Bionote
Boris M.E. Alladassi is a Postdoctoral Research Associate in the Lipka Lab at the University of Illinois Urbana-Champaign. His research focuses on developing new quantitative genetics tools to test emerging models, including the omnigenic model, for complex traits dissection and prediction. Boris obtained his Ph.D. in Plant Breeding with a Statistics minor from Iowa State University.
Email: aboris@illinois.edu
Presentation Abstract
Developing a new hierarchical model for improved cross-subpopulation genomic prediction in plants
Understanding the relationship between an organism’s genotype and phenotype is a central goal in genetics and is essential for developing resilient cultivars better adapted to future climates. Recent advances in genomics-enabled crop performance prediction significantly increased genetic gain per selection cycle for agronomically important traits in plant breeding. However, current genomic prediction models exhibit low predictive ability for cross-population prediction scenarios. Emerging models of genetic architecture, such as the omnigenic model, underscore the need for an integrated framework that incorporates knowledge from molecular and functional genetics into quantitative genetic models to enhance the accuracy of complex trait prediction across subpopulations. Here, we present a new hierarchical model that quantifies epistatic interactions of parental and recombinant haplotypes and enables the estimation of subpopulation-specific additive genetic effects. We are currently simulating various genetic architectures using publicly available genome-wide marker data from a multiparent advanced generation inter-cross population of Arabidopsis. Next, we will compare the prediction accuracy of the new model with that of current genomic prediction models under two scenarios— within-subpopulation and cross-subpopulation using the k-fold and leave-one-population-out cross-validation procedures, respectively. Overall, by incorporating existing biological knowledge, the new hierarchical model holds great promise for improving the accuracy of cross-subpopulation genomic prediction.
Co-Authors: G. P. Morris, G.P.3; A. Lipka1,2
1Department of Crop Sciences, University of Illinois at Urbana-Champaign, 2National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, 3Soil and Crop Sciences Department, Colorado State University