Whole-genome analysis reveals distinct adaptation signatures to diverse environments in Chinese domestic pigs

Whole-genome analysis reveals distinct adaptation signatures to diverse environments in Chinese domestic pigs | 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 Research Article Whole-genome analysis reveals distinct adaptation signatures to diverse environments in Chinese domestic pigs Zhen Wang, Bangmin Song, Jianyu Yao, Xingzheng Li, Yan Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3942411/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background: Long-term natural and artificial selection resulted in many genetic footprints within the genomes of pig breeds across distinct agroecological zones. Nevertheless, the mechanisms of how these signatures contribute to phenotypic diversity and facilitate environmental adaptation remain unclear. Results: Here, we leveraged whole-genome sequencing data of 82 individuals from six domestic pig breeds originating in tropical, frigid, and high-altitude regions. Population genetic analysis suggested that environmental adaptations significantly contributed to population stratification in Chinese local pig breeds. Analysis of selection signals identified regions under selection for tropical adaptation (55.5 Mb), high-altitude adaptation (43.6 Mb), and frigid adaptation (17.72 Mb). The potential functions of the selective sweep regions were linked to certain complex traits that might play critical roles in different geographic environments, including fat coverage in frigid environments and blood indicators in tropical and high-altitude environments. Candidate genes under selection were significantly enriched in the biological pathways involved in environmental adaptations. These pathways contained blood circulation, protein degradation, and inflammation for tropical adaptation; heart and lung development, hypoxia response, and DNA damage repair for high-altitude adaptation; andthermogenesis, cold-induced vasodilation (CIVD), and cell cycle for frigid adaptation. By examining the chromatin state of the selection signatures, we detected the lung and ileum as two critically functional tissues for environmental adaptations. Finally, we unveiled a mutation (chr1: G246,175,129A) in cis-regulatory regions of ABCA1 as a plausible promising variant for tropical adaptation. Conclusions: In this study, we conducted a genome-wide exploration of the genetic mechanisms underlying the tropical, frigid, and high-altitude adaptability of Chinese local pig breeds. Our findings shed light on the prominent role of cis-regulatory elements in impacting environmental adaptation in pigs and may serve as a vital biomodel on human plateau-related disorders and cardiovascular diseases. Pig Whole genome resequencing Chinese local breeds Population genetics Selection signals Environmental adaptations Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Environmental stressors can indeed have negative impacts on the health, welfare, and production efficiency of domesticated animals [ 1 ]. It is well-established that environmental stress can significantly influence the animal genome [ 2 ]. As a result, identifying genomic features in response to environmental pressure has become a focus of evolutionary biology. In natural populations, adaptation is a dynamic and long-term evolutionary process whereby populations enhance their adaptation by accumulating beneficial alleles at gene loci that control adaptive phenotypes [ 3 ]. Compared to natural populations, domesticated animals have experienced a greatly accelerated process of environmental adaptation evolution due to human migration and selection [ 4 ]. Therefore, domestic animals represent an excellent model for probing adaptive mutations in the current genetic studies. Approximately 10,000 years ago, the domestication of pigs occurred in multiple regions across Eurasia [ 5 , 6 ]. Since then, pigs have undergone significant domestication changes and have become one of the most economically important animals globally [ 7 ]. Compared to modern commercial pig breeds under strong selective pressures like Duroc and Landrace, indigenous pig breeds generally exhibit superior and broader environmental adaptability. Given the current and future impacts of climate change on the planet, understanding the genetic adaptability of local pig breeds to different environments and breeding highly adaptable pig breeds to cope with climate change has practical value for the development of modern pig farming and energy conservation. Furthermore, pigs share a high degree of physiological similarity with humans, particularly concerning ecology, metabolism, and immunity [ 8 ]. Thus, exploring the biological processes related to environmental adaptation in domestic pigs could provide valuable insights into treating diseases caused by environmental factors in humans. Recently, a series of genetic variations shaped by environmental adaptations have been reported in multiple farm animals, including chickens [ 9 ], horses [ 10 ], sheep [ 11 ], cattle [ 12 ], bactrian camels [ 13 , 14 ], and pigs [ 15 ]. These studies have provided valuable insights into the environmental adaptability and evolution of domestic animals, yet there has been limited research on the relationship between non-coding regions and adaptability. Given the ability of non-coding regions to regulate gene expression in spatially and temporally specific manners, comprehensive studies integrating multi-tissue and multi-omics analyses are required to gain a deeper understanding of the environmental adaptations. This study collected whole-genome sequencing data of 82 individuals from six Chinese native pig breeds according to our previous study. Based on these data, we conducted comprehensive analyses of the population structure and genome diversity of these pig breeds. Furthermore, we explored the potential relationship among swept genes, functional tissues, and candidate variants associated with the adaptations to tropical, frigid, and high-altitude environments. Materials and methods Resequencing data collection This study utilized a subset of resequencing data from project PRJNA754250 [ 16 ], comprising 82 individuals selected based on the environmental characteristics of their geographic origins. The study included Tibetan pigs from the Tibet plateau; Wuzhishan, Ding'an, and Tunchang pigs from Hainan Island; Hetao pigs from Inner Mongolia; and Min pigs from northeastern regions. The raw sequences were cleaned to remove adaptor and sequencing errors, in which those reads contained the sequencing adaptor, more than 10% unknown nucleotide, and more than 50% bases of low quality were removed (quality scores in the Phred scale less than 5). Reads mapping of whole-genome sequencing data to the reference The reads were first trimmed by fastp [ 17 ] with default parameters. Next, all clean reads, including our newly generated samples, were aligned to the Sscrofa11.1 using the BWA-MEM pipeline [ 18 ]. The mapped reads were then sorted, and duplicates were removed by Picard tools ( https://broadinstitute.github.io/picard/ ) and SAMtools [ 19 ]. Genome-wide screening of SNPs and INDELs The genome-wide variants were called for each sample by the GATK UnifiedGenotyper [ 20 ] with -glm BOTH -rf BadCigar --sample_ploidy 2 option. Then we used the BEAGLE software [ 21 ] to impute and phase the obtained SNPs. To ensure high accuracy of variants calling, SNPs with QD 60.0 || MQ < 40.0 || MQRankSum < -12.5 || ReadPosRankSum < -8.0 were filtered. We then filtered out non-biallelic SNPs. After the quality screening, all the identified SNPs were further annotated using SnpEff v4.3t [ 22 ] based on the gene annotations of the pig reference genome Sscrofa11.1. Based on the genome annotation, SNPs were categorized as occurring in exonic regions, 5' or 3' untranslated regions, intronic regions, splicing sites (within 2 bp of a splicing junction), upstream and downstream regions (within a 1 kb region upstream or downstream from the transcription start site), or intergenic regions. SNPs in coding exons were further grouped as either synonymous SNPs or nonsynonymous SNPs. To check the confidence of SNPs called, we compared the SNPs identified with the dbSNP ( http://www.ncbi.milm.nih.gov/SNP , last accessed Feb 23, 2021). Phylogenetic and population genetic analyses To gain insights into phylogenetic relationships among pig breeds, we further filtered the SNP data set by applying criteria of minor allele frequency < 0.01, Hardy–Weinberg equilibrium 10%. Subsequently, pruning was performed with the PLINK v1.90 [ 23 ] option "-indep-pairwise 50 5 0.2." Following filtering and linkage disequilibrium (LD) pruning, 1,358,458 SNPs were retained for subsequent population genetics analyses. A neighbor-joining (NJ) tree was constructed using the VCF2Dis v1.44 ( https://github.com/BGI-shenzhen/VCF2Dis ; accessed on 23 March 2022). The tree was displayed using the Interactive Tree Of Life (iTOL) [ 24 ]. To infer the population structure, we used ADMIXTURE [ 25 ], which implements a block-relaxation algorithm. To identify the best genetic clusters K, cross-validation error was tested for each K value from 2 to 9. The principal component analysis (PCA) was conducted using the GTAC program [ 26 ]. The pattern of LD for these interest regions was computed using the PopLDdecay [ 27 ]. Selective sweeps during pig domestication and breeding SNPs with minor allele frequency below 1% were removed from this analysis. To identify the genomic signatures of selection in domestic pigs, we calculated the average SNP F ST values and θπ ratios using the VCFtools v1.17 [ 28 ] and the XP-EHH using the selscan [ 29 ]. All genomic regions in 50-kb sliding windows were scanned across genomes of the three populations using 10-kb steps. Windows containing ≥ 100 SNP were used to detect signatures of selection sweeps. Those windows within the top 5% quantile of the two statistics were considered candidate selection targets and annotated using the genomic database search engine BioMart [ 30 ]. Enrichment analysis Biased study towards areas/features of higher value to animal production may lead to overrepresenting certain traits in the QTL database under certain circumstances (such as meat and carcass-related traits in the QTL database for pigs). To eliminate this disturbance, we performed QTL enrichment analyses using a bootstrap simulation against the pig QTL Database [ 31 ]. We only considered p -values less than 0.05 from multiple tests. Furthermore, we identified genes in putative selection regions using the R package GALLO [ 32 ]. We extracted positional candidate genes that overlapped with the selected genomic regions based on the Sscrofa11.1 reference genome assembly. Gene Ontology (GO) enrichment analysis of swept genes was implemented with the R package clusterProfiler 4.0 [ 33 ]. We considered GO terms with corrected p -value < 0.05 to be significantly enriched. We downloaded 15 chromatin states, including promoters (TssA, TssAHet, and TssBiv), TSS-proximal transcribed regions (TxFlnk, TxFlnkWk, and TxFlnkHet), enhancers (EnhA, EnhAMe, EnhAWk, EnhAHet, and EnhPois), repressed regions (Repr and ReprWk), quiescent regions (Qui), and accessible but did not coincide with any other measured epigenetic marks (ATAC islands) for 14 pig tissues (Adipose, Cecum, Cerebellum, Colon, Cortex, Duodenum, Hypothalamus, Ileum, Jejunum, Liver, Lung, Muscle, Spleen, and Stomach) from publicly available datasets [ 34 ]. We calculated the significance of enrichment based on Fisher's exact test using the R package LOLA [ 35 ]. Identification of putative function SNPs To identify putative functional SNPs, we first calculated F ST by site and the top 1% sites located in specifically selected regions with high absolute allele frequency difference (ΔAF > 0.7) considered as candidate SNPs. pCADD scores [ 36 ] were retrieved from public databases to prioritize coding variants. We downloaded the gene expression matrixes of different pig breeds from publicly available datasets, PIGOME. Motif analysis based on the JASPAR database [ 37 ] using the HOMER [ 38 ] for non-coding candidate SNPs located in the promoter or enhancer regions of candidate genes. Generation of Constructs and Dual-Luciferase Reporter Assays The 401-bp partial genomic DNA sequences of the ABCA1 gene, including SNP (G-A, chr1:246175129), were amplified. The PCR products were cloned into pGL4.23-basic Luciferase Reporter Vector (Promega). After 24 h of cell culture, the