The availability of genetic tests to detect different mutations in the myostatin gene allows the identification of heterozygous animals and would warrant the superiority of these animals for slaughter performance if this superiority is confirmed. Thus, 2 mutations of this gene, Q204X and nt821, were studied in 3 French beef breeds in the program Qualvigène. This work was done with 1,114 Charolais, 1,254 Limousin, and 981 Blonde d'Aquitaine young bulls from, respectively, 48, 36, and 30 sires and slaughtered from 2004 to 2006. In addition to the usual carcass traits recorded at slaughter (e.g., carcass yield, muscle score), carcass composition was estimated by weighing internal fat and dissecting the 6th rib. The muscle characteristic traits analyzed were lipid and collagen contents, muscle fiber section area, and pH. Regarding meat quality, sensory qualities of meat samples were evaluated by a taste panel, and Warner-Bratzler shear force was measured. Deoxyribonucleic acid was extracted from the blood samples of all calves, the blood samples of 78% of the dams, and the blood or semen samples of all the sires. Genotypes were determined for 2 disruptive mutations, Q204X and nt821. Analyses were conducted by breed. The superiority of carcass traits of calves carrying one copy of the mutated allele (Q204X or nt821) over noncarrier animals was approximately +1 SD in the Charolais and Limousin breeds but was not significant in the Blonde d'Aquitaine. In the Charolais breed, for which the frequency was the greatest (7%), young bulls carrying the Q204X mutation presented a carcass with less fat, less intramuscular fat and collagen contents, and a clearer and more tender meat than those of homozygous-normal cattle. The meat of these animals also had slightly less flavor. Also in the Charolais breed, 13 of 48 sires were heterozygous. For each sire, the substitution effect of the wild allele by the mutant allele was approximately +1 SD for carcass conformation and yield, showing that the estimate of the substitution effect was independent of family structure, as it ought to be for a causal mutation. These results illustrate the challenge of using genetic tests to detect animals with the genetic potential for greater grades of carcasses and meat quality.

The objectives of the study were to evaluate allelic frequencies and to test the association of polymorphisms in the calpastatin (CAST) and µ-calpain (CAPN1) genes with meat tenderness in 3 French beef breeds. A total of 1,114 Charolais, 1,254 Limousin, and 981 Blonde d'Aquitaine purebred young bulls were genotyped for 3 SNP in the CAST gene and 4 SNP in the CAPN1 gene. Two of these markers, 1 in each gene, can be found in Australian or American commercial genetic tests. Others have previously been reported in American studies or are newly evidenced SNP. The quantitative traits studied were Warner-Bratzler shear force and a tenderness score evaluated by trained sensory panels. All the SNP were informative in the 3 breeds. Associations of individual markers or haplotypes with traits were analyzed. The results differed in the 3 breeds. The G allele of a CAST marker (position 97574679 on Btau4.0) was found to exert a significant effect on the shear force (+0.18 phenotypic SD; RSD) and tenderness score (-0.22 RSD) in the Blonde d'Aquitaine breed. In the same breed, this marker was associated with another CAST SNP (position 97576054 on Btau4.0) such that the GA haplotype appeared to be associated with tougher meat. Two CAPN1 markers (positions 45221250 and 45241089 on Btau4.0) had a significant effect on both traits in the Charolais breed (from |0.11| to |0.25| RSD). In the same breed, these markers were associated with another CAPN1 SNP (position 45219395 on Btau4.0) such that the ACA and AGG haplotypes appeared to be associated with a tender meat and a tougher meat, respectively. Consequently, the present results indicate that the effects of the markers studied are breed-specific and cannot be extended to all Bos taurus breeds. Further studies are also required to identify other more appropriate markers for French beef breeds.

BackgroundWith dense genotyping, many choices exist for methods to detect quantitative trait loci (QTL) in livestock populations. However, no across-species study has been conducted on the performance of different methods using real data. We compared three methods that correct for relatedness either implicitly or explicitly: linkage and linkage disequilibrium haplotype-based analysis (LDLA), efficient mixed-model association (EMMA) analysis, and Bayesian whole-genome regression (BayesC). We analyzed one chromosome in each of five datasets (dairy cattle, beef cattle, sheep, horses, and pigs) using real genotypes based on dense single nucleotide polymorphisms and phenotypes. The P values corrected for multiple testing or Bayes factors greater than 150 were considered to be significant. To complete the real data study, we also simulated quantitative trait loci (QTL) for the same datasets based on the real genotypes. Several scenarios were chosen, with different QTL effects and linkage disequilibrium patterns. A pseudo-null statistical distribution was chosen to make the significance thresholds comparable across methods.ResultsFor the real data, the three methods generally agreed within 1 or 2 cM for the locations of QTL regions and disagreed when no signals were significant (e.g. in pigs). For certain datasets, LDLA had more significant signals than EMMA or BayesC, but they were concentrated around the same peaks. Therefore, the three methods detected approximately the same number of QTL regions. For the simulated data, LDLA was slightly less powerful and accurate than either EMMA or BayesC but this depended strongly on how thresholds were set in the simulations.ConclusionsAll three methods performed similarly for real and simulated data. No method was clearly superior across all datasets or for any particular dataset. For computational efficiency and ease of interpretation, EMMA is recommended, but using more than one method is suggested.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-015-0087-7) contains supplementary material, which is available to authorized users.