Comparative Detection of Selection Signatures in Indigenous and Crossbred Turkish Sheep Breeds

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A total of 1,612 individuals were analyzed, comprising the indigenous Akkaraman breed and four crossbreds: Karacabey Merino, Oamer, Hasak, and Hasmer. PCA revealed clear genetic separation of Akkaraman, partial overlap between Karacabey Merino and Oamer, and distinct variation in the small Hasak and Hasmer populations. Genetic diversity metrics indicated moderate and relatively homogeneous diversity across breeds, with slightly negative FIS, reflecting heterozygote excess. Selection signatures were identified using ROH, iHS, and Tajima’s D statistics. ROH analysis highlighted breed-specific candidate genes associated with growth (e.g., BGLAP, MYF6, GHR), milk production (PRL, LTF, casein cluster), immune response (TLR2, TLR5, IL15), and reproduction (FSHB, BMPR1B). iHS detected additional loci under positive selection, including MSTN, POU1F1, LEPR, and LALBA, while Tajima’s D identified selective sweeps in genes related to muscle development (CAPN2, CAST), reproduction (PAG4), and immune function (IRF2, ITGB2). Fifty candidate genes were shared among all breeds, whereas others were breed-specific, suggesting both common and unique adaptive pathways. These findings provide valuable insights into the genomic architecture and adaptive evolution of indigenous and crossbred Turkish sheep, with implications for conservation and breeding strategies. Biological sciences/Evolution Biological sciences/Genetics Turkish sheep selection signatures ROH iHS Tajima’s D genetic diversity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Sheep ( Ovis aries ) were among the earliest domesticated ungulates, with archaeological, zooarchaeological, and genomic evidence converging on the Fertile Crescent as the primary center of domestication during the early Holocene 1 – 5 . From these origins, pastoral expansions, trade, and repeated episodes of human-mediated selection diversified sheep into a broad array of breeds specialized for meat, milk, wool, and adaptation to heterogeneous agroecological niches 6 . Over thousands of years, deliberate breeding and local environmental pressures have left discernible “fingerprints” in the ovine genome, so-called selection signatures, that reflect both ancient and recent responses to natural and artificial selection 7 – 9 . Systematic detection of these signatures provides a powerful lens for reconstructing the evolutionary and breeding history of populations, identifying candidate genes underlying complex traits, and informing evidence-based improvement and conservation strategies 10 . Türkiye occupies a pivotal position in this history and contemporary sheep production. Its geography spans semi-arid steppes, continental plateaus, and Mediterranean littorals, supporting extensive and semi-extensive grazing systems that remain central to rural livelihoods 11 – 13 . Indigenous Turkish breeds have been shaped by decades to centuries of selection under nutritional constraints, thermal stress, and disease challenges typical of these environments. At the same time, state farms and research institutes have implemented structured crossbreeding and introgression programs to enhance productivity, particularly in wool and lamb production, without compromising local resilience 14 . This dual imperative, preserving adaptation while elevating output, has produced a rich landscape of indigenous and composite populations that are ideal for comparative genomic analysis 15 . Akkaraman (also known as White Karaman) is the most widespread fat-tailed indigenous breed in Central Anatolia. Managed primarily in extensive systems, Akkaraman sheep exhibit notable robustness to feed scarcity, temperature extremes, and endemic disease pressure, coupled with moderate growth and prolificacy. As a genetic reservoir for adaptation to steppe ecosystems, Akkaraman represents a critical reference for understanding the genomic basis of resilience. In parallel, a set of crossbred/composite types has been developed to enhance meat and wool attributes. Karacabey Merino was initiated at the Karacabey State Farm through the crossing of Kivircik with German Mutton Merino, which was stabilized to produce finer wool and acceptable growth under Turkish conditions. The Central Anatolian Merino (oamer; Orta Anadolu Merinosu ) arose from structured introgression of Merino germplasm into local stocks (notably Akkaraman) on Central Anatolian state farms to combine improved fleece quality with environmental hardiness. More recently, the Bandirma Sheep Research Institute established composite meat-type populations, such as Hasmer and Hasak, by integrating genetics from terminal-sire breeds (e.g., German Blackhead Mutton, Hampshire Down) and Merino with Akkaraman. These programs were designed to exploit heterosis and complementarity, improving carcass traits, growth rate, and, where relevant, fleece characteristics while retaining adaptation to Turkish production systems 16 – 21 . Despite their economic prominence, the genomic architecture that differentiates these populations, particularly the balance between introgressed performance alleles and indigenous adaptation loci, remains incompletely characterized. Classical performance testing and quantitative genetic evaluations provide trait-level estimates (e.g., heritability, breeding values). Still, they do not directly reveal the specific genomic regions and biological pathways shaped by selection. Genome-wide selection signature scans fill this gap by interrogating population genetic patterns that deviate from neutrality 22 . Three complementary classes of signals are especially informative for livestock: (i) runs of homozygosity (ROH), (ii) haplotype-based metrics of extended haplotype homozygosity (EHH) such as the integrated haplotype score (iHS), and (iii) site-frequency-spectrum (SFS) statistics such as Tajima’s D 23 , 24 . ROH are long, contiguous stretches of homozygous genotypes that arise when chromosomal segments are inherited identically by descent 25 – 27 . Their frequency, length distribution, and genomic clustering (“ROH islands”) provide insight into recent inbreeding, demographic contractions, and strong directional selection that fixes or nearly fixes haplotypes surrounding advantageous alleles 27 , 28 . Shorter ROH often reflect older shared ancestry, whereas very long ROH indicate recent common ancestors or intense selection 29 . iHS, in contrast, is a within-population haplotype test that contrasts the decay of EHH around the derived versus ancestral allele at a focal SNP 30 , 31 . Because incomplete or ongoing sweeps preserve extended haplotypes at elevated frequency around the favored allele, iHS is particularly sensitive to relatively recent selection where both alleles still segregate 32 . Finally, Tajima’s D summarizes the SFS by comparing pairwise nucleotide diversity to the number of segregating sites; negative values can indicate recent directional selection or population expansion (an excess of rare variants), whereas positive values can reflect balancing selection or recent bottlenecks (an excess of intermediate-frequency variants) 33 . By integrating ROH, iHS, and Tajima’s D, investigators can triangulate signals across timescales and demographic contexts: ROH captures fixation-proximal events and inbreeding structure; iHS targets ongoing sweeps; and Tajima’s D adds SFS sensitivity that is less dependent on haplotype phase 34 , 35 . Applying these tools to Turkish sheep is scientifically and practically valuable for several reasons. First, indigenous and composite breeds have undergone distinct selection regimes and environmental filtering in harsh rangelands compared to artificial selection for growth and fleece in structured programs, resulting in contrasting genomic footprints. Second, crossbreeding and introgression complicate selection scans because admixture reshapes linkage disequilibrium and allele frequencies; a comparative design that includes both indigenous and crossbred populations, combined with explicit population structure analysis, improves interpretability and reduces false positives. Third, the economic traits under selection in these breeds, including growth rate, carcass composition, milk yield and composition, wool fineness and staple strength, parasite resistance, and thermotolerance, are polygenic and mediated by networks of genes related to metabolism, endocrine regulation, immunity, and skin/hair follicle biology. Identifying recurrently targeted regions across populations versus population-specific sweeps can reveal core pathways for small-ruminant productivity and local adaptation, respectively. Finally, from a breeding and conservation standpoint, mapping selection signatures provides candidate markers for genomic selection, helps prioritize haplotypes to maintain or introgress, and informs the management of inbreeding by highlighting genomic regions where homozygosity is most concentrated 11 , 36 – 39 . This study addresses a clear knowledge gap at the interface of adaptation and productivity in Turkish sheep. A rigorous, comparative scan across indigenous and crossbred populations will (i) reveal breed-specific and shared selection footprints, (ii) nominate candidate genes and pathways underpinning economically important and adaptive traits, and (iii) provide actionable genomic targets for breeding programs seeking to balance performance gains with the preservation of local robustness. The overarching objective is to generate a consolidated map of selection in Turkish sheep that can guide marker-assisted and genomic selection, inform introgression and conservation decisions, and deepen our understanding of how historical and contemporary selection have sculpted small-ruminant genomes in the Anatolian context. Materials and methods Animal material A total of 1,612 sheep from five breeds were included in this study: Akkaraman (n = 168), Karacabey Merino (n = 760), Oamer (n = 671), Hasak (n = 7), and Hasmer (n = 6) (Fig. 1 ). Blood samples from the Akkaraman, Oamer, Hasak, and Hasmer breeds were collected at the Bahri Dağdaş International Agricultural Research Institute in Konya. In contrast, samples from Karacabey Merino were collected at the Sheep Breeding Research Institute in Balikesir. Blood was drawn from the jugular vein using vacuum tubes containing ethylenediaminetetraacetic acid (EDTA) as an anticoagulant and stored under appropriate conditions until DNA extraction. DNA Extraction and Genotyping Genomic DNA was isolated from blood samples using a commercial spin-column DNA extraction kit. DNA yield and purity were assessed before genotyping. All individuals were genotyped using the Illumina Ovine SNP50K BeadChip and mapped to the Oar_v4.0 reference assembly. Genotype Quality Control Initial quality filtering was performed using PLINK 40 . SNPs were discarded if more than 10% of their genotypes were missing, if they significantly deviated from Hardy–Weinberg equilibrium (P < 0.0001), or if their MAF fell below 5%. Genetic Diversity and Population Structure Analysis Principal Component Analysis (PCA) To examine genetic differentiation between the two breeds, the SNP matrix was first standardized. PCA was then performed in the R environment using the prcomp() function. The top two principal components were selected, accounting for approximately 70% and 17% of the total variance, respectively. Individuals were plotted in a two-dimensional space, with colors indicating breed membership, using the ggplot2 package for visualization. Selection Signatures Analysis Three complementary approaches, iHS, ROH, and Tajima’s D, were used to detect genomic regions under selection. iHS Analysis Haplotype phasing was conducted in BEAGLE, and iHS scores were calculated in R using the rehh package. Standardized iHS values (along with their corresponding − log₁₀(p) statistics) were computed for all markers. We scanned the genome in non-overlapping 100 kb windows and flagged any window containing at least one SNP with − log₁₀(p) ≥ 4 (i.e., p < 10⁴) as a candidate region. All such windows were merged to form a final list of putative positively selected intervals. ROH Analysis ROH were identified in R environment with the detectRuns package. Parameters included a sliding window of 15 SNPs, a minimum of 15 SNPs required to call a run, and a maximum inter-SNP gap of 1 Mb. We also set the minimum homozygous run length to 1 Mb and enforced a SNP density threshold of at least one SNP per 100 bp. Tajima’s D Calculation To highlight regions potentially under balancing selection, Tajima’s D was calculated using VCFtools. A 10-kb sliding window was implemented across the genome. Overlap among the five breed-specific sets was computed and visualized with the web tool InteractiVenn 41 , which reports exact set intersections. GO and KEGG Functional Enrichment Analysis Genes located within candidate selection regions were subjected to functional enrichment analysis using SRplot 42 . Gene Ontology (GO) terms were classified into three categories: biological process (BP), molecular function (MF), and cellular component (CC). KEGG pathway analysis was then performed to identify pathways that were significantly enriched 43 . SRplot computed enrichment scores, applied FDR correction for p-values, and generated graphical summaries of enriched terms. Genetic diversity analysis All diversity analyses were conducted on an Ubuntu 20.04 system under Windows Subsystem for Linux (WSL). We used PLINK v1.9 for genotype manipulation and basic summary statistics, VCFtools v0.1.17 for sliding-window nucleotide diversity, and R v4.x (with the data. table, knitr, and writeXL packages) for downstream aggregation and reporting. Nucleotide diversity Each breed’s quality-controlled PLINK dataset was first exported to VCF format. We then ran VCFtools to scan the autosomes in 100-kb windows with a 50-kb step. Within every window, the average number of pairwise nucleotide differences per site among all sampled chromosomes was computed. Finally, these windowed estimates were averaged to yield a single genome‐wide nucleotide diversity value for each breed. Minor allele frequency and heterozygosity (MAF, HO, HE) Within-breed allele frequencies were summarized by computing the MAF across all retained SNPs using PLINK’s frequency function. Observed heterozygosity (HO) and expected heterozygosity (HE) were then extracted from PLINK’s Hardy–Weinberg output. HO represents the actual proportion of heterozygous genotypes in the sample, whereas HE reflects the proportion expected under random mating given the allele frequencies. Inbreeding coefficient (FIS) Using the HO and HE values obtained from PLINK, we calculated the FIS for each breed in R. A negative FIS indicates an observed excess of heterozygotes relative to Hardy–Weinberg expectations. In contrast, a positive FIS indicates a deficit. Within-breed allele sharing (DST) and genetic distance (D) Pairwise genomic similarity between individuals was quantified via PLINK’s identity-by‐state (IBS) analysis. From the proportions of loci where individuals shared zero, one, or two alleles identically, we derived an average DST for each breed. D value was then expressed as the complement of DST, with higher D values indicating greater genetic divergence among individuals. Results PCA Analysis in Five Sheep Breeds To investigate the genetic structure of five sheep breeds (Akkaraman, Hasak, Hasmer, Karacabey, and Oamer), a PCA was conducted using SNP data. After filtering out 12,161 monomorphic or missing SNPs, the analysis was performed on the remaining SNPs with less than 10% missing data. The PCA results revealed that the first principal component (PC1) explained 15.81% of the total genetic variance, while the second principal component (PC2) accounted for 5.13%, collectively capturing 20.94% of the variance. The PCA scatter plot (Fig. 1 ) illustrates the distribution of individuals from the five breeds along PC1 and PC2, with axes labeled as PC1 (4.00%) and PC2 (3.00%) for visualization purposes. The Akkaraman breed (n = 168) exhibited distinct separation along PC1, with one individual (A115) showing a notably divergent PC1 score (-0.72), suggesting potential genetic uniqueness or population substructure within this breed. In contrast, Karacabey (n = 760) and Oamer (n = 671) formed larger, partially overlapping clusters, indicating genetic similarity or possible historical gene flow between these breeds. The Hasak (n = 7) and Hasmer (n = 6) breeds, due to their small sample sizes, formed less pronounced clusters but displayed notable variation along PC2 (e.g., PC2 scores ranging from 51.62 to 74.49), suggesting unique genetic contributions despite their limited representation. The relatively low variance explained by PC1 and PC2 (20.94%) may reflect limited genetic diversity in the dataset, possibly due to high rates of missing data or the presence of monomorphic SNPs. These findings suggest that Akkaraman is genetically distinct, while Karacabey and Oamer share closer genetic profiles. The small sample sizes for Hasak and Hasmer warrant caution in interpreting their genetic differentiation, although their PC2 variation indicates potential breed-specific genetic characteristics. Genetic diversity Genome-wide nucleotide diversity varied modestly among the five Turkish sheep breeds, ranging from 6.97 × 10⁶ in Karacabey to 8.30 × 10⁶ in Akkaraman (Table 1 ). MAF was similarly homogeneous, with values ranging from 0.286 in Oamer to 0.306 in Karacabey. HO spanned from 0.381 in Oamer to 0.445 in Hasmer, whereas HE ranged from 0.376 in Oamer to 0.394 in Karacabey. All breeds exhibited slightly negative FIS, indicative of a slight excess of heterozygotes; the most pronounced heterozygote excess occurred in Hasmer (FIS = − 0.161), while Karacabey showed the mildest (FIS = − 0.016). Finally, DST values extended from 0.033 in Oamer to 0.113 in Hasmer, corresponding to genetic distances (D = 1 – DST) of 0.887 to 0.967. Collectively, these metrics reveal moderate and broadly similar levels of genetic diversity across the five breeds, with Hasmer exhibiting the highest internal relatedness and Karacabey displaying the most notable nucleotide diversity. Table 1 Results of genetic diversity among the eleven cattle breeds. Breed Nucleotide diversity MAF HO HE FIS