### INTRODUCTION

### MATERIALS AND METHODS

### Animals and the phenotype

### SNP genotyping

### Linear discriminant and statistical analyses

*y*

*is the phenotype,*

_{ijkl}*μ*is the overall mean,

*S*

*is the random effect of sire*

_{i}*i*with an assumption of independent and identical normal distribution,

*P*

*is the fixed effect of calving place j (14 classes),*

_{j}*G*

*is the fixed effect of genotype*

_{k}*k*,

*β*is the regression coefficient, age is a covariate for age in days at slaughter,

*e*

*is the random residual assumed to have independent and identical normal distribution.*

_{ijkl}### MDR analysis

*i.e.*, high and low groups for phenotypes, and the group’s impurity can be defined as:

*y*is the observation of the j

_{gj}^{th}individual in a total of

*N*individuals in the group (g = high or low). Each individual is assigned to a cell (e.g., if there are two SNPs with three genotypes per SNP, then there are 9 possible cells), and then each cell can be defined as a high or low group. The expected numbers of high and low groups are:

_{g}*n*is the number of total cells and

*I*is an indicator function in which

_{g}(i)*y*is the phenotype of the

_{ij}*j*individual in the

^{th}*i*cell.

^{th}*Step 1*: Data were randomly divided into 10 equal parts (one testing set and nine training sets for cross-validation purpose).*Step 2*: A set of*n*SNPs was selected from the pool of all SNPs.*Step 3*: Based on the observed level of each*n*, steers were partitioned into classes, referred to as cells. If*n =*2, then a set of two SNPs was selected, and, because a SNP had three genotypes, there were 3^{2}= 9 possible cells. Phenotypic means were calculated within each cell.*Step 4*: The impurity function in the CART algorithm used the variance impurity such that the group with the higher average value was labeled as high and the rest was labeled as low.*Step 5*: The expanded MDR model with the smallest ASE was chosen among all two-factor combinations.*Step 6*: For the evaluation of the predictive ability of the model, the predicted ASE (P_ASE) was estimated using a 10-fold cross-validation method. These six steps were repeated for each possible combination of given*n*. The model with the minimum predicted ASE was selected as the best model, but for the selected model, statistical significance was not determined by the predicted ASE. Therefore, permutation tests were performed to determine the empirical significance thresholds by applying the same MDR method (Good, 1994). Before the MDR implementation, phenotypes were adjusted for contemporary and, sire/steer’s age effects by using residuals obtained after fitting the general linear model without SNP effects.

### RESULTS AND DISCUSSION

*de novo*or derived from the diet (Smith et al., 2006). Variations in SCD enzyme activity in mammals are likely to affect a variety of key physiological variables, including cellular differentiation, insulin sensitivity, metabolic rates, adiposity, atherosclerosis, cancer, and obesity (Paton and Ntambi, 2009). Two types of SCD gene isoforms have been characterized in cattle, namely SCD1, expressed mainly in adipose tissue, and SCD5, expressed mainly in the brain (Lengi and Corl, 2007). The expression of SCD1 in bovine adipose tissue is regulated by numerous factors. The SCD1 mRNA level in muscle tissue has been reported to increase after weaning until 12 mon of age (Lee et al., 2005) but also during the late fattening stages (Kwon et al., 2009), and its development was dependent on the breed and diet (Chung et al., 2007). Breed differences in the SCD1 mRNA level have been found Japanese Black and Holstein steers (Taniguchi et al., 2004). Based on these reports, we collected 15 SNPs suggested by dbSNP of NCBI to identify those SNPs related to fatty acids in the SCD1 gene (Oh et al., 2011). These SNPs were located at 3 SNPs in the intron region, 3 SNPs in the exon 5 region and 9 SNPs in the 3′UTR region. By SBE method for verification of 15 SNPs in 45 extreme steers for the high and low MUFAs group of Korean native cattle, 15 polymorphic SNPs were identified (Table 1). And they were excluded in the following genetic association analysis. The other loci had their minor allele frequency ranging from 0.233 to 0.500, and their genotypes did not deviate from the Hardy-Weinberg equilibrium (p>0.05, Table 1).