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Full Research Article

Unveiling the Genomic Signatures for Tropical Adaptation in Kangayam Cattle by De-correlated Composite of Multiple Selection Signals

A
Asad Khan1,2
https://orcid.org/0000-0002-5329-1740
I
Ishmeet Kumar1
J
Jayesh Vyas1
R
Rajesh Kumar Bochlya1,2
P
Prasoon Nayak1,2
K
Kachave Mukund Ramesh1,2
K
K.N. Raja2,*
1Division of Animal Genetics and Breeding, ICAR-National Dairy Research Institute, Karnal-132 001, Haryana, India.
2Division of Animal Genetic Resources, ICAR-National Bureau of Animal Genetic Resources, Karnal-132 001, Haryana, India.

ABSTRACT

Background: This study aimed to identify genomic regions under selection in Kangayam cattle of Tamil Nadu using a de-correlated composite of multiple signals (DCMS) framework.

Methods: BovineHD SNP array data were retrieved from the WIDDE repository and the ICAR Krishi-Kosh portal. After quality control, autosomal SNPs were used for subsequent analyses. Fixation index, integrated haplotype score, modified haplotype homozygosity, Tajima’s D and nucleotide diversity, were calculated and integrated using the DCMS approach. Genomic windows with false discovery rate (FDR) adjusted q<0.001 scores were considered for subsequent analysis. Functional annotation, QTL enrichment, protein–protein interaction (PPI) network analysis and hub gene identification were performed to interpret biological relevance.

Result: Genomic regions identified after DCMS analysis, harbored genes related to muscle development, metabolism, immunity, thermotolerance, reproduction and milk composition, including MSTN, BMP7, PRKAG3, BoLA-DRB3, IL8R, ABCA1 and members of the SLCO gene family. PPI and hub gene analyses highlighted transport and metabolic pathways, with SLC22A7 and ABCC9 emerging as key nodes. This study presents the first DCMS-based selection signature map for Kangayam cattle, uncovering coordinated selection across interconnected biological pathways.

INTRODUCTION

India has the world’s largest livestock population, contributing about 31% to agricultural Gross Value Added (GVA) and 5.5% to the national GVA (BAHS, 2025). Besides its economic importance, livestock enhances resilience to climate variability and supports rural and tribal livelihoods (Khan et al., 2025b; Sharma et al., 2021a). Cattle constitute the largest livestock population with 193.46 million animals, placing India first globally. However, indigenous cattle populations have declined by 6%, highlighting the need for their conservation. Indigenous breeds are valued for their adaptation to local environments, cultural significance and economic importance (Vyas et al., 2026; Sharma et al., 2021b). Tamil Nadu, with ≈9.5 million cattle, is home to four indigenous breeds: Kangayam, Pullikulam, Umblacherry and Bargur. Among these, Kangayam, a Mysore-type breed native to the semi-arid region of western Tamil Nadu, is renowned for its draught ability and cultural significance (Fig 1). Developed by the Pattogar of Palayakottai, it is widely used for heavy transport and cultural events such as Jallikattu. The breed is characterized by a colour transition from red calves to grey adults, average body weights of 540 kg in bulls and 380 kg in cows and distinct morphological features (AGRI-IS). Despite its importance, the genetic basis of its adaptation remains poorly understood.

Fig 1: Breeding tract and geographical distribution of Kangayam cattle (Source: AGRI-IS).


       
Advances in next-generation sequencing and high-density SNP arrays have enabled the identification of genomic regions under selection (Khan et al., 2025a). Commonly used methods, including FST, iHS and XP-EHH, detect different aspects of selection (Voight et al., 2006), whereas composite approaches such as the de-correlated composite of multiple selection signals (DCMS) integrate multiple statistics to improve detection power (Grossman et al., 2010). By accounting for covariance among individual statistics, DCMS further enhances the accuracy of selection signature detection (Ma et al., 2015). Although widely applied in taurine cattle, DCMS-based studies in Indian zebu breeds remain limited to Sahiwal and Belahi (Illa et al., 2021; Muansangi et al., 2025; Chavan et al., 2025). In contrast, DCMS-based genomic studies in indigenous draught breeds from peninsular South India are scarce. Kangayam has historically been selected for strength, speed, trotting endurance and adaptation to hot semi-arid environments (Manomohan et al., 2021). However, agricultural mechanization, increasing maintenance costs and extensive crossbreeding with exotic taurine breeds have reduced the population and economic importance of indigenous draught cattle (Vani et al., 2022). Although conservation and selective breeding programmes have been initiated, the genomic basis of tropical adaptation in this breed remains poorly understood. Therefore, the present study aimed to identify genomic signatures associated with tropical adaptation in Kangayam cattle using DCMS approach.

