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Meat quality is an important trait for pig-breeding programs aiming to meet consumers’ demands. Geneticists must improve meat quality based on their understanding of the underlying genetic mechanisms. Previous studies showed that most meat-quality indicators were low-to-moderate heritability traits; therefore, improving meat quality using conventional techniques remains a challenge. Here, we performed a genome-wide association study of meat-quality traits using the GeneSeek Porcine SNP50K BeadChip in 582 crossbred Duroc × (Landrace × Yorkshire) commercial pigs (249 males and 333 females). Meat conductivity, marbling score, moisture, meat color, pH, and intramuscular fat (IMF) content were investigated. The genome-wide association study was performed using both fixed and random model Circulating Probability Unification (FarmCPU) and a mixed linear model (MLM) with the rMVP software. The genomic heritability of the studied traits ranged from 0.13 ± 0.07 to 0.55 ± 0.08 for conductivity and meat color, respectively. Thirty-two single-nucleotide polymorphisms (SNPs) were identified for meat quality in the crossbred pigs using both FarmCPU and MLM. Among the detected SNPs, five, nine, seven, four, six, and five were significantly associated with conductivity, IMF, marbling score, meat color, moisture, and pH, respectively. Several candidate genes for meat quality were identified in the detected genomic regions. These findings will contribute to the ongoing improvement of meat quality, meeting consumer demands and improving the economic outlook for the swine industry.

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Efforts have been made to investigate the phylogeny of Cetartiodactyla; however, the relationships within this group still remain controversial. Due to the limitation of collecting samples from some key species of the Cetartiodactyla, it is difficult to perform molecular phylogenetic analysis to find out their precise classification scheme. Fortunately, much up-to-date, more molecular data samples of this group are available from GenBank. To further clarify the relationships within the Cetartiodactyla, phylogenetic analyses of the Cetartiodactyla were conducted using Bayesian and maximum likelihood (ML) methods based on complete mitochondrial genomes. The results indicate that Moschidae sister to Bovidae, and recognize the families Moschidae, Bovidae, Cervidae and Giraffidae to be four monophyletic groups. Phylogenetic trees also indicate that the basal divergence within the Cetartiodactyla is between the Suina and a strongly supported clade of the remaining Cetartiodactyla; Tragulidae is the early offshoot within the Ruminantia, followed by the Antilocapridae.

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As an endangered animal group in China, musk deer (genus Moschus) have attracted the attention of deer biologists and wildlife conservationists. Clarifying the taxonomic status and distribution of musk deer species is important to determine the conservation status for each species and establish appropriate conservation strategies. There remains some uncertainty about the species determination of the musk deer in the Guandi Forest District of Shanxi Province, China. The musk deer in Shanxi would appear to represent an extension of the geographical distribution of either the Forest Musk Deer from the southwest or the Siberian Musk Deer from the northeast, or possibly both. The musk deer population in Shanxi Province provides an interesting and significant case to test the value of applying molecular methods to make a genetic species identification. In order to clarify the species status of the Shanxi musk deer, we sequenced 627 bp of the COI gene and ≈723 bp of the D-loop gene in 12 musk deer samples collected from the Guandi Forest District, and the two reference samples collected from Sichuan. Genetic analyses from the data suggest that all of the samples from the Guandi Forest District are M. berezovskii rather than M. moschiferus. It is most likely that the most previous studies had wrong species identification. And it is the first time we use DNA barcoding to prove that Shanxi is a new distribution of M. berezovskii.

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