www.rubic.rdg.ac.uk/~mab/ software.html, a currently distributed version of the original FDIST program as described by [12]. FDIST2 calculates θ, Weir & Cockerham’s [36] estimator of diversity for each locus in the sample. Coalescent simulations are then performed to generate data sets with a distribution of θ centered on the empirical esti- mates. Then, the quantiles of the simulated FST within which the observed FST’s fell and the P-values for each locus were determined. Initially an island model of population differentiation was used and the procedure repeated 50,000 times to generate 95% confidence intervals for neutral differentiation and to estimate P-values for departure of the loci from these expecta- tions. Simulation parameters were under an infinite allele mutation model for 100 demes, 10 sample popu- lations, sample sizes of 100, and a weighted FST similar to the trimmed mean FST calculated from the empiri- cal distribution. Computed by removing the 30% high- est and lowest FST values observed in the empirical data set, the trimmed mean FST is an estimate of the average “neutral” FST value uninfluenced by outlier loci (see [46]). This method provides evidence for selection by looking for outliers with higher/lower observed FST -values, controlling for P-values [12]. The approach is fairly robust regarding variation in muta- tion rate between loci, sample size, and whether popu- lations are at equilibrium or not [9]. Beaumont & Balding’s [12] hierarchical-Bayesian method was performed using the BAYESFST program http://www.reading.ac.uk/Statistics/genetics/software. html package, which generates 2,000 Markov chain Monte Carlo (MCMC) simulated loci on the basis of the distribution of FST given the data. The method combines information over loci and populations in order to simultaneously estimate FST at the ith locus and the jth population, FST(i, j), for all i loci and j populations. A hierarchical model is implemented for FST(i, j) as