600 Prasad et al. 0.3 0.25 0.2 i c o l f o ion 0.15 t r o p o r P 0.1 0.05 0 Holstein Angus <0.05 0.05–0.10 0.05–0.10 0.20–0.30 0.30–0.40 0.40–0.50 MAF Figure 1 The distribution of minor allele frequencies for the SNP markers (MAF > 0.03) used in the LD analysis on BTA19 and BTA29. BTA29. The distribution of MAFs for SNPs used in the LD analyses in both breeds is shown in Fig. 1. It is evident from this ﬁgure that MAF distribution deviates from uniform and Holstein differs from Angus (P < 0.001) for its MAF distri- bution. The presence of a uniform distribution of SNP MAFs is because of the ascertainment bias in SNP discovery and does not represent the true distribution of SNP MAF in the genome, which is more appropriately modelled by a gamma distribution. Any difference between the MAF distributions probably reﬂects a breed of ascertainment effect (i.e. the SNPs were discovered because they were the most common SNPs on these chromosomes in Holstein), which would lead to an excess of high MAF SNPs in one breed and an excess of low MAF in the second. Therefore, the difference probably has no biological signiﬁcance other than identifying the breed of SNP discovery. The distribution of MAF for SNPs used in the estimation of selection signatures using the ﬁve- locus sliding-window approach (results not shown) was not different from Fig. 1, showing that the focus on these SNPs did not introduce an ascertainment bias. Details of the allelic frequencies for both breeds are pro- vided in Table S3. After plotting the differences in rolling average allele frequencies between beef and dairy cattle against the third locus coordinate within a ﬁve-locus sliding window, we observed large ﬂuctuations about the axis on both chromosomes (Figs 2 & 3). The allele frequency thresholds required to achieve statistical signiﬁcance were found by performing permutation tests and were 0.27 and )0.25 on BTA19 and 0.19 and )0.21 on BTA29 respec- tively (shown by the red-coloured lines in Figs 2 & 3). In total, we tested 351 and 171 sliding windows for BTA19 and 29 respectively and the number of chromosomal regions identiﬁed because differing between the breeds was greater than expected by chance. We found evidence of selection in ﬁve regions (6.18–7.35, 9.88–11.93, 14.75– 17.10, 28.64–30.83 and 57.15–59.68 Mb) in Holstein and three regions (4.00–5.40, 24–26 and 60–61 Mb) in Angus on BTA19. On BTA29, there were three regions (11.77– 15.15, 26.42–27.47 and 33–34 Mb) in Holstein and three regions (7.5–8.50, 18.75–19.45 and 27.75–28.68 Mb) in Angus with evidence of selection. Three QTL databases available online (http://genomes.sapac.edu.au/bovineqtl/ index.html, http://www.animalgenome.org/QTLdb/cattle. html, http://www.vetsci.usyd.edu.au/reprogen/QTL_Map/) were used to identify the chromosomal coordinates of pub- lished QTL in beef and dairy cattle on BTA19 and 29. Markers within the reported QTL regions were aligned to the third draft of the bovine genome sequence assembly (Btau 3.1) to obtain the approximate position of these QTL in Mb, and these are reported in Table S4. We found agreement between the regions with large allele frequency differences and those that had previously been identiﬁed to be harbouring beef or dairy QTL (Figs 2 & 3). Using this approach, we sought regions where Angus has been se- lected for alleles that have been selected against in Holstein. Such differences in allelic frequencies, however, may arise because of selection, drift or admixture. Although we can- not completely rule out the possibility of allele frequency differences caused by drift or admixture, the ﬁnding that there is statistically signiﬁcant agreement between 2008 The Authors, Journal compilation 2008 International Society for Animal Genetics, Animal Genetics, 39, 597–605