Genetic parameters for methane emission traits in Australian dairy cows.

Methane is a greenhouse gas of high interest to the dairy industry, with 57% of Australia's dairy emissions attributed to enteric methane. Enteric methane emissions also constitute a loss of approximately 6.5% of ingested energy. Genetic selection offers a unique mitigation strategy to decrease the methane emissions of dairy cattle, while simultaneously improving their energy efficiency. Breeding objectives should focus on improving the overall sustainability of dairy cattle by reducing methane emissions without negatively affecting important economic traits. Common definitions for methane production, methane yield, and methane intensity are widely accepted, but there is not yet consensus for the most appropriate method to calculate residual methane production, as the different methods have not been compared. In this study, we examined 9 definitions of residual methane production. Records of individual cow methane, dry matter intake (DMI), and energy corrected milk (ECM) were obtained from 379 animals and measured over a 5-d period from 12 batches across 5 yr using the SF6 tracer method and an electronic feed recording system, respectively. The 9 methods of calculating residual methane involved genetic and phenotypic regression of methane production on a combination of DMI and ECM corrected for days in milk, parity, and experimental batch using phenotypes or direct genomic values. As direct genomic values (DGV) for DMI are not routinely evaluated in Australia at this time, DGV for FeedSaved, which is derived from DGV for residual feed intake and estimated breeding value for bodyweight, were used. Heritability estimates were calculated using univariate models, and correlations were estimated using bivariate models corrected for the fixed effects of year-batch, days in milk, and lactation number, and fitted using a genomic relationship matrix. Residual methane production candidate traits had low to moderate heritability (0.10 ± 0.09 to 0.21 ± 0.10), with residual methane production corrected for ECM being the highest. All definitions of residual methane were highly correlated phenotypically (>0.87) and genetically (>0.79) with one another and moderately to highly with other methane candidate traits (>0.59), with high standard errors. The results suggest that direct selection for a residual methane production trait would result in indirect, favorable improvement in all other methane traits. The high standard errors highlight the importance of expanding data sets by measuring more animals for their methane emissions and DMI, or through exploration of proxy traits and combining data via international collaboration.

Genotype-by-environment (temperature-humidity) interaction of milk production traits in Australian Holstein cattle.

Dairying in Australia is practiced in highly diverse climatic conditions and production systems, which means that re-ranking of genotypes could occur across environments that vary in temperature and humidity-that is, genotype-by-environment interactions (G × E) may exist. The objective of this study was to investigate G × E for heat tolerance with respect to milk production traits in Australian Holsteins. A total of 6.7 million test-day milk yield records for first, second, and third lactations from 491,562 cows and 6,410 sires that had progeny in different climatic environments were included in the analysis. The environmental gradient used was the temperature-humidity index (THI) calculated from climate data from 163 Australian public weather stations between 2003 and 2017. Data were analyzed using univariate reaction norm (RM) sire model, and the results were compared with multi-trait model (MT). The MT analysis treated test-day yields at 5th percentile (THI = 61; i.e., thermoneutral conditions), 50th percentile (THI = 67; i.e., moderate heat stress conditions), and 95th percentile (THI = 73; i.e., high heat stress conditions) of the trajectory of THI as correlated traits. A THI series of 61, 67, and 73, for example, is equivalent to average temperature and relative humidity of approximately 20°C and 45%, 25°C and 45%, and 31°C and 50%, respectively. We observed some degree of heterogeneity of additive (AG) and permanent environmental (PE) variance over the trajectory THI from RM analysis, with estimates decreasing at higher THI values more steeply for PE than for AG variance. The genetic correlations of the tests between the 5th and 95th percentiles of THI for milk, protein, and fat yield from RM were 0.88 ± 0.01 (standard error), 0.79 ± 0.01, and 0.86 ± 0.01, respectively, whereas the corresponding estimates from MT were 0.86 ± 0.02, 0.84 ± 0.03, and 0.87 ± 0.03. We observed lower genetic correlations between the 5th and 95th percentiles of THI for milk tests from recent years (i.e., 2009 and 2017) compared with earlier years (i.e., 2003 and 2008), which suggests that the level of G × E is increasing in the studied population and should be monitored especially in anticipation of future expected increase in daily average temperature and frequency of heat events. Overall, our results indicate presence of G × E at the upper extreme of the trajectory of THI, but the current extent of sire re-ranking may not justify providing separate genetic evaluations for different levels of heat stress. However, variations observed in the sire sensitivity to heat stress suggest that dairy herds in high heat load conditions could benefit more from using heat-tolerant or resilient sires.

Genomic prediction of serum biomarkers of health in early lactation.

