A0873
Title: Genome-enabled analysis of time-series high-throughput phenotyping data
Authors: Gota Morota - Virginia Polytechnic Institute and State University (United States) [presenting]
Abstract: The advent of plant phenomics, coupled with the wealth of genotypic data generated by next-generation sequencing technologies, provides exciting new resources for studies of complex traits. However, these new technologies also bring new challenges to quantitative genetics, namely, a need to develop robust frameworks that can accommodate these high-dimensional data for genomic prediction and genome-wide association studies. One unique aspect of high-throughput phenotyping data is that phenomics platforms often produce large-scale data with high temporal resolution. We developed a random regression model framework for modeling trait trajectories by accounting for covariances across timepoints to accommodate time-series measurements in genome-enabled analysis. The random regression model has recently been extended to a Bayesian random regression marker effect model that can incorporate mixture priors to marker effects to introduce more meaningful biological assumptions for longitudinal trait analysis. We demonstrate the utility of the random regression model and random regression marker effect model using both simulated and real rice data.
