Whole genome analysis of selection associated with resistance to heat stress in chickens

Whole genome analysis of selection associated with resistance to heat stress in chickens | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Whole genome analysis of selection associated with resistance to heat stress in chickens Sevda Hosseinzadeh, Seyed Abbas Rafat, Arash Javanmard, Karim Hasanpur, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7262298/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Following their domestication, chickens were translocated around the world to novel environments. Through a combination of natural and artificial selection, chickens adapted to these local conditions over the last few thousands of years, creating significant genetic diversity across populations worldwide. Studying this diversity in the context of local environmental conditions may offer insights into mechanisms of adaptation to environmental stressors. In this study, we analyzed genomic data from the Chicken Genomic Diversity Consortium, applying multiple statistical approaches, including fixation index (FST), nucleotide diversity (π), Tajima’s D, and runs of homozygosity (ROH), to identify selective sweeps among indigenous chickens from Afghanistan, China, Indonesia, Iran and Pakistan, compared with White Leghorn chickens. We identified sweeps in 14 genes related to heat tolerance, associated with relevant gene ontology (GO) terms and located within ROH regions. These genes, such as CDH23, NPSR1, MCU, TRPV2, TRPV1, TRPV3, ATP2B4, CALM1, CACNB2, TRAT1, BDNF, SCIN, WIPF3, PRKD1, and DNAJC10 play crucial roles in calcium signaling pathways, thermal sensation, and the plasticity of neurodevelopmental processes. These findings illustrate the significant role of selection in shaping genomic differentiation across chicken populations and provide insights into the genetic basis of adaptation to environmental stressors. Biological sciences/Evolution Biological sciences/Genetics Chicken heat tolerance selection whole genome adaptation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Heat stress (HS) is an important challenge in the poultry industry 1 , especially in warm climates, causing substantial economic losses in both layer and broiler farms. In recent decades, global climate data has shown a clear warming trend across much of the world 2 , amplifying the impact of climate on poultry production. The Temperature Humidity Index (THI) is widely used to evaluate how environmental conditions impact poultry, serving as a measure of livestock productivity in relation to climate factors 3 . This index reflects the external forces that shift an animal’s body temperature from its normal range, providing a useful indicator of climate stress on livestock 4,5 . Chickens' lack of sweat glands 6 , insulating feather cover, and the high density in commercial rearing further exacerbate their susceptibility to HS 7 . Exposure to high temperatures disrupts several physiological processes, including immune function 8 , metabolism 9 , oxidative balance 10 , hemodynamics 11 , and protein synthesis 12 , often leading to reduced feed intake, lower productivity, and higher morbidity and mortality 13 . Over time, indigenous chickens adapt to local environmental conditions through natural selection, detected genetically via distinctive genomic signatures in selected regions. These include more common of selected alleles 14 , extended linkage disequilibrium 15 , increased homozygosity 16 , and reduced local genetic diversity 17 . To identify selection signatures, various statistical methods are used, grouped into two main categories: those analyzing variation within a population (intra-population) and those examining variation between populations (inter-population). Within-population approaches include metrics like runs of homozygosity (ROH) to detect long segments of homozygosity 16 , nucleotide diversity (π) to assess genetic variation levels 18 , and Tajima's D to evaluate deviations from neutral evolution 19 . In contrast, between-population studies often rely on metrics like the fixation index (FST) 20 , which measures genetic differentiation between populations and helps highlight selection patterns that may differ across groups. To investigate mechanisms of adaptation to heat stress, we carried out genomic analyses on indigenous chickens from Afghanistan, China (covering Huaixiang, Huiyang, Huaibeima, Jianghan, and Wuhua regions), Indonesia (Bali), Iran (Shiraz and Zahedan), and Pakistan, comparing these with White Leghorn chickens. Commercial chickens – like the White Leghorn – have been subject to intensive artificial selection, and are highly susceptible to HS due to elevated metabolic demands and extensive selection for high egg production, which often comes at the expense of traits like heat tolerance 7 . Comparing indigenous breeds to the White Leghorn (a lineage selected to exist in temperate environments with a known susceptibility to HS) provides crucial insights into the mechanisms of adaptation to HS, paving the way for breeding strategies aimed at improving heat tolerance while preserving productivity 21 . Materials and Methods Sample collection In this study, we analyzed variant calling data provided by the Chicken Genomic Diversity Consortium 22 . These data, generated using a standardized pipeline detailed in Supplementary information, Fig. S1 , include genomic information from 1,584 commercial and indigenous chickens. All samples were mapped to the bGalGal1.mat.broiler.GRCg7b reference genome. To categorize indigenous chickens based on their local environmental conditions, we calculated a Temperature-Humidity Index (THI) for each population's geographic coordinates, identifying those populations residing in regions characterized by high heat stress based on the hottest season of the year. The average temperature and humidity percentages for each location were based on historical weather reports collected from ( https://www.timeanddate.com ) over the period from 1992 to 2021, using coordinate data to calculate the THI. Additionally, the average temperature and humidity percentages associated with the hottest month of the year were used to calculate a THI. The equation used to calculate the THI was as follow 23 : $$\:THI=\left(1.8\:\times\:\:{T}_{avg}+32\right)-(0.55-0.0055\times\:{RH}_{avg})\times\:(1.8\times\:{T}_{avg}-26)$$ \(\:{T}_{avg}\) = The average air temperature (˚C) \(\:{RH}_{avg}\) = The average relative humidity (%) After sample selection, to minimize potential bias due to missing or unreliable genotypes, we filtered the VCF files for each commercial and indigenous chicken group. Filtering criteria included the removal of indels, SNP call rate, minor allele count (MAC), genotype quality, and minimum and maximum depth. This was performed using VCFtools with the following options: --remove-indels --max-alleles 2 --minGQ 20 --minDP 8 --maxDP 150 --max-missing 0.9 --mac 5. Population structure (dup: abstract ?) Principal Component Analysis (PCA) and ADMIXTURE analyses were conducted to examine genetic variation between populations of indigenous chickens and the Leghorn population, utilizing PLINK V1.9 24 and ADMIXTURE V1.3.0 25 software. Genotypes for ADMIXTURE and PCA clustering were pruned based on linkage disequilibrium (LD) using PLINK with the option --indep-pairwise 50 10 0.1. To investigate potential genetic admixture among populations, the admixture model in the ADMIXTURE V1.3.0 software was applied, with the number of ancestral populations (K) ranging from 2 to 6. Each run was subjected to 1000 bootstrap iterations for robustness. Exploration of selective sweep regions We used three statistical methods—genetic differentiation index (Fst), nucleotide diversity (π), and Tajima’s D—to identify selective sweeps in pairs of indigenous chickens and White leghorn chickens. Our sample pairs include: White Leghorn with indigenous chickens collected from Iran (Shiraz and Zahedan), Afghanistan, Indonesia (Bali), China (Huaixiang, Huiyang, Huaibeima, Jianghan, and Wuhua), and Pakistan. We used the PopGenome R package (version 2.7.5) 26 to calculate Fst nucleotide diversity, and Tajima's D with 100 Kb sliding windows and 10 Kb step size for each population pair separately. To identify candidate regions under selective sweeps, we applied a multi-step filtering strategy. Initially, we selected genomic windows containing at least five single nucleotide polymorphisms (SNPs), suggesting potential regions of interest. We then evaluated genetic differentiation (Fst) between White Leghorn and indigenous chicken breeds, retaining windows that ranked in the top 5% of Fst values. From these, we focused on windows shared in at least 50% of the pairwise comparisons between Leghorn and local breeds. Next, we analyzed nucleotide diversity and Tajima’s D by comparing the values of θπ and Tajima’s D between indigenous and White Leghorn chickens. We selected windows falling within the top and bottom 2.5% of the distributions of log₂ (θπ_indigenous / θπ_WhiteLeghorn) and (Tajima’s D_indigenous – Tajima’s D_WhiteLeghorn), respectively. Finally, we refined the analysis by including only those windows shared across at least 50% of the indigenous populations for both nucleotide diversity and Tajima’s D metrics. Annotation of genetic variants Genomic regions selected as candidate sweeps based on FST, log₂ (θπ_indigenous / θπ_WhiteLeghorn) and (Tajima’s D_indigenous – Tajima’s D_WhiteLeghorn) values were annotated using the GRCg7a genome reference ( https://ftp.ensembl.org/pub/release113/gtf/gallus_gallus/Gallus_gallus.bGalGal1.mat.broiler.GRCg7b.113.gtf.gz ). This annotation process, facilitated by the "rtracklayer" 27 and "GenomicRanges" 28 R packages (R version 4.2.3), and SnpEff 29 software enabled the identification of transcripts. Characteristics of Runs of homozygosity (ROH) This study examined runs of homozygosity (ROH), an indicator of genomic autozygosity, in 10 indigenous chicken populations and White Leghorn populations by analyzing extended segments of homozygous SNPs inherited from a recent common ancestor. ROH were calculated using PLINK v1.9 with the parameters: --homozyg, --homozyg-group, --homozyg-kb 300, --homozyg-window-snp 50, --homozyg-density 100, --homozyg-gap 1000, --homozyg-window-het 3, --homozyg-window-threshold 0.05, and --homozyg-window-missing 3. The inbreeding coefficient (F) was calculated for each individual and across each chicken group using VCFtools and PLINK’s –het option. Detected ROHs were classified into three length categories: 0 to < 1 Mb, 1 to 3 Mb. Gene set enrichment and pathway analysis To identify candidate genes and nearly fixed alleles associated with heat tolerance, we focused on genes that were significant in at least three of the following analyses: Fst, θπ, Tajima’s D, and ROH. Heat tolerance-related candidate genes were further identified based on our previous findings 30 , 31 , and genes listed in the NCBI database ( https://www.ncbi.nlm.nih.gov/ge ). We then constructed a protein-protein interaction (PPI) network for these common genes using the STRING database ( https://string-db.org/ ) to identify hub genes within the network. For gene ontology (GO) pathway enrichment analysis, we used the ClueGO plugin in Cytoscape, considering GO terms and pathways as significantly enriched if they had Bonferroni-corrected p-values < 0.05. Genotype Extraction and Allele Frequency Analysis SNP data for 14 target genes were extracted from a VCF file using R (version 4.2.2), with packages vcfR 32 , dplyr 33 , tidyr 34 , and readr 35 . Allele frequencies were calculated separately for the indigenous and White Leghorn populations. For each SNP, the counts of reference and alternative alleles were obtained, and fixation status was determined. Fisher’s exact test was applied to assess significant differences in allele frequencies between populations, and p-values were adjusted using false discovery rate (FDR) correction. Results PCA analysis of White Leghorn and indigenous chickens The dataset included 51 commercial White Leghorn chickens and indigenous chickens selected from locations with a (THI) above 72, which is commonly regarded as the threshold at which heat stress begins to affect poultry. Each selected group contained 10 or more samples, originating from Pakistan (n = 25), Shiraz (n = 16), Zahedan (n = 16), Afghanistan (n = 11), Bali (n = 15), Huaixiang (n = 10), Huiyang (n = 10), Huaibeima (n = 10), Jianghan (n = 10), and Wuhua (n = 20). Additional details on these samples are provided in Supplementary Table S1 . After excluding variants with low quality, high missing rates, and low frequencies, a total of 15,325,788 SNPs were retained for analysis across the populations. Population structure The PCA analysis indicated that the populations, including Afghanistan, Bali, Huaibei-Partridge-chicken, Huaixiang, Huiyang-Bearded, Jianghan, Pakistan, Shiraz, White-Leghorn, Wuhua-Yellow, and Zahedan, exhibited distinct genetic clustering patterns along the first two principal components (PC1 and PC2), which accounted for 26.86% and 10.20% of the total variance, respectively (Fig. 1 ). Indigenous chickens