Extracting the GEMs: Genotype, Environment, and Microbiome Interactions Shaping Host Phenotypes

Abstract

One of the fundamental tenets of biology is that the phenotype of an organism (Y) is determined by its genotype (G), the environment (E), and their interaction (GE). Quantitative phenotypes can then be modeled as Y = G + E + GE + e, where e is the biological variance. This simple and tractable model has long served as the basis for studies investigating the heritability of traits and decomposing the variability in fitness. The importance and contribution of microbe interactions to a given host phenotype is largely unclear, nor how this relates to the traditional GE model. Here we address this fundamental question and propose an expansion of the original model, referred to as GEM, which explicitly incorporates the contribution of the microbiome (M) to the host phenotype, while maintaining the simplicity and tractability of the original GE model. We show that by keeping host, environment, and microbiome as separate but interacting variables, the GEM model can capture the nuanced ecological interactions between these variables. Finally, we demonstrate with an in vitro experiment how the GEM model can be used to statistically disentangle the relative contributions of each component on specific host phenotypes.

Disentangling the genetic basis of rhizosphere microbiome assembly in tomato

“…In particular, the discontinuity of sweeps in microbiome QTL regions suggests that evolutionary pressure for recombination of key (microbiome associated) traits, such as iron homeostasis and water transport, may have acted against selective sweeps. The results obtained here provide the means to illuminate such complex eco-evolutionary questions, forming the basis of integrating the microbiome into the classic genotype by environment model of host phenotype 10 .…”

“…Developing a blueprint of the genetic architecture for this 'chemical dialogue' and how these interactions lead to specific MAPs is a one of the key focal points in current plant microbiome research. The promise is that these genomic and chemical blueprints can be integrated into microbiome breeding programs for a new generation of crops that can rely, in part, on specific members of the microbiome for stress protection, enhanced growth and higher yields 10 .…”

Do androids dream of electric sorghum?: Predicting Phenotypes from Multi-Scale Genomic and Environmental Data using Neural Networks and Knowledge Graphs

“…There are limitations in the assumptions made by existing methodologies, such as GWAS, that directly attribute plant phenotypes to environmental variables. These methods do not explicitly incorporate biological and molecular interactions, such as post-translational modification of macromolecules [16], the importance of plant-microbe interactions [17], or endogenous siRNA [18]. However, a machine learning approach allows for these biological phenomena to be accounted for as latent variables while probing the interactions of genomes, environments, and phenotypes in a multidimensional manner.…”

Genotype-environment interactions determine microbiota plasticity inNematostella vectensis

“…Starting from these evidences, many studies demonstrated that the host plays an active role in shaping its symbiont microbiota (Augustin et al, 2017; Franzenburg et al, 2012; Fraune & Bosch, 2007; Groussin et al, 2017; Lee et al, 2016). In addition to the effects of the host and the environment, the interaction between these two factors is also discussed as a potential factor influencing the plasticity of the microbiota (Oyserman et al, 2021).…”