Biology subjects

Bohn, M. O.

Publications and source records attributed to Bohn, M. O..

3 recordsLinked to original sources

bioRxiv2023

Genetic variation exists within Zea mays to influence unsustainable nitrogen cycling microbiome function

Overuse of synthetic nitrogen fertilizers in agroecosystems causes environmental pollution and human harm at a global level. Nitrogenous fertilizers provide a short-lived benefit to crops in the agroecosystem, but stimulate microbially-mediated nitrification and denitrification, processes that result in N pollution, greenhouse gas (GHG) production, and reduced soil fertility. Recent advances in plant microbiome science suggest that plants can modulate the composition and activity of rhizosphere microbial communities. These rhizosphere communities act as an extended phenotype, primed by genetic variation in the plant host. Genetic variation in traits (e.g., plant secondary metabolites, root architecture, immune system, etc.) act as mechanistic selective agents on the composition of the microbiome. Here we attempted to determine whether genetic variation exists in Zea mays for the ability to influence the extended phenotype of rhizosphere soil microbiome composition and function. Specifically, we determined whether plants influence on soil nitrogen cycling activities was altered by plant genetics and thereby allowing it to be incorporated into breeding practices. To capture an extensive amount of genetic diversity within maize we sampled the rhizosphere microbiome of a germplasm chronosequence that included ex-PVP inbreds, hybrids, and teosinte (Z. mays ssp. mexicana and Z. mays ssp. parviglumis). We observed that potential N cycling processes were influenced by plant genetics. Teosinte and some hybrid genotypes supported microbial communities with lower potential nitrification and potential denitrification activity in the rhizosphere, while inbreds stimulated/did not inhibit these undesirable N-cycling activities. These potential differences translated to functional differences in N2O production, with teosinte plots producing less GHG than maize plots. Furthermore, within these Zea cultivars we found that plant genetics explained a significant amount of variation in the microbiome, particularly among different nitrification and denitrification functional genes within the community. We found that potential nitrification, potential incomplete denitrification, and overall denitrification rates, but not abundance of N-cycling genes of rhizosphere soils were influenced by growth stage and plant genetics. Taken together, these results suggest that crop selection can lead to changes in root phenotypes that could suppress unsustainable N-cycling processes. Reintroducing stress-adapted and "wild" root characteristics into modern germplasm may be a way to manipulate soil microbiomes at both a composition and functional level to improve sustainability.

ecology↗

bioRxiv2023

Importance of genetic architecture in marker selection decisions for genomic prediction

Breeders commonly use genetic markers to predict the performance of untested individuals as a way to improve the efficiency of breeding programs. These genomic prediction models have almost exclusively used single nucleotide polymorphisms (SNPs) as their source of genetic information, even though other types of markers exist, such as structural variants (SVs). Given that SVs are associated with environmental adaptation and not all of them are in linkage disequilibrium to SNPs, SVs have the potential to bring additional information to multi-environment prediction models that are not captured by SNPs alone. Here, we evaluated different marker types (SNPs and/or SVs) on prediction accuracy across a range of genetic architectures for simulated traits across multiple environments. Our results show that SVs can improve prediction accuracy by up to 19%, but it is highly dependent on the genetic architecture of the trait. Differences in prediction accuracy across marker types were more pronounced for traits with high heritability, high number of QTLs, and SVs as causative variants. In these scenarios, using SV markers resulted in better prediction accuracies than SNP markers, especially when predicting untested genotypes across environments, likely due to more predictors being in linkage disequilibrium with causative variants. The simulations revealed little impact of different effect sizes between SNPs and SVs as causative variants on prediction accuracy. This study demonstrates the importance of knowing the genetic architecture of a trait in deciding what markers and marker types to use in large scale genomic prediction modeling in a breeding program. Key messageWe demonstrate potential for improved multi-environment genomic prediction accuracy using structural variant markers. However, the degree of observed improvement is highly dependent on the genetic architecture of the trait.

plant biology↗

bioRxiv2023

Linking genetic and environmental factors through marker effect networks to understand trait plasticity

Understanding how plants adapt to specific environmental changes and identifying genetic markers associated with phenotypic plasticity can help breeders develop plant varieties adapted to a rapidly changing climate. Here, we propose the use of marker effect networks as a novel method to identify markers associated with environmental adaptability. These marker effect networks are built by adapting commonly used software for building gene co-expression networks with marker effects across growth environments as the input data into the networks. To demonstrate the utility of these networks, we built networks from the marker effects of [~]10,000 non-redundant markers from 400 maize hybrids across nine environments. We demonstrate that networks can be generated using this approach, and that the markers that are co-varying are rarely in linkage disequilibrium, thus representing higher biological relevance. Multiple covarying marker modules associated with different weather factors throughout the growing season were identified within the marker effect networks. Finally, a factorial test of analysis parameters demonstrated marker effect networks are relatively robust to these options, with high overlap in modules associated with the same weather factors across analysis parameters. This novel application of network analysis provides unique insights into phenotypic plasticity, and specific environmental factors that modulate the genome.

plant biology↗