Characterizing breed-shared and breed-specific genetic regulatory effects of gene expression across three pig breeds

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These differences have been attributed to various factors, including genetic variation, selection, and environmental influences. Gene expression regulation, serving as a critical intermediary mechanism that bridges genotypes and phenotypes, may play a pivotal role in driving these differences across breeds. Hence, we characterized the breed-shared and breed-specific pattern in genetic regulatory effects on gene expression via expression quantitative trait loci (eQTL) mapping in three pig breeds (Duroc, Landrace, and Yorkshire), aiming to gain a deeper understanding of the molecular basis underlying complex trait differences across breeds. Results We observed breed differentiation at both the single-nucleotide polymorphism (SNP) and gene expression levels. By eQTL mapping, within each tissue, an average of 71.1% of the eGenes identified in each breed were breed-shared, while the remaining 28.9% were breed-specific. We found that some regulatory effects are relevant to either the difference in average gene expression or expression variance among populations. Breed-shared eGenes were more abundant and showed larger effect sizes and lower evolutionary conservation, and vice versa. Enrichment analysis showed that the genome-wide association studies (GWAS) loci were significantly enriched in the cis-eQTLs of eGenes for an average of 12 of 19 complex traits per breed. These loci exhibited higher enrichment in breed-specific eGenes than breed-shared eGenes. Through colocalization analyses with GWAS loci, we observed 220 colocalization events (PP.H4 > 0.8) with breed-specific eGenes and 758 events with breed-shared eGenes. Conclusions Our study reveals breed-shared and breed-specific effects and characteristics of genetic regulation on gene expression in three pig breeds. Both breed-shared and breed-specific eGenes contribute to the genetic regulation of complex traits, while breed-specific eGenes explain regulatory variation unique to each breed. These findings together improved our understanding of breed-dependent genetic regulation and their contribution to complex traits. cis-eQTL mapping gene expression regulation breed-shared regulation breed-specific regulation complex traits Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background As the most common and important modern commercial pig breeds, Duroc, Landrace, and Yorkshire exhibit distinct differences in complex traits performance, including growth rate, meat quality, immune response, and reproductive capacity, reflecting their different breeding histories and selection goals. In the pig industry, the Duroc pig is one of the most utilized commercial boar lines and is well-known for its growth, feed conversion efficiency, carcass, and meat quality traits [ 1 ], which possess strong skeletal muscle growth potential and high lean meat yield [ 2 ]. In contrast, Landrace pigs are primarily employed as maternal lines in commercial breeding programs due to their superior reproductive performance, characterized by large litter size, strong maternal behavior, and high milk production [ 3 ]. Yorkshire pigs combine excellent growth performance, high lean meat yield, and strong adaptability, which are frequently crossed with Landrace pigs to produce F1 hybrid sows for enhanced reproductive and maternal traits [4; 5]. Intensive artificial selection has strongly targeted genes relating to production performance, reproduction, and carcass traits in these commercial breeds, thereby contributing to differences in complex traits across breeds [ 6 ]. This phenotypic diversity is influenced by a combination of genetic variation, selective breeding, and environmental factors. Although genome-wide association studies have identified numerous loci associated with traits over the past decades, most significant GWAS loci are found in non-coding regions, and these genetic variants often fail to fully explain the observed phenotypic differences, suggesting that additional regulatory mechanisms may play a role in shaping traits [ 7 ]. Besides genetic variation, gene expression differences have also emerged as a key factor shaping complex traits. Gene regulation serves as a key intermediate layer that connects genotypes to phenotypes [ 8 ]. Past research showed that integrating genetic variation with gene expression patterns can help reveal the genetic basis of these molecular traits and their relationships with higher-order phenotypes [ 9 ]. Expression quantitative trait loci (eQTL) are genomic variants that influence gene expression levels across individuals, and the genes they regulate are termed eGenes. Identifying eQTLs and eGenes enables functional interpretation of regulatory variants and provides a crucial link between genetic variation and phenotypic traits. With the establishment of large-scale transcriptomic resources such as cattleGTEx [ 10 ], pigGTEx [ 11 ], and FarmGTEx [ 9 ], integrative eQTL studies across multiple breeds in livestock have emerged, highlighting the importance of molecular traits, particularly gene expression regulation, in understanding the genetic basis of complex traits. In livestock, most studies on molecular phenotypes have focused on a multiple-breed design, aiming to identify genetic effects shared across breeds. However, different breeds can exhibit distinct patterns of complex traits, and approaches that primarily emphasize shared effects may overlook breed-specific regulatory mechanisms [ 12 ]. As a result, such studies may fail to capture the full spectrum of variants contributing to complex traits. Similarly, many human diseases differ among populations in prevalence, severity, or age of onset, and these differences can be partly explained by variation in gene expression regulation [ 13 ]. However, early studies were largely confined to populations of European ancestry, limiting the generalizability of their findings, and may have overlooked genetic effects that are specific to other populations [ 14 ]. More recent research incorporating diverse ancestral backgrounds has revealed that some regulatory effects are shared across ancestries, while others are ancestry-specific, demonstrating that ancestry-specific regulatory mechanisms can explain additional phenotypic variation beyond shared effects [ 15 – 17 ]. However, little is known about the shared and breed-specific regulatory effects across pig breeds. To investigate breed-shared and breed-specific characteristics in gene expression regulation among pig breeds, we applied cis -eQTL mapping in Duroc, Landrace, and Yorkshire pigs. By integrating these results with genome-wide association studies, we further explored the contribution of breed-shared and breed-specific regulatory variation to phenotypic diversity. This work provides a preliminary exploration of breed-dependent genetic regulation and their contribution to complex traits. Methods Population and data The population used in our study consisted of 300 pigs for 3 different breeds (100 Duroc, 100 Landrace, and 100 Yorkshire) generated from [ 18 ]. The genotypic data in VCF format and raw RNA-seq data in FASTQ format of this population were downloaded from the GigaScience GigaDB database ( http://dx.doi.org/10.5524/102388 ). All individuals (n = 300) were genotyped using Whole genome sequencing (WGS) with a depth of ~ 10×. The downloaded genotypic data comprised 31,682,957 SNPs. We retained 15,495,927 biallelic variants with minor allele frequency (MAF) ≥ 5% and minor allele count (MAC) ≥ 6 in 18 autosomes after quality control across 300 pigs. To confirm the breed information of the 300 pigs, we used ADMIXTURE (v1.3.0) [ 19 ] to estimate ancestry proportions of each individual. The same procedure as the breed predication analysis pipeline implemented in PigGTEx [ 11 ]. The raw RNA-seq data used in this study included three tissues, duodenum, muscle, and liver for each individual, resulting in a total of 900 RNA-seq samples. We used Trimmomatic (v0.39) [ 20 ] to trim adaptors and discard reads with poor quality, and then used STAR (v2.7.0) [ 21 ] to align clean reads to the Sscrofa11.1(v100) pig reference genome. Gene raw read counts were acquired with featureCounts (v1.5.2) [ 22 ], and normalized expression (transcripts per million, TPM) was derived from these counts. We retained 29,000 genes after excluding those with low expression, as indicated by TPM < 0.1 and/or raw read counts < 6 in more than 80% of samples within each tissue across 300 pigs. Differential gene expression analysis In order to explore whether there are differences in gene expression levels across breeds, we performed differential gene expression analysis between one breed and the other two breeds. We assessed expression differences across breeds within each tissue using the TPM data with limma (R package) [ 23 ]. We defined genes with |log 2 FC| >1 and FDR < 0.05 as significantly differentially expressed genes. Cis -eQTL mapping To investigate differences in gene expression regulation across breeds, we performed cis -eQTL mapping in each tissue of each breed separately. Quality control of the genotype data in each breed was performed with the criteria of MAF > 0.01, MAC > 6, and het < 0.99. SNPs common to all three breeds after separate quality control were used in the subsequent analyses, with finally 6,521,645 SNPs. For gene expression data, we first split the gene expression profiles (TPM data) by tissue and breed to obtain the TPM matrix for each tissue of each breed. Then we filtered the low-expressed genes in the TPM data for each tissue of each breed, where low-expressed genes refer to genes with a TPM ≤ 0.1 in more than 80% of the samples. After obtaining the filtered genes for each tissue of each breed, we normalized the gene expression across samples using the trimmed mean of M-value (TMM) method, implemented in edgeR [ 24 ], followed by inverse normal transformation of the TMM. We performed cis -eQTL mapping using a linear mixed model implemented in OmiGA [ 25 ] to test associations of the normalized gene expression levels with SNPs in the ± 1Mb of the transcription start site (TSS) of target genes. In order to control the effects of the remaining potential confounders, the principal components of the gene expression (TMM data) were used in this study as covariates, using the function of OmiGA (--geno-pc-covar 0 --dprop-pc-covar 0.001). OmiGA would select the first n PCs as covariates when the increase in the proportion of variance explained by n + 1 PCs and n + 2 PCs was less than 0.1% of the first n PCs. The linear mixed model is as follows: $$\:\mathbf{y}=\mathbf{X}\varvec{\alpha\:}+{\mathbf{s}}_{\text{a}}{\varvec{\beta\:}}_{\text{a}}+{\mathbf{g}}_{\text{a}}+\mathbf{e}$$ , where y is an n×1 vector of normalized gene