MATERIALS AND METHODS

Data retrieval and quality control (QC)
 
A total of 192 cattle genotyped with the BovineHD-BeadChip (777,962 SNPs) were analyzed. Kangayam (n=16), Gir (n=15), Sahiwal (n=13), Tharparkar (n=17) and Ongole (n=17) genotypes were obtained from ICAR-Krishi Kosh portal (Dixit et al., 2020), whereas brown-swiss (n=22), holstein (n=54) and jersey (n=38) were retrieved from WIDDE repository (Sempéré et al., 2015). All analyses were performed at AGR Lab-II, ICAR-NBAGR, Karnal, from January to April 2026. The Kangayam cattle sample size was consistent with FAO guidelines (Lenstra, 2023), which state that approximately 15 animals per population are sufficient for detecting selection signatures when high-density SNP genotypes (i.e., >1 SNP per kb) are available. QC was performed using PLINK v1.9 (Purcell et al., 2007). Non-autosomal markers were excluded and SNPs with a call rate (CR≤95%), minor allele frequency (MAF≤0.05), Hardy-Weinberg equilibrium (HWE p≤0.001) and missing genotypes more than 10% were removed. The filtered genotypes were subsequently phased using SHAPEIT v2.17 for downstream analyses (Delaneau et al., 2012).
 
Inter-and intra-population selection signature analysis
 
Population level selection signatures were investigated using inter-population and intra-population approaches. To enable a Kangayam-versus-all comparison for identifying breed-specific selection signatures in Kangayam cattle, genetic differentiation between Kangayam and the pooled reference population comprising Gir, Sahiwal, Tharparkar, Ongole, Holstein, Jersey and Brown-Swiss was estimated as fixation index (FST) per SNP using PLINK v1.9, followed by smoothing in R v4.6.1 (Weir and Cockerham, 1984). Within population selection signatures were evaluated using haplotype and allele frequency-based statistics. Integrated haplotype score (iHS) was calculated from SHAPEIT v2.17 phased genotypes generated using 35 Markov chain Monte Carlo iterations comprising 7 burn in, 8 pruning and 20 main iterations with a genetic recombination map (Delaneau et al., 2012). SELSCAN v2.0 integrated extended haplotype homozygosity to an EHH threshold of 0.05 after excluding rare variants (MAF<0.05) and iHS values were normalized across derived allele frequency bins (Voight et al., 2006; Szpiech and Hernandez, 2014). Modified haplotype homozygosity (H12) and LASSI were estimated in LASSIP v1.1.1 from phased multilocus genotypes after excluding loci with more than 10% missing genotypes, using the likelihood ratio framework to detect hard and soft selective sweeps (Harris and DeGiorgio, 2020). Tajima’s D and nucleotide diversity (π) were estimated in VCFTOOLS v0.1.16 (Danecek et al., 2011). Tajima’s D was calculated for each breed and chromosome using nonoverlapping 300-kb windows, with missing values replaced by zero. Nucleotide diversity was estimated using the-site-pi option and both statistics were smoothed in R v4.6.1 using the runmed function with a 31 SNP window (k = 31, endrule = “constant”) (Tajima, 1989; Nei and Li, 1979).
 
Integration of multi-statistic selection signals
 
Selection signatures were identified using the DCMS approach by integrating FST, iHS, H12, π and Tajima’s D (Ma et al., 2015). Given the high marker density of the BovineHD-BeadChip (>777,000 SNPs), summary statistics were aggregated into non-overlapping 10-kb windows to minimize stochastic variation from individual SNPs while retaining sufficient resolution for candidate region detection and maintaining a balance between genomic resolution and statistical robustness (Yurchenko et al., 2018). Within each window, weighted mean FST and maximum absolute iHS values were retained. A Spearman correlation matrix derived weights from absolute correlations to reduce redundancy among statistics following the decorrelation procedure implemented in the MINOTAUR framework (Verity et al., 2017). DCMS scores were calculated as the weighted sum of log10 transformed p-values derived from fractional ranks, according to the formula:

 
Significant regions were defined by values exceeding three standard deviations above the mean and FDR adjusted q<0.001, with the top 1% prioritized. Data processing, statistical integration and visualization were performed in R v4.6.1 using dplyr, data.table, ggplot2 and openxlsx for downstream functional analyses.
 
