Longevity is one of the functional traits that considerably affect dairy herd profitability. A Weibull proportional hazards model was used to evaluate the impact of difficult calvings and calf stillbirths on cow functional longevity, defined as length of productive life corrected for milk production. The data for analysis comprised calving ease and calf mortality scores of 2,163,426 calvings, 34.4% of which came from primiparous cows. The percentage of male calves was 53.4%. Calving ease was scored as “without assistance” (34.44%), “with assistance” (62.03%), “difficult—hard pull” (3.39%), and “very difficult, including caesarean section” (0.14%). Calf mortality scores were “live born” (94.21%) and “stillborn or died within 24 h” (5.79%). The Weibull proportional hazards model included classes of calving ease or calf mortality scores × parity (1, ≥2) × sex of calf as time-dependent fixed effect. The model also included time-dependent fixed effects of year × season, parity × stage of lactation, annual change in herd size, fat yield and protein yield, time-independent fixed effect of age at first calving, and time dependent random herd × year × season. In first-parity cows, very difficult birth of a bull or heifer increased the relative risk of culling, respectively, 2.18 or 1.26 times as compared with calving without assistance. In later parities, the relative risk of culling related to very difficult calving was 2.0 times (for male calves) and 1.33 times (for female calves) higher than the relative risk of culling associated with calving without assistance. Calf mortality showed a negative impact on longevity in both heifers and cows. First-parity stillbirth increased the relative risk of culling depending on sex of calf by 18% in females and by 15% in males; in later parities the increase of the relative risk of culling was lower (by 7% for females, 9% for males). Difficult calvings and their consequences, especially in primiparous cows, may negatively influence dairy herd profitability by reducing the length of cows’ productive life.

The goal of our study was to identify the SNPs, metabolic pathways (KEGG), and gene ontology (GO) terms significantly associated with calving and workability traits in dairy cattle. We analysed direct (DCE) and maternal (MCE) calving ease, direct (DSB) and maternal (MSB) stillbirth, milking speed (MSP), and temperament (TEM) based on a Holstein-Friesian dairy cattle population consisting of 35,203 individuals. The number of animals, depending on the trait, ranged from 22,301 bulls for TEM to 30,603 for DCE. We estimated the SNP effects (based on 46,216 polymorphisms from Illumina BovineSNP50 BeadChip Version 2) using a multi-SNP mixed model. The SNP positions were mapped to genes and the GO terms/KEGG pathways of the corresponding genes were assigned. The estimation of the GO term/KEGG pathway effects was based on a mixed model using the SNP effects as dependent variables. The number of significant SNPs comprised 59 for DCE, 25 for DSB and MSP, 17 for MCE and MSB, and 7 for TEM. Significant KEGG pathways were found for MSB (2), TEM (2), and MSP (1) and 11 GO terms were significant for MSP, 10 for DCE, 8 for DSB and TEM, 5 for MCE, and 3 for MSB. From the perspective of a better understanding of the genomic background of the phenotypes, traits with low heritabilities suggest that the focus should be moved from single genes to the metabolic pathways or gene ontologies significant for the phenotype.

(1) Background: The goal of our study was to identify SNPs, metabolic pathways (KEGG), and gene ontology (GO) terms significantly associated with calving and workability; (2) Methods: Based on the EuroGenomics reference data set, we analyzed direct (DCE) and maternal (MCE) calving ease, direct (DSB), and maternal (MSB) stillbirth, milking speed (MSP), and temperament (TEM). We estimated SNP effects using a multi-SNP mixed-model. Further, SNP positions were mapped to genes, and GO terms/KEGG pathways of the corresponding genes were assigned. The estimation of GO term/KEGG pathway effects was based on a mixed-model using SNP effects as dependent variables; (3) Results: The number of significant SNPs comprised 59 for DCE, 25 for DSB and MSP, 17 for MCE and MSB, and 7 for TEM. Significant KEGG pathways were found for MSB (2), TEM (2), and MSP (1), while 11 GO terms were significant for MSP, 10 for DCE, 8 for DSB and TEM, 5 for MCE, and 3 for MSB.; (4) Conclusions: From the perspective of a better understanding of the genomic background of the phenotypes, traits with low heritabilities suggest that the focus should be moved from single genes to metabolic pathways or gene ontologies significant for the phenotype.