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

The overarching goal of this project is to develop and support new statistical tools for the breeding of superior individuals or cultivars in genetic populations with the long-term goal of enhancing the production, sustainability, and climate and disease resilience of crop and livestock species. One way to enhance livestock and crop breeding strategies is by better understanding gene-trait associations and prioritizing causal genes of diverse agriculturally important phenotypic traits. Towards this end, the project will bring together researchers from variety of disciplines, including phenomics, genomics, genetic diversity, and data science. Biologists will bring their own biological questions and datasets from different crop and livestock species. Statisticians, with extensive experience in collaborations with biologists, will build statistical models and methodologies to analyze these datasets. The models and analyses will be updated iteratively following feedback from the biologists.Our objectives in this project are: (1) to build powerful multi-locus methods for combined GWAS, TWAS, and expression quantitative trait loci (eQTL) mapping, (2) to develop user friendly open-source R packages and Python libraries with detailed manuals, vignettes, and video tutorials, and (3) interweave research and education through the integration of training and cross-disciplinary research toward producing a skilled STEM agricultural workforce. We plan to achieve these research objectives by pursuing the following three specific objectives:Objective #1: Develop methods to combine GWAS, TWAS, and eQTL mapping of quantitative traits.Our working hypothesis is that a hierarchy of high-dimensional partial-linear and linear models, with appropriate shrinkage on SNP and gene expression effects, will be able to mitigate the confounding effects. In this objective, we will focus on traits for which the responses can be assumed to be univariate or multivariate Gaussian (normally distributed), possibly after a suitable transformation (e.g., log).Objective #2: Develop methods to combine GWAS, TWAS, and eQTL mapping of ordinal traits.The non-Gaussian traits we will focus on are ordinal scores (e.g., disease and root lodging scores). Our working hypothesis is that we will be able to improve the association results by properly accounting for the nature of the non-Gaussianity through an appropriate hierarchical generalized partial-linear multi-locus model. We will also retain the advantages of Objective #1.Objective #3: Develop methods to combine GWAS, TWAS, and eQTL mapping of functional data traits.Here, we will develop multi-locus methods for traits that are measured by a smooth curve (e.g., repeatedly measured phenotypes such as growth rates, time-series, light curves, A/Ci curves). Our working hypothesis is that we will be able to improve the understanding of the genetic basis of variations in the whole trait curves instead of being limited to univariate analyses of summary measurements or independent analysis of individual time points.Alongside the research outcomes, these initiatives will enhance expertise in agricultural genome-to-phenome research through education and outreach activities. Existing ISU courses will be improved by accommodating GWAS and TWAS methods in the syllabus. Outreach programs will provide education and support the research and training a broad range of crop and livestock scientists at multiple types of U.S. institutions. Hybrid workshops will be organized to facilitate training students and scientists.