Jared Richardson / Mathematics / Faculty Mentor: Jianzhong Su
This collaborative study leverages data-driven agriculture and computational tools to predict causal relationships among rice traits under various growing conditions. Utilizing a combination of multi-omic datasets, we developed unique mixed models for the calculations of Best Linear Unbiased Predictors (BLUPs) and employed Bayesian Networks for the construction of Directed Acyclic Graphs (DAGs). By integrating these datasets with additional single nucleotide polymorphism (SNP) data, we created probabilistic graphical models that illustrate relationships between traits and genetic SNP markers. These findings potentially provide valuable insights for plant pathology and agronomy, with implications for optimizing growing conditions and enhancing our understanding of genomic-trait interactions.
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