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. 2025 Aug 25;104(11):105738. doi: 10.1016/j.psj.2025.105738

The origin patterns, admixture, and selection signatures of the global gamecock populations

Xufang Ren a, Xiurong Zhao a, Haiying Li b, Huie Wang c, Xue Cheng a, Gang Wang a, Xianyao Li d, Lujiang Qu a,⁎
PMCID: PMC12683124  PMID: 40946655

Abstract

The gamecock is a special domestication product of chicken training for cockfighting. With the development of society, factors such as animal protection and social morality have led to a decline in the number of gamecocks. As a crucial category of genetic resources, more measures need to be taken for protection. In this study, we elucidated the origin patterns, admixture, and selection signatures of the global gamecock populations via genome resequencing data. Results suggested multiple origins of global gamecocks. Asian gamecocks have at least five distinct origins, whereas those from Europe and the Americas have a common origin. Admixtures widely occurred from gamecocks to commercial and local chickens. The influences of admixture events are mainly revealed in metabolism and immunity pathways. A total of twenty-four candidate genes were identified in gamecock populations. The TSNARE1 gene associated with emotion regulation and nervous system development, and two genes (the DGKB and GBE1) include specific variants, are regarded as important representatives which experienced strong selection during the domestication of the gamecocks. In summary, our study provides comprehensive genetic insights into the domestication of gamecocks worldwide and the utilization of gamecock genetic resources as breeding materials.

Keywords: Gamecock, Origin, Admixture, Selection signature, Genome resequencing

Introduction

Domestic chickens originated from wild red jungle fowl (RJF) in Southeast Asia (Wang et al., 2020). Driven by both artificial selection and environmental factors, various types of chickens have emerged, including commercial and local breeds. Gamecocks are chickens bred for and trained in cockfighting, and have rich historical and cultural significance in regions including Asia, Europe, and the Americas. The gamecock is an essential genetic resource with excellent muscle features and other unique attributes and has been employed in the process of creating new and elite domestic chicken breeds (Iwamoto et al., 1998; Uemoto et al., 2009), such as commercial Cornish broilers (Abdelmanova et al., 2021). Despite their historical and genetic significance, factors such as a change in social attitude towards cockfighting and the rise of commercial chicken breeds have led to a decrease in their numbers. Therefore, certain conservation strategies must be adopted for protection.

Understanding the genetic basis, including origin, population structure, and phenotypic differences, is important for effective protective measures and utilization of gamecocks. Published studies indicate that gamecocks worldwide have multiple origins and share genetic similarities that distinguish them from non-game chickens (Bendesky et al., 2024; Xu et al., 2025). Introgression exists between the gamecock and RJF (Xu et al., 2025). In addition, admixtures are widely distributed among gamecocks, commercial breeds, and local chicken breeds (Luo et al., 2020; Ren et al., 2023a). Genomic studies have identified several candidate genes associated with specific traits of gamecocks, the majority of which are associated with the development of muscles and the nervous system (Luo et al., 2020; Ren et al., 2023c; Bendesky et al., 2024; Xu et al., 2025). To better understand the origins of global gamecock populations and fully utilize their genetic diversity, it is necessary to incorporate more gamecock breeds from diverse regions and conduct detailed studies on the phenotypic genetic basis of each population. In addition, the role of gamecocks when they are mixed with commercial or local chickens needs to be defined.

In this study, the resequencing genome data of 343 chickens, including 123 gamecocks from different continents, 24 RJF, 164 commercial chickens, and 32 local chickens, were used to reveal the origin patterns, admixture events, and candidate genes for the characteristic traits of global gamecock populations. This study enhances our understanding of global gamecock genetic resources and provides valuable information for conservation and utilization of gamecocks.

Materials and methods

Ethics statements

This study was approved by the Animal Welfare Committee of China Agricultural University (approval number: AW01405202-1-01), and all chickens used in this study were handled according to the relevant regulations.