Gene annotation, functional enrichment and network analysis
 
Genomic regions with q<0.001 were annotated for candidate genes and QTLs using GALLO v1.4 with the UMD3.1.1 assembly and Animal QTL Database (Fonseca et al., 2020; Rosen et al., 2018; Hu et al., 2013). Chromosome based enrichment and pathway analysis were performed using PANTHER v19.0 (Thomas et al., 2003). Protein coding genes within DCMS peaks were further analyzed using STRING v12.0 to construct protein interaction networks (Mering et al., 2003), visualized in Cytoscape v3.10.4 and hub genes were identified using the MCC algorithm in CytoHubba (Chin et al., 2014).

RESULTS AND DISCUSSION

De-correlated composite of multiple signals (DCMS)
 
After QC, 684,048 high quality autosomal SNPs were retained. The genome wide distribution of selection signals is presented in Fig 2. Pairwise correlation analysis showed strong positive correlation between nucleotide diversity (π) and H12, indicating overlapping low diversity regions, whereas iHS showed negative correlations with H12 and π, reflecting different sweep dynamics. FST displayed weak correlations with iHS and π, consistent with its role in population differentiation (Table 1). These complementary patterns justified DCMS integration. More than 5000 significant windows were detected, of which the top 1% were prioritized, yielding 53 genomic windows containing 47 unique annotated genes, with q-values as low as 2.45 × 10-6 and a maximum DCMS score of 4.677 (Supplementary Table S1).

Fig 2: Manhattan plot showing the genomic regions identified by the DCMS in Kangayam cattle.



Table 1: Correlation between various statistical approach used to detect selection signature in Kangayam cattle.



Supplementary Table S1: Candidate genes and their associated biological roles identified through gene annotation and QTL enrichment of significant DCMS genomic windows.


 
Gene annotation functional enrichment and network analysis
 
Analysis of significant DCMS regions using the bovine QTL database identified protein coding genes and QTLs reflecting combined natural and artificial selection in Kangayam cattle. Functional annotation showed enrichment for milk production, growth, feed efficiency, carcass traits, immunity, reproduction and morphology, indicating genomic regions previously associated with tropical adaptation and economically important traits. QTL enrichment revealed trait specific signals across multiple chromosomes, with milk related traits showing the greatest enrichment (Fig 3). Milk fat yield showed the highest enrichment followed by milk fat percentage and the milk C14 index, suggesting strong selection on milk composition. Moderate enrichment was observed for myristoleic, caprylic, capric, palmitoleic and lauric acids, milk phosphorus content, milk protein yield and protein percentage, whereas body weight, ketosis, marbling, tenderness and shear force showed comparatively lower enrichment. Overall, milk related QTLs were most abundant (1766), followed by meat and carcass (587), production (389), reproduction (303) and relatively fewer health and exterior QTLs (Fig 4). Milk fatty acid composition is an economically important trait influenced by genes regulating lipid synthesis and milk quality, including stearoyl CoA desaturase (Yadav et al., 2026).

Fig 3: QTL enrichment across various traits in Kangayam cattle.



Fig 4: Distribution of annotated QTLs across major trait categories in Kangayam cattle.


       
Significant DCMS windows were distributed across multiple autosomes and overlapped genes previously associated with production, tropical adaptation, immunity, reproduction and metabolism. Genes related to muscle development, growth and energy metabolism included MSTN, BMP7, PRKAG3, PHF20 and UQCC1, which have been associated with skeletal muscle development, carcass traits, glycogen metabolism and production traits (Szabó et al., 2025; Costilla et al., 2023; Sun et al., 2023). Genes related to milk production and composition included SLC37A1, CSN3, ABCA1, SLCO1A2, SLCO1B3 and PDE9A, which have previously been linked with milk yield, milk fat composition, mineral transport, lipid metabolism and milk protein traits (Sanchez et al., 2021; Pedrosa et al., 2021; Barwar et al., 2023; Liu et al., 2024), consistent with the predominance of milk related QTLs. Selection signals also overlapped immune related genes including OLFM4, ITCH, BoLA DRB3, PRNP, NINJ2 and CTPS1, previously implicated in immune response, inflammation and disease resistance (McHugo et al., 2025; Holloway et al., 2024; Bykova et al., 2023; Imran et al., 2012; Roshan et al., 2025; Rios et al., 2023). Transporter and metabolic genes including ABCC9, ABCA1, SLC22A7, SLC10A1, SLCO1A2, SLCO1C1, PHYH and GSS have previously been associated with cellular transport, lipid metabolism, oxidative metabolism and homeostasis (Pedrosa et al., 2021; Liu et al., 2024; Yang et al., 2025; Neto et al., 2026), whereas reproductive genes FER1L5, RLF, RIMBP2 and ADAMTSL3 have been linked with fertility and developmental processes (Neupane et al., 2018; Rios et al., 2023; Fathoni et al., 2024).
       