DST D Akkaraman 8.3E-06 0.295207 0.392568 0.385232 -0.01904 0.036079 0.963921 Hasak 7.35E-06 0.295812 0.421154 0.382288 -0.10167 0.04194 0.95806 Hasmer 7.35E-06 0.296168 0.444817 0.383096 -0.16111 0.112967 0.887033 Karacabey 6.97E-06 0.305649 0.399801 0.393631 -0.01567 0.038717 0.961283 Oamer 8.12E-06 0.285933 0.381261 0.375752 -0.01466 0.033206 0.966794 MAF, minor allele frequency; HO, observed heterozygosity; HE, expected heterozygosity; FIS, inbreeding coefficient; DST, within-breed allele‐sharing index; D, genetic distance (D = 1 – DST). ROH-Based Selection Signatures Using a window-based ROH calling strategy, we detected widespread runs of homozygosity across all five breeds, with apparent breed-specific differences in the number and genomic distribution of ROH islands (Fig. 2 ). In Akkaraman sheep, ROH analysis revealed multiple extended homozygous tracts, with the strongest signal centered on BGLAP (osteocalcin, involved in bone mineralization/postnatal growth). Growth-related loci within ROH included MYF6 , MEF2C , FTO , FGF12 ; milk loci spanned the casein cluster ( CSN1S1 , CSN2 , CSN1S2 , CSN3 ) together with LTF and PRL ; immune genes TLR2 , TLR5 , MYD88 , NFKBIA , IL15 showed pronounced homozygosity; reproductive candidates FSHB , GDF9 , OXTR were present; and metabolic/signaling genes ACLY , SHMT1 , STAT5A , AURKA also lay in ROH segments (Table S1 ). In the Hasak crossbred population, ROH islands were widespread. The most pronounced centered on BGLAP ; additional tracts encompassed FTO and GHRHR . Milk‐related ROH included PRL and the casein cluster ( CSN1S1 , CSN2 , CSN1S2 , CSN3 ). Immune genes within the ROH comprised TLR2, TLR5, IFNAR1, IL15, and NFKBIA; reproductive candidate genes included FSHB, LHB, FSHR, and OXTR. Skeletal/muscle loci MYOG, MEF2C, and metabolic loci ACLY and STAT5A also occurred in extended runs of homozygosity (Table S2 ). In the Hasmer crossbred, ROH analysis revealed a pronounced island at POU1F1 (pituitary growth/lactotroph lineage regulator). Extended homozygous tracts also encompassed growth hormone axis genes GHR and GHRHR . Milk/lactation candidates ADIPOQ and AHSG were embedded within ROH, immune loci IL12A , TLR5 , and IFNAR1 resided in ROH, and reproductive candidates INHBA and BMPR1B were likewise detected (Table S3 ). In the Karacabey Merino crossbred, multiple ROH islands were detected, with a prominent signal at BGLAP . Additional extended tracts covered GH , GHR , GHRHR , and POU1F1 (somatotropic axis). Milk‐associated LTF and PRL fell within ROH; immune genes TLR5 , IFNAR1 , IL12A , NFKBIA mapped to homozygous regions; reproductive loci FSHB , LHB resided in ROH; and muscle/metabolic candidates MYOG , FGF12 , ACLY , STAT5A , and IGFBP4 were also included (Table S4 ). In the Oamer crossbred, extended homozygosity spanned multiple chromosomes. The leading ROH island centered on BGLAP ; further tracts encompassed POU1F1 and GHRHR . Milk regions within ROH included the casein cluster ( CSN1S1 , CSN2 , CSN1S2 , CSN3 ) and PRLHR . Immune‐related loci IL12A , IFNAR1 , IFNAR2 , and TLR4 were embedded within ROH; reproductive candidates FSHR, INHBA, and BMPR1B were present; and metabolic/growth genes LEPR, IGFBP3 , and MSTN also occurred in extended runs of homozygosity (Table S5 ). Candidate-gene counts by breed were: Akkaraman (214), Karacabey Merino (871), Oamer (499), Hasak (496), and Hasmer (386). All five breeds shared fifty genes. Breed-specific sets comprised 107 genes unique to Karacabey Merino and one gene unique to Oamer; no breed-specific genes were detected for Akkaraman, Hasak, or Hasmer. The remaining genes were distributed among pairwise, three-way, and four-way intersections as shown in Fig. 3 . iHS-Based Selection Signatures We performed iHS analysis to detect signatures of positive selection within each sheep breed, and then summarized the standardized iHS values genome-wide (Fig. 4 ). In Akkaraman sheep, iHS signals encompassed BGLAP (osteocalcin; bone mineralization and skeletal growth), FTO (nucleic acid demethylase linked to energy balance/adiposity), STAT5A (transcription factor in GH/PRL signaling), PRL (prolactin; lactation), CSN1S1 , CSN2 , CSN1S2 , CSN3 (caseins; major milk proteins), TLR2 , TLR5 (pathogen-recognition receptors), MYD88 (TLR adaptor), IL15 (lymphocyte activation), FSHB (FSH β-subunit), OXT (oxytocin; parturition/milk ejection), GDF9 (oocyte growth/folliculogenesis), and the myogenic regulators MEF2C and MYF6 (Table S6 ). In the Hasak crossbred population, iHS highlighted BMPR1B (BMP receptor; folliculogenesis/prolificacy), FGF2 (skeletal growth/tissue repair), ABCG2 (milk secretion/transport), CSN1S1 , CSN2 , CSN1S2 , CSN3 (caseins), innate immune sensors TLR1 , TLR6 , TLR10 , cytokines IL2 and IL21 , reproductive-axis genes GNRHR and ESR2 , and additional candidates IGFBP7 (IGF-axis modulation/ECM) and GPX1 (oxidative stress defense) (Table S7 ). In the Hasmer crossbred, iHS peaks were detected at FSHR (follicle-stimulating hormone receptor, involved in ovarian follicle maturation) and GNRHR (GnRH receptor), with growth/myogenesis signals at MYF6 and MEF2C, and IGF-axis support from IGFBP7 . Milk synthesis selection involved LALBA (α-lactalbumin; lactose synthesis) and the caseins CSN1S1 , CSN2 , CSN1S2 , CSN3 ; immune adaptation featured TLR1 , TLR6 , TLR10 , IL22 , and IFNG (Table S8 ). In the Karacabey Merino crossbred, iHS signals included MSTN (myostatin; negative regulator of muscle growth), PRL (lactation), BMPR2 (BMP receptor; ovarian/follicular signaling), INHA (inhibin α; feedback on FSH), IGFBP5 (IGF bioavailability), and immune-related TNF , CTLA4 , and CD28 (T-cell costimulation/checkpoint) (Table S9 ). In the Oamer crossbred, iHS highlighted BGLAP (osteocalcin; bone mineralization), POU1F1 (pituitary transcription factor for GH/PRL/TSH lineages), LEPR (leptin receptor; energy balance/reproduction), LALBA (α-lactalbumin; lactose synthesis), innate sensors TLR1 , TLR6 , TLR10 , cytokine/receptor genes IL12A and IFNAR1 , reproductive genes BMPR2 and INHA , and metabolic/growth candidates ADIPOQ , AHSG (fetuin-A), and IGFBP5 (Table S10 ). Tajima’s D-Based Selection Signatures Using a sliding-window framework, we computed Tajima’s D across all autosomes in each breed (Fig. 5 ). In Akkaraman sheep, Tajima’s D identified three loci, CAPN2, PAG4, and IRF2, with CAPN2 showing the most substantial deviation from neutrality. CAPN2 encodes a calcium-dependent protease central to cytoskeletal remodeling and muscle fiber hypertrophy; PAG4 reflects selection on placental function; IRF2 indicates adaptive pressure on immune regulation (Table S11 ). In the Hasak crossbred, Tajima’s D revealed a pronounced signal at CAST , consistent with an excess of low-frequency alleles and a recent sweep. As the endogenous inhibitor of calpains, CAST suggests a selective pressure on somatic growth, with plausible secondary effects on lactation efficiency and disease resilience (Table S12 ). In the Hasmer crossbred, two loci deviated significantly: CAPN3 (strongest) and PAG4 . CAPN3 (muscle-specific calpain) supports selection on sarcomere remodeling and postnatal muscle development, while PAG4 suggests selection on placental/reproductive performance (Table S13 ). In the Karacabey Merino crossbred, three candidates, CAST (the strongest), MEF2C, and CAPN2, exhibited skewed allele-frequency spectra. CAST implicates selection on muscle remodeling and growth; MEF2C indicates pressure on myogenic differentiation; CAPN2 supports recent positive selection on cellular remodeling pathways (Table S14 ). In the Oamer crossbred, a strong signal at GHR underscores selection on somatic growth and metabolic regulation. Additional deviations at DGAT1 , CAST , and CAPN2 suggest involvement in milk-fat synthesis and muscle remodeling; immune genes ITGB2 and IL1B indicate pressure on pathogen defense; B4GALNT2 suggests possible selection related to reproductive tract function (Table S15). Functional Enrichment of Candidate Genes Functional enrichment analysis of the candidate gene set revealed significant overrepresentation of biological processes governing developmental and regulatory pathways. Terms associated with the positive regulation of multicellular organismal processes, positive regulation of developmental processes, and regulation of cell differentiation displayed the highest enrichment, underscoring roles in growth and tissue formation (Fig. 6 ). Cellular component annotations were dominated by membrane-associated locales, including the extracellular space, melanosome, and pigment granule, as well as integral and intrinsic components of the plasma membrane, membrane rafts, microdomains, and receptor complexes. Molecular function categories were principally characterized by signaling and regulatory activities, with signaling receptor regulator activity, signaling receptor activator activity, receptor ligand activity, and signaling receptor binding representing the strongest hits. Additional molecular functions, such as cytokine activity, cytokine receptor binding, hormone activity, growth factor receptor binding, and GTPase activity, further highlighted candidate genes involved in immune signaling, endocrine regulation, and signal transduction. Pathway Analysis of Candidate Genes Pathway analysis identified hormone signaling as the most significantly enriched pathway, reflecting selection on endocrine regulators of growth and reproduction. The cytokine–cytokine receptor interaction pathway emerged as the next highest hit, alongside Toll-like receptor signaling, indicating a strong immune component among the selected loci (Fig. 7 ). Enrichment was also observed for infectious disease-related pathways, including pertussis, measles, leishmaniasis, and malaria, as well as inflammatory bowel disease, suggesting adaptation to a spectrum of pathogen pressures. Cardiovascular‐related pathways, including fluid shear stress and atherosclerosis, were also overrepresented, while Chagas disease appeared at the lower end of significance. These results collectively indicate that coordinated selection has occurred on networks governing hormone action, immune defense, pathogen response, and vascular homeostasis in Turkish sheep breeds. Discussion Population Structure and Genetic Diversity PCA of the five Turkish sheep breeds (Akkaraman, Hasak, Hasmer, Karacabey, and Oamer) showed that the first two components accounted for only 20.94% of the total variance. Akkaraman formed a distinct cluster along the first principal component (PC1), with one outlier (A115) suggesting within‑breed substructure. Karacabey and Oamer overlapped substantially, whereas the crossbreds, Hasak and Hasmer, formed small clusters with greater scatter along PC2. The low variance explained by PC1–PC2 and the modest clustering imply limited population differentiation, possibly because the breeds share recent common ancestry or have experienced gene flow. Similar patterns of low between-breed differentiation have been reported in Turkish sheep using microsatellites and SNPs (Bayraktar and Shoshin, 2022), in Greek breeds where most genetic variation occurs within populations (Michailidou et al., 2025), and in South Asian and Middle Eastern sheep, showing extensive admixture. The overlapping clusters for Karacabey and Oamer suggest recent crossbreeding or shared paternal lineages. In contrast, the distinct Akkaraman cluster aligns with previous reports that this native fat-tailed breed is genetically separate from European breeds 44 . Genetic diversity indices further support moderate but heterogeneous diversity across breeds. Nucleotide diversity ranged from 6.97 × 10 − 6 in Karacabey to 8.30 × 10 − 6 in Akkaraman, with minor allele frequencies around 0.28–0.31. HO varied from 0.381 (oamer) to 0.445 (hasmer), while HE ranged from 0.376 (oamer) to 0.394 (Karacabey). Negative FIS in all breeds indicates an excess of heterozygosity, possibly due to recent admixture or balancing selection. The Hasmer crossbred population showed the most pronounced heterozygote excess, FIS = − 0.161, and the highest DST = 0.113, indicating high internal relatedness. In contrast, Karacabey had the most extraordinary nucleotide diversity and the lowest inbreeding. These patterns resemble those observed in Greek sheep, where heterozygosity was moderate (0.26–0.35) and FIS was negative or low (Michailidou et al., 2025). Similarly, in Turkish and neighbouring breeds, heterozygosity reached 0.44, and FIS was also negative 11 . The high heterozygosity in Hasmer might reflect the crossbreeding between Hasak and native Merino lines, whereas Karacabey’s diversity suggests admixture with diverse paternal lines. ROH analysis revealed signatures of selection and demographic history. Akkaraman had long homozygous tracts centered on BGLAP (osteocalcin) and encompassed skeletal development genes ( MYF6, MEF2C, FGF12 ), metabolic regulators ( FTO, ACLY, SHMT1, SUCLA2 ), milk genes (casein cluster CSN1S1–CSN3, LTF, PRL ), immunity genes ( TLR2, TLR5, MYD88, IFNAR1, IRF2 ), and reproduction genes ( FSHB, GDF9, POU1F1, FSHR ). Long ROH islands also contained STAT5A, AURKA, RCAN1 , and genes associated with protein turnover. Hasak crossbreds shared the BGLAP island and additional tracts containing FTO, GHRHR , and PRKAA2 . Milk‑related ROH contained ADIPOQ, AHSG, BCO1, HSD17B2, AOX3 , and SLC7A11 . Immune‑associated ROH spanned TLR2, TLR5, IFNAR1, IL15, NFKBIA , and FCGR3A, while reproductive tracts included FSHB, LHB, BMPR1B, and INHBA . The Hasmer population showed pronounced ROH at POU1F1, with co-selection on GHR, GHRHR, and PRKAR1A . Karacabey exhibited ROH across GH, GHR, GHRHR and POU1F1 with milk genes ( LTF, UGT1A1, SPP2 ) and immune genes ( TLR5, IFNAR1, IL12A, CRYAA, ITGB2 ). Finally, oamer crossbreds presented extensive ROH encompassing BGLAP, POU1F1, GHRHR , and LEPR with casein cluster and immune genes such as TLR4, NFKBIA, FCGR3A, and reproductive genes ( FSHR, INHBA, BMPR1B, GNRHR ). The distribution of ROH lengths and gene content indicates both ancient and recent selection episodes: long ROH islands often reflect recent inbreeding or strong selection. At the same time, shorter tracts suggest that older selection signals are present. Comparable patterns have been reported in Greek breeds, where ROH lengths largely fall between 1–5 Mb, reflecting moderate autozygosity (Michailidou et al., 2025), and in Polish breeds, where numerous short ROHs indicated historical inbreeding, while longer tracts signalled recent selection 45 . In Anatolian sheep, ROH islands encompassed genes like ZNF208B, CBX1 , and COPZ1 under selection 44 . The presence of similar gene clusters in our study suggests both convergent and breed‑specific selection pressures. Selection Signatures for Growth and Body Size Growth and body size in our Turkish populations were influenced by extensive ROH and iHS signals around genes involved in skeletal development, muscle formation, and metabolic regulation. The principal gene common to all breeds was BGLAP (osteocalcin), a bone matrix protein associated with osteoblast differentiation and mineralization. Surrounding this core region, Akkaraman and Karacabey exhibited homozygosity for MYF6, MEF2C, and FGF12, transcription factors that regulate myogenesis and muscle fibre development. These genes play a key role in controlling muscle fibre composition and carcass traits. STAT5A and AURKA were also identified in multiple breeds; STAT5A mediates growth hormone signalling, while AURKA influences cell cycle progression. Across breeds, iHS analysis highlighted extended haplotype homozygosity at BGLAP and genes involved in the growth hormone axis ( GHR , GHRHR , GH ), metabolic regulators ( ACLY , SHMT1 , SUCLA2 , FTO ), and muscle genes ( MYOG , MSTN , CAPN1 , CAPN2 ). In Oamer and Karacabey, significant haplotypes were identified that covered MSTN (myostatin), a negative regulator of muscle growth. In contrast, in Hasak and Hasmer crossbreds, haplotypes encompassed MYL2, MEF2C, MYF6, CFL1, and small RNAs implicated in muscle differentiation. Tajima’s D results reinforced these signals by identifying departures from neutrality at CAPN2 , CAST (calpastatin), and CAPN3 (muscle-specific calpain), which regulate proteolysis and muscle hypertrophy. The repeated identification of CAPN2 and CAST across breeds implies selection for growth and carcass traits. Our identification of BGLAP as a central growth candidate is consistent with selection signatures in other sheep. South African Merino and Merino-derived breeds showed strong selection at FGF12, ICA1, and HMGA2, which are associated with hair follicle and growth traits 46 . In Iranian Qezel and Afshari sheep, MYF5 and PPP1R12A were implicated in muscle formation 47 . In Hu sheep, the HOXA cluster genes and MSTN were identified as key factors in growth and fat deposition 48 . Genome-wide scans of Chinese breeds identified CRADD, LIN28B, WNT11, HMGA2, and MSTN as influencing body size and muscle development 49 . A BMC Genomics study on Tibetan sheep body size identified 623 significant SNPs and genes, including ASAP1, CDK6, FRYL, NAV2, PTPRM, GPC6, PTPRG, KANK1, NTRK2 , and ADCY8 , which are enriched in cAMP and Rap1 signaling 50 , illustrating the complexity of growth regulation. Although different genes were highlighted across studies, many converge on pathways that control skeletal muscle development, metabolic homeostasis, and growth hormone signaling. Our detection of GHR , GHRHR , GH , STAT5A , and AURKA is aligned with research on Tarim Basin sheep, where SMAD2 , ESR2 , and HAS2 were implicated in growth and reproduction 51 , and on South African Merino breeds, where PRKG genes and HMGA2 influenced body size 46 . In Middle Eastern and South Asian sheep, selection signatures included the genes TNIK, DOCK1, and USH2A , which are related to limb development and disease resistance 52 . These comparisons demonstrate that growth traits are polygenic and shaped by different selective histories; our results underscore the importance of osteogenesis and the growth hormone axis in Turkish breeds. The presence of extended haplotypes at BGLAP and nearby genes suggests that selection has occurred for bone density and skeletal strength, traits that are beneficial for survival and carcass quality. MYF6 and MEF2C regulate myogenic differentiation; their ROH indicates selection for muscle mass. MSTN and CAPN3 are negative