Genomic evaluation for wellness traits in US Jersey cattle.

The number of Jersey cows in the United States has been steadily increasing in recent years according to Council on Dairy Cattle Breeding statistics. To help producers reduce the risk of health disorders in their Jersey animals, Zoetis has developed genomic predictions for wellness traits in Jersey cattle using producer-recorded data. The traits included mastitis (MAST), metritis, retained placenta, displaced abomasum (DA), ketosis, lameness, and milk fever in cows and respiratory disease, scours, and calf livability (DEAD) in calves. Phenotypic data on health events, pedigree, and genotypes were collected directly from producers upon obtaining their permission. Each trait was defined as a binary event, having a value of 1 if an animal has been recorded with a disorder and 0 otherwise. The number of phenotypic records ranged from 216,166 for DA to 628,958 for MAST for cow traits and from 186,505 for scours to 380,429 for DEAD for calf traits. The number of genotyped animals was 41,271. All traits were analyzed using a univariate threshold animal model. The model for cow wellness traits included the fixed effect of parity and random effects of herd × year × season of calving, animal, and permanent environment. The model for calf wellness traits included the fixed effect of year of birth × calving season × region and random effects of herd × year of birth and animal. A total of 45,163 SNP were used in genomic analyses. Animals genotyped with low-density chips were imputed to the required number of markers. All analyses were based on the single-step genomic BLUP. Heritabilities ranged from 0.061 for DA to 0.120 for lameness. Predicted transmitting abilities were expressed in percentage points as deviations from the average estimated probability of a disorder in the base population. Reliabilities of genomic predicted transmitting abilities had average values between 32% (DA) and 51% (MAST and DEAD). The results indicate that a direct evaluation of cow and calf wellness traits under a genomic threshold model is feasible and offers predictions with average reliabilities comparable with other lowly heritable traits for Jersey cattle.

Genomic evaluation for calf wellness traits in Holstein cattle.

Reducing calf morbidity and mortality is important for attaining financial sustainability and improving animal welfare on commercial dairy operations. Zoetis (Kalamazoo, MI) has developed genomic predictions for calf wellness traits in Holsteins that include calf respiratory disease (RESP; recorded between 0 and 365 d of age), calf scours (DIAR; recorded between 2 and 50 d of age), and calf livability (DEAD; recorded between 2 and 365 d of age). Phenotype and pedigree data were from commercial dairies and provided directly by producers upon obtaining their permission. The number of records ranged from 741,484 for DIAR to 1,926,261 for DEAD. The number of genotyped animals was 325,025. All traits were analyzed using a univariate threshold animal model including fixed effect of year of birth × calving season × region, and random effects of herd × year of birth and animal. A total of 45,425 SNP were used in genomic analyses. Animals genotyped with low-density chips were imputed to the required number of SNP. All analyses were conducted using single-step genomic BLUP implementing the "algorithm for proven and young" (APY) animals designed to accommodate very large numbers of genotypes. Estimated heritabilities were 0.042, 0.045, and 0.060 for RESP, DIAR, and DEAD, respectively. The genomic predicted transmitting abilities ranged between -8.0 and 24.0, -11.5 and 28.5, and -6.5 to 22.8 for RESP, DIAR, and DEAD, respectively. Reliabilities of breeding values were obtained by approximation based on partitioning of a function of reliability into contributions from records, pedigree, and genotypes, where the genotype contribution was approximated using the diagonal value of the genomic relationship matrix. The average reliabilities for the genotyped animals were 41.9, 42.6, and 47.3% for RESP, DIAR, and DEAD, respectively. Estimated genomic predicted transmitting abilities and reliabilities were approximately normally distributed for all analyzed traits. Approximated genetic correlations of calf wellness with Zoetis dairy wellness traits and traits included in the US national genetic evaluation were low to moderate. The results indicate that direct evaluation of calf wellness traits under a genomic threshold model is feasible and offers predictions with average reliabilities comparable to other lowly heritable traits. Genetic selection for calf wellness traits presents a compelling opportunity for dairy producers to help manage herd replacement costs and improve overall profitability.

Development of genetic and genomic evaluation for wellness traits in US Holstein cows.

