The large-scale development of pig farming has introduced significant stressors that negatively affect pigs’ mental health, behavior, and production efficiency. The hippocampus, crucial for cognition and stress response regulation, plays a central role in these processes. However, the regulatory mechanisms underlying hippocampal function across pig breeds with different domestication statuses and their implications for behavior and breeding strategies remain unclear. We performed single-nucleus RNA sequencing (snRNA-seq) on hippocampal tissues from 22,342 cells across three pig breeds: Asian wild boar, Jinhua, and Duroc, representing different domestication statuses. We identified six major hippocampal cell types and annotated 108 breed-specific transcription factors, including GATA2, SPI1, and EBF1. Additionally, we characterized 83 co-expression modules and 50 significant ligand-receptor pairs, such as TGFβ, WNT, and SPP1, revealing complex intercellular communication networks. Oligodendrocyte expression patterns were conserved across all breeds. We identified 194 candidate genes linked to stress resilience, mental health, and feeding behavior, including MC4R, RYR2, PDE10A, and ABCG2. Alzheimer’s disease-related gene enrichment was lower in Duroc pigs, consistent with reduced APOE expression. We also developed the Pig Hippocampus Single-cell Atlas (PHiSA, http://alphaindex.zju.edu.cn:8503/), an open-access database allowing breed-specific hippocampal analyses and validation of gene expression at the single-nucleus level. This study offers insights into hippocampal function regulation in pigs, focusing on stress resilience, behavior, and productivity. It highlights conserved and breed-specific molecular features of hippocampal cell types and their roles in adaptability and mental health. By integrating single-nucleus data, the research suggests that genetic strategies could be used to improve animal welfare, stress management, and production efficiency in pig breeding programs. We created a single-nucleus atlas of the hippocampus for pigs representing three domestication statuses: wild boars, a local domesticated breed, and an intensive breed with distinct behavioral traits. This comprehensive atlas revealed both cell type-specific and breed-specific patterns related to their biological functions. Additionally, we identified candidate genes associated with pig mental health across evolutionary stages. These findings strengthen the use of pig breeds as models for human mental disorders and deepen our understanding of hippocampal function at the single-nucleus level. This figure was created with BioRender.

Genomic selection (GS) is one of the most effective approaches for accelerating genetic improvement in animals and plants, but its efficiency largely depends on the size of the training population. However, establishing a large training population is often time-consuming and costly. An alternative strategy is to combine multiple populations distributed across different breeding farms or companies for joint GS, but this is greatly constrained by data-security concerns and the lack of a public platform for secure collaborative analysis. In this study, we developed HEGS (Homomorphic Encryption Genomic Selection), an open-source platform for privacy-preserving joint GS across institutions, which is in principle applicable to diploid species. HEGS uses homomorphic encryption to perform genomic analyses directly on encrypted data without revealing raw information, and extends the encrypted analysis framework from the initial genomic best linear unbiased prediction (GBLUP) model to include both conventional best linear unbiased prediction (BLUP) and single-step GBLUP (ssGBLUP), thereby broadening its applicability in breeding evaluation. To demonstrate the utility of the platform, we constructed a large encrypted pig dataset comprising four breeds (Duroc, Yorkshire, Landrace, and Pietrain), 36 economically important traits, 180 pre-encrypted datasets, and more than 580,000 phenotypic records, enabling immediate joint analyses without exposing raw data. Using both simulated and real datasets, we demonstrated the feasibility and effectiveness of GS under homomorphic encryption. After model fitting, HEGS outputs genomic estimated breeding values (GEBVs) for genotyped candidates without phenotypic records, facilitating selection without additional phenotyping. Overall, HEGS provides a deployable and scalable open-source solution for privacy-preserving cross-institutional collaboration in animal breeding.

