Estimating genetic diversity and population structure of 22 chicken breeds in Asia using microsatellite markers

Objective Estimating the genetic diversity and structures, both within and among chicken breeds, is critical for the identification and conservation of valuable genetic resources. In chickens, microsatellite (MS) marker polymorphisms have previously been widely used to evaluate these distinctions. Our objective was to analyze the genetic diversity and relationships among 22 chicken breeds in Asia based on allelic frequencies. Methods We used 469 genomic DNA samples from 22 chicken breeds from eight Asian countries (South Korea, KNG, KNB, KNR, KNW, KNY, KNO; Laos, LYO, LCH, LBB, LOU; Indonesia, INK, INS, ING; Vietnam, VTN, VNH; Mongolia, MGN; Kyrgyzstan, KGPS; Nepal, NPS; Sri Lanka, SBC) and three imported breeds (RIR, Rhode Island Red; WLG, White Leghorn; CON, Cornish). Their genetic diversity and phylogenetic relationships were analyzed using 20 MS markers. Results In total, 193 alleles were observed across all 20 MS markers, and the number of alleles ranged from 3 (MCW0103) to 20 (LEI0192) with a mean of 9.7 overall. The NPS breed had the highest expected heterozygosity (Hexp, 0.718±0.027) and polymorphism information content (PIC, 0.663±0.030). Additionally, the observed heterozygosity (Hobs) was highest in LCH (0.690±0.039), whereas WLG showed the lowest Hexp (0.372±0.055), Hobs (0.384±0.019), and PIC (0.325±0.049). Nei’s DA genetic distance was the closest between VTN and VNH (0.086), and farthest between KNG and MGN (0.503). Principal coordinate analysis showed similar results to the phylogenetic analysis, and three axes explained 56.2% of the variance (axis 1, 19.17%; 2, 18.92%; 3, 18.11%). STRUCTURE analysis revealed that the 22 chicken breeds should be divided into 20 clusters, based on the highest ΔK value (46.92). Conclusion This study provides a basis for future genetic variation studies and the development of conservation strategies for 22 chicken breeds in Asia.


INTRODUCTION
Prior to the Convention on Biological Diversity (CBD; Rio de Janeiro, Brazil) in 1992, ge netic resources had been recognized as common global resources, however, after the CBD, they began to be regarded not as common, but as the individual resources of each respective country. In 2007, the Interlaken Declaration was adopted by the United Nations Food and Agriculture Organization, which suggested that each country should preserve their own animal genetic resources and promote the sustainable use of local breeds [1]. Moreover, the Nagoya protocol on access to genetic resources and the fair and equitable sharing of their benefits, for the conservation and sustainable use of bio diversity [2], was adopted in October 2010 by the CBD, at the 10th Conference of the Parties. The importance of animal genetic resources has subsequently become established.
Animal genetic diversity is a source of raw genetic material that can be utilized to improve breeds and adapt livestock populations to changing environments and demands. Thus, acquiring information on animal genetic diversity is essen tial to design strategies for their sustainable management [3,4]. In Asia, there are many chicken breeds that are distinguished by phenotypic differences, such as feather color, shank color, and comb type. While there are more than 21 billion chick ens in the world, more than half of these (53%) are found in Asia. Among the common types of livestock kept by humans, chickens have the largest number of different breeds, at ap proximately 1,669, of which 1,514 are local breeds and 155 are regional or from areas that cross international bound aries [5]. However, due to the spread of imported breeds that have good commercial performance, the local breeds with poorer commercial performance have been ignored, to the point that some are now threatened by extinction [6]. The loss of a breed to extinction means the loss of its unique genetic resources, such as environmental adaptability and resistance to endemic diseases [7]. Therefore, it is necessary to develop a conservation strategy for local breeds, by study ing their genetic diversity.
To identify genetic uniqueness, many countries have eval uated the genetic diversity and relatedness of local breeds using DNA markers such as microsatellites (MS), mitochon drial DNA (mtDNA), copy number variation, and single nucleotide polymorphism (SNP) [811]. In recent years, SNPs have been widely used in genetic research. MS markers are comparatively cheap to genotype and provide more genetic information for the population per marker than SNPs, which are biallelic markers. Moreover, MS markers are easily typed in samples with low concentrations of DNA and enable quick identification of breeds in contrast to SNPs [12]. MS markers, also known as simplesequence repeats, have short tandem repeats of approximately 2 to 6 bp, and because they show codominant inheritance, are highly polymorphic, and are distributed throughout the genome [13,14], they are widely used to assess genetic diversity and relationships in many different fields [1518].
Although many studies have analyzed the genetic diversity and phylogenetic relatedness of chickens using MS markers, they have been limited, as their samples generally only come from breeds of their respective country or of a few countries [1922]. The National Institute of Animal Science (NIAS) in South Korea, however, has been carrying out the Asian Food & Agriculture Cooperation Initiative (AFACI) Animal Ge netic Resources (AnGR) project since 2016, for the purpose of improving the value of animal genetic resources across Asia.
Currently, 12 countries, including South Korea, are designated as member countries; NIAS has established a cooperative system by providing information and technologies for the characterization of animal genetic resources from these coun tries. NIAS is consequently able to utilize a large number of Asian chicken breed samples for scientific research. Conse quently, in this study, we have investigated the genetic diversity and relationships among 22 chicken breeds in eight AFACI member countries including three imported breeds, using 20 MS markers. Detailed information on these breeds can be found in Table  1. Ulnar venous blood of the six Korean native breeds and the three imported breeds was collected from the Animal Genetic Resources Research Center at NIAS. Genomic DNA was extracted from the blood using the Wizard Genomic DNA purification Kit (Promega, Madison, WI, USA) ac cording to the manufacturer's instructions. Genomic DNA of the other chicken breeds was obtained from each country, for the purposes of the AFACI AnGR project. The DNA concentrations were quantified by UV Spectrophotometer (Nanodrop ND1000; Thermo Scientific, Waltham, MA, USA) and the samples were diluted to a final concentration of 10 ng/μL in distilled water. This experiment was con ducted with the approval of the NIAS Committee on the Ethics of Animal Experiments (approval number: 2018048).

