- Email: [email protected]
Genetic variability of ten Chinese indigenous goats using MHC-linked microsatellite markers
Veterinary Immunology and Immunopathology 167 (2015) 196–199
Contents lists available at ScienceDirect
Veterinary Immunology and Immunopathology journal homepage: www.elsevier.com/locate/vetimm
Short communication
Genetic variability of ten Chinese indigenous goats using MHC-linked microsatellite markers Guang-Xin E a , Yong-Fu Huang a,∗ , Yong-Ju Zhao a , Yue-Hui Ma b , Ri-Su Na a , Jia-Hua Zhang a , Hui-Jiang Gao b , Xin Wu a a College of Animal Science and Technology, Chongqing Key Laboratory of Forage & Herbivore, Chongqing Engineering Research Centre for Herbivores Resource Protection and Utilization, Southwest University, Chongqing 400716, China b Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
a r t i c l e
i n f o
Article history: Received 16 June 2015 Received in revised form 27 July 2015 Accepted 30 July 2015 Keywords: Major Histocompatibility Complex (MHC) Indigenous goats Microsatellite Diversity
a b s t r a c t In this study, the genetic variability of Chinese indigenous goat breeds (Capra hircus) was analyzed using the MHC-associated microsatellite markers BF1, BM1818, BM1258, and DYMS1. To examine genetic variability, the levels of heterozigosity, degrees of inbreeding, and genetic differences among the breeds were analyzed. The mean number of alleles ranged from 5.50 ± 3.70 in Enshi black goats (EB) to 11.50 ± 3.70 in the Jianyang big ear (JE) breed. The mean observed heterozygosity and mean expected heterozygosity varied from 0.25 ± 0.04 in Jining Qing goats (JQ) to 0.54 ± 0.05 in Chuannan black goats (CN) and from 0.49 ± 0.18 in Hechuan white goats (HW) to 0.78 ± 0.05 in JE, respectively. The mean FIS values ranged from 0.23 in HW to 0.51 in JQ. In addition, the genetic variation among populations and geographic location did indicate a correlation of genetic differences with geographic distance, which was revealed by the phylogenetic network. In conclusion, the high variability and population structure among Chinese native goats in the Major Histocompatibility Complex would be caused by co-evolution between MHC alleles and the epidemic history or pathogens in different agro-ecological zones. © 2015 Elsevier B.V. All rights reserved.
1. Introduction
2. Methods and materials
Indigenous goats (Capra hircus), an important domestic animal in many countries, are well-adapted to harsh local environmental conditions (Ayele and Peacock, 2003; FAO, 2008) and did not undergo extensive artificial selection by humans, especially in developing countries around the world. The Major Histocompatibility Complex (MHC) has long been recognized as an immune-regulatory system important in disease susceptibility or resistance. In addition, MHC genes have become one of the most sought-after molecular markers for investigation of adaptive genetic variation in vertebrates (Eizaguirre et al., 2012; Kubinak et al., 2012). In the current study, BF1, BM1818, BM1258, and DYMS1, which are MHC-related microsatellite loci well-characterized in bovines (Salles et al., 2011), were used to analyze the diversity and population structure of ten Chinese indigenous goat populations.
2.1. Animals
∗ Corresponding author. E-mail address: [email protected] (Y.-F. Huang). http://dx.doi.org/10.1016/j.vetimm.2015.07.013 0165-2427/© 2015 Elsevier B.V. All rights reserved.
We genotyped 287 individuals from 10 populations from different geographic locations in the Chinese mainland (Fig. 1; Table 1) at four MHC-related microsatellite loci (Table 2), which were included in a previous study (Salles et al., 2011): BF1, BM1818, BM1258, and DYMS1. 2.2. Data analysis Genetic diversity (expected heterozygosity (HE ), observed heterozygosity (HO ), mean number of alleles (NA ) and the inbreeding coefficient (FIS ) were estimated from allele frequencies using FSTAT 2.9.3.2 (http://www2.unil.ch/popgen/softwares/fstat.htm) and microsatellite Toolkit (http://courses.washington.edu/fish543/ Software.htm). The pairwise difference of populations (FST , Slatkin, 1995) was displayed using Arlequin software 3.5.1.3 (Excoffier and Lischer, 2010). A phylogenetic neighbour-joining tree was derived from Reynold’s genetic distance (Reynolds et al., 1983) using the PHYLIP (Felsenstein, 2005) software package.
