A Comprehensive Genomic Analysis of Chinese Indigenous Ningxiang Pigs: Genomic Breed Compositions, Runs of Homozygosity, and Beyond

Ningxiang Pig-Derived Microbiota Affects the Growth Performance, Gut Microbiota, and Serum Metabolome of Nursery Pigs

The gut microbiota is crucial for maintaining the host's intestinal homeostasis and metabolism. This study investigated the effects of fecal microbiota transplantation (FMT) from Ningxiang pigs on the growth performance, fecal microbiota, and serum metabolites of the same-old DLY pigs. The results indicated that the average daily gain of FMT pigs was significantly greater than that of the control (CON) group. Compared to the CON group, the FMT group significantly improved the apparent digestibility of crude fiber, crude ash, gross energy, and calcium of the pigs. The analysis of serum antioxidant status revealed that the activities of total superoxide dismutase and catalase in the serum of pigs in the FMT group were significantly elevated, whereas the level of malondialdehyde was significantly reduced. Furthermore, 16S rRNA sequencing analysis revealed that the Ningxiang pig-derived microbiota altered the fecal microbiota structure and modulated the diversity of the gut microbiota in the DLY pigs. Untargeted LC-MS metabolomics demonstrated that pigs in the FMT group exhibited distinct metabolomic profiles compared to those in the CON group. Significant changes were observed in key metabolites involved in amino acid, lipid, and carbohydrate metabolism. Additionally, a correlation analysis between serum differential metabolites and the gut microbiota revealed that the relative abundance of Lachnospiraceae_NK4A136_group and Corynebacterium was highly correlated with lipid compounds. In conclusion, Ningxiang pig-derived microbiota can alleviate oxidative stress and enhance growth performance in DLY pigs by modulating their gut microbiota and metabolic features.

Genomic Inbreeding and Runs of Homozygosity Analysis of Cashmere Goat

Cashmere goats are valuable genetic resources which are famous worldwide for their high-quality fiber. Runs of homozygosity (ROHs) have been identified as an efficient tool to assess inbreeding level and identify related genes under selection. However, there is limited research on ROHs in cashmere goats. Therefore, we investigated the ROH pattern, assessed genomic inbreeding levels and examined the candidate genes associated with the cashmere trait using whole-genome resequencing data from 123 goats. Herein, the Inner Mongolia cashmere goat presented the lowest inbreeding coefficient of 0.0263. In total, we identified 57,224 ROHs. Seventy-four ROH islands containing 50 genes were detected. Certain identified genes were related to meat, fiber and milk production (FGF1, PTPRM, RERE, GRID2, RARA); fertility (BIRC6, ECE2, CDH23, PAK1); disease or cold resistance and adaptability (PDCD1LG2, SVIL, PRDM16, RFX4, SH3BP2); and body size and growth (TMEM63C, SYN3, SDC1, STRBP, SMG6). 135 consensus ROHs were identified, and we found candidate genes (FGF5, DVL3, NRAS, KIT) were associated with fiber length or color. These findings enhance our comprehension of inbreeding levels in cashmere goats and the genetic foundations of traits influenced by selective breeding. This research contributes significantly to the future breeding, reservation and use of cashmere goats and other goat breeds.

Classification of breed combinations for slaughter pigs based on genotypes-modeling DNA samples of crossbreeds as fuzzy sets from purebred founders

In pig production, the production animals are generally three- or four-way crossbreeds. Reliable information regarding the breed of origin of slaughtered pigs is useful, even a prerequisite, for a number of purposes, e.g., evaluating potential breed effects on carcass grading. Genetic data from slaughtered pigs can easily be extracted and used for crossbreed classification. In the current study, four classification methods, namely, random forest (RF), ADMIXTURE, partial least squares regression (PLSR), and partial least squares together with quadratic discriminant analysis (PLS-QDA) were evaluated on simulated (n = 7,500) genomic data of crossbreeds. The derivation of the theory behind PLS-QDA is a major part of the current study, whereas RF and ADMIXTURE are known and well-described in the literature. Classification success (CS) rate, square loss (SL), and Kullback-Leibler (KL) divergence loss for the simulated data were used to compare methods. Overall, PLS-QDA performed best with 99%/0.0018/0.002 (CS/SL/KL) vs. 97%/0.0084/0.051, 97%/0.0087/0.0623, and 17%/0.068/0.39 for PLSR, ADMIXTURE, and RF, respectively. PLS-QDA and ADMIXTURE, as the most relevant methods, were used on a real dataset (n = 1,013) from Norway where the two largest classes contained 532 and 192 (PLS-QDA), and 531 and 193 (ADMIXTURE) individuals, respectively. These two classes were expected to be dominating a priori. The Bayesian nature of PLS-QDA enables inclusion of desirable features such as a separate class "unknown breed combination" and informative priors for crossbreeds, making this a preferable method for the classification of breed combination in the industry.