Objective: This study examines the genetic factors influencing the phenotypes (four economic traits:oleic acid [C18:1], monounsaturated fatty acids, carcass weight, and marbling score) of Hanwoo. Methods: To enhance the accuracy of the genetic analysis, the study proposes a new statistical model tha...
Objective: This study examines the genetic factors influencing the phenotypes (four economic traits:oleic acid [C18:1], monounsaturated fatty acids, carcass weight, and marbling score) of Hanwoo. Methods: To enhance the accuracy of the genetic analysis, the study proposes a new statistical model that excludes environmental factors. A statistically adjusted, analysis of covariance model of environmental and genetic factors was developed, and estimated environmental effects (covariate effects of age and effects of calving farms) were excluded from the model. Results: The accuracy was compared before and after adjustment. The accuracy of the best single nucleotide polymorphism (SNP) in C18:1 increased from 60.16% to 74.26%, and that of the two-factor interaction increased from 58.69% to 87.19%. Also, superior SNPs and SNP interactions were identified using the multifactor dimensionality reduction method in Table 1 to 4. Finally, high- and low-risk genotypes were compared based on their mean scores for each trait. Conclusion: The proposed method significantly improved the analysis accuracy and identified superior gene-gene interactions and genotypes for each of the four economic traits of Hanwoo.
Objective: This study examines the genetic factors influencing the phenotypes (four economic traits:oleic acid [C18:1], monounsaturated fatty acids, carcass weight, and marbling score) of Hanwoo. Methods: To enhance the accuracy of the genetic analysis, the study proposes a new statistical model that excludes environmental factors. A statistically adjusted, analysis of covariance model of environmental and genetic factors was developed, and estimated environmental effects (covariate effects of age and effects of calving farms) were excluded from the model. Results: The accuracy was compared before and after adjustment. The accuracy of the best single nucleotide polymorphism (SNP) in C18:1 increased from 60.16% to 74.26%, and that of the two-factor interaction increased from 58.69% to 87.19%. Also, superior SNPs and SNP interactions were identified using the multifactor dimensionality reduction method in Table 1 to 4. Finally, high- and low-risk genotypes were compared based on their mean scores for each trait. Conclusion: The proposed method significantly improved the analysis accuracy and identified superior gene-gene interactions and genotypes for each of the four economic traits of Hanwoo.
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문제 정의
In addition, the study employs the multifactor dimensionality reduction (MDR) method to test the main and interaction effects of multiple SNPs on the meat quality of Hanwoo and compares the analysis accuracy between the adjusted and unadjusted models. Finally, the study explores superior genotype groups based on interactions between SNPs in exons of the FASN gene.
제안 방법
Data from 513 Hanwoo steer from 17 farms in Gyengsangbukdo were employed to construct an ANCOVA model of how economic traits are influenced by five SNPs and environmental factors. Here economic traits were C18:1, MUFAs, MS, and CWT and five SNPs (g.
Therefore, this study proposes an analysis of covariance (ANCOVA) model that includes environmental and genetic factors influencing the phenotype of Hanwoo and uses a new adjusted model that eliminates the estimated values of environmental factors. In addition, the study employs the multifactor dimensionality reduction (MDR) method to test the main and interaction effects of multiple SNPs on the meat quality of Hanwoo and compares the analysis accuracy between the adjusted and unadjusted models. Finally, the study explores superior genotype groups based on interactions between SNPs in exons of the FASN gene.
The economic traits of Hanwoo are affected by individual SNPs, calving farms, age, etc. In this study, we used C18:1, MUFAs, MS, and CWT as economic traits and SNPs in exons of the FASN gene, calving farms and age as factors. The relationship between economic traits and factors can be expressed as the following ANCOVA model:
The results verify that the proposed MDR nonparametric statistical method for detecting gene interactions is suitable for small samples and can be used to perform an exhaustive search of all n-locus models by collapsing multi-locus genotypes into highand low-risk groups [20]. The MDR method may be used for the genetic assessment of quantitative traits after further development.
Enhancing the accuracy of genetic analysis necessitates a statistical model that excludes environmental effects. Therefore, this study proposes an analysis of covariance (ANCOVA) model that includes environmental and genetic factors influencing the phenotype of Hanwoo and uses a new adjusted model that eliminates the estimated values of environmental factors. In addition, the study employs the multifactor dimensionality reduction (MDR) method to test the main and interaction effects of multiple SNPs on the meat quality of Hanwoo and compares the analysis accuracy between the adjusted and unadjusted models.
Phenotypes are influenced by environmental and genetic factors. To enhance the accuracy of the genetic effect analysis, the study proposes a new statistical model that excludes environmental factors based on an ANCOVA model. The statistical model has qualitative independent variable which has more than two classes (17 farms).
대상 데이터
This study identifies the SNPs in exons of the FASN gene influencing C18:1, MUFAs, CWT, and MS in Hanwoo (Korean native cattle). The FASN gene is significantly related to FAC and carcass traits [7,11].
이론/모형
Second, superior SNPs and SNP interactions were identified using the MDR method. In the case of C18:1 (Table 1) and MUFAs (Table 2), g.
Then, we constructed an adjusted statistical model using an ANCOVA model and calculated adj (Y) values. The MDR method was used for individual SNPs and five-factor interactions for each economic trait (C18:1, MUFAs, MS, and CWT). According to the results, the accuracy of SNP interactions was much higher than that of individual SNPs.
성능/효과
This accuracy was determined before and after environmental factors were adjusted for. According to the results, the higher-order interaction model was significantly more accurate than the SNP model, and adjusted data were more accurate than raw data.
19%. In addition, the accuracy of MUFAs, MS, and CWT increased by 16.78%p, 28.98%p, and 39.98%p, respectively, in the best SNP and by 27.73%p, 43.53%p, and 45.75%p in the two-factor interaction. Therefore, adjusted data were used to identify gene-gene interaction effects.
First, the accuracy was compared before and after adjustment. The accuracy of the best SNP in C18:1 increased from 60.16% to 74.26%, and that of the two-factor interaction increased from 58.69% to 87.19%. In addition, the accuracy of MUFAs, MS, and CWT increased by 16.
Figure 1 shows the contingency tables of the best two-factor interactions for each economic trait. The chi-square test of interactions was conducted to validate the results, and all tests were significant. In the figure, three factors and their possible multifactor classes or cells are represented in a three-dimensional space [20].
The superior genotype groups, which were all and significant, were 44.238 (standard deviation [SD] = 0.896, p<0.001, Cohen’s d = 1.905) for C18:1, 53.162 (SD = 0.911, p<0.001, Cohen’s d = 1.977) for MUFAs, 5.846 (SD = 0.3, p<0.001, Cohen’s d = 1.852) for MS, and 432.251 (SD = 3.787, p<0.001, Cohen’s d = 1.132) for CWT (Table 5).
The t-test was significant (p<0.001), and the effect size of the genotype of the SNP interaction was larger than that of the individual SNP.
Third, gene-gene interactions of C18:1, MUFAs, MS, and CWT were investigated using a detailed MDR interaction model, and the two-factor interaction was selected as the best for each economic trait (Figure 1). Each genotype was divided into a high- or low-risk group in the selected two-factor interaction.
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