Complex farming environments, breed variation, and the high cost of manual annotation remain major obstacles to robust pig detection, while cross-breed detection under few-shot conditions has been insufficiently explored in previous studies. To address this gap, we propose a few-shot pig detection framework that combines an improved YOLOv7 detector with CycleGAN-based pseudo-sample generation. The detector was enhanced through anchor optimization, Efficient Channel Attention (ECA), and Log-Sum-Exp (LSE) pooling to improve localization and feature discrimination in dense pigsty scenes. In addition, an optimized CycleGAN with perceptual loss was used to generate synthetic Duroc-like pig images to enrich the limited target-domain training set. The framework was evaluated using a two-dataset design: a White Pig Base Dataset was used to establish the source-domain detector and validate the architectural improvements, whereas a Duroc Pig Few-Shot Dataset was used to assess cross-breed adaptation under a 10-shot setting. The experimental results show that the proposed method achieved 98.16% mAP on the White pig dataset and 85.52% mAP on the Duroc Few-Shot Dataset. On the Duroc Few-Shot Dataset, the final framework outperformed Faster R-CNN, CenterNet, and YOLOv8, and also surpassed DCGAN- and SRGAN-based augmentation strategies. These results indicate that the proposed method provides an effective and practical solution for cross-breed few-shot pig detection, with potential value for intelligent livestock monitoring under annotation-limited conditions.

The gut microbiota is intricately linked to host phenotypes, yet its influence on host early growth and development, especially regarding the development of meat quality traits remains poorly understood. Here, we applied integrative multi-omics analyses of the gut microbiome, metabolome, and transcriptomes of muscle and colon tissues in Jinhua pigs during the rapid growth stage. At 90 days of age, Jinhua pigs exhibited a marked increase in microbial diversity, accompanied by enhanced microbial carbohydrate utilization and butyrate metabolism. To dissect host-microbe interactions, we integrated microbiome quantitative trait locus mapping with host whole-genome sequencing. Blautia wexlerae (B. wexlerae) emerged as a key microbial species genetically associated with multiple muscle related and meat quality traits. Colocalization and Mendelian randomization analyses implicated CPSF3 as a putative host gene potentially mediating the association of B. wexlerae on phenotypes such as loin muscle depth and lean meat percentage. Functional validation in mouse models showed that B. wexlerae supplementation was associated with improved muscle development, increased markers of type I muscle fibers, and attenuation of dexamethasone-induced muscle atrophy. B. wexlerae was associated with increased Ppargc1a expression in skeletal muscle, suggesting a potential role in muscle metabolism and fiber type specification. Overall, this study not only provides a promising avenue for enhancing pork quality through microbial interventions but also offers valuable insights for developing microbiota-based therapeutic treatments for human muscle related conditions such as sarcopenia and muscle atrophy.

The Jinhua (JH) pig is a renowned Chinese indigenous breed distinguished by their two-end black phenotype, serve as the exclusive genetic resource for producing Jinhua ham. However, there is currently limited knowledge about the genetic structure and characteristics of JH pigs. This study employed whole-genome sequencing of 1238 individuals representing 67 breeds/lines to elucidate the genetic architecture of JH pigs. Population structure analysis revealed five distinct geographic clusters within Chinese indigenous pigs (North China/CCN, Central China/CCN, East China/ECN, South China/SCN, Southwest China/SWCN), with JH forming an early-diverging lineage in the ECN group exhibiting unique ancestry patterns. Genome-wide scans identified 7995 signatures of positive selection (iSAFE; FDR < 0.05). These signals were enriched in pathways related to immunity, neurodevelopment, and lipid metabolism. Key candidate genes (CLEC7A, D2HGDH, NOVA1) demonstrated breed-specific expression. Balancing selection analysis detected 4630 signals converging on PLA2G2A, a metabolic hub gene influencing intramuscular fat deposition. Two haplotypes (Hap1:41%; Hap2:59%) exhibited antagonistic pleiotropy: Hap1 was associated with enhanced growth performance, whereas Hap2 correlated with superior carcass quality. Our integrative analysis demonstrates that germplasm traits of JH pigs, including tender marbling, disease resilience, and roughage utilization efficiency, result from synergistic effects of directional selection on metabolic/immune genes and balancing selection maintaining fitness trade-offs. This study establishes a framework for delineating adaptive mechanisms in indigenous livestock, providing critical insights for genomic conservation and precision breeding.

