U. König von Borstel

Inferring relationships between clinical mastitis, productivity and fertility: a recursive model application including genetics, farm associated herd management, and cow-specific antibiotic treatments.

A dataset of test-day records, fertility traits, and one health trait including 1275 Brown Swiss cows kept in 46 small-scale organic farms was used to infer relationships among these traits based on recursive Gaussian-threshold models. Test-day records included milk yield (MY), protein percentage (PROT-%), fat percentage (FAT-%), somatic cell score (SCS), the ratio of FAT-% to PROT-% (FPR), lactose percentage (LAC-%), and milk urea nitrogen (MUN). Female fertility traits were defined as the interval from calving to first insemination (CTFS) and success of a first insemination (SFI), and the health trait was clinical mastitis (CM). First, a tri-trait model was used which postulated the recursive effect of a test-day observation in the early period of lactation on liability to CM (LCM), and further the recursive effect of LCM on the following test-day observation. For CM and female fertility traits, a bi-trait recursive Gaussian-threshold model was employed to estimate the effects from CM to CTFS and from CM on SFI. The recursive effects from CTFS and SFI onto CM were not relevant, because CM was recorded prior to the measurements for CTFS and SFI. Results show that the posterior heritability for LCM was 0.05, and for all other traits, heritability estimates were in reasonable ranges, each with a small posterior SD. Lowest heritability estimates were obtained for female reproduction traits, i.e. h(2)=0.02 for SFI, and h(2)≈0 for CTFS. Posterior estimates of genetic correlations between LCM and production traits (MY and MUN), and between LCM and somatic cell score (SCS), were large and positive (0.56-0.68). Results confirm the genetic antagonism between MY and LCM, and the suitability of SCS as an indicator trait for CM. Structural equation coefficients describe the impact of one trait on a second trait on the phenotypic pathway. Higher values for FAT-% and FPR were associated with a higher LCM. The rate of change in FAT-% and in FPR in the ongoing lactation with respect to the previous LCM was close to zero. Estimated recursive effects between SCS and CM were positive, implying strong phenotypic impacts between both traits. Structural equation coefficients explained a detrimental impact of CM on female fertility traits CTFS and SFI. The cow-specific CM treatment had no significant impact on performance traits in the ongoing lactation. For most treatments, beta-lactam-antibiotics were used, but test-day SCS and production traits after the beta-lactam-treatment were comparable to those after other antibiotic as well as homeopathic treatments.

Strategy for the simulation and analysis of longitudinal phenotypic and genomic data in the context of a temperature × humidity-dependent covariate

A simulation study was conducted to evaluate the performance of genomic random regression models for the continuous environmental descriptor temperature-humidity index (THI). Statistically innovative aspects of the study included the combined simulation of both longitudinal phenotypic data representing the same trait in the course of THI and genomic data. The longitudinal trait was simulated (phenotypically expressed) at 5 different values of THI. For a moderate heritability trait, heritabilities were 0.30, 0.35, 0.40, 0.40, and 0.35 for THI of 15, 30, 45, 60 and 75, respectively. In a consecutive run, low heritabilities of 0.05, 0.1, 0.15, 0.15, and 0.10 were simulated, respectively. On the genomic level, simulation combined high and low linkage disequilibrium with 5,000-, 15,000-, and 50,000-SNP chip applications to simulate different scenarios of genomic architecture. With regard to data analyses, 2 strategies were applied to evaluate the accuracy of genomic predictions across THI, with special focus on the extreme ends of the environmental scale. In the first strategy, 100, 80, 50, or 20% of phenotypes at THI 75 were deleted randomly and the remaining data set was used to predict the breeding value at THI 75 for non-phenotyped, but genotyped cows. In the second strategy, 1,600 cows had complete information (genotypes and phenotypes) and 400 cows were genotyped, but with missing phenotypes for all THI. For the first strategy and without phenotypic observations at THI 75, accuracies of genomic predictions were lower than 0.34. When only 20% of cows had phenotypic records at THI 75, accuracies increased (~0.60). Such a small proportion of phenotyped cows was sufficient to predict reliable genomic breeding values for cows without phenotypes for extreme THI. For the second strategy, also for low linkage disequilibrium combined with a low density 5,000-SNP chip, the average accuracy of genomic predictions was 0.52, which is substantially higher than accuracies based on pedigree relationships. From a practical perspective, genomic random regression models can be used to predict genomic breeding values for scarce phenotypes (e.g., novel traits) traits measured in extreme environments, or traits measured late in life, such as longevity.