regulators of muscle growth; selection on these genes implies a balancing between lean muscle and fat deposition. FTO influences energy homeostasis and appetite, while ACLY and SHMT1 mediate lipid synthesis and amino acid metabolism, respectively. Together, these genes shape growth rate, body size, and meat quality. The identification of CAPN2 , CAST , and CAPN3 in Tajima’s D analysis indicates that proteolytic regulation is a key target of selection; these calpains influence muscle tenderness and yield. Overall, our results align with international studies that emphasize the significance of osteogenesis, myogenesis, metabolic regulation, and growth hormone signaling in determining body size and carcass traits. Selection Signatures for Milk Production Milk production genes were pervasive in our analyses, with ROH and iHS signals covering the casein gene cluster ( CSN1S1 , CSN1S2 , CSN2 , CSN3 ), lactoferrin ( LTF ), prolactin ( PRL ), and regulators of milk fat and protein synthesis. In Akkaraman, long homozygous tracts encompassed the casein cluster and PRL, UGT1A1, and CYP11A1 , suggesting strong selection for milk composition. Hasak and Hasmer crossbreds showed ROH at ADIPOQ (adiponectin), AHSG (fetuinA), BCO1, HSD17B2, AOX3, and SLC7A11 , all associated with milk fat metabolism. iHS analysis extended these findings by detecting long haplotypes at BMPR1B , FGF2 , ABCG2 , FGF7 , LEPR , SCD5 , LPL , and small RNAs (e.g., oarmir-99a, oarmir-200b) in Hasak crossbreds, and at LALBA, SCD5, BCO1, HSD3B1, and KRT35 in Oamer and Karacabey. The Tajima’s D results identified DGAT1 and B4GALNT2 in oamer, underscoring selection for milk fat synthesis and protein glycosylation. Our identification of the casein cluster and PRL concurs with previous studies. In Greek breeds, ROH islands included milk genes such as ABCG2 , SPP1 , LAP3 , NCAPG , and MEPE 53 , while in Polish breeds, ROH contained SPP1 and ABCG2 45 . A mammalian comparative study across nine breeds found overlapping F_ST and XPEHH signals in DHRS3 , TNFRSF1B , AADACL4 , ARHGEF11 and LRRC71 ; additional genes highlighted by F_ST included PER2 , SH3PXD2A , TMEM117 , DDX6 , PDCD11 and CALHM2 , whereas XPEHH detected CRABP2 , PEAR1 , PGM1 , ALG6 , COX15 and OAT 54 . In selection scans of Lacaune and milkspecialised breeds, SUCNR1 and PPARGC1A were emphasised for milking performance 55 . The Western Pyrenees breeds have been selected at ABCG2 , SPP1 , LAP3 , LCORL , and MEPE 53 . Our detection of ADIPOQ , AHSG , and SLC7A11 parallels findings in Iranian Qezel and Afshari sheep, where PCCA, ACAP3 , and TTK were associated with milk traits 47 . Milk-related genes uncovered in our study are also involved in metabolic regulation and endocrine signalling. ADIPOQ and LEPR are adipokines affecting energy balance and lactation efficiency. ABCG2 transports vitamins and milk constituents; it has been under strong selection in dairy cattle and goats. The detection of BCO1 and SCD5 suggests a selective advantage for carotenoid metabolism and the synthesis of unsaturated fatty acids. KRT35 and other keratin genes might influence teat morphology and udder skin integrity. The presence of UGT1A1 , AOX3 , and HSD17B2 suggests that selection has occurred on steroid metabolism, which impacts milk yield and reproductive cycles. The convergence of ROH and iHS signals at milk genes implies selection for both milk composition and yield in Turkish breeds. Akkaraman and Oamer have historically been valued for milk and meat, and the presence of strong selection at the casein cluster suggests long-standing artificial selection for dairy traits. The identification of metabolic genes, such as ADIPOQ and AHSG , suggests a selection for improved feed efficiency and energy partitioning towards lactation. The crossbred populations may have inherited favourable milk alleles from exotic breeds; for example, ABCG2 , SCD5 , LPL , and SLC7A11 are common targets in European dairy breeds 53 . Overall, the results indicate that selection on lactation traits is widespread across the Turkish breeds, with crossbreds displaying additional metabolic gene signatures. Selection Signatures for Immune Function and Adaptation Immune-related genes represented a substantial proportion of the selection signatures detected. ROH analysis highlighted Toll-like receptors TLR2, TLR4, TLR5 , and TLR6 , interferon receptors IFNAR1 and IFNAR2 , signaling molecules MYD88, NFKBIA , and cytokines IL15, IL12A, and IL12RB2 . FC receptor genes ( FCGR2B , FCGR3A , FCER1A ) and costimulatory molecules ( SLAMF1 , SLAMF9 , CD247 ) also showed extended homozygosity. iHS analysis detected extended haplotypes around these same genes and additional immune genes, such as CD80, CD86, CD28, CTLA4, TNF, IL-22 , and IL-23A , in Hasak, Hasmer, and Karacabey crossbreds. oamer exhibited long haplotypes at MX1 , MX2 , RNF168 and NFKBIA . Tajima’s D indicated selection at IRF2 in Akkaraman and at IL1B, ITGB2 in Oamer, implying directional selection on inflammatory responses. Collectively, these findings suggest that pathogen pressure and environmental adaptation have shaped the immune gene repertoire of Turkish sheep. Similar immune genes have been identified in numerous sheep populations worldwide. In Turkish and Anatolian breeds, selection signatures overlapped with genes involved in olfaction, heat stress, and immune responses, such as ZNF208B and SDK1 44 . In an extensive comparative study across various climatic zones, genes in the interleukin (IL) and cluster of differentiation (CD) families were found to be enriched among selection candidates 56 . Middle Eastern and South Asian sheep displayed selection at TNIK, DOCK1, USH2A , and immune genes, such as TRIM56, CSF2RA , and HGF 52 . Iranian breeds have been identified as having IL23A, STAT2 , and DOCK5 (Yousefi et al., 2025), while Hu sheep selection scans have highlighted immune genes UBR1 and NLRX1 48 . Desert and high-altitude adaptation studies have identified genes such as TRIM62, FOXN1, ALDOC, POLDIP2 , and TXNDC5 as key to responses to hypoxia and heat stress 57 . In South African Merino breeds, IL22 , IL26 , IFNAR1 , IL10RB , and SLC5A3 were identified 46 , while Western Pyrenees breeds showed selection on NFKB2 , an essential immune transcription factor 53 . Our detection of Tolllike receptors ( TLRs ), interferon receptors and interleukins across all breeds echoes findings in the Tarim Basin, where SOD1 , TSHR and DNAJB5 were implicated in oxidative stress and desert adaptation 51 , and in Ethiopian and African breeds where TLR genes were under selection due to endemic diseases and parasites (not directly cited, but widely reported). The presence of NFKBIA , MYD88 , and MX1/MX2 indicates selection on innate immune signaling pathways, which are crucial for defence against viral and bacterial pathogens. Turkish sheep are raised in diverse environments, ranging from Mediterranean coastlines to continental interiors, which exposes them to different pathogens and climatic stressors. The selection signatures in TLR and interferon pathways suggest adaptation to local disease pressures, including bacterial infections such as mastitis and parasitic infestations. NFKBIA modulates inflammatory responses, while MYD88 transduces signals from TLRs to NFκB. The repeated detection of FCGR2B , FCGR3A , and FCER1A suggests selection on antibody-mediated responses, potentially due to vaccination regimes or exposure to parasites. Genes involved in oxidative stress and heat tolerance ( CRYAA , GJA8 , SOD1 ) and circadian regulation ( PER2 ) may reflect adaptation to environmental stress. The extended haplotypes at CD80 , CTLA4 , and CD28 in crossbreds suggest a balance between immune activation and tolerance, possibly due to crossbreeding with exotic lines that introduced novel alleles for disease resistance. Overall, the immune signatures indicate that adaptation to pathogens and climate has been a major driver of genomic evolution in Turkish sheep. Selection Signatures for Reproduction and Fertility Reproductive genes were consistently identified across ROH, iHS, and Tajima’s D analyses. Akkaraman ROH contained FSHB , GDF9 , POU1F1 , FSHR , and OXTR . Hasak and Oamer crossbreds showed ROH at FSHB, LHB, BMPR1B, INHBA, GNRHR , and IGFBP genes. hasmer exhibited long ROH at POU1F1 , GHR , GHRHR and PRKAR1A , and iHS signals at FSHR , GNRHR , INHBA , BMPR2 , POU1F1 and IGFBP7 . Extended haplotypes were detected at genes controlling gonadotropin signalling, including FSHB (βsubunit of folliclestimulating hormone), LHB (βsubunit of luteinising hormone), GDF9 (oocyte growth factor), FSHR (receptor mediating FSH), BMPR1B/BMPR2 (bone morphogenetic protein receptors), INHBA (inhibin subunit α), GNRHR (gonadotropinreleasing hormone receptor), IGFBP3/4/5/6/7 (insulinlike growth factor binding proteins) and OXT/OXTR (oxytocin and receptor). Tajima’s D showed negative values at PAG4 (pregnancy-associated glycoprotein), IRF2, ITGB2 , and IL1B , emphasising selection on early pregnancy and immune regulation during reproduction. Reproductive genes identified here are widely documented in sheep and other species. The prolific Booroola gene, BMPR1B , is a significant determinant of ovulation rate and litter size; its presence in Hasak, Hasmer, Karacabey, and Oamer indicates the introgression of prolific alleles. In Hu sheep, BMPR1B and GNRH2 were among the most significant genes involved in reproduction 48 . Prolific Suffolk sheep showed selection on 23 reproduction genes, including ARHGEF4 , CATIP , and CCDC115 58 . Tarim Basin sheep exhibited candidate genes SMAD2 , ESR2 , HAS2 , DMC1 , and TSHR associated with oocyte maturation, oestrogen receptor signalling, and seasonal reproduction 51 . Iranian breeds were selected for BMP5, ANGPT2, PCCA , and ACAP3 to improve reproduction and milk traits 47 . The Western Pyrenees breeds showed selection on ESR1 , ZNF366 , and H2AFZ (testis histone) 53 . South African Merino breeds had signals at FGF5 , ANTXR2 , BMP2 , GHSR , SPATA16 , RXFP2 , and FGR 46 . The cross-population desert adaptation study identified genes DMC1, TSHR , and HAS2 as being under selection for perennial estrus and reproduction 51 . These examples demonstrate that the reproductive gene network, involving BMP signalling, gonadotropin hormone synthesis and receptor pathways, oxytocin, and insulin-like growth factor binding, is recurrently targeted by selection. The presence of extended haplotypes at FSH , LH , BMPR1B/BMPR2 , and GDF9 suggests that selection has occurred for increased ovulation rates and litter sizes. BMPR1B mutations ( FecB allele) are known to increase ovulation but reduce lamb survival; thus, selection may aim to balance prolificacy with fitness. FSHR and GNRHR regulate follicular development and hormone release; their selection indicates pressure for improved fertility. INHBA modulates FSH secretion, and selection on this gene may fine-tune litter size. IGFBP genes influence follicular growth and embryo development; their selection suggests an emphasis on embryonic survival and growth. PAG4 encodes a trophoblast glycoprotein, and negative Tajima’s D at this locus indicates directional selection, possibly due to improved placental function. OXT/OXTR regulate parturition and maternal behaviour; selection on these genes may improve lamb survival and maternal care. The repeated identification of reproduction genes across all breeds, including the crossbred Hasak, Hasmer, Karacabey, and Oamer populations, implies that reproductive efficiency has been a significant focus of selection, likely due to economic incentives for high lambing rates. Convergence and Divergence of Selection Signals Across the Turkish breeds, there is clear evidence for convergent selection on core pathways involved in growth, milk production, immunity, and reproduction. BGLAP , POU1F1 , and the growth hormone axis (GH– GHR – GHR HR– STAT5A ) are ubiquitous, indicating that body size and skeletal development are central traits under selection. The casein cluster and PRL highlight the importance of lactation. Toll-like receptors, interferon, and interleukin genes demonstrate adaptation to infectious agents and environmental stress. The gonadotropin axis, BMP signalling, and insulin-like growth factor system underscore the emphasis on prolificacy. These convergent signals reflect shared selective pressures, such as meat and milk production, disease resistance, and fertility, across indigenous and crossbred populations. However, each breed also exhibits unique signatures. Akkaraman displayed strong selection on osteogenesis and metabolic regulation, consistent with its adaptation to harsh Anatolian environments and its role as a meat-type, fat-tailed breed. Hasak crossbreds, derived from Akkaraman and German Mutton Merino, showed additional selection at metabolic genes ( PRKAA2, ADIPOQ ) and immune costimulatory molecules ( CD80, CD86 ), reflecting the introgression of exotic alleles. Hasmer, a cross between Hasak and Merino, exhibited pronounced selection at POU1F1 and GHR , which linked growth and milk traits, and at MYF6 and HOXC6 , indicating morphological differences. Karacabey Merino crossbreds exhibited the strongest selection at the somatotropic axis ( GH, GHR, GHRHR ) and immune genes ( ITGB2, CRYAA ), consistent with their dual-purpose use and adaptation to more temperate climates. Oamer crossbreds displayed the widest array of selection signatures, including metabolic regulators ( STAT1, RHEB, CPT1A ), reproduction genes ( GNRHR, IGFBP3 ), and circadian genes ( CLOCK ), suggesting complex polygenic selection due to diverse ancestry. These breed-specific patterns highlight how crossbreeding and local adaptation have produced distinct genetic architectures. Our findings fit within the broader context of sheep genomics. The moderate genetic diversity and overlapping clusters are consistent with other European and Turkish studies, which have shown high within-breed variation and admixture 11 , 59 . The selection on growth hormone and BMP pathways aligns with numerous studies across European, Asian, and African breeds 46 , 51 . The casein cluster and metabolic genes identified here have also been found in dairy breeds and the Western Pyrenees 53 as well as in Lacaune 55 . The immune signatures involving TLRs , interferons, and interleukins are ubiquitous across climate adaptation studies 56 , 57 . Reproductive genes, such as BMPR1B, GDF9, FSHB, and FSHR , are recurrent targets of selection in prolific breeds worldwide 48 , 58 . Thus, our results both corroborate and extend existing knowledge, confirming that selection for productivity and adaptation operates on a relatively conserved set of genes and pathways, with breed-specific variations reflecting local breeding goals and environmental conditions. The identification of core and breed-specific selection signals has practical implications. First, the presence of strong selection at growth and reproduction genes indicates that breeders have prioritized meat and lamb production. However, maintaining genetic diversity is crucial to prevent inbreeding depression and preserve adaptive potential. The moderate heterozygosity and negative FIS values suggest that crossbreeding has mitigated inbreeding, but small populations like Hasak and Hasmer require careful management. Second, the detection of immune gene selection highlights the importance of considering disease resistance in breeding programs, particularly as climate change alters pathogen pressures. Third, the frequent selection of metabolic genes ( ADIPOQ , SCD5 , ACLY ) suggests potential for improving feed efficiency and reducing greenhouse gas emissions. Finally, the identification of reproduction genes can inform marker-assisted selection to optimize litter size and lamb survival while avoiding adverse effects, such as lamb mortality, associated with high prolificacy. Suggested mitigations for future work include expanding and balancing sampling, integrating cross-population statistics with explicit demographic modeling, utilizing whole-genome sequencing for fine-mapping, incorporating phenotype and environmental covariates, and validating key regions through replication and functional follow-up studies. Conclusion The integration of complementary genomic approaches has shed light on the complex landscape of adaptation and production in Turkish sheep, demonstrating the power of combining population-structure analyses with multiple selection-scan methods. By situating indigenous and crossbred populations within a unified analytical framework, this work highlights how shared and breed-specific genetic architectures can be leveraged to strike a balance between productivity and resilience. Looking forward, the candidate regions highlighted here offer a roadmap for targeted breeding and conservation strategies. Functional validation of key loci, coupled with precise phenotype-genotype association studies, will further refine selection targets. Moreover, expanding the survey to include additional Turkish and international populations promises to deepen our understanding of sheep genomic diversity. Collectively, these efforts will support the sustainable improvement of meat, milk, immune robustness, and reproductive performance, while safeguarding the unique genetic heritage of Türkiye’s native breeds. Declarations Ethical statement This study was conducted with an experimental protocol approved by the “ Bandırma Sheep Breeding and Research Institute Ethics Committee for the Use of Animals in Research and Experimentation” , Türkiye (Approval No: 04.10.2021/049). The authors complied with the ARRIVE guidelines, and informed consent was obtained from the Bandırma Sheep Breeding and Research Institute administration prior to the study. Acknowledgements The authors express their gratitude to the General Directorate of Agricultural Research and Policies for invaluable support. We also express our gratitude to our great leader, Mustafa Kemal ATATÜRK, who stated that "Science is the truest guide in the world for everything — for life, for success.". Author contributions Y.Y. and M.B. conceptualized and designed the study, Ş.D. M.K., and B.B. conducted fieldwork, data collection, M.B. performed bioinformatic analysis, Y.Y. and M.B. wrote the draft, Y.Y. edited the manuscript. Funding Funding for this research was provided by the Republic of Turkey Ministry of Agriculture and Forestry, General Directorate of Agricultural Research and Policies (TAGEM) (Project No: TAGEM/HAYSÜD/E/20/A4/P2/2141). Consent for publication Publication consent for the article was obtained from the SBRI administration. Competing interests The authors declare no competing interests. Additional information Correspondence and requests for materials should be addressed to Y.Y. Data availability The datasets generated and/or analysed during the current study are available in the https://figshare.com/repository, https://figshare.com/articles/dataset/Genotype_data_of_Karacabey_Merino_Middle_Anatolian_Merino_Oamer_Akkaraman_HASMER_and_HASAK_sheep_breeds_/29898101 References Daly, K. G. et al. Ancient genomics and the origin, dispersal, and development of domestic sheep. Science 387 , 492–497 (2025). Kaptan, D. et al. The population history of domestic sheep revealed by paleogenomes. Mol. 