GENETICS Genetic characterization and conservation priorities of chicken lines R. Tadano,*1 N. Nagasaka,†‡ N. Goto,‡ K. Rikimaru,§ and M. Tsudzuki‡1 *Faculty of Applied Biological Sciences, Gifu University, Gifu 501-1193, Japan; †Kochi Prefectural Livestock Experimental Station, Kochi, Takaoka-Gun 789-1233, Japan; ‡Graduate School of Biosphere Science, Hiroshima University, Higashi-Hiroshima 739-8528, Japan; and §Livestock Experiment Station, Akita Prefectural Agriculture Forestry and Fisheries Research Center, Daisen 019-1701, Japan cording to line origin and showed no admixture. These results indicated that a substantial degree of genetic differentiation exists among the lines. To decide priorities for conservation, the contribution of each line to the genetic diversity was estimated. The result indicated that a loss of 4 of the 7 lines would lead to a loss from 1.14 to 3.44% of total genetic diversity. The most preferred line for conservation purposes was identified based on multilocus microsatellite analysis. Our results confirmed that characterization by means of molecular markers is helpful for establishing a plan for conservation of chicken genetic resources.
Key words: chicken genetic resource, conservation, genetic diversity, microsatellite marker 2013 Poultry Science 92:2860–2865 http://dx.doi.org/10.3382/ps.2013-03343
INTRODUCTION The importance of preserving chicken biodiversity has received increasing attention in recent years. The modern commercial chicken gene pool lacks the considerable genetic diversity found in noncommercial chicken populations, given that the commercial chicken gene pool has its root in a limited number of breeds (Muir et al., 2008). Conservation of diverse chicken genetic resources is accordingly considered to be important for sustainable poultry production. Multilocus microsatellite analysis is an effective method for estimating within-population genetic diversity and between-population differentiation of farm-animal genetic resources (FAO, 1998). To date, microsatellite analysis has been employed in several genetic diversity studies of chickens (e.g., Dávila et al., 2009; Zanetti et al., 2010; Leroy et al., 2012; Wilkinson et al., 2012). Microsatellite-based genetic characterization has the potential to provide useful information for conservation of chicken genetic resources. For example, assessing conservation priorities among subpopulations (lines) would ©2013 Poultry Science Association Inc. Received May 24, 2013. Accepted August 4, 2013. 1 Corresponding author: [email protected] or tsudzuki@hiro shima-u.ac.jp
help in establishing a conservation plan, although other criteria, including specific traits and future economic interest, should also be considered in conservation decisions (Ruane, 2000; Fabuel et al., 2004). For industrial chicken stocks, a large number of lines (subpopulations) derived from the same breed exists. In this case, it is important to decide which lines should be prioritized for conservation. However, studies of prioritization of industrial chicken lines for conservation are lacking. In the present study, we confirmed the utility of microsatellite marker-based prioritization using several Plymouth Rock lines. To assess conservation priorities for several chicken lines, we incorporated part of the published genotype data of Tadano et al. (2007a).
MATERIALS AND METHODS Genotype Data of Microsatellites Genotype data of 40 microsatellites for 4 Plymouth Rock (PR) lines: PR-1 (n = 48), PR-2 (n = 48), PR-3 (n = 48), and PR-6 (n = 48), published in Tadano et al. (2007a), were included. In addition, we obtained genotypes of the same 40 microsatellites in 3 PR lines: PR-4 (n = 47), PR-5 (n = 48), and PR-7 (n = 30), by the methods of Tadano et al. (2007b). The PR-1, PR-2, PR-3, PR-4, PR-6, and PR-7 are closed flocks
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ABSTRACT Molecular markers are a useful tool for evaluating genetic diversity of chicken genetic resources. Seven chicken lines derived from the Plymouth Rock breed were genotyped using 40 microsatellite markers to quantify genetic differentiation and assess conservation priorities for the lines. Genetic differentiation between pairs of the lines (pairwise FST) ranged from 0.201 to 0.422. A neighbor-joining tree of individuals, based on the proportion of shared alleles, formed clearly defined clusters corresponding to the origins of the lines. In Bayesian model-based clustering, most individuals were clearly assigned to single clusters ac-
CONSERVATION PRIORITIES FOR CHICKEN LINES
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Table 1. Summary information of 7 Plymouth Rock (PR) lines investigated Mean heterozygosity2 Sample size
MNA1
HO
HE
fij3
Plumage color
Start of the line creation
PR-1
48
3.1
0.48
0.48
0.52
White
1975
PR-2
48
3.4
0.54
0.52
0.48
White
1971
PR-3
48
4.2
0.66
0.58
0.42
White
Unknown
PR-4
47
2.6
0.39
0.41
0.60
White
Early 1960s
PR-5
48
3.7
0.49
0.51
0.49
White
1993
PR-6
48
3.1
0.48
0.48
0.50
Barred
1989
PR-7
30
2.7
0.41
0.42
0.59
Barred
1999
Specific features Recessive white line, selected for meat and egg production traits Sex-linked dwarf line, male = 1,520 g, female = 1,150 g for BW at 6 wk of age Dominant white line, originated from commercial broiler dam line Recessive white line, selected for meat production traits (e.g., BW) Dominant white line, selected for egg production traits (e.g., age at first laying, egg weight, and Haugh unit) Large-sized line, created by crossing the White Plymouth Rock at an early creation, selected for meat and egg production traits This line was initiated from individuals of PR-6 in 1999.
