
==== Front
Poult Sci
Poult Sci
Poultry Science
0032-5791
1525-3171
Elsevier

S0032-5791(24)00718-1
10.1016/j.psj.2024.104139
104139
GENETICS AND MOLECULAR BIOLOGY
Whole-genome resequencing identifies candidate genes associated with heat adaptation in chickens
Bai Hao *
Zhao Ning *†
Li Xing *†
Ding Yifan *
Guo Qixin *
Chen Guohong *
Chang Guobin gbchang1975@yzu.edu.cn
*1
⁎ Joint International Research Laboratory of Agriculture and Agri-Product Safety, Institutes of Agricultural Science and Technology Development, The Ministry of Education of China, Yangzhou University, Yangzhou 225009, China
† School of Life Sciences, Southwest University, Chongqing 400715, China
1 Corresponding author: gbchang1975@yzu.edu.cn
31 7 2024
10 2024
31 7 2024
103 10 10413917 3 2024
25 7 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
The wide distribution and diverse varieties of chickens make them important models for studying genetic adaptation. The aim of this study was to identify genes that alter heat adaptation in commercial chicken breeds by comparing genetic differences between tropical and cold-resistant chickens. We analyzed whole-genome resequencing data of 186 chickens across various regions in Asia, including the following breeds: Bian chickens (B), Dagu chickens (DG), Beijing-You chickens (BY), and Gallus gallus jabouillei from China; Gallus gallus murghi from India; Vietnam native chickens (VN); Thailand native chickens (TN) and Gallus gallus spadiceus from Thailand; and Indonesia native chickens (IN), Gallus gallus gallus, and Gallus gallus bankiva from Indonesia. In total, 5,454,765 SNPs were identified for further analyses. Population genetic structure analysis revealed that each local chicken breed had undergone independent evolution. Additionally, when K = 5, B, BY, and DG chickens shared a common ancestor and exhibited high levels of inbreeding, suggesting that northern cold-resistant chickens are likely the result of artificial selection. In contrast, the runs of homozygosity (ROH) and the ROH-based genomic inbreeding coefficient (FROH) results for IN, TN, and VN chickens showed low levels of inbreeding. Low population differentiation index values indicated low differentiation levels, suggesting low genetic diversity in tropical chickens, implying increased vulnerability to environmental changes, decreased adaptability, and disease resistance. Whole-genome selection sweep analysis revealed 69 candidate genes, including LGR4, G6PC, and NBR1, between tropical and cold-resistant chickens. The genes were further subjected to GO and KEGG enrichment analyses, revealing that most of the genes were primarily enriched in biological synthesis processes, metabolic processes, central nervous system development, ion transmembrane transport, and the Wnt signaling pathway. Our study identified heat adaptation genes and their functions in chickens that primarily affect chickens in high-temperature environments through metabolic pathways. These heat-resistance genes provide a theoretical basis for improving the heat-adaptation capacity of commercial chicken breeds.

Key words

chicken
whole genome sequence
extreme environment
heat adaptation
genetic adaptation
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pmcINTRODUCTION

The most important and urgent issue of the 21st century is climate change (Cooper et al., 2008). In the past century, the increase in greenhouse gas levels has been the main factor contributing to climate change (Hertwich and Wood, 2018), and the increase in average surface temperature caused by climate change has led to increasingly frequent and intense extreme weather events (Mutua et al., 2020), posing a huge threat to animal husbandry. The heat stress caused by high temperatures (Mutua et al., 2020; Oke et al., 2021) not only has a negative impact on animal feeding, reproduction, growth, ketone bodies, meat quality, and health, but more importantly, it also affects economic revenue (Ross et al., 2017; Lacetera, 2019; Machado et al., 2021; Oladele et al., 2021).