2.21
Note: (1) ‘SZ’ is sample size, ‘NL’ is north latitude of sampling location, ‘EL’ is east longitude of sampling location, ‘NA ’ is mean number of allele, ‘HE ’ is expected heterozygosity, ‘HO ’ is observed heterozygosity. (2) Above diagonal: average number of pairwise differences between populations (XY); Diagonal elements: Average number of pairwise differences within population (X).
2.00 0.24 2.82 0.07 0.11 2.43 0.06 0.19 0.25 2.75 0.05 0.00 0.10 0.11 3.00 0.02 0.03 0.01 0.10 0.17 2.17 0.06 0.10 0.01 0.09 0.18 0.32 2.10 0.36 0.34 0.17 0.14 0.30 0.13 0.16 0.13
1.95 0.01 0.07 0.10 0.02 0.09 0.20 0.34
JQ HW EB DZ CN CZ
2.38 0.08 0.23 0.22 0.09 0.04 0.17 0.05 0.09 0.05 0.35 0.25 0.38 0.27 0.23 0.51 0.34 0.38 0.38 0.37 0.61 0.71 0.54 0.53 0.49 0.50 0.78 0.72 0.55 0.59 0.40 0.54 0.34 0.39 0.38 0.25 0.51 0.45 0.34 0.38
HO
2.16 2.06 2.50 3.70 3.30 2.45 3.70 2.65 2.83 4.35 ± ± ± ± ± ± ± ± ± ± 6.00 8.75 7.75 5.50 4.75 5.00 11.50 10.50 6.00 5.75 104◦ 36 23.30 104◦ 37 27.32 105◦ 44 14.97 109◦ 28 49.61 106◦ 16 21.20 116◦ 35 20.19 104◦ 31 38.75 103◦ 05 35.76 94◦ 22 21.49 111◦ 17 55.27 30◦ 43 31.04 28◦ 46 0.95 29◦ 39 26.25 30◦ 16 57.55 29◦ 58 29.98 35◦ 23 48.59 30◦ 23 22.17 28◦ 26 25.99 29◦ 46 48.56 30◦ 43 46.09 22 31 50 24 24 24 30 34 24 24 CZ CN DZ EB HW JQ JE MG TG YW
NA EL NL SZ Code
Chuanzhong goat Chuannan black goat Dazu black Enshi black goat Hechuan white goat Jining Qing goat Jianyang big ear Meigu goat Tibetan goat Yichang white goat
Population
Table 1 Descriptive statistics of 4 MHC-related microsatellite loci in 10 Chinese indigenous sheep population.
± ± ± ± ± ± ± ± ± ±
0.05 0.05 0.03 0.05 0.05 0.04 0.05 0.04 0.05 0.05
HE
± ± ± ± ± ± ± ± ± ±
0.10 0.06 0.13 0.18 0.18 0.10 0.05 0.05 0.16 0.12
FIS
Population
JE
MG
TG
YW
G.-X. E et al. / Veterinary Immunology and Immunopathology 167 (2015) 196–199
197
Fig. 1. Geographic locations of ten native goat breeds in this study.
