Abstract
ObjectiveThis 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.MethodsTo 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.ResultsThe 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.ConclusionThe proposed method significantly improved the analysis accuracy and identified superior gene-gene interactions and genotypes for each of the four economic traits of Hanwoo.
Highlights
Beef quality depends on fatty acid composition (FAC) and the degree of marbling
Fatty acid synthase (FASN), which is abundantly expressed in adipose tissue, is a complex homodimeric enzyme that regulates the biosynthesis of long-chain fatty acids [6]
We recently examined the genetic relationships between the FAC of beef and multiple nucleotide sequence variants in the gene encoding fatty acid synthase (FASN) [7]
Summary
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 (cov ariate effects of age and effects of calving farms) were excluded from the model. Superior SNPs and SNP interactions were identified using the multifactor dimensionality reduction method in Table 1 to 4. High- and low-risk genotypes were compared based on their mean scores for each trait.
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