Use of Longitudinal Information, Imaging, and Sensor Data in Genetic Evaluation

Dear Colleagues,

Genetic and genomic selection have been widely adopted by most livestock production systems around the world, which have enabled substantial improvement in animal production efficiency. The second half of the 20th century was marked by a great volume of research and improvements of the statistical methods and applications of genetic evaluation for the selection of individuals within a breeding program. Thanks to the fast advances in molecular technology and computational capacity in both storing and processing data, the 21st century brought the era of genomics to agricultural production systems. Currently, most livestock breeding programs routinely incorporate genomic data for predicting the genetic merit of individual animals for a wide range of traits.

One of the most pressing challenges to the agricultural industry is to further improve production efficiency, animal welfare, and the overall sustainability of the animal production system, in order to provide nutritious food for a rapidly-growing population in an environment that is being disrupted by climate change and limited agricultural lands. In the face of such challenges, researchers are continuously moving their efforts to develop models for genetic evaluation that integrate a wide variety of omics data, environmental descriptors, and novel phenotypes generated by precision farming technologies. All these data sources can be highly valuable when applied to breeding programs, including in the prediction of genomic breeding values and comprehension of the underlying biology that regulates phenotypic expression of key traits of interest. Moreover, a large amount of historical data has been accumulated in many livestock production systems, enabling the comprehensive study of longitudinal patterns, and their association to a series of effects.

This special issue of Animals – Use of longitudinal information, imaging, and sensor data in genetic evaluation – intends to bring the public’s attention to complementary and innovative approaches to the use of novel data available in livestock production. This includes but it is not limited to: applications of environmental and climate data in genetic evaluation or phenotypic prediction; longitudinal population studies; incorporation of multi-omics information for genomic or phenotypic prediction; definition of novel traits and breeding goal based on high-throughput phenotypes; methods and models to process and analyze longitudinal, imaging and sensor data; and, economic or social impacts of novel breeding and phenotyping technologies. Submitted manuscripts should emphasize the uniqueness of their research in terms of methodology, data, population and/or phenotypes, as well as both the strengths and weaknesses of their study or data, with interpretation of the obtained results. We acknowledge as well that negative results are important outcomes in scientific literature, and authors with such results should be encouraged to submit their manuscripts.

Dr. Beatriz C.D. Cuyabano
Dr. Luiz F. Brito
Dr. Gabriel Rovere
Guest Editors

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