HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Abstract Full Text (PDF) _Erratum_ A correction has been published Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Web of Science (2) Citing Articles via Google Scholar Google Scholar Articles by Tadano, R. Articles by Tsudzuki, M. Search for Related Content PubMed Articles by Tadano, R. Articles by Tsudzuki, M.

Assessing Genetic Diversity and Population Structure for Commercial Chicken Lines Based on Forty Microsatellite Analyses

R. Tadano^*, M. Nishibori^*, N. Nagasaka and M. Tsudzuki^*,1

^* Laboratory of Animal Breeding and Genetics, Graduate School of Biosphere Science, Hiroshima University, Higashi-Hiroshima, Hiroshima 739-8528, Japan; and Kochi Prefectural Livestock Experimental Station, Sakawa, Kochi 789-1233, Japan

¹ Corresponding author: tsudzuki{at}hiroshima-u.ac.jp

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 
The aims of the current study were to assess genetic diversity, conduct genetic characterization, and evaluate usefulness of an individual assignment test for 12 commercial chicken lines using 40 microsatellite markers. A total of 268 distinct alleles were observed across the 12 lines, and 42 of the 268 alleles (15.7%) were unique to only 1 line. Mean observed heterozygosity within a line ranged from 0.295 to 0.664, and the highest value was obtained from 1 of the White Plymouth Rock lines. Significant deviations from the Hardy-Weinberg equilibrium were observed at several locus-line combinations, showing excess of heterozygotes in many cases. As a whole, genetic differences among the lines estimated by the fixation index were high at 29.8%, whereas higher genetic similarity was observed among White Leghorn lines despite their different breeding histories. Assignment test could correctly allocate individuals at the line level to their origins, with a high accuracy (96.6%). Individual-based genetic characterization would be a usable step to conserve chicken genetic resources. Here, guidelines for future breeding and management of these lines by the poultry industry are provided.

Key Words: chicken line • genetic diversity • genetic structure • individual assignment • microsatellite

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 
The microsatellite marker is extensively used for assessing genetic structure, diversity, and relationships because of many advantages such as being numerous and ubiquitous throughout the genome, showing a higher degree of polymorphisms, and codominant inheritance (Tautz, 1989). Especially, high degree of polymorphisms is considered to be greatly useful for assessing genetic diversity and relationships among closely related livestock breeds (FAO, 1998). As for conservation of livestock genetic resources, if there are limitations of costs or breeding places, the population may be maintained as a core collection, which possesses as much genetic variability as possible with appropriate population size. To develop the core collection, exact genetic evaluation for the population would be required by molecular markers. It would lead to a correct management of the population as a genetic resource.

To date, several investigations with chickens, using microsatellite markers, have been carried out to assess genetic diversity and genetic relationships in and among various populations of indigenous and commercial chickens including Jungle Fowl (Takahashi et al., 1998; Vanhala et al., 1998; Zhou and Lamont, 1999; Wimmers et al., 2000; Romanov and Weigend, 2001; Zhang et al., 2002; Hillel et al., 2003; Osman et al., 2006; Tadano et al., 2007). Hillel et al. (2003) reported for commercial chicken lines that layer lines generally possessed slightly less variability than broilers. Among layer lines, brown egg layers were the most polymorphic, and White Leghorn lines were the least polymorphic. Furthermore, higher variability in broiler lines was also generally observed in other investigations (Vanhala et al., 1998; Zhang et al., 2002).

The interesting result for clustering and assigning individuals to source population was reported by Rosenberg et al. (2001). They genotyped at 27 microsatellite loci for a total 600 individuals belonging to 20 chicken breeds (30 individuals genotyped in each breed) and discussed relations between clustering success rate and the number of markers used or the number of individuals per breed used. When using all 27 markers, around 98% clustering success rate was obtained. They concluded that at least 12 to 15 highly variable markers should be genotyped in at least 15 to 20 individuals per hypothesized population. Moreover, they mentioned that high expected heterozygosity, number of alleles, and fixation index (F_ST) are appropriate criteria to select the highly variable markers for obtaining high clustering success rate, but F_ST value is weaker than expected heterozygosity and number of alleles.