In March 2016, Zoetis Genetics offered the first commercially available evaluation for wellness traits of Holstein dairy cattle. Phenotypic data on health events, pedigree, and genotypes were collected directly from producers upon obtaining their permission. Among all recorded health events, 6 traits were chosen to be included in the evaluation: mastitis, metritis, retained placenta, displaced abomasum, ketosis, and lameness. Each trait was defined as a binary event, having a value of 1 if a cow has been recorded with a disorder at any point during the lactation and zero otherwise. The number of phenotypic records ranged from 1.8 million for ketosis to 4.1 million for mastitis. Over 14 million pedigree records and 114,216 genotypes were included in the evaluation. All traits were analyzed using univariate threshold animal model with repeated observations, including fixed effect of parity and random effects of herd by year by season of calving, animal, and permanent environment. A total of 45,425 single nucleotide polymorphisms were used in the genomic analyses. Animals genotyped with low-density chips were imputed to the required number of single nucleotide polymorphisms. All analyses were based on the single-step genomic BLUP, a method that combines phenotype, pedigree, and genotype information. Predicted transmitting abilities were expressed in percentage points as a difference from the average estimated probability of a disorder in the base population. Reliabilities of breeding values were obtained by approximation based on partitioning of a function of reliability into contributions from records, pedigree, and genotypes. Reliabilities of genomic predicted transmitting abilities for young genotyped and pedigreed females without recorded health events had average values between 50.2% (displaced abomasum) and 51.9% (mastitis). Genomic predictions for wellness traits can provide new information about an animal's genetic potential for health and new selection tools for dairy wellness improvement.

Real-time quantification of damage in structural materials during mechanical testing.

A novel methodology is introduced for quantifying the severity of damage created during testing in composite components. The method uses digital image correlation combined with image processing techniques to monitor the rate at which the strain field changes during mechanical tests. The methodology is demonstrated using two distinct experimental datasets, a ceramic matrix composite specimen loaded in tension at high temperature and nine polymer matrix composite specimens containing fibre-waviness defects loaded in bending. The changes in the strain field owing to damage creation are shown to be a more effective indicator that the specimen has reached its proportional limit than using load-extension diagrams. The technique also introduces a new approach to using experimental data for creating maps indicating the spatio-temporal distribution of damage in a component. These maps indicate where damage occurs in a component, and provide information about its morphology and its time of occurrence. This presentation format is both easier and faster to interpret than the raw data which, for some tests, can consist of tens of thousands of images. This methodology has the potential to reduce the time taken to interpret large material test datasets while increasing the amount of knowledge that can be extracted from each test.

Glutathione S-transferase gene variations influence BU pharmacokinetics and outcome of hematopoietic SCT in pediatric patients.

BU is a key compound of conditioning regimens in children undergoing hematopoietic SCT (HSCT). Inter-individual differences in BU pharmacokinetics (PKs) might affect BU efficacy and toxicity. As BU is mainly metabolized by glutathione S-transferase (GST), we investigated the relationship between GSTA1, GSTM1 and GSTP1 genotypes with first-dose BU PKs, and the relationship with HSCT outcomes in 69 children receiving myeloablative conditioning regimen. GSTM1 null genotype correlated with higher BU exposure and lower clearance in patients older than 4 years (P ≤ 0.04). In accordance with the suggested functional role, GSTA1*A2 haplotype was associated with lower drug levels and higher drug clearance (P ≤ 0.03). Gene-dosage effect was also observed (P ≤ 0.007). GSTA1 haplotypes were associated with HSCT outcomes. Patients with two copies of haplotype *A2 had better event free survival (P=0.03). In contrast, homozygous individuals for haplotypes *B and *B1 had higher occurrence of veno-occlusive disease (P=0.009). GSTM1 null individuals older than 4 years had more frequently graft versus host disease (P=0.03). In conclusion, we showed that GST gene variants influence BU PK and outcomes of HSCT in children. A model for the dosage adjustment with the inclusion of genetic and non-genetic factors should be evaluated in a future prospective validation cohort.

Maximal height scaling of kinetically growing surfaces.

The scaling properties of the maximal height of a growing self-affine surface with a lateral extent L are considered. In the late-time regime its value measured relative to the evolving average height scales like the roughness: h*(L) approximately L alpha. For large values its distribution obeys logP(h*(L)) approximately (-)A(h*(L)/L(alpha))(a). In the early-time regime where the roughness grows as t(beta), we find h*(L) approximately t(beta)[lnL-(beta/alpha)lnt+C](1/b), where either b = a or b is the corresponding exponent of the velocity distribution. These properties are derived from scaling and extreme-value arguments. They are corroborated by numerical simulations and supported by exact results for surfaces in 1D with the asymptotic behavior of a Brownian path.