Inferring genetic associations among dairy cow behavior components using biomarkers, genetic marker, genome sequence and technical data

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Cow behavior is a major factor influencing dairy herd profitability and is an indicator for animal welfare and disease. Behavior is a complex network of behavioral patterns in response to environmental and social stimuli, and to human handlings. Advances in agriculture technology led to changes in dairy cow husbandry systems worldwide. Gradually increasing herd sizes, less time availability to take care for the animals and installed modern technology such as automatic milking systems (AMS) imply limited human-cow interactions. Vice versa, improving cow behavior response to technical environment (Cow-AMS interactions) becomes increasingly important for efficient production and welfare friendly conditions, and contributes to simplified “cow handling” and reduced labour time. AMS generate objective behavior traits (i.e., “big data”) linked to workability, milkability and health, which can be implemented into genomic selection tools. However, there is insufficient understanding of learning and social behavioral genomics in cows, which affect management, production and welfare in dairy herds essentially. Moreover, selection for adapted behavior to AMS environments requires deep knowledge of the genetic associations among behavior traits, and between behavior traits with production and health traits, which are currently unknown. During the past decade, geneticists and veterinarians from both research groups JLU and MLU implemented so-called cow training sets for genomic selection based on dense phenotyped and genotyped Holstein Friesian cows in Germany. Such comprehensive dataset including more than 20’000 genotyped cows was the basis for implementing genomic selection for health traits. The data pool comprises several large-scale herds using AMS, enabling genomic analyses for AMS behavior traits based on 5’000 genotyped cows imputed to sequence data. AMS technical longitudinal behavior data will be merged with other behavior categories (feeding and activity behavior, social behavior, reproduction behavior, behavior response to human handlings) and behavior biomarkers (heart rate, cortisol concentrations, rectal temperature) in order to infer genomic mechanisms of cow behavior via genome-wide pleiotropy approaches and genome-wide association studies. Results will be annotated with functional gene databases, aiming on the identification of candidate genes and biological pathways contributing to the different cattle behavior categories. Identified genetic variants will be considered in enhanced genetic evaluations, aiming on selection tool developments for cow behaviour.