3. Results and discussion In total, across loci, 74 alleles were found in 10 Chinese native goat breeds, and the mean number of alleles (NA ) ranged from 5.50 ± 3.70 in Enshi black goats (EB) to 11.50 ± 3.70 in Jianyang big ear goats (JE). The expected heterozygosity (HE ) within populations ranged from 0.49 ± 0.18 in Hechuan white goats (HW) to 0.78 ± 0.05 in JE, and the observed heterozygosity (HO ) ranged from 0.25 ± 0.04 in Jining Qing goats (JQ) to 0.54 ± 0.05 in Chuannan black goats (CN); detailed information can be seen in Table 1. The inbreeding coefficient (FIS ) within populations ranged from 0.23 in HW to 0.51 in JQ across the MHC-related microsatellite loci. In total, 14 private alleles distributed across 10 populations and 4 MHC-related microsatellite loci. The frequency of some private alleles within certain population was particularly high. For example, the frequency of a private allele (87 bp) at the locus BM1258 in Tibetan goats was 10.42% (see Supplemental Material I, Table 1a). It is possible to infer that there was a different ancient flow in Tibetan goat in comparison to some other breeds in this study. For other frequencies of alleles in MHC-related markers see Supplemental Material I, Table 1a to Table 1d. In pair-wise difference (FST ) analysis, the largest difference was found between DZ and CN, and the lowest is between MG and JQ. The highest diversity within populations was found in HW (3.00) and the lowest in DZ (1.95) (Table 1). However, the distribution of FST between populations reflected a geographic pattern in general (see Table 1; Fig. 3). The consensus neighbour-joining tree of ten local Chinese goat breeds is seen in Fig. 2. This phylogenetic tree revealed four main clusters, with HW, DZ and CZ breeds in Cluster I, which are three breeds that were bred and sampled in the ChongQing region, JE, CN and MG breeds in Cluster II, which were sampled in the Sichuan province, YW, TG and EB breeds in Cluster III, of which YW and EB were sampled in the Hubei province, and JQ to be a single in Cluster IV, which was sampled from the Shandong province. Such clustering of breeds into four groups highlighted the presence of Table 2 The primer information of four MHC-related microsatellite. Loci
Marker
Tm (◦ C)
Sequence (5 –3 )
BF1
FAM
58
BM1818
HEX
58
BM1258
FAM
56
DYMS1
FAM
52
F-CAACGGTCTGCAACCGAATTACC R-CAATCCGTGGGTTGGAACACAA F-AGCTGGGAATATAACCAAAGG R-AGTGCTTTCAAGGTCCATGC F-GTATGTATTTTTCCCACCCTGC R-GTCAGACATGACTGAGCCTG F-TCCTGGGGATTCCCAATACC R-CATAGAAGTCTTCACTGGTG
Note: Tm is annealing temperature of PCR in each loci.
198
G.-X. E et al. / Veterinary Immunology and Immunopathology 167 (2015) 196–199
Pathogenic-driven selection is more adaptive to explain the high polymorphism in MHC (Kamath and Getz, 2011; Zhang and He, 2013). In addition, many previous studies have revealed that the variability of the Major Histocompatibility Complex was comediated with environment and pathogens (Ejsmond et al., 2010; Sin et al., 2014; Osborne et al., 2015). In the current study, the clustering of breeds into different groups is in accordance with the agro-ecological zone from which they came. This phenomenon would be explained by the different epidemic history around the Chinese mainland. In short, these four MHC-associated markers were polymorphic, with high numbers of alleles, and thus could be useful for future breeding and conservation programmes for goats. In addition, the results indicated a relationship between geographic distance and genetic variation among the Chinese indigenous goat breeds examined in our analysis. Acknowledgements This work was supported by Fundamental Research Funds for the Central Universities (No. SWU114023), the 2013 Innovation Team Building Program in Chongqing universities (KJTD201334). We thank Yuan-Zhi Sun and Chang-Hui Yu (Beijing Tianyi Huiyuan Bioscience & Technology Inc, Beijing, 100070, China) for technology helping. Appendix A. Supplementary data
Fig. 2. Neighbour-joining network derived by Reynold’s genetic distance using MHC-related microsatellites.
Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.vetimm.2015.07. 013 References
Fig. 3. Matrix of Slatkin linearized FSTs as t/M = FST /(1 − FST ) between ten native goat breeds and divergence in each breed. Note: The above diagonal is the average number of pairwise differences between populations (XY); diagonal elements are the average number of pairwise differences within population (X).
clear genetic separation between breeds based on the sampling geographic location. MHC allelic diversity has challenged evolutionary biologists for explanation. Balancing selection is any natural selection process whereby no single allele is absolutely most fit, such as frequencydependent selection and heterozygote advantage (e.g., Niskanen et al., 2014; Huchard et al., 2010; O’Connor et al., 2010; Worley et al., 2010). However, recent studies suggest that a high number of alleles are implausible via heterozygote advantage alone.