HEK-293T cells were transfected with the appropriate plasmids or oligos using Lipofectamine 3000 and Opti-MEM according to the manufacturer's protocols. One day after transfection, cells were collected to measure the luciferase activity by the Dual-Luciferase Reporter Assay System (Promega), and luciferase expression was normalized to renilla luciferase expression. Results Genomic Diversity, Phylogenetic Relationships and Population Structure We collected 82 individuals from six Chinese native pig breeds, including Ding'an pigs (DA), Tunchang pigs (TUC), Wuzhishsan pigs (WZS), Min pigs (MZ), Hetao pigs (HT), and Tibetan pigs (TP), which spread over three classical geographical regions, i.e., tropical, frigid, and high-altitude environments (Fig. 1 A and Table S1 ). All 82 individuals were sequenced at depths larger than 10× (Table S2 ). After applying stringent quality control criteria, we identified a total of 25,602,818 SNPs. By comparing the SNP set with the pig dbSNP database (Fig. 1 B), we found that more than 13.5% of the variants (3,466,300 SNPs) were novel, which substantially expanded the catalog of porcine genetic variants. Further functional annotation revealed that most SNPs (64.12%) were located in the intronic region, followed by intergenic regions (22.82%) (Fig. S1 ). Besides, 1.21% of SNPs were identified in coding regions, of which 102,024 nonsynonymous variants (100,954 missense, 874 stop gain, and 196 stop loss). LD generally decayed as the distance between loci increased, and the strength of LD varied widely between populations. The physical distance between SNPs measured as half of its maximal value occurred at 34.1 kb (r 2 = 0.34) for DA and at 1.8–5.7 kb (r 2 = 0.23–0.31) for the rest five pig breeds. At longer marker distances, the LD value was highest for DA but lowest for Tibetan pigs (Fig. 1 C and Table S3 ). To infer the genetic and evolutionary relationships among pig breeds adapted to different environments, we first constructed a phylogenetic tree of 82 individuals using the Neighbor-Joining (NJ) algorithm (Fig. 1 D). The phylogenetic tree revealed distinct groupings of individuals from different regions. Specifically, the genetic relationships among the six breeds were strongly associated with their habitats, with three pig breeds from Hainan (WZS, DA, and TUC) exhibiting closer genetic relationships. Within the Tibetan pig breed, interestingly, the internal genetic relationships also showed significant geographical partitioning (Fig. 1 D). The results of PCA were consistent with those of the phylogenetic tree, with the first principal component (PC1 = 6.54%) and the second principal component (PC2 = 5.10%) able to separate the six breeds by geographical regions (Fig. S2 ). Population structure analysis revealed that the optimal number of clusters was three, at which point the cross-validation error was lowest, and the results were considered most reliable (Fig. 1 E). When K = 2, the three pig breeds from Hainan, Tibetan pigs, and Hetao pigs shared more ancestral components; when K = 3, we observed that pig breeds clustered by geographical region, consistent with previous phylogenetic tree and PCA results; when K = 4, Hetao pigs were separated, consistent with the fact that their distribution area does not overlap with other pig breeds. The admixture results further confirmed that the genetic relationships among pig breeds were closely related to their geographical distribution. Selection signatures on autosomes and functional annotation To better leverage the diversity of our dataset, we partitioned the three local population samples into four groups based on the putative population structure: the high-temperature group (HP, consisting of Hainan populations), the low-temperature group (NTP, consisting of NorthChina populations and Tibetan populations), the high-altitude group (TP, consisting of Tibetan populations), and the low altitude group (HNP, consisting of Hainan populations and NorthChina populations). To elucidate the selective pattern of pigs in tropical environments, we conducted a comparative analysis between HP and NTP to detect selection signals based on the genomic windows containing more than 100 SNPs (Fig. S3 ). By applying the top 5% cutoffs for both F ST and XP-EHH, we identified 55.50 Mb and 20.47 Mb of selective sweep regions in HP and NTP, respectively (Table S4). A similar approach was employed to compare TP and HNP to investigate the adaptive mechanisms in high-altitude environments, revealing 43.60 Mb of selective sweep regions in TP (Table S5). To focus on the unique regions associated with frigid adaptation, we excluded the overlapped regions between the NTP selection and TP selection from the NTP selection and finally got 17.72 Mb regions (Table S6). The top 25 sweep regions with the highest F ST and XP-EHH scores within the candidate genomic areas were considered as highly significant regions (Fig. 2 ). We first performed an in-depth exploration of the selective pattern in tropical adaptation. Most genes located in the highly significant regions are functionally plausible for tropical adaptation, according to their annotation in previous studies. These genes included VPS13A , GNA14 , and NR6A1 , which were involved in blood coagulation and circulation [ 39 ]; STIMATE and NR5A1 , which participate in the temperature stress response [ 40 – 42 ]; AGMO affecting human inflammation and energy homeostasis [ 43 ]; LMTK2 associated with cell apoptosis [ 44 ]; and CFAP299 , which may affect the hair phenotype of yaks [ 45 ]. We explored the potential biological function of detected signals with publicly available QTLs (Table S7) and gene ontology (GO) (Table S8). QTL enrichment analyses showed that health, meat, and carcass traits were mostly significantly enriched (Fig. S4A). We noticed significant enrichment for "Cholesterol level" and "Mean platelet volume" (Fig. S4B). The positively selected genes (PSGs) in tropical adaptation were mainly associated with blood circulation, protein degradation, and inflammation, including "blood vessel diameter maintenance" ( HRH2 , HTR7 , DBH , ADM , OLR1 , ATP2B1 , and HRH1 ), "lipid translocation" ( MFSD2A , ANO3 , and ANO4 ), "NIK/NF-kappaB signaling" ( LRRC19 , MAP3K7 , NFAT5 , and NLRP3 ), and "proteasomal ubiquitin-independent protein catabolic process" ( PSMA1 , PSMB7 , and PSMB11 ). Improving blood flow to the surrounding skin can mitigate the effects of heat stress in tropical environments [ 46 ]. Previous research has demonstrated that the NF-κB pathway can stimulate HSP activation in immune cells [ 47 ], contributing to reducing heat stress, and the proteasomal ubiquitin-independent protein catabolic process can degrade misconfigured proteins caused by heat stress, reducing damage [ 48 ]. We next explored the mechanisms of hypoxia tolerance in Tibetan pigs. In the highly significant regions of high-altitude adaptation-specific selection, two genes affecting the cardiovascular system were identified: SOX18 , which was associated with the regulation of blood vessel development [ 49 ], and TNNI3K , which affects heart function [ 50 , 51 ]. Additionally, in line with previous research [ 52 ], the candidate gene EPAS1 in the hypoxia-inducible factor pathway [ 53 ] showed substantial selection, but the gene EGLN1 in the same pathway did not. By annotating specific selected regions, we discovered significant enrichment for blood index-associated traits, such as "Hemoglobin" and "Plateletcrit" (Fig. 3 A and Table S9) Gene Ontology analysis revealed an overrepresentation of genes involved in biological processes that contribute to maintaining typical vital signs in high-altitude environments. (Fig. 3 B and Table S10). PSGs detected in Tibetan pigs have particularly enriched in hypoxia adaptation-related processes, including "cardiac cell development" ( MTOR , HEY2 , SMAD4 , SRF , TBX3 ), "coronary vascular development" ( HEY2 , SRF , PTK7 ), "platelet-derived growth factor receptor signaling pathway" ( CBLB , PTGIR , PHF14 , APOD ), "response to hypoxia" ( SCAP , MTOR , SMAD4 , SRF ), and "respiratory tube development" ( SPRY1 , CCDC39 , PHF14 , SRF , CCBE1 , PTK7 ). Additionally, we observed enrichment for the nucleotide metabolism process ( DPYS , MTOR , NME1 , NME2, UOX, RHOQ , DERA ), including "pyrimidine-containing compound metabolic process", "deoxyribose phosphate metabolic process", and "GTP metabolic process", which provided the basis for DNA repair and help to maintain genome stability by repairing UV-induced errors during DNA replication [ 54 ]. For frigid adaptation, we detected four genes in the highly significant regions, including HIF3A associated with adiposity [ 55 ]; JMJD1C affecting de novo lipogenesis [ 56 ]; RLN3 associated with food intake [ 57 , 58 ]; PAQR9 activating thermogenesis of brown adipocyte [ 59 ]. The function of these genes is critical for frigid adaptation. The signal of frigid adaptation was mainly associated with meat and carcass traits, especially for "Fat area percentage in carcass", which may contribute to heat retention (Fig. S5A and Table S11). Biological process enrichment analysis showed the process involved in thermogenesis "regulation of fibroblast growth factor receptor signaling pathway" ( HHIP , SPRY1 ). It is well documented that elevated levels of FGF21 (Fibroblast Growth Factor 21) promote beige adipose tissue and enhance energy expenditure [ 60 , 61 ]. We also found that domestic pigs also exhibit cold-induced vasodilation (CIVD), the enrichment of "vasodilation". CIVD is a dramatic increase in peripheral blood flow observed during cold exposure. It supposedly protects against cold injuries [ 62 ]. Meanwhile, we noticed the enrichment for pathways related to cell cycle ( CENPW , INO80 , DLGAP5 , APC , CCDC61 , NCAPG , SPRY1 , PTPN11 , REEP3 ), in line with the fact that the mammalian cell cycle is temperature sensitive [ 63 ] (Fig. S5B and Table S12). A series of potential promising variants played a vital role in the selection process For better interpreting the genetic basis under domestic selection, we thus annotated the SNPs within selection regions. We identified 21 nonsynonymous variants with high pCADD values (pCADD > 10) in specifically selected regions of tropical adaptation (20 variants) and frigid adaptation (1 variant) (Table S13). As an example, we identified the mutation p.V244G in VPS13A , which was previously reported as a promising mutation that may impact the secretion and aggregation of blood platelets and reduce the risk of thrombosis in southern Chinese pigs from hot environments [ 15 ] (Fig. S6A). Additionally, in NPHP4 , which has been verified by F ST , XP-EHH, θπ Ratio, and genotype patterns as a positively selected gene for tropical adaptation (Fig. 4 A-C), we found a nonsynonymous variant (p.A897T) showed high allele frequency difference between HP and NTP, was predicted to be functional-altering. This variant was highly conservative across multiple vertebrate species (Fig. 4 D). NPHP4 was a cilia-associated protein that negatively regulates the mammalian Hippo signaling pathway and was linked to the severe degenerative renal disease nephronophthisis and blindness in humans [ 64 , 65 ]. We propose that the NPHP4 missense mutation may enhance water reabsorption in the kidneys to mitigate the effects of heat stress on pigs. Chromatin state analysis enhanced the biological interpretations of adaptive evolution Tissue-specific gene regulation plays a crucial role in the process of adaptive evolution [ 34 ]. Thus, we performed chromosome state enrichment analysis for the genomic regions under selective pressure within tropical, frigid, and high-altitude environments, respectively (Fig. 5 A and Table S14-S16). The results showed a high consistency: TssA and TSS-proximal transcribed regions were most enriched, followed by enhancers. Then we examined the tissue-specific promoters (TssA) (Fig. 5 B and Table S17-S19). Using the common promoter as a reference, our analysis revealed that lung-specific and ileum-specific promoters were significantly enriched in all adaptations. Interestingly, most tissue-specific promoters in this study were significantly enriched in tropical adaptation. Several tissue-specific promoters were found to be significantly enriched in one or more selections, including liver-specific promoters in tropical/high-altitude adaptation and cortex-specific promoters in frigid/high-altitude adaptation. Additionally, spleen-specific and stomach-specific promoters were not found to