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Full Research Article

Whole-genome Characterization of Genetic Diversity, Linkage Disequilibrium and Effective Population Size in Four Indian Goat Breeds from Diverse Agroecological Landscapes

M
Mukund Ramesh Kachave1,2
https://orcid.org/0009-0002-6640-5820
R
Reena Arora1
https://orcid.org/0000-0001-8740-7150
A
Asad Khan1,2
https://orcid.org/0000-0002-5329-1740
P
Pravin Babubhai Purohit1,2
R
Rajesh Kumar Bochlya1,2
S
Sonika Ahlawat1
https://orcid.org/0000-0002-4302-2969
R
Raja K.N.1,*
https://orcid.org/0000-0002-9029-6380
1ICAR-National Bureau of Animal Genetic Resources, Karnal-132 001, Haryana, India.
2ICAR-National Dairy Research Institute, Karnal-132 001, Haryana, India.

ABSTRACT

Background: Indian goats represent valuable genetic resources adapted to diverse agro-climatic conditions, yet their genome-wide diversity and demographic history remain inadequately characterized. This study assessed genomic variation, genetic diversity, linkage disequilibrium (LD) and historical effective population size (Ne) in Assam Hill, Bhakarwali, Nandidurga and Kutchi goats.

Methods: Whole-genome sequencing of 40 unrelated animals (10 from each breed) generated an average coverage of 9.14X. Reads were aligned to the Capra hircus ARS1.2 reference genome, followed by variant calling and quality filtering, yielding 22.22-28.20 million processed variants across breeds.

Result: Ts/Tv ratios were consistent (2.3141-2.3175). Observed and expected heterozygosity ranged from 0.2534-0.2746 and 0.2581-0.2763, respectively, while mean MAF ranged from 0.1758-0.1948. Genomic inbreeding was low (-0.0159-0.0323). LD declined progressively with physical distance, with Kutchi showing relatively greater LD persistence. LD-based Ne estimates revealed a substantial decline from historical to recent generations across all breeds. Overall, the breeds exhibited distinct genomic diversity and demographic patterns, providing valuable insights for breed-specific conservation and sustainable genetic utilization of indigenous Indian goats.

INTRODUCTION

The livestock sector is an important component of the Indian economy, contributing 5.5% and 31% to national and agricultural gross value added, respectively (BAHS, 2025). Goats are particularly important to landless and marginal farmers because of their low input requirements, adaptability, prolificacy and ease of marketing, providing meat, milk, skin and fibre under diverse production systems (Khan  et al., 2022, 2024). India has the world’s largest goat population and 45 recognized indigenous breeds (AGRI-IS), representing an important reservoir of genetic resources for production and adaptation (Bhalavi et al., 2021).
       
Despite their importance, the genome-wide diversity and demographic history of several indigenous Indian goat breeds remain inadequately characterized (Khan et al., 2025). Previous studies have demonstrated genetic differentiation among Indian goat populations associated with geography, ecological conditions, traditional management and natural and artificial selection (Radhika et al., 2018; Nair et al., 2021; Khan et al., 2023). However, comparative whole-genome characterization integrating genome-wide variation, genetic diversity, genomic inbreeding, linkage disequilibrium (LD) and historical effective population size (Ne) remains limited for several regionally important breeds. This limits a comprehensive understanding of their current genetic status and demographic history, which is essential for developing informed conservation and sustainable breeding strategies.
       
Assam Hill, Bhakarwali, Nandidurga and Kutchi goats were selected as they represent geographically and ecologically contrasting indigenous populations. Assam hill goats occur in Assam and Meghalaya and are maintained predominantly under low-input and open-grazing systems; Bhakarwali goats are associated with the mountainous and semi-migratory production systems of Jammu and Kashmir; Nandidurga goats are maintained in the rocky and hillock environments of Karnataka and kutchi goats are adapted to the arid conditions of Gujarat (AGRI-IS). Thus, these breeds provide a representative framework for examining how geographic separation and contrasting production environments are reflected in genome-wide genetic variation and demographic patterns.
       
Whole genome sequencing provides high-resolution characterization of genomic variation, including SNPs, insertions and deletions and enables estimation of nucleotide diversity, heterozygosity, minor allele frequency and genomic inbreeding (Benjelloun et al., 2019; Kanaka et al., 2023; Lu et al., 2025). LD patterns and their decay additionally provide insights into population structure and historical demographic processes, while LD-based Ne  helps assess changes in effective population size across generations. Therefore, the present study aimed to (i) characterize genome-wide SNP and indel variation and their functional annotation, (ii) assess genetic diversity and genomic inbreeding, (iii) evaluate genome-wide LD and its decay and reconstruct historical effective population size in Assam Hill, Bhakarwali, Nandidurga and Kutchi goats. By integrating these genomic measures, the study provides a comparative assessment of the genetic status and demographic history of four contrasting indigenous Indian goat breeds, generating information relevant to their conservation, sustainable breeding and utilization.

MATERIALS AND METHODS

Sample collection and library preparation
 
Blood samples had been collected previously from 40 unrelated, healthy adult (>1 year) goats, comprising 10 animals each from Assam Hill (AH), Bhakarwali (BKG), Nandidurga (ND) and Kutchi (KCG), from their respective native breeding tracts. Animals without known pedigree relationships were selected to minimize relatedness and represent within-breed variation. The samples were collected under the approval of the Institutional Animal Ethics Committee (IAEC) of ICAR-NBAGR, Karnal (Approval No. NBAGR/IAEC/2017, dated 21.08.2017). Genomic DNA was extracted using the phenol-chloroform method and assessed by agarose gel electrophoresis and Qubit fluorometry (Green and Sambrook, 2012). Approximately 500-bp insert libraries were prepared and sequenced on an Illumina NovaSeq 6000 platform to generate 150-bp paired-end reads. The resulting whole-genome sequencing data were subsequently processed and analyzed at AGR Lab-II, ICAR-NBAGR, Karnal, from February 2025 to January 2026, including quality control, read alignment, variant calling, variant filtering and genome-wide genetic analyses. No additional animal experimentation or biological sample collection was undertaken during the 2025–2026 genomic analysis period. Breed characteristics, breeding tracts and respective agroclimatic zones are presented in Table 1 and Fig 1.