expression levels (TMM data), X is an n×c matrix of covariates, including a column of 1, with corresponding fixed effect \(\:\varvec{\alpha\:}\) , \(\:{\mathbf{s}}_{\text{a}}\) is an n×1 vector of vector of mean centered genotypes values at the variant being tested, coded as 0, 1, or 2 for the AA, Aa, and aa genotypes. \(\:{\varvec{\beta\:}}_{\mathbf{a}}\) is the variant’s genetic effect, \(\:{\mathbf{g}}_{\text{a}}\) is an n×1 vector of total genetic effects with \(\:{\mathbf{g}}_{\text{a}}\sim\:\text{N}(0,\:\mathbf{G}{{\sigma\:}}_{\text{a}}^{2})\) where the genomic relationship matrix (GRM) is defined as \(\:\mathbf{G}=\frac{\mathbf{M}{\mathbf{M}}^{{\prime\:}}}{2{\sum\:}_{i=1}^{m}{p}_{i}(1-{p}_{i})}\) [ 26 ], of which M is a matrix of mean centered genotypes for genome-wide genetic variants and \(\:{\varvec{p}}_{\varvec{i}}\) is the MAF of the i th variant, e is n×1 vector of residuals with \(\:\mathbf{e}\sim\:\text{N}(0,\:{\mathbf{I}}_{n}{{\sigma\:}}_{\text{e}}^{2})\) where \(\:{\mathbf{I}}_{n}\) is an n×n identity matrix. We considered SNPs with a nominal P -value below the variant-level threshold (obtained from permutations) as significant cis -eQTLs. We considered eGenes with a gene-level P -value corrected by the Benjamini-Hochberg method that was below the significance threshold (qvalue < 0.05). Definition and functional annotation of breed-shared and breed-specific eGenes To understand the shared or specific genetic expression regulatory mechanisms across breeds, we defined breed-shared eGenes and breed-specific eGenes. Breed-shared eGenes were defined as eGenes that exist in at least two breeds within the same tissue. Breed-specific eGenes were defined as eGenes that exist in only one breed within the same tissue. Breed-shared eGenes included two-breed-shared eGenes (eGenes only shared in Duroc and Landrace, Duroc and Yorkshire, Landrace and Yorkshire) and three-breed-shared eGenes (eGenes shared across Duroc, Landrace, and Yorkshire). To further resolve the potential biological functions of eGenes, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on breed-shared eGenes and breed-specific eGenes using clusterProfiler (R package) [ 27 ]. PhastCons score calculation of eGenes To understand the evolutionary sequence conservation of the different types of eGenes, we downloaded PhastCons scores of 100 vertebrate species from UCSC ( http://hgdownload.cse.ucsc.edu/goldenpath/hg38/phastCons100way/hg38.100way.phastCons/ ). We first converted the Wiggle files of PhastCons scores to bed files using the BEDOPS tool (v2.4.40) [ 28 ], and then lifted over from human genome 38 (h38) to Sscrofa11.1 using UCSC’s LiftOver tool [ 29 ]. We used the mean PhastCons scores of sequences within a gene to represent its PhastCons score. Genes were considered only if at least 50% of their sequence length could be mapped in LiftOver. GWAS summary statistics The GWAS summary statistics used in this study were generated from [ 30 ]. We used the meta-GWAS summary statistics results integrating multiple populations and breeds (mainly including Duroc, Landrace, and Yorkshire). Only GWAS summary statistics records containing at least one SNP with a P -value ≤ 5 × 10⁻⁸ were retained for downstream analyses. The traits utilized for enrichment and colocalization analysis include 19 traits, they were average daily gain (ADG, n = 36,943), backfat thickness (BFT, n = 58,725), body weight (BW, n = 42,256), days (DAYS, n = 49,595), gestation days (GD, n = 13,325), lean cuts percentage (LEANCUTP, n = 17,523), loin muscle area (LMA, n = 26,176), loin muscle depth (LMDEP, n = 34,439), number born alive (NBA, n = 13193), number born alive (day21) (NBA_D21, n = 1,083), number born of healthy pigs (NBH, n = 17,746), number born of stillborn pigs (NBS, n = 11,521), total litter weight of piglets born alive (TLWT_BA, n = 19,701), total litter weight of piglets (day 21) (TLWT_D21, n = 9064), total litter weight of piglets (weaning) (TLWT_Weaning, n = 11,166), total number of born (TNB, n = 22,217), teat number (TNUM, n = 38,158), uterine capacity (UC, n = 8,571), and weaning to estrus interval (WSI, n = 16,261). Enrichment analysis of eGenes and GWAS signals To evaluate the association between eGene regulatory regions and complex traits, we performed the enrichment analysis between GWAS signals and the cis-eQTLs of eGenes. We used the significant GWAS SNPs with P -value ≤ 5×10 − 8 , the eGenes region within ± 1Mb of TSS for eGenes. We defined significant GWAS SNPs as the observed set and randomly sampled an equal number of SNPS from the non-significant SNPs as a control set. This sampling procedure was repeated 1,000 times to generate a background distribution. The fold enrichment was then calculated as the proportion of significant GWAS SNPs located in eGene regions divided by the mean proportion of the proportion of control set falling within eGene regions. Colocalization of cis -eQTL and GWAS To investigate the regulatory mechanisms underpinning complex traits in pigs, we performed colocalization analysis between breed-shared, breed-specific eGenes and GWAS summary statistics using coloc (R package) [ 31 ]. Colocalization was performed for each eGene using SNPs within ± 1 Mb of its TSS. We considered colocalization to be significant when the posterior probability for a shared causal variant (PP.H4) exceeded 0.8. Results Population structure and gene expression patterns for three pig breeds After quality control, we kept 6,521,645 common SNPs across three pig breeds (i.e., Duroc, Landrace, and Yorkshire). To explore the population structure of these pigs, we performed PCA using the genotypic data from all 300 individuals. We observed that these individuals clustered mainly according to breeds (Fig. 1 a). The first and second genotype PCs (i.e., PC1 and PC2), explained 18.10% and 11.37% of variance, respectively. To explore the patterns of gene expression among these three pig breeds, we calculated the Pearson’s correlation using gene expression levels from all 900 samples from three tissues. The heatmap of gene expression correlations between samples showed that samples from the same tissue exhibited higher correlation, especially within muscle and liver, indicating that different tissue types contribute more to gene expression variation than different breeds ( Fig. S1 ). We further performed PCA in each of three tissues based on the log-transformed transcript per million (TPM) data from 29,000 genes. We observed that the samples tended to be clustered by different breeds, with Landrace and Yorkshire positioned closer to each other than either is to Duroc (Fig. 1 b-d). Moreover, we detected the differential expressed genes among the three breeds for each tissue. In the duodenum, muscle, and liver, we found that 867, 949, and 2,904 differentially expressed genes for Duroc, 351, 461, and 612 for Landrace, and 763, 282, and 612 for Yorkshire, respectively. Although breed differential at the genomic level and tissue differential at the transcriptomic level have been well pronounced, the present results indicated the existence of breed differential at the transcriptomic level within tissue. Breed-stratified cis -eQTL mapping and eGenes sharing patterns To investigate the contribution of genetic regulation to gene expression differences across pig breeds, we separately conducted cis -eQTL mapping in the duodenum, muscle, and liver for each of the three breeds. We identified 5,204, 4,789, and 5,555 eGenes in the duodenum, muscle, and liver of Duroc, respectively; 5,886, 5,318, and 6,541 eGenes in Landrace; and 7,043, 4,659, and 6,651 eGenes in Yorkshire. We further classed them into breed-specific eGenes and breed-shared eGenes, of which breed-shared eGenes include eGenes shared in two or three breeds (Fig. 2 a). Within each tissue, an average of 71.1% of the eGenes identified in each breed were breed-shared, while the remaining 28.9% were breed-specific. It indicated that the majority of cis -regulatory effects on gene expression are conserved across breeds, which is consistent with findings from the PigGTEx project [ 11 ] and some human studies [ 32 – 34 ]. It must be pointed out that Duroc shared fewer eGenes with the other two breeds in all tissues, such as in the duodenum, 1,567 eGenes were shared between Landrace and Yorkshire, but 683 or 951 were shared between Duroc and Landrace or Yorkshire, respectively. For instance, A Duroc-specific eGene IGF2R was involved in the mannose-6-phosphate (M6P) sorting pathway, which mediates the transport of phosphorylated lysosomal enzymes from the Golgi complex and the cell surface to lysosomes (Fig. 2 b), and a breed-shared eGene PEX7 was involved in Peroxisomal protein import and Ether lipid biosynthesis pathways (Fig. 2 c). These results indicate that there are differences in regulatory effects for gene expression across three pig breeds. Characteristics of breed-shared and breed-specific eGenes To investigate the characteristics of breed-shared and breed-specific eGenes, we compared the fold change values from differential expression analysis between breed-shared and breed-specific eGenes. We found that Duroc-specific eGenes exhibit significantly higher expression specificity in the Duroc population than the other two breeds (Fig. 2 d). For example, the GNB3 gene is a Duroc-specific eGene that exhibits a high average expression level in Duroc (Fig. 2 e). We also observed some breed-specific eGenes like MPHOSPH10 that show large expression variation (Fig. 2 f). Similar patterns were observed in muscle for the Landrace population, muscle and liver for the Yorkshire population ( Fig. S2 ). These results indicate that breed-specific regulatory effects are not only relevant to the difference in average gene expression but also expression variance among populations. To investigate the potential biological functions of these breed-shared and breed-specific eGenes, we performed GO and KEGG pathway enrichment analyses. We observed that the breed-shared eGenes in muscle were significantly enriched in carboxylic acid metabolic process (fold enrichment = 1.64, FDR < 0.05), and other biological processes ( Additional file 1: Table S1 -S2 ). The Duroc-specific eGenes in muscle were enriched in biological processes such as regulation of mitotic cell cycle (fold enrichment = 2.67, FDR < 0.05) and negative regulation of gene expression (fold enrichment = 1.98, FDR < 0.05) ( Additional file 1: Table S3-S4 ). To further explore the genetic regulatory effect and conservation of breed-shared or specific eGenes, we examined their effect sizes (beta) of lead cis -eQTLs and PhastCons score. We found that the lead cis -eQTLs exhibit higher regulatory effect sizes for breed-shared eGenes than breed-specific eGenes (Fig. 3 a). For instance, in muscle, the median effect sizes were 0.60 for breed-specific eGenes, 0.66 for eGenes shared in two breeds, and 0.78 for eGenes shared in