Open Access

Discrimination of the commercial Korean native chicken population using microsatellite markers

  • Nu Ri Choi1,
  • Dong Won Seo1,
  • Slim Ben Jemaa1, 2,
  • Hasina Sultana1,
  • Kang Nyeong Heo3,
  • Cheorun Jo4 and
  • Jun Heon Lee1Email author
Journal of Animal Science and Technology201557:5

DOI: 10.1186/s40781-015-0044-6

Received: 23 December 2014

Accepted: 20 January 2015

Published: 5 February 2015

Abstract

Background

Korean native chicken (KNC) is a well-known breed due to its superior meat taste. This breed, however, owing to a low growth rate, has a high market price. In order to overcome this disadvantage, the National Institute of Animal Science (NIAS) in Korea developed a commercial KNC breed, named Woorimatdag version 2 (WM2), an upgraded version of the Woorimatdag (WM1) breed and the WM2 was created by crossing the KNC with meat type breeds. This study aims to discriminate between WM2 and other chicken breeds using microsatellite (MS) markers.

Methods

A total of 302 individuals from eight Korean chicken populations were examined. The genetic diversity and population structure analysis were investigated using Cervus, API-CALC, STRUCTURE, PowerMarker programs.

Results

Based on heterozygosity and polymorphic information content (PIC) values, 30 MS markers were initially selected from 150 markers. The identified average number of alleles (Na), expected heterozygosity, and PIC values for the WM2 samples were 7.17, 0.741, and 0.682, respectively. Additionally, the paternity of individuals was assigned with a success rate of greater than 99% using 12 markers, the best minimum number of markers. The 12 selected markers contained heterozygosity and PIC values above 0.7 and probability of identity values around zero. Using these markers, the determined probability of identity (PI), PI half-sibs , and PI sibs values were 3.23E-33, 5.03E-22, and 8.61E-08, respectively.

Conclusions

WM2 is well differentiated with respect to other chicken breeds based on estimated genetic distances. The results presented here will contribute to the identification of commercial WM2 chicken in the market.

Keywords

Discrimination Diversity Microsatellite Korean native chicken Woorimatdag

Background

Recently, chicken meat consumption in Korea has rapidly increased to 12 kg per capita due to consumer preferences for healthy white muscle meat [1]. In comparison to red meat, chicken meat is considered a healthier option because of lower fat, cholesterol, and iron levels [2]. Presently, approximately 90% of the Korean poultry industry contains imported chicken breeding stocks. The breeds that existed before the Korean War (1950–1953), unfortunately, are almost all extinct. Since 1992, a Korean native chicken (KNC) conservation project was launched by the National Institute of Animal Science (NIAS) in an attempt to restore local chicken breeds. Recently, five KNC lines and seven others originally imported in the 1960s have been restored [3]. Consumers tend to pay more for the KNCs because of their good taste. The low productivity of the native breeds, however, was disadvantageous for farmers trying to meet feeding and consumption rates. In order to overcome these disadvantages, NIAS developed the Woorimatdag version 1 (WM1) chicken population. WM1 was a commercial, KNC population generated from crossbreeding fast growing native male chickens and good tasting female chickens with increased egg production. WM1 chicken grows faster, reaching the marker weight of 1.8 kg, than the purebred KNC [4]. Moreover, WM1 chickens produce good quality meat with a high oleic acid content, which improves both taste and water holding capacity [4]. Jung et al. [5] reported that WM1 chickens have a significantly higher content of arachidonic acid and meat flavor than commercial broilers (Br). NIAS recently developed Woorimatdag version 2 (WM2) chickens, a modified version of WM1 chickens with increased growth rates.

Traditional methods to identify chicken breeds focused on general appearances such as feather color, shank color, and body type [6]. Because of the process of distribution and market, chicken breeds cannot be effectively classified based on the appearance of meat. Recent advances in molecular biology techniques, however, have provided new opportunities to assess genetic variability at the DNA level [7]. Therefore, many groups have attempted to discriminate breeds using molecular genetic markers. For example, Korean cattle and pork industries developed discrimination and traceability systems using microsatellite (MS) markers [8,9].

