Effects of Short Dry Periods on Performance and Metabolic Status

Genomic breeding programs have been paramount in improving the rates of genetic progress of productive efficiency traits in livestock. Such improvement has been accompanied by the intensification of production systems, use of a wider range of precision technologies in routine management practices, and high-throughput phenotyping. Simultaneously, a greater public awareness of animal welfare has influenced livestock producers to place more emphasis on welfare relative to production traits. Therefore, management practices and breeding technologies in livestock have been developed in recent years to enhance animal welfare. In particular, genomic selection can be used to improve livestock social behavior, resilience to disease and other stress factors, and ease habituation to production system changes. The main requirements for including novel behavioral and welfare traits in genomic breeding schemes are: (1) to identify traits that represent the biological mechanisms of the industry breeding goals; (2) the availability of individual phenotypic records measured on a large number of animals (ideally with genomic information); (3) the derived traits are heritable, biologically meaningful, repeatable, and (ideally) not highly correlated with other traits already included in the selection indexes; and (4) genomic information is available for a large number of individuals (or genetically close individuals) with phenotypic records. In this review, we (1) describe a potential route for development of novel welfare indicator traits (using ideal phenotypes) for both genetic and genomic selection schemes; (2) summarize key indicator variables of livestock behavior and welfare, including a detailed assessment of thermal stress in livestock; (3) describe the primary statistical and bioinformatic methods available for large-scale data analyses of animal welfare; and (4) identify major advancements, challenges, and opportunities to generate high-throughput and large-scale datasets to enable genetic and genomic selection for improved welfare in livestock. A wide variety of novel welfare indicator traits can be derived from information captured by modern technology such as sensors, automatic feeding systems, milking robots, activity monitors, video cameras, and indirect biomarkers at the cellular and physiological levels. The development of novel traits coupled with genomic selection schemes for improved welfare in livestock can be feasible and optimized based on recently developed (or developing) technologies. Efficient implementation of genetic and genomic selection for improved animal welfare also requires the integration of a multitude of scientific fields such as cell and molecular biology, neuroscience, immunology, stress physiology, computer science, engineering, quantitative genomics, and bioinformatics.

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Large-Scale Phenotyping of Livestock Welfare in Commercial Production Systems: A New Frontier in Animal Breeding

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The hedgehog signaling pathway in the mouse ovary

Prospects for gene introgression or gene editing as a strategy for reduction of the impact of heat stress on production and reproduction in cattle.

The flux-linkage characteristics of bearingless induction motors (BIMs) are nonlinear, and the models established by the general analytical method cannot accurately reflect the actual characteristics of BIMs. Thus, a novel method for nonlinear modeling of BIM flux linkage is proposed in this paper. The main objective of this method is to improve the accuracy of the flux linkage model based on the least square support vector machine (LSSVM) technique by applying the gray wolf optimization (GWO) algorithm to determine the optimal kernel parameter and regularization parameter of the LSSVM automatically. In this method, all BIMs flux linkage data are obtained from the finite-element method. In this paper, the relationship between input and output of the nonlinear flux linkage model is studied, and the precision model of GWO-LSSVM flux linkage is obtained. The simulation results demonstrate that the GWO-LSSVM model has high prediction accuracy and strong prediction ability. In addition, the GWO-LSSVM model is compared with other models. From this simulation comparison, it can be concluded that GWO-LSSVM modeling has the characteristics of higher accuracy.

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A Nonlinear Flux Linkage Model for Bearingless Induction Motor Based on GWO-LSSVM

Farm and cow factors and their interactions on the incidence of retained placenta in holstein dairy cows.

This study aimed to investigate the genetic variability of conception rate (CR) and non-return rate (NR) in Iranian dairy cows under heat stress conditions. A total of 34,304 records of CR, and NR at 45 days (NR45) and 90 days (NR90) after the first insemination, from 21,405 Holstein cows were included in this study. The weather records were obtained from seven meteorological stations located at a distance of less than 70 km from the farms. Temperature-Humidity Index (THI) was determined for each record on the insemination day. The statistical models for CR, NR45, and NR90 included the fixed effects of herd-year-season, parity, milk yield, and THI. Genetic components were estimated using an animal model and fitting random regression models on THI based on the Bayesian method. Results showed similar decreasing trends for CR, NR45, and NR90 when increasing the THI levels. The additive genetic variance of heat tolerance for CR, NR45, and NR90 were 0.008 ± 0.0004, 0.0262 ± 0.007, and 0.0254 ± 0.006, respectively. The additive genetic variance of heat tolerance increased directly with THI, and therefore, our findings indicate that a combined selection using heat tolerance can be considered for genetic evaluation of reproduction traits under heat stress conditions.

Investigating the genetic architecture of conception and non-return rates in Holstein cattle under heat stress conditions

. Oxidized low density lipoprotein receptor 1 (OLR1) is the major cell surface receptor for oxidized low density lipoprotein (Ox-LDL). The role of OLR1 in lipid metabolism and the existence of milk-related QTL in the vicinity of the OLR1 gene have prompted the investigation of OLR1 as a candidate gene influencing milk production traits. The present study explored the association of a single nucleotide polymorphism (SNP) in the 3' untranslated region of the OLR1 gene (OLR1g.8232 C>A) with milk-related traits in 408 Iranian Holstein cows. Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) technique was performed for genotyping the animals. Animals with genotype CC had the highest and animals with genotype AA had the lowest fat percentage while genotype AC was intermediate (P

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Association of a polymorphism in the 3' untranslated region of the OLR1 gene with milk fat and protein in dairy cows

Assessing the consequences and economic impact of retained placenta in Holstein dairy cattle.

This study was performed to investigate the association between polymorphisms in the ABCG2 (ATP-binding cassette sub-family G member 2), LEPR (leptin receptor) and SCD1 (stearoyl-coenzyme A desaturase 1) genes and milk production traits in Holstein dairy cows in Iran. The analysis was conducted on 816 lactations from 408 Iranian Holstein cows. Genotyping was carried out using the PCR-RFLP (polymerase chain reaction-restriction fragment length polymorphism) technique. The results of the association study revealed that cows with genotype AC of the ABCG2-Y581S single nucleotide polymorphism (SNP) had lower breeding value for milk yield compared with the AA genotype, but showed significantly higher fat and protein percentages. Cows that were homozygous for allele T at the LEPR-T945M locus had higher breeding value for fat yield than those that were homozygous for the C allele. Regarding the SCD1-A293V SNP, cows with genotype AV produced a higher 305-day milk yield in comparison with cows with the VV genotype. Furthermore, cows that were homozygous for allele V showed a significantly higher protein percentage compared with AA and AV genotypes. The results of this study suggest that these SNPs have the potential to be used in programmes based on genomic selection in Iranian dairy herds. ______________________________________________________________________________________ Keywords : Genetic polymorphism, Iranian Holstein, milk fat, milk protein, SNP

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Saeid Ansari-Mahyari

Papers

Farm and cow factors and their interactions on the incidence of retained placenta in holstein dairy cows.

Investigating the genetic architecture of conception and non-return rates in Holstein cattle under heat stress conditions

Association of a polymorphism in the 3' untranslated region of the OLR1 gene with milk fat and protein in dairy cows

Assessing the consequences and economic impact of retained placenta in Holstein dairy cattle.

Effect of polymorphisms in the ABCG2, LEPR and SCD1 genes on milk production traits in Holstein cows