Sample collection and sequencing

Resequencing genome data from 343 chickens, including 123 gamecocks, 24 RJFs, 164 commercial chickens, and 32 local chickens, were used in this study (Table S1). Gamecocks included populations from China, Japan, Southeast Asia (Laos, Indonesia, Malaysia, Thailand, and Vietnam), Pakistan, Europe (France and Spain), and the Americas (America, Mexico, Peru, Puerto, Colombia, and Brazil). The five commercial breeds were Cornish (n = 2), Rhode Island Red (RIR, n = 66), White Leghorn (WLH, n = 59), an America commercial broiler (n = 19), and a French commercial broiler (n = 18). Among them, 127 commercial chickens (the Cornish, RIR, and WLH) and 10 Chinese gamecocks (the Xishuangbanna, (XSBN, n = 2), Zhangzhou (ZZ, n = 2), Luxi (LX, n = 2), Turpan (Tur, n = 2), and Henan (HN, n = 2)) were newly sequenced, the sequences of others were downloaded from published studies (PRJEB30270 (Qanbari et al., 2019), PRJCA012571 (Zhi et al., 2023), PRJNA973554 (Bendesky et al., 2024), SAMN14651083 (Luo et al., 2020), PRJNA453469 (Tian et al., 2020), PRJNA524911 (Noorai et al., 2019), and ChickenSD database (http://chicken.ynau.edu.cn/) (Wang et al., 2020)).

Blood samples from 137 newly sequenced chickens were obtained from the subpatagial veins. Genomic DNA was extracted using a TIANamp Blood DNA Kit DP348 (Tiangen Biotech Co. Ltd., Beijing, P.R. China). At least 3 μg of genomic DNA of each sample were used to construct the paired-end sequencing library, and then sequenced on the Illumina HiSeq X Ten and NovaSeq 6000 platforms (Illumina, San Diego, CA, United States) following the manufacturer’s instructions from Novogene.

Variants calling and filtering

Clean reads were obtained by removing low-quality reads from the raw data. The filtered reads were mapped to the GRCg6a reference genome using the Burrows-Wheeler Aligner (BWA-V0.7.17) (Li and Durbin, 2010) with default parameters. The mapping results were then converted into BAM format and sorted using SAMtools (version 1.16.1) (Li et al., 2009). Duplicate reads were removed using Picard (version 2.52.2; https://broadinstitute.github.io/picard/). GATK-V3.8 (McKenna et al., 2010) was then used to call the raw single nucleotide polymorphisms (SNPs) (QUAL < 30.0 ||QD < 5.0 || FS > 60.0 || MQ < 40.0 || MQRankSum < −12.5 || ReadPosRankSum < −8.0). PLINK (version 1.90) (Purcell et al., 2007) was used to further filter the data using the criteria –geno 0.1 –maf 0.05. SNPs on the sex chromosomes were also removed. Finally, 28,243,349 high-quality SNPs were selected for subsequent analyses.

Population genetic structure

A maximum likelihood tree for all samples, based on high-quality autosomal SNPs, was constructed using SNPhylo (20180901) (Lee et al., 2014). PCA was performed using PLINK (version 1.90) (Purcell et al., 2007). The genetic structure of the gamecock populations was inferred using ADMIXTURE (version 1.3.0) (Alexander et al., 2009) with default parameters. The predefined genetic clusters (K) were set to a range of 2–10 to encompass the maximum number of lineages, with the optimal K value determined to correspond to the smallest cross-validation error. MapMixture (version 1.2.0) (Jenkins, 2024) was used to visualize the results of the admixture analysis.

Test for gene flow and admixture events

TreeMix software (version 1.13) (Pickrell and Pritchard, 2012) was used to examine the potential gene flow across all populations. Migration events were set from 2 to 10, with RJF designated as the outgroup. The ABBA-BABA statistics (also called D statistics) were used to test for possible gene flow using genome-scale SNP data with Dsuite software (Malinsky et al., 2021). Given the tree topology (((P1, P2), P3), O), the ABBA event indicated that P2 shares more derived alleles with P3 (gene flow between P2 and P3), and the BABA pattern indicated that P1 shares more derived alleles with P3 (gene flow between P1 and P3). In this study, RJF was used as an outgroup, P1 was one of all populations except for RJF, P2 was a commercial or local chicken breed, and P3 was a gamecock population. The Dtrios module calculated the d-value and f4-ratio statistics for all possible population trios. Subsequently, the D investigation module was used to evaluate the introgression level and locate the gene flow region across the whole genome using a sliding window containing 2500 informative SNPs with a step of 500 SNPs. We considered windows in the top 1 ‰ of the distribution of d-values as candidate gene flow regions. We annotated genes in these regions using the Biomart module of Ensembl (http://www.ensembl.org/biomart/martview). KEGG pathway analysis was performed using the KOBAS (version 3.0) online tool (Bu et al., 2021).