Heat stress is one of the environmental factors that reduce the production performance and meat quality of poultry. It can cause overexpression of heat shock factors and heat shock proteins in chicken tissues. Heat shock proteins can regulate several molecular pathways in cells to respond to stress conditions, thereby altering the homeostasis of cells and tissues. This series of changes will affect the physiological function of tissues, thereby affecting the production capacity of chickens (Perini et al., 2021). In addition, due to the lack of sweat glands and the coverage of feathers throughout the body, chickens are particularly sensitive to high temperatures (Loyau et al., 2013). Under artificial selection, commercial chicken products focus more on genetic selection for growth rate and improving nutritional management (Zaboli et al., 2019), which significantly improves the production level of commercial chicken products. However, the temperature regulation system is unable to match conditions of rapid muscle growth and accelerated metabolism (Havenstein et al., 2003; Perini et al., 2021), which leads to the inability to adapt to fluctuations in ambient temperature (Settar et al., 1999; Deeb et al., 2002). In the future development of agriculture, it will be necessary to expand to more regions. However, in the face of environmental changes in different regions, commercial varieties with weak adaptability will obviously not be suitable (McMichael et al., 2007; Thornton et al., 2009; Rothschild et al., 2014). The poultry industry, especially chickens, as one of the most raised species in the world, holds a crucial position in both developed and developing countries (Perini et al., 2021). Faced with such a development situation, the expansion of the poultry industry is inevitable, and the problem that comes with it is how to improve the adaptability of commercial chicken products. Research has shown that modern commercial chickens perform excellently in cold adaptation (Xu et al., 2021), but have no advantage in heat adaptation compared to local chickens. On the contrary, local chickens exhibit excellent adaptability in many traits, such as growth, development, and reproduction (Dana et al., 2011; Haunshi et al., 2011). For example, Wenchang chicken can participate in the response to high temperature stress through sugar metabolism and energy metabolism (Shi et al., 2023). Therefore, how to improve the adaptability of commercial chicken breeds is a pressing issue.

In the present study, we used genomic sequencing data from 186 local chickens from temperate and tropical regions. Genes related to high-temperature adaptation were analyzed and selected by comparing tropical and cold-resistant chickens, providing a theoretical basis for further improving the high-temperature adaptability of commercial chicken breeds.

MATERIALS AND METHODS

Sample Collection

In this study, we downloaded 186 whole-genome resequencing data points from chickens from the Galbase database (http://animal.omics.pro/code/index.php/ChickenVar). The login number of this data is NCBI SRA: PMC9097087, including Bian chickens (B), Dagu chickens (DG), Beijing-You chickens (BY), and Gallus gallus jabouillei (GGJ) from China; Gallus gallus murghi (GGM) from India; Vietnam native chickens (VN); Thailand native chickens (TN) and Gallus gallus spadiceus (GGS) from Thailand; Indonesia native chickens (IN), Gallus gallus gallus (GGG), and Gallus gallus bankiva (GGB) from Indonesia (Figure 1A).Figure 1 Distribution map and SNP density plot of eleven chicken breeds. (A) Geographical changes in the average surface temperature of all chickens used in the study in January. (B) SNP density plot.

Figure 1

Population Structure Analysis

The collected raw data were further filtered using VCFtools (Danecek et al., 2011). Single nucleotide polymorphisms (SNP) were filtered based on a minor allele frequency >0.05, maximum allele frequency <0.99, and maximum missing rate <0.01. Neighbor-joining (NJ) tree, principal component analysis (PCA), and structural analysis were performed to explore genetic relationships. The NJ tree was constructed using the VCF2Dis software (https://github.com/BGI-shenzhen/VCF2Dis), and PCA analysis was performed using PLINK (Purcell et al., 2007). Additionally, 4 methods (t-SNE, PCA-t-SNE, uniform manifold approximation and projection (UMAP), and PCA-UMAP) were used for the dimensionality reduction of the SNP data (Sakaue et al., 2020). Initially, the t-SNE was performed using the multicore TSNE package in Python. Subsequently, t-SNE was applied to the first 50 principal components of the pruned genotypes as PCA-t-SNE. Subsequently, UMAP was executed on the genotype matrix using the UMAP package in Python (n components = 2). Finally, UMAP was applied to the first 50 principal components of the trimmed genotypes with n components = 2 and default parameters, referred to as PCA-UMAP. Moreover, the maximum likelihood method implemented in ADMIXTURE Version 1.3.0 (Alexander et al., 2009) was used to estimate the ancestors of each individual. The default parameters for cross-validation (fold = 2) and the lowest cross-validation error were considered the most likely K values.

