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The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. Traits related to calving have a significant impact on animal welfare and farm profitability in dairy production systems. Identifying genomic regions associated with calving traits could contribute to refining dairy cattle breeding programs and management practices in the dairy industry. Therefore, the primary objectives of this study were to estimate genetic parameters and perform GWAS and functional enrichment analyses for stillbirth, gestation length, calf size, and calving ease traits in North American Jersey cattle. A total of 40,503 animals with phenotypic records and 5,398 animals genotyped for 45,101 SNPs were included in the analyses. Genetic parameters were estimated based on animal models and Bayesian methods. The effects of SNPs were estimated using the single-step GBLUP method. The heritability (SE) estimates ranged from 0.01 (0.01) for stillbirths in heifers to 0.11 (0.01) for gestation length in cows. The genetic correlations ranged from −0.58 (0.11) between calving ease and stillbirths in heifers to 0.44 (0.14) between calving ease and calf size in cows. Calving ease showed the highest genetic correlation between heifers and cows, 0.8 (0.22) respectively. The candidate genes identified, including MTHFR , SERPINA5 , IGFBP3 , and ZRANB1 , are involved in key biological processes and metabolic pathways related to the studied traits. Reducing environmental variation and identifying novel indicators of reproduction traits in the Jersey breed are needed given the low heritability estimates for most traits evaluated in this study. In conclusion, this study provides a characterization of the genetic background of calving-related traits in Jersey cattle. The estimates obtained can be used to improve or build selection indexes in Jersey cattle breeding programs in North America. [ABSTRACT FROM AUTHOR]

Abstract: Records of a pig population used for dry-cured ham production were used to evaluate genetic groups by multivariate analysis. The investigated genetic groups were as follows: DULL=Duroc×(Landrace×Large White), DULA=Duroc×Landrace, DUWI=Duroc×Large White, WIWI=Large White and DUDU=Duroc. Two groups were obtained for the carcass traits hot carcass weight (HCW), backfat thickness (BT) and loin depth (LD), with the groups including 597 and 341 animals harvested at 130kg and at 160kg weights, respectively. Two groups were also found for ham traits gross ham weight (GHW), trimmed ham weight (THW), ham inner layer fat thickness (HIFT), ham outer layer fat thickness (HOFT), pH (PH), and Göfo value, with 393 and 91 animals harvested at 130kg and 160kg weights, respectively. The analysis was performed within each group of traits and harvest weights, and the animals without records were excluded. The first and the second canonical variables explained 97.5% and 93.6% of the total variation for the carcass traits at 130kg and 160kg, respectively, and 88.8% of ham traits at 130kg. In the dispersion graph concerning the canonical means, a significant distance was observed between the genetic groups DUDU and WIWI for the carcass traits at 130kg and 160kg and the ham traits at 130kg. The 50% Duroc animals exhibited little dispersion regarding the carcass traits at 130kg and 160kg and were not divergent from the DUDU genetic group for the ham traits at 130kg. In a cluster analysis using the single linkage method, DULL, DULA and DUWI were grouped with a high similarity level for the carcass traits at 130kg and 160kg and ham traits at 130kg. Using the Tocher optimization method, 50% Duroc crossbred and 100% Duroc purebred animals were grouped for the ham traits at 130kg, suggesting that for ham traits, 50% Duroc animals were similar to 100% Duroc purebred animals. In this context, the genetic groups Duroc×Large White, Duroc×Landrace and Duroc×(Landrace×Large White) are recommended for use in producing dry-cured ham. [Copyright &y& Elsevier]