Results and Discussion
Genetic Variants
Polymerase chain reaction generated a 1,400 bp fragment for CAST28 and a 1,600 bp fragment for CAST67 (Table 1). No polymorphism in the CAST28 segment was found using several restriction enzymes including Hae III, Hinf I, Hha I, Ava II, Xmn I, Msp I, Pst I, Mae III, and Rsa I. A polymorphism was detected in the CAST28 segment using SSCP (Figure 1). A restriction fragment length polymorphism (RFLP) was found in the CAST67 segment using Xmn I (Figure 2). Two alleles were detected for the CAST28 and CAST67 segments. Allele frequencies were calculated for both segments (Table 2). Allele frequencies were tested for chi-square goodness of fit for Mendelian inheritance with no significant departures from expected values. Genetic variants for calpastatin domain L and IV, which are unique and repetitive domains, respectively, have not been previously reported.
Significant Effects
Least squares means for FAT, LEA, KPH, and HCW classified by genotypes are shown in Table 3. Significant differences among CAST67 genotypes were found for KPH (P < 0.01) and CAC (P < 0.05) (Table 5). Genotypes for CAST28 also influenced KPH (P < 0.01) and CAC (P < 0.05). The BB genotype of CAST28 and CAST67 had much higher values for KPH and CAC than the other genotypes. Least significant differences among means for CAST67 and CAST28 genotypes were found for KPH (BB>AB>AA) and CAC (BB>AB>AA). No significant differences among genotypes were detected for FAT, LEA, HCW, MAR, QUL, ADG, MFI, pH, or WBS (Tables 3, 4, and 5). It may be possible to use calpastatin genotypes classified by PCR-RFLP and SSCP in marker assisted selection programs to improve KPH and calpastatin activity.
Residual Correlations
A positive low correlation between CAC and WBS was detected (Table 6); this correlation was lower than in previous studies (r = 0.48, Koohmaraie, 1994; r = 0.31, Lonergan et al., 1995; r = 0.42, Wulf et al., 1996; r = 0.83, Morton et al., 1999). Uytterhaegen et al. (1994) and Koohmaraie et al. (1994) also reported a positive correlation between CAC and WBS. Goll et al. (1985) and Ouali and Talmant (1990) suggested that the activity of endogenous proteolytic enzymes in muscle tissue plays a major role in the conversion of muscle proteins. The CAC was correlated with FAT and MAR (P < 0.05), and was negatively correlated with most of the meat traits. Wiklund et al. (1997) suggested that meat with different pH values has different collagen contents, and, therefore, pH may be related to meat traits. Koohmaraie (1990) and Dransfield (1992) also suggested that pH is one of the main determinants of meat tenderness. However, a significant correlation between pH and WBS was not detected in the current stduy. The pH was not significantly correlated with any of the meat traits other than KPH. Positive relationships were detected between pH and KPH (r = 0.40, P < 0.01) and between KPH and FAT (r = 0.40, P < 0.01).
The calpain-calpastatin system was examined to determine its involvement in the rapid tenderization of fast glycolysing muscle. O'Halloran et al. (1997) reported that fast glycolysing muscles were rated more tender in sensory analysis and had a lower shear force than slow glycolysing muscles. Therefore, slow glycolysing muscles had shorter sarcomere lengths, as reflected in the MFI. Consequently, MFI is expected to be correlated with meat tenderness and pH, but significant correlations were not found in this study.
Conclusions
Genotypes for CAST28 and CAST67 significantly influenced KPH, and also explained significant variation in calpastatin activity. The BB genotypes for both segments had the highest KPH values (BB>AB>AA). Calpastatin activity was somewhat related to WBS (r = 0.26, P = 0.09). Significant negative correlations between CAC and FAT, and CAC and MAR were observed. The KPH was highly correlated with pH and FAT. It may be possible to use calpastatin genotypes classified by PCR-RFLP and SSCP in marker assisted selection programs to improve KPH, as well as calpastatin activity and meat tenderness.