1MNA
= mean number of alleles per locus. = observed heterozygosity; HE = expected heterozygosity. 3f = within-line molecular coancestry coefficient. ij 2H
O
maintained by Japanese public livestock institutes. The PR-1, PR-4, and PR-6 are mainly used for crossing with Japanese native breeds to produce brand meat. The PR-5 is a closed flock selected for egg production traits maintained by a private breeding company in Japan. Summary information of these lines is shown in Table 1.
Data Analysis To assess within-line genetic diversity, the mean number of alleles per locus (MNA), observed heterozygosity (HO), and expected heterozygosity (HE; Nei, 1987) were estimated using an Excel microsatellite toolkit (Park, 2001). The degree of inbreeding within a line was assessed by estimating the molecular coancestry coefficient (fij; Caballero and Toro, 2002) using MolKin (Gutiérrez et al., 2005). Genetic differentiation between each pair of lines (pairwise FST; Weir and Cockerham, 1984) was calculated using FSTAT (Goudet, 2001). Statistical significance was evaluated using the permutation test implemented in FSTAT. We calculated interindividual distances based on the proportion of shared alleles (Dps = 1 – ps; Bowcock et al., 1994) using Microsatellite Analyzer (Dieringer and Schlötterer, 2003). To reveal the population structure of the lines, we clustered individuals based on the neighbor-joining method (Saitou and Nei, 1987) using NEIGHBOR in PHYLIP (Felsenstein, 2005) and TREEEXPLORER in MEGA (Kumar et al., 2004) and performed Bayesian model-based clustering using STRUCTURE (Pritchard et al., 2000). Based on admixture models with correlated allele frequencies, we performed 20 independent runs for each K (the number of assumed genetic clusters) ranging from 1 to 20 with a burn-in period of 10,000 and 100,000 iterations. To
compute average pairwise similarities (H′) and average membership coefficients for the 20 runs, we used the LargeKGreedy algorithm implemented in CLUMPP (Jakobsson and Rosenberg, 2007). Graphic displays of the average membership coefficients were obtained using DISTRUCT (Rosenberg, 2004). To determine the optimal K value, the mean posterior probability of the data [ln Pr (X/K)] (Pritchard et al., 2000) and ∆K (Evanno et al., 2005) was computed. Several methods have been proposed to prioritize subpopulations for conservation purposes (Petit et al., 1998; Caballero and Toro, 2002). To assess conservation priorities for the lines, we quantified the contribution of each line to the genetic diversity (total, withinline, and between-line diversity) based on the methods of Petit et al. (1998) and Caballero and Toro (2002) using MolKin (Gutiérrez et al., 2005). In the methods of Caballero and Toro (2002), the highest negative (−) contribution to the total genetic diversity when a given line is removed from the entire data set identifies a line contributing greatly to the overall diversity and whose conservation should be given high priority. In contrast, in the methods of Petit et al. (1998), the highest positive (+) contribution to total genetic diversity identifies a line making the greatest contribution to the overall diversity and thus to be prioritized for conservation.
RESULTS Within-Line Genetic Diversity Table 1 shows the genetic diversity within a line. The lowest diversity was observed in PR-4 (MNA = 2.6, HO = 0.39, and HE = 0.41) and the highest was in PR-3 (MNA = 4.2, HO = 0.66, and HE = 0.58). The highest degree of inbreeding was found in PR-4 (fij = 0.60) and the lowest was in PR-3 (fij = 0.42).
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Line
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Tadano et al. Table 2. Genetic differentiation index (pairwise FST) among 7 Plymouth Rock (PR) lines1 Line
PR-1
PR-1 PR-2 PR-3 PR-4 PR-5 PR-6 PR-7 1All
0.314 0.262 0.340 0.344 0.276 0.362
PR-2
0.2012 0.336 0.287 0.280 0.326
PR-3
0.283 0.264 0.248 0.253
PR-4
0.4222 0.237 0.345
PR-5
0.336 0.361
PR-6
0.242
PR-7
pairwise FST are significantly different from 0 (P < 0.001). represent both highest and lowest values.
2Numbers
Genetic Differentiation and Population Structure
Figure 1. A neighbor-joining tree of 317 individuals from 7 Plymouth Rock lines (PR-1, PR-2, PR-3, PR-4, PR-5, PR-6, and PR-7), using interindividual genetic distances calculated from the proportion of shared alleles.
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Table 2 shows the genetic differentiation between each pair of lines (pairwise FST) ranging from 0.201 (between PR-2 and PR-3) to 0.422 (between PR-4 and PR-5). All FST values were significantly (P < 0.001) different from 0.
