The increasing importance of meat quality has implications for animal breeding programs. Research has revealed much about the genetic background of pigs, and many studies have revealed the importance of various genetic factors. Since meat quality is a complex trait which is affected by many factors,...
The increasing importance of meat quality has implications for animal breeding programs. Research has revealed much about the genetic background of pigs, and many studies have revealed the importance of various genetic factors. Since meat quality is a complex trait which is affected by many factors, consideration of the overall phenotype is very useful to study meat quality. For integrating the phenotypes, we used principle component analysis (PCA). The significant SNPs refer to results of the GRAMMAR method against PC1, PC2 and PC3 of 14 meat quality traits of 181 Duroc pigs. The Genome-wide association study (GWAS) found 26 potential SNPs affecting various meat quality traits. The loci identified are located in or near 23 genes. The SNPs associated with meat quality are in or near five genes (ANK1, BMP6, SHH, PIP4K2A, and FOXN2) and have been reported previously. Twenty-five of the significant SNPs also located in meat quality-related QTL regions, these result supported the QTL effect indirectly. Each single gene typically affects multiple traits. Therefore, it is a useful approach to use integrated traits for the various traits at the same time. This innovative approach using integrated traits could be applied on other GWAS of complex-traits including meat-quality, and the results will contribute to improving meat-quality of pork.
The increasing importance of meat quality has implications for animal breeding programs. Research has revealed much about the genetic background of pigs, and many studies have revealed the importance of various genetic factors. Since meat quality is a complex trait which is affected by many factors, consideration of the overall phenotype is very useful to study meat quality. For integrating the phenotypes, we used principle component analysis (PCA). The significant SNPs refer to results of the GRAMMAR method against PC1, PC2 and PC3 of 14 meat quality traits of 181 Duroc pigs. The Genome-wide association study (GWAS) found 26 potential SNPs affecting various meat quality traits. The loci identified are located in or near 23 genes. The SNPs associated with meat quality are in or near five genes (ANK1, BMP6, SHH, PIP4K2A, and FOXN2) and have been reported previously. Twenty-five of the significant SNPs also located in meat quality-related QTL regions, these result supported the QTL effect indirectly. Each single gene typically affects multiple traits. Therefore, it is a useful approach to use integrated traits for the various traits at the same time. This innovative approach using integrated traits could be applied on other GWAS of complex-traits including meat-quality, and the results will contribute to improving meat-quality of pork.
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제안 방법
Therefore, consideration of the overall phenotype is a useful approach to evaluate meat quality. This study also used many phenotypes, and extracted non-redundant and integrated information from correlated phenotypes. Other studies have analyzed a single phenotype, or even if several phenotypes were considered, they were analyzed separately then considered together.
대상 데이터
In this study, we used a two-generation resource population of Duroc pigs. Duroc boars were mated with 106 Duroc sows. The 181 female Duroc pigs from four farms were humanely killed at 18±16.
Observations comprised a total of three PCs of 181 pigs. As all 181 pigs were female, we only used age as a covariate.
이론/모형
Residual (e) contains the environmental effect along with a major part of the genetic effect. All parameters were estimated using the ASREML program (Gilmour et al., 2009) facilitated by the AIREML algorithm for a single-trait animal model. Using this model, we calculated the residual for the phenotype related to meat quality used in this GWAS.
Example result of linkage disequilibrium around the significant SNPs of eigenvector 1 PCA results on chromosome 2. Linkage disequilibrium (LD) was determined using Haploview, and LD blocks were defined using the Gabriel rule. Darker boxes represent higher LD percentages.
We performed PCA analysis and got three integrated phenotypes (PCs) related to meat quality, which were then used to estimate meat quality. Residual in Genome-wide rapid association using mixed model and regression (GRAMMAR) (Aulchenko et al., 2007) was used as a phenotype that reflected the genetic factors.
We corrected our figure to produce an accurate number of tests to determine the regions of the genome that should be studied further. The inflation factors seemed to be high, but we corrected for the population stratification in this analysis using the GRAMMAR approach. Yang et al.
The blue colour represents positive correlation between the phenotypes, the red colour means negative correlation between the phenotypes. The order was determined by hierarchical clustering method.
성능/효과
The number of significant SNPs in the genic regions was roughly equal to those in the inter-genic regions. Fourteen significant SNPs were associated with PC1 (six in genic regions, eight in intergenic regions), eleven SNPs were associated with PC2 (six in genic regions, five in intergenic regions), and two SNPs were associated with PC3 (one in a genic region, one in an intergenic region). The coverage of genic and intergenic regions were roughly 1:3 (25.
후속연구
This may lead to an innovative approach of the GWAS of complex-traits including meat-quality. If the significant SNPs of this study and additional SNPs from other studies using the same method as this study are used in marker-assisted selection, it will contribute to improving the meat and carcass quality in pig in a more efficient method than the traditional pig selection.
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