The current study was carried out to estimate genetic diversity of commercial chicken lines and to characterize each line genetically, based on 40 microsatellite markers together with many individuals per line assayed. At the same time, availability of an individual assignment test using a distance-based method (Piry et al., 2004) for chicken populations was demonstrated. The results from the current study would contribute to the appropriate managements avoiding loss of genetic variability in these lines and to future improvements for these lines.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 
Chicken Populations

In total, 536 birds belonging to 12 chicken lines reared in Japan were investigated. These lines were bred based on 5 breeds (Leghorn, Plymouth Rock, Rhode Island Red, Cornish, and New Hampshire Red), respectively. As for a criterion of sample size per breed or population for biodiversity study of domestic animals, FAO guidelines recommend that 25 individuals per breed or population should be assayed (FAO, 1998). Hence, sample size per line in the current study ranged from 34 to 48 individuals. Information about lines sampled is shown in Table 1.

Microsatellite Genotyping

Forty microsatellite markers (Table 2) distributed on 22 autosomes were chosen, in view of covering the genome widely and obtaining precise results. Details for these markers are available from the ArkDB Database Web site by the Roslin Bioinformatics Group (http://www.theark-db.org/). Of 40 markers, 15 markers were a part of 30 recommended markers by a joint International Society of Animal Genetics-FAO working group for biodiversity study of chicken (http://dad.fao.org/). Additionally, 5 markers (ADL0268, LEI0228, MCW0034, MCW0183, and MCW0295), which show high expected heterozygosity, were reported as more effective markers for cluster analysis and individual assignment in chickens (Rosenberg et al., 2001). Expected heterozygosity of these 5 markers reported by Rosenberg et al. (2001) ranged from 0.718 to 0.924. The PCR and genotyping were carried out by the methods of Tadano et al. (2007).

Standard within-line diversity was assessed by calculating total number of alleles (TNA) and mean number of alleles per locus (MNA), observed (direct count) heterozygosity (H_O), unbiased expected heterozygosity (H_E; Nei, 1987), and polymorphic information content (PIC; Botstein et al., 1980), using CERVUS version 2.0 (Marshall et al., 1998). A test of the Hardy-Weinberg equilibrium (HWE) at each locus-line combination was implemented by a test analogous to Fisher’s exact test based on the Markov chain method (Markov chain length, 100,000; dememorization steps, 10,000) using ARLEQUIN version 2.000 (Schneider et al., 2000).

The inbreeding coefficient (F_IS) per line and the pairwise proportion of different alleles between lines (pair-wise F_ST; Weir and Cockerham, 1984) were calculated using FSTAT version 2.9.3.2 [EC] (Goudet, 2001). The level of significance for pairwise F_ST was determined from the permutation test using the sequential Bonferroni procedures (Rice, 1989). Additionally, F_ST per each microsatellite across all lines was also calculated as a measure of highly polymorphic marker, along with expected heterozygosity.

Individual assignment test was implemented by GEN-ECLASS2 (Piry et al., 2004); a distance-based method with modified Cavalli-Sforza chord distance (D_A; Nei et al., 1983) was used. This method does not require the HWE or absence of linkage disequilibrium among loci, and the D_A distance showed higher percentage of individuals assigned to the source population than other distances (Cornuet et al., 1999). The probability that each individual was assigned to or not to candidate lines was estimated using a Monte Carlo resampling method (Paetkau et al., 2004; number of simulated individuals = 10,000; type I error = 0.01, applying rejection threshold of 0.05).

Genetic distance between each line pair based on allele frequencies was evaluated by D_A (Nei et al., 1983). A dendrogram was constructed based on the D_A distance by the neighbor-joining method (Saitou and Nei, 1987). The D_A genetic distance is better suited to obtain correct tree topology than other distances, regardless of a mutation model (Takezaki and Nei, 1996). The robustness of tree topologies was evaluated with a bootstrap test of 1,000 resampling across loci. A series of these processes was carried out using DISPAN (Ota, 1993). The dendrogram was edited using TREEVIEW version 1.6.6 (Page, 1996).