The commercial pork production sector prioritizes genetic improvements in lean meat percentage to enhance profitability and meet consumer preferences. The Pietrain pig, a premier terminal sire breed renowned for its exceptional muscularity and leanness, serves as an ideal model to decipher the genetic underpinnings of these traits. This study investigated two key measures of leanness, backfat thickness and loin muscle depth, in two distinct Pietrain populations to elucidate the genetic architecture underlying these traits. We estimated genetic parameters and performed a meta-analysis of genome-wide association studies, identifying ABCD4, LTBP2, NUMB, and SLC30A9 as candidate genes. To further investigate these associations, we integrated information on molecular quantitative trait loci from the PigGTEx project and single-cell transcriptomic resources. This integrative approach prioritized ABCD4 as a key candidate gene regulating backfat thickness. Functional validation in 3T3-L1 preadipocytes revealed a novel dual regulatory role for ABCD4: its knockdown suppressed cell proliferation while simultaneously stimulating adipogenic differentiation, as demonstrated by the upregulation of key markers. Our findings positioned ABCD4 as a critical modulator of fat deposition, likely through its influence on the core adipogenic transcriptional network. By establishing an analytical framework that integrates large-scale sequencing data from Pietrain pigs with functional validation, our study addresses a key gap in understanding the genetic basis of leanness and provides novel insights for precision breeding.

Feed constitutes the largest cost in pig farming. However, the genetic mechanisms underlying feeding behavior (FB) and feed efficiency (FE), as well as their relationships, remain poorly understood. This study aims to: (1) identify genetic variants associated with FB, FE, and production traits, while improving genomic prediction (GP) accuracy; (2) investigate the relationships between FB, FE, and production traits; and (3) explore potential links between pig feeding-related traits and human health traits. A total of 358 lead SNPs associated with 28 feeding-related traits were identified through genome-wide association studies (GWAS) in 6938 genotyped pigs. In addition, GP was applied to an independent later-born population to validate the reliability of the GWAS summary statistics. By selection SNPs based on the P-values and linkage disequilibrium (LD), GP accuracy was improved by an average of 13.4

Pigs are a major source of animal protein for humans and serve as valuable biomedical models. Compared to Western commercial pig breeds, Jinhua pigs are characterized by superior meat quality due to dynamic muscle development and fat deposition. However, studies investigating dynamic transcriptional regulation of swine meat quality traits across developmental stages remain limited. In this work, we collected longissimus dorsi muscle tissue from three Jinhua and three Landrace x Yorkshire pigs at 1, 90, and 180 days of age, respectively. We have uncovered differentially expressed genes and transcripts, alternative splicing events, and gene fusion events across development stages utilizing RNA sequencing data. CKM exhibited consistent breed-specific alternative splicing and gene fusion events across all three stages, representing a stable regulator of muscle development in Jinhua pigs. On the other hand, our findings highlight day 90 as a critical "window phase" for muscle development and meat quality differences between Jinhua and Landrace x Yorkshire pigs at this stage, exhibiting the greatest number of inter-breed differences in transcriptomic genetic regulation. Additionally, time series analysis revealed that genes with peak expression at day 90 were significantly enriched in pathways associated with muscle development and function. Finally, we identified PFKM, PRKAG3, and CKM as candidate genes with age-specific expression and post-transcriptional regulation that likely influence muscle development. This study advances understanding of transcriptional regulation in pig muscle with implications for meat quality improvement.

Semen quality serves as a vital indicator of male fertility, yet its underlying genetic and regulatory mechanisms remain poorly understood. Here, 1.15 million records of six semen quality traits from 14 210 boars in four distinct breeds were collected. These traits have low to moderate heritability (0.12-0.26), and are genetically correlated with growth traits like average daily gain. Genome-wide association study (GWAS) and multi-breed meta-analysis detected 234 loci associated with semen quality. Systematic integration of the Pig Genotype-Tissue Expression resource with these GWAS loci allowed the prioritization of 93 causal variants targeting 134 genes. For instance, the expression quantitative trait loci (eQTL) of NAXE in multiple tissues were colocalized with a GWAS loci of the number of sperms, while eQTL of LEFTY2 in the testis was exclusively colocalized with in a GWAS loci of semen volume. Through examining GWAS of semen quality traits in cattle and human complex traits, the ortholog genes (e.g., AURKAIP1 and ADRA2A) significant in pigs also regulated bovine semen quality, and were significantly enriched for heritability of human birth weight and height. This study provides novel insights into semen quality traits in mammals, which will provide candidate genes for pig selective breeding and potential targets for human male infertility research.

