| Literature DB >> 18067665 |
Andréa A Egito1, Samuel R Paiva, Maria do Socorro M Albuquerque, Arthur S Mariante, Leonardo D Almeida, Silvia R Castro, Dario Grattapaglia.
Abstract
BACKGROUND: Brazil holds the largest commercial cattle populations worldwide. Local cattle breeds can be classified according to their origin, as exotic or Creole. Exotic breeds imported in the last 100 years, both zebuine and taurine, currently make up the bulk of the intensively managed populations. Locally adapted Creole breeds, originated from cattle introduced by the European conquerors derive from natural selection and events of breed admixture. While historical knowledge exists on the Brazilian Creole breeds very little is known on their genetic composition. The objective of this study was to assess the levels of genetic diversity, phylogenetic relationships and patterns of taurine/zebuine admixture among ten cattle breeds raised in Brazil.Entities:
Mesh:
Year: 2007 PMID: 18067665 PMCID: PMC2228320 DOI: 10.1186/1471-2156-8-83
Source DB: PubMed Journal: BMC Genet ISSN: 1471-2156 Impact factor: 2.797
Description of the ten Brazilian bovine breeds studied.
| Breed name | Subspecies | Code | # of herds | # males | # females | Total |
| Caracu | CAR | 8 | 28 | 49 | 77 | |
| Crioulo Lageano | CRL | 1 | 17 | 83 | 100 | |
| Curraleiro | CUR | 7 | 43 | 56 | 99 | |
| Mocho Nacional | MON | 4 | 27 | 70 | 97 | |
| Pantaneiro | PAN | 2 | 32 | 64 | 96 | |
| Holstein | HOL | 5 | 25 | 75 | 100 | |
| Jersey | JER | 7 | 12 | 42 | 54 | |
| Gyr | GYR | 6 | 22 | 76 | 98 | |
| Guzerat | GUZ | 5 | 24 | 76 | 100 | |
| Nellore | NEL | 7 | 42 | 52 | 94 |
Descriptive statistics of the 22 microsatellite marker loci. Statistics are reported for each Bos subspecies separately and overall, consolidating all breeds and all animals: # alleles (N), observed heterozygosity (Ho), expected heterozygosity (He), polymorphism information content (PIC), Wright F-statistics (FIS, FIT, FST); breed differentiation detected by the marker locus under the step-wise mutation model (RST); statistical significance * = p < 0.05; ** = p < 0.01.
| 11 | 0.463 | 0.597 | 0.561 | 0.224** | 0.233** | 0.077** | 0.102 | 12 | 0.788 | 0.830 | 0.806 | 0.050 | 0.065 | 0.047** | 0.028 | 12 | 0.569 | 0.705 | 0.673 | 0.193** | 0.202** | 0.106** | 0.119 | |
| 13 | 0.790 | 0.888 | 0.877 | 0.110** | 0.115** | 0.038** | 0.075 | 13 | 0.756 | 0.897 | 0.886 | 0.157** | 0.163** | 0.021 | 0.035 | 13 | 0.779 | 0.903 | 0.894 | 0.137** | 0.141** | 0.044** | 0.056 | |
| 8 | 0.643 | 0.680 | 0.622 | 0.055 | 0.066 | 0.083* | 0.109 | 7 | 0.513 | 0.582 | 0.541 | 0.119* | 0.126** | 0.023 | -0.002 | 8 | 0.602 | 0.732 | 0.683 | 0.177** | 0.192** | 0.177** | 0.397 | |