	RESULTS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 
Allelic Diversity and Highly Polymorphic Loci

The variations for each microsatellite are summarized in Table 2. A total of 268 distinct alleles were detected at the 40 microsatellite loci in 536 birds. Across all lines, the average number of alleles per locus was 6.7 (268/40), with the range from 3 (MCW0037, MCW0067, MCW0165, and MCW0248) to 16 (LEI0228). Forty-two of all 268 alleles (15.7%) were unique to only 1 line (unique alleles). The unique alleles were observed in 21 loci of all 40 microsatellite (52.5%). The highest number of unique alleles was detected in ADL0284 and LEI0228, which had 4 unique alleles across all lines, respectively. Most unique alleles distributed with a low frequency, that is, 30 of 42 unique alleles (71.4%) had a frequency of lower than 10%. The frequency of unique alleles ranged from 0.0104 to 0.5729. The H_E per locus ranged from 0.377 (MCW0248) to 0.877 (ADL0284). The F_ST per locus ranged from 0.139 (MCW0078) to 0.453 (MCW0233). In terms of the H_E, higher values were observed at ADL0284, MCW0134, and LEI0228. As for number of alleles, LEI0228, LEI0196, and ADL0284 were more highly polymorphic. In regard to the F_ST value, which indicates component of genetic variation between lines, MCW0233, ADL0169, and MCW0183 had higher values than others. Especially, ADL0284, MCW0134, LEI0228, LEI0196, and ADL0257 showed high expected heterozygosity and many alleles at the same time. These markers would be more effective for this kind of study. On the contrary, a combination of low expected heterozygosity and a small number of alleles were observed at MCW0078 and MCW0248.

Intraline Genetic Diversity

Genetic diversity within each line is summarized in Table 3. The highest number of alleles per line was observed in the Red Cornish (RCN) line (TNA = 187 and MNA = 4.68) with the highest PIC value of 0.585. The least number of alleles was observed in the New Hampshire Red (NHS) line (TNA = 85 and MNA = 2.12) with the lowest PIC value of 0.241. The number of unique alleles ranged from 1 [White Leghorn (WL)-B line and White Cornish line] to 11 (RCN), and the NHS line had no unique alleles. Relatively high frequencies (exceeding 20% in the line) were observed in 6 cases, half of which were detected in the Rhode Island Red (RIR)-B line.

In all 480 HWE tests (40 loci in 12 lines), 25 cases (5.2%) could not be tested because they were monomorphic in the line. Of the remaining 455 tests, significant deviations from the HWE at the 5% level were observed in 46 cases (10.1%). In these deviations, 60.9% (28/46) cases had a heterozygosity excess. Conversely, 39.1% (18/46) cases had a heterozygosity deficit. All lines showed statistically significant deviations from the HWE for at least 1 locus; the number of locus deviated from the HWE ranged from 1 (NHS) to 14 (WR-A). A noticeable case of deviations from the HWE was observed in the WR-A line, in which 13 of 14 deviated loci were caused by excess of heterozygosity. Additionally, all deviations in the 3 WL lines also reflected the heterozygosity excess.

Global Genetic Structure

Large genetic difference was observed (overall F_ST = 0.298, in Table 2); that is, around 30% of the microsatellite variation among 12 lines was due to line differentiation.

The results of an individual assignment test are shown in Table 4. Of all 536 birds, 518 (96.6%) were properly assigned to the source line at the 5% level. The percentage of correct assignment per line ranged from 91.7% (RIR-B) to 100% [RIR-A and Barred Plymouth Rock (BPR)]. A bird that was not assigned to the source line but to another line was not observed. However, some birds were assigned to the source line together with at least another line. Such cases were mostly observed in the WL-A, WL-B, and WL-C (20.6, 21.6, and 12.5%, respectively). Seven WL-A and 8 WL-B individuals were assigned to the WL-C line along with the source line, respectively. A WL-C individual was assigned to the WL-B, 4 individuals to the WL-A, and the remaining 1 individual was assigned to both WL-A and WL-B and the source line. On the contrary, all RIR-A and BPR individuals were assigned only to the source line, respectively.

View larger version (10K): [in this window] [in a new window]   Figure 1. Unrooted neighbor-joining dendrogram of genetic relationships among 12 chicken lines, using modified Cavalli-Sforza chord distance calculated from 40 microsatellite loci. Bootstrap values above 50% are shown at each node. WL-A = White Leghorn A line; WL-B = White Leghorn B line; WL-C = White Leghorn C line; WR-A = White Plymouth Rock A line; WR-B = White Plymouth Rock B line; WR-C = White Plymouth Rock C line; RIR-A = Rhode Island Red A line; RIR-B = Rhode Island Red B line; WCN = White Cornish; RCN = Red Cornish; BPR = Barred Plymouth Rock; NHS = New Hampshire Red.