three breeds. From the PhastCons score, an evolutionary conservation across 100 vertebrates, we observed that eGenes shared across three breeds present the lowest cross-species conservation than those eGenes only detected in one breed or not detected in any breeds (Fig. 3 b). For instance, in liver, the median effect sizes were 0.17 for non-eGenes, 0.14 for breed-specific eGenes, 0.13 for eGenes shared in two breeds, and 0.12 for eGenes shared in three breeds. These findings suggest that breed-shared eGenes have stronger regulatory effect sizes and lower evolutionary conservation, whereas breed-specific eGenes have smaller effect sizes and higher evolutionary conservation. Similarity patterns have also been reported in human studies [35; 36]. Contribution of breed-stratified cis -eQTLs for complex traits of pigs To evaluate the contribution of breed-shared and breed-specific eGenes on the regulation of pig complex traits, we conducted the enrichment analysis using GWAS summary statistics with significant associations from nineteen complex traits. We observed that the GWAS loci were significantly enriched in the cis -eQTLs of eGenes for an average of 12 of 19 complex traits per breed in Duroc, Landrace, and Yorkshire. Notably, these GWAS loci exhibited higher enrichment in breed-specific eGenes than breed-shared eGenes (Fig. 4 a-c). This result suggests that, similar to the cis -eQTLs of breed-shared eGenes, these cis -eQTLs of breed-specific eGenes play a crucial role in regulating the complex traits of pigs. To explore whether causal SNPs shared between the breed-shared and breed-specific eGenes and GWAS signals of pig complex traits, we further performed colocalization analysis to discover shared regulatory effects between gene expression and complex traits. Totally, we observed 758 and 220 colocalization events (PP.H4 > 0.8) with breed-shared and breed-specific eGenes, respectively. The colocalization events from these cis -eQTLs of breed-shared eGenes are substantially higher than those of cis -eQTLs of breed-specific eGenes (Fig. 4 d-e). The more observations of colocalization events on breed-shared eGenes than breed-specific is consistent with the observations on the number of eGenes. For example, there is a GWAS signal in chromosome 5 of the TNUM trait colocalized with the cis -eQTLs of the OS9 gene in liver across three pig breeds (Fig. 5 a), of which the OS9 is a breed-shared eGene in liver across three breeds. OS9 encodes a lectin component of the mammalian HRD1 ubiquitin ligase complex, which is essential for multiple physiological processes, including metabolic regulation, maintenance of intestinal homeostasis, immune cell function, prohormone maturation, and β-cell identity [ 37 ]. In humans, OS9 has been reported as a stable housekeeping gene in breast tissue and has also been implicated in breast cancer biology [ 38 ]. While our findings suggest a potential link between OS9 regulation in the liver and TNUM variation across three pig breeds, the precise molecular mechanisms underlying this association remain to be elucidated. The GWAS signal of the LMDEP trait colocalized with the cis-eQTLs of the PRIMA1 gene in muscle, of which the PRIMA1 gene is a Duroc-specific eGene in muscle (Fig. 5 b). The function of the PRIMA1 gene is to organize acetylcholinesterase (AChE) into tetramers and to anchor AChE at neural cell membranes. A previous study demonstrated that PRiMA -linked G4 acetylcholinesterase (AChE) is localized at neuromuscular junctions (NMJs) and that its expression in motor neurons contributes to this synaptic localization [ 39 ], suggesting a critical role in regulating muscle contraction. PRIMA1 expression is also influenced by neuronal differentiation and maturation [ 40 ], which may contribute to variation in its expression across breeds. Together with our colocalization analysis, these findings suggested that breed-specific regulation of PRIMA1 may be associated with variation in this muscle-related trait. These results together highlight that, as a result of complementarity, the cis -eQTL of breed-specific eGenes could explain additional GWAS signals of complex traits. Discussion Understanding the genetic regulation of gene expression across different breeds is crucial for interpreting the molecular basis of phenotypic diversity and complex traits. In this study, we systematically identified breed-shared and breed-specific eGenes across three pig breeds and evaluated their potential contributions to complex traits by enrichment and colocalization analyses. We explored both breed-shared and breed-specific genetic regulation on gene expression and emphasized their contribution to complex traits, indicating that breed-shared regulatory variation and breed-specific regulatory variation jointly contribute to phenotypic diversity. Breed-shared and breed-specific eGenes exhibited distinct regulatory and characteristics. Within each tissue of each breed, the majority of eGenes identified were shared, and the remaining were breed-specific. We have detected that some eGenes are differentially expressed. Some eGenes have higher expression variance in the specific breed, indicating that genetic variants may influence expression variability rather than mean expression. In line with this concept, recent human studies have identified variance QTL (vQTLs), which capture regulatory effects on phenotypic variability and uncover additional regulatory signals, particularly gene–environment interactions [ 41 – 43 ]. Applying similar approaches in pigs may uncover breed-specific regulation not detected by standard eQTL mapping. Conservation analyses indicated that breed-specific eGenes are likely under stronger selective constraints arising from domestication and breed diversification, and they may play key roles in defining breed-specific adaptive traits. By contrast, shared eGenes evolve under less evolutionary constraint and may tolerate regulatory variation affecting general cellular or metabolic functions, resulting in larger effect sizes across breeds [ 35 ]. Both breed-shard and breed-specific eGenes colocalized with complex traits (Fig. 4 d-e), indicating that genetic regulation of gene expression involves both shared and breed-specific effects. Similar findings in human studies, where ancestry-specific eQTLs provide additional explanatory power for complex traits [ 44 ]. Our findings further highlight the contribution of breed-specific regulatory variation to phenotypic variation in pigs. Duroc pigs exhibited marked genetic and transcriptional divergence from Landrace and Yorkshire, with numerous Duroc-specific eGenes in each tissue. Duroc-specific eGenes in muscle involved with biological processes such as cell differentiation and proliferation stages, coupled with the insights gained from exploring the genetic underpinnings of these eGenes advance our understanding of the genetic architecture of gene regulation. These eGenes are also related to growth traits (e.g., DAYS, LMDEP, and LMA), with the results that are significantly enriched in GWAS loci and have colocalization events with growth traits. Such regulatory variation may influence cellular differentiation and growth in muscle. Previous studies have reported that Duroc pigs possess a stronger myogenic potential, characterized by higher cellular activity and regenerative capacity in muscle development, and ligand–receptor interaction analyses of muscle stem cells revealed that Duroc myogenic lineage cells receive more proliferative signals [ 45 ]. All these findings and studies suggest that the differentiation and growth of muscle in Duroc may be regulated by these specific eGenes, thereby contributing to trait differences across breeds. However, colocalization analysis may be influenced by linkage disequilibrium structure; future studies should integrate genetic variation, GWAS results, context-specific multi-omics (e.g., ATAC-seq, ChIP-seq, Hi-C, single-cell RNA-seq), and functional validations to gain a deeper understanding of the genetic architecture underlying breed-shared and breed-specific regulation. Some previous studies were limited by datasets [ 46 ], which may have constrained the scope of analysis. In addition, most studies in livestock have often focused on shared regulatory effects by combining multiple breeds into the analysis [10; 11]. Such studies may mask breed-specific regulatory variation. The structure of our dataset enabled us to investigate both breed-shared and breed-specific aspects of gene regulation. It is well established that the power of eQTL mapping is correlated to the sample size [ 7 ]. In this study, the sample size for each breed was limited to 100 individuals, which may have constrained our ability to detect eGenes with small effect sizes and thus led to an underestimation of the full extent of regulatory variation. In the future, the collection of multimodal data and eQTL mapping across tissues and diverse breeds can allow a more comprehensive assessment of breed specificity and the exact mechanisms underlying breed differences in gene regulation. Conclusions Our study reveals both breed-shared and breed-specific effects and characteristics of genetic regulation on gene expression in three pig breeds. Both breed-shared and breed-specific eGenes contribute to the genetic regulation of complex traits, while breed-specific eGenes explain regulatory variation unique to each breed. These findings together improved our understanding of breed-dependent genetic regulation and their contribution to complex traits. Abbreviations eQTL Expression quantitative trait loci SNP Single-nucleotide polymorphism GWAS Genome-wide association studies WGS Whole genome sequencing MAF Minor allele frequency MAC Minor allele count Het Heterozygosity rate TPM Log-transformed transcript per million TMM Trimmed mean of M-value TSS Transcription start site GRM Genomic relationship matrix GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes ADG Average daily gain BFT Backfat thickness BW Body weight DAYS Days GD Gestation length LEANCUTP Lean cuts percentage LMA Loin muscle area LMDEP Loin muscle depth NBA Number born alive NBA_D21 Number born alive (day21) NBH Number born of healthy pigs NBS Number born of stillborn pigs TLWT_BA Total litter weight of piglets born alive TLWT_D21 Total litter weight of piglets (day 21) TLWT_Weaning Total litter weight of piglets (weaning) TNB Total number of born TNUM Teat number UC Uterine capacity WSI Weaning to estrus interval PCA Principal component analysis M6P Mannose-6-phosphate AchE Acetylcholinesterase NMJs Neuromuscular junctions vQTL Variance quantitative trait loci Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Competing interests The authors declare that they have no competing interests. Funding This work was supported by the earmarked fund for the China Agriculture Research System (CARS-35), Guangxi Science and Technology Program Project (GuikeJB23023003), and the National Agricultural Science and Technology Major Project (2022). Author Contribution X.L., X.C., X.P., J.T., and Z.Z. conceived and supervised the study. Z.Z. and J.T. designed the experiment. W.G. completed the RNA-seq data. Z.Z. confirmed the breed information. X.L. completed cis-eQTL mapping, differential gene expression analysis, and Enrichment of eGenes and GWAS signals. X.L. and J.C. completed the colocalization analysis. X.L. wrote the manuscript. X.L., X.C., J.T., and Z.Z. revise the manuscript. All the authors reviewed and approved the final manuscript. Acknowledgements Not applicable. Availability of data and material The genotype data and RNA-Seq data are obtained from http://dx.doi.org/10.5524/102388 and the SRA Accession: PRJEB58031. 