Microsatellite markers and polymerase chain reaction amplification
Ten MS markers were selected from among the International Society for Animal Genetics / Food and Agriculture Organi zation of the United Nations (ISAG/FAO) recommended markers. Another Ten MS markers were selected based on their high heterozygosity in the Ark database website (Roslin Bioinformatics Group, Edinburgh, UK). The information for the twenty MS markers used in this study is available in Sup plementary Table A. Extracted DNAs were amplified by the GeneAmp PCR 9700 system (Applied Biosystems, Foster, CA, USA) using AccuPower Negative dye PCR PreMix (Bi oneer, Daejeon, Korea), including DNA polymerase, dNTP, TrisHCl, KCL, and MgCl 2 . The polymerase chain reaction (PCR) reactions were performed in a total reaction volume of 20 μL containing 2 μL of template DNA, and 0.4 to 2.6 μL (2 pmol/μL) of primer based on the multiplex combinations. The initial denaturation was performed at 95°C for 5 min, followed by 35 cycles of 60 s at 95°C, 45 s of annealing at 58°C to 62°C based on the multiplex combination, 60 s of exten sion at 72°C, a final extension at 72°C for 30 min, and then cooling to 4°C.

Determining allele sizes in each marker
After PCR amplification, the genotyping reaction mixtures were made using 1 μL of the PCR products, 10 μL of HiDi Formamide (Applied Biosystems, USA), and the GeneScan 500 LIZ Size Standard (Applied Biosystems, USA) mixture. The genotyping reaction mixture was denatured for 10 min at 95°C and then immediately placed in ice. Electrophoresis was performed using capillary arrays in an ABI PRISM 3130xl Genetic Analyzer (Applied Biosystems, USA). The allele siz es were determined using GeneMapper Software 5 (Applied Biosystems, USA) and was analyzed statistically.

Statistical analysis
The allele frequencies, the number of alleles, expected het erozygosity (H exp ), observed heterozygosity (H obs ), and polymorphism information content (PIC) values for each of the chicken breeds across the 20 loci were calculated using the MS Tool Kit [23]. Nei's D A genetic distances between breeds were calculated using the DISPAN software [24]. The output file for the neighborjoining (NJ) phylogenetic tree was generated using the PHYLIP package [25] and visual ized using TreeView 1.6 [26].
The genetic structures and the degree of admixture among the 22 chicken breeds were analyzed using the Bayesian clus tering procedure of STRUCTURE ver 2.3.4 [27]. Twenty independent runs were performed for each K value from 2 to 22. For all runs, the admixture models had a burnin pe riod of 20,000 repeats, followed by 100,000 repeats of the Markov chain Monte Carlo algorithm. To identify the K val ue that best fits the data, STURCTURE HARVERSTER [28] was used, which implements the Evanno method [29]. The CLUMPP program ver 1.1.2 [30] was used to align the 20 repetitions of each K value. The CLUMPP output files were visualized using the DISTRUCT program ver 1.1 [31]. Prin cipal coordinate analysis (PCoA) was conducted using the adegenet package [32] in R Studio [33].

Genetic diversity of 22 chicken breeds using MS markers
To obtain insight into the genetic diversity and population structures, the H exp , H obs , and the PIC value for each locus were calculated using the MS Tool kit (  [37] reported that MS markers with PIC≥0.5 and H exp ≥0.6 were highly informative for ge netic analysis. Our study demonstrated that 13 of the 20 MS markers were highly informative for discrimination analysis, and would be appropriate for the analysis of the 22 chicken breeds. Furthermore, the genetic diversity parameters of the 22 chicken breeds were calculated using the MS Tool Kit ( Table  3). The mean number of alleles ranged from 2.15±0.93 (MGN)  [38], and this fixation was absent in the other Asian chicken breeds tested. Thus, we further investigated whether the genetic distances could be discriminated amongst between the 22 chicken breeds.

