SUMMARY: Heterosis, also known as hybrid vigor, is the phenomenon wherein a progeny exhibits superior traits relative to one or both parents. In terms of crop breeding, this usually refers to the yield advantage of F₁ hybrids over both inbred parents. The development of high‐yielding hybrid cultivars across a wider range of crops is key to meeting future food demands. However, conventional hybrid breeding strategies are proving to be exceptionally challenging to apply commercially in many self‐pollinating crops, particularly wheat and barley. Currently in these crops, the relative performance advantage of hybrids over inbred line cultivars does not outweigh the cost of hybrid seed production. Here, we review the genetic basis of heterosis, discuss the challenges in hybrid breeding, and propose a strategy to recruit multiple heterosis‐associated genes to develop lines with improved agronomic characteristics. This strategy leverages modern genetic engineering tools to synthesize supergenes by fusing multiple heterotic alleles across multiple heterosis‐associated loci. We outline a plan to assess the feasibility of this approach to improve line performance using barley (Hordeum vulgare) as the model self‐pollinating crop species, and a few heterosis‐associated genes. The proposed method can be applied to all crops for which heterotic gene combinations can be identified.

Gene drives that skew sex ratios offer a new management tool to suppress or eradicate pest populations. Early models and empirical work suggest that these suppression drives can completely eradicate well‐mixed populations, but models that incorporate stochasticity and space (i.e. drift and recolonization events) often result in loss or failure of the drive. We developed a stochastic model to examine these processes in a simple one‐dimensional space. This simple space allows us to map the events and outcomes that emerged and examine how properties of the drive's wave of invasion affect outcomes. Our simulations, across a biologically realistic section of parameter space, suggest that drive failure might be a common outcome in spatially explicit, stochastic systems, and that properties of the drive wave appear to mediate outcomes. Surprisingly, the drives that would be considered fittest in an aspatial model were strongly associated with failure in the spatial setting. The fittest drives cause relatively fast moving, and narrow waves that have a high chance of being penetrated by wild‐types (WTs) leading to WT recolonization, leading to failure. Our results also show that high rates of dispersal reduce the chance of failure because drive waves get disproportionately wider than WT waves as dispersal rates increase. Overall, wide, slow‐moving drive waves were much less prone to failure. Our results point to the complexity inherent in using a genetic system to effect demographic outcomes and speak to a clear need for ecological and evolutionary modelling to inform the drive design process.

The dawn of cost‐effective genome assembly is enabling deep comparative genomics to address fundamental evolutionary questions by comparing the genomes of multiple species. However, comparative genomics analyses frequently deploy multiple, often purpose‐built frameworks, limiting their transferability and replicability. Here, we present compare_genomes, a transferable and extensible comparative genomics workflow package we developed that streamlines the identification of orthologous families within and across eukaryotic genomes and tests for the presence of several mechanisms of evolution (gene family expansion or contraction and substitution rates within protein‐coding sequences). The workflow is available for Linux, written as a Nextflow workflow that calls established genomics and phylogenetics tools to streamline the analysis and visualization of eukaryotic genome divergence. This workflow is freely available at https://github.com/jeffersonfparil/compare_genomes, distributed under the GNU General Public License version 3 (GPLv3). © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.Basic Protocol: Comparative genomics with Nextflow and Conda

Genomic prediction applies to any agro- or ecologically relevant traits, with distinct ontologies and genetic architectures. Selecting the most appropriate model for the distribution of genetic effects and their associated allele frequencies in the training population is crucial. Linear regression models are often preferred for genomic prediction. However, linear models may not suit all genetic architectures and training populations. Machine learning approaches have been proposed to improve genomic prediction owing to their capacity to capture complex biology including epistasis. However, the applicability of different genomic prediction models, including non-linear, non-parametric approaches, has not been rigorously assessed across a wide variety of plant traits in natural outbreeding populations. This study evaluates genomic prediction sensitivity to trait ontology and the impact of population structure on model selection and prediction accuracy. Examining 36 quantitative traits in 1,000+ natural genotypes of the model plant Arabidopsis thaliana, we assessed the performance of penalized regression, random forest, and multilayer perceptron at producing genomic predictions. Regression models were generally the most accurate, except for biochemical traits where random forest performed best. We link this result to the genetic architecture of each trait—notably that biochemical traits have simpler genetic architecture than macroscopic traits. Moreover, complex macroscopic traits, particularly those related to flowering time and yield, were strongly correlated to population structure, while molecular traits were better predicted by fewer, independent markers. This study highlights the relevance of machine learning approaches for simple molecular traits and underscores the need to consider ancestral population history when designing training samples.

Gene drives that skew sex ratios offer a new management tool to suppress or eradicate pest populations. Early models and empirical work suggest that these suppression drives can completely eradicate well-mixed populations, but models that incorporate stochasticity and space (i.e., drift, and founder events) often result in loss or failure of the drive. We developed a stochastic model to examine these processes in a simple 1-dimensional space. This simple space allows us to map the events and outcomes that emerged and examine how properties of the drive’s wave of invasion affect outcomes. Our simulations, across a biologically-realistic section of parameter space, suggest that drive failure might be a common outcome in spatially explicit, stochastic systems, and that properties of the drive wave appear to mediate outcomes. Surprisingly, the drives that would be considered fittest in an aspatial model were strongly associated with failure in the spatial setting. The fittest drives cause fast moving, narrow drive waves that have a high chance of being penetrated by founder events, leading to failure. Our results also show that high rates of dispersal reduce the chance of failure because drive waves get disproportionately wider as dispersal rates increase. Overall, wide, slow-moving drive waves were much less prone to failure. Our results point to the complexity inherent in using a genetic system to effect demographic outcomes and speak to a clear need for ecological and evolutionary modelling to inform the drive design process.