MS markers or simple-sequence repeat (SSR) markers, are highly polymorphic, one to six base pair repeats, widely used since they are numerous, randomly distributed in the genome, and show co-dominant inheritance [10,11]. In addition, MS markers were used in the construction of linkage map of quantitative trait locus (QTL) studies [12]. MS markers may be useful in discriminating individuals. The International Society for Animal Genetics (ISAG) has recommended 30 MS markers for breed identification [13].

In a previous study, 97 MS marker variations, including the 30 MS ISAG recommended markers were investigated in 12 chicken populations. While the majority of the 12 population studies were purebred, the commercial chickens were different, products of three- and four-way crosses. Therefore, in this study, the commercial KNC population, WM2 was investigated to discriminate it from the other chicken populations in the market.

Methods

Sample collection and DNA extraction

A total of 302 individuals from eight Korean chicken population (187 WM2, 17 WM1, 13 Hanhyup-3 (Hh), 14 Hyunin (Hn), 14 Rhode Island Red (RIR), 15 Cornish Black (CoL), 15 Cornish Red (CoR), 17 Ogye (O) and 10 Br) were examined. Chicken populations care facilities and procedures met or exceeded the standards established by the Committee for Accreditation of Laboratory Animal Care at National Institute of Animal Science (NIAS) in Korea. The study also was conducted in accordance with recommendations described in “The Guide for the Care and Use of Laboratory Animals” published by the institutional Animal Care and Use Committee (IACUC) of NIAS (2012-C-037) in Korea. Genomic DNA was extracted from embryo tissues of WM2 using the PrimePrep™ Genomic DNA isolation kit for tissue (GeNetBio, Korea) and blood samples of other population using the PrimePrem™ Genomic DNA isolation kit for blood. The concentration of DNA samples was measured using NanoDrop 2000C spectrophotometer (Thermo Scientific, USA) and stored at −20°C.

Microsatellite (MS) marker genotyping

Previously, 150 MS markers were investigated for the discrimination of five purebred KNC lines [14]. From these results, a total of 30 MS markers were initially selected, which have high expected heterozygosity (Hexp) and polymorphic information content (PIC) values for classification of the WM2 and other commercial populations (Table 1). Selected 30 MS markers were distributed on 15 autosomes.
Table 1

Primer information for 30 microsatellite markers used in this study*

Marker

Chr.

Dye

Forward (5′ → 3′)

Reverse (5′ → 3′)

Allele size (bp)