be significantly enriched in any of the three adaptations. Variation within the cis-regulatory regions involving tropical adaptation The ABCA1 gene was detected to be a positively selected gene in tropical adaptation according to the F ST , XP-EHH, θπ Ratio, and genotype patterns (Fig. S7A-B). Upregulation of ABCA1 has a protective effect against atherosclerosis [ 66 – 68 ]. To investigate whether the expression of the ABCA1 gene was associated with the environmental origins of pig breeds, we incorporated gene expression data. RNA-seq data revealed high expression of ABCA1 in pig liver (Fig. S7C), with upregulation in liver expression associated with increasing annual mean temperature at the pig breed's origin (Fig. 5 C and Table S20). Based on the chromatin state data, we found a variant (chr1: 246,175,129, G-to-A) in the intron of ABCA1 that may regulate the expression as it is located in the enhancer region. The SNP showed high allele frequency in HP (77.8%) compared with TP (11.3%) and NP (0%) (Fig. 5 D). HOMER analysis revealed that the G-to-A mutation might alter the transcription factor binding motif at this position (Fig. S8 and Table S21), leading to changes in the expression of ABCA1 . To confirm this hypothesis, luciferase reporter constructs were engineered. Luciferase activity analysis demonstrated that the G-to-A mutation site exhibited enhancer activity (Fig. 5 E). Therefore, we inferred that the mutation enhanced the expression of ABCA1 , contributing to tropical adaptation. Discussion Domestic pigs are vital agricultural animals, providing a substantial source of animal protein globally. Intense artificial selection and crossbreeding have increased the productivity of modern commercial pig breeds but reduced their adaptive potential [ 69 , 70 ]. With the changing global climate, studying the genetic adaptations of local breeds to diverse environments is crucial. In this study, we conducted a comprehensive investigation of the environmental adaptability of Chinese domestic pigs using whole-genome sequencing data and multiple omics datasets. Our findings underscored the importance of understanding the adaptive potential of domestic pigs to environmental challenges and had significant implications for the breeding of highly adaptable pig breeds. Population genetic analysis Genomic analyses reveal differentiation among pig breeds from distinct geographic regions, with Chinese local breeds likely originating from ancient Yellow River basin domestication centers [ 71 – 74 ]. Compared to modern breeds, Chinese local pigs, particularly Tibetan pigs, exhibit faster linkage disequilibrium decay, though slower than wild boars [ 75 – 78 ]. This suggested that after domestication, local pig populations spread with human migration to diverse agricultural zones and were shaped by combined artificial and natural selection or gene flow. The genomic diversity of Chinese local pig breeds was closely associated with breeding practices, such as the free-range breeding of Tibetan pigs by Tibetans, which may have increased gene flow with local wild boars and resulted in faster linkage disequilibrium decay. Genome-wide selection signatures The efficacy of methods for detecting selection signals is constrained by the comprehension of population complexity, rendering the reliability of a single approach uncertain. To circumvent this limitation, we employed an integrative approach by intersecting selection signals identified through both F ST and XP-EHH analyses to delineate final candidate regions. Subsequently, we corroborated these regions by calculating the θπ Ratio within candidate gene regions. In contrast to the study conducted by Ai et al. [ 15 ], our investigation incorporated the XP-EHH statistic and independently examined the genetic underpinnings of tropical and frigid adaptation in pigs, yielding a more nuanced interpretation. Our findings revealed that genomic regions implicated in tropical adaptation in pigs significantly outnumbered those associated with frigid adaptation, potentially due to the increased complexity of tropical environments. We identified several key candidate genes and genomic regions associated with tropical or frigid adaptation in pigs, providing a foundation for breeding pigs with enhanced temperature adaptability. Additionally, we utilized pig breeds from Hainan (high radiation) and northern regions (frigid) as control populations to elucidate the adaptation of Tibetan pigs to the hypoxic plateau environment. We identified a series of key candidate genes associated with cardiovascular system development, which may serve as valuable references for research on human plateau-related and cardiovascular diseases. Functional annotation Annotation from a single database alone cannot fully reveal the primary roles of genes within regions under selection in the organism. To explore the functions of candidate genes, here, we employed a multi-faceted approach, combining GO enrichment and QTL enrichment analyses. Our analysis confirmed a significant enrichment of traits related to blood circulation in both tropical and high-altitude adaptation in pigs, consistent with prior investigations of blood biochemical indicators in heat-stressed pigs and Tibetan pigs under normal conditions [ 79 – 81 ]. However, upon further analysis of the specific enriched QTLs ("LDL cholesterol" and "Cholesterol level" for tropical adaptation; "Red blood cell count", "Red cell distribution width", and "Hemoglobin" for high-altitude adaptation) and specific enriched GO terms, we found that the overlap was coincidental. Pigs adapted to the tropics aim to increase blood circulation for heat dissipation and to reduce the risk of thrombosis [ 82 , 83 ]. In contrast, pigs adapted to high altitudes aim to reduce blood viscosity caused by high hemoglobin levels and thus reduce cardiac burden [ 84 , 85 ]. This parallel selection of traits warrants consideration in the breeding of pigs with broad environmental adaptability. In contrast to the parallel selection of traits, specific selection for adaptation to distinct environments is more prevalent. Our results support this notion, as evidenced by the selection for fat coverage in cold region pig breeds, granulocyte activity and blood lipid content in tropical pig breeds, and hemoglobin content in high-altitude hypoxic pig breeds. These specifically selected traits may facilitate the breeding of pig breeds adapted to particular environments. Chromatin state annotation and cis-regulatory mutations Gene regulation plays a crucial role in speciation and adaptive diversification [ 86 – 88 ]. Cis-regulatory mutations can alter the expression of proximal genes and have long been considered important targets for adaptive phenotypic evolution, as they may have fewer deleterious effects than changes in protein-coding sequences [ 89 – 91 ]. While protein-coding mutations may affect protein products throughout tissues and developmental stages, cis-regulatory mutations can influence gene expression in spatially and temporally specific manners. Several studies have identified the importance of non-coding region mutations in local adaptation [ 92 , 93 ]. Previous research on the adaptive evolution of domestic pigs primarily focused on protein-coding regions of the genome, annotating candidate gene functions to elucidate environmental adaptation, with little systematic exploration of regulatory regions. To investigate the mechanisms by which variation in regulatory regions affects the environmental adaptability of pigs, we analyzed tissue-specific chromatin states in candidate regions. By examining the enrichment of tissue-specific regulatory factors, we pinpointed the lung and ileum as common functional tissues for tropical, frigid, or high-altitude adaptations. As the undertaker of respiration, the role played by the lung in the process of adapting to various environments was well studied. Numerous investigations have demonstrated that interactions between the host and its microbiome can effectively modulate host adaptability [ 94 – 100 ] and that host genetic selection can affect gut microbiome composition [ 101 , 102 ]. We, therefore, hypothesize that gut-specific regulatory elements indirectly regulate gut microbiome composition during pig environmental adaptations. In contrast, the spleen and stomach did not appear to have a specific role in the adaptation of domestic pigs to various environments. In this study, many tissues showed outstanding contributions to tropical adaptation. This indicated that heat stress affects a wider range of tissues. Interestingly, cortex-specific promoters were especially enriched in the frigid and high-altitude selection, consistent with the general observation that Tibetan pigs and Northern domestic pigs are more active and aggressive [ 103 ]. This finding provided additional evidence of the vital role that tissue-specific gene regulation plays in the adaptive selection process during the domestication of Chinese pigs. Through our elucidation of the positive selection phenomena occurring within regulatory regions during environmental adaptations, our study extended our understanding of pig environmental adaptability to specific tissues, providing a framework for incorporating single-cell data into future adaptability research while also highlighting the crucial role played by cis-regulatory mutations in enabling pig adaptation to tropical environments. Conclusion In summary, we conducted a genome-wide selection scan based on whole-genome sequencing data from six pig breeds (Wuzhishan, Tunchang, Ding'an, Min, Hetao, Tibetan) to identify the selected genomic regions associated with environmental adaptations (tropical, frigid, high-altitude) in Chinese local pigs. Genetic diversity analysis confirmed the strong shaping ability of the environment on the pig genome. We identified biological processes and traits closely related to pig environmental adaptations, and subsequent integration of multi-omics data extended the genetic features of pig environmental adaptations to specific functional tissues and cis-regulatory mutations. Our results enhance our understanding of the process of pig environmental adaptations, providing significant value for pig genetic breeding and human disease reference. Abbreviations CIVD Cold-induced vasodilation SNP Single nucleotide polymorphisms MAF Minor allele frequency LD Linkage disequilibrium NJ Neighbor-Joining PCA Principal component analysis XP-EHH Cross population extend haplotype homozygosity QTL Quantitative trait locus GO Gene Ontology TssA Strongly active promoters/transcripts TssAhet Flanking active TSS without ATAC TssBiv Transcribed at gene TxFlnk Transcribed at gene TxFlnkWk Weak transcribed at gene TxFlnkHet Transcribed region without ATAC EnhA Strong active enhancer EnhAMe Medium enhancer with ATAC EnhAWk Weak active enhancer EnhAHet Active enhancer no ATAC EnhPois Poised enhancer Repr Repressed polycomb ReprWk Weak repressed polycomb Qui Quiescent regions ATAC_Is ATAC island AF Allele frequency pCADD Combined annotation dependent depletion for pig PCR Polymerase chain reaction Declarations Consent for publication Not applicable. Competing interests The authors declare no competing interests. Funding This work was supported by the National Key Research and Development Program of China (2021YFF1000600), the National Natural Science Foundation of China (32002150 and U23A20229), the Basic and Applied Basic Research Foundation of Guangdong Province (2020B1515120053), the Shenzhen Science and Technology Innovation Commission (JCYJ20190813114401691), the Central Government Guiding Funds for Local Science and Technology Development of China (He-Ke ZY220603), and the Open Project of Hainan Provincial Key Laboratory of Tropical Animal Reproduction & Breeding and Epidemic Disease Research (HKL2020101). Authors’ contributions GY conceived and designed the study. ZW collected the data, performed bioinformatics analysis, and visualized the results. BS and JY conducted the experiments. ZW wrote the original draft. GY, ZT, ZW, XL, and YZ reviewed and edited the manuscript. All authors read and approved the final manuscript. Acknowledgements We are grateful to Hongwei Yin for his suggestions for performing selective signal detection. Availability of data and materials The whole genome resequencing data that were analyzed during the current study are available in the NCBI primary data archive (PDA) with accession number PRJNA754250. Chromatin state data used in this study can be found in: http://farm.cse.ucdavis.edu/~zhypan/Nature_Communications_2021 . The RNA-seq data is available in the publicly available datasets, PIGOME ( http://pig123456789.pigome.com/ ). References Miraglia M, Marvin HJP, Kleter GA, Battilani P, Brera C, Coni E, et al. Climate change and food safety: An emerging issue with special focus on Europe. Food Chem Toxicol. 