Identification of copy number variations in the genome of Dairy Gir cattle.

Studying structural variants that can control complex traits is relevant for dairy cattle production, especially for animals that are tolerant to breeding conditions in the tropics, such as the Dairy Gir cattle. This study identified and characterized high confidence copy number variation regions (CNVR) in the Gir breed genome. A total of 38 animals were whole-genome sequenced, and 566 individuals were genotyped with a high-density SNP panel, among which 36 animals had both sequencing and SNP genotyping data available. Two sets of high confidence CNVR were established: one based on common CNV identified in the studied population (CNVR_POP), and another with CNV identified in sires with both sequence and SNP genotyping data available (CNVR_ANI). We found 10 CNVR_POP and 45 CNVR_ANI, which covered 1.05 Mb and 4.4 Mb of the bovine genome, respectively. Merging these CNV sets for functional analysis resulted in 48 unique high confidence CNVR. The overlapping genes were previously related to embryonic mortality, environmental adaptation, evolutionary process, immune response, longevity, mammary gland, resistance to gastrointestinal parasites, and stimuli recognition, among others. Our results contribute to a better understanding of the Gir breed genome. Moreover, the CNV identified in this study can potentially affect genes related to complex traits, such as production, health, and reproduction.

Strategy to Predict High and Low Frequency Behaviors Using Triaxial Accelerometers in Grazing of Beef Cattle.

Knowledge of animal behavior can be indicative of the well-being, health, productivity, and reproduction of animals. The use of accelerometers to classify and predict animal behavior can be a tool for continuous animal monitoring. Therefore, the aim of this study was to provide strategies for predicting more and less frequent beef cattle grazing behaviors. The behavior activities observed were grazing, ruminating, idle, water consumption frequency (WCF), feeding (supplementation) and walking. Three Machine Learning algorithms: Random Forest (RF), Support Vector Machine (SVM) and Naïve Bayes Classifier (NBC) and two resample methods: under and over-sampling, were tested. Overall accuracy was higher for RF models trained with the over-sampled dataset. The greatest sensitivity (0.808) for the less frequent behavior (WCF) was observed in the RF algorithm trained with the under-sampled data. The SVM models only performed efficiently when classifying the most frequent behavior (idle). The greatest predictor in the NBC algorithm was for ruminating behavior, with the over-sampled training dataset. The results showed that the behaviors of the studied animals were classified with high accuracy and specificity when the RF algorithm trained with the resampling methods was used. Resampling training datasets is a strategy to be considered, especially when less frequent behaviors are of interest.