| 17 | 0.771 | 0.833 | 0.811 | 0.074** | 0.084** | 0.067* | 0.097 | 14 | 0.781 | 0.822 | 0.797 | 0.049 | 0.056* | 0.022** | 0.066 | 17 | 0.774 | 0.843 | 0.825 | 0.082** | 0.089** | 0.067** | 0.154 | |
| 9 | 0.442 | 0.601 | 0.565 | 0.265** | 0.277** | 0.111 | 0.124 | 9 | 0.701 | 0.823 | 0.802 | 0.149** | 0.156** | 0.025** | 0.019 | 9 | 0.523 | 0.730 | 0.703 | 0.284** | 0.295** | 0.156** | 0.142 | |
| 13 | 0.652 | 0.895 | 0.885 | 0.272** | 0.277** | 0.045 | 0.081 | 13 | 0.250 | 0.871 | 0.856 | 0.713** | 0.714** | 0.010 | 0.052 | 13 | 0.536 | 0.898 | 0.889 | 0.403** | 0.406** | 0.044** | 0.136 | |
| 10 | 0.739 | 0.796 | 0.771 | 0.071* | 0.078** | 0.057* | 0.070 | 10 | 0.299 | 0.389 | 0.377 | 0.233** | 0.238 | 0.021** | -0.002 | 10 | 0.601 | 0.772 | 0.742 | 0.222** | 0.236** | 0.187** | 0.267 | |
| 11 | 0.548 | 0.719 | 0.674 | 0.238** | 0.250** | 0.102** | 0.173 | 11 | 0.732 | 0.836 | 0.813 | 0.125** | 0.142** | 0.054** | 0.073 | 11 | 0.601 | 0.774 | 0.742 | 0.225** | 0.233** | 0.108** | 0.137 | |
| 10 | 0.738 | 0.759 | 0.723 | 0.028 | 0.029 | 0.012* | 0.009 | 10 | 0.675 | 0.778 | 0.747 | 0.133** | 0.139** | 0.022 | 0.082 | 10 | 0.718 | 0.819 | 0.795 | 0.124** | 0.132** | 0.086** | 0.045 | |
| 15 | 0.794 | 0.877 | 0.864 | 0.094** | 0.098** | 0.032** | 0.028 | 14 | 0.798 | 0.826 | 0.804 | 0.034 | 0.041* | 0.021** | 0.048 | 15 | 0.795 | 0.884 | 0.873 | 0.101** | 0.106** | 0.055** | 0.090 | |
| 15 | 0.686 | 0.822 | 0.798 | 0.166** | 0.177** | 0.089** | 0.057 | 15 | 0.754 | 0.875 | 0.862 | 0.139** | 0.151** | 0.043** | 0.006 | 15 | 0.707 | 0.864 | 0.849 | 0.181** | 0.190** | 0.097** | 0.251 | |
| 20 | 0.782 | 0.858 | 0.844 | 0.088** | 0.096** | 0.059* | 0.018 | 20 | 0.774 | 0.874 | 0.862 | 0.114** | 0.126** | 0.042** | 0.131 | 22 | 0.780 | 0.900 | 0.892 | 0.134** | 0.142** | 0.092** | 0.289 | |
| 11 | 0.793 | 0.855 | 0.841 | 0.072** | 0.080** | 0.054** | 0.068 | 10 | 0.734 | 0.841 | 0.819 | 0.128** | 0.140* | 0.044 | 0.022 | 11 | 0.774 | 0.865 | 0.852 | 0.105** | 0.111** | 0.063** | 0.120 | |
| 9 | 0.695 | 0.768 | 0.734 | 0.094** | 0.105** | 0.080** | 0.123 | 6 | 0.607 | 0.697 | 0.638 | 0.129** | 0.196 | 0.230 | 0.054 | 9 | 0.667 | 0.83 | 0.808 | 0.197** | 0.214** | 0.208** | 0.547 | |
| 9 | 0.550 | 0.609 | 0.580 | 0.096** | 0.104** | 0.059* | 0.107 | 7 | 0.638 | 0.700 | 0.653 | 0.089 | 0.112 | 0.077 | 0.027 | 9 | 0.578 | 0.663 | 0.639 | 0.128** | 0.136** | 0.096** | 0.102 | |
| 23 | 0.817 | 0.927 | 0.921 | 0.119** | 0.123** | 0.033** | 0.032 | 19 | 0.815 | 0.886 | 0.875 | 0.080** | 0.085* | 0.016 | -0.002 | 23 | 0.816 | 0.925 | 0.920 | 0.118** | 0.121** | 0.038** | 0.122 | |
| 12 | 0.779 | 0.855 | 0.837 | 0.089** | 0.096* | 0.051* | 0.015 | 12 | 0.529 | 0.664 | 0.643 | 0.203** | 0.208** | 0.020 | 0.015 | 12 | 0.699 | 0.863 | 0.848 | 0.190** | 0.200** | 0.124** | 0.346 | |
