SNPs are informative to estimate genomic breed composition (GBC) of individual animals, but selected SNPs for this purpose were not made available in the commercial bovine SNP chips prior to the present study. The primary objective of the present study was to select five common SNP panels for estimating GBC of individual animals initially involving 10 cattle breeds (two dairy breeds and eight beef breeds). The performance of the five common SNP panels was evaluated based on admixture model and linear regression model, respectively. Finally, the downstream implication of GBC on genomic prediction accuracies was investigated and discussed in a Santa Gertrudis cattle population. There were 15,708 common SNPs across five currently-available commercial bovine SNP chips. From this set, four subsets (1,000, 3,000, 5,000, and 10,000 SNPs) were selected by maximizing average Euclidean distance (AED) of SNP allelic frequencies among the ten cattle breeds. For 198 animals presented as Akaushi, estimated GBC of the Akaushi breed (GBCA) based on the admixture model agreed very well among the five SNP panels, identifying 166 animals with GBCA = 1. Using the same SNP panels, the linear regression approach reported fewer animals with GBCA = 1. Nevertheless, estimated GBCA using both models were highly correlated (r = 0.953 to 0.992). In the genomic prediction of a Santa Gertrudis population (and crosses), the results showed that the predictability of molecular breeding values using SNP effects obtained from 1,225 animals with no less than 0.90 GBC of Santa Gertrudis (GBCSG) decreased on crossbred animals with lower GBCSG. Of the two statistical models used to compute GBC, the admixture model gave more consistent results among the five selected SNP panels than the linear regression model. The availability of these common SNP panels facilitates identification and estimation of breed compositions using currently-available bovine SNP chips. In view of utility, the 1 K panel is the most cost effective and it is convenient to be included as add-on content in future development of bovine SNP chips, whereas the 10 K and 16 K SNP panels can be more resourceful if used independently for imputation to intermediate or high-density genotypes.

With the aim to study the flavor characteristics of Ningxiang pigs (NX), Duroc (DC) pigs, and their crosses (Duroc × Ningxiang, DN), electronic nose and gas chromatography–mass spectrometry analysis were used to detect the volatile flavor substances in NX, DC, and DN (n = 34 pigs per population). A total of 120 volatile substances were detected in the three populations, of which 18 substances were common. Aldehydes were the main volatile substances in the three populations. Further analysis revealed that tetradecanal, 2-undecenal, and nonanal were the main aldehyde substances in the three kinds of pork, and the relative content of benzaldehyde in the three populations had significant differences. The flavor substances of DN were similar to that of NX and showed certain heterosis in flavor substances. These results provide a theoretical basis for the study of flavor substances of China local pig breeds and new ideas for pig breeding.

Ningxiang pigs, a Chinese bred pig known for its tender meat and high quality unsaturated fatty acids. This study discovers the transcription profiles and functional networks in long non-coding RNA (lncRNA) and messenger RNA (mRNA) in subcutaneous adipose tissue. Subcutaneous adipose tissue was collected from piglet, nursery pig, early fattening, and late fattening stage of Ningxiang piglets, and lncRNA and mRNA transcription of each stage was profiled. A total of 339,204,926 (piglet), 315,609,246 (nursery), 266,798,202 (early fattening), and 343,740,308 (late fattening) clean reads were generated, and 2872 novel lncRNAs were identified. Additionally, 10,084 differential mRNAs (DEmRNAs) and 931 differential lncRNAs were determined. Most DEmRNAs were up-regulated in the piglet stage, while they were down-regulated in late fattening stage. A complicated interaction between mRNAs and lncRNAs was identified via STEM and WGCNA, demonstrated that lncRNAs are a significant regulatory component in mRNAs. The findings showed that modules 2 and 5 have a similar mode of transcription for both mRNA and lncRNA, and were mainly participated in steroid biosynthesis, glycosphingolipid biosynthesis, metabolic pathways, and glycerolipid metabolism. The mRNAs and lncRNAs transcription levels of both modules was higher in the early and late fattening stage, which may be due to the active activity of the metabolism in relation to fatty acids, sterols, steroids, and lipids in the subcutaneous adipose tissue during the early and late fattening stage. These findings could be expected to result in further research of the functional properties of lncRNA from subcutaneous adipose tissue at different stages of development in Ningxiang pigs.