demonstrated an east-west cline in relatedness along PC2, highlighting genetic differentiation between eastern (Chinese) indigenous chickens vs chickens from Afghanistan, Iran and Pakistan. The commercial White Leghorn chickens formed a distinct cluster that was separated from all indigenous breeds along PC1, suggesting substantial differentiation between commercial and indigenous chickens. Genome‑wide selective sweep analysis To identify potential selective sweeps, we analyzed the genome for regions with high differentiation levels between White Leghorn compared to each indigenous chicken population separately. We identified 3,042 genomic windows with Fst values in the top 5% in at least 50% of the pairwise comparisons between White Leghorn and each indigenous chicken population ( see Supplementary Table S2 ). We further selected genomic windows in the top and bottom 2.5% of the distributions of log₂ (θπ_indigenous / θπ_WhiteLeghorn) and (Tajima’s D_indigenous – Tajima’s D_WhiteLeghorn), retaining only those shared by at least 50% of the populations, which resulted in 5,932 ( Supplementary Table S3 ) and 65,074 ( Supplementary Table S4 ) significant windows, respectively. Runs of homozygosity (ROH) A total of 1,924 ROH segments were identified across all studied indigenous populations, while commercial population had 4,814 ROH segments ( Supplementary Information, Table S5 ). Due to differing sample sizes between the populations, direct comparison of the absolute number of ROH segments is not appropriate. The identified ROH segments were also classified into three groups based on their length: less than 1 Mb, between 1–2 Mb, and more than 3 Mb. Most ROH segments fell into the medium category, the proportion of long ROH segments (> 3 Mb) was individually assessed in all ten populations, with values ranging between 68% and 100%. The total number of ROH segments identified in Iran (Shiraz and Zahedan), Afghanistan, Indonesia (Bail), China (Huaixiang, Huiyang, Huaibeima, Jianghan, Wuhua), and Pakistan chicken populations were 310, 281, 242, 311, 16, 10, 38, 71, 40 and 605, respectively. The number of ROH segments exceeding 3 Mb in length in Afghanistan, Bali, Huaibeima, Jianghan, Pakistan, Shiraz, Wuhua and Zahedan chickens was 43, 38, 2, 4, 70, 19, 1, and 28, respectively. We then identified genes in ROH regions for each breed, allowing genes in ROH common to different breeds to be identified, as well as genes that overlap with significant loci in Fst, Tajima’s D, and θπ analyses, and with our previously identified list of heat tolerance–related genes. Genomic inbreeding coefficients based on ROH (FROH) and individual heterozygosity were estimated separately for each population ( Supplementary information, Table S6 ). Potential genes, gene set enrichment and pathways analysis in related with heat tolerance. After identifying selective signatures between White Leghorn and indigenous chicken groups, candidate regions were detected using four statistical methods: Fst, Tajima’s D, θπ, and ROH. These regions were annotated using the "rtracklayer" and "GenomicRanges" packages in R version 4.2.3. Additionally, annotated protein coding genes consistently identified across these methods for each indigenous population by JVenn 36 are shown in Supplementary information, Table S7 and Fig. 2 . Our methods identified numerous candidate genes within high-confidence selected regions potentially linked to heat tolerance, building on our previous findings 30 , 31 and aligning with heat tolerance-associated genes listed in the NCBI database. We identified 267 protein-coding genes that overlap with these selective signature regions. Of the total, 126 genes had been identified in our previous studies, with comprehensive details and expression profiles available in Supplementary Table S7 . To investigate the interactions among 113 candidate genes—selected based on ROH and at least two additional methods (Fst, Tajima’s D, and θπ)—we used the STRING database for interaction analysis (Fig. 3 ) and conducted functional enrichment using the ClueGO plugin in Cytoscape (Fig. 4 ). Gene set enrichment analysis revealed several significantly enriched terms associated with tolerance to heat stress. Among these, “Hsp70 protein binding” was identified within the molecular function category, while terms such as “calcium-mediated signaling,” “response to temperature stimulus,” “positive regulation of actin filament polymerization,” “calcium ion transport,” “calcium ion transmembrane transport,” and “calcium ion transmembrane transporter activity” were significantly enriched within the biological process category. (Supplementary information, Table S8 ) . Functional enrichment analysis We identified 14 genes under selection that are significantly associated with GO terms involved in heat stress, as detailed in Supplementary Table 8 . These genes include CDH23, NPSR1, MCU, TRPV2, TRPV1, TRPV3, ATP2B4, CALM1, CACNB2, TRAT1, BDNF, SCIN, WIPF3, and PRKD1. The enrichment analysis revealed significant involvement of two KEGG pathways: Adrenergic signaling in cardiomyocytes (p = 1.70 × 10⁻²) and the calcium signaling pathway (p = 5.10 × 10⁻²). As illustrated in Fig. 4 , several of the genes identified in our study are mapped onto the adrenergic signaling pathway. Notably, CALM1 (calmodulin, CaM) and ATP2B4 (PMCA) are directly involved in calcium regulation and signal transduction within cardiomyocytes. CALM1 activates downstream targets such as CaMKII, while ATP2B4 facilitates calcium efflux. Although not shown in the pathway diagram, TRPV1, TRPV2, and TRPV3 are thermosensitive calcium channels known to participate in the calcium signaling pathway and contribute to heat stress response. PRKD1 is also connected to PKC signaling downstream of adrenergic activation. Additional genes such as BDNF and CACNB2, while not visualized in this diagram, are associated with neurotrophic and calcium channel functions. Genes including MCU, CDH23, NPSR1, TRAT1, SCIN, and WIPF3 are not represented in the current KEGG pathway map. Analysis of the calcium signaling pathway (Fig. 6 ) revealed the involvement of several key genes, including CDH23, NPSR1, MCU, TRPV1, TRPV2, TRPV3, ATP2B4, CALM1, CACNB2, TRAT1, BDNF, SCIN, WIPF3, and PRKD1, which contribute to distinct downstream cellular processes. MCU and ATP2B4 regulate mitochondrial and cytoplasmic calcium homeostasis, influencing apoptosis and energy metabolism. CALM1, a central calcium sensor, mediates calcium-dependent activation of signaling molecules such as CaMK and CaN, linking calcium influx to memory, learning, and synaptic plasticity. Ion channels TRPV1–3 and CACNB2 modulate calcium entry in response to depolarization or sensory stimuli, which are critical for neuronal excitability and neurotransmitter release. BDNF expression, regulated by calcium signaling, was implicated in synaptic strengthening and neuroplasticity. PRKD1 acts downstream to regulate secretion and cell survival pathways. Immune-related gene TRAT1 and receptor NPSR1 mediate calcium-dependent activation in immune and stress responses. Structural regulators SCIN and WIPF3 may facilitate actin remodeling and exocytosis in response to calcium flux. Collectively, the activation of these genes, as shown in Fig. 6 , underscores the central role of calcium signaling in orchestrating diverse biological processes such as neurotransmission, immune activation, cellular secretion, and gene expression, all of which contribute to the cellular response and adaptation to heat stress 40 – 42 . The fixation of alleles in candidate genes associated with heat tolerance After detection of 14 selected genes associated with heat tolerance, we focused on putatively functional variants in these genes. To assess the genotype distribution and fixation patterns between the indigenous and White Leghorn populations, we analyzed the frequencies of homozygous reference (0/0), heterozygous (0/1), and homozygous alternate (1/1) genotypes. The differential analysis revealed decreases or increases in the percentage of 1/1 or 0/0 genotypes in the White Leghorns compared to the native population, or vice versa. These findings highlight substantial changes that likely reflect underlying genetic and selective pressures acting on the two populations. We discovered 732 SNPs that showed near-complete fixation across all comparisons. Among these, 721 were uniquely fixed in the White Leghorn population, while only 11 were nearly fixed in the indigenous populations. The majority of these indigenous-specific SNPs were situated in intronic regions, though a few exhibited coding variants. The allele frequency distribution for each population is provided in Supplementary Information, Figure S2 . Notably, the SNP rs3386031003 at position 5:3943152 is a missense mutation, while rs3386050400 at position 5:3952604 is an upstream gene variant; both are situated within the BDNF gene. Additional details are provided in Supplementary Information, Table S9 . We identified a set of SNPs that were fixed in over 70% of pairwise comparisons between White Leghorn and several indigenous populations. Notably, the SNP rs1060165215 located at position 5:33441126, a missense variant in the PRKD1 gene, was fixed in the following populations, along with their respective FDR-adjusted p-values: Afghanistan (p = 3.0163E-05), Bali (p = 9.4671E-07), Huaibei (p = 7.43893E-05), Huaixiang (p = 7.92188E-05), Huiyang (p = 8.11157E-05), Jianghan (p = 8.5849E-05), Shiraz (p = 5.17514E-07), Wuhua (p = 6.32765E-09), and Zahedan (p = 5.36687E-08). Additionally, the SNP rs3387803766 at position 5:33330346, located in the splice region in intron 16 of PRKD1, was fixed in Afghanistan (p = 2.46038E-05), Bali (p = 7.82256E-07), Huaibei (p = 7.30574E-05), Huaixiang (p = 7.77693E-05), Huiyang (p = 7.99212E-05), Jianghan (p = 8.45744E-05), Wuhua (p = 2.96E-09), and Pakistan (p = 7.88122E-12). Detailed results are provided in Supplementary Information, Table S10 . Discussion In this study, we identified candidate heat tolerance associated genes under selection. These include CDH23, NPSR1, MCU, TRPV2, TRPV1, TRPV3, ATP2B4, CALM1, CACNB2, TRAT1, BDNF, SCIN, WIPF3, and PRKD1. Selected genes were identified based on ROH and at least two of three population genetic metrics: Fst, Tajima’s D, and θπ. This combined approach provides strong evidence of adaptive evolution in response to environmental heat stress 43 , 44 . GO enrichment analysis showed that these candidate genes cluster significantly in three biological themes: calcium signaling, thermosensation, and neurodevelopmental plasticity. These processes are key for cellular and behavioral adaptation to heat stress. Calcium-related processes, including calcium ion transport (GO:0006816) and calcium-mediated signaling (GO:0019722), were enriched. Also ATP2B4, MCU, CALM1, and TRPV1–3, play central roles in these pathways. This highlights an evolutionarily conserved mechanism that maintains calcium balance under heat stress. Experimental studies support these findings. For example, ATP2B4 (PMCA4) is crucial for calcium homeostasis during hyperthermia 45 , 46 , while MCU regulates mitochondrial calcium uptake and heat-induced apoptosis 47 . The TRPV ion channels, especially TRPV1, TRPV2, and TRPV3, are key thermosensors under selection. TRPV1 activates at approximately 43°C 48 . It enhances peripheral heat sensation and mediates central cooling in the medial preoptic area )mPOA( 49 . Its role has been demonstrated in desert rodents, which show altered thermosensitivity 50 . In mice, TRPV1 activation boosts antioxidant defenses and reduces inflammation under heat stress 51 . TRPV2 responds to temperatures above 52°C, detecting extreme heat 52 , while TRPV3 is active at 33–39°C in keratinocytes. It links thermal signals to calcium pathways, induces HSP expression via calmodulin/CaMK, and supports skin repair 53 – 57 . CALM1 plays a key role in calcium signaling activating kinases such as CaMKs and phosphatases like calcineurin following heat-induced calcium influx. These enzymes regulate heat shock protein expression, inhibit apoptosis, and stabilize intracellular proteins 58 , 59 . Together, these actions enhance cellular resilience to heat stress. Genes such as BDNF, CACNB2, PRKD1, and TRAT1 also contribute to the broader stress adaptation framework. Although BDNF is not directly annotated in KEGG pathways, it plays a well-established role in calcium-dependent synaptic plasticity and neuroprotection, acting through complex signaling networks beyond those currently captured by KEGG databases 60 , 61 . PRKD1 acts downstream of PKCµ to regulate α-catenin phosphorylation and maintain endothelial barrier integrity in response to IL-33 under hypoxic conditions 62 . TRAT1 and NPSR1 participate in immune-related calcium signaling, which may connect heat stress to inflammatory and immune pathways 40 , 41 . SCIN and WIPF3, as cytoskeletal regulators, might facilitate calcium-dependent actin remodeling and vesicle trafficking, thereby enhancing cellular adaptability 63 , 64 . Taken together, these findings show that calcium signaling is a central integrative axis in the heat stress response 42 – it links