Selective signature analysis

Although all gamecock breeds were bred for fighting and some similar traits were identified, the candidate genes selected in gamecocks from different regions need to be studied separately. The selected gene sets of different gamecock populations reflect the selection received during the gamecock domestication process. The gamecock populations were divided into seven groups (China, Japan, Pakistan, Europe, Southeast Asia (SEA), North America (NA), and South America (SA)) according to the country or region. To identify selective sweeps between gamecocks and RJF, we calculated the fixation index (FST) and θπ ratio (gamecock/RJF) using a sliding-window approach with 40-kb windows and 20-kb steps. FST values were computed using the methodology proposed by Weir and Cockerham (1984). Subsequently, the θπ ratio underwent a log10 transformation. Windows that ranked in the top 1 % for both FST and the log10-transformed θπ ratio were designated as candidate selection regions. Finally, we used BioMart from Ensembl (http://www.ensembl.org/biomart/martview) to perform a more detailed annotation of the candidate regions. The frequencies of all the SNPs in the candidate genes were calculated using vcftools (version 0.1.16) (Danecek et al., 2011).

Results

Sequencing mapping quality

Across all chicken samples sequenced in this study, a total of 914.5 million mapped reads was obtained, with an average genome depth of 9.55 ×, an average coverage of 94.82 % (Table S2). Sequences were mapped to the reference genome of chicken with an average of mapping ratio of 99.40 % (Table S2).

Phylogenetic analyses confirm multiple origins of gamecock

According to the maximum likelihood tree (Fig. 1, Figure S1), all samples roughly divided into two clades and gamecocks were not completely separated from the non-gamecock populations. Clade Ⅰ only converged gamecocks from China (the Zhangzhou, Xishuangbanna, Luxi, Henan, Turpan), Japan, Laos, Vietnam, Malaysia, Thailand, and Brazil. Clade Ⅱ not only gathered gamecocks from Indonesia, Pakistan, Europe (the France and Spain) and the Americas (the America, Mexico, Colombia, Peru, Puerto), but also included five commercial breeds and all local chickens. The Asian gamecocks can be traced back to at least five origins. Among them, Chinese gamecocks have two origins, one included the Turpan, Henan, and Luxi gamecocks, while another include the Xishuangbanna and Zhangzhou gamecocks. Gamecocks from Japan, Indonesia, and Pakistan have another three independent origins. Notably, within this phylogenetic tree, one gamecock from Brazil, one from Pakistan, and two from Indonesia did not cluster together in accordance with their respective geographical locations, which indicate that the likelihood of admixtures occurring between gamecock and non-gamecock populations. The PCA clustering result (Fig. 1b) also gave support to the ML tree, which all mirrored the multiple origins of global gamecocks.

Fig. 1.

Fig. 1

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Maximum likelihood phylogenetic tree and population structure of all gamecocks, showing the multiple origins of the global gamecocks. (a). The Genome-wide phylogenetic tree. (b) Principal component plot of the 123 gamecocks. (c-d) Population genetic structure of the 123 gamecocks examined via the program ADMIXTURE. The number of assumed genetic clusters K = 5 is shown.

Gene flows and admixture events widely exist between gamecocks and non-gamecocks

Gene flow detection is crucial for developing effective conservation strategies to maintain genetic diversity and the long-term viability. Migration events between populations were estimated using the TreeMix software. The results showed gene flows from Chinese gamecock to commercial Rhode Island Red (RIR) and Iran local chickens, and from Indonesia gamecock to Tibetan chicken, when the migration event was set to 4 (M = 4) (Figure S2).

Then, a model-based clustering analysis was used to explore the admixture events within gamecocks and provide additional insights to the phylogenetic position distribution. At K = 2, most of the Japan gamecocks and few Chinese gamecocks separated from all other gamecocks (Figure S3). At K = 4, the gamecocks from Pakistan, Japan, Europe and the Americas, formed three groups, while the fourth group included gamecocks from China and Southeast Asia (Figure S2). At K = 5 was selected based on its minimal cross-validation error rate (Table S3) was presented in Fig. 1c-d. Individuals clustered strongly into the five groups (Pakistan, Japan, the Americas and Europe, China, Southeast Asia). However, five groups were not completely isolated especially the gamecocks from China and Southeast Asia, which showed a great deal of admixtures existed within gamecock populations.

Admixtures from gamecock to non-gamecock may cause phenotypic changes. To further elucidate admixture regions between gamecock and non-gamecock, we calculated the d-statistics for each combination of non-gamecock (commercial and local) and gamecock populations. A total of 108 potential admixture events (Z-score > 3, p < 0.001) were identified between the gamecocks and commercial breeds, while 182 such events were detected between the gamecocks and local chickens (Table S5). Part of the admixture events of commercial Cornish chicken (Fig. 2a) and Araucana (Fig. 2b) local chicken are presented. The top 1 ‰ admixture regions of each commercial and local populations were annotated and KEGG pathway analysis was performed (Table S5, Table S6). Both commercial and local chickens were influenced by gamecocks in metabolic pathways, such as the starch and sucrose metabolism (Fig. 2c-d). Besides, some immune-related signaling pathways were enriched, such as the Lysosome pathway and Phagosome pathway.