ADL0268

1

PET

CTCCACCCCTCTCAGAACTA

CAACTTCCCATCTACCTACT

105-117

MCW0111

1

NED

GCTCCATGTGAAGTGGTTTA

ATGTCCACTTGTCAATGATG

98-112

MCW0145

1

FAM

ACTTTATTCTCCAAATTTGGCT

AAACACAATGGCAACGGAAAC

181-211

MCW0063

2

FAM

GGCTCCAAAAGCTTGTTCTTAGCT

GAAAACCAGTAAAGCTTCTTAC

132-150

MCW0087

2

NED

ATTTCTGCAGCCAACTTGGAG

CTCAGGCAGTTCTCAAGAACA

267-283

LEI0141

2

FAM

CGCATTTGATGCATAACACATG

AAGGCAAACTCAGCTGGAACG

220-242

MCW0039

2

VIC

CATTGGACTGAGATGTCACTGCAG

ACATTTGTCTAATGGTACTGTTAC

127-147

MCW0264

2

FAM

CTTACTTTTCACGACAGAAGC

AGACTGAGTCACACTCGTAAG

224-240

MCW0288

2

FAM

GATCTGCTTCTCTGCCCCATG

GGTACTGTCACCAGAATGAGC

108-122

MCW0127

3

VIC

GAGTTCAGCAGGAATGGGATG

TGCAATAAGAGAAGGTAAGGTC

227-241

MCW0040

3

VIC

ACTCAAAAATGTGGTAGAATATAG

ACCGAAATTGAGCAGAAGTTA

121-145

ADL0317

4

FAM

AGTTGGTTTCAGCCATCCAT

CCCAGAGCACACTGTCACTG

178-204

LEI0094

4

FAM

GATCTCACCAGTATGAGCTGC

TCTCACACTGTAACACAGTGC

254-280

ADL0292

5

FAM

CCAAATCAGGCAAAACTTCT

AAATGGCCTAAGGATGAGGA

110-138

MCW0029

5

VIC

GTGGACACCCATTTGTACCCTATG

CATGCAATTCAGGACCGTGCA

139-189

ROS0013

5

NED

TGCTGCTCCTGGRAAATTG

GAAAAGCCATGGAGGAATCA

220-242

ADL0159

6

VIC

GCCATTATTTTTCCCTGTGT

CTCCCCAAAGTCATTAGCAG

107-127

ROS0019

7

NED

ATGTACAGGTTCCAGTGTCCG

CCAGTTCATACAACCTTGAGTTGG

119-143

ADL0259

9

VIC

CTCATTGCAGAGGAAGTTCT

GTAATGGAGGATGCTCAGGT

107-129

GCT0016

9

NED

TCCAAGGTTCTCCAGTTC

GGCATAAGGATAGCAACAG

109-125

MCW0228

10

PET

GATCTCTGCATTACAAGCATG

TTGCTGACCTGCTCATGCAAG

221-239

MCW0104

13

FAM

TAGCACAACTCAAGCTGTGAG

AGACTTGCACAGCTGTGTACC

189-225

ROS0083

13

VIC

CATTACAGCTCAGTGTTGGCA

TTGCAAGTGCTCTCCCATC

109-129

MCW0213

13

NED

GACAAGTCAACAACTTGCCAG

CTGTTCACTTTAAGGACATGG

288-316

MCW0123

14

FAM

CCACTAGAAAAGAACATCCTC

GGCTGATGTAAGAAGGGATGA

79-89

ADL0293

17

PET

GTAATCTAGAAACCCCATCT

ACATACCGCAGTCTTTGTTC

105-119

MCW0330

17

VIC

TGGACCTCATCAGTCTGACAG

AATGTTCTCATAGAGTTCCTGC

254-286

ADL0304

18

FAM

GGGGAGGAACTCTGGAAATG

CCTCATGCTTCGTGCTTTTT

137-159

LEI0074

26

VIC

GACCTGGTCCTGACATGGGTG

GTTTGCTGATTAGCCATCGCG

224-240

LEI0135

28

NED

CACAATGAAGGATGAATAGTGC

AATTCACAGTTACACCTGAGG

131-142

*Bold is selected 12 MS marker combination.

The Polymerase Chain Reaction (PCR) was performed in total volume of 20 μL, 50 ng of genomic DNA, 10 pmol of fluorescent dye (FAM, VIC, NED, PET) labeled modified forward primer and normal reverse primer (Applied Biosystems, USA), 2.5 mM of each dNTPs (GeNet Bio, Korea), 10 X reaction buffer (GeNet Bio, Korea), 2.5 unit of prime Taq DNA polymerase (GeNet Bio, Korea). The PCR was performed in an initial denaturation at 95°C for 10 min followed by 35 cycles of 30 sec of denaturation at 95°C, 30 sec of annealing at 60°C, 30 sec of extension at 72°C and final extension at 72°C for 10 min using My-Genie 96 Thermal Cycler (Bioneer, Korea). The PCR products were initially electrophoresis on 3% agarose gel with ethidium bromide (EtBr) and confirmed whether they gave single PCR DNA band under the UV light. When the bands were clearly appeared, further genotyping was performed. For the microsatellite genotyping, more than 20 times diluted PCR products were used. The genotyping reaction contained 1 μL of diluted PCR products, 10 μL of Hi-Di™ Formamide (Applied Biosystems, USA) and 0.1 μL of GeneScan™-500 LIZ™ size standard marker (Applied Biosystems, USA). After dilution, genotyping reaction mixture was denatured for 2 min at 95°C and fragment analysis was performed using capillary array in Genetic analyzer 3130xl (Applied Biosystems, USA). The MS genotypes were identified using GeneMapper ver.3.7 (Applied Biosystems, USA).