thermosensory input from TRPV channels, calcium homeostasis mediated by ATP2B4, MCU, and CACNB2, and downstream signaling cascades involving CALM1 and PRKD1. These pathways promote protective cellular functions such as protein stabilization, apoptosis inhibition, and immune regulation 65 – 68 . The overlap of positive selection signals and functional enrichment within this network provides strong evidence for evolutionary adaptation aimed at maintaining physiological homeostasis under high-temperature conditions 69 . In White Leghorn chickens, many alleles related to thermosensation, calcium signaling, and cellular stress responses were fixed or nearly fixed (AF ≅ 1). In contrast, these variants were rare or absent in indigenous populations (AF = 0 to 0.08). For instance, we identified fixed variants located in intronic and downstream regions of the TRAT1 gene. Similarly, intronic and upstream variants in CACNB2—a gene involved in calcium channel activity and neural development—were fixed in both Leghorn and indigenous chicken breeds. In Leghorns, fixed variants were also found in the intron, downstream region, and 3' UTR of WIPF3, which is associated with actin cytoskeleton organization and stress response, as well as intronic variants in NPSR1, a gene linked to neuropeptide signaling and immune system regulation. Fixed variants in CDH23, a gene essential for mechanosensation, were detected across several genomic regions, including intronic, splice sites, synonymous positions, upstream, downstream, and the 3' UTR. We also observed variants in the intronic, downstream, and 3' UTR regions of PRKD1, which may influence the heat shock response. Moreover, a fixed variant in MCU—present in the 3' UTR, 5' UTR, upstream area, intron, and including a missense mutation—was found in Leghorns; MCU plays a critical role in regulating mitochondrial calcium uptake. The fixation of these alleles in Leghorns suggests that artificial selection targeting productivity traits might have reduced thermotolerance. Conversely, fixed intronic variants were identified in CACNB2, TRPV3, and PRKD1, alongside missense and upstream variants in BDNF in indigenous chickens. These native breeds appear to have retained more ancestral alleles, likely due to natural adaptation to hot and humid climates. Collectively, these genes stand out as promising candidates for functional studies and selective breeding to improve heat tolerance in commercial poultry. Intronic variants, particularly those located near splice sites, have recently gained significant attention as key regulators of gene expression and post-transcriptional modifications. These variants can induce events such as intronic polyadenylation (IPA), intron retention, or splice site alterations, leading to the production of alternative RNA isoforms and ultimately altering gene expression—without necessarily affecting coding regions. While the functional roles of intronic variants, including intronic polyadenylation (IPA), have been well-documented in human cancer and economically important traits in cattle 70 – 73 , no studies to date have explored their role in the heat stress response in chickens despite the fact that heat stress represents one of the most critical economic and biological challenges in the global poultry industry. In this study, we identify for the first time intronic variants including ones in heat-responsive genes such as WIPF3, TRAT1, and CACNB2, which may influence gene expression through mechanisms such as intronic polyadenylation (IPA) or splice site alterations. Given the strong conservation of RNA processing mechanisms across vertebrates, our findings provide a foundation for future functional investigations (e.g., minigene assays) to assess the direct role of these variants in modulating heat stress responses in indigenous chicken breeds. Conclusion Our findings underscore the potential functional relevance of regulatory variants—particularly intronic ones—in shaping the transcriptional response to heat stress in chickens. This study is the first to highlight the possible involvement of non-coding variants in thermal adaptation in poultry. Candidate genes such as PRKD1, CDH23, WIPF3, TRAT1, and CACNB2 harbor variants that may act through mechanisms like intronic polyadenylation or splicing disruption. These discoveries lay the groundwork for future experimental validation and breeding strategies aimed at enhancing heat tolerance in indigenous breeds. Further functional assays will be crucial to unravel the exact molecular pathways these variants modulate under thermal stress. Declarations Ethics approval All data used in the current work were provided by the Chicken Genomic Diversity consortium 22 . No samples were taken and no sequence data were generated as part of the present study. Consent for publication Not applicable. Competing interests All authors declare that they have no competing interests for this work. Funding S.R.F received support through the UKRI Biotechnology and Biological Sciences Research Council (BBSRC) Institute Strategic Programme and National Bioscience Research Infrastructure grants to the Pirbright Institute: BBS/E/PI/230001A, BBS/E/PI/230001C and BBS/E/PI/23NB0003. Author Contribution S.H. performed all data analyses and wrote the first draft of the manuscript. S.R.F oversaw the analysis and manuscript writing, which was also reviewed and edited by all authors. S.R.F, A.L.S., P.B., M.C. and C.K. prepared the data in their roles as leaders and members of the Chicken Genomic Diversity Consortium. S.A.R, A.J. and K.H provided analytical and editorial assistance. Acknowledgement We gratefully acknowledge the work of Prof. Laurent Frantz in setting up the Chicken Genomic Diversity Consortium dataset. Data Availability All data used in this study are publicly available from online repositories. Table S1 contains accession numbers for all samples. References St-Pierre, N. R., Cobanov, B. & Schnitkey, G. Economic losses from heat stress by US livestock industries. J. Dairy. Sci. 86 , E52–E77 (2003). Wang, Y. R., Hessen, D. O., Samset, B. H. & Stordal, F. Evaluating global and regional land warming trends in the past decades with both MODIS and ERA5-Land land surface temperature data. Remote Sens. Environ. 280 , 113181 (2022). Lin, H., Jiao, H. C., Buyse, J. & Decuypere, E. Strategies for preventing heat stress in. 62 , 71–86 (2006). Lallo, C. H. O. et al. Characterizing heat stress on livestock using the temperature humidity index (THI)—prospects for a warmer Caribbean. Reg. Environ. Chang. 18 , 2329–2340 (2018). Habeeb, A. A., Gad, A. E. & Atta, M. A. Temperature-humidity indices as indicators to heat stress of climatic conditions with relation to production and reproduction of farm animals. Int. J. Biotechnol. Recent. Adv. 1 , 35–50 (2018). Loyau, T. et al. Thermal manipulation of the embryo modifies the physiology and body composition of broiler chickens reared in floor pens without affecting breast meat processing quality. J. Anim. Sci. 91 , 3674–3685 (2013). Brugaletta, G., Teyssier, J. R., Rochell, S. J., Dridi, S. & Sirri, F. A review of heat stress in chickens. Part I: Insights into physiology and gut health. Front Physiol 1535 (2022). Ahmad, R. et al. Influence of heat stress on poultry growth performance, intestinal inflammation, and immune function and potential mitigation by probiotics. Animals 12 , 2297 (2022). Qaid, M. M. & Al-Garadi, M. A. Protein and amino acid metabolism in poultry during and after heat stress: a review. Animals 11 , 1167 (2021). Chauhan, S. S., Rashamol, V. P., Bagath, M., Sejian, V. & Dunshea, F. R. Impacts of heat stress on immune responses and oxidative stress in farm animals and nutritional strategies for amelioration. Int. J. Biometeorol. 65 , 1231–1244 (2021). Etches, R. J., John, T. M. & Gibbins, A. M. V. Behavioural, physiological, neuroendocrine and molecular responses to heat stress. in Poultry Prod. hot climates 48–79 (2008). (CABI Wallingford UK. Chowdhury, V. S. et al. Potential role of amino acids in the adaptation of chicks and market-age broilers to heat stress. Front. Vet. Sci. 7 , 610541 (2021). Khosravinia, H. Mortality, production performance, water intake and organ weight of the heat stressed broiler chicken given savory (Satureja khuzistanica) essential oils through drinking water. J. Appl. Anim. Res. 44 , 273–280 (2016). Buffalo, V. & Coop, G. Estimating the genome-wide contribution of selection to temporal allele frequency change. Proc. Natl. Acad. Sci. 117, 20672–20680 (2020). Dadshani, S., Mathew, B., Ballvora, A., Mason, A. S. & Léon, J. Detection of breeding signatures in wheat using a linkage disequilibrium-corrected mapping approach. Sci. Rep. 11 , 5527 (2021). Li, X. et al. Runs of homozygosity and selection signature analyses reveal putative genomic regions for artificial selection in layer breeding. BMC Genom. 25 , 638 (2024). Charlesworth, B. & Jensen, J. D. Effects of selection at linked sites on patterns of genetic variability. Annu. Rev. Ecol. Evol. Syst. 52 , 177–197 (2021). Shi, H. et al. Whole genome sequencing revealed genetic diversity, population structure, and selective signature of Panou Tibetan sheep. BMC Genom. 24 , 50 (2023). Konopiński, M. K., Fijarczyk, A. M. & Biedrzycka, A. Complex patterns shape immune genes diversity during invasion of common raccoon in Europe–Selection in action despite genetic drift. Evol. Appl. 16 , 134–151 (2023). Ye, S., Song, H., Ding, X., Zhang, Z. & Li, J. Pre-selecting markers based on fixation index scores improved the power of genomic evaluations in a combined Yorkshire pig population. Animal 14 , 1555–1564 (2020). Fathi, M. M., Galal, A., Radwan, L. M., Abou-Emera, O. K. & Al-Homidan, I. H. Using major genes to mitigate the deleterious effects of heat stress in poultry: An updated review. Poult. Sci. 101 , 102157 (2022). No & Title. No Title. National Oceanic and Atmospheric Administration. Livestock Hot Weather Stress. Operations Manual Letter C-31-76, Department of Commerce, NOAA, National Weather Service Central Region, Kansas City;.. (1976). Chang, Y. C. et al. Differential expression patterns of housekeeping genes increase diagnostic and prognostic value in lung cancer. PeerJ 1–17 (2018). (2018). Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19 , 1655–1664 (2009). Pfeifer, B. An introduction to the PopGenome package. at (2020). Lawrence, M., Gentleman, R. & Carey, V. rtracklayer: an R package for interfacing with genome browsers. Bioinformatics 25 , 1841 (2009). Lawrence, M. et al. Software for computing and annotating genomic ranges. PLoS Comput. Biol. 9 , e1003118 (2013). Lu, X. & Ruden, D. M. A program for annotating and predicting the efects of single nucleotide polymorphisms, SnpEf: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. (2012). Hosseinzadeh, S. & Hasanpur, K. Gene expression networks and functionally enriched pathways involved in the response of domestic chicken to acute heat stress. Front. Genet. 14 , 699 (2023). Hosseinzadeh, S. & Hasanpur, K. Whole genome discovery of regulatory genes responsible for the response of chicken to heat stress. Sci. Rep. 14 , 6544 (2024). Knaus, B. J. & Grünwald, N. J. vcfr: a package to manipulate and visualize variant call format data in R. Mol. Ecol. Resour. 17 , 44–53 (2017). Wickham, H. & dplyr A grammar of data manipulation. R Packag version . 04 (3), 156 (2015). Wickham, H., Vaughan, D. & Girlich M. tidyr: tidy messy data. R package version 1.3. 1. at (2024). Wickham, H. et al. Package ‘readr’. Read Rectangular Text Data. Available online https//cran. r (2023). -project . org/web/packages/readr/readr . pdf (accessed 23 August ) (2024). Sawamura, H., Kiyozuka, K. & JVenn A visual reasoning system with diagrams and sentences. in International Conference on Theory and Application of Diagrams 271–285Springer, (2000). Kanehisa, M., Furumichi, M., Sato, Y., Matsuura, Y. & Ishiguro-Watanabe, M. KEGG: biological systems database as a model of the real world. Nucleic Acids Res. 53 , D672–D677 (2025). Kanehisa, M. Toward understanding the origin and evolution of cellular organisms. Protein Sci. 28 , 1947–1951 (2019). Kanehisa, M. & Goto, S. K. E. G. G. Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28 , 27–30 (2000). Shi, T. et al. Single cell transcriptome sequencing indicates the cellular heterogeneity of small intestine tissue in celiac disease. Sci. Rep. 15 , 12385 (2025). Vendelin, J. et al. Downstream target genes of the neuropeptide S–NPSR1 pathway. Hum. Mol. Genet. 15 , 2923–2935 (2006). Yao, X. et al. Hsp90 protected chicken primary myocardial cells from heat-stress injury by inhibiting oxidative stress and calcium overload in mitochondria. Biochem. Pharmacol. 209 , 115434 (2023). Passamonti, M. M. et al. The quest for genes involved in adaptation to climate change in ruminant livestock. Animals 11 , 2833 (2021). Asadollahpour Nanaei, H., Kharrati-Koopaee, H. & Esmailizadeh, A. Genetic diversity and signatures of selection for heat tolerance and immune response in Iranian native chickens. BMC Genom. 23 , 224 (2022). King, A. M. & MacRae, T. H. Insect heat shock proteins during stress and diapause. Annu. Rev. Entomol. 