In a neighbor-joining tree based on Dps (Figure 1), except for 2 individuals of PR-3, all individuals were clustered into 7 groups corresponding to the origins of the lines. The mean posterior probability of the data [ln Pr (X/K)] reached a maximum at K = 7 and the highest ∆K was observed at K = 7 in Bayesian model-based clustering. We accordingly concluded that the optimal
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CONSERVATION PRIORITIES FOR CHICKEN LINES
Table 3. Average individual’s estimated membership coefficient of each line in each of the 7 assumed genetic clusters (K = 7) obtained from Bayesian model-based clustering Cluster No. of individuals
Line1 PR-1 PR-2 PR-3 PR-4 PR-5 PR-6 PR-7 1PR
48 48 48 47 48 48 30
I
II
III
IV
V
VI
VII
0.9892
0.001 0.9442 0.006 0.001 0.002 0.001 0.002
0.002 0.004 0.9812 0.001 0.002 0.001 0.001
0.002 0.002 0.002 0.9942 0.001 0.004 0.006
0.001 0.002 0.003 0.001 0.9912 0.001 0.001
0.003 0.002 0.002 0.001 0.001 0.9872 0.2482
0.002 0.045 0.003 0.001 0.001 0.004 0.7412
0.001 0.003 0.001 0.001 0.001 0.002
= Plymouth Rock. coefficients higher than 0.1.
2Membership
Contribution to Diversity Table 4 shows the contribution of each line to genetic diversity. According to the method of Caballero and Toro (2002), PR-5 made the highest contribution to total genetic diversity (−3.44%), whereas PR-4 made the lowest (+0.65%). The contributions to within-line and between-line diversity ranged from −2.31% (PR-3) to +2.20% (PR-4) and from −3.00% (PR-5) to +1.07% (PR-3), respectively. According to the method described by Petit et al. (1998), PR-5 made the highest contribu-
Figure 2. Bayesian model-based clustering among 317 individuals from 7 Plymouth Rock lines (PR-1, PR-2, PR-3, PR-4, PR-5, PR-6, and PR-7). The number of assumed clusters (K) ranged from 2 to 7. Each individual is represented by a vertical bar. Each shade corresponds to one cluster, and the length of the shaded segment represents the individual’s estimated membership coefficient in that cluster.
tion to total genetic diversity (+6.76%), whereas PR-6 made the lowest (+0.58%). The contributions to within-line and between-line diversity ranged from −2.50% (PR-4) to +3.56% (PR-3) and from +0.90% (PR-3) to +5.64% (PR-5), respectively.
DISCUSSION The diversity estimates of the lines are moderate to high levels compared with other commercial chicken lines (Tadano et al., 2007a). In addition, molecular coancestry coefficients within lines are lower than that reported for a local Italian chicken population that had undergone past inbreeding (Zanetti et al., 2010). These results indicate that the lines used in the present study do not suffer severe inbreeding. Pairwise FST values among 7 PR lines are much higher than those previously reported for chicken lines (subpopulations) derived from the same breed [pairwise FST = 0.120, 0.139, and 0.164 between subpopulations of Hungarian-native chicken breeds (Bodzsar et al., 2009); pairwise FST = 0.071 to 0.259 among 7 White Leghorn lines (Tadano et al., 2011); pairwise FST = 0.022 to 0.250 among 5 lines of a Japanese-native chicken breed (Tadano et al., 2012)]. This result indicates that there is a substantial degree of genetic differentiation among the PR lines and that the gene pool of PR consists of lines with diverse genetic backgrounds. In addition, clustering results demonstrate that the PR lines are highly structured and that there are no admixtures among the lines. We applied different methods to assess conservation priorities for the lines. There were slight differences in results of the 2 methods. For example, the lowest contribution to total genetic diversity was obtained from PR-4 according to the method of Caballero and Toro (2002), whereas the lowest contribution was obtained from PR-6 according to the method of Petit et al. (1998). This may be attributable to their methodological differences [i.e., these methods quantify the importance of each subpopulation (line) in terms of maximization of gene diversity (Caballero and Toro, 2002) or allelic richness (Petit et al., 1998)]. The PR-6 and PR-7 are closely related (i.e., PR-7 was initiated
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number of clusters (K) in our data set was 7. The results of Bayesian model-based clustering (K = 2 to 7) are shown in Figure 2. The average pairwise similarities (H′) among 20 runs were 0.76 (K = 2), 0.59 (K = 3), 0.74 (K = 4), 0.58 (K = 5), 0.83 (K = 6), and 0.87 (K = 7). Table 3 shows the average individual’s estimated membership coefficient of each line at K = 7. Except for PR-7, each line formed an independent cluster with a high average membership coefficient (>0.900).
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Tadano et al.
Table 4. Contributions of each line to genetic diversity, according to the methods described by Caballero and Toro (2002) and Petit et al. (1998) According to Caballero and Toro
According to Petit et al.