Genetic diversity and population structure analysis

The genotyping data were used to estimate mean number of allele (Na), Hexp, observed heterozygosity (Hobs) and PIC using Cervus 3.0 program [15]. The expected probability of identity values among genotypes of random individuals (PI), random half sibs (PI half-sibs ) and random sibs (PI sibs ) were calculated using API-CALC (Average Probability of Identity-Calculate) ver 1.0 [16]. Moreover, we used both model-based and non model-based methods to describe the diversity between pre-defined genetic clusters. We used STRUCTURE software v. 2.3 [17] for model-based and DAPC (Discriminant Analysis of Principal Components) program implemented in adegenet R-package [18-20] for non model-based method. STRUCTURE results were also used to assess population structure of Korean breeds.

DAPC analysis was preceded by the execution of the K-means clustering algorithm implemented in adegenet to identify an optimal number of genetic clusters to describe the data. For this purpose, we ran K-means sequentially with increasing number of clusters. Different clustering solutions are compared using Bayesian Information Criterion (BIC). The optimal number of groups matches the lowest BIC value. Also, genetic distance [21] values were computed by PowerMarker ver 3.25 [22]. We used the number of loci which differ between two individuals as a measure of the genetic distance between individuals. This was computed using the R package ape [23]. A neighbor-joining tree was then constructed based on the resulting distance matrix using the same package. Genetic distance between breeds was computed using Reynolds genetic distance (which is an allele frequency-dependent distance). NeighborNet graph was computed using splitstree software [24].

Results and discussion

Polymorphisms of microsatellite markers

A total of 215 alleles were detected from 30 MS markers in WM2, ranging from 5 to 13 alleles with an average of 7.17 alleles per locus. The Hexp values ranged from 0.474 for ADL0304 to 0.841 for MCW0264. The Hobs values varied from 0.151 for GCT0016 to 0.885 for ADL0159, with an average of 0.741 per locus in the WM2 population. The obtained average PIC value per locus in WM2 was 0.682 and varied from 0.443 for ADL0304 to 0.819 for MCW0264 (Table 2). These markers were also polymorphic in other chicken populations. The lowest value of Na, Hexp, and PIC was calculated in the RIR population (Additional file 1: Table S1). Seo et al. [14] reported the classification of five different lines of KNC using 15 MS markers and determined the mean Na, Hexp, Hobs, and PIC values of 8.4, 0.802, 0.709, and 0.771, respectively. With the exception of the Hobs values, the values in the current study were lower than those reported by Seo et al. [14]. Of the 30 selected markers, six (GCT0016, MCW0029, MCW0063, MCW0087, MCW0264, and MCW0104) were used in the study by Seo et al. [14]. Furthermore, four markers (LEI0135, MCW0111, MCW0145, and MCW0330) were investigated by Suh et al. [25] for the discrimination of four different breeds, including the WM1 population. Our marker combination is more polymorphic than that in the study by Suh et al. [25]. In addition, of the 30 markers in this study, six were among the ISAG recommended markers (ADL0268, LEI0094, MCW0104, MCW0111, MCW0123, and MCW0330). These results indicated that highly polymorphic MS markers were commonly used in diverse populations.
Table 2

The heterozygosity (Hobs and Hexp) and polymorphism information content (PIC) values using 30 MS markers in Woorimatdag version 2 (WM2) chicken population*