Table 1: The characteristics of the goat breeds under study in relation to their agro-climatic regions, breeding tracts and utility.



Fig 1: Breeding tracts and geographical distribution of the indigenous goat breeds included in the present study.


 
Bioinformatics pipeline and read alignment
       
Raw paired-end reads were quality assessed using FastQC v0.11.9 (Andrews, 2010), followed by adapter and low-quality base removal at Phred <20 using Trimmomatic v0.36 (Bolger et al., 2014). High-quality reads were aligned to the Capra hircus ARS1.2 reference genome (GCF_001704415.2) using BWA-MEM v0.7.17 (Li and Durbin, 2009). SAM files were converted to BAM format using SAMtools (Li et al., 2009), followed by coordinate sorting and PCR-duplicate removal using SortSam and MarkDuplicates, respectively, in Picard v2.27.5. SNPs and indels were called independently for each individual using GATK HaplotypeCaller in GVCF mode (Poplin et al., 2017). Breed-wise GVCFs were combined using CombineGVCFs and jointly genotyped using GenotypeGVCFs.
       
Variant quality was assessed using GATK SelectVariants and VariantFiltration. SNPs were hard-filtered using the following criteria: Quality by Depth (QD) <2.0, Mapping Quality (MQ) <40.0, Fisher Strand (FS) >60.0, Strand Odds Ratio (SOR) >4.0, MQRankSum <-12.5 and ReadPosRankSum <-8.0. Indels were filtered using QD <2.0, FS >200.0 and SOR >10.0, following GATK hard-filtering recommendations. Filtered variants were excluded before downstream analyses. The variants were further filtered using VCFtools v0.1.16 and PLINK v1.9. Variants with minor allele frequency (MAF) <0.05, maximum missingness >50%, sequencing depth <10, base quality <50, HWE P<0.001, or more than two alleles were excluded. Individual-level missingness was restricted to 10%. The resulting high-confidence biallelic variants were converted to PLINK binary format for downstream population genetic analyses. Functional annotation of the retained variants was performed using SnpEff v5.2 (Cingolani et al., 2012) against the Capra hircus ARS1.2 annotation files (GCF_001704415.2). Variants were classified according to predicted functional impact as HIGH, MODERATE, LOW and MODIFIER and according to genomic location, including exonic, intronic, intergenic and regulatory regions.
 
Genetic diversity, linkage disequilibrium and effective population size
 
Genome-wide genetic diversity was assessed using PLINK v1.9 (Purcell et al., 2007). Minor allele frequency was calculated from the filtered SNP datasets using the-freq command, while observed heterozygosity (Ho), expected heterozygosity (He) and the genomic inbreeding coefficient (Fis) were estimated using the-het command. The PLINK Fis statistic was estimated from the deviation of observed homozygosity from the expected homozygosity under hardy-weinberg equilibrium.
       
Linkage disequilibrium (LD) was assessed as the squared correlation coefficient (r2) between pairs of SNPs at different physical distances. For LD-based analyses, SNPs were pruned using the PLINK-indep-pairwise 50 5 0.5 comamnd, Genome-wide LD decay within each breed was estimated using PopLDdecay v3.40 (Zhang et al., 2019) using its default parameters and mean r2 values were calculated across increasing physical-distance intervals. The observed LD patterns were subsequently used to estimate historical effective population size (Ne) using SNeP v1.1 (Barbato et al., 2015) with default parameters. The LD-based Ne estimates were used to assess changes in effective population size across generations and infer historical demographic trends in each breed. LD-decay curves and Ne trajectories for the four goat breeds were visualized using R.

RESULTS AND DISCUSSION

Sequencing, alignment and variant discovery
 
Whole-genome sequencing was performed on 40 goats representing four indigenous breeds, with 10 animals each from AH, BKG, ND and KCG. The mean genome coverage of 40 samples was 9.14X and after quality control, reads were aligned to the Capra hircus ARS1.2 reference genome with an average mapping rate of 99.6%. All 40 samples passed quality criteria and were retained for downstream analyses.
 