Genetic variation for worm burdens in laying hens naturally infected with gastro-intestinal nematodes

Abstract Genetic parameters were determined for the worm burden of the most common gastro-intestinal nematodes in two chicken genotypes after being exposed to free-range farming conditions for a laying period. Seventeen-week-old hens of 2 brown genotypes, Lohmann Brown (LB) plus (n = 230) and LB classic (n = 230), were reared for a laying period and subjected to post-mortem parasitological examinations at 79 weeks (LB plus) or 88 weeks (LB classic) of age. There was no significant difference in faecal egg counts between the genotypes. Almost all hens (>99%) were infected with at least one nematode species. Species-specific nematode prevalence ranged from 85.8% to 99.1% between the two genotypes. Heterakis gallinarum was the most prevalent nematode (98.5%), followed by Ascaridia galli (96.2%) and Capillaria spp. (86.1%). Capillaria spp. were composed of C. obsignata (79%), C. caudinflata (16%) and C. bursata (5%). All phenotypic and genetic correlations among worm counts of different parasite species were positive in combined genotypes (rP ranged from 0.05 to 0.30 and rG ranged from 0.29 to 0.88). A strong genetic correlation (rG = 0.88 ± 0.34) between counts of A. galli and H. gallinarum was quantified. Heritability for total worm burden for LB plus and LB classic, respectively, were 0.55 ± 0.18 and 0.55 ± 0.34. Across both genotypes, the heritability of total worm burden was 0.56 ± 0.16. In conclusion, there is a high variation attributable to genetic background of chickens in their responses to naturally acquired nematode infections. The high positive genetic correlation between counts of closely related worm species (e.g. A. galli and H. gallinarum) may indicate existence of similar genetically determined mechanism(s) in chickens for controlling these nematodes.

Short communication: A note on the correction for the effect of freezing on the outcome of pregnancy-associated glycoprotein measurement in blood and serum of cows

Early pregnancy detection is a measure of considerable economic relevance for dairy cattle breeders, and analysis of pregnancy-associated glycoprotein (PAG) values in blood is one of the methods implemented in practice. Starting from d 30 postconception, cows are considered to be pregnant at PAG levels of 2.0 ng of PAG/mL of blood and higher. However, little is known about preanalytic sources of errors that might affect PAG values. Based on blood samples from 65 dairy cows, the present study showed that freezing of samples, such as may be the case during shipping in wintertime, will lower PAG values considerably. Therefore, a Bland-Altman analysis was used to derive a correction factor. Overall, the mean differences (± standard deviation) between frozen and respective fresh samples was -5.5 ± 7.4 ng of PAG/mL of blood and 0.9 ± 6.1 ng of PAG/mL of serum. However, the Bland-Altman plot revealed a concentration-dependent effect of freezing on PAG values with higher variability and larger declines at higher PAG levels. Therefore, to minimize chances of false-negative results, different correction factors are suggested for different levels of PAG (e.g., based on the upper bound of the 95% confidence interval 0.67 for PAG levels between 2.0 and 3.9 ng of PAG/mL and 0.25 for PAG levels between 4.0 and 7.9 ng of PAG/mL). With these concentration-dependent correction factors, implementation into practice will be possible. The accuracy is adequate because no quantitative information but qualitative results (pregnant vs. nonpregnant) are required. However, due to larger chances of false-negative results, the application of the correction factor should only be a last resort if temperature exposure of a sample is unknown.