NIFA AG2PI COLLABORATIVE: IMPROVING CAUSAL GENE DETECTION ACROSS CROP AND LIVESTOCK SPECIES

<b>Project Methods</b><br> The statistical methods include the development of hierarchical Bayesian models for combining GWAS, TWAS, and eQTL mapping. Latent indicator variables will be assumed, and model size will be penalized through Bernoulli priors on these latent indicator vectors. Theoretical results will be developed for choosing the right shrinkage to accurately detect associated genes. Fast scalable computational algorithms based on delayed Cholesky factorization, sparse-matrix algebras will be developed and implemented in C++ programming language. These models and methods will be extended to accommodate more general phenotypic responses through link functions.Three methods will be used to assess the validation of association studies: cross-validation with independent datasets from literature, biological pathway analysis, and network analysis with functional enrichment (GO or gene ontology terms) analysis. In addition, simulation experiments will also be conducted based on the literature.Two hybrid workshops will be organized yearly to disseminate research and the software and train the broader scientific community. Feedback will be sought from the workshop participants to assess the overall effectiveness of the workshops and to improve the accessibility of the software, manuals, and vignettes.