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Metrics details. On the other hand, using haplotypes in the statistical analysis may increase the extent of linkage disequilibrium LD between haplotypes and causal variants and may also potentially capture epistastic interactions between variants within a haplotyped locus, providing an increase in the power and robustness of the association studies. We performed GWAA , SNP markers using haplotypes based on variable-sized sliding windows and compared the results to a single-SNP GWAA using Warner-Bratzler shear force measured in the longissimus thorasis muscle of Nelore bulls to ascertain the optimal window size for identifying the genomic regions that influence meat tenderness.
However, thirty-three putative meat tenderness QTL were detected on BTA 1, 3, 4, 5, 8, 9, 10, 11, 15, 17, 18, 24, 25, 26 and 29 using variable-sized sliding haplotype windows. The genes associated with variation in meat tenderness participate in myogenesis, neurogenesis, lipid and fatty acid metabolism and skeletal muscle structure or composition processes. Following the completion of the first draft bovine reference genome assembly, a high-density single nucleotide polymorphism SNP genotyping assay was developed [ 1 ], enabling genome-wide association analyses GWAA , which are useful in understanding the underlying biology of traits [ 2 , 3 ].
Several GWAA have identified SNP associated with meat tenderness in cattle [ 4 , 5 , 6 , 7 ], which is one of the most important attributes impacting consumer satisfaction and the price of beef [ 8 ].
Moreover, meat tenderness is the most important trait requiring improvement in Nelore cattle in order to increase beef quality and ensure consumer acceptability. However, studies have shown that the traditional single-SNP GWAA can be inefficient because single-SNPs provide limited information about the content of flanking genomic regions [ 9 , 10 ]. On the other hand, using haplotypes in the statistical analysis may increase the extent of linkage disequilibrium LD between haplotypes and causal variants and also potentially capture epistastic interactions between variants within a haplotyped locus, providing increased power and robustness for association studies [ 10 , 11 , 12 , 13 , 14 , 15 , 16 ].