Ayele, Z., Peacock, C., 2003. Improving access to and consumption of animal source foods in rural households: the experiences of a women-focused goat development program in the highlands of Ethiopia. J. Nutr. 133 (11 Suppl 2), 3981S–3986S. Eizaguirre, C., Lenz, T.L., Kalbe, M., Milinski, M., 2012. Rapid and adaptive evolution of MHC genes under parasite selection in experimental vertebrate populations. Nat. Commun. 3, http://dx.doi.org/10.1038/ncomms1632 Ejsmond, M.J., Babik, W., Radwan, J., 2010. MHC allele frequency distributions under parasite-driven selection: a simulation model. BMC Evol. Biol. 10, 332, http://dx.doi.org/10.1186/1471-2148-10-332 Excoffier, L., Lischer, H.E.L., 2010. Arlequin 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567. Felsenstein J., 2005. PHYLIP (Phylogeny Inference Package) version 3.6 Distributed by the author. Department of Genome Sciences, University of Washington, Seattle. Available: http://evolution.genetics.washington.edu/phylip/getme. html (accessed 24.03.2014). Food and Agriculture Organization (FAO), 2008. The State of the World’s Animal Genetic Resources for Food and Agriculture. FAO, Rome. Huchard, E., Knapp, L.A., Wang, J., Raymond, M., Cowlishaw, G., 2010. MHC, mate choice and heterozygote advantage in a wild social primate. Mol. Ecol. 19 (12), 2545–2561, http://dx.doi.org/10.1111/j.1365-294X.2010.04644.x (Epub 2010 May 13). Kamath, P.L., Getz, W.M., 2011. Adaptive molecular evolution of the Major Histocompatibility Complex genes, DRA and DQA, in the genus Equus. BMC Evol. Biol. 11, 128. Kubinak, J.L., Ruff, J.S., Hyzer, C.W., Slev, P.R., Potts, W.K., 2012. Experimental viral evolution to specific host MHC genotypes reveals fitness and virulence trade-offs in alternative MHC types. PNAS 109, 3422. Niskanen, A.K., Kennedy, L.J., Ruokonen, M., Kojola, I., Lohi, H., Isomursu, M., Jansson, E., Pyhäjärvi, T., Aspi, J., 2014. Balancing selection and heterozygote advantage in major histocompatibility complex loci of the bottlenecked Finnish wolf population. Mol. Ecol. 23 (4), 875–889, http://dx.doi.org/10.1111/ mec.12647 O’Connor, S.L., Lhost, J.J., Becker, E.A., Detmer, A.M., Johnson, R.C., Macnair, C.E., Wiseman, R.W., Karl, J.A., Greene, J.M., Burwitz, B.J., Bimber, B.N., Lank, S.M., Tuscher, J.J., Mee, E.T., Rose, N.J., Desrosiers, R.C., Hughes, A.L., Friedrich, T.C., Carrington, M., O’Connor, D.H., 2010. MHC heterozygote advantage in simian immunodeficiency virus-infected Mauritian cynomolgus macaques. Sci. Transl. Med. 2 (22), 22ra18, http://dx.doi.org/10.1126/scitranslmed.3000524
G.-X. E et al. / Veterinary Immunology and Immunopathology 167 (2015) 196–199 Osborne, A.J., Pearson, J., Negro, S.S., Chilvers, B.L., Kennedy, M.A., Gemmell, N.J., 2015. Heterozygote advantage at MHC DRB may influence response to infectious disease epizootics. Mol. Ecol. 24 (7), 1419–1432, http://dx.doi.org/ 10.1111/mec.13128 Reynolds, J., Weir, B.S., Cockerham, C.C., 1983. Estimation of the coancestry coefficient: basis for a short-term genetic distance. Genetics 105 (3), 767–779. Salles, P.de.A., Santos, S.C., Rondina, D., Weller, M., 2011. Genetic variability of six indigenous goat breeds using major histocompatibility complex-associated microsatellite markers. J. Vet. Sci. 12 (2), 127–132, http://dx.doi.org/10.4142/ jvs.2011, 12.2.127 Sin, Y.W., Annavi, G., Dugdale, H.L., Newman, C., Burke, T., MacDonald, D.W., 2014. Pathogen burden, co-infection and major histocompatibility complex
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variability in the European badger (Meles meles). Mol. Ecol. 23 (20), 5072–5088, http://dx.doi.org/10.1111/mec.12917 Slatkin, M., 1995. A measure of population subdivision based on microsatellite allele frequencies. Genetics 139, 457–462. Worley, K., Collet, J., Spurgin, L.G., Cornwallis, C., Pizzari, T., Richardson, D.S., 2010. MHC heterozygosity and survival in red junglefowl. Mol. Ecol. 19 (15), 3064–3075, http://dx.doi.org/10.1111/j.1365-294X.2010.04724.x Zhang, M., He, H., 2013. Parasite-mediated selection of major histocompatibility complex variability in wild brandt’s voles (Lasiopodomys brandtii) from Inner Mongolia, China. BMC Evol. Biol. 13, 149, http://dx.doi.org/10.1186/14712148-13-149.