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Wanjala, G. et al. Genetic diversity and adaptability of native sheep breeds from different climatic zones. Sci. Rep. 15 , 14143 (2025). Patiabadi, Z. et al. Whole-genome scan for selection signature associated with temperature adaptation in Iranian sheep breeds. Plos one . 19 , e0309023 (2024). Yang, H. et al. Genome-wide comparative analysis reveals selection signatures for reproduction traits in prolific Suffolk sheep. Front. Genet. 15 , 1404031 (2024). Michailidou, S., Kyritsi, M., Pavlou, E., Tsoureki, A. & Argiriou, A. Genetic Diversity, Population Structure, and Historical Gene Flow Patterns of Nine Indigenous Greek Sheep Breeds. Biology 14 , 845 (2025). Acknowledgements The authors express their gratitude to the General Directorate of Agricultural Research and Policies for invaluable support. We also express our gratitude to our great leader, Mustafa Kemal ATATÜRK, who stated that Science is the truest guide in the world for everything — for life, for success. Supplementary Table S4 Supplementary Table S4 is not available with this version. Additional Declarations No competing interests reported. Supplementary Files TableS1ROHakkaramanROHfinalresults100kb.xlsx TableS2ROHHASAKROHfinalresults100kb.xlsx TableS3ROHHASMERROHfinalresults100kb.xlsx TableS5ROHOAMERROHfinalresults100kb.xlsx TableS6iHSakkaramanfinalresults100kb.xlsx TableS7iHSHASAKFINALihsfinalresults100kb.xlsx TableS8iHSHASMERFINALfinalresults100kb.xlsx TableS9iHSKaracabeyfinalresults100kb.xlsx TableS10iHSOAMERfinalresults100kb.xlsx TableS11Tajimaakkaramangenesinbalsel.txt TableS12TajimaHASAKFINALgenesinbalsel.txt TableS13TajimaHASMERFINAL1genesinbalsel.txt TableS14TajimaKaracabeygenesinbalsel.txt TableS15TajimaOAMERgenesinbalsel.txt Cite Share Download PDF Status: Published Journal Publication published 23 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted Reviewers invited by journal 02 Sep, 2025 Editor assigned by journal 02 Sep, 2025 Editor invited by journal 29 Aug, 2025 Submission checks completed at journal 21 Aug, 2025 First submitted to journal 21 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7364156","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":509761096,"identity":"3fec2a84-59cb-49bd-96c7-e5f51bb70943","order_by":0,"name":"Mervan BAYRAKTAR","email":"","orcid":"","institution":"Çukurova University","correspondingAuthor":false,"prefix":"","firstName":"Mervan","middleName":"","lastName":"BAYRAKTAR","suffix":""},{"id":509761097,"identity":"049a0bd5-1c8b-4e0b-97fe-b83fc7c857d7","order_by":1,"name":"Şükrü DOĞAN","email":"","orcid":"","institution":"Dagdas International Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Şükrü","middleName":"","lastName":"DOĞAN","suffix":""},{"id":509761098,"identity":"c1729af3-a111-4a20-bcb5-e3acca9af091","order_by":2,"name":"Mesut KIRBAŞ","email":"","orcid":"","institution":"Dagdas International Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Mesut","middleName":"","lastName":"KIRBAŞ","suffix":""},{"id":509761099,"identity":"5e312dc4-88f6-4e0b-9639-e31fd6b0ea12","order_by":3,"name":"Bülent BÜLBÜL","email":"","orcid":"","institution":"Dokuz Eylül University","correspondingAuthor":false,"prefix":"","firstName":"Bülent","middleName":"","lastName":"BÜLBÜL","suffix":""},{"id":509761100,"identity":"05e59b88-acd4-4aa8-969b-22ff0d14994b","order_by":4,"name":"Yalçın YAMAN","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYFAC5gaGBAiL8QGQ4OEjrIURroXZAKSFjSgtUMAmASYJaTA4frDxw8Mdh+V1288eq/yaYyfDxsD88NENfFrOJDZLJJ45bLjtTF7abdltyUCHsRkb5+DTciCxjSGx7TDjtgM5ZrcltzEDtfCwSePVcv4hWIv9tvNvzIolt9UToeUGxJbEbTdyzBg/bjtMWIvkjYdAv7SlJ2+78cZYmnHbcR42ZgJ+4TuffPDjzzZr223ncww//txWbc/P3vzwMT4tCgfAVDOYZOYBk3iUg4B8A5iqA5OMPwioHgWjYBSMgpEJAEIfTnV483LiAAAAAElFTkSuQmCC","orcid":"","institution":"Siirt University","correspondingAuthor":true,"prefix":"","firstName":"Yalçın","middleName":"","lastName":"YAMAN","suffix":""}],"badges":[],"createdAt":"2025-08-13 10:53:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7364156/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7364156/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-29969-1","type":"published","date":"2025-11-23T15:57:19+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90908848,"identity":"871f0497-6cde-411d-901c-1bf4a0f3c369","added_by":"auto","created_at":"2025-09-09 13:28:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":780006,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative Turkish sheep breeds, a. Akkaraman, b. Karacabey Merino, c. Oamer, d. Hasmer, e. Hasak\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7364156/v1/bd153a3f95544b27cd58dfb6.png"},{"id":90908849,"identity":"c303a1dd-8149-4b42-9a09-f957fc37231d","added_by":"auto","created_at":"2025-09-09 13:28:10","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":64026,"visible":true,"origin":"","legend":"\u003cp\u003ePCA illustrating genetic differentiation among five Turkish sheep breeds\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7364156/v1/e435a7bd3df8b3f15e36656b.jpeg"},{"id":90910179,"identity":"d4c6d848-d43c-49a2-a32e-8eb22ddb6a41","added_by":"auto","created_at":"2025-09-09 13:36:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":526133,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 2.\u003c/strong\u003e Comparative Genome-wide ROH Analysis in Five Sheep Breeds\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7364156/v1/f120acf39cd5de86725095f6.png"},{"id":90911752,"identity":"57e4c399-b5ac-4ed5-bf0e-54f609317669","added_by":"auto","created_at":"2025-09-09 13:44:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":118591,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 3. \u003c/strong\u003eVenn analysis\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7364156/v1/440bdecfccc65b696d6f51aa.png"},{"id":90910190,"identity":"e4580a46-dacf-4c36-9f20-f932c978db14","added_by":"auto","created_at":"2025-09-09 13:36:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":980333,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 4.\u003c/strong\u003e Genome-wide iHS Profiles Across Five Sheep Breeds\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7364156/v1/3fdc9a3488cee3b88bb6b110.png"},{"id":90912696,"identity":"5182db23-e322-49a4-b15f-db3b86845828","added_by":"auto","created_at":"2025-09-09 13:52:11","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":373912,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 5.\u003c/strong\u003e Comparative Genome-wide Tajima’s D Analysis in Five Sheep Breeds\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7364156/v1/6e4e7af11c738056348da527.png"},{"id":90908863,"identity":"303f72c4-d831-4906-8b60-d6381923ea2d","added_by":"auto","created_at":"2025-09-09 13:28:11","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":45371,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 6. \u003c/strong\u003eGene Ontology Enrichment\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7364156/v1/d91e0fe81a9f3ed6a5ccd218.png"},{"id":90910182,"identity":"4de4df59-4181-4ae4-a152-c4814b4541b2","added_by":"auto","created_at":"2025-09-09 13:36:11","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":41658,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 7. \u003c/strong\u003eKEGG Pathway Enrichment\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7364156/v1/badbbfb4c897fdf105b18de1.png"},{"id":96650942,"identity":"52e9a4c2-44ff-4686-90be-180159d6bf81","added_by":"auto","created_at":"2025-11-24 16:12:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4238847,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7364156/v1/55cf7f1a-1675-499b-b173-efbf89bfc3ee.pdf"},{"id":90908854,"identity":"e2745a88-3c3e-4ea1-ae2a-aa2ea5de3cc1","added_by":"auto","created_at":"2025-09-09 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13:36:11","extension":"txt","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":5189,"visible":true,"origin":"","legend":"","description":"","filename":"TableS12TajimaHASAKFINALgenesinbalsel.txt","url":"https://assets-eu.researchsquare.com/files/rs-7364156/v1/11cd3866d44a863673a1548d.txt"},{"id":90911758,"identity":"4de05f2a-a3fb-4cfe-80e2-b13f7e48d0fe","added_by":"auto","created_at":"2025-09-09 13:44:11","extension":"txt","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":3833,"visible":true,"origin":"","legend":"","description":"","filename":"TableS13TajimaHASMERFINAL1genesinbalsel.txt","url":"https://assets-eu.researchsquare.com/files/rs-7364156/v1/64c9956673683abf380558f6.txt"},{"id":90910200,"identity":"c0fc956f-7233-416a-a5d2-4af9a7e849b7","added_by":"auto","created_at":"2025-09-09 13:36:11","extension":"txt","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":8299,"visible":true,"origin":"","legend":"","description":"","filename":"TableS14TajimaKaracabeygenesinbalsel.txt","url":"https://assets-eu.researchsquare.com/files/rs-7364156/v1/ae7ddcd4012030171a9c3d82.txt"},{"id":90910198,"identity":"f5bf4471-cb3d-471a-b0ee-dbc440772148","added_by":"auto","created_at":"2025-09-09 13:36:11","extension":"txt","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":11952,"visible":true,"origin":"","legend":"","description":"","filename":"TableS15TajimaOAMERgenesinbalsel.txt","url":"https://assets-eu.researchsquare.com/files/rs-7364156/v1/21a7cefdac6de4cabf891655.txt"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative Detection of Selection Signatures in Indigenous and Crossbred Turkish Sheep Breeds","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSheep (\u003cem\u003eOvis aries\u003c/em\u003e) were among the earliest domesticated ungulates, with archaeological, zooarchaeological, and genomic evidence converging on the Fertile Crescent as the primary center of domestication during the early Holocene \u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. From these origins, pastoral expansions, trade, and repeated episodes of human-mediated selection diversified sheep into a broad array of breeds specialized for meat, milk, wool, and adaptation to heterogeneous agroecological niches \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Over thousands of years, deliberate breeding and local environmental pressures have left discernible \u0026ldquo;fingerprints\u0026rdquo; in the ovine genome, so-called selection signatures, that reflect both ancient and recent responses to natural and artificial selection \u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Systematic detection of these signatures provides a powerful lens for reconstructing the evolutionary and breeding history of populations, identifying candidate genes underlying complex traits, and informing evidence-based improvement and conservation strategies \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eT\u0026uuml;rkiye occupies a pivotal position in this history and contemporary sheep production. Its geography spans semi-arid steppes, continental plateaus, and Mediterranean littorals, supporting extensive and semi-extensive grazing systems that remain central to rural livelihoods \u003csup\u003e\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Indigenous Turkish breeds have been shaped by decades to centuries of selection under nutritional constraints, thermal stress, and disease challenges typical of these environments. At the same time, state farms and research institutes have implemented structured crossbreeding and introgression programs to enhance productivity, particularly in wool and lamb production, without compromising local resilience \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. This dual imperative, preserving adaptation while elevating output, has produced a rich landscape of indigenous and composite populations that are ideal for comparative genomic analysis \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAkkaraman (also known as White Karaman) is the most widespread fat-tailed indigenous breed in Central Anatolia. Managed primarily in extensive systems, Akkaraman sheep exhibit notable robustness to feed scarcity, temperature extremes, and endemic disease pressure, coupled with moderate growth and prolificacy. As a genetic reservoir for adaptation to steppe ecosystems, Akkaraman represents a critical reference for understanding the genomic basis of resilience. In parallel, a set of crossbred/composite types has been developed to enhance meat and wool attributes. Karacabey Merino was initiated at the Karacabey State Farm through the crossing of Kivircik with German Mutton Merino, which was stabilized to produce finer wool and acceptable growth under Turkish conditions. The Central Anatolian Merino (oamer; \u003cem\u003eOrta Anadolu Merinosu\u003c/em\u003e) arose from structured introgression of Merino germplasm into local stocks (notably Akkaraman) on Central Anatolian state farms to combine improved fleece quality with environmental hardiness. More recently, the Bandirma Sheep Research Institute established composite meat-type populations, such as Hasmer and Hasak, by integrating genetics from terminal-sire breeds (e.g., German Blackhead Mutton, Hampshire Down) and Merino with Akkaraman. These programs were designed to exploit heterosis and complementarity, improving carcass traits, growth rate, and, where relevant, fleece characteristics while retaining adaptation to Turkish production systems \u003csup\u003e\u003cspan additionalcitationids=\"CR17 CR18 CR19 CR20\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDespite their economic prominence, the genomic architecture that differentiates these populations, particularly the balance between introgressed performance alleles and indigenous adaptation loci, remains incompletely characterized.\u003c/p\u003e\u003cp\u003eClassical performance testing and quantitative genetic evaluations provide trait-level estimates (e.g., heritability, breeding values). Still, they do not directly reveal the specific genomic regions and biological pathways shaped by selection. Genome-wide selection signature scans fill this gap by interrogating population genetic patterns that deviate from neutrality \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Three complementary classes of signals are especially informative for livestock: (i) runs of homozygosity (ROH), (ii) haplotype-based metrics of extended haplotype homozygosity (EHH) such as the integrated haplotype score (iHS), and (iii) site-frequency-spectrum (SFS) statistics such as Tajima\u0026rsquo;s D \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eROH are long, contiguous stretches of homozygous genotypes that arise when chromosomal segments are inherited identically by descent \u003csup\u003e\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Their frequency, length distribution, and genomic clustering (\u0026ldquo;ROH islands\u0026rdquo;) provide insight into recent inbreeding, demographic contractions, and strong directional selection that fixes or nearly fixes haplotypes surrounding advantageous alleles \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Shorter ROH often reflect older shared ancestry, whereas very long ROH indicate recent common ancestors or intense selection \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. iHS, in contrast, is a within-population haplotype test that contrasts the decay of EHH around the derived versus ancestral allele at a focal SNP \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Because incomplete or ongoing sweeps preserve extended haplotypes at elevated frequency around the favored allele, iHS is particularly sensitive to relatively recent selection where both alleles still segregate \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Finally, Tajima\u0026rsquo;s D summarizes the SFS by comparing pairwise nucleotide diversity to the number of segregating sites; negative values can indicate recent directional selection or population expansion (an excess of rare variants), whereas positive values can reflect balancing selection or recent bottlenecks (an excess of intermediate-frequency variants) \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. By integrating ROH, iHS, and Tajima\u0026rsquo;s D, investigators can triangulate signals across timescales and demographic contexts: ROH captures fixation-proximal events and inbreeding structure; iHS targets ongoing sweeps; and Tajima\u0026rsquo;s D adds SFS sensitivity that is less dependent on haplotype phase \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eApplying these tools to Turkish sheep is scientifically and practically valuable for several reasons. First, indigenous and composite breeds have undergone distinct selection regimes and environmental filtering in harsh rangelands compared to artificial selection for growth and fleece in structured programs, resulting in contrasting genomic footprints. Second, crossbreeding and introgression complicate selection scans because admixture reshapes linkage disequilibrium and allele frequencies; a comparative design that includes both indigenous and crossbred populations, combined with explicit population structure analysis, improves interpretability and reduces false positives. Third, the economic traits under selection in these breeds, including growth rate, carcass composition, milk yield and composition, wool fineness and staple strength, parasite resistance, and thermotolerance, are polygenic and mediated by networks of genes related to metabolism, endocrine regulation, immunity, and skin/hair follicle biology. Identifying recurrently targeted regions across populations versus population-specific sweeps can reveal core pathways for small-ruminant productivity and local adaptation, respectively. Finally, from a breeding and conservation standpoint, mapping selection signatures provides candidate markers for genomic selection, helps prioritize haplotypes to maintain or introgress, and informs the management of inbreeding by highlighting genomic regions where homozygosity is most concentrated \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan additionalcitationids=\"CR37 CR38\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThis study addresses a clear knowledge gap at the interface of adaptation and productivity in Turkish sheep. A rigorous, comparative scan across indigenous and crossbred populations will (i) reveal breed-specific and shared selection footprints, (ii) nominate candidate genes and pathways underpinning economically important and adaptive traits, and (iii) provide actionable genomic targets for breeding programs seeking to balance performance gains with the preservation of local robustness. The overarching objective is to generate a consolidated map of selection in Turkish sheep that can guide marker-assisted and genomic selection, inform introgression and conservation decisions, and deepen our understanding of how historical and contemporary selection have sculpted small-ruminant genomes in the Anatolian context.