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In livestock breeding populations, regions of the genome with a high frequency of runs of homozygosity (ROH) have reduced diversity. Metrics that reduce ROH frequency may provide an attractive way to manage the diversity and ensure long-term gains while avoiding inbreeding accumulation. The 2 objectives of the current work were to characterize the frequency of ROH in Landrace (LR), Large White (LW), and their cross (LR x LW) through a combination of real and simulated genotypes and to determine the impact of optimizing different inbreeding metrics for nucleus and crossbred populations. A ROH statistic (ROH5Mb: "1" if SNP was in ROH of 5 Mb and "0" otherwise) was calculated across the genome for genotyped LR (n = 1206) and LW (n = 1349) dams and simulated crossbred genotypes derived from mating 81 LR sires to 100 LW dams. High ROH5Mb frequencies were declared for a contiguous set of SNP within the top 5%. In addition to random mating, pedigree, genomic, or shared ROH-based relationship matrices were used to minimize relationships between mating pairs within breed and the latter 2 were in crossings. Mating plans with 25 sires available with a restriction on the maximum number of mating were devised for within-breed and across-breed mating populations of 625 and 1250 dams, respectively. Each plan was replicated 25 times. Regions of shared highROH5Mb frequency were found on SSC1, SSC3, and SSC14 and regions with a high ROH5Mb frequency within a breed were found to persist in the crossbreeds. Runs of homozygosity and genom-ic-based relationship matrices decreased the proportion of the overall genome in a ROH by 2.45- and 2.19-fold when compared with pedigree-based relationships. Furthermore, the use of pedigree-based relationships was not able to decrease regions with high ROH5Mb frequency more heavily than ROH-or G-based relationships. The use of alternative genomic re-latedness metrics such as ROH allow for relationships to be minimized for targeted regions of low diversity. [ABSTRACT FROM AUTHOR]

The objective of the study was to estimate variance components and inbreeding effect for sow reproductive performance considered as different traits according to the season of conception. Reproductive and pedigree data were obtained for 18,648 Landrace litters from nucleus farms in Texas (n = 1) and North Carolina (n = 2). Traits included number born alive (NBA), total number born (TNB), number born dead (BD), and fetal loss (FL) calculated as BD/TNB. Season of conception was defined as winter (December-February), spring (March-May), summer (June-August), and fall (September-November). Variance components and genetic correlations were estimated with gibbslf90 using a multiple-trait model with trait by season represented in the model. The model included fixed effects of contemporary group (herd by year) and parity and the random additive genetic effect of sow. For the inbreeding estimates, level of inbreeding was also included in die model as a covariate. Heritability estimates were greatest for NBA, TNB, and BD for conception in summer months with estimates of 0.198, 0.208, and 0.165, respectively, and for FL for spring conception with an estimate of 0.172. Heritability estimates were lowest for spring conception for NBA (0.107) and TNB (0.086), for fall conception for BD (0.122), and for summer conception for FL (0.138). Genetic correlations were greatest for NBA (0.946) and TNB (0.934) in spring and winter, and the relationship between spring and fall for BD (0.987) and PWM (0.935). Genetic correlations were lowest for NBA (0.794) and TNB (0.733) for spring and summer, for spring and winter for BD (0.817), and for fall and winter for FL (0.823). The estimates and SE of inbreeding depression for each trait by season are shown in Table 029. The results suggest that NBA, TNB, BD, and FL should be treated as different traits according to season of conception, and summer performance appears to be determined by a different genetic background compared with the other seasons. Selection for increased performance during the summer months may be a more effective method to mitigate seasonal infertility than selection for performance across the year. It is also suggested that increased inbreeding may be especially detrimental for sows conceiving during the summer and fall season. [ABSTRACT FROM AUTHOR]

