Towards genetic architecture and genomic prediction of crop traits from time-series data: Challenges and breakthroughs

Advances in remote and proximal sensing have facilitated temporal high-throughput phenotyping of crop populations grown in field conditions. The resulting time-series phenotypic data capture single or multiple growth- and yield-related traits at different temporal resolution. Whilst classical quantitative genetics approaches can readily be used with these temporal data by considering the measurement of a character at a given time point as a separate trait, this strategy fully neglects inter-trait integration over time. Here, we provide a classification of computational approaches that can be used to effectively analyze temporal phenotyping data from crop populations, focusing on genomic prediction, identification of quantitative trait loci, and genome-wide association studies. We point out the existing challenges due to the consideration of time-resolved data, and stress the extent to which these challenges are addressed by the available computational solutions. Finally, we highlight recent breakthroughs that make use of time-resolved data for multiple traits and are poised to revolutionize breeding efforts of climate-resilient crops.