Research

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, Jun Heon Lee1
Author Information & Copyright
1Department of Animal Science and Biotechnology, College of Agriculture and Life Sciences, Chungnam National University, Daejeon, 305-764 Republic of Korea
2INRA-Tunisie, Laboratoire des Productions Animales et Fourragères, Rue Hédi Karray, 2049 Ariana Tunisia
3Poultry Science Division, National Institute of Animal Science, RDA, Cheonan, 331-801 Republic of Korea
4Department of Agricultural Biotechnology, Center for Food and Bioconvergence, and Research Institute for Agriculture and Life Science, Seoul National University, Seoul, 151-921 Republic of Korea

© 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.

Received: Dec 23, 2014 ; Accepted: Jan 20, 2015

Published Online: Feb 5, 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), PIhalf-sibs, and PIsibs 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.

Electronic supplementary material

The online version of this article (doi:10.1186/s40781-015-0044-6) contains supplementary material, which is available to authorized users.

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*
MarkerChr.DyeForward (5′ → 3′)Reverse (5′ → 3′)Allele size (bp)
ADL02681PETCTCCACCCCTCTCAGAACTACAACTTCCCATCTACCTACT105-117
MCW01111NEDGCTCCATGTGAAGTGGTTTAATGTCCACTTGTCAATGATG98-112
MCW01451FAMACTTTATTCTCCAAATTTGGCTAAACACAATGGCAACGGAAAC181-211
MCW00632FAMGGCTCCAAAAGCTTGTTCTTAGCTGAAAACCAGTAAAGCTTCTTAC132-150
MCW00872NEDATTTCTGCAGCCAACTTGGAGCTCAGGCAGTTCTCAAGAACA267-283
LEI01412FAMCGCATTTGATGCATAACACATGAAGGCAAACTCAGCTGGAACG220-242
MCW00392VICCATTGGACTGAGATGTCACTGCAGACATTTGTCTAATGGTACTGTTAC127-147
MCW02642FAMCTTACTTTTCACGACAGAAGCAGACTGAGTCACACTCGTAAG224-240
MCW02882FAMGATCTGCTTCTCTGCCCCATGGGTACTGTCACCAGAATGAGC108-122
MCW01273VICGAGTTCAGCAGGAATGGGATGTGCAATAAGAGAAGGTAAGGTC227-241
MCW00403VICACTCAAAAATGTGGTAGAATATAGACCGAAATTGAGCAGAAGTTA121-145
ADL03174FAMAGTTGGTTTCAGCCATCCATCCCAGAGCACACTGTCACTG178-204
LEI00944FAMGATCTCACCAGTATGAGCTGCTCTCACACTGTAACACAGTGC254-280
ADL02925FAMCCAAATCAGGCAAAACTTCTAAATGGCCTAAGGATGAGGA110-138
MCW00295VICGTGGACACCCATTTGTACCCTATGCATGCAATTCAGGACCGTGCA139-189
ROS00135NEDTGCTGCTCCTGGRAAATTGGAAAAGCCATGGAGGAATCA220-242
ADL01596VICGCCATTATTTTTCCCTGTGTCTCCCCAAAGTCATTAGCAG107-127
ROS00197NEDATGTACAGGTTCCAGTGTCCGCCAGTTCATACAACCTTGAGTTGG119-143
ADL02599VICCTCATTGCAGAGGAAGTTCTGTAATGGAGGATGCTCAGGT107-129
GCT00169NEDTCCAAGGTTCTCCAGTTCGGCATAAGGATAGCAACAG109-125
MCW022810PETGATCTCTGCATTACAAGCATGTTGCTGACCTGCTCATGCAAG221-239
MCW010413FAMTAGCACAACTCAAGCTGTGAGAGACTTGCACAGCTGTGTACC189-225
ROS008313VICCATTACAGCTCAGTGTTGGCATTGCAAGTGCTCTCCCATC109-129
MCW021313NEDGACAAGTCAACAACTTGCCAGCTGTTCACTTTAAGGACATGG288-316
MCW012314FAMCCACTAGAAAAGAACATCCTCGGCTGATGTAAGAAGGGATGA79-89
ADL029317PETGTAATCTAGAAACCCCATCTACATACCGCAGTCTTTGTTC105-119
MCW033017VICTGGACCTCATCAGTCTGACAGAATGTTCTCATAGAGTTCCTGC254-286
ADL030418FAMGGGGAGGAACTCTGGAAATGCCTCATGCTTCGTGCTTTTT137-159
LEI007426VICGACCTGGTCCTGACATGGGTGGTTTGCTGATTAGCCATCGCG224-240
LEI013528NEDCACAATGAAGGATGAATAGTGCAATTCACAGTTACACCTGAGG131-142

*Bold is selected 12 MS marker combination.