2009;47(5):1009–21. https://doi.org/10.1016/j.fct.2009.02.005 . Nevo E. Evolution of genome–phenome diversity under environmental stress. Proceedings of the National Academy of Sciences. 2001;98(11):6233–40. https://doi.org/10.1073/pnas.101109298 . 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Supplementary Files Additionalfile1.docx Additionalfile2.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 18 Feb, 2024 Reviewers invited by journal 16 Feb, 2024 Editor assigned by journal 08 Feb, 2024 First submitted to journal 07 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3942411","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273445519,"identity":"79de9108-9445-4859-b736-7b0ab599ae51","order_by":0,"name":"Zhen Wang","email":"","orcid":"https://orcid.org/0000-0001-7387-3724","institution":"Chinese Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Wang","suffix":""},{"id":273445520,"identity":"cf99c0dc-1feb-4be9-937d-801fcc37ce79","order_by":1,"name":"Bangmin Song","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Bangmin","middleName":"","lastName":"Song","suffix":""},{"id":273445521,"identity":"28eaac4c-5659-46e4-968a-bb854cd5a018","order_by":2,"name":"Jianyu Yao","email":"","orcid":"","institution":"The University of Hong Kong","correspondingAuthor":false,"prefix":"","firstName":"Jianyu","middleName":"","lastName":"Yao","suffix":""},{"id":273445522,"identity":"65c80dcb-0e8e-492a-9921-91406e8d409f","order_by":3,"name":"Xingzheng Li","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xingzheng","middleName":"","lastName":"Li","suffix":""},{"id":273445523,"identity":"2ef40e3c-3dd5-4a6e-b110-4978d7a7ce4c","order_by":4,"name":"Yan Zhang","email":"","orcid":"","institution":"Hainan Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Zhang","suffix":""},{"id":273445524,"identity":"e7ce41f5-333f-4eb2-bcac-4471c5c94773","order_by":5,"name":"Zhonglin Tang","email":"","orcid":"https://orcid.org/0000-0002-4538-4349","institution":"Chinese Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zhonglin","middleName":"","lastName":"Tang","suffix":""},{"id":273445525,"identity":"4db603b3-2ea2-49ce-bc4a-96c64451d799","order_by":6,"name":"Guoqiang Yi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYDACZjApAWIdIFkLWwLJ9vEYEKfO4Dj7NYkPZRby5vxrPn74UVPHwD+7gYCWwzxlkjPOSRjunPF2s2TPscMMEncOENSSJs3bJsG44cbZbQw8bAcYDCQSiNNiv+HGmWeMf/7VEaOF/RhIS+KG8z1szLxtzIS1SB7mYbYE+iV5ww02Y2nZvsM8EjcIaOE7f/zhjQ9ldbYbzh9++PHNtzo5/hkEtCgcAEUHG5AFdQ8PfvVAIN/A/gCihf8AQcWjYBSMglEwQgEAM6xDtu/EnE4AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-8248-9126","institution":"Chinese Academy of Agricultural Sciences","correspondingAuthor":true,"prefix":"","firstName":"Guoqiang","middleName":"","lastName":"Yi","suffix":""}],"badges":[],"createdAt":"2024-02-09 08:16:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3942411/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3942411/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51382122,"identity":"1c9d4e5a-41e7-4e9e-954f-bc89a27a230f","added_by":"auto","created_at":"2024-02-20 16:15:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":163880,"visible":true,"origin":"","legend":"\u003cp\u003eGeographic distribution and population genetics analyses of six domestic pig breeds. \u003cstrong\u003eA \u003c/strong\u003eSampling locations of the 82 individuals in this study. The point size indicates the population size (4 - 18); the dot color indicates the annual average temperature. The elevation is shown on the map with a gradient. \u003cstrong\u003eB\u003c/strong\u003e Venn diagrams for novel variants detected in this study. \u003cstrong\u003eC\u003c/strong\u003e Decay of Linkage disequilibrium (LD) for six breeds, with one line per breed. (Ding'anpigs, DA; Tunchang pigs, TUC; Wuzhishan pigs, WZS; Hetao pigs, HT; Min pigs, MZ; Tibetan pigs, TP). \u003cstrong\u003eD\u003c/strong\u003e The neighbor-joining tree was constructed using p-distance between individuals. \u003cstrong\u003eE\u003c/strong\u003e Population genetic structure of the 82 individuals. The number of assumed genetic clusters K ranged from 2 to 4 are shown.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-3942411/v1/689b29a21a9ee2ae63312984.png"},{"id":51382119,"identity":"987510ef-7933-4fe5-a2e4-7142d7cc0b47","added_by":"auto","created_at":"2024-02-20 16:15:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":57989,"visible":true,"origin":"","legend":"\u003cp\u003eGenome-wide selective signals across environmental adaptations based on \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e and XP-EHH. Candidate selection regions detected by two statistics (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e and XP-EHH) are plotted across the genome. All dots represent regions identified as outliers with the \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e method. Red, blue, green, and yellow dots represent the regions identified as outliers in specific selection. Genes in the selected regions were marked.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-3942411/v1/7722b38446a8c303a474f0a4.png"},{"id":51382120,"identity":"e96030b4-bb92-4772-b8e0-0fe8ff762e88","added_by":"auto","created_at":"2024-02-20 16:15:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":31017,"visible":true,"origin":"","legend":"\u003cp\u003eAnnotation of the regions and genes detected by high-altitude specific selection based on the Animal QTLdb and the Gene Ontology Resource, respectively. \u003cstrong\u003eA \u003c/strong\u003eSignificantly enriched QTL terms of high-altitude specific selection. \u003cstrong\u003eB\u003c/strong\u003e Significantly enriched GO terms (Biological process, top 10) of selected genes in high-altitude specific selection.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-3942411/v1/42c5cc50efacba9455acd18d.png"},{"id":51382118,"identity":"b15e9aee-1d11-46cb-af46-dcf1227a1175","added_by":"auto","created_at":"2024-02-20 16:15:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":59904,"visible":true,"origin":"","legend":"\u003cp\u003ePutative promising variant (p.A897T) in \u003cem\u003eNPHP4\u003c/em\u003e for tropical adaptation. \u003cstrong\u003eA \u003c/strong\u003eθπ ratios (50kb-window, 10kb-step), \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e values, and XP-EHH values around the \u003cem\u003eNPHP4\u003c/em\u003e gene locus. The blue line represents θπ ratios. The red and black lines represent \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e and XP-EHH values, respectively. \u003cstrong\u003eB\u003c/strong\u003e Haplotype pattern in the genomic region of \u003cem\u003eNPHP4\u003c/em\u003e among HP, TP, and NP. \u003cstrong\u003eC\u003c/strong\u003e Allele frequency of the mutation site. \u003cstrong\u003eD\u003c/strong\u003e Multispecies alignment of the protein sequences around the variant.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-3942411/v1/a8593aae3637959aec16c081.png"},{"id":51382586,"identity":"ff2a32ec-a3f5-4c70-99cf-d028a5011dcd","added_by":"auto","created_at":"2024-02-20 16:23:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":52621,"visible":true,"origin":"","legend":"\u003cp\u003eChromatin state plays an important role in the environmental adaptations of Chinese domestic pigs. \u003cstrong\u003eA \u003c/strong\u003eSelection signature enrichment within chromatin states in Hainan, NorthChina, and Tibet plateau pigs. \u003cstrong\u003eB\u003c/strong\u003e Selection signature enrichment within tissue-specific promoters (TssA) in Hainan, NorthChina, and Tibet plateau populations. \u003cstrong\u003eC\u003c/strong\u003e The \u003cem\u003eABCA1\u003c/em\u003e gene shows the highest expression in Wuzhishan pig's liver. From left to right, the average annual temperature of pig breeding areas is decreasing. The significance of the genotype difference is tested. ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 (t-test), *** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 (t-test). \u003cstrong\u003eD\u003c/strong\u003e Allele frequency of the mutation site in the intron of \u003cem\u003eABCA1\u003c/em\u003e(chr1: G246,175,129A). \u003cstrong\u003eE\u003c/strong\u003e Luciferase reporter assays in HEK-293T cells to compare enhancer activity between the two alleles. ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 (t-test).\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-3942411/v1/7803add1a067ec67683e67b2.png"},{"id":51383035,"identity":"d5cb2796-f545-4d32-8b6f-003a3396f9d6","added_by":"auto","created_at":"2024-02-20 16:31:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2006795,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3942411/v1/e50f1649-943d-4472-bc02-f12d2cc9e178.pdf"},{"id":51382124,"identity":"094cd6c3-b224-4683-a3d0-584b6d39f295","added_by":"auto","created_at":"2024-02-20 16:15:06","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":1258397,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3942411/v1/803f5310b52a948fe85f0b8b.docx"},{"id":51382123,"identity":"560e58eb-d2d5-464e-9665-8e45a1494562","added_by":"auto","created_at":"2024-02-20 16:15:06","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":500328,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3942411/v1/7e09161558002a7e30043877.xlsx"}],"financialInterests":"","formattedTitle":"Whole-genome analysis reveals distinct adaptation signatures to diverse environments in Chinese domestic pigs","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEnvironmental stressors can indeed have negative impacts on the health, welfare, and production efficiency of domesticated animals [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is well-established that environmental stress can significantly influence the animal genome [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. As a result, identifying genomic features in response to environmental pressure has become a focus of evolutionary biology. In natural populations, adaptation is a dynamic and long-term evolutionary process whereby populations enhance their adaptation by accumulating beneficial alleles at gene loci that control adaptive phenotypes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Compared to natural populations, domesticated animals have experienced a greatly accelerated process of environmental adaptation evolution due to human migration and selection [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, domestic animals represent an excellent model for probing adaptive mutations in the current genetic studies.\u003c/p\u003e \u003cp\u003eApproximately 10,000 years ago, the domestication of pigs occurred in multiple regions across Eurasia [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Since then, pigs have undergone significant domestication changes and have become one of the most economically important animals globally [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Compared to modern commercial pig breeds under strong selective pressures like Duroc and Landrace, indigenous pig breeds generally exhibit superior and broader environmental adaptability. Given the current and future impacts of climate change on the planet, understanding the genetic adaptability of local pig breeds to different environments and breeding highly adaptable pig breeds to cope with climate change has practical value for the development of modern pig farming and energy conservation. Furthermore, pigs share a high degree of physiological similarity with humans, particularly concerning ecology, metabolism, and immunity [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Thus, exploring the biological processes related to environmental adaptation in domestic pigs could provide valuable insights into treating diseases caused by environmental factors in humans.