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14:29:12","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":15413,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7717815/v1/4db9bc492333c4bfda8c6a4e.png"},{"id":94115569,"identity":"2cd124bd-3922-4b62-96a7-d84168945aaf","added_by":"auto","created_at":"2025-10-22 14:21:12","extension":"xml","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":131689,"visible":true,"origin":"","legend":"","description":"","filename":"d42904d3790140ed9a53033dc9f9c2e71structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7717815/v1/52b3e678e1ebc04a74d3eb09.xml"},{"id":94116686,"identity":"88bc964c-3fbf-47e1-a458-98e2a852e48c","added_by":"auto","created_at":"2025-10-22 14:37:12","extension":"html","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":146953,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7717815/v1/46a54edcd7bc0d7989f708d6.html"},{"id":94115240,"identity":"d433c062-b175-4a6e-8bd6-37f835e1004f","added_by":"auto","created_at":"2025-10-22 14:13:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1464953,"visible":true,"origin":"","legend":"\u003cp\u003eClustering of genotype and gene expression data. a. The PCA result of genotype data across three breeds. b. The PCA result of 29,000 genes in the duodenum across three breeds. c. The PCA result of 29,000 genes in muscle across three breeds. d. The PCA result of 29,000 genes in the liver across three breeds.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7717815/v1/8a8124b3fbdbb8bb2dbeb835.png"},{"id":94115242,"identity":"569dcbad-c0fd-4952-8cc6-41b3ede4ca11","added_by":"auto","created_at":"2025-10-22 14:13:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3078553,"visible":true,"origin":"","legend":"\u003cp\u003eBreed-shared and breed-specific eGenes. a. The number of eGenes in three tissues across three breeds. b. Manhattan plot of a Duroc-specific eGenes \u003cem\u003eIGF2R\u003c/em\u003e in muscle. c. Manhattan plot of a breed-shared eGenes \u003cem\u003ePEX7\u003c/em\u003e in muscle. d: The fold change result of Duroc-shared eGenes and Duroc-specific eGenes (removed outliers, and significance test was performed using Mann-Whitney-Wilcoxon test, * p\u0026lt;0.05, * * p\u0026lt;0.01, * * * p\u0026lt;0.001). e. The gene expression levels of \u003cem\u003eGNB3\u003c/em\u003e, a differentially expressed gene and Duroc-specific eGene in Duroc muscle. f. The gene expression levels of \u003cem\u003eMPHOSPH1\u003c/em\u003e0 across three breeds (\u003cem\u003eMPHOSPH1\u003c/em\u003e0 is a Durco-specific eGenes and non-differentially expressed gene, and is not an eGenes for Landrace and Yorkshire).\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7717815/v1/edff8a612633d9703cbd61e5.png"},{"id":94115244,"identity":"d9e2fad2-3752-4839-bacd-613a739d4e35","added_by":"auto","created_at":"2025-10-22 14:13:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":766452,"visible":true,"origin":"","legend":"\u003cp\u003eThe effect sizes and PhastCons score of different types of eGenes. a. The effect sizes of lead \u003cem\u003ecis\u003c/em\u003e-eQTL for eGenes. Breed-specific eGenes included Duroc-specific eGenes, Landrace-specific eGenes, and Yorkshire-specific eGenes. Shared (two breed) eGenes were eGenes only shared in Duroc and Landrace, eGenes only shared in Duroc and Yorkshire, and eGenes only shared in Landrace and Yorkshire. Shared (three breed) eGenes were eGenes shared across Duroc, Landrace, and Yorkshire. b. The PhastCons score of eGenes. Non-eGenes were non-eGenes across Duroc, Landrace, and Yorkshire. Other types of eGenes were the same as Figure 3a.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7717815/v1/a37ceaf3cda2e39f9ea87746.png"},{"id":94115245,"identity":"4f393fd1-a6c0-49f4-bfc0-581838b16cef","added_by":"auto","created_at":"2025-10-22 14:13:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1488587,"visible":true,"origin":"","legend":"\u003cp\u003eThe result of GWAS signal enrichment and colocalization analyses. a. The fold enrichment result of Duroc-shared and -specific eGenes. b. The fold enrichment result of Landrace-shared and -specific eGenes. c: The fold enrichment result of Yorkshire-shared and -specific eGenes (* p\u0026lt;0.05). e. The significance colocalization result of breed-specific eGenes. f. The significance colocalization result of breed-shared eGenes.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7717815/v1/b733eec47675eea756777b81.png"},{"id":94115563,"identity":"65c45e4b-d38d-4d40-957c-796feb738c3b","added_by":"auto","created_at":"2025-10-22 14:21:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4422749,"visible":true,"origin":"","legend":"\u003cp\u003eThe colocalization examples of breed-shared eGenes and breed-specific eGenes. a. A breed-shared eGene \u003cem\u003eOS9\u003c/em\u003e on chr5 was significantly colocalized with the TNUM trait across Duroc, Landrace, and Yorkshire. b. A breed-specific eGenes \u003cem\u003ePRIMA1\u003c/em\u003e on chr7 was significantly colocalized with the LMDEP trait in Duroc, and was not significantly colocalized with Landrace and Yorkshire.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7717815/v1/daaffdcf9a5d1137e745c910.png"},{"id":106810672,"identity":"c59ca872-fc06-4c95-a200-d43e68d9f664","added_by":"auto","created_at":"2026-04-13 16:16:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12177768,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7717815/v1/0ee92fc2-bde8-46c8-a35d-4ad09647c25f.pdf"},{"id":94115562,"identity":"b10d45a0-deb3-47d6-96b5-dc9c508ec282","added_by":"auto","created_at":"2025-10-22 14:21:12","extension":"xls","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":151040,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.xls","url":"https://assets-eu.researchsquare.com/files/rs-7717815/v1/f2e242d0ef5a33a3a6f5aac2.xls"},{"id":94115253,"identity":"d258be93-adeb-4d13-ad73-e8bb2ce7dbee","added_by":"auto","created_at":"2025-10-22 14:13:12","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18261777,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7717815/v1/1766ed6118e4566ac71dca7f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Characterizing breed-shared and breed-specific genetic regulatory effects of gene expression across three pig breeds","fulltext":[{"header":"Background","content":"\u003cp\u003eAs the most common and important modern commercial pig breeds, Duroc, Landrace, and Yorkshire exhibit distinct differences in complex traits performance, including growth rate, meat quality, immune response, and reproductive capacity, reflecting their different breeding histories and selection goals. In the pig industry, the Duroc pig is one of the most utilized commercial boar lines and is well-known for its growth, feed conversion efficiency, carcass, and meat quality traits [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], which possess strong skeletal muscle growth potential and high lean meat yield [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In contrast, Landrace pigs are primarily employed as maternal lines in commercial breeding programs due to their superior reproductive performance, characterized by large litter size, strong maternal behavior, and high milk production [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Yorkshire pigs combine excellent growth performance, high lean meat yield, and strong adaptability, which are frequently crossed with Landrace pigs to produce F1 hybrid sows for enhanced reproductive and maternal traits [4; 5]. Intensive artificial selection has strongly targeted genes relating to production performance, reproduction, and carcass traits in these commercial breeds, thereby contributing to differences in complex traits across breeds [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This phenotypic diversity is influenced by a combination of genetic variation, selective breeding, and environmental factors. Although genome-wide association studies have identified numerous loci associated with traits over the past decades, most significant GWAS loci are found in non-coding regions, and these genetic variants often fail to fully explain the observed phenotypic differences, suggesting that additional regulatory mechanisms may play a role in shaping traits [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBesides genetic variation, gene expression differences have also emerged as a key factor shaping complex traits. Gene regulation serves as a key intermediate layer that connects genotypes to phenotypes [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Past research showed that integrating genetic variation with gene expression patterns can help reveal the genetic basis of these molecular traits and their relationships with higher-order phenotypes [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Expression quantitative trait loci (eQTL) are genomic variants that influence gene expression levels across individuals, and the genes they regulate are termed eGenes. Identifying eQTLs and eGenes enables functional interpretation of regulatory variants and provides a crucial link between genetic variation and phenotypic traits. With the establishment of large-scale transcriptomic resources such as cattleGTEx [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], pigGTEx [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and FarmGTEx [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], integrative eQTL studies across multiple breeds in livestock have emerged, highlighting the importance of molecular traits, particularly gene expression regulation, in understanding the genetic basis of complex traits.