60 , 59–75 (2015). Chandan, K., Gupta, M., Ahmad, A. & Sarwat, M. P-type calcium ATPases play important roles in biotic and abiotic stress signaling. Planta 260 , 37 (2024). Enomoto, A. & Fukasawa, T. The role of calcium-calpain pathway in hyperthermia. Front. Mol. Med. 2 , 1005258 (2022). Sánchez-Moreno, A. et al. Irreversible temperature gating in trpv1 sheds light on channel activation. Elife 7 , e36372 (2018). Lezama-García, K. et al. Transient Receptor Potential (TRP) and thermoregulation in animals: Structural biology and neurophysiological aspects. Animals 12 , 106 (2022). Wang, B. et al. Genetic Diversity of a Heat Activated Channel—TRPV1 in Two Desert Gerbil Species with Different Heat Sensitivity. Int. J. Mol. Sci. 24 , 9123 (2023). Li, Z., Zhang, J., Cheng, K., Zhang, L. & Wang, T. Capsaicin alleviates the intestinal oxidative stress via activation of TRPV1/PKA/UCP2 and Keap1/Nrf2 pathways in heat-stressed mice. J. Funct. Foods . 108 , 105749 (2023). Caterina, M. J., Rosen, T. A., Tominaga, M. & Brake, A. J. Julius, D. A capsaicin-receptor homologue with a high threshold for noxious heat. Nature 398 , 436–441 (1999). Guo, Y. et al. Novel insights into the role of keratinocytes-expressed TRPV3 in the skin. Biomolecules 13 , 513 (2023). Thiriet, M., Thiriet, M., Ion & Carriers Signal Cell. Surf. Circ. Vent. Syst 89–156 (2012). Lei, J. & Tominaga, M. Unlocking the therapeutic potential of TRPV3: Insights into thermosensation, channel modulation, and skin homeostasis involving TRPV3. BioEssays 46, 2400047 (2024). Scott, V. E. et al. 534 Defining a mechanistic link between TRPV3 activity and psoriasis through IL-1α and EGFR signaling pathways. J. Invest. Dermatol. 136 , S94 (2016). Szöllősi, A. G. et al. Activation of TRPV3 regulates inflammatory actions of human epidermal keratinocytes. J. Invest. Dermatol. 138 , 365–374 (2018). Kubik, R. M. Genomic investigation of beta agonist supplementation and heat stress in livestock species. (2018). Ren, H. et al. Calcium signaling-mediated transcriptional reprogramming during abiotic stress response in plants. Theor. Appl. Genet. 136 , 210 (2023). Want, A. A functional role for brain-derived neurotrophic factor from circulating blood platelets and potential neuroprotective applications. at (2022). Chernyavskaya, Y., Ebert, A. M., Milligan, E. & Garrity, D. M. Voltage-gated calcium channel CACNB2 (β2. 1) protein is required in the heart for control of cell proliferation and heart tube integrity. Dev. Dyn. 241 , 648–662 (2012). Sharma, D. et al. IL-33 via PKCµ/PRKD1 mediated α-catenin phosphorylation regulates endothelial cell-barrier integrity and ischemia-induced vascular leakage. Cells 12 , 703 (2023). Wang, K. et al. Autophagy regulation and protein kinase activity of PIK3C3 controls sertoli cell polarity through its negative regulation on SCIN (scinderin). Autophagy 19 , 2934–2957 (2023). De Luca, F., Kha, M., Swärd, K. & Johansson, M. E. Identification of ARMH4 and WIPF3 as human podocyte proteins with potential roles in immunomodulation and cytoskeletal dynamics. PLoS One . 18 , e0280270 (2023). Wei, Z. et al. Calcium induces death is associated with Pyroptosis and the anti-tumor immunity in breast cancer. (2022). Clucas, J. & Meier, P. Roles of RIPK1 as a stress sentinel coordinating cell survival and immunogenic cell death. Nat. Rev. Mol. Cell. Biol. 24 , 835–852 (2023). Palkar, R., Lippoldt, E. K. & McKemy, D. D. The molecular and cellular basis of thermosensation in mammals. Curr. Opin. Neurobiol. 34 , 14–19 (2015). Hasan, A. R. et al. The Alteration of Microglial Calcium Homeostasis in Central Nervous System Disorders: A Comprehensive Review. Neuroglia 5 , 410–444 (2024). Sajjanar, B., Krishnaswamy, N., Saxena, V. K. & Dhara, S. K. Stress Responses to Changing Environmental Factors in the Domestic Animals: An Epigenetic Perspective. J Anim. Physiol. Anim. Nutr. (Berl) (2025). Guan, D. et al. Genetic regulation of gene expression across multiple tissues in chickens. Nat Genet 1–11 (2025). Shuli, L. et al. A multi-tissue atlas of regulatory variants in cattle. (2022). Wang, L. et al. Trans-ancestry transcriptome-wide association and functional studies to uncover novel susceptibility genes and therapeutic targets for colorectal cancer. npj Precis Oncol. 9 , 124 (2025). Zhao, Z. et al. Comprehensive characterization of somatic variants associated with intronic polyadenylation in human cancers. Nucleic Acids Res. 49 , 10369–10381 (2021). Additional Declarations No competing interests reported. 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19:50:14","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":1889217,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformationTableS10.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7262298/v1/f27fc76b61114fcad9b49b66.xlsx"},{"id":96241865,"identity":"947c529c-bb3b-449f-96e8-8c15e29383bd","added_by":"auto","created_at":"2025-11-19 07:11:32","extension":"xls","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":93184,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryinformationTableS1updated.xls","url":"https://assets-eu.researchsquare.com/files/rs-7262298/v1/5229e908a5fc22211dbd631f.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"Whole genome analysis of selection associated with resistance to heat stress in chickens","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHeat stress (HS) is an important challenge in the poultry industry\u003csup\u003e1\u003c/sup\u003e, especially in warm climates, causing substantial economic losses in both layer and broiler farms. In recent decades, global climate data has shown a clear warming trend across much of the world\u003csup\u003e2\u003c/sup\u003e, amplifying\u0026nbsp;the impact of climate on poultry production. The Temperature Humidity Index (THI) is widely used to evaluate\u0026nbsp;how environmental conditions impact poultry, serving as a measure of livestock productivity in relation to climate factors\u003csup\u003e3\u003c/sup\u003e. This index reflects the external forces that shift an animal’s body temperature from its normal range, providing a useful indicator of climate stress on livestock\u003csup\u003e4,5\u003c/sup\u003e.\u0026nbsp;Chickens' lack of sweat glands\u003csup\u003e6\u003c/sup\u003e, insulating feather cover, and the high density in commercial rearing further exacerbate their susceptibility to HS\u003csup\u003e7\u003c/sup\u003e. Exposure to high temperatures disrupts several physiological processes, including immune function\u003csup\u003e8\u003c/sup\u003e, metabolism\u003csup\u003e9\u003c/sup\u003e, oxidative balance\u003csup\u003e10\u003c/sup\u003e, hemodynamics\u003csup\u003e11\u003c/sup\u003e, and protein synthesis\u003csup\u003e12\u003c/sup\u003e, often leading to reduced feed intake, lower productivity, and higher morbidity and mortality\u003csup\u003e13\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOver time, indigenous chickens adapt to local environmental conditions through natural selection, detected genetically via distinctive genomic signatures in selected regions. These include more common of selected alleles\u003csup\u003e14\u003c/sup\u003e, extended linkage disequilibrium\u003csup\u003e15\u003c/sup\u003e, increased homozygosity\u003csup\u003e16\u003c/sup\u003e, and reduced local genetic diversity\u003csup\u003e17\u003c/sup\u003e. To identify selection signatures, various statistical methods are used, grouped\u0026nbsp;into two main categories: those analyzing variation\u0026nbsp;within\u0026nbsp;a population (intra-population) and those examining variation\u0026nbsp;between populations (inter-population). Within-population approaches include metrics like runs of homozygosity (ROH) to detect long segments of homozygosity\u003csup\u003e16\u003c/sup\u003e, nucleotide diversity (π) to assess genetic variation levels\u003csup\u003e18\u003c/sup\u003e, and Tajima's D to evaluate deviations from neutral evolution\u003csup\u003e19\u003c/sup\u003e. In contrast, between-population studies often rely on metrics like the fixation index (FST)\u003csup\u003e20\u003c/sup\u003e, which measures genetic differentiation between populations and helps highlight selection patterns that may differ across groups.\u003c/p\u003e\n\u003cp\u003eTo investigate mechanisms of adaptation to heat stress, we carried out genomic analyses on indigenous chickens from Afghanistan, China (covering Huaixiang, Huiyang, Huaibeima, Jianghan, and Wuhua regions), Indonesia (Bali), Iran (Shiraz and Zahedan), and Pakistan, comparing these with White Leghorn chickens. Commercial chickens – like the White Leghorn – have been subject to intensive artificial selection, and are highly susceptible to HS due to elevated metabolic demands and extensive selection for high egg production, which often comes at the expense of traits like heat tolerance\u003csup\u003e7\u003c/sup\u003e. Comparing indigenous breeds to the White Leghorn (a lineage selected to exist in temperate environments with a known susceptibility to HS) provides crucial insights into the mechanisms of adaptation to HS, paving the way for breeding strategies aimed at improving heat tolerance while preserving productivity\u003csup\u003e21\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\n\u003ch3\u003eSample collection\u003c/h3\u003e\n\u003cp\u003eIn this study, we analyzed variant calling data provided by the Chicken Genomic Diversity Consortium\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. These data, generated using a standardized pipeline detailed in \u003cb\u003eSupplementary information, Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e, include genomic information from 1,584 commercial and indigenous chickens. All samples were mapped to the bGalGal1.mat.broiler.GRCg7b reference genome. To categorize indigenous chickens based on their local environmental conditions, we calculated a Temperature-Humidity Index (THI) for each population's geographic coordinates, identifying those populations residing in regions characterized by high heat stress based on the hottest season of the year.\u003c/p\u003e\u003cp\u003eThe average temperature and humidity percentages for each location were based on historical weather reports collected from (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.timeanddate.com\u003c/span\u003e\u003cspan address=\"https://www.timeanddate.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) over the period from 1992 to 2021, using coordinate data to calculate the THI. Additionally, the average temperature and humidity percentages associated with the hottest month of the year were used to calculate a THI.\u003c/p\u003e\u003cp\u003eThe equation used to calculate the THI was as follow\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:THI=\\left(1.8\\:\\times\\:\\:{T}_{avg}+32\\right)-(0.55-0.0055\\times\\:{RH}_{avg})\\times\\:(1.8\\times\\:{T}_{avg}-26)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{T}_{avg}\\)\u003c/span\u003e\u003c/span\u003e = The average air temperature (˚C)\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{RH}_{avg}\\)\u003c/span\u003e\u003c/span\u003e= The average relative humidity (%)\u003c/p\u003e\u003cp\u003eAfter sample selection, to minimize potential bias due to missing or unreliable genotypes, we filtered the VCF files for each commercial and indigenous chicken group. Filtering criteria included the removal of indels, SNP call rate, minor allele count (MAC), genotype quality, and minimum and maximum depth. This was performed using VCFtools with the following options: --remove-indels --max-alleles 2 --minGQ 20 --minDP 8 --maxDP 150 --max-missing 0.9 --mac 5.\u003c/p\u003e\n\u003ch3\u003ePopulation structure (dup: abstract ?)\u003c/h3\u003e\n\u003cp\u003ePrincipal Component Analysis (PCA) and ADMIXTURE analyses were conducted to examine genetic variation between populations of indigenous chickens and the Leghorn population, utilizing PLINK V1.9\u003csup\u003e24\u003c/sup\u003e and ADMIXTURE V1.3.0\u003csup\u003e25\u003c/sup\u003e software. Genotypes for ADMIXTURE and PCA clustering were pruned based on linkage disequilibrium (LD) using PLINK with the option --indep-pairwise 50 10 0.1. To investigate potential genetic admixture among populations, the admixture model in the ADMIXTURE V1.3.0 software was applied, with the number of ancestral populations (K) ranging from 2 to 6. Each run was subjected to 1000 bootstrap iterations for robustness.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eExploration of selective sweep regions\u003c/h2\u003e\u003cp\u003eWe used three statistical methods\u0026mdash;genetic differentiation index (Fst), nucleotide diversity (π), and Tajima\u0026rsquo;s D\u0026mdash;to identify selective sweeps in pairs of indigenous chickens and White leghorn chickens. Our sample pairs include: White Leghorn with indigenous chickens collected from Iran (Shiraz and Zahedan), Afghanistan, Indonesia (Bali), China (Huaixiang, Huiyang, Huaibeima, Jianghan, and Wuhua), and Pakistan.\u003c/p\u003e\u003cp\u003eWe used the PopGenome R package (version 2.7.5)\u003csup\u003e26\u003c/sup\u003e to calculate Fst nucleotide diversity, and Tajima's D with 100 Kb sliding windows and 10 Kb step size for each population pair separately.