Genome-wide variant discovery and functional annotation
 
The four goat breeds exhibited extensive genome wide variation, with processed variants ranging from 22.22 million in KCG to 28.20 million in BKG, followed by ND (24.20 million) and AH (24.19 million), of which approximately 86 to 87% were SNPs. Ts/Tv ratios were highly consistent, ranging from 2.3141 in AH to 2.3175 in ND, with BKG and KCG recording 2.3157 and 2.3156, respectively, indicating comparable nucleotide substitution patterns. These values were similar to those reported by Guan et al., (2016) (2.3347 to 2.3552) and Xiong et al., (2023) (2.338) and within the range reported for Indian goats by Nandhini (2023) (1.547 to 2.4908). Chromosome wise variant distribution and density are presented in Fig 2 and 3, respectively. Variant numbers generally increased with chromosome length, although breed specific differences in density were observed, ranging genome wide from one variant per 87bp in BKG to one per 111 bp in KCG. Functional annotation revealed predominance of MODIFIER variants (>99.1%), whereas HIGH impact variants represented only 0.037 to 0.045%. Approximately 54% and 35% of variants occurred in intronic and intergenic regions, respectively, while less than 1% were exonic. This distribution was broadly comparable with Ethiopian goats (Belay et al., 2024) and Indian breeds (Nandhini, 2023), although differences may reflect reference assemblies, annotation approaches, variant calling, filtering and population composition. Synonymous and missense variants predominated among coding consequences, with a missense to synonymous ratio of approximately 0.69. High impact variants included frameshift, stop gained, stop lost and splice site variants, numbering 13,749 in BKG, 10,944 in AH, 10,235 in ND and 8,203 in KCG. The relatively low proportion of HIGH-impact variants is consistent with the expectation that strongly deleterious coding variants are generally less frequent in populations. However, this pattern alone does not provide direct evidence of purifying selection, as variant annotation, demographic history, allele-frequency distributions and stochastic processes may also influence the observed distribution of functional variants. Direct assessment of purifying selection would require additional functional or population-genetic analyses. Overall variant annotation characteristics are summarized in Table 2.

Fig 2: Chromosome-wise distribution of genomic variants among the four indigenous goat breeds under study (AH- Assam hill goat, BKG- Bhakarwali, ND- Nandidurga, KCG- Kutchi).



Fig 3: Chromosome-wise variant density (variants/Mb) across the four indigenous goat breeds under study (AH- Assam hill goat, BKG- Bhakarwali, ND- Nandidurga, KCG-Kutchi).



Table 2: Comparative genome-wide characterization of genetic variants and annotation profile of variants in the four indigenous goat breeds.


 
Genomic diversity, inbreeding and minor allele frequency
 
Genomic diversity showed modest variation among the four goat breeds (Table 3). Mean observed heterozygosity (Ho) ranged from 0.2534 in BKG to 0.2746 in ND, with the highest value in ND (0.2746), followed by KCG (0.2676), AH (0.2564) and BKG (0.2534). Expected heterozygosity (He) ranged from 0.2581 in BKG to 0.2763 in KCG, with KCG showing the highest value, followed by ND (0.2709), AH (0.2656) and BKG (0.2581). These estimates were generally within or below the ranges reported across Indian goat populations by Nandhini (2023) (Ho: 0.1194 to 0.5054; He: 0.2491 to 0.3789). Vijh et al., (2023) reported Ho values ranging from 0.2587 to 0.4470 across Indian goat breeds, for breeds common to both studies, the present Ho estimates were lower for Assam Hill (0.2564 vs. 0.4159), Bhakarwali (0.2534 vs. 0.2986) and Kutchi (0.2676 vs. 0.2834), but slightly higher for ND (0.2746 vs. 0.2587). Comparable levels of heterozygosity (Ho≈0.233; He≈0.308) were reported in Ethiopian goats by Sheriff et al., (2024). In contrast, microsatellite-based assessment of 27 Indian goat breeds by Dixit et al., (2021) reported higher overall Ho  (0.549) and He (0.669), including Ho of 0.586 and He of 0.726 in Kutchi goats. Differences among studies should be interpreted in the context of marker type and ascertainment, sample size, SNP filtering criteria, marker density, sequencing depth and population composition, in addition to underlying demographic history, genetic drift, effective population size, breeding practices and gene flow.

Table 3: Genome-wide observed heterozygosity (Ho), expected heterozygosity (He), minor allele frequency (MAF) and inbreeding coefficient (FIS) across the four indigenous goat breeds.


       
Mean minor allele frequency (MAF) ranged from 0.1758 in BKG to 0.1948 in KCG, with ND (0.1901) and AH (0.1863) showing intermediate values (Table 3). The comparatively lower MAF in BKG indicates a greater representation of low-frequency alleles, potentially reflecting differences in allele frequency distribution, genetic drift and demographic history, whereas the higher MAF in KCG suggests a relatively greater representation of intermediate-frequency alleles. These interpretations should therefore be considered indicative rather than definitive, as the sample size of 10 animals per breed may influence allele-frequency estimates, particularly for low-frequency variants. These estimates were lower than those reported by Nandhini (2023), which may partly reflect differences in SNP filtering and sample composition. Genomic FIS ranged from -0.0159 in ND to 0.0323 in AH. Positive FIS values in AH (0.0323), KCG (0.0286) and BKG (0.0181), accompanied by Ho lower than He, indicated only slight heterozygosity deficits, whereas the negative FIS in ND (-0.0159), where Ho exceeded He, indicated a slight heterozygote excess. Given the modest magnitude of these estimates and the sample size of 10 animals per breed, they are best interpreted as indications of the observed heterozygosity patterns in the sampled animals rather than as definitive evidence of population-level inbreeding. These estimates were comparable with the ranges reported in Ethiopian goats (-0.18 to 0.04) and Kazakh goats (-0.027 to 0.052), suggesting generally low deviations from hardy-weinberg expectations in the sampled animals (Gebreselase et al., 2024). Dixit et al., (2021), using microsatellite markers, reported a higher overall FIS of 0.192 across 27 Indian goat breeds and 0.205 specifically in Kutchi goats, further illustrating the influence of marker systems and analytical approaches on diversity and inbreeding estimates.
 