\u003c/p\u003e \u003cp\u003eRecently, a series of genetic variations shaped by environmental adaptations have been reported in multiple farm animals, including chickens [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], horses [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], sheep [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], cattle [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], bactrian camels [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and pigs [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These studies have provided valuable insights into the environmental adaptability and evolution of domestic animals, yet there has been limited research on the relationship between non-coding regions and adaptability. Given the ability of non-coding regions to regulate gene expression in spatially and temporally specific manners, comprehensive studies integrating multi-tissue and multi-omics analyses are required to gain a deeper understanding of the environmental adaptations.\u003c/p\u003e \u003cp\u003eThis study collected whole-genome sequencing data of 82 individuals from six Chinese native pig breeds according to our previous study. Based on these data, we conducted comprehensive analyses of the population structure and genome diversity of these pig breeds. Furthermore, we explored the potential relationship among swept genes, functional tissues, and candidate variants associated with the adaptations to tropical, frigid, and high-altitude environments.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResequencing data collection\u003c/h2\u003e \u003cp\u003eThis study utilized a subset of resequencing data from project PRJNA754250 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], comprising 82 individuals selected based on the environmental characteristics of their geographic origins. The study included Tibetan pigs from the Tibet plateau; Wuzhishan, Ding'an, and Tunchang pigs from Hainan Island; Hetao pigs from Inner Mongolia; and Min pigs from northeastern regions. The raw sequences were cleaned to remove adaptor and sequencing errors, in which those reads contained the sequencing adaptor, more than 10% unknown nucleotide, and more than 50% bases of low quality were removed (quality scores in the Phred scale less than 5).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eReads mapping of whole-genome sequencing data to the reference\u003c/h2\u003e \u003cp\u003eThe reads were first trimmed by fastp [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] with default parameters. Next, all clean reads, including our newly generated samples, were aligned to the Sscrofa11.1 using the BWA-MEM pipeline [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The mapped reads were then sorted, and duplicates were removed by Picard tools (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://broadinstitute.github.io/picard/\u003c/span\u003e\u003cspan address=\"https://broadinstitute.github.io/picard/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and SAMtools [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGenome-wide screening of SNPs and INDELs\u003c/h2\u003e \u003cp\u003eThe genome-wide variants were called for each sample by the GATK UnifiedGenotyper [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] with \u003cem\u003e-glm\u003c/em\u003e BOTH \u003cem\u003e-rf\u003c/em\u003e BadCigar \u003cem\u003e--sample_ploidy 2\u003c/em\u003e option. Then we used the BEAGLE software [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] to impute and phase the obtained SNPs. To ensure high accuracy of variants calling, SNPs with \u003cem\u003eQD\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;2.0 || \u003cem\u003eFS\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;60.0 || \u003cem\u003eMQ\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;40.0 || \u003cem\u003eMQRankSum\u003c/em\u003e \u0026lt; -12.5 || \u003cem\u003eReadPosRankSum\u003c/em\u003e \u0026lt; -8.0 were filtered. We then filtered out non-biallelic SNPs. After the quality screening, all the identified SNPs were further annotated using SnpEff v4.3t [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] based on the gene annotations of the pig reference genome Sscrofa11.1. Based on the genome annotation, SNPs were categorized as occurring in exonic regions, 5' or 3' untranslated regions, intronic regions, splicing sites (within 2 bp of a splicing junction), upstream and downstream regions (within a 1 kb region upstream or downstream from the transcription start site), or intergenic regions. SNPs in coding exons were further grouped as either synonymous SNPs or nonsynonymous SNPs. To check the confidence of SNPs called, we compared the SNPs identified with the dbSNP (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.milm.nih.gov/SNP\u003c/span\u003e\u003cspan address=\"http://www.ncbi.milm.nih.gov/SNP\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, last accessed Feb 23, 2021).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePhylogenetic and population genetic analyses\u003c/h2\u003e \u003cp\u003eTo gain insights into phylogenetic relationships among pig breeds, we further filtered the SNP data set by applying criteria of minor allele frequency\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Hardy\u0026ndash;Weinberg equilibrium\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and a proportion of missing genotypes\u0026thinsp;\u0026gt;\u0026thinsp;10%. Subsequently, pruning was performed with the PLINK v1.90 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] option \"-indep-pairwise 50 5 0.2.\" Following filtering and linkage disequilibrium (LD) pruning, 1,358,458 SNPs were retained for subsequent population genetics analyses. A neighbor-joining (NJ) tree was constructed using the VCF2Dis v1.44 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/BGI-shenzhen/VCF2Dis\u003c/span\u003e\u003cspan address=\"https://github.com/BGI-shenzhen/VCF2Dis\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; accessed on 23 March 2022). The tree was displayed using the Interactive Tree Of Life (iTOL) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. To infer the population structure, we used ADMIXTURE [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], which implements a block-relaxation algorithm. To identify the best genetic clusters K, cross-validation error was tested for each K value from 2 to 9. The principal component analysis (PCA) was conducted using the GTAC program [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The pattern of LD for these interest regions was computed using the PopLDdecay [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSelective sweeps during pig domestication and breeding\u003c/h2\u003e \u003cp\u003eSNPs with minor allele frequency below 1% were removed from this analysis. To identify the genomic signatures of selection in domestic pigs, we calculated the average SNP \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e values and θπ ratios using the VCFtools v1.17 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] and the XP-EHH using the selscan [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. All genomic regions in 50-kb sliding windows were scanned across genomes of the three populations using 10-kb steps. Windows containing\u0026thinsp;\u0026ge;\u0026thinsp;100 SNP were used to detect signatures of selection sweeps. Those windows within the top 5% quantile of the two statistics were considered candidate selection targets and annotated using the genomic database search engine BioMart [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEnrichment analysis\u003c/h2\u003e \u003cp\u003eBiased study towards areas/features of higher value to animal production may lead to overrepresenting certain traits in the QTL database under certain circumstances (such as meat and carcass-related traits in the QTL database for pigs). To eliminate this disturbance, we performed QTL enrichment analyses using a bootstrap simulation against the pig QTL Database [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. We only considered \u003cem\u003ep\u003c/em\u003e-values less than 0.05 from multiple tests. Furthermore, we identified genes in putative selection regions using the R package GALLO [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. We extracted positional candidate genes that overlapped with the selected genomic regions based on the Sscrofa11.1 reference genome assembly.\u003c/p\u003e \u003cp\u003eGene Ontology (GO) enrichment analysis of swept genes was implemented with the R package clusterProfiler 4.0 [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. We considered GO terms with corrected \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 to be significantly enriched.\u003c/p\u003e \u003cp\u003eWe downloaded 15 chromatin states, including promoters (TssA, TssAHet, and TssBiv), TSS-proximal transcribed regions (TxFlnk, TxFlnkWk, and TxFlnkHet), enhancers (EnhA, EnhAMe, EnhAWk, EnhAHet, and EnhPois), repressed regions (Repr and ReprWk), quiescent regions (Qui), and accessible but did not coincide with any other measured epigenetic marks (ATAC islands) for 14 pig tissues (Adipose, Cecum, Cerebellum, Colon, Cortex, Duodenum, Hypothalamus, Ileum, Jejunum, Liver, Lung, Muscle, Spleen, and Stomach) from publicly available datasets [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. We calculated the significance of enrichment based on Fisher's exact test using the R package LOLA [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of putative function SNPs\u003c/h2\u003e \u003cp\u003eTo identify putative functional SNPs, we first calculated \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e by site and the top 1% sites located in specifically selected regions with high absolute allele frequency difference (ΔAF\u0026thinsp;\u0026gt;\u0026thinsp;0.7) considered as candidate SNPs. pCADD scores [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] were retrieved from public databases to prioritize coding variants. We downloaded the gene expression matrixes of different pig breeds from publicly available datasets, PIGOME. Motif analysis based on the JASPAR database [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] using the HOMER [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] for non-coding candidate SNPs located in the promoter or enhancer regions of candidate genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eGeneration of Constructs and Dual-Luciferase Reporter Assays\u003c/h2\u003e \u003cp\u003eThe 401-bp partial genomic DNA sequences of the \u003cem\u003eABCA1\u003c/em\u003e gene, including SNP (G-A, chr1:246175129), were amplified. The PCR products were cloned into pGL4.23-basic Luciferase Reporter Vector (Promega). After 24 h of cell culture, the HEK-293T cells were transfected with the appropriate plasmids or oligos using Lipofectamine 3000 and Opti-MEM according to the manufacturer's protocols. One day after transfection, cells were collected to measure the luciferase activity by the Dual-Luciferase Reporter Assay System (Promega), and luciferase expression was normalized to renilla luciferase expression.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGenomic Diversity, Phylogenetic Relationships and Population Structure\u003c/h2\u003e \u003cp\u003eWe collected 82 individuals from six Chinese native pig breeds, including Ding'an pigs (DA), Tunchang pigs (TUC), Wuzhishsan pigs (WZS), Min pigs (MZ), Hetao pigs (HT), and Tibetan pigs (TP), which spread over three classical geographical regions, i.e., tropical, frigid, and high-altitude environments (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). All 82 individuals were sequenced at depths larger than 10\u0026times; (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). After applying stringent quality control criteria, we identified a total of 25,602,818 SNPs. By comparing the SNP set with the pig dbSNP database (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), we found that more than 13.5% of the variants (3,466,300 SNPs) were novel, which substantially expanded the catalog of porcine genetic variants. Further functional annotation revealed that most SNPs (64.12%) were located in the intronic region, followed by intergenic regions (22.82%) (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Besides, 1.21% of SNPs were identified in coding regions, of which 102,024 nonsynonymous variants (100,954 missense, 874 stop gain, and 196 stop loss).