Background: Genomic selection involves choosing as parents those elite individuals with the higher genomic estimated breeding values (GEBV) to accelerate the speed of genetic improvement in domestic animals. But after multi-generation selection, the rate of inbreeding and the occurrence of homozygous harmful alleles might increase, which would reduce performance and genetic diversity. To mitigate the above problems, we can utilize genomic mating (GM) based upon optimal mate allocation to construct the best genotypic combinations in the next generation. In this study, we used stochastic simulation to investigate the impact of various factors on the efficiencies of GM to optimize pairing combinations after genomic selection of candidates in a pig population. These factors included: the algorithm used to derive inbreeding coefficients; the trait heritability (0.1, 0.3 or 0.5); the kind of GM scheme (focused average GEBV or inbreeding); the approach for computing the genomic relationship matrix (by SNP or runs of homozygosity (ROH)). The outcomes were compared to three traditional mating schemes (random, positive assortative or negative assortative matings). In addition, the performance of the GM approach was tested on real datasets obtained from a Large White pig breeding population. Results: Genomic mating outperforms other approaches in limiting the inbreeding accumulation for the same expected genetic gain. The use of ROH-based genealogical relatedness in GM achieved faster genetic gains than using relatedness based on individual SNPs. The G_ROH-based GM schemes with the maximum genetic gain resulted in 0.9%–2.6% higher rates of genetic gain ΔG, and 13%–83.3% lower ΔF than positive assortative mating regardless of heritability. The rates of inbreeding were always the fastest with positive assortative mating. Results from a purebred Large White pig population, confirmed that GM with ROH-based GRM was more efficient than traditional mating schemes. Conclusion: Compared with traditional mating schemes, genomic mating can not only achieve sustainable genetic progress but also effectively control the rates of inbreeding accumulation in the population. Our findings demonstrated that breeders should consider using genomic mating for genetic improvement of pigs.

An important economic reason for the loss of local breeds is that they tend to be less productive, and hence having less market value than commercial breeds. Nevertheless, local breeds often have irreplaceable values, genetically and sociologically. In the breeding programs with local breeds, it is crucial to balance the selection for genetic gain and the maintaining of genetic diversity. These two objectives are often conflicting, and finding the optimal point of the trade-off has been a challenge for breeders. Genomic selection (GS) provides a revolutionary tool for the genetic improvement of farm animals. At the same time, it can increase inbreeding and produce a more rapid depletion of genetic variability of the selected traits in future generations. Optimum-contribution selection (OCS) represents an approach to maximize genetic gain while constraining inbreeding within a targeted range. In the present study, 515 Ningxiang pigs were genotyped with the Illumina Porcine SNP60 array or the GeneSeek Genomic Profiler Porcine 50K array. The Ningxiang pigs were found to be highly inbred at the genomic level. Average locus-wise inbreeding coefficients were 0.41 and 0.37 for the two SNP arrays used, whereas genomic inbreeding coefficients based on runs of homozygosity were 0.24 and 0.25, respectively. Simulated phenotypic data were used to assess the utility of genomic OCS (GOCS) in comparison with GS without inbreeding control. GOCS was conducted under two scenarios, selecting sires only (GOCS_S) or selecting sires and dams (GOCS_SD), while kinships were constrained on selected parents. The genetic gain for average daily body weight gain (ADG) per generation was between 18.99 and 20.55 g with GOCS_S, and between 23.20 and 28.92 with GOCS_SD, and it varied from 25.38 to 48.38 g under GS without controlling inbreeding. While the rate of genetic gain per generation obtained using GS was substantially larger than that obtained by the two scenarios of genomic OCS in the beginning generations of selection, the difference in the genetic gain of ADG between GS and GOCS reduced quickly in latter generations. At generation ten, the difference in the realized rates of genetic gain between GS and GOCS_SD diminished and ended up with even a slightly higher genetic gain with GOCS_SD, due to the rapid loss of genetic variance with GS and fixation of causative genes. The rate of inbreeding was mostly maintained below 5% per generation with genomic OCS, whereas it increased to between 10.5% and 15.3% per generation with GS. Therefore, genomic OCS appears to be a sustainable strategy for the genetic improvement of local breeds such as Ningxiang pigs, but keeping mind that a variety of GOCS methods exist and the optimal forms remain to be exploited further.