Linkage disequilibrium and effective population size
 
Genome wide linkage disequilibrium (LD), assessed as the squared correlation coefficient (r2) between SNP pairs, declined consistently with increasing physical distance across all four breeds (Table 4; Fig 4), reflecting the progressive breakdown of allelic associations through recombination. At 20kb, mean r2 values were 0.342, 0.315, 0.349 and 0.332 for AH, BKG, KC and ND, respectively, declining to 0.161, 0.152, 0.178 and 0.156 at 200kb and further to 0.139, 0.135, 0.157 and 0.137 at 1000 kb. KCG consistently exhibited the highest LD, whereas BKG showed the lowest values and comparatively faster LD decay. Similar patterns have been reported in other goat populations. Nandhini (2023) reported r2 values of 0.1361 to 0.3028 at 30kb, while Sun et al., (2022) observed a rapid decline from approximately 0.57 at short distances to 0.06 at 100 kb. The relatively persistent LD observed in KCG may reflect differences in effective population size, genetic drift, historical population structure, gene flow, or breed-specific mating patterns; however, the present data do not allow these factors to be distinguished. Similarly, the faster LD decay observed in BKG may be associated with differences in demographic history or population structure, but the underlying mechanism cannot be determined from the present analysis alone.

Table 4: Decay of linkage disequilibrium with increasing physical distance among the four indigenous goat breeds under study.



Fig 4: Genome-wide linkage disequilibrium decay based on mean r2 across increasing physical distances among the four indigenous goat breeds under study.


       
LD-based historical effective population size (Ne) estimates revealed a marked reduction from historical to recent generations across all breeds (Table 5; Fig 5). At approximately 995 generations ago, Ne was estimated at 2,674, 3,180, 2,592 and 3,060 for AH, BKG, Kutchi and ND, respectively, declining to 49, 49, 46 and 47 at approximately 13 generations ago. Thus, BKG exhibited the highest historical Ne and Kutchi the lowest, although all breeds converged towards similarly low recent values. A comparable historical decline in Ne has been reported in Ethiopian goats by Gebreselase et al., (2024). The observed decline may be consistent with demographic contraction, genetic drift, population subdivision, breed formation, or changes in breeding management; however, these potential drivers cannot be distinguished from LD patterns alone. Furthermore, LD-based Ne estimates should be regarded as approximate indicators of effective population size and should not be interpreted as direct estimates of census population size or genomic inbreeding.

Table 5: Estimates of effective population size (Ne) at different generations in the four goat breeds under study.



Fig 5: Dynamics of effective population size (Ne) among the four indigenous goat breeds under study over the generations.


       
Collectively, Ho, He, MAF, FIS, LD and Ne revealed distinct but complementary genomic and demographic patterns among breeds. ND showed the highest Ho and negative FIS, indicating relatively greater heterozygosity and slight heterozygote excess. KCG exhibited the highest He and MAF together with the highest LD, while BKG showed the lowest Ho, He and MAF but the highest historical Ne and fastest LD decay. AH exhibited intermediate diversity but the highest positive FIS, indicating a slight heterozygosity deficit. The consistently low recent LD-based Ne estimates may reflect reduced effective population size and greater potential influence of genetic drift in recent generations. However, given the sample size of 10 animals per breed, these estimates should be interpreted as indicative of the genomic patterns observed in the sampled populations rather than as definitive estimates of long-term demographic history. Overall, these breed-specific genomic and demographic profiles provide a basis for tailored conservation and sustainable genetic utilization strategies.

CONCLUSION

The present whole genome sequencing study revealed substantial genomic variation and distinct diversity and demographic patterns among Assam Hill, Bhakarwali, Nandidurga and Kutchi goats. Nandidurga exhibited the highest observed heterozygosity and a slight heterozygote excess, whereas Kutchi showed the highest expected heterozygosity and MAF, indicating comparatively greater representation of intermediate frequency alleles. The low genomic inbreeding estimates across all breeds suggest that substantial genetic variability is still maintained within these populations. However, breed specific LD patterns and the pronounced decline in effective population size from historical to recent generations indicate contrasting demographic trajectories and a progressive reduction in the effective breeding population. Particularly, the consistently low recent Ne across breeds highlights the potential for increased genetic drift and gradual loss of genetic diversity if this trend persists. The contrasting diversity, LD and demographic patterns further demonstrate that no single parameter adequately represents the genomic status of a breed and emphasize the value of their integrated assessment. Overall, these findings establish a genome wide baseline for these indigenous goat genetic resources and suggest that conservation and breeding programmes should focus not only on maintaining census numbers but also on preserving within breed genetic diversity, minimizing relatedness and maintaining adequate effective population sizes. Such breed specific genomic management would facilitate their long-term conservation while retaining the genetic variability required for future adaptation and sustainable utilization.

ACKNOWLEDGEMENT

This study was financially supported by the ICAR Consortium Research Platform on Genomics. The authors gratefully acknowledge the ICAR, New Delhi and the Director, ICAR–NBAGR, Karnal, for providing financial and institutional support. The authors also express their sincere gratitude to the Director, ICAR–NDRI, Karnal and the Head, AGB Division, ICAR–NDRI, for providing the necessary research facilities and support. The first author gratefully acknowledges ICAR–NDRI, Karnal, for the Institute Fellowship received during the PhD programme. The authors also acknowledge the Institutional Fellowship awarded by ICAR-NDRI to the first author in support of this research during the PhD programme.
 
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
 
The biological samples used in this study were collected under the approval of the Institutional Animal Ethics Committee (IAEC) of ICAR–NBAGR, Karnal (Approval No. NBAGR/IAEC/2017 dated 21.08.2017). The raw whole-genome sequencing data generated from these samples were subsequently processed and analysed during 2025-2026, including quality control, read alignment, variant calling, variant filtering and genome-wide genetic diversity analyses. No additional animal experimentation or sample collection was undertaken during the genomic analyses.