| 14 | 0.755 | 0.870 | 0.857 | 0.133** | 0.145** | 0.090** | 0.187 | 13 | 0.384 | 0.400 | 0.385 | 0.042 | 0.045 | 0.009 | 0.044 | 14 | 0.635 | 0.794 | 0.778 | 0.201** | 0.215** | 0.169** | 0.370 | |
| 20 | 0.724 | 0.870 | 0.860 | 0.168** | 0.175** | 0.053* | 0.044 | 21 | 0.646 | 0.787 | 0.775 | 0.180** | 0.188** | 0.027 | 0.003 | 21 | 0.700 | 0.850 | 0.840 | 0.177** | 0.181** | 0.048** | 0.034 | |
| 13 | 0.739 | 0.785 | 0.761 | 0.059* | 0.068** | 0.066** | 0.027 | 12 | 0.738 | 0.781 | 0.762 | 0.056 | 0.064 | 0.025 | -0.000 | 13 | 0.738 | 0.794 | 0.776 | 0.071** | 0.077** | 0.063** | 0.018 | |
| 11 | 0.705 | 0.787 | 0.765 | 0.105** | 0.114** | 0.073** | 0.058 | 9 | 0.602 | 0.593 | 0.536 | -0.015 | 0.002 | 0.051 | 0.045 | 11 | 0.672 | 0.770 | 0.739 | 0.127** | 0.138** | 0.122** | 0.085 | |
| 12 | 0.696 | 0.790 | 0.759 | 0.119** | 0.125** | 0.040** | 0.047 | 12 | 0.693 | 0.710 | 0.664 | 0.024 | 0.031 | 0.021 | 0.015 | 12 | 0.695 | 0.781 | 0.749 | 0.109** | 0.114** | 0.052** | 0.044 | |
| 13 | 0.6955 | 0.793 | 0.769 | 0.123** | 0.131** | 0.061** | 0.0606 | 12,23 | 0.6458 | 0.748 | 0.723 | 0.137** | 0.149** | 0.040** | 0.0549 | 13.18 | 0.680 | 0.816 | 0.796 | 0.167** | 0.176** | 0.098** | 0.1861 | |
Summary statistics of population genetic parameters for the ten studied breeds. Estimates were obtained averaging over all 22 microsatellites: number of individuals (N); allelic richness, i.e. mean number of alleles/locus (AR); observed heterozygosity (Ho); expected heterozygosity (He); number of Hardy-Weinberg equilibrium deviated loci at p < 0.001 (#HWE); average proportion of shared alleles among animals within breed (APSA) with its standard deviation (SD); * = p < 0.05; ** = p < 0.01.
| 77 | 7.822 | 0.6802 (0.0115) | 0.7151 (0.0310) | 0.0491* | 3 | 0.3839 (0.0780) | |
| 100 | 9.067 | 0.7102 (0.0098) | 0.7625 (0.0292) | 0.0682** | 3 | 0.3244 (0.0784) | |
| 99 | 8.838 | 0.6702 (0.0103) | 0.7435 (0.0275) | 0.0948** | 5 | 0.3437 (0.0831) | |
| 97 | 8.773 | 0.7409 (0.0097) | 0.7763 (0.0225) | 0.0454* | 1 | 0.3213 (0.0791) | |
| 96 | 9.003 | 0.7229 (0.0100) | 0.7839 (0.0184) | 0.0775** | 4 | 0.3051 (0.0822) | |
| 100 | 8.175 | 0.6847 (0.0103) | 0.7406 (0.0232) | 0.0755** | 6 | 0.3574 (0.0793) | |
| 54 | 8.061 | 0.6316 (0.0146) | 0.7142 (0.0314) | 0.1210** | 4 | 0.3686 (0.0918) | |
| 94 | 8.375 | 0.6454 (0.0109) | 0.7220 (0.0318) | 0.0957** | 6 | 0.3711 (0.0771) | |
| 98 | 8.633 | 0.6357 (0.0108) | 0.7235 (0.0326) | 0.1196** | 5 | 0.3638 (0.0786) | |
| 100 | 8.751 | 0.6542 (0.0104) | 0.7384 (0.0330) | 0.1132** | 6 | 0.3469 (0.0763) |
Partitioning of genetic variation at different levels among and within the 10 cattle breeds. Microsatellite marker variation was partitioned by an Analysis of Molecular Variance (AMOVA) under different proposed structures based on subspecies and historical information; Fst values correspond to the AMOVA among population variance; *p < 0.001.