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eAnimal material\u003c/h2\u003e\u003cp\u003eA total of 1,612 sheep from five breeds were included in this study: Akkaraman (n\u0026thinsp;=\u0026thinsp;168), Karacabey Merino (n\u0026thinsp;=\u0026thinsp;760), Oamer (n\u0026thinsp;=\u0026thinsp;671), Hasak (n\u0026thinsp;=\u0026thinsp;7), and Hasmer (n\u0026thinsp;=\u0026thinsp;6) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Blood samples from the Akkaraman, Oamer, Hasak, and Hasmer breeds were collected at the Bahri Dağdaş International Agricultural Research Institute in Konya. In contrast, samples from Karacabey Merino were collected at the Sheep Breeding Research Institute in Balikesir. Blood was drawn from the jugular vein using vacuum tubes containing ethylenediaminetetraacetic acid (EDTA) as an anticoagulant and stored under appropriate conditions until DNA extraction.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDNA Extraction and Genotyping\u003c/h3\u003e\n\u003cp\u003eGenomic DNA was isolated from blood samples using a commercial spin-column DNA extraction kit. DNA yield and purity were assessed before genotyping. All individuals were genotyped using the Illumina Ovine SNP50K BeadChip and mapped to the Oar_v4.0 reference assembly.\u003c/p\u003e\n\u003ch3\u003eGenotype Quality Control\u003c/h3\u003e\n\u003cp\u003eInitial quality filtering was performed using PLINK \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. SNPs were discarded if more than 10% of their genotypes were missing, if they significantly deviated from Hardy\u0026ndash;Weinberg equilibrium (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), or if their MAF fell below 5%.\u003c/p\u003e\n\u003ch3\u003eGenetic Diversity and Population Structure Analysis\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003ePrincipal Component Analysis (PCA)\u003c/h2\u003e\u003cp\u003eTo examine genetic differentiation between the two breeds, the SNP matrix was first standardized. PCA was then performed in the R environment using the prcomp() function. The top two principal components were selected, accounting for approximately 70% and 17% of the total variance, respectively. Individuals were plotted in a two-dimensional space, with colors indicating breed membership, using the ggplot2 package for visualization.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eSelection Signatures Analysis\u003c/h2\u003e\u003cp\u003eThree complementary approaches, iHS, ROH, and Tajima\u0026rsquo;s D, were used to detect genomic regions under selection.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eiHS Analysis\u003c/h3\u003e\n\u003cp\u003eHaplotype phasing was conducted in BEAGLE, and iHS scores were calculated in R using the rehh package. Standardized iHS values (along with their corresponding\u0026thinsp;\u0026minus;\u0026thinsp;log₁₀(p) statistics) were computed for all markers. We scanned the genome in non-overlapping 100 kb windows and flagged any window containing at least one SNP with \u0026minus;\u0026thinsp;log₁₀(p)\u0026thinsp;\u0026ge;\u0026thinsp;4 (i.e., p\u0026thinsp;\u0026lt;\u0026thinsp;10⁴) as a candidate region. All such windows were merged to form a final list of putative positively selected intervals.\u003c/p\u003e\n\u003ch3\u003eROH Analysis\u003c/h3\u003e\n\u003cp\u003eROH were identified in R environment with the detectRuns package. Parameters included a sliding window of 15 SNPs, a minimum of 15 SNPs required to call a run, and a maximum inter-SNP gap of 1 Mb. We also set the minimum homozygous run length to 1 Mb and enforced a SNP density threshold of at least one SNP per 100 bp.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eTajima\u0026rsquo;s D Calculation\u003c/h2\u003e\u003cp\u003eTo highlight regions potentially under balancing selection, Tajima\u0026rsquo;s D was calculated using VCFtools. A 10-kb sliding window was implemented across the genome. Overlap among the five breed-specific sets was computed and visualized with the web tool InteractiVenn \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, which reports exact set intersections.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eGO and KEGG Functional Enrichment Analysis\u003c/h2\u003e\u003cp\u003eGenes located within candidate selection regions were subjected to functional enrichment analysis using SRplot \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Gene Ontology (GO) terms were classified into three categories: biological process (BP), molecular function (MF), and cellular component (CC). KEGG pathway analysis was then performed to identify pathways that were significantly enriched \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. SRplot computed enrichment scores, applied FDR correction for p-values, and generated graphical summaries of enriched terms.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eGenetic diversity analysis\u003c/h2\u003e\u003cp\u003eAll diversity analyses were conducted on an Ubuntu 20.04 system under Windows Subsystem for Linux (WSL). We used PLINK v1.9 for genotype manipulation and basic summary statistics, VCFtools v0.1.17 for sliding-window nucleotide diversity, and R v4.x (with the data. table, knitr, and writeXL packages) for downstream aggregation and reporting.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eNucleotide diversity\u003c/h2\u003e\u003cp\u003eEach breed\u0026rsquo;s quality-controlled PLINK dataset was first exported to VCF format. We then ran VCFtools to scan the autosomes in 100-kb windows with a 50-kb step. Within every window, the average number of pairwise nucleotide differences per site among all sampled chromosomes was computed. Finally, these windowed estimates were averaged to yield a single genome‐wide nucleotide diversity value for each breed.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eMinor allele frequency and heterozygosity (MAF, HO, HE)\u003c/h2\u003e\u003cp\u003eWithin-breed allele frequencies were summarized by computing the MAF across all retained SNPs using PLINK\u0026rsquo;s frequency function. Observed heterozygosity (HO) and expected heterozygosity (HE) were then extracted from PLINK\u0026rsquo;s Hardy\u0026ndash;Weinberg output. HO represents the actual proportion of heterozygous genotypes in the sample, whereas HE reflects the proportion expected under random mating given the allele frequencies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eInbreeding coefficient (FIS)\u003c/h2\u003e\u003cp\u003eUsing the HO and HE values obtained from PLINK, we calculated the FIS for each breed in R. A negative FIS indicates an observed excess of heterozygotes relative to Hardy\u0026ndash;Weinberg expectations. In contrast, a positive FIS indicates a deficit.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eWithin-breed allele sharing (DST) and genetic distance (D)\u003c/h2\u003e\u003cp\u003ePairwise genomic similarity between individuals was quantified via PLINK\u0026rsquo;s identity-by‐state (IBS) analysis. From the proportions of loci where individuals shared zero, one, or two alleles identically, we derived an average DST for each breed. D value was then expressed as the complement of DST, with higher D values indicating greater genetic divergence among individuals.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003ePCA Analysis in Five Sheep Breeds\u003c/h2\u003e\u003cp\u003eTo investigate the genetic structure of five sheep breeds (Akkaraman, Hasak, Hasmer, Karacabey, and Oamer), a PCA was conducted using SNP data. After filtering out 12,161 monomorphic or missing SNPs, the analysis was performed on the remaining SNPs with less than 10% missing data. The PCA results revealed that the first principal component (PC1) explained 15.81% of the total genetic variance, while the second principal component (PC2) accounted for 5.13%, collectively capturing 20.94% of the variance.\u003c/p\u003e\u003cp\u003eThe PCA scatter plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) illustrates the distribution of individuals from the five breeds along PC1 and PC2, with axes labeled as PC1 (4.00%) and PC2 (3.00%) for visualization purposes. The Akkaraman breed (n\u0026thinsp;=\u0026thinsp;168) exhibited distinct separation along PC1, with one individual (A115) showing a notably divergent PC1 score (-0.72), suggesting potential genetic uniqueness or population substructure within this breed. In contrast, Karacabey (n\u0026thinsp;=\u0026thinsp;760) and Oamer (n\u0026thinsp;=\u0026thinsp;671) formed larger, partially overlapping clusters, indicating genetic similarity or possible historical gene flow between these breeds. The Hasak (n\u0026thinsp;=\u0026thinsp;7) and Hasmer (n\u0026thinsp;=\u0026thinsp;6) breeds, due to their small sample sizes, formed less pronounced clusters but displayed notable variation along PC2 (e.g., PC2 scores ranging from 51.62 to 74.49), suggesting unique genetic contributions despite their limited representation.\u003c/p\u003e\u003cp\u003eThe relatively low variance explained by PC1 and PC2 (20.94%) may reflect limited genetic diversity in the dataset, possibly due to high rates of missing data or the presence of monomorphic SNPs. These findings suggest that Akkaraman is genetically distinct, while Karacabey and Oamer share closer genetic profiles. The small sample sizes for Hasak and Hasmer warrant caution in interpreting their genetic differentiation, although their PC2 variation indicates potential breed-specific genetic characteristics.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eGenetic diversity\u003c/h2\u003e\u003cp\u003eGenome-wide nucleotide diversity varied modestly among the five Turkish sheep breeds, ranging from 6.97 \u0026times; 10⁶ in Karacabey to 8.30 \u0026times; 10⁶ in Akkaraman (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). MAF was similarly homogeneous, with values ranging from 0.286 in Oamer to 0.306 in Karacabey. HO spanned from 0.381 in Oamer to 0.445 in Hasmer, whereas HE ranged from 0.376 in Oamer to 0.394 in Karacabey. All breeds exhibited slightly negative FIS, indicative of a slight excess of heterozygotes; the most pronounced heterozygote excess occurred in Hasmer (FIS = \u0026minus;\u0026thinsp;0.161), while Karacabey showed the mildest (FIS = \u0026minus;\u0026thinsp;0.016). Finally, DST values extended from 0.033 in Oamer to 0.113 in Hasmer, corresponding to genetic distances (D\u0026thinsp;=\u0026thinsp;1 \u0026ndash; DST) of 0.887 to 0.967. Collectively, these metrics reveal moderate and broadly similar levels of genetic diversity across the five breeds, with Hasmer exhibiting the highest internal relatedness and Karacabey displaying the most notable nucleotide diversity.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of genetic diversity among the eleven cattle breeds.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBreed\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNucleotide diversity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMAF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHO\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFIS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDST\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAkkaraman\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.3E-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.295207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.392568\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.385232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.01904\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.036079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.963921\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHasak\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.35E-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.295812\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.421154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.382288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.10167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.04194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.95806\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHasmer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.35E-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.296168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.444817\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.383096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.16111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.112967\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.887033\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKaracabey\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.97E-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.305649\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.399801\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.393631\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.01567\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.038717\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.961283\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOamer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.12E-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.285933\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.381261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.375752\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.01466\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.033206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.966794\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eMAF, minor allele frequency; HO, observed heterozygosity; HE, expected heterozygosity; FIS, inbreeding coefficient; DST, within-breed allele‐sharing index; D, genetic distance (D\u0026thinsp;=\u0026thinsp;1 \u0026ndash; DST).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eROH-Based Selection Signatures\u003c/h2\u003e\u003cp\u003eUsing a window-based ROH calling strategy, we detected widespread runs of homozygosity across all five breeds, with apparent breed-specific differences in the number and genomic distribution of ROH islands (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In Akkaraman sheep, ROH analysis revealed multiple extended homozygous tracts, with the strongest signal centered on \u003cem\u003eBGLAP\u003c/em\u003e (osteocalcin, involved in bone mineralization/postnatal growth). Growth-related loci within ROH included \u003cem\u003eMYF6\u003c/em\u003e, \u003cem\u003eMEF2C\u003c/em\u003e, \u003cem\u003eFTO\u003c/em\u003e, \u003cem\u003eFGF12\u003c/em\u003e; milk loci spanned the casein cluster (\u003cem\u003eCSN1S1\u003c/em\u003e, \u003cem\u003eCSN2\u003c/em\u003e, \u003cem\u003eCSN1S2\u003c/em\u003e, \u003cem\u003eCSN3\u003c/em\u003e) together with \u003cem\u003eLTF\u003c/em\u003e and \u003cem\u003ePRL\u003c/em\u003e; immune genes \u003cem\u003eTLR2\u003c/em\u003e, \u003cem\u003eTLR5\u003c/em\u003e, \u003cem\u003eMYD88\u003c/em\u003e, \u003cem\u003eNFKBIA\u003c/em\u003e, \u003cem\u003eIL15\u003c/em\u003e showed pronounced homozygosity; reproductive candidates \u003cem\u003eFSHB\u003c/em\u003e, \u003cem\u003eGDF9\u003c/em\u003e, \u003cem\u003eOXTR\u003c/em\u003e were present; and metabolic/signaling genes \u003cem\u003eACLY\u003c/em\u003e, \u003cem\u003eSHMT1\u003c/em\u003e, \u003cem\u003eSTAT5A\u003c/em\u003e, \u003cem\u003eAURKA\u003c/em\u003e also lay in ROH segments (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In the Hasak crossbred population, ROH islands were widespread. The most pronounced centered on \u003cem\u003eBGLAP\u003c/em\u003e; additional tracts encompassed \u003cem\u003eFTO\u003c/em\u003e and \u003cem\u003eGHRHR\u003c/em\u003e. Milk‐related ROH included \u003cem\u003ePRL\u003c/em\u003e and the casein cluster (\u003cem\u003eCSN1S1\u003c/em\u003e, \u003cem\u003eCSN2\u003c/em\u003e, \u003cem\u003eCSN1S2\u003c/em\u003e, \u003cem\u003eCSN3\u003c/em\u003e). Immune genes within the ROH comprised TLR2, TLR5, IFNAR1, IL15, and NFKBIA; reproductive candidate genes \u003cem\u003eincluded FSHB, LHB, FSHR, and OXTR. Skeletal/muscle loci MYOG, MEF2C, and metabolic loci ACLY and STAT5A\u003c/em\u003e also occurred in extended runs of homozygosity (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). In the Hasmer crossbred, ROH analysis revealed a pronounced island at \u003cem\u003ePOU1F1\u003c/em\u003e (pituitary growth/lactotroph lineage regulator). Extended homozygous tracts also encompassed growth hormone axis genes \u003cem\u003eGHR\u003c/em\u003e and \u003cem\u003eGHRHR\u003c/em\u003e. Milk/lactation candidates \u003cem\u003eADIPOQ\u003c/em\u003e and \u003cem\u003eAHSG\u003c/em\u003e were embedded within ROH, immune loci \u003cem\u003eIL12A\u003c/em\u003e, \u003cem\u003eTLR5\u003c/em\u003e, and \u003cem\u003eIFNAR1\u003c/em\u003e resided in ROH, and reproductive candidates \u003cem\u003eINHBA\u003c/em\u003e and \u003cem\u003eBMPR1B\u003c/em\u003e were likewise detected (Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). In the Karacabey Merino crossbred, multiple ROH islands were