Bionote
Alexander E. Lipka is an Associate Professor of Biometry in the Department of Crop Sciences at the University of Illinois in Urbana-Champaign. His research applies cutting-edge statistical approaches to quantitative genetics analyses, resulting in more accurate quantification of genomic signals underlying phenotypic variation and prediction of breeding values of agronomically important traits. A statistician by training, Dr. Lipka has co-developed statistical quantitative genetics software including GAPIT, simplePHENOTYPES, SPAEML, and MSTEP.
Associate Professor of Biometry
University of Illinois at Urbana-Champaign
College of Agricultural, Consumer and Environmental Sciences
Department of Crop Sciences
W-201A Turner Hall | M/C 046
Urbana, IL 61801, USA
+1-217-300-0726 | alipka@illinois.edu
thelipkalab.web.illinois.edu/
Presentation Abstract
Typical GWAS and GS statistical models make assumptions on genetic architecture that were derived from RA Fisher’s milestone 1918 paper. The omnigenic model of Boyle et al. (2017) and Liu et al. (2019) is a prominent example of how findings in molecular and functional biology that have occurred since 1918 can be incorporated into the established framework of complex genetic architecture. This model partitions genes underlying complex traits into a core set that directly controls the trait and a peripheral set that indirectly controls a trait through trans regulation. A refinement of the omnigenic model from Mathieson (2021) postulates that for a given trait, core gene effects are the same across subpopulations, while peripheral gene effect networks change across subpopulations. We hypothesize is that the omnigenic model accurately depicts the genetic architecture across subpopulations. Therefore, the purpose of this ongoing work is to explore how genetic architecture evolves across subpopulations. We are conducting a forward-in-time simulation study to simulate three divergent breeding programs, and then study the evolution of trait genetic architecture. For each generation, traits that are dependent upon additive and epistatic effects of core and peripheral genes are being simulated. This study is a work in progress, and the latest results as of April 2025 are summarized. If this simulation study suggests that the omnigenic model is an accurate depiction of cross-subpopulation genetic architecture, then GWAS and GS models that incorporate features of this model should become more widely used in practice. Such models could result in a refined understanding of genetic architecture and could help optimize breeding values on a species-wide basis.
Co-Authors: Gregor Gorjanc, Geoffrey P. Morris