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

    Whole-genome Characterization of Genetic Diversity, Linkage Disequilibrium and Effective Population Size in Four Indian Goat Breeds from Diverse Agroecological Landscapes

    M
    Mukund Ramesh Kachave1,2
    https://orcid.org/0009-0002-6640-5820
    R
    Reena Arora1
    https://orcid.org/0000-0001-8740-7150
    A
    Asad Khan1,2
    https://orcid.org/0000-0002-5329-1740
    P
    Pravin Babubhai Purohit1,2
    R
    Rajesh Kumar Bochlya1,2
    S
    Sonika Ahlawat1
    https://orcid.org/0000-0002-4302-2969
    R
    Raja K.N.1,*
    https://orcid.org/0000-0002-9029-6380
    1ICAR-National Bureau of Animal Genetic Resources, Karnal-132 001, Haryana, India.
    2ICAR-National Dairy Research Institute, Karnal-132 001, Haryana, India.

    ABSTRACT

    Background: Indian goats represent valuable genetic resources adapted to diverse agro-climatic conditions, yet their genome-wide diversity and demographic history remain inadequately characterized. This study assessed genomic variation, genetic diversity, linkage disequilibrium (LD) and historical effective population size (Ne) in Assam Hill, Bhakarwali, Nandidurga and Kutchi goats.

    Methods: Whole-genome sequencing of 40 unrelated animals (10 from each breed) generated an average coverage of 9.14X. Reads were aligned to the Capra hircus ARS1.2 reference genome, followed by variant calling and quality filtering, yielding 22.22-28.20 million processed variants across breeds.

    Result: Ts/Tv ratios were consistent (2.3141-2.3175). Observed and expected heterozygosity ranged from 0.2534-0.2746 and 0.2581-0.2763, respectively, while mean MAF ranged from 0.1758-0.1948. Genomic inbreeding was low (-0.0159-0.0323). LD declined progressively with physical distance, with Kutchi showing relatively greater LD persistence. LD-based Ne estimates revealed a substantial decline from historical to recent generations across all breeds. Overall, the breeds exhibited distinct genomic diversity and demographic patterns, providing valuable insights for breed-specific conservation and sustainable genetic utilization of indigenous Indian goats.

    INTRODUCTION

    The livestock sector is an important component of the Indian economy, contributing 5.5% and 31% to national and agricultural gross value added, respectively (BAHS, 2025). Goats are particularly important to landless and marginal farmers because of their low input requirements, adaptability, prolificacy and ease of marketing, providing meat, milk, skin and fibre under diverse production systems (Khan  et al., 2022, 2024). India has the world’s largest goat population and 45 recognized indigenous breeds (AGRI-IS), representing an important reservoir of genetic resources for production and adaptation (Bhalavi et al., 2021).
           
    Despite their importance, the genome-wide diversity and demographic history of several indigenous Indian goat breeds remain inadequately characterized (Khan et al., 2025). Previous studies have demonstrated genetic differentiation among Indian goat populations associated with geography, ecological conditions, traditional management and natural and artificial selection (Radhika et al., 2018; Nair et al., 2021; Khan et al., 2023). However, comparative whole-genome characterization integrating genome-wide variation, genetic diversity, genomic inbreeding, linkage disequilibrium (LD) and historical effective population size (Ne) remains limited for several regionally important breeds. This limits a comprehensive understanding of their current genetic status and demographic history, which is essential for developing informed conservation and sustainable breeding strategies.
           
    Assam Hill, Bhakarwali, Nandidurga and Kutchi goats were selected as they represent geographically and ecologically contrasting indigenous populations. Assam hill goats occur in Assam and Meghalaya and are maintained predominantly under low-input and open-grazing systems; Bhakarwali goats are associated with the mountainous and semi-migratory production systems of Jammu and Kashmir; Nandidurga goats are maintained in the rocky and hillock environments of Karnataka and kutchi goats are adapted to the arid conditions of Gujarat (AGRI-IS). Thus, these breeds provide a representative framework for examining how geographic separation and contrasting production environments are reflected in genome-wide genetic variation and demographic patterns.
           
    Whole genome sequencing provides high-resolution characterization of genomic variation, including SNPs, insertions and deletions and enables estimation of nucleotide diversity, heterozygosity, minor allele frequency and genomic inbreeding (Benjelloun et al., 2019; Kanaka et al., 2023; Lu et al., 2025). LD patterns and their decay additionally provide insights into population structure and historical demographic processes, while LD-based Ne  helps assess changes in effective population size across generations. Therefore, the present study aimed to (i) characterize genome-wide SNP and indel variation and their functional annotation, (ii) assess genetic diversity and genomic inbreeding, (iii) evaluate genome-wide LD and its decay and reconstruct historical effective population size in Assam Hill, Bhakarwali, Nandidurga and Kutchi goats. By integrating these genomic measures, the study provides a comparative assessment of the genetic status and demographic history of four contrasting indigenous Indian goat breeds, generating information relevant to their conservation, sustainable breeding and utilization.

    MATERIALS AND METHODS

    Sample collection and library preparation
     
    Blood samples had been collected previously from 40 unrelated, healthy adult (>1 year) goats, comprising 10 animals each from Assam Hill (AH), Bhakarwali (BKG), Nandidurga (ND) and Kutchi (KCG), from their respective native breeding tracts. Animals without known pedigree relationships were selected to minimize relatedness and represent within-breed variation. The samples were collected under the approval of the Institutional Animal Ethics Committee (IAEC) of ICAR-NBAGR, Karnal (Approval No. NBAGR/IAEC/2017, dated 21.08.2017). Genomic DNA was extracted using the phenol-chloroform method and assessed by agarose gel electrophoresis and Qubit fluorometry (Green and Sambrook, 2012). Approximately 500-bp insert libraries were prepared and sequenced on an Illumina NovaSeq 6000 platform to generate 150-bp paired-end reads. The resulting whole-genome sequencing data were subsequently processed and analyzed at AGR Lab-II, ICAR-NBAGR, Karnal, from February 2025 to January 2026, including quality control, read alignment, variant calling, variant filtering and genome-wide genetic analyses. No additional animal experimentation or biological sample collection was undertaken during the 2025–2026 genomic analysis period. Breed characteristics, breeding tracts and respective agroclimatic zones are presented in Table 1 and Fig 1.