Although DCMS studies in Indian cattle have been limited to Sahiwal and Belahi (Muansangi et al., 2025; Chavan et al., 2025), selection signature studies in Gir, Ongole, Hariana and Tharparkar have also identified candidate genes associated with production, immunity, reproduction and tropical adaptation (Dixit et al., 2021; Sukhija et al., 2024; Dash et al., 2025). Similarly, the present analysis identified candidate genomic regions overlapping genes previously associated with these biological processes, while the genomic signatures observed in Kangayam likely reflect its distinct evolutionary history and long-term selection as a specialized Indian draught breed of southern peninsular.
 
Gene network analysis and hub genes identification
 
The PPI network revealed coordinated biological interactions among candidate genes (Fig 5), indicating that DCMS identified interconnected genomic regions rather than isolated loci. A major cluster comprised transporter genes ABCC9, SLCO1A2, SLCO1C1 and ABCA1, previously associated with transport and cellular homeostasis (Pedrosa et al., 2021; Liu et al., 2024). Another cluster containing SLC37A1, ABCA1 and ADAMTSL3 supported coordinated metabolic regulation (Sanchez et al., 2021), whereas MSTN, BMP7 and PRKAG3 linked muscle development with metabolic processes (Szabó et al., 2025). Cytoscape identified SLC22A7 and ABCC9 as major hub genes, with SLC10A1 and SLCO1A2 as secondary hubs (Fig 6). SLC22A7 mediates transport of endogenous metabolites and xenobiotics and contributes to metabolic homeostasis (Momper and Nigam, 2018), whereas ABCC9 encodes the sulfonylurea-receptor-2, a regulatory subunit of ATP sensitive potassium channels involved in cellular energy sensing, ion transport and protection against metabolic stress (Flagg et al., 2010).

Fig 5: PPI network of candidate genes under selection in Kangayam cattle.



Fig 6: Hub gene network of candidate genes under selection in Kangayam cattle.


       
Although these candidate genes overlap genomic regions previously associated with tropical adaptation and economically important traits in cattle, the present findings are based on computational analyses. Furthermore, high density SNP array data may underrepresent rare or population specific variants because of ascertainment bias. Future integration of whole genome sequencing, transcriptomics, functional genomics and larger populations will enable higher resolution characterization and functional validation of candidate genes underlying tropical adaptation in Kangayam cattle.

CONCLUSION

The DCMS framework identified genomic regions under selection in Kangayam cattle and provided insights into the genetic basis of tropical adaptation, endurance and production traits. A total of 53 significant regions enriched 47 genes related to muscle development, metabolism, immunity, thermotolerance, reproduction and milk traits, including MSTN, BMP7, PRKAG3, BoLA DRB3, IL8R, ABCA1 and SLCO family genes. QTL enrichment demonstrated the predominance of milk, muscle and health related traits, whereas network analysis identified SLC22A7 and ABCC9 as major hub genes, highlighting coordinated metabolic and transport pathways. Although these findings provide valuable genomic resources, they are based on computational analyses and require functional validation. Future integration of whole genome sequencing, transcriptomics and functional genomics will improve understanding of the molecular mechanisms underlying tropical adaptation in Kangayam cattle and support the conservation, genomic characterization and future breeding of indigenous Indian cattle.

ACKNOWLEDGEMENT

The authors acknowledge the WIDDE database (http://widde.toulouse.inra.fr/widde/), ICAR Krishi-Kosh portal (http://krishi.icar.gov.in/jspui/handle/123456789/31167) and all contributors of these datasets for providing cattle SNP array data. The authors express sincere gratitude to the Directors of ICAR-NDRI, Karnal and ICAR-NBAGR, Karnal, the Heads of AGB Division, ICAR-NDRI and AG Division, ICAR-NBAGR, for providing the necessary research facilities.
 
Disclaimers
 
The views and conclusions expressed in this article are solely those of the authors and do not necessarily represent the views of their affiliated institutions. The authors are responsible for the accuracy and completeness of the information provided, but do not accept any liability for any direct or indirect losses resulting from the use of this content.
 
Informed consent
 
Not applicable as this study did not involve any live animal experimentation.

CONFLICT OF INTEREST

The authors declare that there are no conflicts of interest regarding the publication of this article. No funding or sponsorship influenced the design of the study, data collection, analysis, decision to publish, or preparation of the manuscript.