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLD generally decayed as the distance between loci increased, and the strength of LD varied widely between populations. The physical distance between SNPs measured as half of its maximal value occurred at 34.1 kb (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.34) for DA and at 1.8\u0026ndash;5.7 kb (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.23\u0026ndash;0.31) for the rest five pig breeds. At longer marker distances, the LD value was highest for DA but lowest for Tibetan pigs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC and Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo infer the genetic and evolutionary relationships among pig breeds adapted to different environments, we first constructed a phylogenetic tree of 82 individuals using the Neighbor-Joining (NJ) algorithm (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). The phylogenetic tree revealed distinct groupings of individuals from different regions. Specifically, the genetic relationships among the six breeds were strongly associated with their habitats, with three pig breeds from Hainan (WZS, DA, and TUC) exhibiting closer genetic relationships. Within the Tibetan pig breed, interestingly, the internal genetic relationships also showed significant geographical partitioning (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). The results of PCA were consistent with those of the phylogenetic tree, with the first principal component (PC1\u0026thinsp;=\u0026thinsp;6.54%) and the second principal component (PC2\u0026thinsp;=\u0026thinsp;5.10%) able to separate the six breeds by geographical regions (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePopulation structure analysis revealed that the optimal number of clusters was three, at which point the cross-validation error was lowest, and the results were considered most reliable (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). When K\u0026thinsp;=\u0026thinsp;2, the three pig breeds from Hainan, Tibetan pigs, and Hetao pigs shared more ancestral components; when K\u0026thinsp;=\u0026thinsp;3, we observed that pig breeds clustered by geographical region, consistent with previous phylogenetic tree and PCA results; when K\u0026thinsp;=\u0026thinsp;4, Hetao pigs were separated, consistent with the fact that their distribution area does not overlap with other pig breeds. The admixture results further confirmed that the genetic relationships among pig breeds were closely related to their geographical distribution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSelection signatures on autosomes and functional annotation\u003c/h2\u003e \u003cp\u003eTo better leverage the diversity of our dataset, we partitioned the three local population samples into four groups based on the putative population structure: the high-temperature group (HP, consisting of Hainan populations), the low-temperature group (NTP, consisting of NorthChina populations and Tibetan populations), the high-altitude group (TP, consisting of Tibetan populations), and the low altitude group (HNP, consisting of Hainan populations and NorthChina populations). To elucidate the selective pattern of pigs in tropical environments, we conducted a comparative analysis between HP and NTP to detect selection signals based on the genomic windows containing more than 100 SNPs (Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). By applying the top 5% cutoffs for both \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e and XP-EHH, we identified 55.50 Mb and 20.47 Mb of selective sweep regions in HP and NTP, respectively (Table S4). A similar approach was employed to compare TP and HNP to investigate the adaptive mechanisms in high-altitude environments, revealing 43.60 Mb of selective sweep regions in TP (Table S5). To focus on the unique regions associated with frigid adaptation, we excluded the overlapped regions between the NTP selection and TP selection from the NTP selection and finally got 17.72 Mb regions (Table S6). The top 25 sweep regions with the highest \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e and XP-EHH scores within the candidate genomic areas were considered as highly significant regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe first performed an in-depth exploration of the selective pattern in tropical adaptation. Most genes located in the highly significant regions are functionally plausible for tropical adaptation, according to their annotation in previous studies. These genes included \u003cem\u003eVPS13A\u003c/em\u003e, \u003cem\u003eGNA14\u003c/em\u003e, and \u003cem\u003eNR6A1\u003c/em\u003e, which were involved in blood coagulation and circulation [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]; \u003cem\u003eSTIMATE\u003c/em\u003e and \u003cem\u003eNR5A1\u003c/em\u003e, which participate in the temperature stress response [\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]; \u003cem\u003eAGMO\u003c/em\u003e affecting human inflammation and energy homeostasis [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]; \u003cem\u003eLMTK2\u003c/em\u003e associated with cell apoptosis [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]; and \u003cem\u003eCFAP299\u003c/em\u003e, which may affect the hair phenotype of yaks [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. We explored the potential biological function of detected signals with publicly available QTLs (Table S7) and gene ontology (GO) (Table S8). QTL enrichment analyses showed that health, meat, and carcass traits were mostly significantly enriched (Fig. S4A). We noticed significant enrichment for \"Cholesterol level\" and \"Mean platelet volume\" (Fig. S4B). The positively selected genes (PSGs) in tropical adaptation were mainly associated with blood circulation, protein degradation, and inflammation, including \"blood vessel diameter maintenance\" (\u003cem\u003eHRH2\u003c/em\u003e, \u003cem\u003eHTR7\u003c/em\u003e, \u003cem\u003eDBH\u003c/em\u003e, \u003cem\u003eADM\u003c/em\u003e, \u003cem\u003eOLR1\u003c/em\u003e, \u003cem\u003eATP2B1\u003c/em\u003e, and \u003cem\u003eHRH1\u003c/em\u003e), \"lipid translocation\" (\u003cem\u003eMFSD2A\u003c/em\u003e, \u003cem\u003eANO3\u003c/em\u003e, and \u003cem\u003eANO4\u003c/em\u003e), \"NIK/NF-kappaB signaling\" (\u003cem\u003eLRRC19\u003c/em\u003e, \u003cem\u003eMAP3K7\u003c/em\u003e, \u003cem\u003eNFAT5\u003c/em\u003e, and \u003cem\u003eNLRP3\u003c/em\u003e), and \"proteasomal ubiquitin-independent protein catabolic process\" (\u003cem\u003ePSMA1\u003c/em\u003e, \u003cem\u003ePSMB7\u003c/em\u003e, and \u003cem\u003ePSMB11\u003c/em\u003e). Improving blood flow to the surrounding skin can mitigate the effects of heat stress in tropical environments [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Previous research has demonstrated that the NF-κB pathway can stimulate HSP activation in immune cells [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], contributing to reducing heat stress, and the proteasomal ubiquitin-independent protein catabolic process can degrade misconfigured proteins caused by heat stress, reducing damage [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe next explored the mechanisms of hypoxia tolerance in Tibetan pigs. In the highly significant regions of high-altitude adaptation-specific selection, two genes affecting the cardiovascular system were identified: \u003cem\u003eSOX18\u003c/em\u003e, which was associated with the regulation of blood vessel development [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], and \u003cem\u003eTNNI3K\u003c/em\u003e, which affects heart function [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Additionally, in line with previous research [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], the candidate gene \u003cem\u003eEPAS1\u003c/em\u003e in the hypoxia-inducible factor pathway [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] showed substantial selection, but the gene \u003cem\u003eEGLN1\u003c/em\u003e in the same pathway did not. By annotating specific selected regions, we discovered significant enrichment for blood index-associated traits, such as \"Hemoglobin\" and \"Plateletcrit\" (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and Table S9) Gene Ontology analysis revealed an overrepresentation of genes involved in biological processes that contribute to maintaining typical vital signs in high-altitude environments. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and Table S10). PSGs detected in Tibetan pigs have particularly enriched in hypoxia adaptation-related processes, including \"cardiac cell development\" (\u003cem\u003eMTOR\u003c/em\u003e, \u003cem\u003eHEY2\u003c/em\u003e, \u003cem\u003eSMAD4\u003c/em\u003e, \u003cem\u003eSRF\u003c/em\u003e, \u003cem\u003eTBX3\u003c/em\u003e), \"coronary vascular development\" (\u003cem\u003eHEY2\u003c/em\u003e, \u003cem\u003eSRF\u003c/em\u003e, \u003cem\u003ePTK7\u003c/em\u003e), \"platelet-derived growth factor receptor signaling pathway\" (\u003cem\u003eCBLB\u003c/em\u003e, \u003cem\u003ePTGIR\u003c/em\u003e, \u003cem\u003ePHF14\u003c/em\u003e, \u003cem\u003eAPOD\u003c/em\u003e), \"response to hypoxia\" (\u003cem\u003eSCAP\u003c/em\u003e, \u003cem\u003eMTOR\u003c/em\u003e, \u003cem\u003eSMAD4\u003c/em\u003e, \u003cem\u003eSRF\u003c/em\u003e), and \"respiratory tube development\" (\u003cem\u003eSPRY1\u003c/em\u003e, \u003cem\u003eCCDC39\u003c/em\u003e, \u003cem\u003ePHF14\u003c/em\u003e, \u003cem\u003eSRF\u003c/em\u003e, \u003cem\u003eCCBE1\u003c/em\u003e, \u003cem\u003ePTK7\u003c/em\u003e). Additionally, we observed enrichment for the nucleotide metabolism process (\u003cem\u003eDPYS\u003c/em\u003e, \u003cem\u003eMTOR\u003c/em\u003e, \u003cem\u003eNME1\u003c/em\u003e, \u003cem\u003eNME2, UOX, RHOQ\u003c/em\u003e, \u003cem\u003eDERA\u003c/em\u003e), including \"pyrimidine-containing compound metabolic process\", \"deoxyribose phosphate metabolic process\", and \"GTP metabolic process\", which provided the basis for DNA repair and help to maintain genome stability by repairing UV-induced errors during DNA replication [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor frigid adaptation, we detected four genes in the highly significant regions, including \u003cem\u003eHIF3A\u003c/em\u003e associated with adiposity [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]; \u003cem\u003eJMJD1C\u003c/em\u003e affecting de novo lipogenesis [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]; \u003cem\u003eRLN3\u003c/em\u003e associated with food intake [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]; \u003cem\u003ePAQR9\u003c/em\u003e activating thermogenesis of brown adipocyte [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. The function of these genes is critical for frigid adaptation. The signal of frigid adaptation was mainly associated with meat and carcass traits, especially for \"Fat area percentage in carcass\", which may contribute to heat retention (Fig. S5A and Table S11). Biological process enrichment analysis showed the process involved in thermogenesis \"regulation of fibroblast growth factor receptor signaling pathway\" (\u003cem\u003eHHIP\u003c/em\u003e, \u003cem\u003eSPRY1\u003c/em\u003e). It is well documented that elevated levels of FGF21 (Fibroblast Growth Factor 21) promote beige adipose tissue and enhance energy expenditure [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. We also found that domestic pigs also exhibit cold-induced vasodilation (CIVD), the enrichment of \"vasodilation\". CIVD is a dramatic increase in peripheral blood flow observed during cold exposure. It supposedly protects against cold injuries [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Meanwhile, we noticed the enrichment for pathways related to cell cycle (\u003cem\u003eCENPW\u003c/em\u003e, \u003cem\u003eINO80\u003c/em\u003e, \u003cem\u003eDLGAP5\u003c/em\u003e, \u003cem\u003eAPC\u003c/em\u003e, \u003cem\u003eCCDC61\u003c/em\u003e, \u003cem\u003eNCAPG\u003c/em\u003e, \u003cem\u003eSPRY1\u003c/em\u003e, \u003cem\u003ePTPN11\u003c/em\u003e, \u003cem\u003eREEP3\u003c/em\u003e), in line with the fact that the mammalian cell cycle is temperature sensitive [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e] (Fig. S5B and Table S12).