| Local breeds (Creole) | Among populations | 4 | Fst = 0.04429* |
| Within populations | 933 | ||
| All taurine breeds | Among populations | 6 | Fst = 0.06202* |
| Within populations | 1239 | ||
| Specialized taurine breeds | Among populations | 1 | Fst = 0.08309* |
| Within populations | 306 | ||
| Zebuine breeds | Among populations | 2 | Fst = 0.04959* |
| Within populations | 581 | ||
| Zebuine and taurine specialized breeds | Among populations | 4 | Fst = 0.16878* |
| Within populations | 887 | ||
| Among all ten breeds | Among populations | 9 | Fst = 0.11875* |
| Within populations | 1820 | ||
| Taurine vs Zebuine | Among populations | 1 | Fst = 0.13428* |
| Within populations | 1828 | ||
| Specialized taurine vs Creole vs zebuine | Among populations | 2 | Fst = 0.11777* |
| Within populations | 1827 |
Pairwise estimates of genetic differentiation and genetic distance among all ten Brazilian cattle breeds. FST estimates above diagonal and Nei genetic distance (DA) below diagonal. All estimates of FST were found significant (p < 0.01).
| 0.084 | 0.068 | 0.178 | 0.193 | 0.105 | 0.118 | 0.047 | 0.185 | 0.062 | ||
| 0.153 | 0.045 | 0.103 | 0.117 | 0.075 | 0.103 | 0.034 | 0.120 | 0.042 | ||
| 0.124 | 0.099 | 0.141 | 0.157 | 0.079 | 0.095 | 0.041 | 0.157 | 0.036 | ||
| 0.326 | 0.180 | 0.220 | 0.033 | 0.190 | 0.210 | 0.125 | 0.051 | 0.106 | ||
| 0.330 | 0.185 | 0.232 | 0.086 | 0.197 | 0.216 | 0.137 | 0.048 | 0.122 | ||
| 0.185 | 0.153 | 0.175 | 0.343 | 0.345 | 0.083 | 0.059 | 0.197 | 0.077 | ||
| 0.209 | 0.191 | 0.194 | 0.368 | 0.377 | 0.156 | 0.076 | 0.215 | 0.081 | ||
| 0.100 | 0.086 | 0.105 | 0.238 | 0.254 | 0.147 | 0.168 | 0.138 | 0.036 | ||
| 0.346 | 0.210 | 0.263 | 0.108 | 0.103 | 0.376 | 0.382 | 0.275 | 0.125 | ||
| 0.133 | 0.088 | 0.084 | 0.194 | 0.199 | 0.179 | 0.175 | 0.088 | 0.232 |
Figure 1Genetic relationship among ten Brazilian cattle breeds. (a) UPGMA dendrogram and (b) Neighbor-Net graph of genetic relationship among the ten cattle breeds studied based on DA genetic distances (Nei, 1983) estimated with 22 microsatellites. Number on the nodes in UPGMA dendrogram are bootstrap values of 10,000 replications.
Figure 2Dendrogram of genetic relationship among all 915 bovine animals. Neighbor-joining tree based on the pairwise genetic distances between all animals estimated by the logarithm of the proportions of shared alleles. Each tip represents a single animal and breeds are distinguished by different colors according to the legend.
Figure 3Clustering assignment of the ten Brazilian bovine breeds obtained by STRUCTURE analyses. Each of the 915 animals is represented by a thin vertical line that is divided into segments whose size and color correspond to the relative proportion of the animal genome corresponding to a particular cluster. Breeds are separated by thin black lines. Panels with K = 2 inferred clusters, taurine (red) and zebuine (green) breeds are discriminated; with K = 3, taurine Creole breeds of Iberian origin (blue) are further separated from the specialized taurine breeds (red) and zebuine (green); with K = 10 inferred clusters corresponding to the ten breeds, complex breed admixture patterns can be visualized.