Locus

No. of allele

HObs

HExp

PIC

ADL0268

5

0.783

0.742

0.703

MCW0111

5

0.789

0.695

0.654

MCW0145

7

0.754

0.692

0.655

MCW0063

7

0.775

0.783

0.746

MCW0087

9

0.833

0.785

0.751

LEI0141

7

0.832

0.797

0.767

MCW0039

7

0.738

0.765

0.726

MCW0264

8

0.877

0.841

0.819

MCW0288

6

0.608

0.631

0.578

MCW0127

6

0.742

0.679

0.627

MCW0040

6

0.813

0.695

0.656

ADL0317

7

0.819

0.775

0.739

LEI0094

7

0.845

0.716

0.665

ADL0292

7

0.774

0.733

0.684

MCW0029

13

0.805

0.815

0.790

ROS0013

8

0.871

0.771

0.734

ADL0159

7

0.885

0.789

0.757

ROS0019

8

0.766

0.643

0.581

ADL0259

6

0.674

0.657

0.620

GCT0016

9

0.151

0.804

0.773

MCW0228

6

0.821

0.773

0.740

MCW0104

11

0.743

0.813

0.786

ROS0083

8

0.818

0.749

0.707

MCW0213

11

0.807

0.798

0.771

MCW0123

5

0.707

0.687

0.627

ADL0293

7

0.701

0.554

0.522

MCW0330

6

0.484

0.643

0.592

ADL0304

5

0.513

0.474

0.443

LEI0074

6

0.85

0.745

0.704

LEI0135

5

0.659

0.596

0.544

Mean

7.17

0.741

0.721

0.682

*Bold is selected 12 MS marker combination.

Bostein et al. [26] reported that markers with PIC values >0.5 and Hexp values >0.6 have a high polymorphic information content and are sufficient for breed discrimination. Thus, our results confirmed that all markers, with the exception of ADL0304, have high polymorphic information and suitable allele frequencies and polymorphisms, and can be used to discriminate the WM2 population.

Discrimination of WM2 population

While all 30 selected markers had the ability to discriminate the WM2 population from other populations, the best minimum number of markers was required from an economic point of view. For this reason, 12 MS markers were selected for the best minimum MS marker combination based on the highest Hexp and PIC values. Using this combination of 12 MS markers, the calculated PI, PI half-sibs , and PI sibs values were 3.23E-33, 5.03E-22, and 8.61E-08, respectively. A previous study, using 12 markers, reported PI, PI half-sibs, and PI sibs values of 7.98E-29, 2.28E-20, and 1.25E-8, respectively, for the discrimination of 5 lines of purebred KNCs [14]. These results suggest that the selected 12 markers have high polymorphism and are effective in discriminating the WM2 population from other populations (Figure 1).
Figure 1

The expected probability of identity values among genotypes of random individuals ( PI ), random half-sib (PI half-sibs ) and random sibs ( PI sibs ) were suggested markers for discrimination of chicken lines.

Genetic distance among WM2 and other populations

To establish genetic relationships among WM2 and the other populations, genetic distances were calculated using the alleles from the 12 selected MS markers. Nei et al. [21]’s genetic distance was calculated between WM2 and the other populations using a pairwise co-ancestry matrix according to the allele frequencies (Table 3). The lowest genetic distance (0.1375) was observed between the WM1 and CoL populations. The genetic distance between O chicken population and WM2 was the highest (0.791), followed by RIR and WM2 (0.788) (Table 3). Similarly, Suh et al. [25] reported the lowest genetic distance (0.092) between the WM1 and Hh populations and the highest genetic distance (0.690) between the RIR and White Leghorn breeds. This indicated that the WM1 and Hh populations originated from the same breed/ancestor for constructing the populations. Furthermore, according to the genetic distance values, our marker combination has a stronger discriminating power than that in the findings by Suh et al. [25]. On the other hand, close genetic distances of the WM1 population with the CoL, Hh, and RIR populations (0.1375, 0.2453, and 0.2478, respectively) were observed. These results support the findings by Suh et al. [25] that the WM1 and Hh populations have the same founder breeds as their genetic distances are close (Table 3). The WM2 population, however, has a genetic distance >0.690 indicating that different crossing combinations were applied between the WM2 and WM1 populations.
Table 3

