### INTRODUCTION

*longissimus dorsi*muscle area (LMA) in the 2000’s were 8 kg and 2.9 cm

^{2}, respectively, in Hanwoo (NIAS, 2009).

### MATERIALS AND METHODS

### Animals, phenotypes and molecular data

### Models used

#### LDRM

*y*is the phenotypic record,

*μ*is the average phenotypic performance, X is the design matrix of SNP genotype (e.g. individuals with marker genotypes ‘11’, ‘12’, and ‘22’ are assumed to have genetic values μ

_{kAA}, 0, and μ

_{kBB}),

*a*is the fixed substitution effect for the SNP, Z

_{u}is the incidence matrix for animal effects, u is the infinitesimal genetic effect, which is distributed as

*e*is a random residual for animal

*i,*which is distributed as

*I*and

*x*

^{2}distribution of the likelihood ratio test with one degree of freedom. Association with false discovery rate (FDR) <0.01 on chromosome-wide level were considered significant.

#### LDLA

*ϕ*

*) at the midpoint of each SNP interval (*

_{p}*p*) were computed for all pairs of haplotypes conditional on the identity-by-state status of flanking markers (Meuwissen and Goddard, 2001). A dendrogram was generated by using the unweighted pair group method with arithmetic mean (UPGMA) hierarchical clustering algorithm with 1-

*ϕ*

*as the distance measure at QTL location (*

_{p}*p*). Starting at the ancestral node and sequentially descending into the dendrogram, all possible combinations of haplotype clusters were analyzed in place of individual haplotypes. This process identified the set of nodes at which the likelihood of the data were maximized.

*h*is the vector of random QTL effects corresponding to the defined haplotype clusters.

*Z*

*is an incidence matrix relating maternal haplotypes of sons and sire haplotypes to individual sons. Likelihood ratio tests were performed by removing the haplotype cluster effects, and p-values were obtained assuming a*

_{h}*x*

^{2}distribution of the likelihood ratio test with one degree of freedom. Association with FDR <0.01 on chromosome-wide level were considered significant.

#### BayesCπ

_{j}is the vector of genotypes at SNP

*,*

_{j}*a*

*is the random substitution effect for the SNP, which condition on the variance*

_{j}*δ*

*=1, while*

_{j}*δ*

*=0,*

_{j}*in the model. The prior for π was treated as unknown with uniform (0, 1). Gibbs sampling was applied to calculate the posterior means of model parameters μ,*

_{j}*a*

*,*

_{j}### RESULTS AND DISCUSSION

### Quantitative trait loci analysis by LDRM and LDLA

*Bos taurus*autosome (BTA) 14, and a 0.2Mb (between 52.72 and 52.88Mb) for BFT on BTA13, and this indicated that they are associated with the same QTL.

*i.e.*5 SNPs are located within 0.2 Mb (between 26.24 to 26.45 Mb) for BFT on BTA29, which harbors the neuron navigator 2 (

*NAV2*) gene.

### Quantitative trait loci analysis by BayesCπ

### Comparison of result

*GRIA1*), that encodes glutamate receptor 1. The QTL for CWT was detected in the proximal region (24.3 to 25.4 Mb) of BTA14, which encompasses family with sequence similarity 110, member B (FAM110B), ubx domain protein 2B (UBXN2B), and thymocyte selection-associated high mobility group box (TOX) as positional and functional candidate genes for the CWT QTL in cattle. A QTL for BFT was detected in the proximal region (0.5 to 1.5 Mb) of the BTA 6, in which Locus (

*LOC*)

*100139637*gene was located. Another QTL was detected for Marb in the proximal region of the BTA29 (26.3 to 33.4 Mb), around which

*NAV2*gene was located. These QTL locations that were found by each method were as expected because the chromosomes were suspected to contain QTL.

*i.e.*LDRM tests a single marker at a time and regards the markers as independent of all other markers (Grapes et al., 2004; Zhao et al., 2007; MacLeod et al., 2010), LDVCM tests the midpoint of the marker brackets, which corresponded best when the QTL was masked between analyzed markers(Kim and Georges, 2002; Blott et al., 2003), and BayesCπ test the effects of all markers, which are fitted simultaneously. However, QTL with large effects can be detected by both the Bayesian shrinkage and linear regression mapping methods. By specifying proper prior distributions for SNP effects, the ignorable small SNP effects are coerced to zero and only SNPs with larger effects on the phenotype are fitted in the model, hence Bayesian shrinkage analysis could reduce possible spurious QTL effects by adjusting all other QTL effects. This was also explained by Xu (2003) and Sun (2011). Therefore, here we attempted to minimize the number of false positives by combined these three methods result.