BACKGROUND:African swine fever (ASF) remains a persistent threat to global pig production, with no licensed vaccines or effective treatments available. Observations of surviving individuals within low-virulence infected herds suggest that host genetic resistance plays a crucial role. RESULTS:Here, we present a multi-dimensional integrative analysis to uncover host genomic variants associated with ASF resistance. Combining genome-wide association studies (GWAS), genetic differentiation, and functional genomic approaches, including TWAS, SMR, colocalization, and Bayesian network GWAS, we prioritized 135 high-priority candidate resistance genes from an initial gene set of 1,102 candidates. These prioritized genes are enriched in immune-related pathways, such as chemokine signaling and IL-15-mediated activation. Heritability enrichment and transcriptomic analyses further revealed tissue- and cell-type-specific expression patterns, particularly in peripheral immune organs and pulmonary alveolar macrophages. Dynamic infection-responsive genes, including CXCL10, CXCL11, and IL15, exhibited robust antiviral signatures, which highlighted Mac_CD163 as key cellular mediators in the immune response to ASF. Moreover, multiple genes (such as SOS1, FCGR2B, FCGR3) converged on the PI3K-AKT and Fcγ receptor signaling axes pathways, underscoring their functional importance. Finally, we developed a polygenic resistance score using 40 prioritized independent SNPs, which effectively discriminates phenotypic outcomes and showed a positive correlation with health traits such as platelet distribution width. CONCLUSIONS:These findings provided a genomic foundation for the precision breeding of ASF-resistant pigs and inform host-targeted disease control strategies.

High-throughput genome sequencing and genotyping have significantly accelerated genetic research. However, the high cost of whole-genome sequencing (WGS) remains a barrier to large-scale studies like genome-wide association studies (GWAS) and genomic prediction. Genotype imputation offers a cost-effective alternative by inferring unobserved variants from lower-density data using haplotype reference panels. In this study, we present the updated Pig Haplotype Reference Panel (PHARP) 4.0, comprising 6449 pig genomes from 154 breeds. PHARP 4.0 encompasses 50.3 million SNPs and 5.8 million indels, making it the largest and most diverse pig reference panel to date. PHARP 4.0 demonstrated superior imputation accuracy compared to existing panels (SWIM, AHC, AGIDB, and PGRP), achieving concordance rates (CR > 0.99) and correlation coefficients (R² > 0.98) in European breeds and improved accuracy in Chinese Jinhua pigs (CR = 0.936, R² = 0.924) when imputing from 80 K SNP chip data to whole-genome sequencing (WGS). We further optimized an RNA-seq-based imputation pipeline by incorporating multiple breeds and applying a 6× sequencing depth filter, achieving CR > 0.95 and R² > 0.90 in European breeds, and a CR of 0.93 with an R² = 0.92 in Chinese Jinhua pigs. Additionally, increasing the specific reference panel size to approximately 400 samples improved the imputation of rare variants. Utilizing PHARP 4.0, we successfully imputed low-density SNP chip data for two GWAS, identifying significant SNPs likely representing causal variants. Overall, PHARP 4.0 serves as a valuable resource for advancing pig genetic research and supporting breeding programs. PHARP 4.0 is an updated pig haplotype reference panel with 6449 genomes from 154 breeds. It demonstrates superior imputation accuracy from chip to WGS data, enables an optimized RNA-seq imputation pipeline, and successfully identifies novel causal variants in GWAS.

Improving reproductive performance in Yorkshire pigs, a key maternal line in three-way crossbreeding systems, remains challenging due to low heritability and historical selection pressures favoring production traits. Identifying pleiotropic genetic variants that influence both reproduction and production traits is crucial for understanding their genetic interplay and enhancing molecular breeding strategies. Genome-wide association studies (GWAS) using 2,764 individuals identified 264,660 significant loci associated with reproduction traits and 12,460 loci for production traits, with 73 independent signals, including genes such as SCLT1 and CAPN9. A total of 465,047 independent loci were identified, resulting in a genome-wide significance threshold of 2.15×10^-6 . Genetic correlations analysis between reproduction and production traits across parities revealed varying trends, including a strengthening negative correlation between mean litter weight (MLW) and backfat thickness (BFT) with increasing parity (P1: r_g =-0.0376; P2: r_g =-0.1371; P3: r_g =-0.1475). Given 1062 shared significant loci between MLW and BFT, local genetic correlation was calculated within the corresponding genomic regions, resulting in a weak correlation of 0.014. Transcriptome-wide association studies (TWAS) leveraging data from the PigGTEx project, which includes 9,530 RNA-sequencing samples across 34 tissues, revealed 2,143 significant genes, with 31 linked to total number of piglets born (TNB) and 133 to number of piglets born alive (NBA). These results highlight the importance of these genes in reproductive performance, with SCLT1 being notably significant in reproductive tissues. For MLW, integrating results from multiple analyses revealed CENPE as a strong candidate gene, exhibiting significant association and colocalization. Validation in an independent population (n = 300) showed that incorporating the top 0.2