\u003c/p\u003e\u003cp\u003eIn livestock, most studies on molecular phenotypes have focused on a multiple-breed design, aiming to identify genetic effects shared across breeds. However, different breeds can exhibit distinct patterns of complex traits, and approaches that primarily emphasize shared effects may overlook breed-specific regulatory mechanisms [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. As a result, such studies may fail to capture the full spectrum of variants contributing to complex traits. Similarly, many human diseases differ among populations in prevalence, severity, or age of onset, and these differences can be partly explained by variation in gene expression regulation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, early studies were largely confined to populations of European ancestry, limiting the generalizability of their findings, and may have overlooked genetic effects that are specific to other populations [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. More recent research incorporating diverse ancestral backgrounds has revealed that some regulatory effects are shared across ancestries, while others are ancestry-specific, demonstrating that ancestry-specific regulatory mechanisms can explain additional phenotypic variation beyond shared effects [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, little is known about the shared and breed-specific regulatory effects across pig breeds.\u003c/p\u003e\u003cp\u003eTo investigate breed-shared and breed-specific characteristics in gene expression regulation among pig breeds, we applied \u003cem\u003ecis\u003c/em\u003e-eQTL mapping in Duroc, Landrace, and Yorkshire pigs. By integrating these results with genome-wide association studies, we further explored the contribution of breed-shared and breed-specific regulatory variation to phenotypic diversity. This work provides a preliminary exploration of breed-dependent genetic regulation and their contribution to complex traits.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePopulation and data\u003c/h2\u003e\u003cp\u003eThe population used in our study consisted of 300 pigs for 3 different breeds (100 Duroc, 100 Landrace, and 100 Yorkshire) generated from [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The genotypic data in VCF format and raw RNA-seq data in FASTQ format of this population were downloaded from the GigaScience GigaDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.5524/102388\u003c/span\u003e\u003cspan address=\"10.5524/102388\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). All individuals (n\u0026thinsp;=\u0026thinsp;300) were genotyped using Whole genome sequencing (WGS) with a depth of ~\u0026thinsp;10\u0026times;. The downloaded genotypic data comprised 31,682,957 SNPs. We retained 15,495,927 biallelic variants with minor allele frequency (MAF)\u0026thinsp;\u0026ge;\u0026thinsp;5% and minor allele count (MAC)\u0026thinsp;\u0026ge;\u0026thinsp;6 in 18 autosomes after quality control across 300 pigs. To confirm the breed information of the 300 pigs, we used ADMIXTURE (v1.3.0) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] to estimate ancestry proportions of each individual. The same procedure as the breed predication analysis pipeline implemented in PigGTEx [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe raw RNA-seq data used in this study included three tissues, duodenum, muscle, and liver for each individual, resulting in a total of 900 RNA-seq samples. We used Trimmomatic (v0.39) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] to trim adaptors and discard reads with poor quality, and then used STAR (v2.7.0) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] to align clean reads to the Sscrofa11.1(v100) pig reference genome. Gene raw read counts were acquired with featureCounts (v1.5.2) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and normalized expression (transcripts per million, TPM) was derived from these counts. We retained 29,000 genes after excluding those with low expression, as indicated by TPM\u0026thinsp;\u0026lt;\u0026thinsp;0.1 and/or raw read counts\u0026thinsp;\u0026lt;\u0026thinsp;6 in more than 80% of samples within each tissue across 300 pigs.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDifferential gene expression analysis\u003c/h3\u003e\n\u003cp\u003eIn order to explore whether there are differences in gene expression levels across breeds, we performed differential gene expression analysis between one breed and the other two breeds. We assessed expression differences across breeds within each tissue using the TPM data with limma (R package) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. We defined genes with |log\u003csub\u003e2\u003c/sub\u003eFC| \u0026gt;1 and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as significantly differentially expressed genes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCis\u003c/b\u003e\u003cb\u003e-eQTL mapping\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo investigate differences in gene expression regulation across breeds, we performed \u003cem\u003ecis\u003c/em\u003e-eQTL mapping in each tissue of each breed separately. Quality control of the genotype data in each breed was performed with the criteria of MAF\u0026thinsp;\u0026gt;\u0026thinsp;0.01, MAC\u0026thinsp;\u0026gt;\u0026thinsp;6, and het\u0026thinsp;\u0026lt;\u0026thinsp;0.99. SNPs common to all three breeds after separate quality control were used in the subsequent analyses, with finally 6,521,645 SNPs. For gene expression data, we first split the gene expression profiles (TPM data) by tissue and breed to obtain the TPM matrix for each tissue of each breed. Then we filtered the low-expressed genes in the TPM data for each tissue of each breed, where low-expressed genes refer to genes with a TPM\u0026thinsp;\u0026le;\u0026thinsp;0.1 in more than 80% of the samples. After obtaining the filtered genes for each tissue of each breed, we normalized the gene expression across samples using the trimmed mean of M-value (TMM) method, implemented in edgeR [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], followed by inverse normal transformation of the TMM.\u003c/p\u003e\u003cp\u003eWe performed \u003cem\u003ecis\u003c/em\u003e-eQTL mapping using a linear mixed model implemented in OmiGA [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] to test associations of the normalized gene expression levels with SNPs in the \u0026plusmn;\u0026thinsp;1Mb of the transcription start site (TSS) of target genes. In order to control the effects of the remaining potential confounders, the principal components of the gene expression (TMM data) were used in this study as covariates, using the function of OmiGA (--geno-pc-covar 0 --dprop-pc-covar 0.001). OmiGA would select the first n PCs as covariates when the increase in the proportion of variance explained by n\u0026thinsp;+\u0026thinsp;1 PCs and n\u0026thinsp;+\u0026thinsp;2 PCs was less than 0.1% of the first n PCs.\u003c/p\u003e\u003cp\u003eThe linear mixed model is as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\mathbf{y}=\\mathbf{X}\\varvec{\\alpha\\:}+{\\mathbf{s}}_{\\text{a}}{\\varvec{\\beta\\:}}_{\\text{a}}+{\\mathbf{g}}_{\\text{a}}+\\mathbf{e}$$\u003c/div\u003e\u003c/div\u003e,\u003c/p\u003e\u003cp\u003ewhere \u003cb\u003ey\u003c/b\u003e is an n\u0026times;1 vector of normalized gene expression levels (TMM data), \u003cb\u003eX\u003c/b\u003e is an n\u0026times;c matrix of covariates, including a column of 1, with corresponding fixed effect \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\alpha\\:}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mathbf{s}}_{\\text{a}}\\)\u003c/span\u003e\u003c/span\u003e is an n\u0026times;1 vector of vector of mean centered genotypes values at the variant being tested, coded as 0, 1, or 2 for the AA, Aa, and aa genotypes. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{\\beta\\:}}_{\\mathbf{a}}\\)\u003c/span\u003e\u003c/span\u003e is the variant\u0026rsquo;s genetic effect, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mathbf{g}}_{\\text{a}}\\)\u003c/span\u003e\u003c/span\u003e is an n\u0026times;1 vector of total genetic effects with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mathbf{g}}_{\\text{a}}\\sim\\:\\text{N}(0,\\:\\mathbf{G}{{\\sigma\\:}}_{\\text{a}}^{2})\\)\u003c/span\u003e\u003c/span\u003e where the genomic relationship matrix (GRM) is defined as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mathbf{G}=\\frac{\\mathbf{M}{\\mathbf{M}}^{{\\prime\\:}}}{2{\\sum\\:}_{i=1}^{m}{p}_{i}(1-{p}_{i})}\\)\u003c/span\u003e\u003c/span\u003e [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], of which \u003cb\u003eM\u003c/b\u003e is a matrix of mean centered genotypes for genome-wide genetic variants and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{p}}_{\\varvec{i}}\\)\u003c/span\u003e\u003c/span\u003e is the MAF of the i\u003csup\u003eth\u003c/sup\u003e variant, \u003cb\u003ee\u003c/b\u003e is n\u0026times;1 vector of residuals with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mathbf{e}\\sim\\:\\text{N}(0,\\:{\\mathbf{I}}_{n}{{\\sigma\\:}}_{\\text{e}}^{2})\\)\u003c/span\u003e\u003c/span\u003e where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mathbf{I}}_{n}\\)\u003c/span\u003e\u003c/span\u003e is an n\u0026times;n identity matrix.\u003c/p\u003e\u003cp\u003eWe considered SNPs with a nominal \u003cem\u003eP\u003c/em\u003e-value below the variant-level threshold (obtained from permutations) as significant \u003cem\u003ecis\u003c/em\u003e-eQTLs. We considered eGenes with a gene-level \u003cem\u003eP\u003c/em\u003e-value corrected by the Benjamini-Hochberg method that was below the significance threshold (qvalue\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003ch3\u003eDefinition and functional annotation of breed-shared and breed-specific eGenes\u003c/h3\u003e\n\u003cp\u003eTo understand the shared or specific genetic expression regulatory mechanisms across breeds, we defined breed-shared eGenes and breed-specific eGenes. Breed-shared eGenes were defined as eGenes that exist in at least two breeds within the same tissue. Breed-specific eGenes were defined as eGenes that exist in only one breed within the same tissue. Breed-shared eGenes included two-breed-shared eGenes (eGenes only shared in Duroc and Landrace, Duroc and Yorkshire, Landrace and Yorkshire) and three-breed-shared eGenes (eGenes shared across Duroc, Landrace, and Yorkshire). To further resolve the potential biological functions of eGenes, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on breed-shared eGenes and breed-specific eGenes using clusterProfiler (R package) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003ePhastCons score calculation of eGenes\u003c/h3\u003e\n\u003cp\u003eTo understand the evolutionary sequence conservation of the different types of eGenes, we downloaded PhastCons scores of 100 vertebrate species from UCSC (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://hgdownload.cse.ucsc.edu/goldenpath/hg38/phastCons100way/hg38.100way.phastCons/\u003c/span\u003e\u003cspan address=\"http://hgdownload.cse.ucsc.edu/goldenpath/hg38/phastCons100way/hg38.100way.phastCons/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We first converted the Wiggle files of PhastCons scores to bed files using the BEDOPS tool (v2.4.40) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and then lifted over from human genome 38 (h38) to Sscrofa11.1 using UCSC\u0026rsquo;s LiftOver tool [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. We used the mean PhastCons scores of sequences within a gene to represent its PhastCons score. Genes were considered only if at least 50% of their sequence length could be mapped in LiftOver.