Modeling genome-wide by environment interactions through omnigenic interactome networks

Abstract

How genes interact with the environment to shape phenotypic variation and evolution is a fundamental question intriguing to biologists from various fields. Existing linear models built on single genes are inadequate to reveal the complexity of genotype-environment (G-E) interactions. Here, we develop a conceptual model for mechanistically dissecting G-E interplay by integrating previously disconnected theories and methods. Under this integration, evolutionary game theory, developmental modularity theory, and a variable selection method allow us to reconstruct environment-induced, maximally informative, sparse, and casual multilayer genetic networks. We design and conduct two mapping experiments by using a desert-adapted tree species to validate the biological application of the model proposed. The model identifies previously uncharacterized molecular mechanisms that mediate trees' response to saline stress. Our model provides a tool to comprehend the genetic architecture of trait variation and evolution and trace the information flow of each gene toward phenotypes within omnigenic networks.

Identifying Gene–Environment Interactions With Robust Marginal Bayesian Variable Selection

“…Besides, the variable selection conducted in our study is based on the L1 penalty within the Bayesian framework. As this structure ignores the correlation among genetic features, a possible direction for future improvement is to incorporate network or gene set information in the identification of important gene–environment interactions (Wang et al, 2021 ). Furthermore, in our study, the genetic factor is represented by one SNP coded as a triadic factor.…”

Modularity in Biological Networks

“…Despite underlying mutational mechanisms have been proposed to explain the emergence of modularity, selection and other evolutionary forces have also been part of this discussion (Wagner et al, 2001(Wagner et al, , 2007Espinosa-Soto and Wagner, 2010;Clune et al, 2013;Friedlander et al, 2013;Banerjee et al, 2017;Verd et al, 2019;Jaeger and Monk, 2021), as are ecological factors such as spatial distribution and population dynamics (Gilarranz, 2020). Biological modularity arise in the contexts of dynamical process that may even challenge compartmentalization and cause the breakdown of modularity or its rearrangement (Valverde, 2017;Wang et al, 2021).…”

Specialty Grand Challenge: Systems Genetics

“…We argue that the omnigenic model can be tested by inferring omnigenic interactome networks (OGIN) that cover a complete set of SNPs genotyped from the whole genome in a GWAS design (Wang et al, 2021). Such OGIN have many desirable properties that can fill two major gaps of systems genetics.…”

“…Chen and Mar (2018) further pinpointed that existing approaches lack performance to reconstruct gene networks using heterogeneous single cell data that are becoming popular in genetic studies. Several attempts have been made to combine elements of multiple distinct disciplines to reconstruct maximally informative genetic networks that function at different levels of organization (Sun et al, 2021;Wu and Jiang 2021), producing some promising results that may advance systems genetics (Wang et al, 2021).…”