Line1
Total (%)
Within line (%)
Between lines (%)
Total (%)
Within line (%)
Between lines (%)
PR-1 PR-2 PR-3 PR-4 PR-5 PR-6 PR-7
–1.16 –1.14 –1.24 +0.65 –3.44 +0.47 +0.33
+0.21 –0.75 –2.31 +2.20 –0.44 –0.22 +1.22
–1.37 –0.38 +1.07 –1.55 –3.00 +0.69 –0.89
+1.65 +2.76 +4.46 +1.02 +6.76 +0.58 +0.90
–0.50 +0.71 +3.56 –2.50 +1.13 –0.42 –1.97
+2.15 +2.06 +0.90 +3.52 +5.64 +1.00 +2.87
1PR
= Plymouth Rock.
ation by molecular marker-based diversity and other information would be important for making a final conservation decision. In conclusion, we identified the most preferred line for conservation purposes using multilocus microsatellite markers. Genetic characterization by means of molecular markers yields useful information for conservation of chicken genetic resources.
ACKNOWLEDGMENTS The authors thank the members of the public livestock institutes and a private company for providing blood samples used in the present study.
REFERENCES Bodzsar, N., H. Eding, T. Revay, A. Hidas, and S. Weigend. 2009. Genetic diversity of Hungarian indigenous chicken breeds based on microsatellite markers. Anim. Genet. 40:516–523. Bowcock, A. M., A. Ruiz-Linares, J. Tomfohrde, E. Minch, J. R. Kidd, and L. L. Cavalli-Sforza. 1994. High resolution of human evolutionary trees with polymorphic microsatellite. Nature 368:455–457. Caballero, A., and M. A. Toro. 2002. Analysis of genetic diversity for the management of conserved subdivided populations. Conserv. Genet. 3:289–299. Dávila, S. G., M. G. Gil, P. Resino-Talaván, and J. L. Campo. 2009. Evaluation of diversity between different Spanish chicken breeds, a tester line, and a White Leghorn population based on microsatellite markers. Poult. Sci. 88:2518–2525. Dieringer, D., and C. Schlötterer. 2003. Microsatellite analyser (MSA): A platform independent analysis tool for large microsatellite data sets. Mol. Ecol. Notes 3:167–169. Evanno, G., S. Regnaut, and J. Goudet. 2005. Detecting the number of clusters of individuals using the software STRUCTRE: A simulation study. Mol. Ecol. 14:2611–2620. Fabuel, E., C. Barragán, L. Silió, M. C. Rodríguez, and M. A. Toro. 2004. Analysis of genetic diversity and conservation priorities in Iberian pigs based on microsatellite markers. Heredity 93:104– 113. 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, Rome, Italy. Felsenstein, J. 2005. PHYLIP (Phylogeny Inference Package) version 3.6. Department of Genome Sciences, University of Washington, Seattle. Goudet, J. 2001. FSTAT, a program to estimate and test gene diversities and fixation indices, version 2.9.3. Accessed Apr 10, 2013. http://www2.unil.ch/popgen/softwares/fstat.htm.
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from individuals of PR-6 about 15 yr ago). These lines made low contributions to the total genetic diversity in both methods. This indicates that if 1 of the 2 lines was removed from the data set, there is still similar genetic component in the data set. According to Caballero and Toro (2002), quantifying both the contribution to within-subpopulation (line) diversity and the contribution to between-subpopulations (lines) diversity is required for conservation decisions. More specifically, a subpopulation that harbors low genetic diversity or extreme allele frequencies is more distant from other subpopulations and shows a higher contribution to between-subpopulation diversity. As a result, the contribution to between-subpopulation diversity is overvalued and conservation priority may be misjudged. This misjudgment is corrected by quantifying the contribution to within-subpopulation diversity. Thus, quantifying not only the contribution to between-subpopulations diversity but also to withinsubpopulation diversity is necessary for precise assessment of conservation priorities (Toro and Caballero, 2005). In the present study, PR-4 made a relatively high contribution to between-line diversity. However, PR-4 made the lowest contributions to within-line diversity in all of the lines analyzed. Thus, PR-4 does not contribute much to total genetic diversity. This result indicates that PR-4 harbors lower genetic diversity and demonstrates that quantifying the contribution to within-line diversity is important for assessing conservation priorities. We identified lines that should be given high priority for conservation purposes in terms of genetic diversity based on microsatellite polymorphisms. Needless to say, genetic diversity inferred from these neutral loci is not linked to genes relevant to specific and productive traits (Ruane, 2000). Conservation decisions for animal genetic resources should take into account not only molecular marker-based genetic diversity but also other factors such as specific traits, productive performance, and future economic interest (Fabuel et al., 2004; Ruane, 2000). For example, in the present study, although PR-2 made moderate contributions to total genetic diversity, this line carries a sex-linked dwarf gene that is valuable in poultry production. Comprehensive evalu-
CONSERVATION PRIORITIES FOR CHICKEN LINES