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eA series of potential promising variants played a vital role in the selection process\u003c/h2\u003e \u003cp\u003eFor better interpreting the genetic basis under domestic selection, we thus annotated the SNPs within selection regions. We identified 21 nonsynonymous variants with high pCADD values (pCADD\u0026thinsp;\u0026gt;\u0026thinsp;10) in specifically selected regions of tropical adaptation (20 variants) and frigid adaptation (1 variant) (Table S13). As an example, we identified the mutation p.V244G in \u003cem\u003eVPS13A\u003c/em\u003e, which was previously reported as a promising mutation that may impact the secretion and aggregation of blood platelets and reduce the risk of thrombosis in southern Chinese pigs from hot environments [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] (Fig. S6A). Additionally, in \u003cem\u003eNPHP4\u003c/em\u003e, which has been verified by \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e, XP-EHH, θπ Ratio, and genotype patterns as a positively selected gene for tropical adaptation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-C), we found a nonsynonymous variant (p.A897T) showed high allele frequency difference between HP and NTP, was predicted to be functional-altering. This variant was highly conservative across multiple vertebrate species (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). \u003cem\u003eNPHP4\u003c/em\u003e was a cilia-associated protein that negatively regulates the mammalian Hippo signaling pathway and was linked to the severe degenerative renal disease nephronophthisis and blindness in humans [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. We propose that the \u003cem\u003eNPHP4\u003c/em\u003e missense mutation may enhance water reabsorption in the kidneys to mitigate the effects of heat stress on pigs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eChromatin state analysis enhanced the biological interpretations of adaptive evolution\u003c/h2\u003e \u003cp\u003eTissue-specific gene regulation plays a crucial role in the process of adaptive evolution [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Thus, we performed chromosome state enrichment analysis for the genomic regions under selective pressure within tropical, frigid, and high-altitude environments, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and Table S14-S16). The results showed a high consistency: TssA and TSS-proximal transcribed regions were most enriched, followed by enhancers. Then we examined the tissue-specific promoters (TssA) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB and Table S17-S19). Using the common promoter as a reference, our analysis revealed that lung-specific and ileum-specific promoters were significantly enriched in all adaptations. Interestingly, most tissue-specific promoters in this study were significantly enriched in tropical adaptation. Several tissue-specific promoters were found to be significantly enriched in one or more selections, including liver-specific promoters in tropical/high-altitude adaptation and cortex-specific promoters in frigid/high-altitude adaptation. Additionally, spleen-specific and stomach-specific promoters were not found to be significantly enriched in any of the three adaptations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eVariation within the cis-regulatory regions involving tropical adaptation\u003c/h2\u003e \u003cp\u003eThe \u003cem\u003eABCA1\u003c/em\u003e gene was detected to be a positively selected gene in tropical adaptation according to the \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e, XP-EHH, θπ Ratio, and genotype patterns (Fig. S7A-B). Upregulation of \u003cem\u003eABCA1\u003c/em\u003e has a protective effect against atherosclerosis [\u003cspan additionalcitationids=\"CR67\" citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. To investigate whether the expression of the \u003cem\u003eABCA1\u003c/em\u003e gene was associated with the environmental origins of pig breeds, we incorporated gene expression data. RNA-seq data revealed high expression of \u003cem\u003eABCA1\u003c/em\u003e in pig liver (Fig. S7C), with upregulation in liver expression associated with increasing annual mean temperature at the pig breed's origin (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC and Table S20). Based on the chromatin state data, we found a variant (chr1: 246,175,129, G-to-A) in the intron of \u003cem\u003eABCA1\u003c/em\u003e that may regulate the expression as it is located in the enhancer region. The SNP showed high allele frequency in HP (77.8%) compared with TP (11.3%) and NP (0%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). HOMER analysis revealed that the G-to-A mutation might alter the transcription factor binding motif at this position (Fig. S8 and Table S21), leading to changes in the expression of \u003cem\u003eABCA1\u003c/em\u003e. To confirm this hypothesis, luciferase reporter constructs were engineered. Luciferase activity analysis demonstrated that the G-to-A mutation site exhibited enhancer activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Therefore, we inferred that the mutation enhanced the expression of \u003cem\u003eABCA1\u003c/em\u003e, contributing to tropical adaptation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDomestic pigs are vital agricultural animals, providing a substantial source of animal protein globally. Intense artificial selection and crossbreeding have increased the productivity of modern commercial pig breeds but reduced their adaptive potential [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. With the changing global climate, studying the genetic adaptations of local breeds to diverse environments is crucial. In this study, we conducted a comprehensive investigation of the environmental adaptability of Chinese domestic pigs using whole-genome sequencing data and multiple omics datasets. Our findings underscored the importance of understanding the adaptive potential of domestic pigs to environmental challenges and had significant implications for the breeding of highly adaptable pig breeds.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ePopulation genetic analysis\u003c/h2\u003e \u003cp\u003eGenomic analyses reveal differentiation among pig breeds from distinct geographic regions, with Chinese local breeds likely originating from ancient Yellow River basin domestication centers [\u003cspan additionalcitationids=\"CR72 CR73\" citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Compared to modern breeds, Chinese local pigs, particularly Tibetan pigs, exhibit faster linkage disequilibrium decay, though slower than wild boars [\u003cspan additionalcitationids=\"CR76 CR77\" citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. This suggested that after domestication, local pig populations spread with human migration to diverse agricultural zones and were shaped by combined artificial and natural selection or gene flow. The genomic diversity of Chinese local pig breeds was closely associated with breeding practices, such as the free-range breeding of Tibetan pigs by Tibetans, which may have increased gene flow with local wild boars and resulted in faster linkage disequilibrium decay.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eGenome-wide selection signatures\u003c/h2\u003e \u003cp\u003eThe efficacy of methods for detecting selection signals is constrained by the comprehension of population complexity, rendering the reliability of a single approach uncertain. To circumvent this limitation, we employed an integrative approach by intersecting selection signals identified through both \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e and XP-EHH analyses to delineate final candidate regions. Subsequently, we corroborated these regions by calculating the θπ Ratio within candidate gene regions.\u003c/p\u003e \u003cp\u003eIn contrast to the study conducted by Ai et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], our investigation incorporated the XP-EHH statistic and independently examined the genetic underpinnings of tropical and frigid adaptation in pigs, yielding a more nuanced interpretation. Our findings revealed that genomic regions implicated in tropical adaptation in pigs significantly outnumbered those associated with frigid adaptation, potentially due to the increased complexity of tropical environments. We identified several key candidate genes and genomic regions associated with tropical or frigid adaptation in pigs, providing a foundation for breeding pigs with enhanced temperature adaptability. Additionally, we utilized pig breeds from Hainan (high radiation) and northern regions (frigid) as control populations to elucidate the adaptation of Tibetan pigs to the hypoxic plateau environment. We identified a series of key candidate genes associated with cardiovascular system development, which may serve as valuable references for research on human plateau-related and cardiovascular diseases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eFunctional annotation\u003c/h2\u003e \u003cp\u003eAnnotation from a single database alone cannot fully reveal the primary roles of genes within regions under selection in the organism. To explore the functions of candidate genes, here, we employed a multi-faceted approach, combining GO enrichment and QTL enrichment analyses. Our analysis confirmed a significant enrichment of traits related to blood circulation in both tropical and high-altitude adaptation in pigs, consistent with prior investigations of blood biochemical indicators in heat-stressed pigs and Tibetan pigs under normal conditions [\u003cspan additionalcitationids=\"CR80\" citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. However, upon further analysis of the specific enriched QTLs (\"LDL cholesterol\" and \"Cholesterol level\" for tropical adaptation; \"Red blood cell count\", \"Red cell distribution width\", and \"Hemoglobin\" for high-altitude adaptation) and specific enriched GO terms, we found that the overlap was coincidental. Pigs adapted to the tropics aim to increase blood circulation for heat dissipation and to reduce the risk of thrombosis [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. In contrast, pigs adapted to high altitudes aim to reduce blood viscosity caused by high hemoglobin levels and thus reduce cardiac burden [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]. This parallel selection of traits warrants consideration in the breeding of pigs with broad environmental adaptability. In contrast to the parallel selection of traits, specific selection for adaptation to distinct environments is more prevalent. Our results support this notion, as evidenced by the selection for fat coverage in cold region pig breeds, granulocyte activity and blood lipid content in tropical pig breeds, and hemoglobin content in high-altitude hypoxic pig breeds. These specifically selected traits may facilitate the breeding of pig breeds adapted to particular environments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eChromatin state annotation and cis-regulatory mutations\u003c/h2\u003e \u003cp\u003eGene regulation plays a crucial role in speciation and adaptive diversification [\u003cspan additionalcitationids=\"CR87\" citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. Cis-regulatory mutations can alter the expression of proximal genes and have long been considered important targets for adaptive phenotypic evolution, as they may have fewer deleterious effects than changes in protein-coding sequences [\u003cspan additionalcitationids=\"CR90\" citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e]. While protein-coding mutations may affect protein products throughout tissues and developmental stages, cis-regulatory mutations can influence gene expression in spatially and temporally specific manners. Several studies have identified the importance of non-coding region mutations in local adaptation [\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. Previous research on the adaptive evolution of domestic pigs primarily focused on protein-coding regions of the genome, annotating candidate gene functions to elucidate environmental adaptation, with little systematic exploration of regulatory regions.\u003c/p\u003e \u003cp\u003eTo investigate the mechanisms by which variation in regulatory regions affects the environmental adaptability of pigs, we analyzed tissue-specific chromatin states in candidate regions. By examining the enrichment of tissue-specific regulatory factors, we pinpointed the lung and ileum as common functional tissues for tropical, frigid, or high-altitude adaptations. As the undertaker of respiration, the role played by the lung in the process of adapting to various environments was well studied. Numerous investigations have demonstrated that interactions between the host and its microbiome can effectively modulate host adaptability [\u003cspan additionalcitationids=\"CR95 CR96 CR97 CR98 CR99\" citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e] and that host genetic selection can affect gut microbiome composition [\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e, \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e]. We, therefore, hypothesize that gut-specific regulatory elements indirectly regulate gut microbiome composition during pig environmental adaptations. In contrast, the spleen and stomach did not appear to have a specific role in the adaptation of domestic pigs to various environments.