Genetic Diversity, Linkage Disequilibrium Detection, and Homozygosity Analysis

VCFtools were used to calculate the nucleotide diversity for each population, with a window size of 50 kb and a step size of 20 kb. The linkage disequilibrium (LD) was computed using PopLDdecay (Zhang et al., 2019). For runs of homozygosity (ROH) detection, PLINK was used to trim the long homozygous segments in the data based on the following parameters: a window size of 50 SNPs (-homozyg-window-snp 50), an allowance for up to 5 missing SNP (-homozyg-window-missing 5), and a maximum of 3 heterozygous SNP per window (-homozyg-window-het 3). Additionally, the proportion of SNP included in the sliding window was specified to be at least 0.05 (-homozyg-window-threshold 0.05). Following the analysis, the length and average number of ROH for each population were estimated and categorized into 5 groups: ≤1 Mb, 1 to 2 Mb, 2 to 4 Mb, 4 to 6 Mb, and ≥6 Mb. The ROH-based genomic inbreeding coefficients (FROH) for each chicken was computed based on the ROH using the following formula: FROH = LROH/Ltotal (McQuillan et al., 2008).

Genome-Wide Selective Sweep Analysis

We used VCFtools to calculate the population differentiation index (FST) and nucleotide polymorphisms (π-ratio) between the cold-tolerant breeds (B, BY, and DG) and the tropical breeds (GGB, GGG, GGJ, GGM, GGS, IN, TN, and VN), with a window size of 50 kb and a step size of 20 kb. Subsequently, the top 1% of windows with the highest FST and π-ratio values were selected as candidate regions. The identified selective regions were annotated to the reference genome (GRCg6a), and genes located in selective regions were identified as candidate genes and submitted to KOBAS 3.0 for GO and KEGG analysis.

