A Boolean gene regulatory model of heterosis and speciation

Abstract

BackgroundModelling genetic phenomena affecting biological traits is important for the development of agriculture as it allows breeders to predict the potential of breeding for certain traits. One such phenomenon is heterosis or hybrid vigor: crossing individuals from genetically distinct populations often results in improvements in quantitative traits, such as growth rate, biomass production and stress resistance. Heterosis has become a very useful tool in global agriculture, but its genetic basis remains controversial and its effects hard to predict. We have taken a computational approach to studying heterosis, developing a simulation of evolution, independent reassortment of alleles and hybridization of Gene Regulatory Networks (GRNs) in a Boolean framework. These artificial regulatory networks exhibit topological properties that reflect those observed in biology, and fitness is measured as the ability of a network to respond to external inputs in a pre-defined way.ResultsOur model reproduced common experimental observations on heterosis using only biologically justified parameters, such as mutation rates. Hybrid vigor was observed and its extent was seen to increase as parental populations diverged, up until a point of sudden collapse of hybrid fitness. Thus, the model also describes a process akin to speciation due to genetic incompatibility of the separated populations. We also reproduce, for the first time in a model, the fact that hybrid vigor cannot easily be fixed by within a breeding line, currently an important limitation of the use of hybrid crops. The simulation allowed us to study the effects of three standard models for the genetic basis of heterosis: dominance, over-dominance, and epistasis.ConclusionThis study describes the most detailed simulation of heterosis using gene regulatory networks to date and reproduces several phenomena associated with heterosis for the first time in a model. The level of detail in our model allows us to suggest possible warning signs of the impending collapse of hybrid vigor in breeding. In addition, the simulation provides a framework that can be extended to study other aspects of heterosis and alternative evolutionary scenarios.Electronic supplementary materialThe online version of this article (doi:10.1186/s12862-015-0298-0) contains supplementary material, which is available to authorized users.

Parental Expression Variation of Small RNAs Is Negatively Correlated with Grain Yield Heterosis in a Maize Breeding Population

“…The features of ha-sRNAs are to large extent compatible with driving factors of these epistatic interactions. In agreement with the prevailing negative associations of sRNAs with heterosis, a recent modeling of heterosis indicates epistasis as a major factor for the collapse of heterosis after prolonged separation of parental populations (Emmrich et al, 2015 ). The underlying mechanism requires beneficial mutations to accumulate for one parental population with negative effects on hybrid phenotypes—a situation, which resembles our model on the action of 22-nt negatively ha-sRNA (Figure 7 ).…”

Heterosis of Fitness and Phenotypic Variance in the Evolution of Diploid Gene Regulatory Network

“…Although a few studies have addressed heterosis using GRN models (26,27), the consequences of GRN dynamics and the robustness of phenotypes to noise and genomic mixing by sexual recombination have not previously been addressed.…”

Poster abstracts of GCB 2015

“…We will create a gene regulatory network (GRN), which will be integrated in the heterosis model beside phenotypic and enviromental models. With the help of a reference transcriptome and a heterosis simulation model [ERP15] representative amplicons will be selected, which will be sequenced for 662 genotypes. For the de novo transcriptome sequencing we established an RNA-Seq method to get 400bp long reads from the Ion Torrent platform, that enables the correct transcriptome assembly for the two closely related progenitor subgenomes in Brassica napus [HKJ + 08, CTD + 09].…”