\u003c/p\u003e\u003cp\u003eTo identify candidate regions under selective sweeps, we applied a multi-step filtering strategy. Initially, we selected genomic windows containing at least five single nucleotide polymorphisms (SNPs), suggesting potential regions of interest. We then evaluated genetic differentiation (Fst) between White Leghorn and indigenous chicken breeds, retaining windows that ranked in the top 5% of Fst values. From these, we focused on windows shared in at least 50% of the pairwise comparisons between Leghorn and local breeds. Next, we analyzed nucleotide diversity and Tajima\u0026rsquo;s D by comparing the values of θπ and Tajima\u0026rsquo;s D between indigenous and White Leghorn chickens. We selected windows falling within the top and bottom 2.5% of the distributions of log₂ (θπ_indigenous / θπ_WhiteLeghorn) and (Tajima\u0026rsquo;s D_indigenous \u0026ndash; Tajima\u0026rsquo;s D_WhiteLeghorn), respectively. Finally, we refined the analysis by including only those windows shared across at least 50% of the indigenous populations for both nucleotide diversity and Tajima\u0026rsquo;s D metrics.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAnnotation of genetic variants\u003c/h3\u003e\n\u003cp\u003eGenomic regions selected as candidate sweeps based on FST, log₂ (θπ_indigenous / θπ_WhiteLeghorn) and (Tajima\u0026rsquo;s D_indigenous \u0026ndash; Tajima\u0026rsquo;s D_WhiteLeghorn) values were annotated using the GRCg7a genome reference (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ftp.ensembl.org/pub/release113/gtf/gallus_gallus/Gallus_gallus.bGalGal1.mat.broiler.GRCg7b.113.gtf.gz\u003c/span\u003e\u003cspan address=\"https://ftp.ensembl.org/pub/release113/gtf/gallus_gallus/Gallus_gallus.bGalGal1.mat.broiler.GRCg7b.113.gtf.gz\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This annotation process, facilitated by the \"rtracklayer\"\u003csup\u003e27\u003c/sup\u003e and \"GenomicRanges\"\u003csup\u003e28\u003c/sup\u003e R packages (R version 4.2.3), and SnpEff \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e software enabled the identification of transcripts.\u003c/p\u003e\n\u003ch3\u003eCharacteristics of Runs of homozygosity (ROH)\u003c/h3\u003e\n\u003cp\u003eThis study examined runs of homozygosity (ROH), an indicator of genomic autozygosity, in 10 indigenous chicken populations and White Leghorn populations by analyzing extended segments of homozygous SNPs inherited from a recent common ancestor. ROH were calculated using PLINK v1.9 with the parameters: --homozyg, --homozyg-group, --homozyg-kb 300, --homozyg-window-snp 50, --homozyg-density 100, --homozyg-gap 1000, --homozyg-window-het 3, --homozyg-window-threshold 0.05, and --homozyg-window-missing 3.\u003c/p\u003e\u003cp\u003eThe inbreeding coefficient (F) was calculated for each individual and across each chicken group using VCFtools and PLINK\u0026rsquo;s \u0026ndash;het option. Detected ROHs were classified into three length categories: 0 to \u0026lt;\u0026thinsp;1 Mb, 1 to \u0026lt;\u0026thinsp;3 Mb, and \u0026gt;\u0026thinsp;3 Mb.\u003c/p\u003e\n\u003ch3\u003eGene set enrichment and pathway analysis\u003c/h3\u003e\n\u003cp\u003eTo identify candidate genes and nearly fixed alleles associated with heat tolerance, we focused on genes that were significant in at least three of the following analyses: Fst, θπ, Tajima\u0026rsquo;s D, and ROH. Heat tolerance-related candidate genes were further identified based on our previous findings\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, and genes listed in the NCBI database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/ge\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/ge\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We then constructed a protein-protein interaction (PPI) network for these common genes using the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to identify hub genes within the network. For gene ontology (GO) pathway enrichment analysis, we used the ClueGO plugin in Cytoscape, considering GO terms and pathways as significantly enriched if they had Bonferroni-corrected p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003ch3\u003eGenotype Extraction and Allele Frequency Analysis\u003c/h3\u003e\n\u003cp\u003eSNP data for 14 target genes were extracted from a VCF file using R (version 4.2.2), with packages vcfR\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, dplyr\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, tidyr\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, and readr\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Allele frequencies were calculated separately for the indigenous and White Leghorn populations. For each SNP, the counts of reference and alternative alleles were obtained, and fixation status was determined. Fisher\u0026rsquo;s exact test was applied to assess significant differences in allele frequencies between populations, and p-values were adjusted using false discovery rate (FDR) correction.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003ePCA analysis of White Leghorn and indigenous chickens\u003c/h2\u003e\u003cp\u003eThe dataset included 51 commercial White Leghorn chickens and indigenous chickens selected from locations with a (THI) above 72, which is commonly regarded as the threshold at which heat stress begins to affect poultry. Each selected group contained 10 or more samples, originating from Pakistan (n\u0026thinsp;=\u0026thinsp;25), Shiraz (n\u0026thinsp;=\u0026thinsp;16), Zahedan (n\u0026thinsp;=\u0026thinsp;16), Afghanistan (n\u0026thinsp;=\u0026thinsp;11), Bali (n\u0026thinsp;=\u0026thinsp;15), Huaixiang (n\u0026thinsp;=\u0026thinsp;10), Huiyang (n\u0026thinsp;=\u0026thinsp;10), Huaibeima (n\u0026thinsp;=\u0026thinsp;10), Jianghan (n\u0026thinsp;=\u0026thinsp;10), and Wuhua (n\u0026thinsp;=\u0026thinsp;20). Additional details on these samples are provided in \u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e. After excluding variants with low quality, high missing rates, and low frequencies, a total of 15,325,788 SNPs were retained for analysis across the populations.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePopulation structure\u003c/h3\u003e\n\u003cp\u003eThe PCA analysis indicated that the populations, including Afghanistan, Bali, Huaibei-Partridge-chicken, Huaixiang, Huiyang-Bearded, Jianghan, Pakistan, Shiraz, White-Leghorn, Wuhua-Yellow, and Zahedan, exhibited distinct genetic clustering patterns along the first two principal components (PC1 and PC2), which accounted for 26.86% and 10.20% of the total variance, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Indigenous chickens demonstrated an east-west cline in relatedness along PC2, highlighting genetic differentiation between eastern (Chinese) indigenous chickens vs chickens from Afghanistan, Iran and Pakistan. The commercial White Leghorn chickens formed a distinct cluster that was separated from all indigenous breeds along PC1, suggesting substantial differentiation between commercial and indigenous chickens.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eGenome‑wide selective sweep analysis\u003c/h2\u003e\u003cp\u003eTo identify potential selective sweeps, we analyzed the genome for regions with high differentiation levels between White Leghorn compared to each indigenous chicken population separately. We identified 3,042 genomic windows with Fst values in the top 5% in at least 50% of the pairwise comparisons between White Leghorn and each indigenous chicken population (\u003cb\u003esee Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). We further selected genomic windows in the top and bottom 2.5% of the distributions of log₂ (θπ_indigenous / θπ_WhiteLeghorn) and (Tajima\u0026rsquo;s D_indigenous \u0026ndash; Tajima\u0026rsquo;s D_WhiteLeghorn), retaining only those shared by at least 50% of the populations, which resulted in 5,932 (\u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/b\u003e) and 65,074 (\u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e\u003c/b\u003e) significant windows, respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eRuns of homozygosity (ROH)\u003c/h2\u003e\u003cp\u003eA total of 1,924 ROH segments were identified across all studied indigenous populations, while commercial population had 4,814 ROH segments (\u003cb\u003eSupplementary Information, Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e\u003c/b\u003e). Due to differing sample sizes between the populations, direct comparison of the absolute number of ROH segments is not appropriate. The identified ROH segments were also classified into three groups based on their length: less than 1 Mb, between 1\u0026ndash;2 Mb, and more than 3 Mb. Most ROH segments fell into the medium category, the proportion of long ROH segments (\u0026gt;\u0026thinsp;3 Mb) was individually assessed in all ten populations, with values ranging between 68% and 100%. The total number of ROH segments identified in Iran (Shiraz and Zahedan), Afghanistan, Indonesia (Bail), China (Huaixiang, Huiyang, Huaibeima, Jianghan, Wuhua), and Pakistan chicken populations were 310, 281, 242, 311, 16, 10, 38, 71, 40 and 605, respectively. The number of ROH segments exceeding 3 Mb in length in Afghanistan, Bali, Huaibeima, Jianghan, Pakistan, Shiraz, Wuhua and Zahedan chickens was 43, 38, 2, 4, 70, 19, 1, and 28, respectively. We then identified genes in ROH regions for each breed, allowing genes in ROH common to different breeds to be identified, as well as genes that overlap with significant loci in Fst, Tajima\u0026rsquo;s D, and θπ analyses, and with our previously identified list of heat tolerance\u0026ndash;related genes. Genomic inbreeding coefficients based on ROH (FROH) and individual heterozygosity were estimated separately for each population (\u003cb\u003eSupplementary information, Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003ePotential genes, gene set enrichment and pathways analysis in related with heat tolerance.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAfter identifying selective signatures between White Leghorn and indigenous chicken groups, candidate regions were detected using four statistical methods: Fst, Tajima\u0026rsquo;s D, θπ, and ROH. These regions were annotated using the \"rtracklayer\" and \"GenomicRanges\" packages in R version 4.2.3. Additionally, annotated protein coding genes consistently identified across these methods for each indigenous population by JVenn\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e are shown in \u003cb\u003eSupplementary information, Table \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e and\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOur methods identified numerous candidate genes within high-confidence selected regions potentially linked to heat tolerance, building on our previous findings\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e and aligning with heat tolerance-associated genes listed in the NCBI database. We identified 267 protein-coding genes that overlap with these selective signature regions. Of the total, 126 genes had been identified in our previous studies, with comprehensive details and expression profiles available in \u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e\u003c/b\u003e. To investigate the interactions among 113 candidate genes\u0026mdash;selected based on ROH and at least two additional methods (Fst, Tajima\u0026rsquo;s D, and θπ)\u0026mdash;we used the STRING database for interaction analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and conducted functional enrichment using the ClueGO plugin in Cytoscape (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGene set enrichment analysis revealed several significantly enriched terms associated with tolerance to heat stress. Among these, \u0026ldquo;Hsp70 protein binding\u0026rdquo; was identified within the molecular function category, while terms such as \u0026ldquo;calcium-mediated signaling,\u0026rdquo; \u0026ldquo;response to temperature stimulus,\u0026rdquo; \u0026ldquo;positive regulation of actin filament polymerization,\u0026rdquo; \u0026ldquo;calcium ion transport,\u0026rdquo; \u0026ldquo;calcium ion transmembrane transport,\u0026rdquo; and \u0026ldquo;calcium ion transmembrane transporter activity\u0026rdquo; were significantly enriched within the biological process category. \u003cb\u003e(Supplementary information, Table \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003e)\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eFunctional enrichment analysis\u003c/h2\u003e\u003cp\u003eWe identified 14 genes under selection that are significantly associated with GO terms involved in heat stress, as detailed in \u003cb\u003eSupplementary Table\u0026nbsp;8\u003c/b\u003e. These genes include CDH23, NPSR1, MCU, TRPV2, TRPV1, TRPV3, ATP2B4, CALM1, CACNB2, TRAT1, BDNF, SCIN, WIPF3, and PRKD1.