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    Full Research Article

    Unveiling the Genomic Signatures for Tropical Adaptation in Kangayam Cattle by De-correlated Composite of Multiple Selection Signals

    A
    Asad Khan1,2
    https://orcid.org/0000-0002-5329-1740
    I
    Ishmeet Kumar1
    J
    Jayesh Vyas1
    R
    Rajesh Kumar Bochlya1,2
    P
    Prasoon Nayak1,2
    K
    Kachave Mukund Ramesh1,2
    K
    K.N. Raja2,*
    1Division of Animal Genetics and Breeding, ICAR-National Dairy Research Institute, Karnal-132 001, Haryana, India.
    2Division of Animal Genetic Resources, ICAR-National Bureau of Animal Genetic Resources, Karnal-132 001, Haryana, India.

    ABSTRACT

    Background: This study aimed to identify genomic regions under selection in Kangayam cattle of Tamil Nadu using a de-correlated composite of multiple signals (DCMS) framework.

    Methods: BovineHD SNP array data were retrieved from the WIDDE repository and the ICAR Krishi-Kosh portal. After quality control, autosomal SNPs were used for subsequent analyses. Fixation index, integrated haplotype score, modified haplotype homozygosity, Tajima’s D and nucleotide diversity, were calculated and integrated using the DCMS approach. Genomic windows with false discovery rate (FDR) adjusted q<0.001 scores were considered for subsequent analysis. Functional annotation, QTL enrichment, protein–protein interaction (PPI) network analysis and hub gene identification were performed to interpret biological relevance.

    Result: Genomic regions identified after DCMS analysis, harbored genes related to muscle development, metabolism, immunity, thermotolerance, reproduction and milk composition, including MSTN, BMP7, PRKAG3, BoLA-DRB3, IL8R, ABCA1 and members of the SLCO gene family. PPI and hub gene analyses highlighted transport and metabolic pathways, with SLC22A7 and ABCC9 emerging as key nodes. This study presents the first DCMS-based selection signature map for Kangayam cattle, uncovering coordinated selection across interconnected biological pathways.

    INTRODUCTION

    India has the world’s largest livestock population, contributing about 31% to agricultural Gross Value Added (GVA) and 5.5% to the national GVA (BAHS, 2025). Besides its economic importance, livestock enhances resilience to climate variability and supports rural and tribal livelihoods (Khan et al., 2025b; Sharma et al., 2021a). Cattle constitute the largest livestock population with 193.46 million animals, placing India first globally. However, indigenous cattle populations have declined by 6%, highlighting the need for their conservation. Indigenous breeds are valued for their adaptation to local environments, cultural significance and economic importance (Vyas et al., 2026; Sharma et al., 2021b). Tamil Nadu, with ≈9.5 million cattle, is home to four indigenous breeds: Kangayam, Pullikulam, Umblacherry and Bargur. Among these, Kangayam, a Mysore-type breed native to the semi-arid region of western Tamil Nadu, is renowned for its draught ability and cultural significance (Fig 1). Developed by the Pattogar of Palayakottai, it is widely used for heavy transport and cultural events such as Jallikattu. The breed is characterized by a colour transition from red calves to grey adults, average body weights of 540 kg in bulls and 380 kg in cows and distinct morphological features (AGRI-IS). Despite its importance, the genetic basis of its adaptation remains poorly understood.

    Fig 1: Breeding tract and geographical distribution of Kangayam cattle (Source: AGRI-IS).


           
    Advances in next-generation sequencing and high-density SNP arrays have enabled the identification of genomic regions under selection (Khan et al., 2025a). Commonly used methods, including FST, iHS and XP-EHH, detect different aspects of selection (Voight et al., 2006), whereas composite approaches such as the de-correlated composite of multiple selection signals (DCMS) integrate multiple statistics to improve detection power (Grossman et al., 2010). By accounting for covariance among individual statistics, DCMS further enhances the accuracy of selection signature detection (Ma et al., 2015). Although widely applied in taurine cattle, DCMS-based studies in Indian zebu breeds remain limited to Sahiwal and Belahi (Illa et al., 2021; Muansangi et al., 2025; Chavan et al., 2025). In contrast, DCMS-based genomic studies in indigenous draught breeds from peninsular South India are scarce. Kangayam has historically been selected for strength, speed, trotting endurance and adaptation to hot semi-arid environments (Manomohan et al., 2021). However, agricultural mechanization, increasing maintenance costs and extensive crossbreeding with exotic taurine breeds have reduced the population and economic importance of indigenous draught cattle (Vani et al., 2022). Although conservation and selective breeding programmes have been initiated, the genomic basis of tropical adaptation in this breed remains poorly understood. Therefore, the present study aimed to identify genomic signatures associated with tropical adaptation in Kangayam cattle using DCMS approach.