\u003c/p\u003e\n\u003ch3\u003eGWAS summary statistics\u003c/h3\u003e\n\u003cp\u003eThe GWAS summary statistics used in this study were generated from [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. We used the meta-GWAS summary statistics results integrating multiple populations and breeds (mainly including Duroc, Landrace, and Yorkshire). Only GWAS summary statistics records containing at least one SNP with a \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026le;\u0026thinsp;5 \u0026times; 10⁻⁸ were retained for downstream analyses. The traits utilized for enrichment and colocalization analysis include 19 traits, they were average daily gain (ADG, n\u0026thinsp;=\u0026thinsp;36,943), backfat thickness (BFT, n\u0026thinsp;=\u0026thinsp;58,725), body weight (BW, n\u0026thinsp;=\u0026thinsp;42,256), days (DAYS, n\u0026thinsp;=\u0026thinsp;49,595), gestation days (GD, n\u0026thinsp;=\u0026thinsp;13,325), lean cuts percentage (LEANCUTP, n\u0026thinsp;=\u0026thinsp;17,523), loin muscle area (LMA, n\u0026thinsp;=\u0026thinsp;26,176), loin muscle depth (LMDEP, n\u0026thinsp;=\u0026thinsp;34,439), number born alive (NBA, n\u0026thinsp;=\u0026thinsp;13193), number born alive (day21) (NBA_D21, n\u0026thinsp;=\u0026thinsp;1,083), number born of healthy pigs (NBH, n\u0026thinsp;=\u0026thinsp;17,746), number born of stillborn pigs (NBS, n\u0026thinsp;=\u0026thinsp;11,521), total litter weight of piglets born alive (TLWT_BA, n\u0026thinsp;=\u0026thinsp;19,701), total litter weight of piglets (day 21) (TLWT_D21, n\u0026thinsp;=\u0026thinsp;9064), total litter weight of piglets (weaning) (TLWT_Weaning, n\u0026thinsp;=\u0026thinsp;11,166), total number of born (TNB, n\u0026thinsp;=\u0026thinsp;22,217), teat number (TNUM, n\u0026thinsp;=\u0026thinsp;38,158), uterine capacity (UC, n\u0026thinsp;=\u0026thinsp;8,571), and weaning to estrus interval (WSI, n\u0026thinsp;=\u0026thinsp;16,261).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eEnrichment analysis of eGenes and GWAS signals\u003c/h2\u003e\u003cp\u003eTo evaluate the association between eGene regulatory regions and complex traits, we performed the enrichment analysis between GWAS signals and the cis-eQTLs of eGenes. We used the significant GWAS SNPs with \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026le;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e, the eGenes region within \u0026plusmn;\u0026thinsp;1Mb of TSS for eGenes. We defined significant GWAS SNPs as the observed set and randomly sampled an equal number of SNPS from the non-significant SNPs as a control set. This sampling procedure was repeated 1,000 times to generate a background distribution. The fold enrichment was then calculated as the proportion of significant GWAS SNPs located in eGene regions divided by the mean proportion of the proportion of control set falling within eGene regions.\u003c/p\u003e\u003cp\u003e\u003cb\u003eColocalization of\u003c/b\u003e \u003cb\u003ecis\u003c/b\u003e\u003cb\u003e-eQTL and GWAS\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo investigate the regulatory mechanisms underpinning complex traits in pigs, we performed colocalization analysis between breed-shared, breed-specific eGenes and GWAS summary statistics using coloc (R package) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Colocalization was performed for each eGene using SNPs within \u0026plusmn;\u0026thinsp;1 Mb of its TSS. We considered colocalization to be significant when the posterior probability for a shared causal variant (PP.H4) exceeded 0.8.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003ePopulation structure and gene expression patterns for three pig breeds\u003c/h2\u003e\u003cp\u003eAfter quality control, we kept 6,521,645 common SNPs across three pig breeds (i.e., Duroc, Landrace, and Yorkshire). To explore the population structure of these pigs, we performed PCA using the genotypic data from all 300 individuals. We observed that these individuals clustered mainly according to breeds (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). The first and second genotype PCs (i.e., PC1 and PC2), explained 18.10% and 11.37% of variance, respectively. To explore the patterns of gene expression among these three pig breeds, we calculated the Pearson\u0026rsquo;s correlation using gene expression levels from all 900 samples from three tissues. The heatmap of gene expression correlations between samples showed that samples from the same tissue exhibited higher correlation, especially within muscle and liver, indicating that different tissue types contribute more to gene expression variation than different breeds (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). We further performed PCA in each of three tissues based on the log-transformed transcript per million (TPM) data from 29,000 genes. We observed that the samples tended to be clustered by different breeds, with Landrace and Yorkshire positioned closer to each other than either is to Duroc (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb-d). Moreover, we detected the differential expressed genes among the three breeds for each tissue. In the duodenum, muscle, and liver, we found that 867, 949, and 2,904 differentially expressed genes for Duroc, 351, 461, and 612 for Landrace, and 763, 282, and 612 for Yorkshire, respectively. Although breed differential at the genomic level and tissue differential at the transcriptomic level have been well pronounced, the present results indicated the existence of breed differential at the transcriptomic level within tissue.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eBreed-stratified\u003c/b\u003e \u003cb\u003ecis\u003c/b\u003e\u003cb\u003e-eQTL mapping and eGenes sharing patterns\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo investigate the contribution of genetic regulation to gene expression differences across pig breeds, we separately conducted \u003cem\u003ecis\u003c/em\u003e-eQTL mapping in the duodenum, muscle, and liver for each of the three breeds. We identified 5,204, 4,789, and 5,555 eGenes in the duodenum, muscle, and liver of Duroc, respectively; 5,886, 5,318, and 6,541 eGenes in Landrace; and 7,043, 4,659, and 6,651 eGenes in Yorkshire. We further classed them into breed-specific eGenes and breed-shared eGenes, of which breed-shared eGenes include eGenes shared in two or three breeds (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Within each tissue, an average of 71.1% of the eGenes identified in each breed were breed-shared, while the remaining 28.9% were breed-specific. It indicated that the majority of \u003cem\u003ecis\u003c/em\u003e-regulatory effects on gene expression are conserved across breeds, which is consistent with findings from the PigGTEx project [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and some human studies [\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. It must be pointed out that Duroc shared fewer eGenes with the other two breeds in all tissues, such as in the duodenum, 1,567 eGenes were shared between Landrace and Yorkshire, but 683 or 951 were shared between Duroc and Landrace or Yorkshire, respectively. For instance, A Duroc-specific eGene \u003cem\u003eIGF2R\u003c/em\u003e was involved in the mannose-6-phosphate (M6P) sorting pathway, which mediates the transport of phosphorylated lysosomal enzymes from the Golgi complex and the cell surface to lysosomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), and a breed-shared eGene \u003cem\u003ePEX7\u003c/em\u003e was involved in Peroxisomal protein import and Ether lipid biosynthesis pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). These results indicate that there are differences in regulatory effects for gene expression across three pig breeds.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eCharacteristics of breed-shared and breed-specific eGenes\u003c/h2\u003e\u003cp\u003eTo investigate the characteristics of breed-shared and breed-specific eGenes, we compared the fold change values from differential expression analysis between breed-shared and breed-specific eGenes. We found that Duroc-specific eGenes exhibit significantly higher expression specificity in the Duroc population than the other two breeds (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). For example, the \u003cem\u003eGNB3\u003c/em\u003e gene is a Duroc-specific eGene that exhibits a high average expression level in Duroc (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). We also observed some breed-specific eGenes like \u003cem\u003eMPHOSPH10\u003c/em\u003e that show large expression variation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef). Similar patterns were observed in muscle for the Landrace population, muscle and liver for the Yorkshire population (\u003cb\u003eFig. S2\u003c/b\u003e). These results indicate that breed-specific regulatory effects are not only relevant to the difference in average gene expression but also expression variance among populations.\u003c/p\u003e\u003cp\u003eTo investigate the potential biological functions of these breed-shared and breed-specific eGenes, we performed GO and KEGG pathway enrichment analyses. We observed that the breed-shared eGenes in muscle were significantly enriched in carboxylic acid metabolic process (fold enrichment\u0026thinsp;=\u0026thinsp;1.64, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and other biological processes (\u003cb\u003eAdditional file 1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-S2\u003c/b\u003e). The Duroc-specific eGenes in muscle were enriched in biological processes such as regulation of mitotic cell cycle (fold enrichment\u0026thinsp;=\u0026thinsp;2.67, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and negative regulation of gene expression (fold enrichment\u0026thinsp;=\u0026thinsp;1.98, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (\u003cb\u003eAdditional file 1: Table S3-S4\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eTo further explore the genetic regulatory effect and conservation of breed-shared or specific eGenes, we examined their effect sizes (beta) of lead \u003cem\u003ecis\u003c/em\u003e-eQTLs and PhastCons score. We found that the lead \u003cem\u003ecis\u003c/em\u003e-eQTLs exhibit higher regulatory effect sizes for breed-shared eGenes than breed-specific eGenes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). For instance, in muscle, the median effect sizes were 0.60 for breed-specific eGenes, 0.66 for eGenes shared in two breeds, and 0.78 for eGenes shared in three breeds. From the PhastCons score, an evolutionary conservation across 100 vertebrates, we observed that eGenes shared across three breeds present the lowest cross-species conservation than those eGenes only detected in one breed or not detected in any breeds (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). For instance, in liver, the median effect sizes were 0.17 for non-eGenes, 0.14 for breed-specific eGenes, 0.13 for eGenes shared in two breeds, and 0.12 for eGenes shared in three breeds. These findings suggest that breed-shared eGenes have stronger regulatory effect sizes and lower evolutionary conservation, whereas breed-specific eGenes have smaller effect sizes and higher evolutionary conservation. Similarity patterns have also been reported in human studies [35; 36].