Rosenberg, N. A. 2004. DISTRUCT: A program for the graphical display of population structure. Mol. Ecol. Notes 4:137–138. Ruane, J. 2000. A framework for prioritizing domestic animal breeds for conservation purposes at the national level: A Norwegian case study. Conserv. Biol. 14:1385–1393. Saitou, N., and M. Nei. 1987. The neighbor-joining method: A new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 4:406–425. Tadano, R., N. Goto, and M. Tsudzuki. 2011. Genetic differentiation among White Leghorn lines: Application of individual-based clustering approaches. Poult. Sci. 90:725–730. Tadano, R., A. Nakamura, and K. Kino. 2012. Analysis of genetic divergence between closely related lines of chickens. Poult. Sci. 91:327–333. Tadano, R., M. Nishibori, N. Nagasaka, and M. Tsudzuki. 2007a. Assessing genetic diversity and population structure for commercial chicken lines based on forty microsatellite analyses. Poult. Sci. 86:2301–2308. Tadano, R., M. Sekino, M. Nishibori, and M. Tsudzuki. 2007b. Microsatellite marker analysis for the genetic relationships among Japanese long-tailed chicken breeds. Poult. Sci. 86:460–469. Toro, M. A., and A. Caballero. 2005. Characterization and conservation of genetic diversity in subdivided populations. Philos. Trans. R. Soc. Lond. B Biol. Sci. 360:1367–1378. Weir, B. S., and C. C. Cockerham. 1984. Estimating F-statistics for the analysis of population structure. Evolution 38:1358–1370. Wilkinson, S., P. Wiener, D. Teverson, C. S. Haley, and P. M. Hocking. 2012. Characterization of the genetic diversity, structure and admixture of British chicken breeds. Anim. Genet. 43:552–563. Zanetti, E., M. De Marchi, C. Dalvit, and M. Cassandro. 2010. Genetic characterization of local Italian breeds of chickens undergoing in situ conservation. Poult. Sci. 89:420–427.
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Gutiérrez, J. P., L. J. Royo, I. Alvarez, and F. Goyache. 2005. MolKin v2.0: A computer program for genetic analysis of populations using molecular coancestry information. J. Hered. 96:718–721. Jakobsson, M., and N. A. Rosenberg. 2007. CLUMPP: A cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23:1801–1806. Kumar, S., K. Tamura, and M. Nei. 2004. MEGA3: Integrated software for molecular evolutionary genetics analysis and sequence alignment. Brief. Bioinform. 5:150–163. Leroy, G., B. B. Kayang, I. A. Youssao, C. V. Yapi-Gnaoré, R. Osei-Amponsah, N. E. Loukou, J. C. Fotsa, K. Benabdeljelil, B. Bed’hom, M. Tixier-Boichard, and X. Rognon. 2012. Gene diversity, agroecological structure and introgression patterns among village chicken populations across North, West and Central Africa. BMC Genet. 13:34. Muir, W. M., G. K. Wong, Y. Zhang, J. Wang, M. A. Groenen, R. P. Crooijmans, H. J. Megens, H. Zhang, R. Okimoto, A. Vereijken, A. Jungerius, G. A. Albers, C. T. Lawley, M. E. Delany, S. MacEachern, and H. H. Cheng. 2008. Genome-wide assessment of worldwide chicken SNP genetic diversity indicates significant absence of rare alleles in commercial breeds. Proc. Natl. Acad. Sci. USA 105:17312–17317. Nei, M. 1987. Molecular Evolutionary Genetics. Columbia Univ. Press, New York, NY. Park, S. E. D. 2001. Trypanotolerance in West African cattle and the population genetic effects of selection. PhD thesis. Univ. Dublin, Ireland. Petit, R. J., A. El Mousadik, and O. Pons. 1998. Identifying populations for conservation on the basis of genetic markers. Conserv. Biol. 12:844–855. Pritchard, J. K., M. Stephens, and P. Donnelly. 2000. Inference of population structure using multilocus genotype data. Genetics 155:945–959.
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Key words: chicken genetic resource, conservation, genetic diversity, microsatellite marker 2013 Poultry Science 92:2860–2865 http://dx.doi.org/10.3382/ps.2013-03343
INTRODUCTION The importance of preserving chicken biodiversity has received increasing attention in recent years. The modern commercial chicken gene pool lacks the considerable genetic diversity found in noncommercial chicken populations, given that the commercial chicken gene pool has its root in a limited number of breeds (Muir et al., 2008). Conservation of diverse chicken genetic resources is accordingly considered to be important for sustainable poultry production. Multilocus microsatellite analysis is an effective method for estimating within-population genetic diversity and between-population differentiation of farm-animal genetic resources (FAO, 1998). To date, microsatellite analysis has been employed in several genetic diversity studies of chickens (e.g., Dávila et al., 2009; Zanetti et al., 2010; Leroy et al., 2012; Wilkinson et al., 2012). Microsatellite-based genetic characterization has the potential to provide useful information for conservation of chicken genetic resources. For example, assessing conservation priorities among subpopulations (lines) would ©2013 Poultry Science Association Inc. Received May 24, 2013. Accepted August 4, 2013. 1 Corresponding author: [email protected] or tsudzuki@hiro shima-u.ac.jp
help in establishing a conservation plan, although other criteria, including specific traits and future economic interest, should also be considered in conservation decisions (Ruane, 2000; Fabuel et al., 2004). For industrial chicken stocks, a large number of lines (subpopulations) derived from the same breed exists. In this case, it is important to decide which lines should be prioritized for conservation. However, studies of prioritization of industrial chicken lines for conservation are lacking. In the present study, we confirmed the utility of microsatellite marker-based prioritization using several Plymouth Rock lines. To assess conservation priorities for several chicken lines, we incorporated part of the published genotype data of Tadano et al. (2007a).