RESULTS AND DISCUSSION

SNP Distribution and Population Structure

We analyzed resequencing data from 186 chickens using NJ tree, PCA, and Bayesian clustering to assess the population structure and genetic relationships among different chicken populations. We obtained 5,454,765 SNP from these data, and all filtered SNP were distributed across 32 chromosomes, with an average density of 5676.1 SNP/Mb (Figure 1B). The NJ tree analysis showed that all individuals clustered together according to their breed, except for IN and TN (Figure 2A). We applied 5 dimensionality reduction methods to reveal the finest structure among the 186 individuals. Among these methods, the PCA plot displayed the first 2 components that explained 33.73% and 13.31% of the total variance. The results indicated significant differences between chickens from different regions, with the geographical area being the main factor contributing to these differences, suggesting genetic differentiation among different breeds of chickens due to temperature variations brought about by the geographical region. UMAP and PCA-UMAP clearly and discretely distinguished the Chinese cold-resistant chickens (B, BY, and DG) from the tropical chickens (GGB, GGG, GGJ, GGM, GGS, IN, TN, and VN). Studies have demonstrated a clear distinction between tropical and temperate chickens, indicating genetic variation between the 2 groups. Our research further validates these findings. However, significant differences were observed among different breeds of tropical chickens inhabiting the same tropical environment, which can be attributed to the different climate types within the tropical region. For example, GGJ is found in a subtropical monsoon climate, while GGM, GGS, and VN are found in tropical monsoon climates. In addition, TN inhabits a tropical rainforest climate, whereas IN, GGG, and GGB are found in tropical climates. Conversely, t-SNE showed individuals with continuous or adjacent distributions, making it difficult to distinguish them (Figure 2B). Based on the analysis of the population genetic differentiation index, the pairwise FST values of the 11 populations ranged from 0.02054 to 0.39078, with an average of 0.17362. The FST values between wild chickens (FST = 0.11006–0.37218, average 0.21101) were higher than those between domestic chickens (FST = 0.02054–0.14592, average 0.11085), indicating significant genetic differentiation between wild chickens and domestic chickens (Figure 2C). Previous studies have also indicated that domestic chickens and red junglefowl diverged before 8,093 years ago (range: 7,014–8,768 years) (Eda, 2021). The FST values of GGB, GGG, and GGM were higher than those of domestic chickens, whereas GGS showed a lower FST value, indicating lower differentiation. The result is consistent with the findings of previous studies, suggesting a close genetic relationship between G. g. spadiceus and domestic chicken populations (Wang et al., 2020). The FST values of B, BY, DG, IN, TN, and VN chickens were low, indicating low differentiation, consistent with the PCA results. To assess the historical hybridization patterns of chickens, an ADMIXTURE analysis was conducted. When K = 2, the gene flow between domestic chickens and wild chickens was identified. The optimal K value was 5, which could differentiate Chinese cold-resistant chickens (B, BY, and DG) from tropical chickens and the red-colored wild chickens (Figure 2D), revealing that B, BY, and DG chickens shared a common ancestor.Figure 2 Population genetic analyses. (A) NJ tree generated using polymorphisms detected in the 186 individual chickens. Each of the 11 breeds has been assigned a distinct color. (B) Using 5 dimensionality reduction methods to perform 2-dimensional graphical analysis of 186 individual genomic data: PCA; UMAP; t-SNE; PCA–UMAP; PCA-t-SNE. (C) Population divergence (FST) across the 11 groups. The values displayed in the heat map are FST values between breeds. (D) Genetic structure of samples from 186 individuals for K groups using the ADMIXTURE program. K is the number of presumed ancestral groups. The optimal K value was obtained with the least CV error value.

Figure 2

Genetic Diversity, LD, and ROH Analysis

To explore the genomic variation, linkage disequilibrium, and ROH patterns of 11 chicken populations, we analyzed π, LD, ROH size, average number of ROH, and FROH. The results showed that IN had the highest π value among domestic chickens (π = 0.001498), while B had the lowest π value (π = 0.001113). Among wild chickens, GGJ had the highest π value (π = 0.001405), while GGB had the lowest π value (π = 0.000537) (Figure 3A). Furthermore, whole-genome linkage disequilibrium significantly varied across populations. IN, GGM, and GGJ exhibited rapid decay rates and low levels of linkage disequilibrium, whereas GGB and GGG exhibited slow decay rates and high linkage disequilibrium levels (Figure 3B). In conclusion, IN, GGM, and GGJ displayed low selection levels, whereas GGB and GGG displayed high levels. We further analyzed the size and number of ROH within each population. The results indicated that B, BY, and DG had large and numerous ROH, whereas IN, TN, and VN had small and few ROH. Among the wild chickens, GGG showed the largest ROH and highest ROH (Figure 3C and 3D). Additionally, we calculated the FROH, with B, BY, DG, and GGG having high FROH values, suggesting a high level of inbreeding in northern cold-resistant chickens and GGG chickens from northern China. IN, TN, and VN had low FROH values, indicating low levels of inbreeding (Figure 3E). These results suggest that northern cold-resistant chickens in China might have undergone artificial selective breeding and that GGG and domestic chickens exhibited high differentiation, possibly due to inbreeding among populations.Figure 3 Analysis of genomic variation, LD, and FROH for eleven chicken populations. (A) Distributions of π in different groups. (B) Decay of LD in different groups, with 1 line per breed. (C) Length of ROHs in different chicken groups. (D) Average number of ROH of different chicken group. (E) FROH in different chicken groups.