In this study, we estimated genetic parameters and genomic prediction accuracies of serum biomarkers of health in early-lactation dairy cows. A single serum sample was taken from 1,393 cows, located on 14 farms in southeastern Australia, within 30 d after calving. Sera were analyzed for biomarkers of energy balance (ß-hydroxybutyrate and fatty acids), macromineral status (Ca and Mg), protein nutritional status (urea and albumin), and immune status (globulins, albumin-to-globulin ratio, and haptoglobin). After editing, 47,162 SNP marker genotypes were used to estimate genomic heritabilities and breeding values (GEBV) for these traits in ASReml. Heritabilities were low for ß-hydroxybutyrate, fatty acids, Ca, Mg, and urea (0.09 ± 0.04, 0.18 ± 0.05, 0.07 ± 0.04, 0.19 ± 0.06, and 0.18 ± 0.05, respectively), and moderate for albumin, globulins, and albumin-to-globulin ratio (0.27 ± 0.06, 0.46 ± 0.06, and 0.41 ± 0.06, respectively). The heritability of haptoglobin concentration was close to 0. The magnitude of genetic correlations between traits (estimated using bivariate models) varied considerably (0.01 to 0.96), and standard errors of these correlations were high (0.02 to 0.44). Interestingly, the direction of most genetic correlations was favorable, suggesting that selecting for more optimal concentrations of one biomarker may result in more optimal concentrations of other biomarkers. Correlations between biomarker GEBV and existing breeding values for survival, somatic cell count, and daughter fertility were small to moderate (0.07 to 0.45) and favorable, whereas correlations with breeding values for milk production traits were small (≤0.15). Accuracies of GEBV were evaluated by using 5-fold cross validation, and by calculating accuracies from prediction error variances associated with the GEBV. Accuracies of GEBV predicted using 5-fold cross validation were low (0.05 to 0.27), whereas the means of individual accuracies were greater, ranging from 0.31 to 0.51. Although increasing the size of the reference population should theoretically improve accuracies, our results suggest that genomic prediction of health biomarkers may allow identification of cows that are less susceptible to diseases in early lactation.

Genetic parameters for health traits using data collected from genomic information nucleus herds.

As with many other countries, data availability has been a limitation in Australia for developing breeding values for health traits. A genomic information nucleus of approximately 100 herds across the country, selected on the basis of their record keeping, has recently been established in Australia and is known as Ginfo. The objective of this study was to evaluate the feasibility of developing genomic breeding values for health traits using farmer-collected data from Ginfo herds. Having this genotyped population opens up opportunities to develop new genomic breeding values, such as dairy health traits. In these herds, the 4 most recorded groups of health diseases identified were mastitis, reproductive problems, lameness, and metabolic disorders with incidence levels of 16, 9, 2, and 1.5%, respectively. Heritability estimates from pedigree and genomic analysis ranged from 0.01 to 0.03 for mastitis, 0.005 to 0.02 for reproductive disorders, 0 to 0.02 for lameness, and 0.00 to 0.06 for metabolic disorders. However, although heritability is low, there is sufficient genetic variation to make genetic progress feasible (coefficient of variation ranging from 3 to 26.3%). Genetic correlations between health and milk production traits (0.08 to 0.48) and overall type (-0.00 to 0.57) are unfavorable, but favorable with other economical traits, such as fertility (0.10 to 0.51), survival (-0.16 to -0.37), and somatic cell count (0.07 to 0.55). For a genomic reference population of 11,458 cows, the reliability of genomic predictions is comparatively low (<30%), but is promising for health traits, such as mastitis (∼30%) and a broader-based all disease trait (∼28%), when bulls have daughters in the reference population, but not when they only have genomic information (0 to 18%). Further improvement of the reliabilities of health breeding values continues to be an important objective. The study has provided a good foundation for future research to develop breeding values for health traits.

Invited review: Opportunities for genetic improvement of metabolic diseases.

Metabolic disorders are disturbances to one or more of the metabolic processes in dairy cattle. Dysfunction of any of these processes is associated with the manifestation of metabolic diseases or disorders. In this review, data recording, incidences, genetic parameters, predictors, and status of genetic evaluations were examined for (1) ketosis, (2) displaced abomasum, (3) milk fever, and (4) tetany, as these are the most prevalent metabolic diseases where published genetic parameters are available. The reported incidences of clinical cases of metabolic disorders are generally low (less than 10% of cows are recorded as having a metabolic disease per herd per year or parity/lactation). Heritability estimates are also low and are typically less than 5%. Genetic correlations between metabolic traits are mainly positive, indicating that selection to improve one of these diseases is likely to have a positive effect on the others. Furthermore, there may also be opportunities to select for general disease resistance in terms of metabolic stability. Although there is inconsistency in published genetic correlation estimates between milk yield and metabolic traits, selection for milk yield may be expected to lead to a deterioration in metabolic disorders. Under-recording and difficulty in diagnosing subclinical cases are among the reasons why interest is growing in using easily measurable predictors of metabolic diseases, either recorded on-farm by using sensors and milk tests or off-farm using data collected from routine milk recording. Some countries have already initiated genetic evaluations of metabolic disease traits and currently most of these use clinical observations of disease. However, there are opportunities to use clinical diseases in addition to predictor traits and genomic information to strengthen genetic evaluations for metabolic health in the future.

Renal carcinoma: diagnostic and therapeutic aspects.

Angiographic diagnosis of renal cell carcinoma depends primarily upon the demonstration of abnormal vasculature within the tumor. Unless the renal vein is well visualized on the arteriogram, a venacavogram and renal venogram should be performed to detect tumor in these vessels. At present, the only effective treatment for renal carcinoma is radical nephrectomy.

Avulsion of the ureter caused by stone basket manipulation.

A case of an iatrogenic loss of 18 cm. of the distal ureter owing to attempted basket retrieval of a lower ureteral stone is reported. The defect was bridged by mobilizing the kidney, constructing a Boari flap and fixing the bladder with a Psoas hitch. The complications of stone manipulating are reviewed and discussed.

Spontaneous passage of bullet during voiding.

The second instance of a bullet voided spontaneously via the urethra was reported and the related literature was briefly reviewed.

Renal tumors involving the inferior vena cava: plan for management.

Our experience in the management of 12 malignant renal tumors involving the inferior vena cava is analyzed and a plan to manage the different degrees of caval involvement is discussed. The use of Pringle maneuver to control hepatic circulation during removal of suprahepatic caval extensions is recommended. Followup to date supports aggressive operative management in these patients.