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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 (PIhalf-sibs) and random sibs (PIsibs) 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*
LocusNo. of alleleHObsHExpPIC
ADL026850.7830.7420.703
MCW011150.7890.6950.654
MCW014570.7540.6920.655
MCW006370.7750.7830.746
MCW008790.8330.7850.751
LEI014170.8320.7970.767
MCW003970.7380.7650.726
MCW026480.8770.8410.819
MCW028860.6080.6310.578
MCW012760.7420.6790.627
MCW004060.8130.6950.656
ADL031770.8190.7750.739
LEI009470.8450.7160.665
ADL029270.7740.7330.684
MCW0029130.8050.8150.790
ROS001380.8710.7710.734
ADL015970.8850.7890.757
ROS001980.7660.6430.581
ADL025960.6740.6570.620
GCT001690.1510.8040.773
MCW022860.8210.7730.740
MCW0104110.7430.8130.786
ROS008380.8180.7490.707
MCW0213110.8070.7980.771
MCW012350.7070.6870.627
ADL029370.7010.5540.522
MCW033060.4840.6430.592
ADL030450.5130.4740.443
LEI007460.850.7450.704
LEI013550.6590.5960.544
Mean7.170.7410.7210.682

*Bold is selected 12 MS marker combination.

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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, PIhalf-sibs, and PIsibs values were 3.23E-33, 5.03E-22, and 8.61E-08, respectively. A previous study, using 12 markers, reported PI, PIhalf-sibs, and PIsibs 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).

jast-57-0-5-g1
Figure 1. The expected probability of identity values among genotypes of random individuals (PI), random half-sib(PIhalf-sibs) and random sibs (PIsibs) were suggested markers for discrimination of chicken lines.
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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*
BbCoLCoRHhHnORIRWm1Wm2
Bb-
CoL0.3267-
CoR0.32510.3906-
Hh0.27150.31140.3805-
Hn0.41890.40900.47380.4460-
O0.45870.40270.45430.49720.5287-
RIR0.43710.47020.53820.34940.54920.6302-
Wm10.27910.13750.33650.24530.36670.43410.2478-
Wm20.71020.72250.70950.71210.76180.79140.78780.6904-

*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).

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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.

jast-57-0-5-g2
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).
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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.

jast-57-0-5-g3
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).
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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.

jast-57-0-5-g4
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).
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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.

Additional file

The calculated number of alleles (k), observed heterozygosity (HObs), expected heterozygosity (HExp), and polymorphic information content (PIC) values in eight chicken populations. jast-57-0-5-suppl1.xlsx

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

Notes

Competing interests

The author declares that they have no competing interests.

Authors’ contributions

NRC, DWS, KNH, CJ and JHL designed experiment, NRC, DWS and HS Carried out lab works, NRC, DWS, SBJ, HS, KNH analyzed data, NRC, DWS, SBJ, CJ, JHL written manuscript. All authors read and approved the final manuscript.

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.

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We are happy to announce some very good news to all of our authors and members.

Journal of Animal Science and Technology (JAST) is confirmed for inclusion into Scopus, and all articles published since 2018 have already been listed in SCOPUS.

Scopus is the world's largest abstract and citation database of peer-reviewed literature.
This news means that even more researchers and scientists will have access to high-quality research papers published in JAST.

JAST is currently indexed in SCOPUS, PubMed Central, DOAJ, Google Scholar, Crossref and Korea Citation Index.

We would like to ask for your continued interest in our journal.

Please feel free to reach out should you have any questions, comments or concerns.

Thank you.

JAST Editorial Office

SCOPUS Screenshot PDF
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