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eContribution of breed-stratified\u003c/b\u003e \u003cb\u003ecis\u003c/b\u003e\u003cb\u003e-eQTLs for complex traits of pigs\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo evaluate the contribution of breed-shared and breed-specific eGenes on the regulation of pig complex traits, we conducted the enrichment analysis using GWAS summary statistics with significant associations from nineteen complex traits. We observed that the GWAS loci were significantly enriched in the \u003cem\u003ecis\u003c/em\u003e-eQTLs of eGenes for an average of 12 of 19 complex traits per breed in Duroc, Landrace, and Yorkshire. Notably, these GWAS loci exhibited higher enrichment in breed-specific eGenes than breed-shared eGenes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea-c). This result suggests that, similar to the \u003cem\u003ecis\u003c/em\u003e-eQTLs of breed-shared eGenes, these \u003cem\u003ecis\u003c/em\u003e-eQTLs of breed-specific eGenes play a crucial role in regulating the complex traits of pigs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo explore whether causal SNPs shared between the breed-shared and breed-specific eGenes and GWAS signals of pig complex traits, we further performed colocalization analysis to discover shared regulatory effects between gene expression and complex traits. Totally, we observed 758 and 220 colocalization events (PP.H4\u0026thinsp;\u0026gt;\u0026thinsp;0.8) with breed-shared and breed-specific eGenes, respectively. The colocalization events from these \u003cem\u003ecis\u003c/em\u003e-eQTLs of breed-shared eGenes are substantially higher than those of \u003cem\u003ecis\u003c/em\u003e-eQTLs of breed-specific eGenes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed-e). The more observations of colocalization events on breed-shared eGenes than breed-specific is consistent with the observations on the number of eGenes.\u003c/p\u003e\u003cp\u003eFor example, there is a GWAS signal in chromosome 5 of the TNUM trait colocalized with the \u003cem\u003ecis\u003c/em\u003e-eQTLs of the \u003cem\u003eOS9\u003c/em\u003e gene in liver across three pig breeds (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea), of which the \u003cem\u003eOS9\u003c/em\u003e is a breed-shared eGene in liver across three breeds. \u003cem\u003eOS9\u003c/em\u003e encodes a lectin component of the mammalian HRD1 ubiquitin ligase complex, which is essential for multiple physiological processes, including metabolic regulation, maintenance of intestinal homeostasis, immune cell function, prohormone maturation, and β-cell identity [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In humans, \u003cem\u003eOS9\u003c/em\u003e has been reported as a stable housekeeping gene in breast tissue and has also been implicated in breast cancer biology [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. While our findings suggest a potential link between \u003cem\u003eOS9\u003c/em\u003e regulation in the liver and TNUM variation across three pig breeds, the precise molecular mechanisms underlying this association remain to be elucidated.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe GWAS signal of the LMDEP trait colocalized with the cis-eQTLs of the \u003cem\u003ePRIMA1\u003c/em\u003e gene in muscle, of which the \u003cem\u003ePRIMA1\u003c/em\u003e gene is a Duroc-specific eGene in muscle (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). The function of the \u003cem\u003ePRIMA1\u003c/em\u003e gene is to organize acetylcholinesterase (AChE) into tetramers and to anchor AChE at neural cell membranes. A previous study demonstrated that \u003cem\u003ePRiMA\u003c/em\u003e-linked G4 acetylcholinesterase (AChE) is localized at neuromuscular junctions (NMJs) and that its expression in motor neurons contributes to this synaptic localization [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], suggesting a critical role in regulating muscle contraction. \u003cem\u003ePRIMA1\u003c/em\u003e expression is also influenced by neuronal differentiation and maturation [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], which may contribute to variation in its expression across breeds. Together with our colocalization analysis, these findings suggested that breed-specific regulation of \u003cem\u003ePRIMA1\u003c/em\u003e may be associated with variation in this muscle-related trait. These results together highlight that, as a result of complementarity, the \u003cem\u003ecis\u003c/em\u003e-eQTL of breed-specific eGenes could explain additional GWAS signals of complex traits.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eUnderstanding the genetic regulation of gene expression across different breeds is crucial for interpreting the molecular basis of phenotypic diversity and complex traits. In this study, we systematically identified breed-shared and breed-specific eGenes across three pig breeds and evaluated their potential contributions to complex traits by enrichment and colocalization analyses. We explored both breed-shared and breed-specific genetic regulation on gene expression and emphasized their contribution to complex traits, indicating that breed-shared regulatory variation and breed-specific regulatory variation jointly contribute to phenotypic diversity.\u003c/p\u003e\u003cp\u003eBreed-shared and breed-specific eGenes exhibited distinct regulatory and characteristics. Within each tissue of each breed, the majority of eGenes identified were shared, and the remaining were breed-specific. We have detected that some eGenes are differentially expressed. Some eGenes have higher expression variance in the specific breed, indicating that genetic variants may influence expression variability rather than mean expression. In line with this concept, recent human studies have identified variance QTL (vQTLs), which capture regulatory effects on phenotypic variability and uncover additional regulatory signals, particularly gene\u0026ndash;environment interactions [\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Applying similar approaches in pigs may uncover breed-specific regulation not detected by standard eQTL mapping. Conservation analyses indicated that breed-specific eGenes are likely under stronger selective constraints arising from domestication and breed diversification, and they may play key roles in defining breed-specific adaptive traits. By contrast, shared eGenes evolve under less evolutionary constraint and may tolerate regulatory variation affecting general cellular or metabolic functions, resulting in larger effect sizes across breeds [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBoth breed-shard and breed-specific eGenes colocalized with complex traits (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed-e), indicating that genetic regulation of gene expression involves both shared and breed-specific effects. Similar findings in human studies, where ancestry-specific eQTLs provide additional explanatory power for complex traits [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Our findings further highlight the contribution of breed-specific regulatory variation to phenotypic variation in pigs. Duroc pigs exhibited marked genetic and transcriptional divergence from Landrace and Yorkshire, with numerous Duroc-specific eGenes in each tissue. Duroc-specific eGenes in muscle involved with biological processes such as cell differentiation and proliferation stages, coupled with the insights gained from exploring the genetic underpinnings of these eGenes advance our understanding of the genetic architecture of gene regulation. These eGenes are also related to growth traits (e.g., DAYS, LMDEP, and LMA), with the results that are significantly enriched in GWAS loci and have colocalization events with growth traits. Such regulatory variation may influence cellular differentiation and growth in muscle. Previous studies have reported that Duroc pigs possess a stronger myogenic potential, characterized by higher cellular activity and regenerative capacity in muscle development, and ligand\u0026ndash;receptor interaction analyses of muscle stem cells revealed that Duroc myogenic lineage cells receive more proliferative signals [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. All these findings and studies suggest that the differentiation and growth of muscle in Duroc may be regulated by these specific eGenes, thereby contributing to trait differences across breeds. However, colocalization analysis may be influenced by linkage disequilibrium structure; future studies should integrate genetic variation, GWAS results, context-specific multi-omics (e.g., ATAC-seq, ChIP-seq, Hi-C, single-cell RNA-seq), and functional validations to gain a deeper understanding of the genetic architecture underlying breed-shared and breed-specific regulation.\u003c/p\u003e\u003cp\u003eSome previous studies were limited by datasets [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], which may have constrained the scope of analysis. In addition, most studies in livestock have often focused on shared regulatory effects by combining multiple breeds into the analysis [10; 11]. Such studies may mask breed-specific regulatory variation. The structure of our dataset enabled us to investigate both breed-shared and breed-specific aspects of gene regulation. It is well established that the power of eQTL mapping is correlated to the sample size [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In this study, the sample size for each breed was limited to 100 individuals, which may have constrained our ability to detect eGenes with small effect sizes and thus led to an underestimation of the full extent of regulatory variation. In the future, the collection of multimodal data and eQTL mapping across tissues and diverse breeds can allow a more comprehensive assessment of breed specificity and the exact mechanisms underlying breed differences in gene regulation.