Matrix of genetic distances among nine populations*

 

Bb

CoL

CoR

Hh

Hn

O

RIR

Wm1

Wm2

Bb

-

        

CoL

0.3267

-

       

CoR

0.3251

0.3906

-

      

Hh

0.2715

0.3114

0.3805

-

     

Hn

0.4189

0.4090

0.4738

0.4460

-

    

O

0.4587

0.4027

0.4543

0.4972

0.5287

-

   

RIR

0.4371

0.4702

0.5382

0.3494

0.5492

0.6302

-

  

Wm1

0.2791

0.1375

0.3365

0.2453

0.3667

0.4341

0.2478

-

 

Wm2

0.7102

0.7225

0.7095

0.7121

0.7618

0.7914

0.7878

0.6904

-

*WM1 (Woorimatdag version 1), WM2 (Woorimatdag version 2), Hh (Hanhyup-3), Hn (Hyunin), RIR (Rhode Island Red), CoL (Cornish black), CoR (Cornish red), O (Ogye) and Br (Broiler).

Phylogenetic and structure analysis of nine populations

Based on Nei’s equations [21], an unrooted neighbor-joining (NJ) phylogenetic tree was constructed for 263 animals from nine chicken populations using 12 MS marker variations (Figure 2). In our individual phylogenetic analysis, the WM2 population was identified as a distinct population from other populations. O chicken population was also well separated from other populations and a mixture clade contained the WM1, Hh, CoL, and Br populations.
Figure 2

Phylogenetic analysis for each individual from nine populations using Reynolds genetic distance. The color codes are indicated different populations. The population acronyms are as follows: WM1 (Woorimatdag version 1), WM2 (Woorimatdag version 2), Hh (Hanhyup-3), Hn (Hyunin), RIR (Rhode Island Red), CoL (Cornish black), CoR (Cornish red), O (Ogye) and Br (Broiler).

The genetic structure of nine native chicken populations using microsatellite marker genotypes was investigated based on population clustering (Figure 3). The purpose of structure analysis, performed using a Bayesian approach based on the marker genotypes, was to delineate clusters of individuals [27]. Using 12 MS markers and a K value of 2, WM2 was fully separated from the other populations. This result was also observed in the individual phylogenetic and discriminant analyses. Based on these results, the combination of 12 MS markers could discriminate WM2 from the other populations. Furthermore, with a K value of 9, most populations classified well with different groups. WM1, however, was found to be a mixture population, a finding consistent with the results obtained from the phylogenetic and DAPC analyses. The discriminant analysis confirmed the WM2 populations were distinct from the other populations.
Figure 3

Structure analysis using twelve MS markers from nine populations. The population acronyms are as follows: WM1 (Woorimatdag version 1), WM2 (Woorimatdag version 2), Hh (Hanhyup-3), Hn (Hyunin), RIR (Rhode Island Red), CoL (Cornish black), CoR (Cornish red), O (Ogye) and Br (Broiler).

The assignment of the individuals from the 9 populations was 9 clusters (group), represent genetic groups and they were inferred using K-means algorithm implemented in the R package adegenet (Figure 4). Furthermore, individuals (represented by dots) were plotted according to their coordinates on the first two principal components. The populations were represented as inertia ellipses, which characterize the dispersion of each population around its center of gravity. Bar graph insets indicate the amount of variance determined by the two discriminant values used for plotting. WM2 is clearly separated from the other populations, a result supported by the phylogenetic analysis using 12 selected MS markers. The 12 selected MS markers were also used for the separation of the O chicken and RIR groups.
Figure 4

Scatter plot of DAPC analysis of the nine populations using adegent R package. The population acronyms are as follows: WM1 (Woorimatdag version 1), WM2 (Woorimatdag version 2), Hh (Hanhyup-3), Hn (Hyunin), RIR (Rhode Island Red), CoL (Cornish black), CoR (Cornish red), O (Ogye) and Br (Broiler).