BACKGROUND:Pigs are crucial sources of meat and protein, valuable animal models, and potential donors for xenotransplantation. However, the existing reference genome for pigs is incomplete, with thousands of segments and centromeres and telomeres missing, which limits our understanding of the important traits in these genomic regions. FINDINGS:We present a near-complete genome assembly for the Jinhua pig (JH-T2T) and provide a set of diploid Jinhua reference genomes, constructed using PacBio HiFi, ONT long reads, and Hi-C reads. This assembly includes all 18 autosomes and the X and Y sex chromosomes, with only 6 gaps. It features annotations of 46.90% repetitive sequences, 33 telomeres, 17 centromeres, and 23,924 high-confident genes. Compared to the Sscrofa11.1, JH-T2T closes nearly all gaps, extends sequences by 177 Mb, predicts more intact telomeres and centromeres, and gains 799 more genes and loses 114 genes. Moreover, it enhances the mapping rate for both Western and Chinese local pigs, outperforming Sscrofa11.1 as a reference genome. Additionally, this comprehensive genome assembly will facilitate large-scale variant detection. CONCLUSIONS:This study produced a near-gapless assembly of the pig genome and provides a set of haploid Jinhua reference genomes. Our findings represent a significant advance in pig genomics, providing a robust resource that enhances genetic research, breeding programs, and biomedical applications.

The Beigang pig was recently identified as one of the endangered breeds during a Chinese indigenous pig genetic resource survey. The Beigang breed is notable for its remarkable roughage tolerance and high reproductive capacity according to historical records. Morphologically, the Beigang pig resembles many indigenous pigs in eastern China, especially in its large ears. This makes the Beigang pig a valuable reference for studying the genetic mechanisms on large ear size in pigs. However, there is currently a lack of clear understanding regarding the genetic structure and inbreeding levels of the Beigang pig population. This study used whole-genome sequencing data from Beigang pig (N = 145 pigs) and integrated genetic information from commercial pigs and indigenous pigs in eastern China to conduct a comprehensive analysis of the Beigang pig's genetic structure. Three selection signal detection methods-runs of homozygosity, fixation index, and integrated haplotype score-were employed to explore the differences in genomic selection signatures between Beigang pig and other pig populations. Additionally, we used a public project for regulatory variants discovery and molecular phenotype prediction in farm animal species called FarmGtex to explore the expression of three genes (WIF1, LEMD3, and MSRB3) related to ear size in Beigang pig. This research identified five homozygous variant sites in the WIF1 gene as important candidate loci potentially influencing ear size in Beigang pig. The results indicate that the Beigang pig holds a unique status among Chinese indigenous pigs, characterized by high genetic diversity and low levels of inbreeding. The study also revealed that WIF1 may play a significant role in influencing ear size in this breed. These findings contribute to a deeper understanding of the population structure and genetic characteristics of Beigang pig.

Carcass composition traits, such as lean meat percentage, bone percentage, and number of ribs, are critical factors determining meat production and profitability of pigs. Traditional slaughter measurements are time-consuming, labor-intensive and invasive and cannot be evaluated on selection candidates. However, computed tomography scanning, a non-invasive technique, enables in vivo measurement of these traits, facilitating rapid accumulation of extensive phenotypic data. Despite these advances, the genetic mechanisms underlying computed tomography-based carcass traits remain largely unexplored. In this study, we performed a multi-ancestry genome-wide association meta-analysis (MA-GWAMA) using low-coverage whole-genome sequencing data from four breeds (1222 Duroc, 582 Landrace, 1018 Yorkshire, and 448 Piétrain). In total, we identified 11 independent genome-wide significant loci associated with carcass composition traits in the meta-analysis. Compared to standard genomic best linear unbiased prediction, weighting MA-GWAMA-significant SNPs increased genomic prediction accuracy in an independent population (N = 365, including 136 Duroc, 65 Landrace, 50 Piétrain, and 114 Yorkshire) by 16.3