    MATERIALS AND METHODS

    Data retrieval and quality control (QC)
     
    A total of 192 cattle genotyped with the BovineHD-BeadChip (777,962 SNPs) were analyzed. Kangayam (n=16), Gir (n=15), Sahiwal (n=13), Tharparkar (n=17) and Ongole (n=17) genotypes were obtained from ICAR-Krishi Kosh portal (Dixit et al., 2020), whereas brown-swiss (n=22), holstein (n=54) and jersey (n=38) were retrieved from WIDDE repository (Sempéré et al., 2015). All analyses were performed at AGR Lab-II, ICAR-NBAGR, Karnal, from January to April 2026. The Kangayam cattle sample size was consistent with FAO guidelines (Lenstra, 2023), which state that approximately 15 animals per population are sufficient for detecting selection signatures when high-density SNP genotypes (i.e., >1 SNP per kb) are available. QC was performed using PLINK v1.9 (Purcell et al., 2007). Non-autosomal markers were excluded and SNPs with a call rate (CR≤95%), minor allele frequency (MAF≤0.05), Hardy-Weinberg equilibrium (HWE p≤0.001) and missing genotypes more than 10% were removed. The filtered genotypes were subsequently phased using SHAPEIT v2.17 for downstream analyses (Delaneau et al., 2012).
     
    Inter-and intra-population selection signature analysis
     
    Population level selection signatures were investigated using inter-population and intra-population approaches. To enable a Kangayam-versus-all comparison for identifying breed-specific selection signatures in Kangayam cattle, genetic differentiation between Kangayam and the pooled reference population comprising Gir, Sahiwal, Tharparkar, Ongole, Holstein, Jersey and Brown-Swiss was estimated as fixation index (FST) per SNP using PLINK v1.9, followed by smoothing in R v4.6.1 (Weir and Cockerham, 1984). Within population selection signatures were evaluated using haplotype and allele frequency-based statistics. Integrated haplotype score (iHS) was calculated from SHAPEIT v2.17 phased genotypes generated using 35 Markov chain Monte Carlo iterations comprising 7 burn in, 8 pruning and 20 main iterations with a genetic recombination map (Delaneau et al., 2012). SELSCAN v2.0 integrated extended haplotype homozygosity to an EHH threshold of 0.05 after excluding rare variants (MAF<0.05) and iHS values were normalized across derived allele frequency bins (Voight et al., 2006; Szpiech and Hernandez, 2014). Modified haplotype homozygosity (H12) and LASSI were estimated in LASSIP v1.1.1 from phased multilocus genotypes after excluding loci with more than 10% missing genotypes, using the likelihood ratio framework to detect hard and soft selective sweeps (Harris and DeGiorgio, 2020). Tajima’s D and nucleotide diversity (π) were estimated in VCFTOOLS v0.1.16 (Danecek et al., 2011). Tajima’s D was calculated for each breed and chromosome using nonoverlapping 300-kb windows, with missing values replaced by zero. Nucleotide diversity was estimated using the-site-pi option and both statistics were smoothed in R v4.6.1 using the runmed function with a 31 SNP window (k = 31, endrule = “constant”) (Tajima, 1989; Nei and Li, 1979).
     
    Integration of multi-statistic selection signals
     
    Selection signatures were identified using the DCMS approach by integrating FST, iHS, H12, π and Tajima’s D (Ma et al., 2015). Given the high marker density of the BovineHD-BeadChip (>777,000 SNPs), summary statistics were aggregated into non-overlapping 10-kb windows to minimize stochastic variation from individual SNPs while retaining sufficient resolution for candidate region detection and maintaining a balance between genomic resolution and statistical robustness (Yurchenko et al., 2018). Within each window, weighted mean FST and maximum absolute iHS values were retained. A Spearman correlation matrix derived weights from absolute correlations to reduce redundancy among statistics following the decorrelation procedure implemented in the MINOTAUR framework (Verity et al., 2017). DCMS scores were calculated as the weighted sum of log10 transformed p-values derived from fractional ranks, according to the formula:

     
    Significant regions were defined by values exceeding three standard deviations above the mean and FDR adjusted q<0.001, with the top 1% prioritized. Data processing, statistical integration and visualization were performed in R v4.6.1 using dplyr, data.table, ggplot2 and openxlsx for downstream functional analyses.
     