Conclusions

Since our 12 MS marker combination can effectively discriminate WM2, they can be used for breed identification. Moreover, to the best of our knowledge, this is the first study demonstrating the discrimination of the commercial KNC population, and the results presented here may be applied in the commercial market.

Abbreviations

BIC: 

Bayesian Information Criterion

Br: 

Broiler

CoL: 

Cornish Black

CoR: 

Cornish Red

DAPC: 

Discriminant Analysis of Principal Components

EtBr: 

Ethidium Bromide

Hexp: 

Expected heterozygosity

Hh: 

Hanhyup-3

Hobs: 

Observed heterozygosity

Hn: 

Hyunin

ISAG: 

International Society for Animal Genetics

KNC: 

Korean native chicken

MS: 

Microsatellite

Na: 

Number of allele

NIAS: 

National Institute of Animal Science

NJ: 

Neighbor-joining

O: 

Korean Ogye

PCR: 

Polymerase Chain Reaction

PI: 

Probability of identity

PIC: 

Polymorphic Information content

QTL: 

Quantitative Trait Locus

RIR: 

Rhode Island Red

SSR: 

Simple-Sequence Repeat

WM1: 

Woorimatdag version 1

WM2: 

Woorimatdag verson 2

Declarations

Acknowledgements

This study was supported by a grant from the Golden Seed Project (No. 2013005042SB730), Korea Institute of Planning & Evaluation for Technology in Food, Agriculture Forestry & Fisheries (IPET), Republic of Korea.

Authors’ Affiliations

(1)
Department of Animal Science and Biotechnology, College of Agriculture and Life Sciences, Chungnam National University
(2)
INRA-Tunisie, Laboratoire des Productions Animales et Fourragères
(3)
Poultry Science Division, National Institute of Animal Science, RDA
(4)
Department of Agricultural Biotechnology, Center for Food and Bioconvergence, and Research Institute for Agriculture and Life Science, Seoul National University

