Modern plant breeding relies on data-driven approaches for decision-making. The development of improved lines and product placement is facilitated by our knowledge of genomic regions associated with higher-yielding genetics. The elucidation of such genetic mechanisms relies on our knowledge of genomics, enviromics, and phenomics; however, omic data require specialized expertise for collection, processing, sophisticated modeling, interpretation of results, and prediction applications. The intersections of scalable omics are influenced by the know-how of mixed models, machine learning, envirotyping, quantitative genetics, plant physiology, and genotype-by-environment interaction.
In this Research Topic, we will prioritize methodologies and proofs of concept that describe how authors include phenomics and environmental information in genomic analyses that can translate into actionable breeding applications. Those may include novel insights and applications of genome-wide association studies, benchmarking or genomic prediction approaches, and genotype-by-environment analysis, through mixed models and machine learning. We envision addressing the gap in plant breeding between statistical genomics and sensor-collected data describing crop growth, environmental parameters, and the interactions between them. Our goal is to accelerate successful applications of newly-enabled insights on climate resilience and environmental responses into modern genomics-assisted crop breeding schemes, including identifying genomic regions responsible for change over time.
We encourage contributions that (1) provide insight on knowledge gaps based on the big-picture of the deployment of novel technologies; (2) evaluation of statistical genomics methodological approaches that are suitable to analyze complex data and integrate various sources of information; (3) proof-of-concept - breeding applications where authors have combined the use of genomic prediction or genome-wide screenings with one of the following: high-throughput phenotyping, genomic studies of longitudinal traits, genomics-assisted breeding conditional to environmental information, genome-by-environment interactions, and genomic-enabled crop growth modeling.
Modern plant breeding relies on data-driven approaches for decision-making. The development of improved lines and product placement is facilitated by our knowledge of genomic regions associated with higher-yielding genetics. The elucidation of such genetic mechanisms relies on our knowledge of genomics, enviromics, and phenomics; however, omic data require specialized expertise for collection, processing, sophisticated modeling, interpretation of results, and prediction applications. The intersections of scalable omics are influenced by the know-how of mixed models, machine learning, envirotyping, quantitative genetics, plant physiology, and genotype-by-environment interaction.
In this Research Topic, we will prioritize methodologies and proofs of concept that describe how authors include phenomics and environmental information in genomic analyses that can translate into actionable breeding applications. Those may include novel insights and applications of genome-wide association studies, benchmarking or genomic prediction approaches, and genotype-by-environment analysis, through mixed models and machine learning. We envision addressing the gap in plant breeding between statistical genomics and sensor-collected data describing crop growth, environmental parameters, and the interactions between them. Our goal is to accelerate successful applications of newly-enabled insights on climate resilience and environmental responses into modern genomics-assisted crop breeding schemes, including identifying genomic regions responsible for change over time.
We encourage contributions that (1) provide insight on knowledge gaps based on the big-picture of the deployment of novel technologies; (2) evaluation of statistical genomics methodological approaches that are suitable to analyze complex data and integrate various sources of information; (3) proof-of-concept - breeding applications where authors have combined the use of genomic prediction or genome-wide screenings with one of the following: high-throughput phenotyping, genomic studies of longitudinal traits, genomics-assisted breeding conditional to environmental information, genome-by-environment interactions, and genomic-enabled crop growth modeling.