Fig. 2.

Fig. 2

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Admixture analysis of gamecocks. d-statistics between gamecocks and commercial Cornish (a) and local Araucana (b). More details in Table S4. KEGG pathway analysis of candidate genes annotated by admixture regions from gamecocks to commercial (c) and local chickens (d).

Positively selected genes in gamecocks compared with the red junglefowl

Combined with FST and θπ ratio methods (Table S7, Table S8), candidate genes were identified range from 17 to 40 in different gamecock populations. Share selected genes varied from 10 to 31 (Fig. 3a, Table S9), whereas 5∼14 breed-specific genes were identified (Fig. 3b). Finally, a total of 24 protein coding genes were selected at least in two gamecock populations (Fig. 3c, Table S9). Among these genes, none of them is selected by all seven populations, and only the TSNARE1 gene is selected in six gamecock populations, and only the DGKB gene is selected in five gamecock populations (Fig. 3c). Scanning of the frequence of all SNPs in candidate genes, three SNPs in the intron of DGKB (chr2:27456686, chr2:27529268, chr2:27597083) (Fig. 3d-i), and one SNP in the intron of GBE1 (chr1:96515705) (Fig. 3j-m) showed higher mutation rate in gamecocks.

Fig. 3.

Fig. 3

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Genome-wide selection in gamecocks. (a) The numbers of shared genes and breed-specific genes in the candidate regions of each gamecock population. “Shared selected gene number” is the number of genes selected exceeding 2 gamecock populations. (b) Upset plot showing the overlap of differentially expressed genes in seven gamecock populations. (c) Veen plot shows methods using to define candidate genes and the number of candidate genes of gamecocks. Fst and the log10(θπ ratio) of gene DGKB (d-e). The white dotted lines represent the boundaries of gene. (f-i) The position of specific variants located in DGKB gene and the frequence of chr2:27456686, chr2:27529268, and chr2:27597083. The red triangles represent the positions of specific variants. Fst and the log10(θπ ratio) of gene GBE1 (j-k). (l-m) The position and the frequence of specific variant chr1:96515705 located in GBE1 gene.

Discussion

Using genomic data from 123 gamecocks from Asia, Europe, and the Americas, we systematically investigated the origin patterns, selection signatures of global gamecock populations, and admixture events from gamecocks to commercial and local populations to seek valuable insights into the breeding and utilization of gamecock genetic resources.

Our phylogenetic and population structure analysis results corroborate multiple origins of global gamecocks. The phylogenetic relationships of gamecocks from China, Japan, Vietnam, Malaysia, and Thailand were consistent with those of previous studies (Komiyama et al., 2003; Ren et al., 2023b; Bendesky et al., 2024; Xu et al., 2025). Gamecocks from Indonesia and Pakistan have two distinct and independent origins. Cluster analysis of gamecocks from Europe and the Americas uncovered a shared origin, providing compelling evidence that American gamecock breeds likely originated from gamecock breeds transported by early settlers from Europe. However, two Indonesian gamecocks, one Pakistani gamecock, and one Brazilian gamecock failed to cluster according to their geographical location, indicating the existence of admixture events within global gamecock populations. One published study reported introgression from wild jungle fowl into gamecocks (Xu et al., 2025). Considering the importance of gamecocks as breeding materials, we further elucidated admixture events from gamecocks to commercial and local chicken populations. The results indicate that admixture events were widespread between gamecocks and both commercial and local chicken populations. This suggests that gamecock and non-gamecock populations were not completely isolated during their evolutionary or breeding histories, with frequent gene flow occurring between them. Such patterns imply that they share relatively close genetic affinities or exert a long-term mutual influence on their genetic backgrounds.