Figure 3

Whole-Genome Selection Scans and Functional Analysis

To identify candidate genes potentially associated with heat adaptation, we conducted a genome-wide selection sweep analysis based on the genetic variation data obtained in this study. We performed whole-genome selection scans in the high-temperature groups and low-temperature groups. Using the top 1% as a threshold to identify potential candidate regions, annotated genes were considered potential candidate genes. We found a total of 468 FST high/low regions (top 1%) and 467 π-ratio high/low regions (top 1%) identified as candidate regions for heat adaptation, with 104 overlapped regions for both methods (Figure 4A and 4B). Through SNP annotation in the NCBI database, we identified 69 candidate genes (Table 1). The GO enrichment results (P < 0.05) indicated that these genes not only participated in transmembrane transport processes, including L-glutamate transmembrane transporter activity, lactate transmembrane transport, and chloride transmembrane transport, but also participated in positive regulation of canonical Wnt signaling pathway and glucose homeostasis pathway (Figure 4C). KEGG analysis results showed that these genes were predominantly enriched in metabolic pathways, such as glycine, serine and threonine metabolism, galactose metabolism, tyrosine metabolism, starch and sucrose metabolism, and beta-alanine metabolism, and also participated in the Wnt signaling pathway (Figure 4D). The review article of Li et al. (2015) summarized that leucine-rich repeat containing G protein-coupled receptor 4 (LGR4) exhibits a distinct circadian rhythm in lipid metabolism in LGR4 KO mice, with high lipid consumption during the light phase. In tropical chickens, which typically experience extended periods of light, increased lipid consumption may reduce fat deposition and help them adapt to the thermal environment. In cold environments, nonshivering thermogenesis, in addition to shivering thermogenesis in skeletal muscle, is a way of generating heat. In mammals, 2 types of thermogenic cells have been identified: white adipocytes and brown adipocytes, which play crucial roles in regulating whole-body energy homeostasis. The primary protein involved in the mechanism of nonshivering thermogenesis is uncoupling protein (UCP) 1, which generates heat by embedding into the mitochondrial inner membrane and dissipating the proton gradient (Cinti et al., 1989). A UCP (avian UCP) has also been found in avian skeletal muscle. When chickens are exposed to cold environments, the expression of avian UCP increases, indicating the acquisition of thermogenic capacity by differentiating adipose tissue into beige fat (Sotome et al., 2021). Previous studies have found that peroxisome proliferator-activated receptor γ coactivator 1-alpha (PGC-1α) can regulate nonshivering thermogenesis in adipocytes (Lin et al., 2002), and the Ampk-Sirt1-Pgc-1α pathway is considered critical for muscle energy metabolism. This pathway involves LGR4 and UCP1, and the knockout of LGR4 can significantly upregulate the expression of UCP1 and PGC-1α (Wang et al., 2013). Therefore, LGR4 may further regulate avian nonshivering thermogenesis by modulating the expression of avian UCP through the Ampk-Sirt1-Pgc-1α pathway. In addition, Shi et al. (2023) analyzed the whole-genome sequences