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China is the world recognized pig big country in the global pig production occupies an important position. Pig growth, carcass and meat quality traits as a major economic traits, breeding has been a focus of the work. Pig growth traits including weight gain (Weight gain, WG), feed conversion efficiency (Feed conversion ratio, FCR), etc.; carcass traits including backfat (Backfat, BFT), tare (Skin Weight, SW) and fat Weight (Fat Weight, FW) and the like; meat quality traits including flesh (Meat Color, MC), fat content (Fat content, FC), protein content (Protein content, PC), water content (Water content, WC) and so on. These traits are quantitative traits controlled by multiple genes. Genes or pathways through passage between the interaction of other genes regulate body growth and development to achieve or metabolic activity, so a single gene or a single SNP loci as a research unit has some limitations. Therefore, this study proposes a candidate path method, digging pig growth, carcass and meat quality traits related candidate genes. This paper selected pig fatty acid metabolic pathways and metabolic pathways as a candidate pathway, mice and fatty acid pathway genes within the pathway to the pig genome compared to the initial screening candidate genes and further information with PigQTLdb QTL mapping database final selection the 17 candidate genes. Tag SNP loci through the extraction and prediction of potential SNP sites were excavated 57 SNP loci, including 24 tag SNP loci and 33 predicted SNP loci, and through SNaPshot genotyping methods. Least-squares model, multivariate multiple regression analysis model and MB-MDR method and pig growth, meat and carcass quality traits were analyzed. The results detected in genes SUCLA2, SUCLG2, ALDH1B1, ACADS gene on four SNP loci with growth traits were significantly associated; located genes ACSS1, SUCLG2 the two SNP loci and meat quality traits were significantly associated, located genes MUT, MCEE, PCCB on three SNP loci were significantly associated with carcass traits. In addition, this paper model-based multifactor dimensionality reduction (MB-MDR) method passage interaction effects between genes were analyzed and found that a single SNP locus association analysis did not show a significant method of loci MBMDR Analysis of the results showed a very significant. This paper further significant loci bioinformatics analysis, SNP loci sequence of the transcription factor binding sites were predicted and found that gene SUCLG2 located on the first intron nucleotide polymorphic loci mutated to T of C When preexisting four transcription factor binding sites disappeared, appeared POU1F1 transcription factor binding sites. By fluorescence quantitative PCR for propionic acid metabolic pathway studied traits were significantly associated with the five gene expression analysis found MCEE, MUT, PCCB, SUCLA2 five genes in nine individuals showed the same trend, infer that this 4 gene expression and regulation may have a certain relationship.
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