	DISCUSSION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

Of all 40 microsatellites used, few markers (ADL0257, ADL0284, LEI0196, and LEI0228) were more variable. These markers possessed relatively lower F_ST values with high H_E and many alleles. These findings indicate that more common alleles did not greatly differ in terms of frequency across lines, and these markers are greatly effective for clustering and individual assignment (Rosenberg et al., 2001). These would be more useful to distinguish populations and lead to a reduction of cost for typing.

The highest heterozygosity was observed in the WR-A line, with the HWE deviations due to extreme heterozygosity excess. This line had its origin from a broiler dam line bred by a multinational company. High variability for broiler lines was previously reported in some studies (Vanhala et al., 1998; Zhang et al., 2002; Hillel et al., 2003). Three white egg layer lines in the current study generally showed lower allelic diversity (average TNA and MNA = 112 and 2.81, respectively) and heterozygosity (average H_O and H_E = 0.468 and 0.454, respectively) than the other lines. As compared with a broiler (WR-A) line, these layer lines showed clearly low genetic variability. However, broiler and layer lines possessed sufficient heterozygosity in the population (F_IS = –0.020 to –0.141). The commercial lines are mostly created from linebreeding based on genetically different grandparental lines. These breeding practices among highly selected lines may cause deviations from the HWE due to a large excess of heterozygosity. Fixed alleles were observed in some lines; the NHS showed the most number of monomorphic loci with the lowest diversity. This result indicates that the NHS line has been maintained as a closed flock without introgressions of birds from other lines since creation. Conversely, a high level of allelic diversity was observed in the RIR-A and RCN lines, respectively. Higher allelic diversity of them is thought to be after effects of the introgressions of birds from other lines in the past 10 yr. The NHS line needs to be crossed with other lines in the near future to maintain or improve its productive performances. Other lines, except for WL lines and the NHS, did not show especially low heterozygosity than previously reported (Hillel et al., 2003).

Overall F_ST value of 0.298 across all lines showed that 29.8% of the genetic variation was explained by the line differences. The result was approximately equivalent to the previous report by Vanhala et al. (1998). They reported overall F_ST of 0.303 in 8 chicken lines including commercial Leghorn and broiler hybrids. These F_ST values are higher than those of other livestock species. For instance, MacHugh et al. (1998) reported a F_ST value of 0.112 from European cattle breeds, and SanCristobal et al. (2006) reported a F_ST value of 0.21 from European pig breeds.

In the dendrogram based on D_A genetic distance, WL lines showed the greatest difference from other lines. Among 3 WL lines, genetic similarities were observed with shallow divergence despite their different breeding histories. Individual assignment tests also showed that the WL lines were genetically close to each other, because 12.5 to 21.6% of individuals of WL lines were simultaneously assigned to other WL lines together with their respected source lines. At the present time, Leghorn lines constitute a large percentage of white egg layers in the poultry industry; however, their genetic foundations may be narrow, because white egg layer lines mostly have their roots in Single Comb White Leghorn (Hillel et al., 2003). On the contrary, genetic relationships among other lines (Plymouth Rock, Rhode Island Red, and Cornish) were not consistent with an original breed of line (their genetic bases) in the dendrogram and showed large divergences on average. For instance, the RIR-A and RIR-B could have the same genetic base, however, the 2 were not genetically close (D_A = 0.3517 and pairwise F_ST = 0.3008). The large genetic difference may attribute to pre-existing genetic variations originating in the dual-purpose breed, along with artificial selection based on economic traits, which largely changes the primary genetic structure. Otherwise, it is thought to be associated with introgressions of birds from other lines into the RIR-A line in the past.

In the poultry industry, commercial layers and broilers, which match present economic demands, occupy a large percentage of commercial birds. Many breeds that are not used in intensive commercial productions have a tendency to be reduced in population size with some exceptions. The current status of the poultry industry leads to the loss of genetic diversity in domestic chickens. The conservation of chicken genetic resources will be more important to fill unanticipated breeding demands for production and research in the future.