of 119 Chinese native chickens from 4 breeds. Their findings revealed that tropical environmental pressures exert strong selection pressure on new genes linked to the glucose metabolism and energy metabolism pathways. Glucose-6-phosphatase catalytic subunit (G6PC), a target gene of miR-5100, participates in liver lipid regulation, a key pathway for heat adaptation (Yoshida et al., 2016). Sequestosome 1 (P62), which connects organelle autophagy and signal transduction, plays a crucial role in cell metabolism and energy balance regulation (Huang et al., 2021). Previous studies have demonstrated that heat stress can inhibit mitochondrial respiratory chain activity in the liver of broiler chickens, resulting in excessive reactive oxygen species (ROS) production and thus oxidative damage (Tan et al., 2010; Huang et al., 2015). In this study, we discovered that the neighbor of BRCA1 gene 1 (NBR1) is involved in the mitochondrial autophagy pathway, which involves ROS generation from mitochondrial damage and mTOR inhibition mediated by AMPK activation due to ATP depletion. Therefore, this gene may indirectly regulate the physiological response of chickens to heat stress through the mitochondrial autophagy pathway (Ding and Yin, 2012). The development and function of the kidneys are crucial for heat adaptation, and one of the screened genes, dishevelled associated activator of morphogenesis 2 (DAAM2), was confirmed to be associated with normal kidney development and is a factor in the Wnt signaling pathway (Goggolidou et al., 2014), consistent with our KEGG enrichment results. We propose that DAAM2 is evidence of tropical chicken adaptation to high-temperature environments. A hot environment can induce beneficial central nervous adaptations, maintaining autonomous activation ability during prolonged periods of elevated body temperature (Racinais et al., 2017). Additionally, as environmental temperature increases, decreased vascular resistance and blood volume lead to reduced blood pressure and cardiac output (Vogel et al., 1963). This study identified genes related to central nervous system development, such as Rho family GTPase 2 (RND2), a novel regulator of cortical neuron migration (Heng et al., 2008). Zinc finger homeobox 3 (ZFHX3) is not only a zinc finger homeodomain transcription factor associated with neural development (Del Rocío Pérez Baca et al., 2023), but it is also involved in angiogenesis. Structurally, it is an essential part of HIF-1α angiogenic activity, and functionally, it is necessary for HIF-1α to exert its angiogenic effects (Fu et al., 2020), suggesting that these genes may be related to tropical environmental adaptability. Similarly, amine oxidase copper containing 3 (AOC3) plays a role in intracellular fat storage and insulation, is important for energy balance, and is directly related to heat adaptation (Shen, 2010).Figure 4 Functional enrichment analysis of the heat adaptation candidate genes. The putatively selected genomic regions in tropical populations were identified using bot. (A) FST and (B) π-ratio (High/Low) approaches with a sliding window strategy (50 kb windows with 20 kb steps). (C) GO enrichment analysis of candidate genes under selection in high temperature chickens. (D) KEGG enrichment analysis of candidate genes under selection in high temperature chickens.