detected, with a prominent signal at \u003cem\u003eBGLAP\u003c/em\u003e. Additional extended tracts covered \u003cem\u003eGH\u003c/em\u003e, \u003cem\u003eGHR\u003c/em\u003e, \u003cem\u003eGHRHR\u003c/em\u003e, and \u003cem\u003ePOU1F1\u003c/em\u003e (somatotropic axis). Milk‐associated \u003cem\u003eLTF\u003c/em\u003e and \u003cem\u003ePRL\u003c/em\u003e fell within ROH; immune genes \u003cem\u003eTLR5\u003c/em\u003e, \u003cem\u003eIFNAR1\u003c/em\u003e, \u003cem\u003eIL12A\u003c/em\u003e, \u003cem\u003eNFKBIA\u003c/em\u003e mapped to homozygous regions; reproductive loci \u003cem\u003eFSHB\u003c/em\u003e, \u003cem\u003eLHB\u003c/em\u003e resided in ROH; and muscle/metabolic candidates \u003cem\u003eMYOG\u003c/em\u003e, \u003cem\u003eFGF12\u003c/em\u003e, \u003cem\u003eACLY\u003c/em\u003e, \u003cem\u003eSTAT5A\u003c/em\u003e, and \u003cem\u003eIGFBP4\u003c/em\u003e were also included (Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). In the Oamer crossbred, extended homozygosity spanned multiple chromosomes. The leading ROH island centered on \u003cem\u003eBGLAP\u003c/em\u003e; further tracts encompassed \u003cem\u003ePOU1F1\u003c/em\u003e and \u003cem\u003eGHRHR\u003c/em\u003e. Milk regions within ROH included the casein cluster (\u003cem\u003eCSN1S1\u003c/em\u003e, \u003cem\u003eCSN2\u003c/em\u003e, \u003cem\u003eCSN1S2\u003c/em\u003e, \u003cem\u003eCSN3\u003c/em\u003e) and \u003cem\u003ePRLHR\u003c/em\u003e. Immune‐related loci \u003cem\u003eIL12A\u003c/em\u003e, \u003cem\u003eIFNAR1\u003c/em\u003e, \u003cem\u003eIFNAR2\u003c/em\u003e, and \u003cem\u003eTLR4\u003c/em\u003e were embedded within \u003cem\u003eROH; reproductive candidates FSHR, INHBA, and BMPR1B\u003c/em\u003e were present; and \u003cem\u003emetabolic/growth\u003c/em\u003e genes \u003cem\u003eLEPR, IGFBP3\u003c/em\u003e, and \u003cem\u003eMSTN\u003c/em\u003e also occurred in extended runs of homozygosity (Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). Candidate-gene counts by breed were: Akkaraman (214), Karacabey Merino (871), Oamer (499), Hasak (496), and Hasmer (386). All five breeds shared fifty genes. Breed-specific sets comprised 107 genes unique to Karacabey Merino and one gene unique to Oamer; no breed-specific genes were detected for Akkaraman, Hasak, or Hasmer. The remaining genes were distributed among pairwise, three-way, and four-way intersections as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eiHS-Based Selection Signatures\u003c/h2\u003e\u003cp\u003eWe performed iHS analysis to detect signatures of positive selection within each sheep breed, and then summarized the standardized iHS values genome-wide (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In Akkaraman sheep, iHS signals encompassed \u003cem\u003eBGLAP\u003c/em\u003e (osteocalcin; bone mineralization and skeletal growth), \u003cem\u003eFTO\u003c/em\u003e (nucleic acid demethylase linked to energy balance/adiposity), \u003cem\u003eSTAT5A\u003c/em\u003e (transcription factor in GH/PRL signaling), \u003cem\u003ePRL\u003c/em\u003e (prolactin; lactation), \u003cem\u003eCSN1S1\u003c/em\u003e, \u003cem\u003eCSN2\u003c/em\u003e, \u003cem\u003eCSN1S2\u003c/em\u003e, \u003cem\u003eCSN3\u003c/em\u003e (caseins; major milk proteins), \u003cem\u003eTLR2\u003c/em\u003e, \u003cem\u003eTLR5\u003c/em\u003e (pathogen-recognition receptors), \u003cem\u003eMYD88\u003c/em\u003e (TLR adaptor), \u003cem\u003eIL15\u003c/em\u003e (lymphocyte activation), \u003cem\u003eFSHB\u003c/em\u003e (FSH β-subunit), \u003cem\u003eOXT\u003c/em\u003e (oxytocin; parturition/milk ejection), \u003cem\u003eGDF9\u003c/em\u003e (oocyte growth/folliculogenesis), and the myogenic regulators \u003cem\u003eMEF2C\u003c/em\u003e and \u003cem\u003eMYF6\u003c/em\u003e (Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e). In the Hasak crossbred population, iHS highlighted \u003cem\u003eBMPR1B\u003c/em\u003e (BMP receptor; folliculogenesis/prolificacy), \u003cem\u003eFGF2\u003c/em\u003e (skeletal growth/tissue repair), \u003cem\u003eABCG2\u003c/em\u003e (milk secretion/transport), \u003cem\u003eCSN1S1\u003c/em\u003e, \u003cem\u003eCSN2\u003c/em\u003e, \u003cem\u003eCSN1S2\u003c/em\u003e, \u003cem\u003eCSN3\u003c/em\u003e (caseins), innate immune sensors \u003cem\u003eTLR1\u003c/em\u003e, \u003cem\u003eTLR6\u003c/em\u003e, \u003cem\u003eTLR10\u003c/em\u003e, cytokines \u003cem\u003eIL2\u003c/em\u003e and \u003cem\u003eIL21\u003c/em\u003e, reproductive-axis genes \u003cem\u003eGNRHR\u003c/em\u003e and \u003cem\u003eESR2\u003c/em\u003e, and additional candidates \u003cem\u003eIGFBP7\u003c/em\u003e (IGF-axis modulation/ECM) and \u003cem\u003eGPX1\u003c/em\u003e (oxidative stress defense) (Table \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e). In the Hasmer crossbred, iHS peaks were detected at \u003cem\u003eFSHR\u003c/em\u003e (follicle-stimulating hormone receptor, involved in ovarian follicle maturation) and GNRHR (GnRH receptor), with growth/myogenesis signals at MYF6 and MEF2C, and IGF-axis support from \u003cem\u003eIGFBP7\u003c/em\u003e. Milk synthesis selection involved \u003cem\u003eLALBA\u003c/em\u003e (α-lactalbumin; lactose synthesis) and the caseins \u003cem\u003eCSN1S1\u003c/em\u003e, \u003cem\u003eCSN2\u003c/em\u003e, \u003cem\u003eCSN1S2\u003c/em\u003e, \u003cem\u003eCSN3\u003c/em\u003e; immune adaptation featured \u003cem\u003eTLR1\u003c/em\u003e, \u003cem\u003eTLR6\u003c/em\u003e, \u003cem\u003eTLR10\u003c/em\u003e, \u003cem\u003eIL22\u003c/em\u003e, and \u003cem\u003eIFNG\u003c/em\u003e (Table \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003e). In the Karacabey Merino crossbred, iHS signals included \u003cem\u003eMSTN\u003c/em\u003e (myostatin; negative regulator of muscle growth), \u003cem\u003ePRL\u003c/em\u003e (lactation), \u003cem\u003eBMPR2\u003c/em\u003e (BMP receptor; ovarian/follicular signaling), \u003cem\u003eINHA\u003c/em\u003e (inhibin α; feedback on FSH), \u003cem\u003eIGFBP5\u003c/em\u003e (IGF bioavailability), and immune-related \u003cem\u003eTNF\u003c/em\u003e, \u003cem\u003eCTLA4\u003c/em\u003e, and \u003cem\u003eCD28\u003c/em\u003e (T-cell costimulation/checkpoint) (Table \u003cspan refid=\"MOESM9\" class=\"InternalRef\"\u003eS9\u003c/span\u003e). In the Oamer crossbred, iHS highlighted \u003cem\u003eBGLAP\u003c/em\u003e (osteocalcin; bone mineralization), \u003cem\u003ePOU1F1\u003c/em\u003e (pituitary transcription factor for GH/PRL/TSH lineages), \u003cem\u003eLEPR\u003c/em\u003e (leptin receptor; energy balance/reproduction), \u003cem\u003eLALBA\u003c/em\u003e (α-lactalbumin; lactose synthesis), innate sensors \u003cem\u003eTLR1\u003c/em\u003e, \u003cem\u003eTLR6\u003c/em\u003e, \u003cem\u003eTLR10\u003c/em\u003e, cytokine/receptor genes \u003cem\u003eIL12A\u003c/em\u003e and \u003cem\u003eIFNAR1\u003c/em\u003e, reproductive genes \u003cem\u003eBMPR2\u003c/em\u003e and \u003cem\u003eINHA\u003c/em\u003e, and metabolic/growth candidates \u003cem\u003eADIPOQ\u003c/em\u003e, \u003cem\u003eAHSG\u003c/em\u003e (fetuin-A), and \u003cem\u003eIGFBP5\u003c/em\u003e (Table \u003cspan refid=\"MOESM10\" class=\"InternalRef\"\u003eS10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eTajima\u0026rsquo;s D-Based Selection Signatures\u003c/h2\u003e\u003cp\u003eUsing a sliding-window framework, we computed Tajima\u0026rsquo;s D across all autosomes in each breed (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In Akkaraman sheep, Tajima\u0026rsquo;s D identified three loci, CAPN2, PAG4, and IRF2, with \u003cem\u003eCAPN2\u003c/em\u003e showing the most substantial deviation from neutrality. \u003cem\u003eCAPN2\u003c/em\u003e encodes a calcium-dependent protease central to cytoskeletal remodeling and muscle fiber hypertrophy; \u003cem\u003ePAG4\u003c/em\u003e reflects selection on placental function; \u003cem\u003eIRF2\u003c/em\u003e indicates adaptive pressure on immune regulation (Table \u003cspan refid=\"MOESM11\" class=\"InternalRef\"\u003eS11\u003c/span\u003e). In the Hasak crossbred, Tajima\u0026rsquo;s D revealed a pronounced signal at \u003cem\u003eCAST\u003c/em\u003e, consistent with an excess of low-frequency alleles and a recent sweep. As the endogenous inhibitor of calpains, \u003cem\u003eCAST\u003c/em\u003e suggests a selective pressure on somatic growth, with plausible secondary effects on lactation efficiency and disease resilience (Table \u003cspan refid=\"MOESM12\" class=\"InternalRef\"\u003eS12\u003c/span\u003e). In the Hasmer crossbred, two loci deviated significantly: \u003cem\u003eCAPN3\u003c/em\u003e (strongest) and \u003cem\u003ePAG4\u003c/em\u003e. \u003cem\u003eCAPN3\u003c/em\u003e (muscle-specific calpain) supports selection on sarcomere remodeling and postnatal muscle development, while \u003cem\u003ePAG4\u003c/em\u003e suggests selection on placental/reproductive performance (Table \u003cspan refid=\"MOESM13\" class=\"InternalRef\"\u003eS13\u003c/span\u003e). In the Karacabey Merino crossbred, three candidates, CAST (the strongest), MEF2C, and CAPN2, exhibited skewed allele-frequency spectra. \u003cem\u003eCAST\u003c/em\u003e implicates selection on muscle remodeling and growth; \u003cem\u003eMEF2C\u003c/em\u003e indicates pressure on myogenic differentiation; \u003cem\u003eCAPN2\u003c/em\u003e supports recent positive selection on cellular remodeling pathways (Table \u003cspan refid=\"MOESM14\" class=\"InternalRef\"\u003eS14\u003c/span\u003e). In the Oamer crossbred, a strong signal at \u003cem\u003eGHR\u003c/em\u003e underscores selection on somatic growth and metabolic regulation. Additional deviations at \u003cem\u003eDGAT1\u003c/em\u003e, \u003cem\u003eCAST\u003c/em\u003e, and \u003cem\u003eCAPN2\u003c/em\u003e suggest involvement in milk-fat synthesis and muscle remodeling; immune genes ITGB2 and IL1B indicate pressure on pathogen defense; B4GALNT2 suggests possible selection related to reproductive tract function (Table S15).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eFunctional Enrichment of Candidate Genes\u003c/h2\u003e\u003cp\u003eFunctional enrichment analysis of the candidate gene set revealed significant overrepresentation of biological processes governing developmental and regulatory pathways. Terms associated with the positive regulation of multicellular organismal processes, positive regulation of developmental processes, and regulation of cell differentiation displayed the highest enrichment, underscoring roles in growth and tissue formation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Cellular component annotations were dominated by membrane-associated locales, including the extracellular space, melanosome, and pigment granule, as well as integral and intrinsic components of the plasma membrane, membrane rafts, microdomains, and receptor complexes. Molecular function categories were principally characterized by signaling and regulatory activities, with signaling receptor regulator activity, signaling receptor activator activity, receptor ligand activity, and signaling receptor binding representing the strongest hits. Additional molecular functions, such as cytokine activity, cytokine receptor binding, hormone activity, growth factor receptor binding, and GTPase activity, further highlighted candidate genes involved in immune signaling, endocrine regulation, and signal transduction.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003ePathway Analysis of Candidate Genes\u003c/h2\u003e\u003cp\u003ePathway analysis identified hormone signaling as the most significantly enriched pathway, reflecting selection on endocrine regulators of growth and reproduction. The cytokine\u0026ndash;cytokine receptor interaction pathway emerged as the next highest hit, alongside Toll-like receptor signaling, indicating a strong immune component among the selected loci (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Enrichment was also observed for infectious disease-related pathways, including pertussis, measles, leishmaniasis, and malaria, as well as inflammatory bowel disease, suggesting adaptation to a spectrum of pathogen pressures. Cardiovascular‐related pathways, including fluid shear stress and atherosclerosis, were also overrepresented, while Chagas disease appeared at the lower end of significance. These results collectively indicate that coordinated selection has occurred on networks governing hormone action, immune defense, pathogen response, and vascular homeostasis in Turkish sheep breeds.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003ePopulation Structure and Genetic Diversity\u003c/h2\u003e\u003cp\u003ePCA of the five Turkish sheep breeds (Akkaraman, Hasak, Hasmer, Karacabey, and Oamer) showed that the first two components accounted for only 20.94% of the total variance. Akkaraman formed a distinct cluster along the first principal component (PC1), with one outlier (A115) suggesting within‑breed substructure. Karacabey and Oamer overlapped substantially, whereas the crossbreds, Hasak and Hasmer, formed small clusters with greater scatter along PC2. The low variance explained by PC1\u0026ndash;PC2 and the modest clustering imply limited population differentiation, possibly because the breeds share recent common ancestry or have experienced gene flow. Similar patterns of low between-breed differentiation have been reported in Turkish sheep using microsatellites and SNPs (Bayraktar and Shoshin, 2022), in Greek breeds where most genetic variation occurs within populations (Michailidou et al., 2025), and in South Asian and Middle Eastern sheep, showing extensive admixture. The overlapping clusters for Karacabey and Oamer suggest recent crossbreeding or shared paternal lineages. In contrast, the distinct Akkaraman cluster aligns with previous reports that this native fat-tailed breed is genetically separate from European breeds \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Genetic diversity indices further support moderate but heterogeneous diversity across breeds. Nucleotide diversity ranged from 6.97 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e in Karacabey to 8.30 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e in Akkaraman, with minor allele frequencies around 0.28\u0026ndash;0.31. HO varied from 0.381 (oamer) to 0.445 (hasmer), while HE ranged from 0.376 (oamer) to 0.394 (Karacabey). Negative FIS in all breeds indicates an excess of heterozygosity, possibly due to recent admixture or balancing selection. The Hasmer crossbred population showed the most pronounced heterozygote excess, FIS = \u0026minus;\u0026thinsp;0.161, and the highest DST\u0026thinsp;=\u0026thinsp;0.113, indicating high internal relatedness. In contrast, Karacabey had the most extraordinary nucleotide diversity and the lowest inbreeding. These patterns resemble those observed in Greek sheep, where heterozygosity was moderate (0.26\u0026ndash;0.35) and FIS was negative or low (Michailidou et al., 2025). Similarly, in Turkish and neighbouring breeds, heterozygosity reached 0.44, and FIS was also negative \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. The high heterozygosity in Hasmer might reflect the crossbreeding between Hasak and native Merino lines, whereas Karacabey\u0026rsquo;s diversity suggests admixture with diverse paternal lines.\u003c/p\u003e\u003cp\u003eROH analysis revealed signatures of selection and demographic history. Akkaraman had long homozygous tracts centered on \u003cem\u003eBGLAP\u003c/em\u003e (osteocalcin) and encompassed skeletal development genes (\u003cem\u003eMYF6, MEF2C, FGF12\u003c/em\u003e), metabolic regulators (\u003cem\u003eFTO, ACLY, SHMT1, SUCLA2\u003c/em\u003e), milk genes (casein cluster \u003cem\u003eCSN1S1\u0026ndash;CSN3, LTF, PRL\u003c/em\u003e), immunity genes (\u003cem\u003eTLR2, TLR5, MYD88, IFNAR1, IRF2\u003c/em\u003e), and reproduction genes (\u003cem\u003eFSHB, GDF9, POU1F1, FSHR\u003c/em\u003e). Long ROH islands also contained \u003cem\u003eSTAT5A, AURKA, RCAN1\u003c/em\u003e, and genes associated with protein turnover. Hasak crossbreds shared the \u003cem\u003eBGLAP\u003c/em\u003e island and additional tracts containing \u003cem\u003eFTO, GHRHR\u003c/em\u003e, and \u003cem\u003ePRKAA2\u003c/em\u003e. Milk‑related ROH contained \u003cem\u003eADIPOQ, AHSG, BCO1, HSD17B2, AOX3\u003c/em\u003e, and \u003cem\u003eSLC7A11\u003c/em\u003e. Immune‑associated ROH spanned \u003cem\u003eTLR2, TLR5, IFNAR1, IL15, NFKBIA\u003c/em\u003e, and FCGR3A, while reproductive tracts included FSHB, LHB, BMPR1B, and \u003cem\u003eINHBA\u003c/em\u003e. The Hasmer population showed pronounced ROH at POU1F1, with co-selection on GHR, GHRHR, and \u003cem\u003ePRKAR1A\u003c/em\u003e. Karacabey exhibited ROH across \u003cem\u003eGH, GHR, GHRHR and POU1F1\u003c/em\u003e with milk genes (\u003cem\u003eLTF, UGT1A1, SPP2\u003c/em\u003e) and immune genes (\u003cem\u003eTLR5, IFNAR1, IL12A, CRYAA, ITGB2\u003c/em\u003e). Finally, oamer crossbreds presented extensive ROH encompassing \u003cem\u003eBGLAP, POU1F1, GHRHR\u003c/em\u003e, and LEPR with casein cluster and immune genes such as TLR4, NFKBIA, FCGR3A, and reproductive genes (\u003cem\u003eFSHR, INHBA, BMPR1B, GNRHR\u003c/em\u003e). The distribution of ROH lengths and gene content indicates both ancient and recent selection episodes: long ROH islands often reflect recent inbreeding or strong selection. At the same time, shorter tracts suggest that older selection signals are present. Comparable patterns have been reported in Greek breeds, where ROH lengths largely fall between 1\u0026ndash;5 Mb, reflecting moderate autozygosity (Michailidou et al., 2025), and in Polish breeds, where numerous short ROHs indicated historical inbreeding, while longer tracts signalled recent selection \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. In Anatolian sheep, ROH islands encompassed genes like \u003cem\u003eZNF208B, CBX1\u003c/em\u003e, and \u003cem\u003eCOPZ1\u003c/em\u003e under selection \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. The presence of similar gene clusters in our study suggests both convergent and breed‑specific selection pressures.