Contents lists available at ScienceDirect
Veterinary Immunology and Immunopathology journal homepage: www.elsevier.com/locate/vetimm
Short communication
Genetic variability of ten Chinese indigenous goats using MHC-linked microsatellite markers Guang-Xin E a , Yong-Fu Huang a,∗ , Yong-Ju Zhao a , Yue-Hui Ma b , Ri-Su Na a , Jia-Hua Zhang a , Hui-Jiang Gao b , Xin Wu a a College of Animal Science and Technology, Chongqing Key Laboratory of Forage & Herbivore, Chongqing Engineering Research Centre for Herbivores Resource Protection and Utilization, Southwest University, Chongqing 400716, China b Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
a r t i c l e
i n f o
Article history: Received 16 June 2015 Received in revised form 27 July 2015 Accepted 30 July 2015 Keywords: Major Histocompatibility Complex (MHC) Indigenous goats Microsatellite Diversity
a b s t r a c t In this study, the genetic variability of Chinese indigenous goat breeds (Capra hircus) was analyzed using the MHC-associated microsatellite markers BF1, BM1818, BM1258, and DYMS1. To examine genetic variability, the levels of heterozigosity, degrees of inbreeding, and genetic differences among the breeds were analyzed. The mean number of alleles ranged from 5.50 ± 3.70 in Enshi black goats (EB) to 11.50 ± 3.70 in the Jianyang big ear (JE) breed. The mean observed heterozygosity and mean expected heterozygosity varied from 0.25 ± 0.04 in Jining Qing goats (JQ) to 0.54 ± 0.05 in Chuannan black goats (CN) and from 0.49 ± 0.18 in Hechuan white goats (HW) to 0.78 ± 0.05 in JE, respectively. The mean FIS values ranged from 0.23 in HW to 0.51 in JQ. In addition, the genetic variation among populations and geographic location did indicate a correlation of genetic differences with geographic distance, which was revealed by the phylogenetic network. In conclusion, the high variability and population structure among Chinese native goats in the Major Histocompatibility Complex would be caused by co-evolution between MHC alleles and the epidemic history or pathogens in different agro-ecological zones. © 2015 Elsevier B.V. All rights reserved.
1. Introduction
2. Methods and materials
Indigenous goats (Capra hircus), an important domestic animal in many countries, are well-adapted to harsh local environmental conditions (Ayele and Peacock, 2003; FAO, 2008) and did not undergo extensive artificial selection by humans, especially in developing countries around the world. The Major Histocompatibility Complex (MHC) has long been recognized as an immune-regulatory system important in disease susceptibility or resistance. In addition, MHC genes have become one of the most sought-after molecular markers for investigation of adaptive genetic variation in vertebrates (Eizaguirre et al., 2012; Kubinak et al., 2012). In the current study, BF1, BM1818, BM1258, and DYMS1, which are MHC-related microsatellite loci well-characterized in bovines (Salles et al., 2011), were used to analyze the diversity and population structure of ten Chinese indigenous goat populations.
2.1. Animals
∗ Corresponding author. E-mail address: [email protected] (Y.-F. Huang). http://dx.doi.org/10.1016/j.vetimm.2015.07.013 0165-2427/© 2015 Elsevier B.V. All rights reserved.
We genotyped 287 individuals from 10 populations from different geographic locations in the Chinese mainland (Fig. 1; Table 1) at four MHC-related microsatellite loci (Table 2), which were included in a previous study (Salles et al., 2011): BF1, BM1818, BM1258, and DYMS1. 2.2. Data analysis Genetic diversity (expected heterozygosity (HE ), observed heterozygosity (HO ), mean number of alleles (NA ) and the inbreeding coefficient (FIS ) were estimated from allele frequencies using FSTAT 2.9.3.2 (http://www2.unil.ch/popgen/softwares/fstat.htm) and microsatellite Toolkit (http://courses.washington.edu/fish543/ Software.htm). The pairwise difference of populations (FST , Slatkin, 1995) was displayed using Arlequin software 3.5.1.3 (Excoffier and Lischer, 2010). A phylogenetic neighbour-joining tree was derived from Reynold’s genetic distance (Reynolds et al., 1983) using the PHYLIP (Felsenstein, 2005) software package.
