Accuracy of genotype imputation with different low density panels in Braford and Hereford cattle.

The main objective of this research was to test alternative low density SNP panels to impute Illumina 50K SNP panel genotypes in Braford and Hereford cattle. Genotypes from 3,768 Hereford, Braford and Nellore animals were used for testing imputation from low density SNP panels (3K, 6K, 8K, 15K and 20K) to the Illumina 50K SNP panel, under four different scenarios: including or not Nellore genotypes in the reference population in combination with the use or not of pedigree information. There were no significant differences in imputation accuracy among these four scenarios within each panel. However, significant differences between panels were found. The best accuracy was given by a customized 15K SNP panel, with an overall genotype concordance rate of 0.977, with 93.3% of the animals imputed with a concordance rate above 0.95. The concordance rates for the other SNP panels were 0.872, 0.952, 0.957 and 0.958 for 3K, 6K, 8K and 20K SNP panel, respectively. Therefore, in the Braford/Hereford population considered in this study, all the alternative panels denser than 3K could be used for imputing to the 50K SNP panel with an overall high imputation accuracy. However, the best results were obtained with the customized 15K SNP instead of the alternative commercial panels. The use of Nellore sire genotypes and pedigree information did not increase accuracy of imputation in this population

Local formation of nitrogen-vacancy centers in diamond by swift heavy ions

We exposed nitrogen-implanted diamonds to beams of swift uranium and gold ions (~1 GeV) and find that these irradiations lead directly to the formation of nitrogen vacancy (NV) centers, without thermal annealing. We compare the photoluminescence intensities of swift heavy ion activated NV- centers to those formed by irradiation with low-energy electrons and by thermal annealing. NV- yields from irradiations with swift heavy ions are 0.1 of yields from low energy electrons and 0.02 of yields from thermal annealing. We discuss possible mechanisms of NV-center formation by swift heavy ions such as electronic excitations and thermal spikes. While forming NV centers with low efficiency, swift heavy ions enable the formation of three dimensional NV- assemblies over relatively large distances of tens of micrometers. Further, our results show that NV-center formation is a local probe of (partial) lattice damage relaxation induced by electronic excitations from swift heavy ions in diamond.Comment: to be published in Journal of Applied Physic

Effects of low energy electron irradiation on formation of nitrogen-vacancy centers in single-crystal diamond

Exposure to beams of low energy electrons (2 to 30 keV) in a scanning electron microscope locally induces formation of NV-centers without thermal annealing in diamonds that have been implanted with nitrogen ions. We find that non-thermal, electron beam induced NV-formation is about four times less efficient than thermal annealing. But NV-center formation in a consecutive thermal annealing step (800C) following exposure to low energy electrons increases by a factor of up to 1.8 compared to thermal annealing alone. These observations point to reconstruction of nitrogen-vacancy complexes induced by electronic excitations from low energy electrons as an NV-center formation mechanism and identify local electronic excitations as a means for spatially controlled room-temperature NV-center formation

Accuracy of genome-wide imputation in Braford and Hereford beef cattle.

Article 157

Review: Opportunities and challenges for the genetic selection of dairy calf disease traits.

Interest in dairy cow health continues to grow as we better understand health's relationship with production potential and animal welfare. Over the past decade, efforts have been made to incorporate health traits into national genetic evaluations. However, they have focused on the mature cow, with calf health largely being neglected. Diarrhoea and respiratory disease comprise the main illnesses with regard to calf health. Conventional methods to control calf disease involve early separation of calves from the dam and housing calves individually. However, public concern regarding these methods, and growing evidence that these methods may negatively impact calf development, mean the dairy industry may move away from these practices. Genetic selection may be a promising tool to address these major disease issues. In this review, we examined current literature for enhancing calf health through genetics and discussed alternative approaches to improve calf health via the use of epidemiological modelling approaches, and the potential of indirectly selecting for improved calf health through improving colostrum quality. Heritability estimates on the observed scale for diarrhoea ranged from 0.03 to 0.20, while for respiratory disease, estimates ranged from 0.02 to 0.24. The breadth in these ranges is due, at least in part, to differences in disease prevalence, population structure, data editing and models, as well as data collection practices, which should be all considered when comparing literature values. Incorporation of epidemiological theory into quantitative genetics provides an opportunity to better determine the level of genetic variation in disease traits, as it accounts for disease transmission among contemporaries. Colostrum intake is a major determinant of whether a calf develops either respiratory disease or diarrhoea. Colostrum traits have the advantage of being measured and reported on a continuous scale, which removes the issues classically associated with binary disease traits. Overall, genetic selection for improved calf health is feasible. However, to ensure the maximum response, first steps by any industry members should focus efforts on standardising recording practices and encouragement of uploading information to genetic evaluation centres through herd management software, as high-quality phenotypes are the backbone of any successful breeding programme

Investigating the potential for genetic selection of dairy calf disease traits using management data.