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003eSelection Signatures for Growth and Body Size\u003c/h2\u003e\u003cp\u003eGrowth and body size in our Turkish populations were influenced by extensive ROH and iHS signals around genes involved in skeletal development, muscle formation, and metabolic regulation. The principal gene common to all breeds was \u003cem\u003eBGLAP\u003c/em\u003e (osteocalcin), a bone matrix protein associated with osteoblast differentiation and mineralization. Surrounding this core region, Akkaraman and Karacabey exhibited homozygosity for MYF6, MEF2C, and FGF12, transcription factors that regulate myogenesis and muscle fibre development. These genes play a key role in controlling muscle fibre composition and carcass traits. \u003cem\u003eSTAT5A\u003c/em\u003e and \u003cem\u003eAURKA\u003c/em\u003e were also identified in multiple breeds; \u003cem\u003eSTAT5A\u003c/em\u003e mediates growth hormone signalling, while \u003cem\u003eAURKA\u003c/em\u003e influences cell cycle progression.\u003c/p\u003e\u003cp\u003eAcross breeds, iHS analysis highlighted extended haplotype homozygosity at \u003cem\u003eBGLAP\u003c/em\u003e and genes involved in the growth hormone axis (\u003cem\u003eGHR\u003c/em\u003e, \u003cem\u003eGHRHR\u003c/em\u003e, \u003cem\u003eGH\u003c/em\u003e), metabolic regulators (\u003cem\u003eACLY\u003c/em\u003e, \u003cem\u003eSHMT1\u003c/em\u003e, \u003cem\u003eSUCLA2\u003c/em\u003e, \u003cem\u003eFTO\u003c/em\u003e), and muscle genes (\u003cem\u003eMYOG\u003c/em\u003e, \u003cem\u003eMSTN\u003c/em\u003e, \u003cem\u003eCAPN1\u003c/em\u003e, \u003cem\u003eCAPN2\u003c/em\u003e). In Oamer and Karacabey, significant haplotypes were identified that covered \u003cem\u003eMSTN\u003c/em\u003e (myostatin), a negative regulator of muscle growth. In contrast, in Hasak and Hasmer crossbreds, haplotypes encompassed MYL2, MEF2C, MYF6, CFL1, and small RNAs implicated in muscle differentiation. Tajima\u0026rsquo;s D results reinforced these signals by identifying departures from neutrality at \u003cem\u003eCAPN2\u003c/em\u003e, \u003cem\u003eCAST\u003c/em\u003e (calpastatin), and CAPN3 (muscle-specific calpain), which regulate proteolysis and muscle hypertrophy. The repeated identification of \u003cem\u003eCAPN2\u003c/em\u003e and \u003cem\u003eCAST\u003c/em\u003e across breeds implies selection for growth and carcass traits.\u003c/p\u003e\u003cp\u003eOur identification of \u003cem\u003eBGLAP\u003c/em\u003e as a central growth candidate is consistent with selection signatures in other sheep. South African Merino and Merino-derived breeds showed strong selection at FGF12, ICA1, and HMGA2, which are associated with hair follicle and growth traits \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. In Iranian Qezel and Afshari sheep, \u003cem\u003eMYF5\u003c/em\u003e and \u003cem\u003ePPP1R12A\u003c/em\u003e were implicated in muscle formation \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. In Hu sheep, the \u003cem\u003eHOXA\u003c/em\u003e cluster genes and \u003cem\u003eMSTN\u003c/em\u003e were identified as key factors in growth and fat deposition \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Genome-wide scans of Chinese breeds identified CRADD, LIN28B, WNT11, HMGA2, and MSTN as influencing body size and muscle development \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. A BMC Genomics study on Tibetan sheep body size identified 623 significant SNPs and genes, including \u003cem\u003eASAP1, CDK6, FRYL, NAV2, PTPRM, GPC6, PTPRG, KANK1, NTRK2\u003c/em\u003e, and \u003cem\u003eADCY8\u003c/em\u003e, which are enriched in cAMP and Rap1 signaling \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, illustrating the complexity of growth regulation. Although different genes were highlighted across studies, many converge on pathways that control skeletal muscle development, metabolic homeostasis, and growth hormone signaling.\u003c/p\u003e\u003cp\u003eOur detection of \u003cem\u003eGHR\u003c/em\u003e, \u003cem\u003eGHRHR\u003c/em\u003e, \u003cem\u003eGH\u003c/em\u003e, \u003cem\u003eSTAT5A\u003c/em\u003e, and \u003cem\u003eAURKA\u003c/em\u003e is aligned with research on Tarim Basin sheep, where \u003cem\u003eSMAD2\u003c/em\u003e, \u003cem\u003eESR2\u003c/em\u003e, and \u003cem\u003eHAS2\u003c/em\u003e were implicated in growth and reproduction \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, and on South African Merino breeds, where \u003cem\u003ePRKG\u003c/em\u003e genes and \u003cem\u003eHMGA2\u003c/em\u003e influenced body size \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. In Middle Eastern and South Asian sheep, selection signatures included the genes \u003cem\u003eTNIK, DOCK1, and USH2A\u003c/em\u003e, which are related to limb development and disease resistance \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. These comparisons demonstrate that growth traits are polygenic and shaped by different selective histories; our results underscore the importance of osteogenesis and the growth hormone axis in Turkish breeds.\u003c/p\u003e\u003cp\u003eThe presence of extended haplotypes at \u003cem\u003eBGLAP\u003c/em\u003e and nearby genes suggests that selection has occurred for bone density and skeletal strength, traits that are beneficial for survival and carcass quality. \u003cem\u003eMYF6\u003c/em\u003e and \u003cem\u003eMEF2C\u003c/em\u003e regulate myogenic differentiation; their ROH indicates selection for muscle mass. \u003cem\u003eMSTN\u003c/em\u003e and \u003cem\u003eCAPN3\u003c/em\u003e are negative regulators of muscle growth; selection on these genes implies a balancing between lean muscle and fat deposition. \u003cem\u003eFTO\u003c/em\u003e influences energy homeostasis and appetite, while \u003cem\u003eACLY\u003c/em\u003e and \u003cem\u003eSHMT1\u003c/em\u003e mediate lipid synthesis and amino acid metabolism, respectively. Together, these genes shape growth rate, body size, and meat quality. The identification of \u003cem\u003eCAPN2\u003c/em\u003e, \u003cem\u003eCAST\u003c/em\u003e, and \u003cem\u003eCAPN3\u003c/em\u003e in Tajima\u0026rsquo;s D analysis indicates that proteolytic regulation is a key target of selection; these calpains influence muscle tenderness and yield. Overall, our results align with international studies that emphasize the significance of osteogenesis, myogenesis, metabolic regulation, and growth hormone signaling in determining body size and carcass traits.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003eSelection Signatures for Milk Production\u003c/h2\u003e\u003cp\u003eMilk production genes were pervasive in our analyses, with ROH and iHS signals covering the casein gene cluster (\u003cem\u003eCSN1S1\u003c/em\u003e, \u003cem\u003eCSN1S2\u003c/em\u003e, \u003cem\u003eCSN2\u003c/em\u003e, \u003cem\u003eCSN3\u003c/em\u003e), lactoferrin (\u003cem\u003eLTF\u003c/em\u003e), prolactin (\u003cem\u003ePRL\u003c/em\u003e), and regulators of milk fat and protein synthesis. In Akkaraman, long homozygous tracts encompassed the casein cluster and PRL, UGT1A1, and \u003cem\u003eCYP11A1\u003c/em\u003e, suggesting strong selection for milk composition. Hasak and Hasmer crossbreds showed ROH at ADIPOQ (adiponectin), AHSG (fetuinA), BCO1, HSD17B2, AOX3, and \u003cem\u003eSLC7A11\u003c/em\u003e, all associated with milk fat metabolism. iHS analysis extended these findings by detecting long haplotypes at \u003cem\u003eBMPR1B\u003c/em\u003e, \u003cem\u003eFGF2\u003c/em\u003e, \u003cem\u003eABCG2\u003c/em\u003e, \u003cem\u003eFGF7\u003c/em\u003e, \u003cem\u003eLEPR\u003c/em\u003e, \u003cem\u003eSCD5\u003c/em\u003e, \u003cem\u003eLPL\u003c/em\u003e, and small RNAs (e.g., oarmir-99a, oarmir-200b) in Hasak crossbreds, and at LALBA, SCD5, BCO1, HSD3B1, and KRT35 in Oamer and Karacabey. The Tajima\u0026rsquo;s D results identified \u003cem\u003eDGAT1\u003c/em\u003e and \u003cem\u003eB4GALNT2\u003c/em\u003e in oamer, underscoring selection for milk fat synthesis and protein glycosylation.\u003c/p\u003e\u003cp\u003eOur identification of the casein cluster and \u003cem\u003ePRL\u003c/em\u003e concurs with previous studies. In Greek breeds, ROH islands included milk genes such as \u003cem\u003eABCG2\u003c/em\u003e, \u003cem\u003eSPP1\u003c/em\u003e, \u003cem\u003eLAP3\u003c/em\u003e, \u003cem\u003eNCAPG\u003c/em\u003e, and \u003cem\u003eMEPE\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e, while in Polish breeds, ROH contained \u003cem\u003eSPP1\u003c/em\u003e and \u003cem\u003eABCG2\u003c/em\u003e \u003csup\u003e45\u003c/sup\u003e. A mammalian comparative study across nine breeds found overlapping F_ST and XPEHH signals in \u003cem\u003eDHRS3\u003c/em\u003e, \u003cem\u003eTNFRSF1B\u003c/em\u003e, \u003cem\u003eAADACL4\u003c/em\u003e, \u003cem\u003eARHGEF11\u003c/em\u003e and \u003cem\u003eLRRC71\u003c/em\u003e; additional genes highlighted by F_ST included \u003cem\u003ePER2\u003c/em\u003e, \u003cem\u003eSH3PXD2A\u003c/em\u003e, \u003cem\u003eTMEM117\u003c/em\u003e, \u003cem\u003eDDX6\u003c/em\u003e, \u003cem\u003ePDCD11\u003c/em\u003e and \u003cem\u003eCALHM2\u003c/em\u003e, whereas XPEHH detected \u003cem\u003eCRABP2\u003c/em\u003e, \u003cem\u003ePEAR1\u003c/em\u003e, \u003cem\u003ePGM1\u003c/em\u003e, \u003cem\u003eALG6\u003c/em\u003e, \u003cem\u003eCOX15\u003c/em\u003e and \u003cem\u003eOAT\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. In selection scans of Lacaune and milkspecialised breeds, \u003cem\u003eSUCNR1\u003c/em\u003e and \u003cem\u003ePPARGC1A\u003c/em\u003e were emphasised for milking performance \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. The Western Pyrenees breeds have been selected at \u003cem\u003eABCG2\u003c/em\u003e, \u003cem\u003eSPP1\u003c/em\u003e, \u003cem\u003eLAP3\u003c/em\u003e, \u003cem\u003eLCORL\u003c/em\u003e, and \u003cem\u003eMEPE\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Our detection of \u003cem\u003eADIPOQ\u003c/em\u003e, \u003cem\u003eAHSG\u003c/em\u003e, and \u003cem\u003eSLC7A11\u003c/em\u003e parallels findings in Iranian Qezel and Afshari sheep, where \u003cem\u003ePCCA, ACAP3\u003c/em\u003e, and \u003cem\u003eTTK\u003c/em\u003e were associated with milk traits \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMilk-related genes uncovered in our study are also involved in metabolic regulation and endocrine signalling. \u003cem\u003eADIPOQ\u003c/em\u003e and \u003cem\u003eLEPR\u003c/em\u003e are adipokines affecting energy balance and lactation efficiency. \u003cem\u003eABCG2\u003c/em\u003e transports vitamins and milk constituents; it has been under strong selection in dairy cattle and goats. The detection of \u003cem\u003eBCO1\u003c/em\u003e and \u003cem\u003eSCD5\u003c/em\u003e suggests a selective advantage for carotenoid metabolism and the synthesis of unsaturated fatty acids. \u003cem\u003eKRT35\u003c/em\u003e and other keratin genes might influence teat morphology and udder skin integrity. The presence of \u003cem\u003eUGT1A1\u003c/em\u003e, \u003cem\u003eAOX3\u003c/em\u003e, and \u003cem\u003eHSD17B2\u003c/em\u003e suggests that selection has occurred on steroid metabolism, which impacts milk yield and reproductive cycles.\u003c/p\u003e\u003cp\u003eThe convergence of ROH and iHS signals at milk genes implies selection for both milk composition and yield in Turkish breeds. Akkaraman and Oamer have historically been valued for milk and meat, and the presence of strong selection at the casein cluster suggests long-standing artificial selection for dairy traits. The identification of metabolic genes, such as \u003cem\u003eADIPOQ\u003c/em\u003e and \u003cem\u003eAHSG\u003c/em\u003e, suggests a selection for improved feed efficiency and energy partitioning towards lactation. The crossbred populations may have inherited favourable milk alleles from exotic breeds; for example, \u003cem\u003eABCG2\u003c/em\u003e, \u003cem\u003eSCD5\u003c/em\u003e, \u003cem\u003eLPL\u003c/em\u003e, and \u003cem\u003eSLC7A11\u003c/em\u003e are common targets in European dairy breeds \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Overall, the results indicate that selection on lactation traits is widespread across the Turkish breeds, with crossbreds displaying additional metabolic gene signatures.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSelection Signatures for Immune Function and Adaptation\u003c/h3\u003e\n\u003cp\u003eImmune-related genes represented a substantial proportion of the selection signatures detected. ROH analysis highlighted Toll-like receptors \u003cem\u003eTLR2, TLR4, TLR5\u003c/em\u003e, and \u003cem\u003eTLR6\u003c/em\u003e, interferon receptors \u003cem\u003eIFNAR1\u003c/em\u003e and \u003cem\u003eIFNAR2\u003c/em\u003e, signaling molecules \u003cem\u003eMYD88, NFKBIA\u003c/em\u003e, and cytokines IL15, IL12A, and \u003cem\u003eIL12RB2\u003c/em\u003e. FC receptor genes (\u003cem\u003eFCGR2B\u003c/em\u003e, \u003cem\u003eFCGR3A\u003c/em\u003e, \u003cem\u003eFCER1A\u003c/em\u003e) and costimulatory molecules (\u003cem\u003eSLAMF1\u003c/em\u003e, \u003cem\u003eSLAMF9\u003c/em\u003e, \u003cem\u003eCD247\u003c/em\u003e) also showed extended homozygosity. iHS analysis detected extended haplotypes around these same genes and additional immune genes, such as \u003cem\u003eCD80, CD86, CD28, CTLA4, TNF, IL-22\u003c/em\u003e, and \u003cem\u003eIL-23A\u003c/em\u003e, in Hasak, Hasmer, and Karacabey crossbreds. oamer exhibited long haplotypes at \u003cem\u003eMX1\u003c/em\u003e, \u003cem\u003eMX2\u003c/em\u003e, \u003cem\u003eRNF168\u003c/em\u003e and \u003cem\u003eNFKBIA\u003c/em\u003e. Tajima\u0026rsquo;s D indicated selection at \u003cem\u003eIRF2\u003c/em\u003e in Akkaraman and at \u003cem\u003eIL1B, ITGB2\u003c/em\u003e in Oamer, implying directional selection on inflammatory responses. Collectively, these findings suggest that pathogen pressure and environmental adaptation have shaped the immune gene repertoire of Turkish sheep.\u003c/p\u003e\u003cp\u003eSimilar immune genes have been identified in numerous sheep populations worldwide. In Turkish and Anatolian breeds, selection signatures overlapped with genes involved in olfaction, heat stress, and immune responses, such as \u003cem\u003eZNF208B\u003c/em\u003e and \u003cem\u003eSDK1\u003c/em\u003e \u003csup\u003e44\u003c/sup\u003e. In an extensive comparative study across various climatic zones, genes in the interleukin (IL) and cluster of differentiation (CD) families were found to be enriched among selection candidates \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Middle Eastern and South Asian sheep displayed selection at \u003cem\u003eTNIK, DOCK1, USH2A\u003c/em\u003e, and immune genes, such as \u003cem\u003eTRIM56, CSF2RA\u003c/em\u003e, and \u003cem\u003eHGF\u003c/em\u003e \u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e. Iranian breeds have been identified as having \u003cem\u003eIL23A, STAT2\u003c/em\u003e, and \u003cem\u003eDOCK5\u003c/em\u003e (Yousefi et al., 2025), while Hu sheep selection scans have highlighted immune genes \u003cem\u003eUBR1\u003c/em\u003e and \u003cem\u003eNLRX1\u003c/em\u003e \u003csup\u003e48\u003c/sup\u003e. Desert and high-altitude adaptation studies have identified genes such as TRIM62, \u003cem\u003eFOXN1, ALDOC, POLDIP2\u003c/em\u003e, and \u003cem\u003eTXNDC5\u003c/em\u003e as key to responses to hypoxia and heat stress \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. In South African Merino breeds, \u003cem\u003eIL22\u003c/em\u003e, \u003cem\u003eIL26\u003c/em\u003e, \u003cem\u003eIFNAR1\u003c/em\u003e, \u003cem\u003eIL10RB\u003c/em\u003e, and \u003cem\u003eSLC5A3\u003c/em\u003e were identified \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, while Western Pyrenees breeds showed selection on \u003cem\u003eNFKB2\u003c/em\u003e, an essential immune transcription factor \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Our detection of Tolllike receptors (\u003cem\u003eTLRs\u003c/em\u003e), interferon receptors and interleukins across all breeds echoes findings in the Tarim Basin, where \u003cem\u003eSOD1\u003c/em\u003e, \u003cem\u003eTSHR\u003c/em\u003e and \u003cem\u003eDNAJB5\u003c/em\u003e were implicated in oxidative stress and desert adaptation \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, and in Ethiopian and African breeds where \u003cem\u003eTLR\u003c/em\u003e genes were under selection due to endemic diseases and parasites (not directly cited, but widely reported). The presence of \u003cem\u003eNFKBIA\u003c/em\u003e, \u003cem\u003eMYD88\u003c/em\u003e, and \u003cem\u003eMX1/MX2\u003c/em\u003e indicates selection on innate immune signaling pathways, which are crucial for defence against viral and bacterial pathogens.\u003c/p\u003e\u003cp\u003eTurkish sheep are raised in diverse environments, ranging from Mediterranean coastlines to continental interiors, which exposes them to different pathogens and climatic stressors. The selection signatures in \u003cem\u003eTLR\u003c/em\u003e and interferon pathways suggest adaptation to local disease pressures, including bacterial infections such as mastitis and parasitic infestations. \u003cem\u003eNFKBIA\u003c/em\u003e modulates inflammatory responses, while \u003cem\u003eMYD88\u003c/em\u003e transduces signals from TLRs to NFκB. The repeated detection of \u003cem\u003eFCGR2B\u003c/em\u003e, \u003cem\u003eFCGR3A\u003c/em\u003e, and \u003cem\u003eFCER1A\u003c/em\u003e suggests selection on antibody-mediated responses, potentially due to vaccination regimes or exposure to parasites. Genes involved in oxidative stress and heat tolerance (\u003cem\u003eCRYAA\u003c/em\u003e, \u003cem\u003eGJA8\u003c/em\u003e, \u003cem\u003eSOD1\u003c/em\u003e) and circadian regulation (\u003cem\u003ePER2\u003c/em\u003e) may reflect adaptation to environmental stress. The extended haplotypes at \u003cem\u003eCD80\u003c/em\u003e, \u003cem\u003eCTLA4\u003c/em\u003e, and \u003cem\u003eCD28\u003c/em\u003e in crossbreds suggest a balance between immune activation and tolerance, possibly due to crossbreeding with exotic lines that introduced novel alleles for disease resistance. Overall, the immune signatures indicate that adaptation to pathogens and climate has been a major driver of genomic evolution in Turkish sheep.