References

  1. Ministry of Agriculture, Food and Rural Affairs. Primary statistics of Ministry of Agriculture, Food and Rural Affairs. 2014, Korea
  2. Jaturasitha S, Srikanchai T, Kreuzer M, Wicke M. Differences in carcass and meat characteristics between chicken indigenous to northern Thailand (Black-boned and Thai native) and imported extensive breeds (Bresse and Rhode Island Red). Poult Sci. 2008;87:160–9.View ArticlePubMedGoogle Scholar
  3. Heo KN, Choo HJ, Seo BY, Park MN, Jung KC, Hwangbo J, et al. Investigation of TYR and MC1R polymorphisms in Korean native chickens and the commercial chickens. CNU J Agric Sci. 2011;38:465–71.Google Scholar
  4. Park MN, Hong EC, Kang BS, Kim HK, Kim JH, Na SH, et al. Chemical composition and meat quality of crossbred Korean native chickens (KNC). Korean J Poult Sci. 2010;37:415–21.View ArticleGoogle Scholar
  5. Jung YK, Jeon HJ, Jung S, Choe JH, Lee HJ, Heo KN, et al. Comparison of quality traits of thigh meat from Korean native chickens and broilers. Korean J Food Sci Ani resour. 2011;31:684–92.View ArticleGoogle Scholar
  6. Park MN, Kim TH, Lee HJ, Choi JA, Heo KN, Kim CD, et al. Genetic variatiion of chicken MC1R gene and associations with feather color of Korean native chicken (KNC) ‘Woorimatdag’. Korean J Poult Sci. 2013;40:139–45.View ArticleGoogle Scholar
  7. Hillel J, Groenen MA, Tixier-Boichard M, Korol AB, David L, Kirzhner VM, et al. Biodiversity of 52 chicken populations assessed by microsatellite typing of DNA pools. Genet Sel Evo. 2003;35:533–58.View ArticleGoogle Scholar
  8. Lim HT, Seo BY, Jung EJ, Yoo CK, Yoon DH, Jeon JT. A comparison of discriminating powers between 14 microsatellite markers and 60 SNP markers applicable to the cattle identification test. J Anim Sci Technol. 2009;51:353–60.View ArticleGoogle Scholar
  9. Lim HT, Seo BY, Jung EJ, Yoo CK, Zhong T, Cho IC, et al. Estabilishment of a microsatellite marker set for individual, pork brand and product origin identification in pigs. J Anim Sci Technol. 2009;51:201–6.View ArticleGoogle Scholar
  10. Cheng HH, Crittenden LB. Microsatellite markers for genetic mapping in the chicken. Poult Sci. 1994;73:539–46.View ArticlePubMedGoogle Scholar
  11. Crooijmans RP, Groen AF, Van Kampen AJ, Van der Beek S, Van der Poel JJ, Groenen MA. Microsatellite polymorphism in commercial broiler and layer lines estimated using pooled blood samples. Poult Sci. 1996;75:904–9.View ArticlePubMedGoogle Scholar
  12. Abasht B, Dekkers JCM, Lamont SJ. Review of quantitative trait loci identified in the chicken. Poult Sci. 2006;85:2079–96.View ArticlePubMedGoogle Scholar
  13. Food and Agriculture Organization of the United Nations. Molecular genetic characterization of animal genetic resources. Rome: FAO Animal production and Health Guidelines; 2011. No. 9: 84–85.Google Scholar
  14. Seo DW, Hoque MR, Choi NR, Sultana H, Park HB, Heo KN, et al. Discrimination of Korean native chicken lines using fifteen selected Microsatellite markers. Asian-Aust J Anim Sci. 2013;26:316–22.View ArticleGoogle Scholar
  15. Marshall T, Slate J, Kruuk L, Pemberton J. Statistical confidence for likelihood‐based paternity inference in natural populations. Mol Ecol. 1998;7:639–55.View ArticlePubMedGoogle Scholar
  16. Ayres KL, Overall AD. api‐calc 1.0: a computer program for calculating the average probability of identity allowing for substructure, inbreeding and the presence of close relatives. Mol Ecol Notes. 2004;4:315–8.View ArticleGoogle Scholar
  17. Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000;155:945–59.PubMed CentralPubMedGoogle Scholar
  18. Jombart T. adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics. 2008;24:1403–5.View ArticlePubMedGoogle Scholar
  19. Jombart T, Devillard S, Balloux F. Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genetics. 2010;11:94.View ArticlePubMed CentralPubMedGoogle Scholar
  20. Jombart T, Collins C, Solymos P, Ahmed I, Calboli F, Cori A. Package ‘adegenet’. ftp://mint.c3sl.ufpr.br/CRAN/web/packages/adegenet/adegenet.pdf. Accessed June 7, 2014.
  21. Nei M, Tajima F, Tateno Y. Accuracy of estimated phylogenetic trees from molecular data. J Mol Evol. 1983;19:153–70.View ArticlePubMedGoogle Scholar
  22. Liu K, Muse SV. PowerMarker: an integrated analysis environment for genetic marker analysis. Bioinformatics. 2005;21:2128–9.View ArticlePubMedGoogle Scholar
  23. Paradis E, Claude J, Strimmer K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics. 2004;20:289–90.View ArticlePubMedGoogle Scholar
  24. Huson DH, Bryant D. Application of phylogenetic networks in evolutionary studies. Mol Biol Evol. 2006;23:254–67.View ArticlePubMedGoogle Scholar
  25. Suh S, Cho CY, Kim JH, Choi SB, Kim YS, Kim H, et al. Analysis of genetic characteristics and probability of individual discrimination in Korean indigenous chicken brands by microsatellite marker. J Anim Sci Technol. 2013;55:185–94.View ArticleGoogle Scholar
  26. Botstein D, White RL, Skolnik M, Davis RW. Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am J Hum Genet. 1980;32:314–31.PubMed CentralPubMedGoogle Scholar
  27. Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol. 2005;14:2611–20.View ArticlePubMedGoogle Scholar

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© Choi et al.; licensee BioMed Central. 2015

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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