    Table 1: The characteristics of the goat breeds under study in relation to their agro-climatic regions, breeding tracts and utility.



    Fig 1: Breeding tracts and geographical distribution of the indigenous goat breeds included in the present study.


     
    Bioinformatics pipeline and read alignment
           
    Raw paired-end reads were quality assessed using FastQC v0.11.9 (Andrews, 2010), followed by adapter and low-quality base removal at Phred <20 using Trimmomatic v0.36 (Bolger et al., 2014). High-quality reads were aligned to the Capra hircus ARS1.2 reference genome (GCF_001704415.2) using BWA-MEM v0.7.17 (Li and Durbin, 2009). SAM files were converted to BAM format using SAMtools (Li et al., 2009), followed by coordinate sorting and PCR-duplicate removal using SortSam and MarkDuplicates, respectively, in Picard v2.27.5. SNPs and indels were called independently for each individual using GATK HaplotypeCaller in GVCF mode (Poplin et al., 2017). Breed-wise GVCFs were combined using CombineGVCFs and jointly genotyped using GenotypeGVCFs.
           
    Variant quality was assessed using GATK SelectVariants and VariantFiltration. SNPs were hard-filtered using the following criteria: Quality by Depth (QD) <2.0, Mapping Quality (MQ) <40.0, Fisher Strand (FS) >60.0, Strand Odds Ratio (SOR) >4.0, MQRankSum <-12.5 and ReadPosRankSum <-8.0. Indels were filtered using QD <2.0, FS >200.0 and SOR >10.0, following GATK hard-filtering recommendations. Filtered variants were excluded before downstream analyses. The variants were further filtered using VCFtools v0.1.16 and PLINK v1.9. Variants with minor allele frequency (MAF) <0.05, maximum missingness >50%, sequencing depth <10, base quality <50, HWE P<0.001, or more than two alleles were excluded. Individual-level missingness was restricted to 10%. The resulting high-confidence biallelic variants were converted to PLINK binary format for downstream population genetic analyses. Functional annotation of the retained variants was performed using SnpEff v5.2 (Cingolani et al., 2012) against the Capra hircus ARS1.2 annotation files (GCF_001704415.2). Variants were classified according to predicted functional impact as HIGH, MODERATE, LOW and MODIFIER and according to genomic location, including exonic, intronic, intergenic and regulatory regions.
     
    Genetic diversity, linkage disequilibrium and effective population size
     
    Genome-wide genetic diversity was assessed using PLINK v1.9 (Purcell et al., 2007). Minor allele frequency was calculated from the filtered SNP datasets using the-freq command, while observed heterozygosity (Ho), expected heterozygosity (He) and the genomic inbreeding coefficient (Fis) were estimated using the-het command. The PLINK Fis statistic was estimated from the deviation of observed homozygosity from the expected homozygosity under hardy-weinberg equilibrium.
           
    Linkage disequilibrium (LD) was assessed as the squared correlation coefficient (r2) between pairs of SNPs at different physical distances. For LD-based analyses, SNPs were pruned using the PLINK-indep-pairwise 50 5 0.5 comamnd, Genome-wide LD decay within each breed was estimated using PopLDdecay v3.40 (Zhang et al., 2019) using its default parameters and mean r2 values were calculated across increasing physical-distance intervals. The observed LD patterns were subsequently used to estimate historical effective population size (Ne) using SNeP v1.1 (Barbato et al., 2015) with default parameters. The LD-based Ne estimates were used to assess changes in effective population size across generations and infer historical demographic trends in each breed. LD-decay curves and Ne trajectories for the four goat breeds were visualized using R.

    RESULTS AND DISCUSSION

    Sequencing, alignment and variant discovery
     
    Whole-genome sequencing was performed on 40 goats representing four indigenous breeds, with 10 animals each from AH, BKG, ND and KCG. The mean genome coverage of 40 samples was 9.14X and after quality control, reads were aligned to the Capra hircus ARS1.2 reference genome with an average mapping rate of 99.6%. All 40 samples passed quality criteria and were retained for downstream analyses.
     