The dairy sheep industry faces ongoing challenges in optimizing cheese production and enhancing efficiency across different breeds. This study provides crucial insights into how breed-specific factors, lactation stages, parity, and milk composition, including mineral concentrations, affect cheese yield and nutrients recovery in the curd. The aims of this study were to characterize individual sheep milk samples for cheesemaking efficiency by measuring 3 cheese yield traits (%CY; fresh curd, TS, and water retained) and 5 recovery of nutrients traits (%REC; fat, protein, lactose, TS, and energy) and to examine how these traits change throughout the lactation, considering different sheep breeds, parities, and variations in milk composition and mineral concentrations. A total of 760 ewes from Massese and Comisana breeds were sampled during the morning milking at the National Association of Sheep Breeders nucleus farm (Tuscany, Italy). The application of generalized additive mixed models (GAMM) was a key element of this study, enabling a more nuanced analysis of complex relationships and nonlinear trends. The GAMM accounted for variations in breed and parity, which were included as parametric terms, as well as milk minerals, rennet coagulation time (RCT), sampling day, and interactions between DIM and milk yield (MY), fat and casein, and Ca and P, which were treated as smooth terms. This approach provided insights that would have been difficult to capture with traditional linear models. Results evidenced breed and parity-specific variations. Indeed, the Massese had overall lower cheesemaking efficiency compared with the Comisana ewes, and primiparous ewes had higher percentages of both %CY and %REC traits. The interaction between DIM × MY was less significant in the Comisana breed compared with the Massese, with notable effects only on protein and lactose recovery in the Comisana. The interaction between fat × casein was also breed specific and affected with different extent and patterns the cheesemaking traits between the 2 breeds. Longer RCT increased the water retained in the curd and reduced the recovery of TS and the individual recovery rates of the main milk components in both breeds. The effect of Ca × P was not always linear on the cheesemaking traits and between breeds. Overall, an increase of both Ca and P was associated with higher %CY and %REC, except for the recovery of fat and energy. High Na and Cl were detrimental for the cheesemaking process in both breeds, whereas the destabilizing effect of K was particularly notable on the fresh curd, curd TS and the recovery of protein from the Massese milk. These findings highlight that sheep breed and parity, alongside specific milk components and mineral concentrations, significantly affect cheesemaking efficiency, with important differences between the Massese and Comisana breeds. Efficient cheesemaking requires careful consideration of mineral concentrations, especially in terms of Ca, P, Na, Cl, and K, to optimize yields and nutrients recovery in the curd., (© 2025, The Authors. Published by Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Traits such as meat quality and composition are becoming valuable in modern pork production; however, they are difficult to include in genetic evaluations because of the high phenotyping costs. Combining genomic information with multiple-trait indirect selection with cheaper indicator traits is an alternative for continued cost-effective genetic improvement. Additionally, gut microbiome information is becoming more affordable to measure using targeted rRNA sequencing, and its applications in animal breeding are becoming relevant. In this paper, we investigated the usefulness of microbial information as a correlated trait in selecting meat quality in swine. This study incorporated phenotypic data encompassing marbling, colour, tenderness, loin muscle and backfat depth, along with the characterization of gut (rectal) microbiota through 16S rRNA sequencing at three distinct time points of the animal's growth curve. Genetic progress estimation and cross-validation were employed to evaluate the utility of utilizing host genomic and gut microbiota information for selecting expensive-to-record traits in crossbred individuals. Initial steps involved variance components estimation using multiple-trait models on a training dataset, where the top 25 associated operational taxonomic units (OTU) for each meat quality trait and time point were included. The second step compared the predictive ability of multiple-trait models incorporating different numbers of OTU with single-trait models in a validation set. Results demonstrated the advantage of including genomic information for some traits, while in some instances, gut microbial information proved advantageous, namely, for marbling and pH. The study suggests further investigation into the shared genetic architecture between microbial features and traits, considering microbial data's compositional and high-dimensional nature. This research proposes a straightforward method to enhance swine breeding programs for improving costly-to-record traits like meat quality by incorporating gut microbiome information., (© 2024 The Author(s). Journal of Animal Breeding and Genetics published by John Wiley & Sons Ltd.)