Breeds genetically distant from the reference genome often show considerable differences in DNA fragments, making it difficult to achieve accurate mappings. The genetic differences between pig reference genome (Sscrofa11.1) and Chinese indigenous pigs may lead to mapping bias and affect subsequent analyses. Our analysis revealed that pangenome exhibited superior mapping accuracy to the Sscrofa11.1, reducing false-positive mappings by 1.4

Carcass segmentation and composition (CSC) traits are important indicators for assessing the economic efficiency of pig production. Conventional determination of these traits by slaughter has the drawbacks of high costs and the inability to retain breeding stock. Combining computed tomography (CT) with deep learning enables the non-invasive evaluation of live animal carcass characteristics. In this study, we proposed UPPECT for predicting CSC traits of live pigs based on deep learning. A labeled dataset comprising 300 pigs with a total of 63,708 CT images was constructed for training the nnU-Net model to automatically segment different cuts of pig carcasses. The composition quantification process was optimized using adaptive thresholding and bone filling to achieve accurate prediction of 16 CSC traits. At last, the genetic parameters of CSC traits obtained by UPPECT were estimated for 4,063 pigs. The segmentation model demonstrated excellent performance with a PA of 0.9992, an IoU of 0.9910 and an F1-score of 0.9955. We slaughtered and dissected 50 pigs to obtain real CSC trait values as the validation dataset. The results showed that our method improved the accuracy of composition quantification after optimization, and our predictions for all traits were highly correlated with manual dissection results, with correlation coefficients up to 0.9568. The heritability estimates ranged from 0.52 to 0.85 for all traits. Our study enables non-invasive and precise measurement of CSC traits of live pigs, which makes an important contribution to the breeding practice. A graphical user interface software for UPPECT is freely accessible at https://github.com/StMerce/UPPECT.

Transcriptome-wide association study (TWAS) is a powerful strategy for elucidating the molecular mechanisms behind the genetic loci of complex phenotypes. However, TWAS analysis is still daunting in many species due to the complication of the TWAS analysis pipeline, including the construction of the gene expression reference panel, gene expression prediction, and the subsequent association analysis in the large cohorts of genome-wide association study (GWAS). Farm animals are major protein sources and biomedical models for humans. To facilitate the translation of genetic findings across species, here we provide an interactive and easy-to-use multi-species TWAS web server for the entire community, called the FarmGTEx TWAS-server ( http://twas.farmgtex.org ), which is based on the GTEx and FarmGTEx projects. It includes gene expression data from 49, 34, and 23 tissues in 838 humans, 5,457 pigs, and 4,889 cattle, representing 38,180, 21,037, and 17,942 distinct eGenes in prediction models for humans, pigs, and cattle, respectively. It allows users to conduct gene expression prediction for any individuals with genotypes, GWAS summary statistics imputation, customized TWAS, and popular downstream functional annotation. It also provides 479,203, 1,208, and 657 tissue-gene-trait association trios for the research community, representing 1,129 human traits, 41 cattle traits, and 11 pig traits. In summary, the FarmGTEx TWAS-server is a one-stop solution for performing TWAS analysis for researchers without programming skills in both human and farm animal research communities. It will be maintained and updated timely within the FarmGTEx project to facilitate gene mapping and phenotype prediction within and across species.

Understanding the molecular and cellular mechanisms underlying complex traits in pigs is crucial for enhancing genetic gain via artificial selection and utilizing pigs as models for human disease and biology.Here,we conducted comprehensive genome-wide association studies(GWAS) followed by a cross-breed meta-analysis for 232 complex traits and a within-breed met a-analysis for 12 traits,using 28.3 million imputed sequence variants in 70 328 animals across 14 pig breeds.We identified 6878 quantitative trait loci(QTL) for 139 complex traits.Leveraging the Pig Genotype-Tissue Expression resource,we systematically investigated the biological context and regulatory me chanisms behind these trait-QTLs,ultimately prioritizing 14 829 variant-gene-tissue-trait regulatory circuits.For instance,rs344053754 regulates UGT2B31 expression in the liver and intestines,potentially by modulating enhancer activity,ultimately influencing litter weight at weaning in pigs.Furthermore,we observed conservation of certain genetic and regulatory mechanisms underlying complex traits between humans and pigs.Overall,our cross-breed meta-GWAS in pigs provides invaluable resources and novel insights into the genetic regulatory and evolutionary mechanisms of complex traits in mammals.