During the domestication of gamecocks, a series of characteristic traits emerged, including more robust muscles, comb shapes, and aggressive behavior. These phenotypic alterations invariably left footprints in the genome. Twenty-four protein coding genes were identified using Fst and θπ ratio methods. These genes were associated with transcriptional regulation, metabolism, cell signal transduction, cell structure and transport, and neurodevelopment. Among these, SOX5, ETV1, LDB2, NPAS3, PLRG1, MEOX2, and RELA play crucial roles in cell differentiation, growth, and development, and responses to external stimuli by regulating gene transcription (Storbeck et al., 2009; Wright et al., 2009; Sha et al., 2011; Shekhar et al., 2018; Wang et al., 2022; Choi et al., 2023; Marie et al., 2024). Metabolism-related genes include GBE1, AGMO, DGKB, and SLC37A2, which are involved in the metabolism of glycogen, arginine, lipids, and carbohydrates, respectively, and maintain the cellular energy supply and metabolic balance (Chou and Mansfield, 2014; Li et al., 2020; Sailer et al., 2021). PTPRB, ADGRB3, STK32B, and DRAP1 are associated with cell signal transduction pathways that regulate cell behaviors, such as proliferation, differentiation, and migration (Hamann et al., 2015; Soady et al., 2017; Ciuculete et al., 2018; Huang et al., 2024). Cell structure and transport category included TSNARE1, SNX9, EPB41L1, MSANTD2, and MARK2. They are mainly involved in intracellular vesicle transport, membrane fusion, and cytoskeleton regulation, all of which affect cell morphology and structural stability (Lundmark and Carlsson, 2009; Liang et al., 2020; Plooster et al., 2021; Etchegaray et al., 2022; Pasapera et al., 2022). Four genes related to neurodevelopment and function–NRXN3, STXBP6, DLGAP4, and HEPACAM–are important for nervous system development, synapse formation, and neural signal transmission (Baldwin et al., 2021; Romero et al., 2022; Vinci et al., 2023; Zhang et al., 2023). Notably, TSNARE1 was the only gene that was identified in six gamecock populations, except for South American gamecocks. This gene plays a key role in multiple processes, such as neural signal transmission, neuroendocrine regulation, and intracellular substance transport (Ma et al., 2019; Alameda et al., 2023; Kimbrel et al., 2023). This further indicates that the nervous system of gamecocks underwent intense selection during domestication.

SNPs are important sources of genetic diversity. Some SNPs occur at a relatively high frequency in specific populations and can be used as genetic markers. We further screened all SNPs located in the candidate genes. Our results indicate three intronic variants in DGKB and one intronic variant in GBE1 as the most pronounced genetic distinctions between gamecocks and non-game chickens. DGKB is important for bone remodeling and glucose and lipid metabolism (Dupuis et al., 2010; Lovšin, 2023), whereas the biological function of GBE1 is mainly reflected in the synthesis and metabolism of glycogen (Malinska et al., 2021). Both of these genes may play a role in energy metabolism during the domestication of gamecocks, which aligns with the energy-demand characteristics of gamecocks. This suggests that their involvement in energy-related pathways is shaped by the selective pressures associated with the domestication process, ultimately contributing to the unique physiological requirements of gamecocks for activities, such as fighting and intense physical exertion. However, the specific role of these SNPs in the regulation of phenotypes requires further investigation.

In conclusion, we systematically examined the origin patterns, admixture events, and selective signatures of global gamecocks by using resequencing data. These results suggest that the global gamecocks have several origins. Admixture events occurred widely between gamecocks and commercial and local chickens. Gamecocks affect the metabolism- and immunity-related pathways in both commercial and local chickens. A range of genes, especially those related to nervous system and musculoskeletal development, were identified. Several intronic SNPs were found to have higher allele mutation frequencies in gamecock populations. Therefore, our results offer an in-depth and comprehensive perspective on the understanding and utilization of global gamecock genetic resources.

Funding

This work was supported by the Open Project of the Key Laboratory of Tarim Animal Husbandry Science and Technology Corps (HS202303) and Tarim University President's Fund Project (Natural Science) (ZNLH202501).

Acknowledgments

We gratefully acknowledge for support from High-performance Computing Platform of China Agricultural University and the SolAriot high-performance computing platform of the National Research Facility for Phenotypic and Genotypic Analysis of Model Animals (Beijing).

CRediT authorship contribution statement

Xufang Ren: Writing – original draft, Visualization, Software, Methodology, Investigation, Data curation. Xiurong Zhao: Software, Methodology, Data curation. Haiying Li: Writing – review & editing, Conceptualization. Huie Wang: Visualization, Resources, Data curation. Xue Cheng: Data curation. Gang Wang: Software. Xianyao Li: Resources, Data curation. Lujiang Qu: Writing – review & editing, Writing – original draft, Funding acquisition, Conceptualization.

Disclosures

The authors have no conflicts of interest to declare.

Footnotes

Scientific section: Genetics and Genomics

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.psj.2025.105738.

Appendix. Supplementary materials

mmc1.docx (621.5KB, docx)
mmc2.xlsx (235.8KB, xlsx)

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