Recent developments in the area of precision livestock promoted the concept of high-throughput phenotyping to be exploited under the Animal Breeding and Genetics framework. In this context, genomic prediction, GWAS and gene expression (transcriptome) researches, among others, must be revisited to accommodate new challenges that arise from technological phenotyping issues. One of the most relevant challenges is the handling of large-scale data generated by a fully automated process such as images collection and continuous real-time measurements (body weight, milk yield, feed intake, and other traits). Thus, the data complexity is characterized not only by increasing amounts of records but also by the dynamic nature of its constant collection over time. Several computational and statistical methods have been successfully proposed to treat this massive information. In general, these methods provide the technical basis to extract only relevant attributes from these large databases. Thus, we encourage contributions that apply innovative methods and strategies for high-throughput phenotypic data analysis in order to report novel results under a livestock genomic viewpoint.
This Research Topic welcomes original research, method and review articles exploring, evaluating and proposing future developments in next-generation phenotyping according to the specific themes:
1) Next-generation phenotyping in livestock genomics
2) New insights on high-throughput phenotypic data analysis
3) High-performance computing using next-generation phenotyping
4) Breeding program strategies based on high-throughput phenotyping
5) High-throughput phenotyping in different species: dairy and beef cattle, pig, chicken, sheep, goat and fish
6) Functional genomics based on high-throughput phenotyping
7) Nutrigenomics in next-generation phenotyping era
Recent developments in the area of precision livestock promoted the concept of high-throughput phenotyping to be exploited under the Animal Breeding and Genetics framework. In this context, genomic prediction, GWAS and gene expression (transcriptome) researches, among others, must be revisited to accommodate new challenges that arise from technological phenotyping issues. One of the most relevant challenges is the handling of large-scale data generated by a fully automated process such as images collection and continuous real-time measurements (body weight, milk yield, feed intake, and other traits). Thus, the data complexity is characterized not only by increasing amounts of records but also by the dynamic nature of its constant collection over time. Several computational and statistical methods have been successfully proposed to treat this massive information. In general, these methods provide the technical basis to extract only relevant attributes from these large databases. Thus, we encourage contributions that apply innovative methods and strategies for high-throughput phenotypic data analysis in order to report novel results under a livestock genomic viewpoint.
This Research Topic welcomes original research, method and review articles exploring, evaluating and proposing future developments in next-generation phenotyping according to the specific themes:
1) Next-generation phenotyping in livestock genomics
2) New insights on high-throughput phenotypic data analysis
3) High-performance computing using next-generation phenotyping
4) Breeding program strategies based on high-throughput phenotyping
5) High-throughput phenotyping in different species: dairy and beef cattle, pig, chicken, sheep, goat and fish
6) Functional genomics based on high-throughput phenotyping
7) Nutrigenomics in next-generation phenotyping era