MATERIALS AND METHODS Genotype Data of Microsatellites Genotype data of 40 microsatellites for 4 Plymouth Rock (PR) lines: PR-1 (n = 48), PR-2 (n = 48), PR-3 (n = 48), and PR-6 (n = 48), published in Tadano et al. (2007a), were included. In addition, we obtained genotypes of the same 40 microsatellites in 3 PR lines: PR-4 (n = 47), PR-5 (n = 48), and PR-7 (n = 30), by the methods of Tadano et al. (2007b). The PR-1, PR-2, PR-3, PR-4, PR-6, and PR-7 are closed flocks
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ABSTRACT Molecular markers are a useful tool for evaluating genetic diversity of chicken genetic resources. Seven chicken lines derived from the Plymouth Rock breed were genotyped using 40 microsatellite markers to quantify genetic differentiation and assess conservation priorities for the lines. Genetic differentiation between pairs of the lines (pairwise FST) ranged from 0.201 to 0.422. A neighbor-joining tree of individuals, based on the proportion of shared alleles, formed clearly defined clusters corresponding to the origins of the lines. In Bayesian model-based clustering, most individuals were clearly assigned to single clusters ac-
CONSERVATION PRIORITIES FOR CHICKEN LINES
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Table 1. Summary information of 7 Plymouth Rock (PR) lines investigated Mean heterozygosity2 Sample size
MNA1
HO
HE
fij3
Plumage color
Start of the line creation
PR-1
48
3.1
0.48
0.48
0.52
White
1975
PR-2
48
3.4
0.54
0.52
0.48
White
1971
PR-3
48
4.2
0.66
0.58
0.42
White
Unknown
PR-4
47
2.6
0.39
0.41
0.60
White
Early 1960s
PR-5
48
3.7
0.49
0.51
0.49
White
1993
PR-6
48
3.1
0.48
0.48
0.50
Barred
1989
PR-7
30
2.7
0.41
0.42
0.59
Barred
1999
Specific features Recessive white line, selected for meat and egg production traits Sex-linked dwarf line, male = 1,520 g, female = 1,150 g for BW at 6 wk of age Dominant white line, originated from commercial broiler dam line Recessive white line, selected for meat production traits (e.g., BW) Dominant white line, selected for egg production traits (e.g., age at first laying, egg weight, and Haugh unit) Large-sized line, created by crossing the White Plymouth Rock at an early creation, selected for meat and egg production traits This line was initiated from individuals of PR-6 in 1999.
1MNA
= mean number of alleles per locus. = observed heterozygosity; HE = expected heterozygosity. 3f = within-line molecular coancestry coefficient. ij 2H
O
maintained by Japanese public livestock institutes. The PR-1, PR-4, and PR-6 are mainly used for crossing with Japanese native breeds to produce brand meat. The PR-5 is a closed flock selected for egg production traits maintained by a private breeding company in Japan. Summary information of these lines is shown in Table 1.
Data Analysis To assess within-line genetic diversity, the mean number of alleles per locus (MNA), observed heterozygosity (HO), and expected heterozygosity (HE; Nei, 1987) were estimated using an Excel microsatellite toolkit (Park, 2001). The degree of inbreeding within a line was assessed by estimating the molecular coancestry coefficient (fij; Caballero and Toro, 2002) using MolKin (Gutiérrez et al., 2005). Genetic differentiation between each pair of lines (pairwise FST; Weir and Cockerham, 1984) was calculated using FSTAT (Goudet, 2001). Statistical significance was evaluated using the permutation test implemented in FSTAT. We calculated interindividual distances based on the proportion of shared alleles (Dps = 1 – ps; Bowcock et al., 1994) using Microsatellite Analyzer (Dieringer and Schlötterer, 2003). To reveal the population structure of the lines, we clustered individuals based on the neighbor-joining method (Saitou and Nei, 1987) using NEIGHBOR in PHYLIP (Felsenstein, 2005) and TREEEXPLORER in MEGA (Kumar et al., 2004) and performed Bayesian model-based clustering using STRUCTURE (Pritchard et al., 2000). Based on admixture models with correlated allele frequencies, we performed 20 independent runs for each K (the number of assumed genetic clusters) ranging from 1 to 20 with a burn-in period of 10,000 and 100,000 iterations. To
compute average pairwise similarities (H′) and average membership coefficients for the 20 runs, we used the LargeKGreedy algorithm implemented in CLUMPP (Jakobsson and Rosenberg, 2007). Graphic displays of the average membership coefficients were obtained using DISTRUCT (Rosenberg, 2004). To determine the optimal K value, the mean posterior probability of the data [ln Pr (X/K)] (Pritchard et al., 2000) and ∆K (Evanno et al., 2005) was computed. Several methods have been proposed to prioritize subpopulations for conservation purposes (Petit et al., 1998; Caballero and Toro, 2002). To assess conservation priorities for the lines, we quantified the contribution of each line to the genetic diversity (total, withinline, and between-line diversity) based on the methods of Petit et al. (1998) and Caballero and Toro (2002) using MolKin (Gutiérrez et al., 2005). In the methods of Caballero and Toro (2002), the highest negative (−) contribution to the total genetic diversity when a given line is removed from the entire data set identifies a line contributing greatly to the overall diversity and whose conservation should be given high priority. In contrast, in the methods of Petit et al. (1998), the highest positive (+) contribution to total genetic diversity identifies a line making the greatest contribution to the overall diversity and thus to be prioritized for conservation.
