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Mitochondrial DNA copy number (mtDNA CN) is heritable and easily obtained from low-pass sequencing (LPS). This study investigated the genetic correlation of mtDNA CN with growth and carcass traits in a multi-breed and crossbred beef cattle population. Blood, leucocyte, and semen samples were obtained from 2,371 animals and subjected to LPS that resulted in nuclear DNA (nuDNA) and mtDNA sequence reads. Mitochondrial DNA CN was estimated as the ratio of mtDNA to nuDNA coverages. Variant calling was performed from mtDNA, and 11 single nucleotide polymorphisms (SNP) were identified in the population. Samples were classified in taurine haplogroups. Haplogroup and mtDNA type were further classified based on the 11 segregating SNP. Growth and carcass traits were available for between 7,249 and 60,989 individuals. Associations of mtDNA CN, mtDNA haplogroups, mtDNA types, and mtDNA SNP with growth and carcass traits were estimated with univariate animal models, and genetic correlations were estimated with a bivariate animal model based on pedigree. Mitochondrial DNA CN tended (P -value ≤0.08) to be associated with birth weight and weaning weight. There was no association (P -value >0.10) between mtDNA SNP, haplogroups, or types with growth and carcass traits. Genetic correlation estimates of mtDNA CN were −0.30 ± 0.16 with birth weight, −0.31 ± 0.16 with weaning weight, −0.15 ± 0.14 with post-weaning gain, −0.11 ± 0.19 with average daily dry-matter intake, −0.04 ± 0.22 with average daily gain, −0.29 ± 0.13 with mature cow weight, −0.11 ± 0.13 with slaughter weight, −0.14 ± 0.13 with carcass weight, −0.07 ± 0.14 with carcass backfat, 0.14 ± 0.14 with carcass marbling, and −0.06 ± 0.14 with ribeye area. In conclusion, mtDNA CN was negatively correlated with most traits investigated, and the genetic correlation was stronger with growth traits than with carcass traits. [ABSTRACT FROM AUTHOR]

Beginning with a large push from consumers to improve meat and carcass quality in the 1990s, through selection, the beef industry has successfully made significant genetic gains towards achieving these improvements. For producers looking to increase the genetic potential of their own herds for carcass quality there are multiple considerations that will inform the time it takes to see improvement. Some of the primary factors influencing the rate of genetic gain for carcass quality phenotypes include: 1) available selection candidates, 2) trait heritability, 3) selection accuracy, and 4) selection intensity. Using the Germplasm Evaluation beef herd at the US Meat Animal Research Center, many of the possible factors influencing this timeline have been evaluated. Collection of carcass quality phenotypes has been an ongoing, long-term objective of the project and has allowed for the estimation of trait heritabilities, breed differences, heterosis effects, and genetic trends over time for this trait complex. Additionally, this herd facilitated the development of decision support tools for creating selection indices and breeding programs, representing the culmination of many of these research outcomes. There are multiple ways to achieve genetic improvement for carcass quality, and our primary objective is to equip producers and other stakeholders with the necessary information to make informed selection decisions. The USDA is an equal opportunity provider and employer [ABSTRACT FROM AUTHOR]

The objective of this study was to enhance estimates of additive, dominance, and epistatic effects of marker polymorphisms on beef carcass and quality traits. Myostatin (MSTN) F94L SNP and the µ-calpain (CAPN1) 316 and 4751 SNP haplotype have previously been associated with fat and muscle traits in beef cattle. Multiyear selection in a composite population segregating these polymorphisms increased minor allele (F94L L) and chosen haplotype (CAPN1 CC and GT) frequencies to intermediate levels resulting in more precise estimates of additive and nonadditive genetic effects. During the 3 yr after selection, 176 steers were evaluated for growth, carcass, meat quality, tenderness (n = 103), and meat color traits. The statistical model included year, age of dam, age of the steer, and genotype in a random animal model. The 9 genotypes (3 CAPN1 diplotypes × 3 F94L genotypes) affected marbling score, ribeye area, adjusted fat thickness, vision yield grade (all P < 0.001), slice shear force (P = 0.03), and CIE L* reflectance (P = 0.01). Linear contrasts of the 9 genotypes estimated additive, recessive, and epistatic genetic effects. Significant additive effects of the F94L L allele decreased marbling score, adjusted fat thickness, vision yield grade, and slice shear force; and increased ribeye area and CIE L* reflectance. The homozygous F94L FF and LL genotypes differed by 1.3 to 1.9 phenotypic SD for most carcass traits and by 0.8 to 0.9 SD for slice shear force and CIE L * reflectance but carcass weight differed by only 3 kg (0.1 SD). The L allele was partially recessive to F for ribeye area (P = 0.02) and the heterozygous FL means tended to be closer to the FF genotype than the LL genotype for other carcass traits but differences from additive were not significant. The CAPN1 additive × F94L additive effect on slice shear force was the only significant epistatic estimate. The F94L L allele is prevalent in Limousin but nearly absent in other U.S. purebreds. This allele had about half of the effects on birth weight, muscle, and fat traits reported for severe MSTN mutations in Belgian Blue and Piedmontese breeds. The interaction between MSTN and CAPN1 genotypes may reflect the strong additive effects of MSTN F94L L allele on fat and muscle traits interfering with the phenotypic effect of CAPN1 genotype on meat tenderness. [ABSTRACT FROM AUTHOR]

Failure to meet consumer expectations for meat quality attributes such as color, tenderness, and flavor leads to loss of customer satisfaction and market share. Thus, much effort has been dedicated to understanding the biological variation in these traits. However, traditional approaches explained only a limited amount of biological variation. Investigations of the genome, transcriptome, proteome, and metabolome of meat animals has greatly increased the understanding of biochemical processes affecting meat quality attributes. Gene expression in response to environmental factors results in protein production. These proteins, which vary in functionality, are involved in cellular processes which produce metabolites. Thus, the proteome comprises the machinery of cellular functions and provides a great deal of information about genomic expression resulting in the phenotype. However, quantifying the metabolome provides additional information about the functionality of the machinery, and thus, even greater information about the phenotype. Untargeted metabolomic investigations have suggested novel mechanisms influencing meat tenderness, lean color stability, and flavor. Future work with targeted metabolomic approaches should focus on validating these mechanisms and studying these mechanisms under variable production systems. Moreover, future work should integrate metabolomic data with genomic, transcriptomic, and proteomic data to fully understand the biological basis of meat quality phenotypes. [ABSTRACT FROM AUTHOR]