Background: Reproductive performance of livestock is an economically important aspect of global food production. The Chinese Meishan pig is a prolific breed, with an average of three to five more piglets per litter than European breeds; however, the genetic basis for this difference is not well understood. Results: In this study, we investigated copy number variations (CNVs) of 32 Meishan pigs and 29 Duroc pigs by next-generation sequencing. A genome-wide analysis of 61 pigs revealed 12,668 copy number variable regions (CNVRs) that were further divided into three categories based on copy number (CN) of the whole population, i.e., gain (n = 7,638), and loss (n = 5,030) CNVRs. We then compared Meishan and Duroc pigs and identified 17.17 Mb of 6,387 CNVRs that only existing in Meishan pigs CNVRs that overlapped the reproduction-related gene encoding the aryl hydrocarbon receptor (AHR) gene. We found that normal AHR CN was more frequent than CN loss in four different pig breeds. An association analysis showed that AHR CN had a positive effect on litter size (P < 0.05) and that a higher CN was associated with higher total number born (P < 0.05), number born alive (P < 0.05), number of weaned piglets, and birth weight. Conclusions: The present study provides comprehensive CNVRs for Meishan and Duroc pigs through large-scale population resequencing. Our results provide a supplement for the high-resolution map of copy number variation in the porcine genome and valuable information for the investigation of genomic structural variation underlying traits of interest in pig. In addition, the association results provide evidence for AHR as a candidate gene associated with reproductive traits that can be used as a genetic marker in pig breeding programs.

Piglet uniformity (PU) and farrowing interval (FI) are important reproductive traits related to production and economic profits in the pig industry. However, the genetic architecture of the longitudinal trends of reproductive traits still remains elusive. Herein, we performed a genome-wide association study (GWAS) to detect potential genetic variation and candidate genes underlying the phenotypic records at different parities for PU and FI in a population of 884 Large White pigs. In total, 12 significant SNPs were detected on SSC1, 3, 4, 9, and 14, which collectively explained 1–1.79% of the phenotypic variance for PU from parity 1 to 4, and 2.58–4.11% for FI at different stages. Of these, seven SNPs were located within 16 QTL regions related to swine reproductive traits. One QTL region was associated with birth body weight (related to PU) and contained the peak SNP MARC0040730, and another was associated with plasma FSH concentration (related to FI) and contained the SNP MARC0031325. Finally, some positional candidate genes for PU and FI were identified because of their roles in prenatal skeletal muscle development, fetal energy substrate, pre-implantation, and the expression of mammary gland epithelium. Identification of novel variants and candidate genes will greatly advance our understanding of the genetic mechanisms of PU and FI, and suggest a specific opportunity for improving marker assisted selection or genomic selection in pigs.

Long noncoding RNA (lncRNA) play a critical role in mammary development and breast cancer biology. Despite their important role in the mammary gland, little is known of the roles of lncRNA in bovine lactation, particularly regarding the molecular processes underlying it. To characterize the role of lncRNA in bovine lactation, 4 samples of Holstein cow mammary gland tissue at peak and late lactation stages were examined after biopsy. We then profiled the transcriptome of the mammary gland using RNA sequencing technology. Further, functional lncRNA-mRNA coexpression pairs were constructed to infer the function of lncRNA using a generalized linear model, followed by gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. More than 1,000 putative lncRNA were identified, 117 of which were differentially expressed between peak and late lactation stages. Bovine lncRNA were shorter, with fewer exon numbers, and expressed at significantly lower levels than protein-coding genes. Seventy-two differentially expressed (DE) lncRNA were coexpressed with 340 different protein-coding genes. The KEGG pathway analysis showed that target mRNA for DE lncRNA were mainly related to lipid and glucose metabolism, including the peroxisome proliferator-activated receptors and 5' adenosine monophosphate-activated protein kinase signaling pathways. Further bioinformatics and integrative analyses revealed that 12 DE lncRNA potentially played important roles in bovine lactation. Our findings provide a valuable resource for future bovine transcriptome studies, facilitate the understanding of bovine lactation biology, and offer functional information for cattle lactation.

Low-coverage sequencing (LCS) followed by imputation has been proposed as a cost-effective genotyping approach for obtaining genotypes of whole-genome variants. Imputation performance is essential for the effectiveness of this approach. Several imputation methods have been proposed and successfully applied in genomic studies in human and other species. However, there are few reports on the performance of these methods in livestock. Here, we evaluated a variety of imputation methods, including Beagle v4.1, GeneImp v1.3, GLIMPSE v1.1.0, QUILT v1.0.0, Reveel, and STITCH v1.6.5, with varying sequencing depth, sample size, and reference panel size using LCS data of Holstein cattle. We found that all of these methods, except Reveel, performed well in most cases with an imputation accuracy over 0.9; on the whole, GLIMPSE, QUILT, and STITCH performed better than the other methods. For species with no reference panel available, STITCH followed by Beagle would be an optimal strategy, whereas for species with reference panel available, QUILT would be the method of choice. Overall, this study illustrated the promising potential of LCS for genomic analysis in livestock.

Complex traits with multiple phenotypic values changing over time are called longitudinal traits. In traditional genome-wide association studies (GWAS) for longitudinal traits, a combined/averaged estimated breeding value (EBV) or deregressed proof (DRP) instead of multiple phenotypic measurements per se for each individual was frequently treated as response variable in statistical model. This can result in power losses or even inflate false positive rates (FPRs) in the detection due to failure of exploring time-dependent relationship among measurements. Aiming at overcoming such limitation, we developed two random regression-based models for functional GWAS on longitudinal traits, which could directly use original time-dependent records as response variable and fit the time-varied Quantitative Trait Nucleotide (QTN) effect. Simulation studies showed that our methods could control the FPRs and increase statistical powers in detecting QTN in comparison with traditional methods where EBVs, DRPs or estimated residuals were considered as response variables. Besides, our proposed models also achieved reliable powers in gene detection when implementing into two real datasets, a Chinese Holstein Cattle data and the Genetic Analysis Workshop 18 data. Our study herein offers an optimal way to enhance the power of gene detection and further understand genetic control of developmental processes for complex longitudinal traits.

DNA methylation changes play essential roles in regulating the activities of genes involved in immune responses. Understanding of variable DNA methylation linked to immune responses may contribute to identifying biologically promising epigenetic markers for pathogenesis of diseases. Here, we generated genome-wide DNA methylation and transcriptomic profiles of six pairs of polyinosinic-polycytidylic acid-treated pig peripheral blood mononuclear cell (PBMC) samples and corresponding controls using methylated DNA immunoprecipitation sequencing and RNA sequencing. Comparative methylome analyses identified 5,827 differentially methylated regions and 615 genes showing differential expression between the two groups. Integrative analyses revealed inverse associations between DNA methylation around transcriptional start site and gene expression levels. Furthermore, 70 differentially methylated and expressed genes were identified such as TNFRSF9, IDO1 and EBI3. Functional annotation revealed the enriched categories including positive regulation of immune system process and regulation of leukocyte activation. These findings demonstrated DNA methylation changes occurring in immune responses of PBMCs to poly I:C stimulation and a subset of genes potentially regulated by DNA methylation in the immune responses. The PBMC DNA methylome provides an epigenetic overview of this physiological system in response to viral infection, and we expect it to constitute a valuable resource for future epigenetic epidemiology studies in pigs.