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study reveals both breed-shared and breed-specific effects and characteristics of genetic regulation on gene expression in three pig breeds. Both breed-shared and breed-specific eGenes contribute to the genetic regulation of complex traits, while breed-specific eGenes explain regulatory variation unique to each breed. These findings together improved our understanding of breed-dependent genetic regulation and their contribution to complex traits.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eeQTL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eExpression quantitative trait loci\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 polymorphism\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGWAS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGenome-wide association studies\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWGS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWhole genome sequencing\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\"\u003eMAC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMinor allele count\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHet\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHeterozygosity rate\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTPM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLog-transformed transcript per million\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTMM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTrimmed mean of M-value\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTSS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTranscription start site\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGRM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGenomic relationship matrix\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\"\u003eKEGG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eADG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAverage daily gain\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBFT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBackfat thickness\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBW\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBody weight\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDAYS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDays\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGestation length\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLEANCUTP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLean cuts percentage\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLMA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLoin muscle area\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLMDEP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLoin muscle depth\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNBA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNumber born alive\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNBA_D21\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNumber born alive (day21)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNBH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNumber born of healthy pigs\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNBS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNumber born of stillborn pigs\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTLWT_BA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTotal litter weight of piglets born alive\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTLWT_D21\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTotal litter weight of piglets (day 21)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTLWT_Weaning\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTotal litter weight of piglets (weaning)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTNB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTotal number of born\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTNUM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTeat number\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eUterine capacity\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWSI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWeaning to estrus interval\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\"\u003eM6P\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMannose-6-phosphate\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAchE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAcetylcholinesterase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNMJs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNeuromuscular junctions\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003evQTL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eVariance quantitative trait loci\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the earmarked fund for the China Agriculture Research System (CARS-35), Guangxi Science and Technology Program Project (GuikeJB23023003), and the National Agricultural Science and Technology Major Project (2022).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eX.L., X.C., X.P., J.T., and Z.Z. conceived and supervised the study. Z.Z. and J.T. designed the experiment. W.G. completed the RNA-seq data. Z.Z. confirmed the breed information. X.L. completed cis-eQTL mapping, differential gene expression analysis, and Enrichment of eGenes and GWAS signals. X.L. and J.C. completed the colocalization analysis. X.L. wrote the manuscript. X.L., X.C., J.T., and Z.Z. revise the manuscript. All the authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and material\u003c/h2\u003e\n\u003cp\u003eThe genotype data and RNA-Seq data are obtained from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.5524/102388\u003c/span\u003e\u003c/span\u003e and the SRA Accession: PRJEB58031.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChen Z, Teng J, Diao S, Xu Z, Ye S, Qiu D, et al. 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BMC Genomics. 2019;20(1):518. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12864-019-5889-5\u003c/span\u003e\u003cspan address=\"10.1186/s12864-019-5889-5\" 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":false,"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":"cis-eQTL mapping, gene expression regulation, breed-shared regulation, breed-specific regulation, complex traits","lastPublishedDoi":"10.21203/rs.3.rs-7717815/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7717815/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eDifferences across breeds in adaptation to various environments and performance on complex traits such as growth rate are very common in livestock animals. These differences have been attributed to various factors, including genetic variation, selection, and environmental influences. Gene expression regulation, serving as a critical intermediary mechanism that bridges genotypes and phenotypes, may play a pivotal role in driving these differences across breeds. Hence, we characterized the breed-shared and breed-specific pattern in genetic regulatory effects on gene expression via expression quantitative trait loci (eQTL) mapping in three pig breeds (Duroc, Landrace, and Yorkshire), aiming to gain a deeper understanding of the molecular basis underlying complex trait differences across breeds.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eWe observed breed differentiation at both the single-nucleotide polymorphism (SNP) and gene expression levels. By eQTL mapping, within each tissue, an average of 71.1% of the eGenes identified in each breed were breed-shared, while the remaining 28.9% were breed-specific. We found that some regulatory effects are relevant to either the difference in average gene expression or expression variance among populations. Breed-shared eGenes were more abundant and showed larger effect sizes and lower evolutionary conservation, and vice versa. Enrichment analysis showed that the genome-wide association studies (GWAS) loci were significantly enriched in the cis-eQTLs of eGenes for an average of 12 of 19 complex traits per breed. These loci exhibited higher enrichment in breed-specific eGenes than breed-shared eGenes. Through colocalization analyses with GWAS loci, we observed 220 colocalization events (PP.H4\u0026thinsp;\u0026gt;\u0026thinsp;0.8) with breed-specific eGenes and 758 events with breed-shared eGenes.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eOur study reveals breed-shared and breed-specific effects and characteristics of genetic regulation on gene expression in three pig breeds. Both breed-shared and breed-specific eGenes contribute to the genetic regulation of complex traits, while breed-specific eGenes explain regulatory variation unique to each breed. These findings together improved our understanding of breed-dependent genetic regulation and their contribution to complex traits.\u003c/p\u003e","manuscriptTitle":"Characterizing breed-shared and breed-specific genetic regulatory effects of gene expression across three pig breeds","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-22 14:13:07","doi":"10.21203/rs.3.rs-7717815/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-11T01:59:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-10T20:46:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-10T15:58:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"70313955970808585802038653303586415245","date":"2025-11-26T14:42:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"330531035160346717948516284441890154055","date":"2025-11-26T14:17:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"152181381310293859661002707577193368437","date":"2025-11-24T09:16:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"225828664644487640792067348503375940322","date":"2025-10-16T02:39:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-09T08:10:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-30T03:11:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-30T03:10:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Animal Science and Biotechnology","date":"2025-09-26T05:00:20+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":"aa6940a1-a54e-4878-9f65-beac68d6b03a","owner":[],"postedDate":"October 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T16:14:14+00:00","versionOfRecord":{"articleIdentity":"rs-7717815","link":"https://doi.org/10.1186/s40104-026-01374-2","journal":{"identity":"journal-of-animal-science-and-biotechnology","isVorOnly":false,"title":"Journal of Animal Science and Biotechnology"},"publishedOn":"2026-04-09 15:58:37","publishedOnDateReadable":"April 9th, 2026"},"versionCreatedAt":"2025-10-22 14:13:07","video":"","vorDoi":"10.1186/s40104-026-01374-2","vorDoiUrl":"https://doi.org/10.1186/s40104-026-01374-2","workflowStages":[]},"version":"v1","identity":"rs-7717815","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7717815","identity":"rs-7717815","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}