\u003c/p\u003e \u003cp\u003eIn this study, many tissues showed outstanding contributions to tropical adaptation. This indicated that heat stress affects a wider range of tissues. Interestingly, cortex-specific promoters were especially enriched in the frigid and high-altitude selection, consistent with the general observation that Tibetan pigs and Northern domestic pigs are more active and aggressive [\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e]. This finding provided additional evidence of the vital role that tissue-specific gene regulation plays in the adaptive selection process during the domestication of Chinese pigs.\u003c/p\u003e \u003cp\u003eThrough our elucidation of the positive selection phenomena occurring within regulatory regions during environmental adaptations, our study extended our understanding of pig environmental adaptability to specific tissues, providing a framework for incorporating single-cell data into future adaptability research while also highlighting the crucial role played by cis-regulatory mutations in enabling pig adaptation to tropical environments.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003e In summary, we conducted a genome-wide selection scan based on whole-genome sequencing data from six pig breeds (Wuzhishan, Tunchang, Ding'an, Min, Hetao, Tibetan) to identify the selected genomic regions associated with environmental adaptations (tropical, frigid, high-altitude) in Chinese local pigs. Genetic diversity analysis confirmed the strong shaping ability of the environment on the pig genome. We identified biological processes and traits closely related to pig environmental adaptations, and subsequent integration of multi-omics data extended the genetic features of pig environmental adaptations to specific functional tissues and cis-regulatory mutations. Our results enhance our understanding of the process of pig environmental adaptations, providing significant value for pig genetic breeding and human disease reference.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCIVD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCold-induced vasodilation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSNP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSingle nucleotide polymorphisms\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMAF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMinor allele frequency\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLinkage disequilibrium\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNJ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNeighbor-Joining\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrincipal component analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eXP-EHH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCross population extend haplotype homozygosity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eQTL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQuantitative trait locus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Ontology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTssA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStrongly active promoters/transcripts\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTssAhet\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFlanking active TSS without ATAC\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTssBiv\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTranscribed at gene\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTxFlnk\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTranscribed at gene\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTxFlnkWk\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWeak transcribed at gene\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTxFlnkHet\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTranscribed region without ATAC\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEnhA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStrong active enhancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEnhAMe\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMedium enhancer with ATAC\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEnhAWk\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWeak active enhancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEnhAHet\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eActive enhancer no ATAC\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEnhPois\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePoised enhancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRepr\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRepressed polycomb\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eReprWk\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWeak repressed polycomb\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eQui\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQuiescent regions\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eATAC_Is\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eATAC island\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAllele frequency\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003epCADD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCombined annotation dependent depletion for pig\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePolymerase chain reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Key Research and Development Program of China (2021YFF1000600), the National Natural Science Foundation of China (32002150 and U23A20229), the Basic and Applied Basic Research Foundation of Guangdong Province (2020B1515120053), the Shenzhen Science and Technology Innovation Commission (JCYJ20190813114401691), the Central Government Guiding Funds for Local Science and Technology Development of China (He-Ke ZY220603), and the Open Project of Hainan Provincial Key Laboratory of Tropical Animal Reproduction \u0026amp; Breeding and Epidemic Disease Research (HKL2020101).\u003c/p\u003e\u003ch2\u003eAuthors\u0026rsquo; contributions\u003c/h2\u003e \u003cp\u003eGY conceived and designed the study. ZW collected the data, performed bioinformatics analysis, and visualized the results. BS and JY conducted the experiments. ZW wrote the original draft. GY, ZT, ZW, XL, and YZ reviewed and edited the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe are grateful to Hongwei Yin for his suggestions for performing selective signal detection.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eThe whole genome resequencing data that were analyzed during the current study are available in the NCBI primary data archive (PDA) with accession number PRJNA754250. Chromatin state data used in this study can be found in: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://farm.cse.ucdavis.edu/~zhypan/Nature_Communications_2021\u003c/span\u003e\u003cspan address=\"http://farm.cse.ucdavis.edu/~zhypan/Nature_Communications_2021\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The RNA-seq data is available in the publicly available datasets, PIGOME (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://pig123456789.pigome.com/\u003c/span\u003e\u003cspan address=\"http://pig123456789.pigome.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMiraglia M, Marvin HJP, Kleter GA, Battilani P, Brera C, Coni E, et al. Climate change and food safety: An emerging issue with special focus on Europe. 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Nat Genet. 2015;47(3):190\u0026ndash;2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/ng.3191\u003c/span\u003e\u003cspan address=\"10.1038/ng.3191\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\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":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-animal-science-and-biotechnology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jasb","sideBox":"Learn more about [Journal of Animal Science and Biotechnology](http://jasbsci.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jasb/default.aspx","title":"Journal of Animal Science and Biotechnology","twitterHandle":"@animalplantsci","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Pig, Whole genome resequencing, Chinese local breeds, Population genetics, Selection signals, Environmental adaptations","lastPublishedDoi":"10.21203/rs.3.rs-3942411/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3942411/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003eLong-term natural and artificial selection resulted in many genetic footprints within the genomes of pig breeds across distinct agroecological zones. Nevertheless, the mechanisms of how these signatures contribute to phenotypic diversity and facilitate environmental adaptation remain unclear.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eHere, we leveraged whole-genome sequencing data of 82 individuals from six domestic pig breeds originating in tropical, frigid, and high-altitude regions. Population genetic analysis suggested that environmental adaptations significantly contributed to population stratification in Chinese local pig breeds. Analysis of selection signals identified regions under selection for tropical adaptation (55.5 Mb), high-altitude adaptation (43.6 Mb), and frigid adaptation (17.72 Mb). The potential functions of the selective sweep regions were linked to certain complex traits that might play critical roles in different geographic environments, including fat coverage in frigid environments and blood indicators in tropical and high-altitude environments. Candidate genes under selection were significantly enriched in the biological pathways involved in environmental adaptations. These pathways contained blood circulation, protein degradation, and inflammation for tropical adaptation; heart and lung development, hypoxia response, and DNA damage repair for high-altitude adaptation; andthermogenesis, cold-induced vasodilation (CIVD), and cell cycle for frigid adaptation. By examining the chromatin state of the selection signatures, we detected the lung and ileum as two critically functional tissues for environmental adaptations. Finally, we unveiled a mutation (chr1: G246,175,129A) in cis-regulatory regions of \u003cem\u003eABCA1\u003c/em\u003e as a plausible promising variant for tropical adaptation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eIn this study, we conducted a genome-wide exploration of the genetic mechanisms underlying the tropical, frigid, and high-altitude adaptability of Chinese local pig breeds. Our findings shed light on the prominent role of cis-regulatory elements in impacting environmental adaptation in pigs and may serve as a vital biomodel on human plateau-related disorders and cardiovascular diseases.\u003c/p\u003e","manuscriptTitle":"Whole-genome analysis reveals distinct adaptation signatures to diverse environments in Chinese domestic pigs","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-20 16:15:01","doi":"10.21203/rs.3.rs-3942411/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-02-18T11:44:43+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-17T01:52:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-08T06:17:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Animal Science and Biotechnology","date":"2024-02-07T09:41:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-animal-science-and-biotechnology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jasb","sideBox":"Learn more about [Journal of Animal Science and Biotechnology](http://jasbsci.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jasb/default.aspx","title":"Journal of Animal Science and Biotechnology","twitterHandle":"@animalplantsci","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6e1f3d1c-9ab6-4e46-a27b-3c68d67e9947","owner":[],"postedDate":"February 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-05-21T01:19:39+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-20 16:15:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3942411","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3942411","identity":"rs-3942411","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}