Genetic selection could be a tool to help improve the health and welfare of calves, however, to date, there is limited research on the genetics of calfhood diseases. This study aimed to understand the current impact of calf diseases, by investigating incidence rates, estimating genetic parameters, and providing industry recommendations to improve calf disease recording practices on farms. Available calf disease data comprised of 69,695 Holstein calf disease records for respiratory problems (RESP) and diarrhea (DIAR), from 62,361 calves collected on 1,617 Canadian dairy herds from 2006 to 2021. Single and multiple trait analysis using both a threshold and linear animal model for each trait were evaluated. Furthermore, each trait was analyzed using 2 scenarios with respect to minimum disease incidence threshold criterion (herd-year incidence of at least 1% and 5%) to highlight the impact of different filtering thresholds on selection potential. Observed scale heritability estimates for RESP and DIAR ranged from 0.02 to 0.07 across analyses, while estimated genetic correlations between the traits ranged from 0.50 to 0.62. Sires were compared based on their estimated breeding value and their diseased daughter incidence rates. On average, calves born to the bottom 10% of sires were 1.8 times more likely to develop RESP and 1.9 times to develop DIAR compared with daughters born to the top 10% of sires. Results from the current study are promising for the inclusion of both DIAR and RESP in Canadian genetic evaluations. However, for effective genetic evaluation we require standardized approaches on data collection and industry outreach to highlight the importance of collecting and uploading this information to herd management software. In particular, it is important that the herd management software is accessible to the national milk recording system to allow for use in national genetic evaluation

Genomic predictions for economically important traits in Brazilian Braford and Hereford beef cattle using true and imputed genotypes.

Genomic selection (GS) has played an important role in cattle breeding programs. However, genotyping prices are still a challenge for implementation of GS in beef cattle and there is still a lack of information about the use of low-density Single Nucleotide Polymorphisms (SNP) chip panels for genomic predictions in breeds such as Brazilian Braford and Hereford. Therefore, this study investigated the effect of using imputed genotypes in the accuracy of genomic predictions for twenty economically important traits in Brazilian Braford and Hereford beef cattle. Various scenarios composed by different percentages of animals with imputed genotypes and different sizes of the training population were compared.Article 2

Phenotypic analysis of heat stress in Holsteins using test-day production records and NASA POWER meteorological data.

Weather station data and test-day production records can be combined to quantify the effects of heat stress on production traits in dairy cattle. However, meteorological data sets that are retrieved from ground-based weather stations can be limited by spatial and temporal data gaps. The National Aeronautics and Space Administration Prediction of Worldwide Energy Resources (NASA POWER) database provides meteorological data over regions where surface measurements are sparse or nonexistent. The first aim of this study was to determine whether NASA POWER data are a viable alternative resource of weather data for studying heat stress in Canadian Holsteins. The results showed that average, minima, and maxima ambient temperature and dewpoint temperature as well as 4 different types of temperature-humidity index (THI) values from NASA POWER were highly correlated to the corresponding values from weather stations (regression R2 > 0.80). However, the NASA POWER values for the daily average, minima, and maxima wind speed and relative humidity were poorly correlated to the corresponding weather station values (regression R2 = 0.10 to 0.49). The second aim of this study was to quantify the influence of heat stress on Canadian dairy cattle. This was achieved by determining the THI values at which milk, protein, and fat yield started to decline due to heat stress as well as the rates of decline in these traits after the respective thresholds, using segmented polynomial regression models. This was completed for both primiparous and multiparous cows from 5 regions in Canada (Ontario, Quebec, British Columbia, the Prairies, and the Atlantic Maritime). The results showed that all production traits were negatively affected by heat stress and that the patterns of responses for milk, fat, and protein yields to increasing THI differed from each other. We found 3 THI thresholds for milk yield, 1 for fat yield, and 2 for protein yield. All thresholds marked a change in rate of decrease in production yield per unit THI, except for the first milk yield threshold, which marked a greater rate of increase. The first thresholds for milk yield ranged between 47 and 50, the second thresholds ranged between 61 and 69, and the third thresholds ranged between 72 and 76 THI units. The single THI threshold for fat yield ranged between 48 and 55 THI units. Finally, the first and second thresholds ranged between 58 and 62 THI units and 72 and 73 THI units for protein yield, respectively

Machine learning classification of breeding protocol descriptions from Canadian Holsteins.

Dairy farmers are motivated to ensure cows become pregnant in an optimal and timely manner. Although timed artificial insemination (TAI) is a successful management tool in dairy cattle, it masks an animal's innate fertility performance, likely reducing the accuracy of genetic evaluations for fertility traits. Therefore, separating fertility traits based on the recorded management technique involved in the breeding process or adding the breeding protocol as an effect to the model can be viable approaches to address the potential bias caused by such management decisions. Nevertheless, there is a lack of specificity and uniformity in the recording of breeding protocol descriptions by dairy farmers. Therefore, this study investigated the use of 8 supervised machine learning algorithms to classify 1,835 unique breeding protocol descriptions from 981 herds into the following 2 classes: TAI or other than TAI. Our results showed that models that used a stacking classifier algorithm had the highest Matthews correlation coefficient (0.94 ± 0.04, mean ± SD) and maximized precision and recall (F1-score = 0.96 ± 0.03) on test data. Nonetheless, their F1-scores on test data were not different from 5 out of the other 7 algorithms considered. Altogether, results presented herein suggest machine learning algorithms can be used to produce robust models that correctly identify TAI protocols from dairy cattle breeding records, thus opening the opportunity for unbiased genetic evaluation of animals based on their natural fertility