An extended omnigenic model explains genome-phenome relationships for complex traits in global sorghum diversity

A central finding of complex trait genetics is the geometric distribution of effect sizes, but the biological basis of this phenomena is not understood. The omnigenic model (OM) could explain this architecture, with oligogenic variation arising from direct regulatory genes (core genes) and polygenic variation from indirect regulators (peripheral trans regulators). Plant yield is a canonical complex trait and here we tested the OM using genome-phenome analysis of biomass yield in global sorghum diversity. We used field-based phenomics to characterize dynamic growth and yield formation traits, then decomposed oligogenic and polygenic components of variation using genome-wide association studies (GWAS), genome-wide prediction (GWP), and tissue-specific transcriptome analyses. We identified major dynamic QTL, including several persistent ( Dw1 , Dw3 ) or transient ( ELF3 ) QTL at known genes, consistent with the oligogenic-core of the OM. Next, we evaluated a key prediction on the peripheral-polygenic component – a positive correlation between GWP marker loadings and gene expression in relevant tissues. GWP loadings are indeed correlated with gene expression in relevant tissues. However, correlations are often higher with non-relevant tissues from earlier growth stages or tissues, which is not predicted by within-tissue trans regulation for the peripheral-polygenic component (the “omnigenic in space” model). Therefore, these findings suggest that genes expressed in early growth stages, with indirect effects on later traits, are contributing to polygenic variation (“omnigenic in time”). Together, our findings suggest that an extended OM, with regulatory effects both in space and in time, could explain the ubiquitous geometric genetic architecture of complex traits.