\u003c/p\u003e\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\u003ch2\u003eSelection Signatures for Reproduction and Fertility\u003c/h2\u003e\u003cp\u003eReproductive genes were consistently identified across ROH, iHS, and Tajima\u0026rsquo;s D analyses. Akkaraman ROH contained \u003cem\u003eFSHB\u003c/em\u003e, \u003cem\u003eGDF9\u003c/em\u003e, \u003cem\u003ePOU1F1\u003c/em\u003e, \u003cem\u003eFSHR\u003c/em\u003e, and \u003cem\u003eOXTR\u003c/em\u003e. Hasak and Oamer crossbreds showed ROH at \u003cem\u003eFSHB, LHB, BMPR1B, INHBA, GNRHR\u003c/em\u003e, and \u003cem\u003eIGFBP\u003c/em\u003e genes. hasmer exhibited long ROH at \u003cem\u003ePOU1F1\u003c/em\u003e, \u003cem\u003eGHR\u003c/em\u003e, \u003cem\u003eGHRHR\u003c/em\u003e and \u003cem\u003ePRKAR1A\u003c/em\u003e, and iHS signals at \u003cem\u003eFSHR\u003c/em\u003e, \u003cem\u003eGNRHR\u003c/em\u003e, \u003cem\u003eINHBA\u003c/em\u003e, \u003cem\u003eBMPR2\u003c/em\u003e, \u003cem\u003ePOU1F1\u003c/em\u003e and \u003cem\u003eIGFBP7\u003c/em\u003e. Extended haplotypes were detected at genes controlling gonadotropin signalling, including \u003cem\u003eFSHB\u003c/em\u003e (βsubunit of folliclestimulating hormone), \u003cem\u003eLHB\u003c/em\u003e (βsubunit of luteinising hormone), \u003cem\u003eGDF9\u003c/em\u003e (oocyte growth factor), \u003cem\u003eFSHR\u003c/em\u003e (receptor mediating FSH), \u003cem\u003eBMPR1B/BMPR2\u003c/em\u003e (bone morphogenetic protein receptors), \u003cem\u003eINHBA\u003c/em\u003e (inhibin subunit α), \u003cem\u003eGNRHR\u003c/em\u003e (gonadotropinreleasing hormone receptor), \u003cem\u003eIGFBP3/4/5/6/7\u003c/em\u003e (insulinlike growth factor binding proteins) and \u003cem\u003eOXT/OXTR\u003c/em\u003e (oxytocin and receptor). Tajima\u0026rsquo;s D showed negative values at \u003cem\u003ePAG4\u003c/em\u003e (pregnancy-associated glycoprotein), \u003cem\u003eIRF2, ITGB2\u003c/em\u003e, and \u003cem\u003eIL1B\u003c/em\u003e, emphasising selection on early pregnancy and immune regulation during reproduction.\u003c/p\u003e\u003cp\u003eReproductive genes identified here are widely documented in sheep and other species. The prolific Booroola gene, \u003cem\u003eBMPR1B\u003c/em\u003e, is a significant determinant of ovulation rate and litter size; its presence in Hasak, Hasmer, Karacabey, and Oamer indicates the introgression of prolific alleles. In Hu sheep, \u003cem\u003eBMPR1B\u003c/em\u003e and \u003cem\u003eGNRH2\u003c/em\u003e were among the most significant genes involved in reproduction \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Prolific Suffolk sheep showed selection on 23 reproduction genes, including \u003cem\u003eARHGEF4\u003c/em\u003e, \u003cem\u003eCATIP\u003c/em\u003e, and \u003cem\u003eCCDC115\u003c/em\u003e \u003csup\u003e58\u003c/sup\u003e. Tarim Basin sheep exhibited candidate genes \u003cem\u003eSMAD2\u003c/em\u003e, \u003cem\u003eESR2\u003c/em\u003e, \u003cem\u003eHAS2\u003c/em\u003e, \u003cem\u003eDMC1\u003c/em\u003e, and TSHR associated with oocyte maturation, oestrogen receptor signalling, and seasonal reproduction \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Iranian breeds were selected for \u003cem\u003eBMP5, ANGPT2, PCCA\u003c/em\u003e, and \u003cem\u003eACAP3\u003c/em\u003e to improve reproduction and milk traits \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. The Western Pyrenees breeds showed selection on \u003cem\u003eESR1\u003c/em\u003e, \u003cem\u003eZNF366\u003c/em\u003e, and \u003cem\u003eH2AFZ\u003c/em\u003e (testis histone) \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. South African Merino breeds had signals at \u003cem\u003eFGF5\u003c/em\u003e, \u003cem\u003eANTXR2\u003c/em\u003e, \u003cem\u003eBMP2\u003c/em\u003e, \u003cem\u003eGHSR\u003c/em\u003e, \u003cem\u003eSPATA16\u003c/em\u003e, \u003cem\u003eRXFP2\u003c/em\u003e, and \u003cem\u003eFGR\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. The cross-population desert adaptation study identified genes \u003cem\u003eDMC1, TSHR\u003c/em\u003e, and \u003cem\u003eHAS2\u003c/em\u003e as being under selection for perennial estrus and reproduction \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. These examples demonstrate that the reproductive gene network, involving \u003cem\u003eBMP\u003c/em\u003e signalling, gonadotropin hormone synthesis and receptor pathways, oxytocin, and insulin-like growth factor binding, is recurrently targeted by selection.\u003c/p\u003e\u003cp\u003eThe presence of extended haplotypes at \u003cem\u003eFSH\u003c/em\u003e, \u003cem\u003eLH\u003c/em\u003e, \u003cem\u003eBMPR1B/BMPR2\u003c/em\u003e, and \u003cem\u003eGDF9\u003c/em\u003e suggests that selection has occurred for increased ovulation rates and litter sizes. \u003cem\u003eBMPR1B\u003c/em\u003e mutations (\u003cem\u003eFecB\u003c/em\u003e allele) are known to increase ovulation but reduce lamb survival; thus, selection may aim to balance prolificacy with fitness. \u003cem\u003eFSHR\u003c/em\u003e and \u003cem\u003eGNRHR\u003c/em\u003e regulate follicular development and hormone release; their selection indicates pressure for improved fertility. \u003cem\u003eINHBA\u003c/em\u003e modulates FSH secretion, and selection on this gene may fine-tune litter size. \u003cem\u003eIGFBP\u003c/em\u003e genes influence follicular growth and embryo development; their selection suggests an emphasis on embryonic survival and growth. \u003cem\u003ePAG4\u003c/em\u003e encodes a trophoblast glycoprotein, and negative Tajima\u0026rsquo;s D at this locus indicates directional selection, possibly due to improved placental function. \u003cem\u003eOXT/OXTR\u003c/em\u003e regulate parturition and maternal behaviour; selection on these genes may improve lamb survival and maternal care. The repeated identification of reproduction genes across all breeds, including the crossbred Hasak, Hasmer, Karacabey, and Oamer populations, implies that reproductive efficiency has been a significant focus of selection, likely due to economic incentives for high lambing rates.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec32\" class=\"Section2\"\u003e\u003ch2\u003eConvergence and Divergence of Selection Signals\u003c/h2\u003e\u003cp\u003eAcross the Turkish breeds, there is clear evidence for convergent selection on core pathways involved in growth, milk production, immunity, and reproduction. \u003cem\u003eBGLAP\u003c/em\u003e, \u003cem\u003ePOU1F1\u003c/em\u003e, and the growth hormone axis (GH\u0026ndash;\u003cem\u003eGHR\u003c/em\u003e\u0026ndash;\u003cem\u003eGHR\u003c/em\u003eHR\u0026ndash;\u003cem\u003eSTAT5A\u003c/em\u003e) are ubiquitous, indicating that body size and skeletal development are central traits under selection. The casein cluster and \u003cem\u003ePRL\u003c/em\u003e highlight the importance of lactation. Toll-like receptors, interferon, and interleukin genes demonstrate adaptation to infectious agents and environmental stress. The gonadotropin axis, BMP signalling, and insulin-like growth factor system underscore the emphasis on prolificacy. These convergent signals reflect shared selective pressures, such as meat and milk production, disease resistance, and fertility, across indigenous and crossbred populations.\u003c/p\u003e\u003cp\u003eHowever, each breed also exhibits unique signatures. Akkaraman displayed strong selection on osteogenesis and metabolic regulation, consistent with its adaptation to harsh Anatolian environments and its role as a meat-type, fat-tailed breed. Hasak crossbreds, derived from Akkaraman and German Mutton Merino, showed additional selection at metabolic genes (\u003cem\u003ePRKAA2, ADIPOQ\u003c/em\u003e) and immune costimulatory molecules (\u003cem\u003eCD80, CD86\u003c/em\u003e), reflecting the introgression of exotic alleles. Hasmer, a cross between Hasak and Merino, exhibited pronounced selection at \u003cem\u003ePOU1F1\u003c/em\u003e and \u003cem\u003eGHR\u003c/em\u003e, which linked growth and milk traits, and at \u003cem\u003eMYF6\u003c/em\u003e and \u003cem\u003eHOXC6\u003c/em\u003e, indicating morphological differences. Karacabey Merino crossbreds exhibited the strongest selection at the somatotropic axis (\u003cem\u003eGH, GHR, GHRHR\u003c/em\u003e) and immune genes (\u003cem\u003eITGB2, CRYAA\u003c/em\u003e), consistent with their dual-purpose use and adaptation to more temperate climates. Oamer crossbreds displayed the widest array of selection signatures, including metabolic regulators (\u003cem\u003eSTAT1, RHEB, CPT1A\u003c/em\u003e), reproduction genes (\u003cem\u003eGNRHR, IGFBP3\u003c/em\u003e), and circadian genes (\u003cem\u003eCLOCK\u003c/em\u003e), suggesting complex polygenic selection due to diverse ancestry. These breed-specific patterns highlight how crossbreeding and local adaptation have produced distinct genetic architectures.\u003c/p\u003e\u003cp\u003eOur findings fit within the broader context of sheep genomics. The moderate genetic diversity and overlapping clusters are consistent with other European and Turkish studies, which have shown high within-breed variation and admixture \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. The selection on growth hormone and \u003cem\u003eBMP\u003c/em\u003e pathways aligns with numerous studies across European, Asian, and African breeds \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. The casein cluster and metabolic genes identified here have also been found in dairy breeds and the Western Pyrenees \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e as well as in Lacaune \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. The immune signatures involving \u003cem\u003eTLRs\u003c/em\u003e, interferons, and interleukins are ubiquitous across climate adaptation studies \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Reproductive genes, such as \u003cem\u003eBMPR1B, GDF9, FSHB, and FSHR\u003c/em\u003e, are recurrent targets of selection in prolific breeds worldwide \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Thus, our results both corroborate and extend existing knowledge, confirming that selection for productivity and adaptation operates on a relatively conserved set of genes and pathways, with breed-specific variations reflecting local breeding goals and environmental conditions.\u003c/p\u003e\u003cp\u003eThe identification of core and breed-specific selection signals has practical implications. First, the presence of strong selection at growth and reproduction genes indicates that breeders have prioritized meat and lamb production. However, maintaining genetic diversity is crucial to prevent inbreeding depression and preserve adaptive potential. The moderate heterozygosity and negative FIS values suggest that crossbreeding has mitigated inbreeding, but small populations like Hasak and Hasmer require careful management. Second, the detection of immune gene selection highlights the importance of considering disease resistance in breeding programs, particularly as climate change alters pathogen pressures. Third, the frequent selection of metabolic genes (\u003cem\u003eADIPOQ\u003c/em\u003e, \u003cem\u003eSCD5\u003c/em\u003e, \u003cem\u003eACLY\u003c/em\u003e) suggests potential for improving feed efficiency and reducing greenhouse gas emissions. Finally, the identification of reproduction genes can inform marker-assisted selection to optimize litter size and lamb survival while avoiding adverse effects, such as lamb mortality, associated with high prolificacy.\u003c/p\u003e\u003cp\u003eSuggested mitigations for future work include expanding and balancing sampling, integrating cross-population statistics with explicit demographic modeling, utilizing whole-genome sequencing for fine-mapping, incorporating phenotype and environmental covariates, and validating key regions through replication and functional follow-up studies.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe integration of complementary genomic approaches has shed light on the complex landscape of adaptation and production in Turkish sheep, demonstrating the power of combining population-structure analyses with multiple selection-scan methods. By situating indigenous and crossbred populations within a unified analytical framework, this work highlights how shared and breed-specific genetic architectures can be leveraged to strike a balance between productivity and resilience.\u003c/p\u003e\u003cp\u003eLooking forward, the candidate regions highlighted here offer a roadmap for targeted breeding and conservation strategies. Functional validation of key loci, coupled with precise phenotype-genotype association studies, will further refine selection targets. Moreover, expanding the survey to include additional Turkish and international populations promises to deepen our understanding of sheep genomic diversity. Collectively, these efforts will support the sustainable improvement of meat, milk, immune robustness, and reproductive performance, while safeguarding the unique genetic heritage of T\u0026uuml;rkiye\u0026rsquo;s native breeds.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted with an experimental protocol approved by the \u0026ldquo;\u003cem\u003eBandırma Sheep Breeding and Research Institute Ethics Committee for the Use of Animals in Research and Experimentation\u0026rdquo;\u003c/em\u003e, T\u0026uuml;rkiye (Approval No: 04.10.2021/049). The authors complied with the ARRIVE guidelines, and informed consent was obtained from the Bandırma Sheep Breeding and Research Institute administration prior to the study.\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors express their gratitude to the General Directorate of Agricultural Research and Policies for invaluable support. We also express our gratitude to our great leader, Mustafa Kemal ATAT\u0026Uuml;RK, who stated that\u0026nbsp;\u0026quot;Science is the truest guide in the world for everything \u0026mdash; for life, for success.\u0026quot;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY.Y. and M.B. conceptualized and designed the study, Ş.D. M.K., and B.B. conducted fieldwork, data collection, M.B. performed bioinformatic analysis, Y.Y. and M.B. wrote the draft, Y.Y. edited the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding for this research was provided by the Republic of Turkey Ministry of Agriculture and Forestry, General Directorate of Agricultural Research and Policies (TAGEM) (Project No: TAGEM/HAYS\u0026Uuml;D/E/20/A4/P2/2141).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublication consent for the article was obtained from the SBRI administration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrespondence\u0026nbsp;\u003c/strong\u003eand requests for materials should be addressed to Y.Y.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available in the https://figshare.com/repository, \u0026nbsp;https://figshare.com/articles/dataset/Genotype_data_of_Karacabey_Merino_Middle_Anatolian_Merino_Oamer_Akkaraman_HASMER_and_HASAK_sheep_breeds_/29898101\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDaly, K. 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Genetic Diversity, Population Structure, and Historical Gene Flow Patterns of Nine Indigenous Greek Sheep Breeds. \u003cem\u003eBiology\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 845 (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAcknowledgements The authors express their gratitude to the General Directorate of Agricultural Research and Policies for invaluable support. We also express our gratitude to our great leader, Mustafa Kemal ATAT\u0026Uuml;RK, who stated that Science is the truest guide in the world for everything \u0026mdash; for life, for success.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Supplementary Table S4","content":"\u003cp\u003eSupplementary Table S4 is not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Turkish sheep, selection signatures, ROH, iHS, Tajima’s D, genetic diversity","lastPublishedDoi":"10.21203/rs.3.rs-7364156/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7364156/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aimed to comparatively detect genomic signatures of selection in indigenous and crossbred Turkish sheep breeds using high-density SNP genotyping. A total of 1,612 individuals were analyzed, comprising the indigenous Akkaraman breed and four crossbreds: Karacabey Merino, Oamer, Hasak, and Hasmer. PCA revealed clear genetic separation of Akkaraman, partial overlap between Karacabey Merino and Oamer, and distinct variation in the small Hasak and Hasmer populations. Genetic diversity metrics indicated moderate and relatively homogeneous diversity across breeds, with slightly negative FIS, reflecting heterozygote excess. Selection signatures were identified using ROH, iHS, and Tajima\u0026rsquo;s D statistics. ROH analysis highlighted breed-specific candidate genes associated with growth (e.g., BGLAP, MYF6, GHR), milk production (PRL, LTF, casein cluster), immune response (TLR2, TLR5, IL15), and reproduction (FSHB, BMPR1B). iHS detected additional loci under positive selection, including MSTN, POU1F1, LEPR, and LALBA, while Tajima\u0026rsquo;s D identified selective sweeps in genes related to muscle development (CAPN2, CAST), reproduction (PAG4), and immune function (IRF2, ITGB2). Fifty candidate genes were shared among all breeds, whereas others were breed-specific, suggesting both common and unique adaptive pathways. These findings provide valuable insights into the genomic architecture and adaptive evolution of indigenous and crossbred Turkish sheep, with implications for conservation and breeding strategies.\u003c/p\u003e","manuscriptTitle":"Comparative Detection of Selection Signatures in Indigenous and Crossbred Turkish Sheep Breeds","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-09 13:28:06","doi":"10.21203/rs.3.rs-7364156/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-09-02T20:20:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-02T15:48:43+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-29T05:22:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-21T12:00:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-08-21T10:18:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8945f822-993c-4b2f-b308-180fbb3e7f2a","owner":[],"postedDate":"September 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":54144269,"name":"Biological sciences/Evolution"},{"id":54144270,"name":"Biological sciences/Genetics"}],"tags":[],"updatedAt":"2025-11-24T16:09:02+00:00","versionOfRecord":{"articleIdentity":"rs-7364156","link":"https://doi.org/10.1038/s41598-025-29969-1","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-23 15:57:19","publishedOnDateReadable":"November 23rd, 2025"},"versionCreatedAt":"2025-09-09 13:28:06","video":"","vorDoi":"10.1038/s41598-025-29969-1","vorDoiUrl":"https://doi.org/10.1038/s41598-025-29969-1","workflowStages":[]},"version":"v1","identity":"rs-7364156","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7364156","identity":"rs-7364156","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}