Growth traits are important economic traits of pigs that are controlled by several major genes and multiple minor genes. To better understand the genetic architecture of growth traits, we performed a weighted single-step genome-wide association study (wssGWAS) to identify genomic regions and candidate genes that are associated with days to 100 kg (AGE), average daily gain (ADG), backfat thickness (BF) and lean meat percentage (LMP) in a Duroc pig population. In this study, 3945 individuals with phenotypic and genealogical information, of which 2084 pigs were genotyped with a 50 K single-nucleotide polymorphism (SNP) array, were used for association analyses. We found that the most significant regions explained 2.56–3.07% of genetic variance for four traits, and the detected significant regions (>1%) explained 17.07%, 18.59%, 23.87% and 21.94% for four traits. Finally, 21 genes that have been reported to be associated with metabolism, bone growth, and fat deposition were treated as candidate genes for growth traits in pigs. Moreover, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses implied that the identified genes took part in bone formation, the immune system, and digestion. In conclusion, such full use of phenotypic, genotypic, and genealogical information will accelerate the genetic improvement of growth traits in pigs.

Feed efficiency (FE) is an important trait in the porcine industry. Therefore, understanding the molecular mechanisms of FE is vital for the improvement of this trait. In this study, 6 extreme high-FE and 6 low-FE pigs were selected from 225 Duroc × (Landrace × Yorkshire) (DLY) pigs for transcriptomic analysis. RNA-seq analysis was performed to determine differentially expressed genes (DEGs) in the liver tissues of the 12 individuals, and 507 DEGs were identified between high-FE pigs (HE- group) and low-FE pigs (LE- group). A gene ontology (GO) enrichment and pathway enrichment analysis were performed and revealed that glycolytic metabolism and lipid synthesis-related pathways were significantly enriched within DEGs; all of these DEGs were downregulated in the HE- group. Moreover, Weighted gene co-expression analysis (WGCNA) revealed that oxidative phosphorylation, thermogenesis, and energy metabolism-related pathways were negatively related to HE- group, which might result in lower energy consumption in higher efficiency pigs. These results implied that the higher FE in the HE- group may be attributed to a lower glycolytic, energy consumption and lipid synthesizing potential in the liver. Furthermore, our findings suggested that the inhibition of lipid synthesis and glucose metabolic activity in the liver may be strategies for improving the FE of DLY pigs.

Background Improving feed efficiency is economically and environmentally beneficial in the pig industry. A deeper understanding of feed efficiency is essential on many levels for its highly complex nature. The aim of this project is to explore the relationship between fecal metabolites and feed efficiency-related traits, thereby identifying metabolites that may assist in the screening of the feed efficiency of pigs. Results We performed fecal metabolomics analysis on 50 individuals selected from 225 Duroc x (Landrace x Yorkshire) (DLY) commercial pigs, 25 with an extremely high feed efficiency and 25 with an extremely low feed efficiency. A total of 6749 and 5644 m/z features were detected in positive and negative ionization modes by liquid chromatography-mass spectrometry (LC/MS). Regrettably, the PCA could not classify the the samples accurately. To improve the classification, OPLS-DA was introduced. However, the predictive ability of the OPLS-DA model did not perform well. Then, through weighted coexpression network analysis (WGCNA), we found that one module in each positive and negative mode was related to residual feed intake (RFI), and six and three metabolites were further identified. The nine metabolites were found to be involved in multiple metabolic pathways, including lipid metabolism (primary bile acid synthesis, linoleic acid metabolism), vitamin D, glucose metabolism, and others. Then, Lasso regression analysis was used to evaluate the importance of nine metabolites obtained by the annotation process. Conclusions Altogether, this study provides new insights for the subsequent evaluation of commercial pig feed efficiency through small molecule metabolites, but also provide a reference for the development of new feed additives.