\u003c/p\u003e\u003cp\u003eThe enrichment analysis revealed significant involvement of two KEGG pathways: Adrenergic signaling in cardiomyocytes (p\u0026thinsp;=\u0026thinsp;1.70 \u0026times; 10⁻\u0026sup2;) and the calcium signaling pathway (p\u0026thinsp;=\u0026thinsp;5.10 \u0026times; 10⁻\u0026sup2;). As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, several of the genes identified in our study are mapped onto the adrenergic signaling pathway. Notably, CALM1 (calmodulin, CaM) and ATP2B4 (PMCA) are directly involved in calcium regulation and signal transduction within cardiomyocytes. CALM1 activates downstream targets such as CaMKII, while ATP2B4 facilitates calcium efflux. Although not shown in the pathway diagram, TRPV1, TRPV2, and TRPV3 are thermosensitive calcium channels known to participate in the calcium signaling pathway and contribute to heat stress response. PRKD1 is also connected to PKC signaling downstream of adrenergic activation. Additional genes such as BDNF and CACNB2, while not visualized in this diagram, are associated with neurotrophic and calcium channel functions. Genes including MCU, CDH23, NPSR1, TRAT1, SCIN, and WIPF3 are not represented in the current KEGG pathway map.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAnalysis of the calcium signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) revealed the involvement of several key genes, including CDH23, NPSR1, MCU, TRPV1, TRPV2, TRPV3, ATP2B4, CALM1, CACNB2, TRAT1, BDNF, SCIN, WIPF3, and PRKD1, which contribute to distinct downstream cellular processes. MCU and ATP2B4 regulate mitochondrial and cytoplasmic calcium homeostasis, influencing apoptosis and energy metabolism. CALM1, a central calcium sensor, mediates calcium-dependent activation of signaling molecules such as CaMK and CaN, linking calcium influx to memory, learning, and synaptic plasticity. Ion channels TRPV1\u0026ndash;3 and CACNB2 modulate calcium entry in response to depolarization or sensory stimuli, which are critical for neuronal excitability and neurotransmitter release. BDNF expression, regulated by calcium signaling, was implicated in synaptic strengthening and neuroplasticity. PRKD1 acts downstream to regulate secretion and cell survival pathways. Immune-related gene TRAT1 and receptor NPSR1 mediate calcium-dependent activation in immune and stress responses. Structural regulators SCIN and WIPF3 may facilitate actin remodeling and exocytosis in response to calcium flux. Collectively, the activation of these genes, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, underscores the central role of calcium signaling in orchestrating diverse biological processes such as neurotransmission, immune activation, cellular secretion, and gene expression, all of which contribute to the cellular response and adaptation to heat stress\u003csup\u003e\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eThe fixation of alleles in candidate genes associated with heat tolerance\u003c/h2\u003e\u003cp\u003eAfter detection of 14 selected genes associated with heat tolerance, we focused on putatively functional variants in these genes. To assess the genotype distribution and fixation patterns between the indigenous and White Leghorn populations, we analyzed the frequencies of homozygous reference (0/0), heterozygous (0/1), and homozygous alternate (1/1) genotypes. The differential analysis revealed decreases or increases in the percentage of 1/1 or 0/0 genotypes in the White Leghorns compared to the native population, or vice versa. These findings highlight substantial changes that likely reflect underlying genetic and selective pressures acting on the two populations. We discovered 732 SNPs that showed near-complete fixation across all comparisons. Among these, 721 were uniquely fixed in the White Leghorn population, while only 11 were nearly fixed in the indigenous populations. The majority of these indigenous-specific SNPs were situated in intronic regions, though a few exhibited coding variants. The allele frequency distribution for each population is provided in \u003cb\u003eSupplementary Information, Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e. Notably, the SNP rs3386031003 at position 5:3943152 is a missense mutation, while rs3386050400 at position 5:3952604 is an upstream gene variant; both are situated within the BDNF gene. Additional details are provided in \u003cb\u003eSupplementary Information, Table \u003cspan refid=\"MOESM9\" class=\"InternalRef\"\u003eS9\u003c/span\u003e\u003c/b\u003e. We identified a set of SNPs that were fixed in over 70% of pairwise comparisons between White Leghorn and several indigenous populations. Notably, the SNP rs1060165215 located at position 5:33441126, a missense variant in the PRKD1 gene, was fixed in the following populations, along with their respective FDR-adjusted p-values: Afghanistan (p\u0026thinsp;=\u0026thinsp;3.0163E-05), Bali (p\u0026thinsp;=\u0026thinsp;9.4671E-07), Huaibei (p\u0026thinsp;=\u0026thinsp;7.43893E-05), Huaixiang (p\u0026thinsp;=\u0026thinsp;7.92188E-05), Huiyang (p\u0026thinsp;=\u0026thinsp;8.11157E-05), Jianghan (p\u0026thinsp;=\u0026thinsp;8.5849E-05), Shiraz (p\u0026thinsp;=\u0026thinsp;5.17514E-07), Wuhua (p\u0026thinsp;=\u0026thinsp;6.32765E-09), and Zahedan (p\u0026thinsp;=\u0026thinsp;5.36687E-08). Additionally, the SNP rs3387803766 at position 5:33330346, located in the splice region in intron 16 of PRKD1, was fixed in Afghanistan (p\u0026thinsp;=\u0026thinsp;2.46038E-05), Bali (p\u0026thinsp;=\u0026thinsp;7.82256E-07), Huaibei (p\u0026thinsp;=\u0026thinsp;7.30574E-05), Huaixiang (p\u0026thinsp;=\u0026thinsp;7.77693E-05), Huiyang (p\u0026thinsp;=\u0026thinsp;7.99212E-05), Jianghan (p\u0026thinsp;=\u0026thinsp;8.45744E-05), Wuhua (p\u0026thinsp;=\u0026thinsp;2.96E-09), and Pakistan (p\u0026thinsp;=\u0026thinsp;7.88122E-12). Detailed results are provided in \u003cb\u003eSupplementary Information, Table \u003cspan refid=\"MOESM10\" class=\"InternalRef\"\u003eS10\u003c/span\u003e\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we identified candidate heat tolerance associated genes under selection. These include CDH23, NPSR1, MCU, TRPV2, TRPV1, TRPV3, ATP2B4, CALM1, CACNB2, TRAT1, BDNF, SCIN, WIPF3, and PRKD1. Selected genes were identified based on ROH and at least two of three population genetic metrics: Fst, Tajima\u0026rsquo;s D, and θπ. This combined approach provides strong evidence of adaptive evolution in response to environmental heat stress\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eGO enrichment analysis showed that these candidate genes cluster significantly in three biological themes: calcium signaling, thermosensation, and neurodevelopmental plasticity. These processes are key for cellular and behavioral adaptation to heat stress. Calcium-related processes, including calcium ion transport (GO:0006816) and calcium-mediated signaling (GO:0019722), were enriched. Also ATP2B4, MCU, CALM1, and TRPV1\u0026ndash;3, play central roles in these pathways. This highlights an evolutionarily conserved mechanism that maintains calcium balance under heat stress. Experimental studies support these findings. For example, ATP2B4 (PMCA4) is crucial for calcium homeostasis during hyperthermia\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, while MCU regulates mitochondrial calcium uptake and heat-induced apoptosis\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe TRPV ion channels, especially TRPV1, TRPV2, and TRPV3, are key thermosensors under selection. TRPV1 activates at approximately 43\u0026deg;C\u003csup\u003e48\u003c/sup\u003e. It enhances peripheral heat sensation and mediates central cooling in the medial preoptic area )mPOA(\u003csup\u003e49\u003c/sup\u003e. Its role has been demonstrated in desert rodents, which show altered thermosensitivity\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. In mice, TRPV1 activation boosts antioxidant defenses and reduces inflammation under heat stress\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. TRPV2 responds to temperatures above 52\u0026deg;C, detecting extreme heat\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e, while TRPV3 is active at 33\u0026ndash;39\u0026deg;C in keratinocytes. It links thermal signals to calcium pathways, induces HSP expression via calmodulin/CaMK, and supports skin repair\u003csup\u003e\u003cspan additionalcitationids=\"CR54 CR55 CR56\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCALM1 plays a key role in calcium signaling activating kinases such as CaMKs and phosphatases like calcineurin following heat-induced calcium influx. These enzymes regulate heat shock protein expression, inhibit apoptosis, and stabilize intracellular proteins\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. Together, these actions enhance cellular resilience to heat stress.\u003c/p\u003e\u003cp\u003eGenes such as BDNF, CACNB2, PRKD1, and TRAT1 also contribute to the broader stress adaptation framework. Although BDNF is not directly annotated in KEGG pathways, it plays a well-established role in calcium-dependent synaptic plasticity and neuroprotection, acting through complex signaling networks beyond those currently captured by KEGG databases\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. PRKD1 acts downstream of PKC\u0026micro; to regulate α-catenin phosphorylation and maintain endothelial barrier integrity in response to IL-33 under hypoxic conditions\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. TRAT1 and NPSR1 participate in immune-related calcium signaling, which may connect heat stress to inflammatory and immune pathways\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. SCIN and WIPF3, as cytoskeletal regulators, might facilitate calcium-dependent actin remodeling and vesicle trafficking, thereby enhancing cellular adaptability\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e,\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTaken together, these findings show that calcium signaling is a central integrative axis in the heat stress response\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e \u0026ndash; it links thermosensory input from TRPV channels, calcium homeostasis mediated by ATP2B4, MCU, and CACNB2, and downstream signaling cascades involving CALM1 and PRKD1. These pathways promote protective cellular functions such as protein stabilization, apoptosis inhibition, and immune regulation\u003csup\u003e\u003cspan additionalcitationids=\"CR66 CR67\" citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. The overlap of positive selection signals and functional enrichment within this network provides strong evidence for evolutionary adaptation aimed at maintaining physiological homeostasis under high-temperature conditions\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn White Leghorn chickens, many alleles related to thermosensation, calcium signaling, and cellular stress responses were fixed or nearly fixed (AF \u0026cong; 1). In contrast, these variants were rare or absent in indigenous populations (AF\u0026thinsp;=\u0026thinsp;0 to 0.08). For instance, we identified fixed variants located in intronic and downstream regions of the TRAT1 gene. Similarly, intronic and upstream variants in CACNB2\u0026mdash;a gene involved in calcium channel activity and neural development\u0026mdash;were fixed in both Leghorn and indigenous chicken breeds. In Leghorns, fixed variants were also found in the intron, downstream region, and 3' UTR of WIPF3, which is associated with actin cytoskeleton organization and stress response, as well as intronic variants in NPSR1, a gene linked to neuropeptide signaling and immune system regulation. Fixed variants in CDH23, a gene essential for mechanosensation, were detected across several genomic regions, including intronic, splice sites, synonymous positions, upstream, downstream, and the 3' UTR. We also observed variants in the intronic, downstream, and 3' UTR regions of PRKD1, which may influence the heat shock response. Moreover, a fixed variant in MCU\u0026mdash;present in the 3' UTR, 5' UTR, upstream area, intron, and including a missense mutation\u0026mdash;was found in Leghorns; MCU plays a critical role in regulating mitochondrial calcium uptake. The fixation of these alleles in Leghorns suggests that artificial selection targeting productivity traits might have reduced thermotolerance. Conversely, fixed intronic variants were identified in CACNB2, TRPV3, and PRKD1, alongside missense and upstream variants in BDNF in indigenous chickens. These native breeds appear to have retained more ancestral alleles, likely due to natural adaptation to hot and humid climates. Collectively, these genes stand out as promising candidates for functional studies and selective breeding to improve heat tolerance in commercial poultry.