Figure 4

Table 1 The detailed information of 69 candidate genes.

Table 1Chromosome	Gene	Gene name	Fst (high/low)	π-ratio (high/low)	
27	AARSD1	Alanyl-tRNA synthetase domain containing 1	0.248	2.912	
5	ANO3	Anoctamin 3	0.280	1.213	
5	ANO5	Anoctamin 5	0.267	1.489	
27	AOC3	Amine oxidase copper containing 3	0.248	2.912	
2	ARHGAP39L	Rho GTPase activating protein 39 like	0.394	0.973	
5	BBOX1	Gamma-butyrobetaine hydroxylase 1	0.266	1.264	
27	BRCA1	BRCA1 DNA repair associated	0.286	2.283	
5	CCDC34	Coiled-coil domain containing 34	0.247	1.220	
1	CMAS	Cytidine monophosphate N-acetylneuraminic acid synthetase	0.257	0.918	
3	CSMD1	CUB and Sushi multiple domains 1	0.282	1.308	
3	DAAM2	Dishevelled associated activator of morphogenesis 2	0.299	1.586	
3	KIF6	Kinesin family member 6	0.306	2.085	
5	DLK1	Delta like noncanonical Notch ligand 1	0.283	1.360	
5	FANCF	FA complementation group F	0.256	1.542	
5	FIBIN	Fin bud initiation factor homolog	0.285	1.160	
27	G6PC	Glucose-6-phosphatase catalytic subunit	0.248	2.912	
5	GAS2	Growth arrest specific 2	0.288	1.251	
27	IFI35	Interferon induced protein 35	0.254	2.856	
2	INHBA	Inhibin subunit beta A	0.389	1.611	
2	IRX2	Iroquois homeobox 2	0.288	1.145	
3	KCNK17	Potassium 2 pore domain channel subfamily K member 17	0.276	1.422	
3	KCNK5	Potassium 2 pore domain channel subfamily K member 5	0.334	1.826	
3	KIF6	Kinesin family member 6	0.288	2.079	
5	LGR4	Leucine rich repeat containing G protein-coupled receptor 4	0.257	1.151	
5	LIN7C	Lin-7 homolog C, crumbs cell polarity complex component	0.274	0.924	
3	LRPPRC	Leucine rich pentatricopeptide repeat containing	0.281	0.930	
5	MIR1775	MicroRNA 1775	0.300	1.039	
1	MIR6606	MicroRNA 6606	0.373	0.999	
27	MPP2	MAGUK p55 scaffold protein 2	0.341	1.194	
27	NBR1	NBR1 autophagy cargo receptor	0.314	1.460	
5	NELL1	Neural EGFL like 1	0.308	1.327	
27	PTGES3L	Prostaglandin E synthase 3 like	0.248	2.912	
27	RND2	Rho family GTPase 2	0.255	3.042	
27	RPL27	Ribosomal protein L27	0.254	2.856	
27	RUNDC1	RUN domain containing 1	0.248	2.912	
5	SLC17A6	Solute carrier family 17 member 6	0.278	1.273	
5	SLC5A12	Solute carrier family 5 member 12	0.277	1.036	
5	SVIP	Small VCP interacting protein	0.313	1.162	
7	UNC80	Unc-80 homolog, NALCN channel complex subunit	0.248	1.136	
27	VAT1	Vesicle amine transport 1	0.255	3.042	
11	ZFHX3	Zinc finger homeobox 3	0.265	0.124	
31	ZNF624	Zinc finger protein 624	0.263	1.103	
2	LOC100858452	/	0.424	1.571	
31	LOC101747443	/	0.250	1.284	
3	LOC101748090	/	0.336	2.146	
2	LOC101749399	/	0.407	1.157	
2	LOC101749489	/	0.463	1.226	
5	LOC101749701	/	0.283	1.360	
3	LOC101750546	/	0.281	0.930	
2	LOC101751137	/	0.295	1.021	
2	LOC107050162	/	0.424	1.571	
2	LOC107052665	/	0.288	1.145	
2	LOC107052804	/	0.392	1.037	
2	LOC107052808	/	0.392	1.037	
3	LOC107052914	/	0.310	1.764	
5	LOC107053348	/	0.263	1.090	
5	LOC107053349	/	0.266	1.264	
5	LOC107053350	/	0.277	1.593	
5	LOC107053449	/	0.245	1.693	
5	LOC107053451	/	0.245	1.693	
31	LOC112531036	/	0.250	1.284	
31	LOC112531042	/	0.250	1.284	
3	LOC112532188	/	0.302	1.175	
4	LOC112532427	/	0.253	1.025	
5	LOC112532481	/	0.286	1.220	
5	LOC112532484	/	0.288	1.726	
5	LOC112532525	/	0.330	1.208	
5	LOC112532530	/	0.315	1.234	
1	LOC427933	/	0.257	0.918	

Our findings reveal numerous candidate regions and genes related to heat adaptation in tropical chickens. The discovery of these heat resistance genes provides a theoretical basis for improving heat adaptation in commercial chickens.

DISCLOSURES

This manuscript has not been published or presented elsewhere in part or in entirety and is not under consideration by another journal. The study design was approved by the appropriate ethics review board. We have read and understood your journal's policies, and we believe that neither the manuscript nor the study violates any of these. There are no conflicts of interest to declare.

ACKNOWLEDGMENTS

This work was supported by the earmarked fund for CARS (CARS-41 ) and the Jiangsu Key Research and Development Program (BE2022341 ).
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