Genetic distance and phylogenetic analysis among 22 chicken breeds
To further investigate the genetic divergences among the  breeds using the 20 MS marker allele frequencies, we esti mated Nei's D A genetic distance between pairs of breeds, for all 22 chicken breeds, using the DISPAN program (Supple  mentary Table B); the shortest genetic distance was between VNH and VTN at 0.086, and the longest was between KNG and MGN at 0.503. Moreover, to understand the evolution ary relationships among the chicken breeds, an NJ phylogenetic tree was constructed using the PHYLIP program based on the D A genetic distance (Figure 1). In our study, three main branches appear in the phylogenetic tree. The first main branch comprised the KNC breeds (except for KNO), and the RIR and SBC breeds. The WLG, CON, KNO, MGN, and KGPS breeds constituted the second major branch, and this branch was further subdivided into the WLG and CON groups. KNO was grouped with CON, whereas MGN and KGPS were grouped with WLG. The third main branch was comprised of the other Asian chicken breeds. This suggests that the KNC breeds are clearly genetically separated from the other Asian chicken breeds that we studied.

Clustering and principal coordinate analysis
We conducted clustering analysis using Bayesian clustering, which provided more accurate estimates of relatedness of the breeds [39]. According to the STRUCTURE analysis the most probable number of inferred clusters and the K value (ΔK), was K = 20 (46.92). The genetic structures of each chicken breed (for K = 2, 4, 9, 14, and 20) were visualized using DIS TRUCT (Figure 2). For K = 2, the KNC breeds (excepting KNO) and SBC were clustered with RIR, whereas the other Asian chicken breeds were clustered with WLG and CON. WLG and MGN were distinguished from the other breeds at K = 4. These two breeds were found to differ the most in terms of genetic composition, compared with the other breeds. Differentiation from the other breeds began for RIR and KGPS at K = 9, and for CON at K = 14. Based on the CLUMPP analysis at K = 20 (Supplementary Table C), 11 breeds (RIR, WLG, CON, KNG, KNB, KNR, KNW, KNY, KNO, KPGS, and LCH) were detected in independent clus ter and each of these breeds occurred predominantly in one cluster (with more than 84% of its membership in one cluster). Moreover, MGN had 88.9% and WLG had 95.1% member ship in cluster 18, and the genetic distances between them were short (0.193). It was difficult to distinguish between WLG and MGN, suggesting that MGN was derived from WLG. SBC was also detected independently in cluster 1, but with a relatively low proportion of membership (68.3%). The proportion of membership of the other breeds ranged from 25.3% to 79.5% in cluster 13 (LOU) and 17 (INS), respectively. Generally, the genetic uniformity of the imported breeds (RIR, WLG, CON) and KNC breeds was higher than that of the other Asian chicken breeds, except for MGN, KPGS, and LCH. This is probably because these Asian chicken breeds have not gone through genetic fixation processes via strong selection processes. It means they were crossbreed. If genetic uniformity is low, it is difficult to determine whether a breed is distinct from other breeds. Therefore, to increase genetic uniformity, as long as it does not reduce genetic diversity through planned breeding, these Asian chicken breeds should be selected according to their specific purposes.
To assess the relatedness of breeds, we carried out PCoA analysis using the allele frequencies of the 20 MS markers (Figure 3). These results were similar to those of our phylo genetic tree and structure analysis. The percentages in the label of each axis indicate the variance explained by the axis. Three axes explained 56.2% of the variance; the first two ex plained 19.17% and 18.92%, respectively, and the third axis explained 18.11%. Remarkably, on the first axis, the Laotian, Vietnamese, and Indonesian chicken breeds were clearly sepa rated from the other breeds. In addition, WLG and MGN were completely isolated from the other breeds on the sec ond axis, whereas the Asian chicken breeds were not clearly separated from each other on this axis. On the other hand, CON and KNO were separated from the other breeds on the third axis.
In conclusion, the genetic diversity of the KNC and im ported breeds was lower than that of the other Asian chicken breeds, whereas their genetic uniformity was higher, except in the MGN, KGPS, and LCH. Therefore, selection for the specific characteristics of Asian chicken breeds used in this study is necessary to increase their genetic uniformity. More over, estimating the genetic diversity between the 22 Asian chicken breeds is the first step in a strategic plan for the genetic characterization and conservation of these breeds. Although the sample size of some of the breeds was small, our findings are meaningful in that the study was conducted using vari ous breeds from different countries. Additionally, this study may be useful as an initial guide for defining conservation objectives, for designing future investigations of genetic vari ation, and for developing conservation strategies for 22 Asian chicken breeds. However, further research is required to elu cidate the specific reasons for the genetic differences between the Asian breeds and the native Korean and imported breeds.

CONFLICT OF INTEREST
We certify that there is no conflict of interest with any financial organization regarding the material discussed in the manu script.

ACKNOWLEDGMENTS
This work was carried out with the support of the Asian Food & Agriculture Cooperation Initiative (AFACI) Animal Ge netic Resources (AnGR) project "Improving Animal Genetic Resources Value and Productive Performance in Asia, " and the Production and Management of Livestock Genetic Re sources Characteristic Information project (Project No. PJ 01098402) in Rural Development Administration, Republic of Korea.