RESULTS Within-Line Genetic Diversity Table 1 shows the genetic diversity within a line. The lowest diversity was observed in PR-4 (MNA = 2.6, HO = 0.39, and HE = 0.41) and the highest was in PR-3 (MNA = 4.2, HO = 0.66, and HE = 0.58). The highest degree of inbreeding was found in PR-4 (fij = 0.60) and the lowest was in PR-3 (fij = 0.42).
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Line
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Tadano et al. Table 2. Genetic differentiation index (pairwise FST) among 7 Plymouth Rock (PR) lines1 Line
PR-1
PR-1 PR-2 PR-3 PR-4 PR-5 PR-6 PR-7 1All
0.314 0.262 0.340 0.344 0.276 0.362
PR-2
0.2012 0.336 0.287 0.280 0.326
PR-3
0.283 0.264 0.248 0.253
PR-4
0.4222 0.237 0.345
PR-5
0.336 0.361
PR-6
0.242
PR-7
pairwise FST are significantly different from 0 (P < 0.001). represent both highest and lowest values.
2Numbers
Genetic Differentiation and Population Structure
Figure 1. A neighbor-joining tree of 317 individuals from 7 Plymouth Rock lines (PR-1, PR-2, PR-3, PR-4, PR-5, PR-6, and PR-7), using interindividual genetic distances calculated from the proportion of shared alleles.
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Table 2 shows the genetic differentiation between each pair of lines (pairwise FST) ranging from 0.201 (between PR-2 and PR-3) to 0.422 (between PR-4 and PR-5). All FST values were significantly (P < 0.001) different from 0.
In a neighbor-joining tree based on Dps (Figure 1), except for 2 individuals of PR-3, all individuals were clustered into 7 groups corresponding to the origins of the lines. The mean posterior probability of the data [ln Pr (X/K)] reached a maximum at K = 7 and the highest ∆K was observed at K = 7 in Bayesian model-based clustering. We accordingly concluded that the optimal
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Table 3. Average individual’s estimated membership coefficient of each line in each of the 7 assumed genetic clusters (K = 7) obtained from Bayesian model-based clustering Cluster No. of individuals
Line1 PR-1 PR-2 PR-3 PR-4 PR-5 PR-6 PR-7 1PR
48 48 48 47 48 48 30
I
II
III
IV
V
VI
VII
0.9892
0.001 0.9442 0.006 0.001 0.002 0.001 0.002
0.002 0.004 0.9812 0.001 0.002 0.001 0.001
0.002 0.002 0.002 0.9942 0.001 0.004 0.006
0.001 0.002 0.003 0.001 0.9912 0.001 0.001
0.003 0.002 0.002 0.001 0.001 0.9872 0.2482
0.002 0.045 0.003 0.001 0.001 0.004 0.7412
0.001 0.003 0.001 0.001 0.001 0.002
= Plymouth Rock. coefficients higher than 0.1.
2Membership
Contribution to Diversity Table 4 shows the contribution of each line to genetic diversity. According to the method of Caballero and Toro (2002), PR-5 made the highest contribution to total genetic diversity (−3.44%), whereas PR-4 made the lowest (+0.65%). The contributions to within-line and between-line diversity ranged from −2.31% (PR-3) to +2.20% (PR-4) and from −3.00% (PR-5) to +1.07% (PR-3), respectively. According to the method described by Petit et al. (1998), PR-5 made the highest contribu-
Figure 2. Bayesian model-based clustering among 317 individuals from 7 Plymouth Rock lines (PR-1, PR-2, PR-3, PR-4, PR-5, PR-6, and PR-7). The number of assumed clusters (K) ranged from 2 to 7. Each individual is represented by a vertical bar. Each shade corresponds to one cluster, and the length of the shaded segment represents the individual’s estimated membership coefficient in that cluster.
tion to total genetic diversity (+6.76%), whereas PR-6 made the lowest (+0.58%). The contributions to within-line and between-line diversity ranged from −2.50% (PR-4) to +3.56% (PR-3) and from +0.90% (PR-3) to +5.64% (PR-5), respectively.