2.21
Note: (1) ‘SZ’ is sample size, ‘NL’ is north latitude of sampling location, ‘EL’ is east longitude of sampling location, ‘NA ’ is mean number of allele, ‘HE ’ is expected heterozygosity, ‘HO ’ is observed heterozygosity. (2) Above diagonal: average number of pairwise differences between populations (XY); Diagonal elements: Average number of pairwise differences within population (X).
2.00 0.24 2.82 0.07 0.11 2.43 0.06 0.19 0.25 2.75 0.05 0.00 0.10 0.11 3.00 0.02 0.03 0.01 0.10 0.17 2.17 0.06 0.10 0.01 0.09 0.18 0.32 2.10 0.36 0.34 0.17 0.14 0.30 0.13 0.16 0.13
1.95 0.01 0.07 0.10 0.02 0.09 0.20 0.34
JQ HW EB DZ CN CZ
2.38 0.08 0.23 0.22 0.09 0.04 0.17 0.05 0.09 0.05 0.35 0.25 0.38 0.27 0.23 0.51 0.34 0.38 0.38 0.37 0.61 0.71 0.54 0.53 0.49 0.50 0.78 0.72 0.55 0.59 0.40 0.54 0.34 0.39 0.38 0.25 0.51 0.45 0.34 0.38
HO
2.16 2.06 2.50 3.70 3.30 2.45 3.70 2.65 2.83 4.35 ± ± ± ± ± ± ± ± ± ± 6.00 8.75 7.75 5.50 4.75 5.00 11.50 10.50 6.00 5.75 104◦ 36 23.30 104◦ 37 27.32 105◦ 44 14.97 109◦ 28 49.61 106◦ 16 21.20 116◦ 35 20.19 104◦ 31 38.75 103◦ 05 35.76 94◦ 22 21.49 111◦ 17 55.27 30◦ 43 31.04 28◦ 46 0.95 29◦ 39 26.25 30◦ 16 57.55 29◦ 58 29.98 35◦ 23 48.59 30◦ 23 22.17 28◦ 26 25.99 29◦ 46 48.56 30◦ 43 46.09 22 31 50 24 24 24 30 34 24 24 CZ CN DZ EB HW JQ JE MG TG YW
NA EL NL SZ Code
Chuanzhong goat Chuannan black goat Dazu black Enshi black goat Hechuan white goat Jining Qing goat Jianyang big ear Meigu goat Tibetan goat Yichang white goat
Population
Table 1 Descriptive statistics of 4 MHC-related microsatellite loci in 10 Chinese indigenous sheep population.
± ± ± ± ± ± ± ± ± ±
0.05 0.05 0.03 0.05 0.05 0.04 0.05 0.04 0.05 0.05
HE
± ± ± ± ± ± ± ± ± ±
0.10 0.06 0.13 0.18 0.18 0.10 0.05 0.05 0.16 0.12
FIS
Population
JE
MG
TG
YW
G.-X. E et al. / Veterinary Immunology and Immunopathology 167 (2015) 196–199
197
Fig. 1. Geographic locations of ten native goat breeds in this study.