\u003c/p\u003e\u003cp\u003eIntronic variants, particularly those located near splice sites, have recently gained significant attention as key regulators of gene expression and post-transcriptional modifications. These variants can induce events such as intronic polyadenylation (IPA), intron retention, or splice site alterations, leading to the production of alternative RNA isoforms and ultimately altering gene expression\u0026mdash;without necessarily affecting coding regions. While the functional roles of intronic variants, including intronic polyadenylation (IPA), have been well-documented in human cancer and economically important traits in cattle \u003csup\u003e\u003cspan additionalcitationids=\"CR71 CR72\" citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e, no studies to date have explored their role in the heat stress response in chickens despite the fact that heat stress represents one of the most critical economic and biological challenges in the global poultry industry. In this study, we identify for the first time intronic variants including ones in heat-responsive genes such as WIPF3, TRAT1, and CACNB2, which may influence gene expression through mechanisms such as intronic polyadenylation (IPA) or splice site alterations. Given the strong conservation of RNA processing mechanisms across vertebrates, our findings provide a foundation for future functional investigations (e.g., minigene assays) to assess the direct role of these variants in modulating heat stress responses in indigenous chicken breeds.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur findings underscore the potential functional relevance of regulatory variants\u0026mdash;particularly intronic ones\u0026mdash;in shaping the transcriptional response to heat stress in chickens. This study is the first to highlight the possible involvement of non-coding variants in thermal adaptation in poultry. Candidate genes such as PRKD1, CDH23, WIPF3, TRAT1, and CACNB2 harbor variants that may act through mechanisms like intronic polyadenylation or splicing disruption. These discoveries lay the groundwork for future experimental validation and breeding strategies aimed at enhancing heat tolerance in indigenous breeds. Further functional assays will be crucial to unravel the exact molecular pathways these variants modulate under thermal stress.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003cp\u003eAll data used in the current work were provided by the Chicken Genomic Diversity consortium\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. No samples were taken and no sequence data were generated as part of the present study.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eConsent for publication\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cp\u003eAll authors declare that they have no competing interests for this work.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eS.R.F received support through the UKRI Biotechnology and Biological Sciences Research Council (BBSRC) Institute Strategic Programme and National Bioscience Research Infrastructure grants to the Pirbright Institute: BBS/E/PI/230001A, BBS/E/PI/230001C and BBS/E/PI/23NB0003.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eS.H. performed all data analyses and wrote the first draft of the manuscript. S.R.F oversaw the analysis and manuscript writing, which was also reviewed and edited by all authors. S.R.F, A.L.S., P.B., M.C. and C.K. prepared the data in their roles as leaders and members of the Chicken Genomic Diversity Consortium. S.A.R, A.J. and K.H provided analytical and editorial assistance.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe gratefully acknowledge the work of Prof. Laurent Frantz in setting up the Chicken Genomic Diversity Consortium dataset.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data used in this study are publicly available from online repositories. Table S1 contains accession numbers for all samples.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSt-Pierre, N. R., Cobanov, B. \u0026amp; Schnitkey, G. Economic losses from heat stress by US livestock industries. \u003cem\u003eJ. Dairy. Sci.\u003c/em\u003e \u003cb\u003e86\u003c/b\u003e, E52\u0026ndash;E77 (2003).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang, Y. R., Hessen, D. O., Samset, B. H. \u0026amp; Stordal, F. Evaluating global and regional land warming trends in the past decades with both MODIS and ERA5-Land land surface temperature data. \u003cem\u003eRemote Sens. Environ.\u003c/em\u003e \u003cb\u003e280\u003c/b\u003e, 113181 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin, H., Jiao, H. C., Buyse, J. \u0026amp; Decuypere, E. Strategies for preventing heat stress in. \u003cb\u003e62\u003c/b\u003e, 71\u0026ndash;86 (2006).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLallo, C. H. O. et al. Characterizing heat stress on livestock using the temperature humidity index (THI)\u0026mdash;prospects for a warmer Caribbean. \u003cem\u003eReg. Environ. Chang.\u003c/em\u003e \u003cb\u003e18\u003c/b\u003e, 2329\u0026ndash;2340 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHabeeb, A. A., Gad, A. E. \u0026amp; Atta, M. A. Temperature-humidity indices as indicators to heat stress of climatic conditions with relation to production and reproduction of farm animals. \u003cem\u003eInt. J. Biotechnol. Recent. Adv.\u003c/em\u003e \u003cb\u003e1\u003c/b\u003e, 35\u0026ndash;50 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLoyau, T. et al. Thermal manipulation of the embryo modifies the physiology and body composition of broiler chickens reared in floor pens without affecting breast meat processing quality. \u003cem\u003eJ. Anim. Sci.\u003c/em\u003e \u003cb\u003e91\u003c/b\u003e, 3674\u0026ndash;3685 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrugaletta, G., Teyssier, J. R., Rochell, S. J., Dridi, S. \u0026amp; Sirri, F. A review of heat stress in chickens. Part I: Insights into physiology and gut health. \u003cem\u003eFront Physiol\u003c/em\u003e 1535 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAhmad, R. et al. Influence of heat stress on poultry growth performance, intestinal inflammation, and immune function and potential mitigation by probiotics. \u003cem\u003eAnimals\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 2297 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQaid, M. M. \u0026amp; Al-Garadi, M. A. Protein and amino acid metabolism in poultry during and after heat stress: a review. \u003cem\u003eAnimals\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 1167 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChauhan, S. S., Rashamol, V. P., Bagath, M., Sejian, V. \u0026amp; Dunshea, F. R. Impacts of heat stress on immune responses and oxidative stress in farm animals and nutritional strategies for amelioration. \u003cem\u003eInt. J. Biometeorol.\u003c/em\u003e \u003cb\u003e65\u003c/b\u003e, 1231\u0026ndash;1244 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEtches, R. J., John, T. M. \u0026amp; Gibbins, A. M. V. Behavioural, physiological, neuroendocrine and molecular responses to heat stress. in \u003cem\u003ePoultry Prod. hot climates\u003c/em\u003e 48\u0026ndash;79 (2008). (CABI Wallingford UK.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChowdhury, V. S. et al. Potential role of amino acids in the adaptation of chicks and market-age broilers to heat stress. \u003cem\u003eFront. Vet. Sci.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e, 610541 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKhosravinia, H. Mortality, production performance, water intake and organ weight of the heat stressed broiler chicken given savory (Satureja khuzistanica) essential oils through drinking water. \u003cem\u003eJ. Appl. Anim. Res.\u003c/em\u003e \u003cb\u003e44\u003c/b\u003e, 273\u0026ndash;280 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBuffalo, V. \u0026amp; Coop, G. Estimating the genome-wide contribution of selection to temporal allele frequency change. \u003cem\u003eProc. Natl. Acad. Sci.\u003c/em\u003e 117, 20672\u0026ndash;20680 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDadshani, S., Mathew, B., Ballvora, A., Mason, A. S. \u0026amp; L\u0026eacute;on, J. Detection of breeding signatures in wheat using a linkage disequilibrium-corrected mapping approach. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 5527 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi, X. et al. Runs of homozygosity and selection signature analyses reveal putative genomic regions for artificial selection in layer breeding. \u003cem\u003eBMC Genom.\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e, 638 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCharlesworth, B. \u0026amp; Jensen, J. D. Effects of selection at linked sites on patterns of genetic variability. \u003cem\u003eAnnu. Rev. Ecol. Evol. Syst.\u003c/em\u003e \u003cb\u003e52\u003c/b\u003e, 177\u0026ndash;197 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShi, H. et al. Whole genome sequencing revealed genetic diversity, population structure, and selective signature of Panou Tibetan sheep. \u003cem\u003eBMC Genom.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e, 50 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKonopiński, M. K., Fijarczyk, A. M. \u0026amp; Biedrzycka, A. Complex patterns shape immune genes diversity during invasion of common raccoon in Europe\u0026ndash;Selection in action despite genetic drift. \u003cem\u003eEvol. Appl.\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e, 134\u0026ndash;151 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYe, S., Song, H., Ding, X., Zhang, Z. \u0026amp; Li, J. Pre-selecting markers based on fixation index scores improved the power of genomic evaluations in a combined Yorkshire pig population. \u003cem\u003eAnimal\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 1555\u0026ndash;1564 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFathi, M. M., Galal, A., Radwan, L. M., Abou-Emera, O. K. \u0026amp; Al-Homidan, I. H. Using major genes to mitigate the deleterious effects of heat stress in poultry: An updated review. \u003cem\u003ePoult. Sci.\u003c/em\u003e \u003cb\u003e101\u003c/b\u003e, 102157 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNo \u0026amp; Title.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNo Title. National Oceanic and Atmospheric Administration. Livestock Hot Weather Stress. Operations Manual Letter C-31-76, Department of Commerce, NOAA, National Weather Service Central Region, Kansas City;.. (1976).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChang, Y. C. et al. Differential expression patterns of housekeeping genes increase diagnostic and prognostic value in lung cancer. \u003cem\u003ePeerJ\u003c/em\u003e 1\u0026ndash;17 (2018). (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlexander, D. H., Novembre, J. \u0026amp; Lange, K. Fast model-based estimation of ancestry in unrelated individuals. \u003cem\u003eGenome Res.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e, 1655\u0026ndash;1664 (2009).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePfeifer, B. An introduction to the PopGenome package. at (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLawrence, M., Gentleman, R. \u0026amp; Carey, V. rtracklayer: an R package for interfacing with genome browsers. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e, 1841 (2009).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLawrence, M. et al. Software for computing and annotating genomic ranges. \u003cem\u003ePLoS Comput. Biol.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, e1003118 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLu, X. \u0026amp; Ruden, D. M. A program for annotating and predicting the efects of single nucleotide polymorphisms, SnpEf: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHosseinzadeh, S. \u0026amp; Hasanpur, K. Gene expression networks and functionally enriched pathways involved in the response of domestic chicken to acute heat stress. \u003cem\u003eFront. Genet.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 699 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHosseinzadeh, S. \u0026amp; Hasanpur, K. Whole genome discovery of regulatory genes responsible for the response of chicken to heat stress. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 6544 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKnaus, B. J. \u0026amp; Gr\u0026uuml;nwald, N. J. vcfr: a package to manipulate and visualize variant call format data in R. \u003cem\u003eMol. Ecol. Resour.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e, 44\u0026ndash;53 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWickham, H. \u0026amp; dplyr A grammar of data manipulation. \u003cem\u003eR Packag version\u003c/em\u003e. \u003cb\u003e04\u003c/b\u003e (3), 156 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWickham, H., Vaughan, D. \u0026amp; Girlich M. tidyr: tidy messy data. R package version 1.3. 1. at (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWickham, H. et al. Package \u0026lsquo;readr\u0026rsquo;. \u003cem\u003eRead Rectangular Text Data. Available online https//cran. r\u003c/em\u003e (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e-project\u003c/span\u003e\u003cspan address=\"http://-project\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. \u003cem\u003eorg/web/packages/readr/readr\u003c/em\u003e. \u003cem\u003epdf (accessed 23 August\u003c/em\u003e) (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSawamura, H., Kiyozuka, K. \u0026amp; JVenn A visual reasoning system with diagrams and sentences. in \u003cem\u003eInternational Conference on Theory and Application of Diagrams\u003c/em\u003e 271\u0026ndash;285Springer, (2000).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKanehisa, M., Furumichi, M., Sato, Y., Matsuura, Y. \u0026amp; Ishiguro-Watanabe, M. KEGG: biological systems database as a model of the real world. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e53\u003c/b\u003e, D672\u0026ndash;D677 (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKanehisa, M. Toward understanding the origin and evolution of cellular organisms. \u003cem\u003eProtein Sci.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e, 1947\u0026ndash;1951 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKanehisa, M. \u0026amp; Goto, S. K. E. G. G. Kyoto Encyclopedia of Genes and Genomes. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e, 27\u0026ndash;30 (2000).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShi, T. et al. Single cell transcriptome sequencing indicates the cellular heterogeneity of small intestine tissue in celiac disease. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 12385 (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVendelin, J. et al. Downstream target genes of the neuropeptide S\u0026ndash;NPSR1 pathway. \u003cem\u003eHum. Mol. Genet.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 2923\u0026ndash;2935 (2006).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYao, X. et al. Hsp90 protected chicken primary myocardial cells from heat-stress injury by inhibiting oxidative stress and calcium overload in mitochondria. \u003cem\u003eBiochem. Pharmacol.\u003c/em\u003e \u003cb\u003e209\u003c/b\u003e, 115434 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePassamonti, M. M. et al. The quest for genes involved in adaptation to climate change in ruminant livestock. \u003cem\u003eAnimals\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 2833 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAsadollahpour Nanaei, H., Kharrati-Koopaee, H. \u0026amp; Esmailizadeh, A. Genetic diversity and signatures of selection for heat tolerance and immune response in Iranian native chickens. \u003cem\u003eBMC Genom.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e, 224 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKing, A. M. \u0026amp; MacRae, T. H. Insect heat shock proteins during stress and diapause. \u003cem\u003eAnnu. Rev. Entomol.\u003c/em\u003e \u003cb\u003e60\u003c/b\u003e, 59\u0026ndash;75 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChandan, K., Gupta, M., Ahmad, A. \u0026amp; Sarwat, M. P-type calcium ATPases play important roles in biotic and abiotic stress signaling. \u003cem\u003ePlanta\u003c/em\u003e \u003cb\u003e260\u003c/b\u003e, 37 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEnomoto, A. \u0026amp; Fukasawa, T. The role of calcium-calpain pathway in hyperthermia. \u003cem\u003eFront. Mol. Med.\u003c/em\u003e \u003cb\u003e2\u003c/b\u003e, 1005258 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eS\u0026aacute;nchez-Moreno, A. et al. Irreversible temperature gating in trpv1 sheds light on channel activation. \u003cem\u003eElife\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e, e36372 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLezama-Garc\u0026iacute;a, K. et al. Transient Receptor Potential (TRP) and thermoregulation in animals: Structural biology and neurophysiological aspects. \u003cem\u003eAnimals\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 106 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang, B. et al. Genetic Diversity of a Heat Activated Channel\u0026mdash;TRPV1 in Two Desert Gerbil Species with Different Heat Sensitivity. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e, 9123 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi, Z., Zhang, J., Cheng, K., Zhang, L. \u0026amp; Wang, T. Capsaicin alleviates the intestinal oxidative stress via activation of TRPV1/PKA/UCP2 and Keap1/Nrf2 pathways in heat-stressed mice. \u003cem\u003eJ. Funct. Foods\u003c/em\u003e. \u003cb\u003e108\u003c/b\u003e, 105749 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCaterina, M. J., Rosen, T. A., Tominaga, M. \u0026amp; Brake, A. J. Julius, D. A capsaicin-receptor homologue with a high threshold for noxious heat. \u003cem\u003eNature\u003c/em\u003e \u003cb\u003e398\u003c/b\u003e, 436\u0026ndash;441 (1999).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuo, Y. et al. Novel insights into the role of keratinocytes-expressed TRPV3 in the skin. \u003cem\u003eBiomolecules\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 513 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThiriet, M., Thiriet, M., Ion \u0026amp; Carriers \u003cem\u003eSignal Cell. Surf. Circ. Vent. Syst\u003c/em\u003e 89\u0026ndash;156 (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLei, J. \u0026amp; Tominaga, M. Unlocking the therapeutic potential of TRPV3: Insights into thermosensation, channel modulation, and skin homeostasis involving TRPV3. \u003cem\u003eBioEssays\u003c/em\u003e 46, 2400047 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eScott, V. E. et al. 534 Defining a mechanistic link between TRPV3 activity and psoriasis through IL-1α and EGFR signaling pathways. \u003cem\u003eJ. Invest. Dermatol.\u003c/em\u003e \u003cb\u003e136\u003c/b\u003e, S94 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSz\u0026ouml;llősi, A. G. et al. Activation of TRPV3 regulates inflammatory actions of human epidermal keratinocytes. \u003cem\u003eJ. Invest. Dermatol.\u003c/em\u003e \u003cb\u003e138\u003c/b\u003e, 365\u0026ndash;374 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKubik, R. M. Genomic investigation of beta agonist supplementation and heat stress in livestock species. (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRen, H. et al. Calcium signaling-mediated transcriptional reprogramming during abiotic stress response in plants. \u003cem\u003eTheor. Appl. Genet.\u003c/em\u003e \u003cb\u003e136\u003c/b\u003e, 210 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWant, A. A functional role for brain-derived neurotrophic factor from circulating blood platelets and potential neuroprotective applications. at (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChernyavskaya, Y., Ebert, A. M., Milligan, E. \u0026amp; Garrity, D. M. Voltage-gated calcium channel CACNB2 (β2. 1) protein is required in the heart for control of cell proliferation and heart tube integrity. \u003cem\u003eDev. Dyn.\u003c/em\u003e \u003cb\u003e241\u003c/b\u003e, 648\u0026ndash;662 (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSharma, D. et al. IL-33 via PKC\u0026micro;/PRKD1 mediated α-catenin phosphorylation regulates endothelial cell-barrier integrity and ischemia-induced vascular leakage. \u003cem\u003eCells\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 703 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang, K. et al. Autophagy regulation and protein kinase activity of PIK3C3 controls sertoli cell polarity through its negative regulation on SCIN (scinderin). \u003cem\u003eAutophagy\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e, 2934\u0026ndash;2957 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDe Luca, F., Kha, M., Sw\u0026auml;rd, K. \u0026amp; Johansson, M. E. Identification of ARMH4 and WIPF3 as human podocyte proteins with potential roles in immunomodulation and cytoskeletal dynamics. \u003cem\u003ePLoS One\u003c/em\u003e. \u003cb\u003e18\u003c/b\u003e, e0280270 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWei, Z. et al. Calcium induces death is associated with Pyroptosis and the anti-tumor immunity in breast cancer. (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eClucas, J. \u0026amp; Meier, P. Roles of RIPK1 as a stress sentinel coordinating cell survival and immunogenic cell death. \u003cem\u003eNat. Rev. Mol. Cell. Biol.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e, 835\u0026ndash;852 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePalkar, R., Lippoldt, E. K. \u0026amp; McKemy, D. D. The molecular and cellular basis of thermosensation in mammals. \u003cem\u003eCurr. Opin. Neurobiol.\u003c/em\u003e \u003cb\u003e34\u003c/b\u003e, 14\u0026ndash;19 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHasan, A. R. et al. The Alteration of Microglial Calcium Homeostasis in Central Nervous System Disorders: A Comprehensive Review. \u003cem\u003eNeuroglia\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e, 410\u0026ndash;444 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSajjanar, B., Krishnaswamy, N., Saxena, V. K. \u0026amp; Dhara, S. K. Stress Responses to Changing Environmental Factors in the Domestic Animals: An Epigenetic Perspective. \u003cem\u003eJ Anim. Physiol. Anim. Nutr. (Berl)\u003c/em\u003e (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuan, D. et al. Genetic regulation of gene expression across multiple tissues in chickens. \u003cem\u003eNat Genet\u003c/em\u003e 1\u0026ndash;11 (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShuli, L. et al. A multi-tissue atlas of regulatory variants in cattle. (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang, L. et al. Trans-ancestry transcriptome-wide association and functional studies to uncover novel susceptibility genes and therapeutic targets for colorectal cancer. \u003cem\u003enpj Precis Oncol.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 124 (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao, Z. et al. Comprehensive characterization of somatic variants associated with intronic polyadenylation in human cancers. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e49\u003c/b\u003e, 10369\u0026ndash;10381 (2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"Chicken, heat tolerance, selection, whole genome, adaptation","lastPublishedDoi":"10.21203/rs.3.rs-7262298/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7262298/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFollowing their domestication, chickens were translocated around the world to novel environments. Through a combination of natural and artificial selection, chickens adapted to these local conditions over the last few thousands of years, creating significant genetic diversity across populations worldwide. Studying this diversity in the context of local environmental conditions may offer insights into mechanisms of adaptation to environmental stressors. In this study, we analyzed genomic data from the Chicken Genomic Diversity Consortium, applying multiple statistical approaches, including fixation index (FST), nucleotide diversity (π), Tajima\u0026rsquo;s D, and runs of homozygosity (ROH), to identify selective sweeps among indigenous chickens from Afghanistan, China, Indonesia, Iran and Pakistan, compared with White Leghorn chickens. We identified sweeps in 14 genes related to heat tolerance, associated with relevant gene ontology (GO) terms and located within ROH regions. These genes, such as CDH23, NPSR1, MCU, TRPV2, TRPV1, TRPV3, ATP2B4, CALM1, CACNB2, TRAT1, BDNF, SCIN, WIPF3, PRKD1, and DNAJC10 play crucial roles in calcium signaling pathways, thermal sensation, and the plasticity of neurodevelopmental processes. These findings illustrate the significant role of selection in shaping genomic differentiation across chicken populations and provide insights into the genetic basis of adaptation to environmental stressors.\u003c/p\u003e","manuscriptTitle":"Whole genome analysis of selection associated with resistance to heat stress in chickens","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 19:50:09","doi":"10.21203/rs.3.rs-7262298/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-03T05:50:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-19T15:41:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-04T11:39:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-04T04:31:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"320157184861679379496172078423572859959","date":"2025-11-04T02:10:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"329272500615678386124221613462047005720","date":"2025-11-03T12:51:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"206667178508013809956962579890301952941","date":"2025-11-03T12:45:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-03T11:16:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-31T15:18:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-31T07:03:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-27T09:43:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-27T09:39:11+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":"94931917-4ea4-4543-bdf9-871e484570ec","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":57439896,"name":"Biological sciences/Evolution"},{"id":57439897,"name":"Biological sciences/Genetics"}],"tags":[],"updatedAt":"2026-04-13T16:00:50+00:00","versionOfRecord":{"articleIdentity":"rs-7262298","link":"https://doi.org/10.1038/s41598-026-41813-8","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-04-07 15:57:55","publishedOnDateReadable":"April 7th, 2026"},"versionCreatedAt":"2025-11-13 19:50:09","video":"","vorDoi":"10.1038/s41598-026-41813-8","vorDoiUrl":"https://doi.org/10.1038/s41598-026-41813-8","workflowStages":[]},"version":"v1","identity":"rs-7262298","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7262298","identity":"rs-7262298","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}