    Genome-wide variant discovery and functional annotation
     
    The four goat breeds exhibited extensive genome wide variation, with processed variants ranging from 22.22 million in KCG to 28.20 million in BKG, followed by ND (24.20 million) and AH (24.19 million), of which approximately 86 to 87% were SNPs. Ts/Tv ratios were highly consistent, ranging from 2.3141 in AH to 2.3175 in ND, with BKG and KCG recording 2.3157 and 2.3156, respectively, indicating comparable nucleotide substitution patterns. These values were similar to those reported by Guan et al., (2016) (2.3347 to 2.3552) and Xiong et al., (2023) (2.338) and within the range reported for Indian goats by Nandhini (2023) (1.547 to 2.4908). Chromosome wise variant distribution and density are presented in Fig 2 and 3, respectively. Variant numbers generally increased with chromosome length, although breed specific differences in density were observed, ranging genome wide from one variant per 87bp in BKG to one per 111 bp in KCG. Functional annotation revealed predominance of MODIFIER variants (>99.1%), whereas HIGH impact variants represented only 0.037 to 0.045%. Approximately 54% and 35% of variants occurred in intronic and intergenic regions, respectively, while less than 1% were exonic. This distribution was broadly comparable with Ethiopian goats (Belay et al., 2024) and Indian breeds (Nandhini, 2023), although differences may reflect reference assemblies, annotation approaches, variant calling, filtering and population composition. Synonymous and missense variants predominated among coding consequences, with a missense to synonymous ratio of approximately 0.69. High impact variants included frameshift, stop gained, stop lost and splice site variants, numbering 13,749 in BKG, 10,944 in AH, 10,235 in ND and 8,203 in KCG. The relatively low proportion of HIGH-impact variants is consistent with the expectation that strongly deleterious coding variants are generally less frequent in populations. However, this pattern alone does not provide direct evidence of purifying selection, as variant annotation, demographic history, allele-frequency distributions and stochastic processes may also influence the observed distribution of functional variants. Direct assessment of purifying selection would require additional functional or population-genetic analyses. Overall variant annotation characteristics are summarized in Table 2.

    Fig 2: Chromosome-wise distribution of genomic variants among the four indigenous goat breeds under study (AH- Assam hill goat, BKG- Bhakarwali, ND- Nandidurga, KCG- Kutchi).



    Fig 3: Chromosome-wise variant density (variants/Mb) across the four indigenous goat breeds under study (AH- Assam hill goat, BKG- Bhakarwali, ND- Nandidurga, KCG-Kutchi).



    Table 2: Comparative genome-wide characterization of genetic variants and annotation profile of variants in the four indigenous goat breeds.


     
    Genomic diversity, inbreeding and minor allele frequency
     
    Genomic diversity showed modest variation among the four goat breeds (Table 3). Mean observed heterozygosity (Ho) ranged from 0.2534 in BKG to 0.2746 in ND, with the highest value in ND (0.2746), followed by KCG (0.2676), AH (0.2564) and BKG (0.2534). Expected heterozygosity (He) ranged from 0.2581 in BKG to 0.2763 in KCG, with KCG showing the highest value, followed by ND (0.2709), AH (0.2656) and BKG (0.2581). These estimates were generally within or below the ranges reported across Indian goat populations by Nandhini (2023) (Ho: 0.1194 to 0.5054; He: 0.2491 to 0.3789). Vijh et al., (2023) reported Ho values ranging from 0.2587 to 0.4470 across Indian goat breeds, for breeds common to both studies, the present Ho estimates were lower for Assam Hill (0.2564 vs. 0.4159), Bhakarwali (0.2534 vs. 0.2986) and Kutchi (0.2676 vs. 0.2834), but slightly higher for ND (0.2746 vs. 0.2587). Comparable levels of heterozygosity (Ho≈0.233; He≈0.308) were reported in Ethiopian goats by Sheriff et al., (2024). In contrast, microsatellite-based assessment of 27 Indian goat breeds by Dixit et al., (2021) reported higher overall Ho  (0.549) and He (0.669), including Ho of 0.586 and He of 0.726 in Kutchi goats. Differences among studies should be interpreted in the context of marker type and ascertainment, sample size, SNP filtering criteria, marker density, sequencing depth and population composition, in addition to underlying demographic history, genetic drift, effective population size, breeding practices and gene flow.

    Table 3: Genome-wide observed heterozygosity (Ho), expected heterozygosity (He), minor allele frequency (MAF) and inbreeding coefficient (FIS) across the four indigenous goat breeds.


           
    Mean minor allele frequency (MAF) ranged from 0.1758 in BKG to 0.1948 in KCG, with ND (0.1901) and AH (0.1863) showing intermediate values (Table 3). The comparatively lower MAF in BKG indicates a greater representation of low-frequency alleles, potentially reflecting differences in allele frequency distribution, genetic drift and demographic history, whereas the higher MAF in KCG suggests a relatively greater representation of intermediate-frequency alleles. These interpretations should therefore be considered indicative rather than definitive, as the sample size of 10 animals per breed may influence allele-frequency estimates, particularly for low-frequency variants. These estimates were lower than those reported by Nandhini (2023), which may partly reflect differences in SNP filtering and sample composition. Genomic FIS ranged from -0.0159 in ND to 0.0323 in AH. Positive FIS values in AH (0.0323), KCG (0.0286) and BKG (0.0181), accompanied by Ho lower than He, indicated only slight heterozygosity deficits, whereas the negative FIS in ND (-0.0159), where Ho exceeded He, indicated a slight heterozygote excess. Given the modest magnitude of these estimates and the sample size of 10 animals per breed, they are best interpreted as indications of the observed heterozygosity patterns in the sampled animals rather than as definitive evidence of population-level inbreeding. These estimates were comparable with the ranges reported in Ethiopian goats (-0.18 to 0.04) and Kazakh goats (-0.027 to 0.052), suggesting generally low deviations from hardy-weinberg expectations in the sampled animals (Gebreselase et al., 2024). Dixit et al., (2021), using microsatellite markers, reported a higher overall FIS of 0.192 across 27 Indian goat breeds and 0.205 specifically in Kutchi goats, further illustrating the influence of marker systems and analytical approaches on diversity and inbreeding estimates.
     
