Rat monoclonal antibodies (MoAbs) against mouse mannan-binding lectin (MBL)-A and MBL-C were generated and assays for MBL-A and MBL-C were constructed. This allowed for the quantitative analysis of both proteins for the first time. Previously only MBL-A has been quantified using less standardized methods. In a mouse serum pool the concentrations were now determined at 7.5 mg MBL-A and 45 mg MBL-C per ml. On gel permeation chromatography of mouse serum, MBL-A eluted corresponding to a M r of 850 kDa whereas the majority of MBL-C eluted corresponding to a M r of 950 kDa. On sucrose density gradient centrifugation the sedimentation velocities of MBL-A and MBL-C were estimated at 7.3 S and 10.8 S, respectively. The MBL-A and MBL-C levels in 10 laboratory mice strains were compared and found to vary between 4 mg/ml to 12 mg/ml, and 16 mg/ml to 118 mg/ml, respectively. After the induction of acute phase responses by intraperitoneal injection of either casein or lipopolysaccharide (LPS), MBL-A was found to increase approximately two-fold, with a maximum after 32 h, while MBL-C did not increase significantly. In comparison, serum amyloid A component (SAA) peaked at 15 h with an approximate 100-fold increase.

Both small dairy cattle populations and dairy cattle populations with a low level of linkage disequilibrium (LD) suffer from low reliability of genomic prediction. In this study, we investigated whether adding more genotyped cows to the reference population influences the rate of genetic gain and rate of inbreeding by affecting the reliability. A standard breeding program with a large reference population and high LD, which mimicked a breeding program for Danish Holstein population, was simulated as a reference. A Danish Jersey population with a small reference population and high LD and a Red Dairy Cattle population with a large reference population and low LD were also simulated. Two additional breeding programs were simulated for Danish Jersey and Red Dairy Cattle populations, where 2,000 additional genotyped cows were included in the population for genomic selection. All 5 simulated breeding programs were initiated by a founder population to generate LD resembling the real LD pattern, followed by a 20-yr conventional progeny-testing scheme with 1,000 or 10,000 genotyped progeny-tested bulls and a 10-yr genomic selection scheme with or without 2,000 additional genotyped cows. Evaluation criteria were annual monetary genetic gain and rate of true inbreeding. Our results showed that adding more genotyped cows to the reference in dairy cattle populations has the potential to increase genetic gain and reduce the rate of inbreeding, regardless of reference population size and level of LD. However, it is still not possible to reach the same genetic gain as in the simulated Danish Holstein population with either a small reference population or low LD. Our results also showed that in a small reference population with high LD, it is difficult to manage inbreeding because of lower accuracy compared with the simulated Danish Holstein population and a smaller number of relevant families to select from. Therefore, breeding strategies need to be chosen to match population size and structure. The rate of true inbreeding is always underestimated by pedigree inbreeding and even more in genomic breeding programs, indicating that some forms of genome-wide inbreeding, instead of pedigree-based inbreeding, should be used to monitor inbreeding when genomic selection is implemented.

We tested the hypothesis that mating strategies with genomic information realise lower rates of inbreeding (∆F) than with pedigree information without compromising rates of genetic gain (∆G). We used stochastic simulation to compare ∆F and ∆G realised by two mating strategies with pedigree and genomic information in five breeding schemes. The two mating strategies were minimum-coancestry mating (MC) and minimising the covariance between ancestral genetic contributions (MCAC). We also simulated random mating (RAND) as a reference point. Generations were discrete. Animals were truncation-selected for a single trait that was controlled by 2000 quantitative trait loci, and the trait was observed for all selection candidates before selection. The criterion for selection was genomic-breeding values predicted by a ridge-regression model. Our results showed that MC and MCAC with genomic information realised 6% to 22% less ∆F than MC and MCAC with pedigree information without compromising ∆G across breeding schemes. MC and MCAC realised similar ∆F and ∆G. In turn, MC and MCAC with genomic information realised 28% to 44% less ∆F and up to 14% higher ∆G than RAND. These results indicated that MC and MCAC with genomic information are more effective than with pedigree information in controlling rates of inbreeding. This implies that genomic information should be applied to more than just prediction of breeding values in breeding schemes with truncation selection.

ADAM software is a stochastic simulation tool intended to model a variety of selective breeding schemes for animals and plants. ADAM can be used to generate genetic data to test statistical tools and the implemented algorithms can evaluate breeding schemes by tracing genetic change under different selective breeding scenarios. In practice, crossbreeding has been used extensively in beef cattle, pig, and poultry production systems, and the use of crossbreeding is increasing in dairy cattle breeding system due to changes in the dairy market. The aim of this paper was to present a new crossbreeding feature of the ADAM software. The software was updated to include multiple purebred populations and crossbred populations in animals with different genetic backgrounds considering both additive and dominance effects. Our simulated results showed that, by using the new features in ADAM, breeding organizations can optimize their design of crossbreeding schemes and, thereby, optimize profitability.

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National and international across-population selection is often recommended and fairly common in the current breeding practice of dairy cattle, with the primary aims to increase genetic gain and genetic variability. The aim of this study was to test the hypothesis that the strategy of truncation selection of sires across populations [i.e., competitive gene flow strategy (CGF)] may not necessarily maximize genetic gain in the long term in the presence of genotype-by-environment interaction (G×E). Two alternative strategies used to be compared with CGF were forced gene flow (FGF) strategies, with 10 or 50% of domestic dams forced to be mated with foreign sires (FGF10%, FGF50%). Two equal-size populations (N dams = 1,000) that were selected for the same breeding goal trait (h 2 = 0.3) under G×E correlation (r g ) of either 0.9 or 0.8 were simulated to test these 3 different strategies. Each population first experienced either 5 or 20 differentiation generations (G d ), then 15 migration generations. Discrete generations were simulated for simplicity. Each population performed a within-population conventional breeding program during differentiation generations and the 3 across-population sire selection strategies based on joint genomic prediction during migration generations. The 4 G d_ r g combinations defined 4 different levels of differentiation degree between the 2 populations at the start of migration. The true rate of inbreeding over the last 10 migration generations in each scenario was constrained at 0.01 to provide a fair basis for comparison of genetic gain across scenarios. Results showed that CGF maximized the genetic gain after 15 migration generations in 5_0.9 combination only, the case of the lowest differentiation degree, with a superiority of 0.4% (0.04 genetic SD units) over the suboptimal strategy.While in 5_0.8, 20_0.9, and 20_0.8 combinations, 2 FGF strategies had a superiority in genetic gain of 2.3 to 12.5% (0.21-1.07 genetic SD units) over CGF after 15 migration generations, especially FGF50%. The superiority of FGF strategies over CGF was that they alleviated inbreeding, introduced new genetic variance in the early migration period, and improved accuracy in the entire migration period. Therefore, we concluded that CGF does not necessarily maximize the genetic gain of across-population genomic breeding programs given moderate G×E. The across-population selection strategy remains to be optimized to maximize genetic gain.

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