    Gene annotation, functional enrichment and network analysis
     
    Genomic regions with q<0.001 were annotated for candidate genes and QTLs using GALLO v1.4 with the UMD3.1.1 assembly and Animal QTL Database (Fonseca et al., 2020; Rosen et al., 2018; Hu et al., 2013). Chromosome based enrichment and pathway analysis were performed using PANTHER v19.0 (Thomas et al., 2003). Protein coding genes within DCMS peaks were further analyzed using STRING v12.0 to construct protein interaction networks (Mering et al., 2003), visualized in Cytoscape v3.10.4 and hub genes were identified using the MCC algorithm in CytoHubba (Chin et al., 2014).

    RESULTS AND DISCUSSION

    De-correlated composite of multiple signals (DCMS)
     
    After QC, 684,048 high quality autosomal SNPs were retained. The genome wide distribution of selection signals is presented in Fig 2. Pairwise correlation analysis showed strong positive correlation between nucleotide diversity (π) and H12, indicating overlapping low diversity regions, whereas iHS showed negative correlations with H12 and π, reflecting different sweep dynamics. FST displayed weak correlations with iHS and π, consistent with its role in population differentiation (Table 1). These complementary patterns justified DCMS integration. More than 5000 significant windows were detected, of which the top 1% were prioritized, yielding 53 genomic windows containing 47 unique annotated genes, with q-values as low as 2.45 × 10-6 and a maximum DCMS score of 4.677 (Supplementary Table S1).

    Fig 2: Manhattan plot showing the genomic regions identified by the DCMS in Kangayam cattle.



    Table 1: Correlation between various statistical approach used to detect selection signature in Kangayam cattle.



    Supplementary Table S1: Candidate genes and their associated biological roles identified through gene annotation and QTL enrichment of significant DCMS genomic windows.


     
    Gene annotation functional enrichment and network analysis
     
    Analysis of significant DCMS regions using the bovine QTL database identified protein coding genes and QTLs reflecting combined natural and artificial selection in Kangayam cattle. Functional annotation showed enrichment for milk production, growth, feed efficiency, carcass traits, immunity, reproduction and morphology, indicating genomic regions previously associated with tropical adaptation and economically important traits. QTL enrichment revealed trait specific signals across multiple chromosomes, with milk related traits showing the greatest enrichment (Fig 3). Milk fat yield showed the highest enrichment followed by milk fat percentage and the milk C14 index, suggesting strong selection on milk composition. Moderate enrichment was observed for myristoleic, caprylic, capric, palmitoleic and lauric acids, milk phosphorus content, milk protein yield and protein percentage, whereas body weight, ketosis, marbling, tenderness and shear force showed comparatively lower enrichment. Overall, milk related QTLs were most abundant (1766), followed by meat and carcass (587), production (389), reproduction (303) and relatively fewer health and exterior QTLs (Fig 4). Milk fatty acid composition is an economically important trait influenced by genes regulating lipid synthesis and milk quality, including stearoyl CoA desaturase (Yadav et al., 2026).

    Fig 3: QTL enrichment across various traits in Kangayam cattle.



    Fig 4: Distribution of annotated QTLs across major trait categories in Kangayam cattle.


           
    Significant DCMS windows were distributed across multiple autosomes and overlapped genes previously associated with production, tropical adaptation, immunity, reproduction and metabolism. Genes related to muscle development, growth and energy metabolism included MSTN, BMP7, PRKAG3, PHF20 and UQCC1, which have been associated with skeletal muscle development, carcass traits, glycogen metabolism and production traits (Szabó et al., 2025; Costilla et al., 2023; Sun et al., 2023). Genes related to milk production and composition included SLC37A1, CSN3, ABCA1, SLCO1A2, SLCO1B3 and PDE9A, which have previously been linked with milk yield, milk fat composition, mineral transport, lipid metabolism and milk protein traits (Sanchez et al., 2021; Pedrosa et al., 2021; Barwar et al., 2023; Liu et al., 2024), consistent with the predominance of milk related QTLs. Selection signals also overlapped immune related genes including OLFM4, ITCH, BoLA DRB3, PRNP, NINJ2 and CTPS1, previously implicated in immune response, inflammation and disease resistance (McHugo et al., 2025; Holloway et al., 2024; Bykova et al., 2023; Imran et al., 2012; Roshan et al., 2025; Rios et al., 2023). Transporter and metabolic genes including ABCC9, ABCA1, SLC22A7, SLC10A1, SLCO1A2, SLCO1C1, PHYH and GSS have previously been associated with cellular transport, lipid metabolism, oxidative metabolism and homeostasis (Pedrosa et al., 2021; Liu et al., 2024; Yang et al., 2025; Neto et al., 2026), whereas reproductive genes FER1L5, RLF, RIMBP2 and ADAMTSL3 have been linked with fertility and developmental processes (Neupane et al., 2018; Rios et al., 2023; Fathoni et al., 2024).
           