    Linkage disequilibrium and effective population size
     
    Genome wide linkage disequilibrium (LD), assessed as the squared correlation coefficient (r2) between SNP pairs, declined consistently with increasing physical distance across all four breeds (Table 4; Fig 4), reflecting the progressive breakdown of allelic associations through recombination. At 20kb, mean r2 values were 0.342, 0.315, 0.349 and 0.332 for AH, BKG, KC and ND, respectively, declining to 0.161, 0.152, 0.178 and 0.156 at 200kb and further to 0.139, 0.135, 0.157 and 0.137 at 1000 kb. KCG consistently exhibited the highest LD, whereas BKG showed the lowest values and comparatively faster LD decay. Similar patterns have been reported in other goat populations. Nandhini (2023) reported r2 values of 0.1361 to 0.3028 at 30kb, while Sun et al., (2022) observed a rapid decline from approximately 0.57 at short distances to 0.06 at 100 kb. The relatively persistent LD observed in KCG may reflect differences in effective population size, genetic drift, historical population structure, gene flow, or breed-specific mating patterns; however, the present data do not allow these factors to be distinguished. Similarly, the faster LD decay observed in BKG may be associated with differences in demographic history or population structure, but the underlying mechanism cannot be determined from the present analysis alone.

    Table 4: Decay of linkage disequilibrium with increasing physical distance among the four indigenous goat breeds under study.



    Fig 4: Genome-wide linkage disequilibrium decay based on mean r2 across increasing physical distances among the four indigenous goat breeds under study.


           
    LD-based historical effective population size (Ne) estimates revealed a marked reduction from historical to recent generations across all breeds (Table 5; Fig 5). At approximately 995 generations ago, Ne was estimated at 2,674, 3,180, 2,592 and 3,060 for AH, BKG, Kutchi and ND, respectively, declining to 49, 49, 46 and 47 at approximately 13 generations ago. Thus, BKG exhibited the highest historical Ne and Kutchi the lowest, although all breeds converged towards similarly low recent values. A comparable historical decline in Ne has been reported in Ethiopian goats by Gebreselase et al., (2024). The observed decline may be consistent with demographic contraction, genetic drift, population subdivision, breed formation, or changes in breeding management; however, these potential drivers cannot be distinguished from LD patterns alone. Furthermore, LD-based Ne estimates should be regarded as approximate indicators of effective population size and should not be interpreted as direct estimates of census population size or genomic inbreeding.

    Table 5: Estimates of effective population size (Ne) at different generations in the four goat breeds under study.



    Fig 5: Dynamics of effective population size (Ne) among the four indigenous goat breeds under study over the generations.


           
    Collectively, Ho, He, MAF, FIS, LD and Ne revealed distinct but complementary genomic and demographic patterns among breeds. ND showed the highest Ho and negative FIS, indicating relatively greater heterozygosity and slight heterozygote excess. KCG exhibited the highest He and MAF together with the highest LD, while BKG showed the lowest Ho, He and MAF but the highest historical Ne and fastest LD decay. AH exhibited intermediate diversity but the highest positive FIS, indicating a slight heterozygosity deficit. The consistently low recent LD-based Ne estimates may reflect reduced effective population size and greater potential influence of genetic drift in recent generations. However, given the sample size of 10 animals per breed, these estimates should be interpreted as indicative of the genomic patterns observed in the sampled populations rather than as definitive estimates of long-term demographic history. Overall, these breed-specific genomic and demographic profiles provide a basis for tailored conservation and sustainable genetic utilization strategies.

    CONCLUSION

    The present whole genome sequencing study revealed substantial genomic variation and distinct diversity and demographic patterns among Assam Hill, Bhakarwali, Nandidurga and Kutchi goats. Nandidurga exhibited the highest observed heterozygosity and a slight heterozygote excess, whereas Kutchi showed the highest expected heterozygosity and MAF, indicating comparatively greater representation of intermediate frequency alleles. The low genomic inbreeding estimates across all breeds suggest that substantial genetic variability is still maintained within these populations. However, breed specific LD patterns and the pronounced decline in effective population size from historical to recent generations indicate contrasting demographic trajectories and a progressive reduction in the effective breeding population. Particularly, the consistently low recent Ne across breeds highlights the potential for increased genetic drift and gradual loss of genetic diversity if this trend persists. The contrasting diversity, LD and demographic patterns further demonstrate that no single parameter adequately represents the genomic status of a breed and emphasize the value of their integrated assessment. Overall, these findings establish a genome wide baseline for these indigenous goat genetic resources and suggest that conservation and breeding programmes should focus not only on maintaining census numbers but also on preserving within breed genetic diversity, minimizing relatedness and maintaining adequate effective population sizes. Such breed specific genomic management would facilitate their long-term conservation while retaining the genetic variability required for future adaptation and sustainable utilization.

    ACKNOWLEDGEMENT

    This study was financially supported by the ICAR Consortium Research Platform on Genomics. The authors gratefully acknowledge the ICAR, New Delhi and the Director, ICAR–NBAGR, Karnal, for providing financial and institutional support. The authors also express their sincere gratitude to the Director, ICAR–NDRI, Karnal and the Head, AGB Division, ICAR–NDRI, for providing the necessary research facilities and support. The first author gratefully acknowledges ICAR–NDRI, Karnal, for the Institute Fellowship received during the PhD programme. The authors also acknowledge the Institutional Fellowship awarded by ICAR-NDRI to the first author in support of this research during the PhD programme.
     
    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
     
    The biological samples used in this study were collected under the approval of the Institutional Animal Ethics Committee (IAEC) of ICAR–NBAGR, Karnal (Approval No. NBAGR/IAEC/2017 dated 21.08.2017). The raw whole-genome sequencing data generated from these samples were subsequently processed and analysed during 2025-2026, including quality control, read alignment, variant calling, variant filtering and genome-wide genetic diversity analyses. No additional animal experimentation or sample collection was undertaken during the genomic analyses.

    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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