Meat and carcass quality of Korean native cattle, Hanwoo, are of great value and thus of great concern to Korean consumers. Among carcass quality traits, carcass weight, backfat thickness, longissimus dorsi muscle area, and marbling score are the most influential on beef profit in Hanwoo industry.

Hanwoo breeding programs that were implemented using Animal Model has enabled significant genetic progress in carcass quality. For example, the estimates of annual gain over the last 10 yr were 8 kg for CWT and 2.9 cm² for LMA (NIAS, 2009). However, genomic prediction at young stage can further accelerate genetic gain, because prediction errors at breeding age would be reduced by exploiting information on the value of every chromosomal fragment and on the transmission of chromosome fragments from parents to selection candidates (Garrick, 2011).

Meuwissen et al. (2002) proposed a fine-mapping method that was based on the evolutionary history of adjacent markers under a coalescent model. In the mapping approach, QTL variance is estimated using a matrix of covariances among marker haplotypes flanking the QTL. If the haplotypes are similar, e.g. identical alleles between the haplotypes, there is an increased chance that the QTL alleles in the haplotypes that were descended from unobserved founders of the population are identical by descent (IBD), which was called linkage disequilibrium (LD) mapping. If the common ancestors occur within the known pedigrees, then the IBD probability between individuals can be calculated by investigating inheritance patterns of the marker alleles from the parents, i.e. by linkage analysis (LA).

Kim and Georges (2002) reported a modified version of Meuwissen et al. (2002) with the addition of a hierarchical haplotype clustering step in the QTL fine-mapping analysis, which clustered haplotype groups that were based on kinship relatedness and effectively solved numerical computation problems, e.g. singularities in the haplotype covariance matrix between similar haplotypes (Druet et al., 2008).

Chromosomal regions, genes, and their specific alleles on BTA6 that were associated with carcass traits in Hanwoo have been reported (Lee et al., 2008; Lee et al., 2010a; b).

The aim of this study was to further refine the QTL region on BTA6, and to search for candidate genes affecting carcass qualities in Hanwoo by applying the LDVCM method of Kim and Georges (2002).

A total of 47 significant signals (SNPs) were detected, for which 13, 11, 21, and two SNPs were associated with CWT, BFT, LMA, and Marb, respectively (Table 1, Figure 1). The significant signals were grouped into 15 QTLs, comprising five, three, five, and two QTL for the respective traits (Table 1).

In general, compared with Li et al. (2011), in which QTL analyses were performed under four different LD mapping models using the same phenotypes and genotypes as in this study, the LDVCM analyses gave similar results on QTL locations. However, the LDVCM outperformed in power in detecting QTL, i.e. only 12 QTL were detected in Li et al. (2011). This result may be partly due to genetic characteristics of Hanwoo breed. In Holstein-Friesian dairy cattle, high LDs, e.g. r²>0.62, were detectable within 10 kb distances between SNPs, suggesting that 300,000 evenly spaced SNPs are sufficient to cover the genome to exploit LD for QTL fine-mapping (Khatkar et al., 2008). In Hanwoo, however, LDs between linked SNPs were much weaker, compared to other western commercial breeds (Lee et al., 2010a). For this reason, the single marker regression analysis that was applied in Li at al. (2011) would cause less power in detecting QTL, because there is less chance that a QTL is closely located to one SNP marker with high LD. However, the LDVCM analysis, which was based on haplotypes and thus LD information was exploited from multiple SNPs flanking the QTL, would enable detection of QTL with greater power (Meuwissen and Goddard, 2001). Further, the LDVCM analysis exploited LD information within the observable pedigrees, i.e. inheritance of marker alleles from sires to progeny, as well as LDs at a population-wide level between chromosomes of sires and maternal chromosomes of steers (Blott et al., 2003). Thus, the LDVCM method would be suitable in QTL mapping efficiency when marker densities was limited and a structured population was used, e.g. paternal halfsib pedigrees.

Fifteen chromosomal regions (QTL) were identified (Table 1) and many significant signals (SNPs) were observed in the QTL regions (Figure 1). The signals may be due to high LDs with the causal mutation for the trait. Another reason of the clustering of significant SNPs might be due to the chromosomal regions harboring groups of co-regulated genes that are related to restriction sites or transcription factor binding sites (Sun et al., 2006). Therefore, even if more than one significant signals (SNPs) were detected within ±1 Mb region (2-Mb QTL interval), only one QTL was declared. The QTL can be accessed in the Ensembl (Hubbard et al., 2009).

Half of the significant signals (11 out of 21) that were associated with LMA were located on the distal region (110,285,672 to 110,633,096 bp) (Table 1, Figure 1). The QTL region was close to one gene, CLNK, which encodes cytokine-dependent hematopoietic cell linker. In particular, a signal (BTB-00280923 ~ Hapmap44204-BTA-117480) that was located at 110,360,573 bp was the most significant among all the tests of the four traits (p<10⁻⁵, Table 1).

In the proximal region flanking 33,596,515 to 33,897,434 bp, one QTL with pleiotropic effects for CWT, BFT, and LMA was detected (Table 1, Figures 1). On the region, one gene, BBS7 (Bardet-Biedl syndrome 7), was located. The QTL region was previously reported in other studies (McClure et al., 2010; Mizoguchi et al., 2006; Setoguchi et al., 2009). However, further studies are needed to characterize the QTL, i.e. with effects of pleiotropy of one gene or of multiple genes that are clustered together in the QTL region.

In conclusion, our results suggest that the LDVCM approach is beneficial in detection of QTL for carcass quality in a Hanwoo population, under whose conditions availability of marker density and exploitation of LDs (genetic structure) are limited. The detected QTL regions need to be verified in other Hanwoo populations as well as by conducting candidate gene studies, before implementing marker-assisted selection in Hanwoo commercial populations.