3. Results and discussion In total, across loci, 74 alleles were found in 10 Chinese native goat breeds, and the mean number of alleles (NA ) ranged from 5.50 ± 3.70 in Enshi black goats (EB) to 11.50 ± 3.70 in Jianyang big ear goats (JE). The expected heterozygosity (HE ) within populations ranged from 0.49 ± 0.18 in Hechuan white goats (HW) to 0.78 ± 0.05 in JE, and the observed heterozygosity (HO ) ranged from 0.25 ± 0.04 in Jining Qing goats (JQ) to 0.54 ± 0.05 in Chuannan black goats (CN); detailed information can be seen in Table 1. The inbreeding coefficient (FIS ) within populations ranged from 0.23 in HW to 0.51 in JQ across the MHC-related microsatellite loci. In total, 14 private alleles distributed across 10 populations and 4 MHC-related microsatellite loci. The frequency of some private alleles within certain population was particularly high. For example, the frequency of a private allele (87 bp) at the locus BM1258 in Tibetan goats was 10.42% (see Supplemental Material I, Table 1a). It is possible to infer that there was a different ancient flow in Tibetan goat in comparison to some other breeds in this study. For other frequencies of alleles in MHC-related markers see Supplemental Material I, Table 1a to Table 1d. In pair-wise difference (FST ) analysis, the largest difference was found between DZ and CN, and the lowest is between MG and JQ. The highest diversity within populations was found in HW (3.00) and the lowest in DZ (1.95) (Table 1). However, the distribution of FST between populations reflected a geographic pattern in general (see Table 1; Fig. 3). The consensus neighbour-joining tree of ten local Chinese goat breeds is seen in Fig. 2. This phylogenetic tree revealed four main clusters, with HW, DZ and CZ breeds in Cluster I, which are three breeds that were bred and sampled in the ChongQing region, JE, CN and MG breeds in Cluster II, which were sampled in the Sichuan province, YW, TG and EB breeds in Cluster III, of which YW and EB were sampled in the Hubei province, and JQ to be a single in Cluster IV, which was sampled from the Shandong province. Such clustering of breeds into four groups highlighted the presence of Table 2 The primer information of four MHC-related microsatellite. Loci
Marker
Tm (◦ C)
Sequence (5 –3 )
BF1
FAM
58
BM1818
HEX
58
BM1258
FAM
56
DYMS1
FAM
52
F-CAACGGTCTGCAACCGAATTACC R-CAATCCGTGGGTTGGAACACAA F-AGCTGGGAATATAACCAAAGG R-AGTGCTTTCAAGGTCCATGC F-GTATGTATTTTTCCCACCCTGC R-GTCAGACATGACTGAGCCTG F-TCCTGGGGATTCCCAATACC R-CATAGAAGTCTTCACTGGTG
Note: Tm is annealing temperature of PCR in each loci.
198
G.-X. E et al. / Veterinary Immunology and Immunopathology 167 (2015) 196–199
Pathogenic-driven selection is more adaptive to explain the high polymorphism in MHC (Kamath and Getz, 2011; Zhang and He, 2013). In addition, many previous studies have revealed that the variability of the Major Histocompatibility Complex was comediated with environment and pathogens (Ejsmond et al., 2010; Sin et al., 2014; Osborne et al., 2015). In the current study, the clustering of breeds into different groups is in accordance with the agro-ecological zone from which they came. This phenomenon would be explained by the different epidemic history around the Chinese mainland. In short, these four MHC-associated markers were polymorphic, with high numbers of alleles, and thus could be useful for future breeding and conservation programmes for goats. In addition, the results indicated a relationship between geographic distance and genetic variation among the Chinese indigenous goat breeds examined in our analysis. Acknowledgements This work was supported by Fundamental Research Funds for the Central Universities (No. SWU114023), the 2013 Innovation Team Building Program in Chongqing universities (KJTD201334). We thank Yuan-Zhi Sun and Chang-Hui Yu (Beijing Tianyi Huiyuan Bioscience & Technology Inc, Beijing, 100070, China) for technology helping. Appendix A. Supplementary data
Fig. 2. Neighbour-joining network derived by Reynold’s genetic distance using MHC-related microsatellites.
Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.vetimm.2015.07. 013 References
Fig. 3. Matrix of Slatkin linearized FSTs as t/M = FST /(1 − FST ) between ten native goat breeds and divergence in each breed. Note: The above diagonal is the average number of pairwise differences between populations (XY); diagonal elements are the average number of pairwise differences within population (X).
clear genetic separation between breeds based on the sampling geographic location. MHC allelic diversity has challenged evolutionary biologists for explanation. Balancing selection is any natural selection process whereby no single allele is absolutely most fit, such as frequencydependent selection and heterozygote advantage (e.g., Niskanen et al., 2014; Huchard et al., 2010; O’Connor et al., 2010; Worley et al., 2010). However, recent studies suggest that a high number of alleles are implausible via heterozygote advantage alone.
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