In the current study, most individuals were correctly assigned to their source lines in an individual assignment test using the distance-based method (Piry et al., 2004). Genetic characterization at an individual level demonstrated here would be a useful process to conserve chicken genetic resources.

	ACKNOWLEDGMENTS

 
We thank the members of Poultry Laboratory, Animal Husbandry Research Division, Aichiken Agricultural Research Center, and Okazaki and Hyogo Stations, National Livestock Breeding Center, for providing blood samples.

Received for publication June 7, 2007. Accepted for publication August 4, 2007.

	REFERENCES

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 
Botstein, D., R. L. White, M. Skolnick, and R. W. Davis. 1980. Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am. J. Hum. Genet. 32:314–331.[Web of Science][Medline]

Cornuet, J. M., S. Piry, G. Luikart, A. Estoup, and M. Solignac. 1999. New methods employing multilocus genotypes to select or exclude populations as origins of individuals. Genetics 153:1989–2000.[Abstract/Free Full Text]

FAO. 1998. Secondary guidelines for development of national farm animal genetic resources management plans. Measurement of Domestic Animal Diversity (MoDAD): Original Working Group Report. FAO, Roma, Italy.

Goudet, J. 2001. FSTAT, a program to estimate and test gene diversities and fixation indices, version 2.9.3. http://www2.unil.ch/popgen/softwares/fstat.htm Accessed Mar. 26, 2007.

Hillel, J., M. A. M. Groenen, M. Tixier-Boichard, A. B. Korol, L. David, V. M. Kirzhner, T. Burke, A. Barre-Dirie, R. P. M. A. Crooijmans, K. Elo, M. W. Feldman, P. J. Freidlin, A. Mäki-Tanila, M. Oortwijn, P. Thomson, A. Vignal, K. Wimmers, and S. Weigend. 2003. Biodiversity of 52 chicken populations assessed by microsatellite typing of DNA pools. Genet. Sel. Evol. 35:533–557.[CrossRef][Web of Science][Medline]

MacHugh, D. E., R. T. Loftus, P. Cunningham, and D. G. Bradley. 1998. Genetic structure of seven European cattle breeds assessed using 20 microsatellite markers. Anim. Genet. 29:333–340.[CrossRef][Web of Science][Medline]

Marshall, T. C., J. Slate, L. E. B. Kruuk, and J. M. Pemberton. 1998. Statistical confidence for likelihood-based paternity inference in natural populations. Mol. Ecol. 7:639–655.[CrossRef][Medline]

Nei, M. 1987. Molecular Evolutionary Genetics. Columbia Univ. Press, New York, NY.

Nei, M., F. Tajima, and Y. Tateno. 1983. Accuracy of estimated phylogenetic trees from molecular data. II. Gene frequency data. J. Mol. Evol. 19:153–170.[CrossRef][Web of Science][Medline]

Osman, S. A. M., M. Sekino, A. Nishihata, Y. Kobayashi, W. Takenaka, K. Kinoshita, T. Kuwayama, M. Nishibori, Y. Yamamoto, and M. Tsudzuki. 2006. The genetic variability and relationships of Japanese and foreign chickens assessed by microsatellite DNA profiling. Asian-australas. J. Anim. Sci. 19:1369–1378.

Ota, T. 1993. DISPAN: Genetic Distance and Phylogenetic Analysis. Pennsylvania State Univ., University Park.

Paetkau, D., R. Slade, M. Burden, and A. Estoup. 2004. Genetic assignment methods for the direct, real-time estimation of migration rate: A simulation-based exploration of accuracy and power. Mol. Ecol. 13:55–65.[CrossRef][Medline]

Page, R. D. M. 1996. TreeView: An application to display phylogenetic trees on personal computers. Comp. Appl. Biol. Sci. 12:357–358.