DISCUSSION The diversity estimates of the lines are moderate to high levels compared with other commercial chicken lines (Tadano et al., 2007a). In addition, molecular coancestry coefficients within lines are lower than that reported for a local Italian chicken population that had undergone past inbreeding (Zanetti et al., 2010). These results indicate that the lines used in the present study do not suffer severe inbreeding. Pairwise FST values among 7 PR lines are much higher than those previously reported for chicken lines (subpopulations) derived from the same breed [pairwise FST = 0.120, 0.139, and 0.164 between subpopulations of Hungarian-native chicken breeds (Bodzsar et al., 2009); pairwise FST = 0.071 to 0.259 among 7 White Leghorn lines (Tadano et al., 2011); pairwise FST = 0.022 to 0.250 among 5 lines of a Japanese-native chicken breed (Tadano et al., 2012)]. This result indicates that there is a substantial degree of genetic differentiation among the PR lines and that the gene pool of PR consists of lines with diverse genetic backgrounds. In addition, clustering results demonstrate that the PR lines are highly structured and that there are no admixtures among the lines. We applied different methods to assess conservation priorities for the lines. There were slight differences in results of the 2 methods. For example, the lowest contribution to total genetic diversity was obtained from PR-4 according to the method of Caballero and Toro (2002), whereas the lowest contribution was obtained from PR-6 according to the method of Petit et al. (1998). This may be attributable to their methodological differences [i.e., these methods quantify the importance of each subpopulation (line) in terms of maximization of gene diversity (Caballero and Toro, 2002) or allelic richness (Petit et al., 1998)]. The PR-6 and PR-7 are closely related (i.e., PR-7 was initiated
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number of clusters (K) in our data set was 7. The results of Bayesian model-based clustering (K = 2 to 7) are shown in Figure 2. The average pairwise similarities (H′) among 20 runs were 0.76 (K = 2), 0.59 (K = 3), 0.74 (K = 4), 0.58 (K = 5), 0.83 (K = 6), and 0.87 (K = 7). Table 3 shows the average individual’s estimated membership coefficient of each line at K = 7. Except for PR-7, each line formed an independent cluster with a high average membership coefficient (>0.900).
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Table 4. Contributions of each line to genetic diversity, according to the methods described by Caballero and Toro (2002) and Petit et al. (1998) According to Caballero and Toro
According to Petit et al.
Line1
Total (%)
Within line (%)
Between lines (%)
Total (%)
Within line (%)
Between lines (%)
PR-1 PR-2 PR-3 PR-4 PR-5 PR-6 PR-7
–1.16 –1.14 –1.24 +0.65 –3.44 +0.47 +0.33
+0.21 –0.75 –2.31 +2.20 –0.44 –0.22 +1.22
–1.37 –0.38 +1.07 –1.55 –3.00 +0.69 –0.89
+1.65 +2.76 +4.46 +1.02 +6.76 +0.58 +0.90
–0.50 +0.71 +3.56 –2.50 +1.13 –0.42 –1.97
+2.15 +2.06 +0.90 +3.52 +5.64 +1.00 +2.87
1PR
= Plymouth Rock.
ation by molecular marker-based diversity and other information would be important for making a final conservation decision. In conclusion, we identified the most preferred line for conservation purposes using multilocus microsatellite markers. Genetic characterization by means of molecular markers yields useful information for conservation of chicken genetic resources.
ACKNOWLEDGMENTS The authors thank the members of the public livestock institutes and a private company for providing blood samples used in the present study.
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from individuals of PR-6 about 15 yr ago). These lines made low contributions to the total genetic diversity in both methods. This indicates that if 1 of the 2 lines was removed from the data set, there is still similar genetic component in the data set. According to Caballero and Toro (2002), quantifying both the contribution to within-subpopulation (line) diversity and the contribution to between-subpopulations (lines) diversity is required for conservation decisions. More specifically, a subpopulation that harbors low genetic diversity or extreme allele frequencies is more distant from other subpopulations and shows a higher contribution to between-subpopulation diversity. As a result, the contribution to between-subpopulation diversity is overvalued and conservation priority may be misjudged. This misjudgment is corrected by quantifying the contribution to within-subpopulation diversity. Thus, quantifying not only the contribution to between-subpopulations diversity but also to withinsubpopulation diversity is necessary for precise assessment of conservation priorities (Toro and Caballero, 2005). In the present study, PR-4 made a relatively high contribution to between-line diversity. However, PR-4 made the lowest contributions to within-line diversity in all of the lines analyzed. Thus, PR-4 does not contribute much to total genetic diversity. This result indicates that PR-4 harbors lower genetic diversity and demonstrates that quantifying the contribution to within-line diversity is important for assessing conservation priorities. We identified lines that should be given high priority for conservation purposes in terms of genetic diversity based on microsatellite polymorphisms. Needless to say, genetic diversity inferred from these neutral loci is not linked to genes relevant to specific and productive traits (Ruane, 2000). Conservation decisions for animal genetic resources should take into account not only molecular marker-based genetic diversity but also other factors such as specific traits, productive performance, and future economic interest (Fabuel et al., 2004; Ruane, 2000). For example, in the present study, although PR-2 made moderate contributions to total genetic diversity, this line carries a sex-linked dwarf gene that is valuable in poultry production. Comprehensive evalu-
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