    Although DCMS studies in Indian cattle have been limited to Sahiwal and Belahi (Muansangi et al., 2025; Chavan et al., 2025), selection signature studies in Gir, Ongole, Hariana and Tharparkar have also identified candidate genes associated with production, immunity, reproduction and tropical adaptation (Dixit et al., 2021; Sukhija et al., 2024; Dash et al., 2025). Similarly, the present analysis identified candidate genomic regions overlapping genes previously associated with these biological processes, while the genomic signatures observed in Kangayam likely reflect its distinct evolutionary history and long-term selection as a specialized Indian draught breed of southern peninsular.
     
    Gene network analysis and hub genes identification
     
    The PPI network revealed coordinated biological interactions among candidate genes (Fig 5), indicating that DCMS identified interconnected genomic regions rather than isolated loci. A major cluster comprised transporter genes ABCC9, SLCO1A2, SLCO1C1 and ABCA1, previously associated with transport and cellular homeostasis (Pedrosa et al., 2021; Liu et al., 2024). Another cluster containing SLC37A1, ABCA1 and ADAMTSL3 supported coordinated metabolic regulation (Sanchez et al., 2021), whereas MSTN, BMP7 and PRKAG3 linked muscle development with metabolic processes (Szabó et al., 2025). Cytoscape identified SLC22A7 and ABCC9 as major hub genes, with SLC10A1 and SLCO1A2 as secondary hubs (Fig 6). SLC22A7 mediates transport of endogenous metabolites and xenobiotics and contributes to metabolic homeostasis (Momper and Nigam, 2018), whereas ABCC9 encodes the sulfonylurea-receptor-2, a regulatory subunit of ATP sensitive potassium channels involved in cellular energy sensing, ion transport and protection against metabolic stress (Flagg et al., 2010).

    Fig 5: PPI network of candidate genes under selection in Kangayam cattle.



    Fig 6: Hub gene network of candidate genes under selection in Kangayam cattle.


           
    Although these candidate genes overlap genomic regions previously associated with tropical adaptation and economically important traits in cattle, the present findings are based on computational analyses. Furthermore, high density SNP array data may underrepresent rare or population specific variants because of ascertainment bias. Future integration of whole genome sequencing, transcriptomics, functional genomics and larger populations will enable higher resolution characterization and functional validation of candidate genes underlying tropical adaptation in Kangayam cattle.

    CONCLUSION

    The DCMS framework identified genomic regions under selection in Kangayam cattle and provided insights into the genetic basis of tropical adaptation, endurance and production traits. A total of 53 significant regions enriched 47 genes related to muscle development, metabolism, immunity, thermotolerance, reproduction and milk traits, including MSTN, BMP7, PRKAG3, BoLA DRB3, IL8R, ABCA1 and SLCO family genes. QTL enrichment demonstrated the predominance of milk, muscle and health related traits, whereas network analysis identified SLC22A7 and ABCC9 as major hub genes, highlighting coordinated metabolic and transport pathways. Although these findings provide valuable genomic resources, they are based on computational analyses and require functional validation. Future integration of whole genome sequencing, transcriptomics and functional genomics will improve understanding of the molecular mechanisms underlying tropical adaptation in Kangayam cattle and support the conservation, genomic characterization and future breeding of indigenous Indian cattle.

    ACKNOWLEDGEMENT

    The authors acknowledge the WIDDE database (http://widde.toulouse.inra.fr/widde/), ICAR Krishi-Kosh portal (http://krishi.icar.gov.in/jspui/handle/123456789/31167) and all contributors of these datasets for providing cattle SNP array data. The authors express sincere gratitude to the Directors of ICAR-NDRI, Karnal and ICAR-NBAGR, Karnal, the Heads of AGB Division, ICAR-NDRI and AG Division, ICAR-NBAGR, for providing the necessary research facilities.
     
    Disclaimers
     
    The views and conclusions expressed in this article are solely those of the authors and do not necessarily represent the views of their affiliated institutions. The authors are responsible for the accuracy and completeness of the information provided, but do not accept any liability for any direct or indirect losses resulting from the use of this content.
     
    Informed consent
     
    Not applicable as this study did not involve any live animal experimentation.

    CONFLICT OF INTEREST

    The authors declare that there are no conflicts of interest regarding the publication of this article. No funding or sponsorship influenced the design of the study, data collection, analysis, decision to publish, or preparation of the manuscript.

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