Piry, S., A. Alapetite, J. M. Cornuet, D. Paetkau, L. Baudouin, and A. Estoup. 2004. GENECLASS2: A software for genetic assignment and first-generation migrant detection. J. Hered. 95:536–539.[Abstract/Free Full Text]

Rice, W. R. 1989. Analysing tables of statistical tests. Evolution 43:223–225.[CrossRef][Web of Science]

Romanov, M. N., and S. Weigend. 2001. Analysis of genetic relationships between various populations of domestic and jungle fowl using microsatellite markers. Poult. Sci. 80:1057–1063.[Abstract/Free Full Text]

Rosenberg, N. A., T. Burke, K. Elo, M. W. Feldman, P. J. Freidlin, M. A. M. Groenen, J. Hillel, A. Mäki-Tanila, M. Tixier-Boichard, A. Vignal, K. Wimmers, and S. Weigend. 2001. Empirical evaluation of genetic clustering methods using multilocus genotypes from 20 chicken breeds. Genetics 159:699–713.[Abstract/Free Full Text]

Saitou, N., and M. Nei. 1987. The neighbor-joining method: A new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 4:406–425.[Abstract]

SanCristobal, M., C. Chevalet, C. S. Haley, R. Joosten, A. P. Rattink, B. Harlizius, M. A. M. Groenen, Y. Amigues, M. Y. Boscher, G. Russell, A. Law, R. Davoli, V. Russo, C. Desautes, L. Alderson, E. Fimland, M. Bagga, J. V. Delgado, J. L. Vega-Pla, A. M. Martinez, M. Ramos, P. Glodek, J. N. Meyer, G. C. Gandini, D. Matassino, G. S. Plastow, K. W. Siggens, G. Laval, A. L. Archibald, D. Milan, K. Hammond, and R. Cardellino. 2006. Genetic diversity within and between European pig breeds using microsatellite markers. Anim. Genet. 37:189–198.[CrossRef][Web of Science][Medline]

Schneider, S., D. Roessli, and L. Excoffier. 2000. Arlequin: A Software for Population Genetics Data Analysis. Genetics and Biometry Laboratory, Univ. Geneva, Geneva, Switzerland.

Tadano, R., M. Sekino, M. Nishibori, and M. Tsudzuki. 2007. Microsatellite marker analysis for the genetic relationships among Japanese long-tailed chicken breeds. Poult. Sci. 86:460–469.[Abstract/Free Full Text]

Takahashi, H., K. Nirasawa, Y. Nagamine, M. Tsudzuki, and Y. Yamamoto. 1998. Genetic relationships among Japanese native breeds of chicken based on microsatellite DNA polymorphisms. J. Hered. 89:543–546.[Abstract/Free Full Text]

Takezaki, N., and M. Nei. 1996. Genetic distances and reconstruction of phylogenetic trees from microsatellite DNA. Genetics 144:389–399.[Abstract]

Tautz, D. 1989. Hypervariability of simple sequences as a general source for polymorphic DNA markers. Nucleic Acids Res. 17:6463–6471.[Abstract/Free Full Text]

Vanhala, T., M. Tuiskula-Haavisto, K. Elo, J. Vilkki, and A. Mäki-Tanila. 1998. Evaluation of genetic variability and genetic distances between eight chicken lines using microsatellite markers. Poult. Sci. 77:783–790.[Abstract/Free Full Text]

Weir, B. S., and C. C. Cockerham. 1984. Estimating F-statistics for the analysis of population structure. Evolution 38:1358–1370.[CrossRef][Web of Science]

Wimmers, K., S. Ponsuksili, T. Hardge, A. Valle-Zarate, P. K. Mathur, and P. Horst. 2000. Genetic distinctness of African, Asian and South American local chickens. Anim. Genet. 31:159–165.[CrossRef][Web of Science][Medline]

Zhang, X., F. C. Leung, D. K. O. Chan, G. Yang, and C. Wu. 2002. Genetic diversity of Chinese native chicken breeds based on protein polymorphism, randomly amplified polymorphic DNA, and microsatellite polymorphism. Poult. Sci. 81:1463–1472.[Abstract/Free Full Text]

Zhou, H., and S. J. Lamont. 1999. Genetic characterization of biodiversity in highly inbred chicken lines by microsatellite markers. Anim. Genet. 30:256–264.[CrossRef][Web of Science][Medline]

This Article Abstract Full Text (PDF) _Erratum_ A correction has been published Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Web of Science (2) Citing Articles via Google Scholar Google Scholar Articles by Tadano, R. Articles by Tsudzuki, M. Search for Related Content PubMed Articles by Tadano, R. Articles by Tsudzuki, M.