A model framework for identifying genes that guide the evolution of heterochrony

Multiple dynamic models reveal the genetic architecture for growth in height of Catalpa bungei in the field

Growth in height (GH) is a critical determinant for tree survival and development in forests and can be depicted using logistic growth curves. Our understanding of the genetic mechanism underlying dynamic GH, however, is limited, particularly under field conditions. We applied two mapping models (Funmap and FVTmap) to find quantitative trait loci responsible for dynamic GH and two epistatic models (2HiGWAS and 1HiGWAS) to detect epistasis in Catalpa bungei grown in the field. We identified 13 co-located quantitative trait loci influencing the growth curve by Funmap and three heterochronic parameters (the timing of the inflection point, maximum acceleration and maximum deceleration) by FVTmap. The combined use of FVTmap and Funmap reduced the number of candidate genes by >70%. We detected 76 significant epistatic interactions, amongst which a key gene, COMT14, co-located by three models (but not 1HiGWAS) interacted with three other genes, implying that a novel network of protein interaction centered on COMT14 may control the dynamic GH of C. bungei. These findings provide new insights into the genetic mechanisms underlying the dynamic growth in tree height in natural environments and emphasize the necessity of incorporating multiple dynamic models for screening more reliable candidate genes.

Inferring multilayer interactome networks shaping phenotypic plasticity and evolution

Phenotypic plasticity represents a capacity by which the organism changes its phenotypes in response to environmental stimuli. Despite its pivotal role in adaptive evolution, how phenotypic plasticity is genetically controlled remains elusive. Here, we develop a unified framework for coalescing all single nucleotide polymorphisms (SNPs) from a genome-wide association study (GWAS) into a quantitative graph. This framework integrates functional genetic mapping, evolutionary game theory, and predator-prey theory to decompose the net genetic effect of each SNP into its independent and dependent components. The independent effect arises from the intrinsic capacity of a SNP, only expressed when it is in isolation, whereas the dependent effect results from the extrinsic influence of other SNPs. The dependent effect is conceptually beyond the traditional definition of epistasis by not only characterizing the strength of epistasis but also capturing the bi-causality of epistasis and the sign of the causality. We implement functional clustering and variable selection to infer multilayer, sparse, and multiplex interactome networks from any dimension of genetic data. We design and conduct two GWAS experiments using Staphylococcus aureus, aimed to test the genetic mechanisms underlying the phenotypic plasticity of this species to vancomycin exposure and Escherichia coli coexistence. We reconstruct the two most comprehensive genetic networks for abiotic and biotic phenotypic plasticity. Pathway analysis shows that SNP-SNP epistasis for phenotypic plasticity can be annotated to protein-protein interactions through coding genes. Our model can unveil the regulatory mechanisms of significant loci and excavate missing heritability from some insignificant loci. Our multilayer genetic networks provide a systems tool for dissecting environment-induced evolution.

Modeling genome-wide by environment interactions through omnigenic interactome networks

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.

The genetic architecture of heterochrony as a quantitative trait: lessons from a computational model

Heterochrony is known as a developmental change in the timing or rate of ontogenetic events across phylogenetic lineages. It is a key concept synthesizing development into ecology and evolution to explore the mechanisms of how developmental processes impact on phenotypic novelties. A number of molecular experiments using contrasting organisms in developmental timing have identified specific genes involved in heterochronic variation. Beyond these classic approaches that can only identify single genes or pathways, quantitative models derived from current next-generation sequencing data serve as a more powerful tool to precisely capture heterochronic variation and systematically map a complete set of genes that contribute to heterochronic processes. In this opinion note, we discuss a computational framework of genetic mapping that can characterize heterochronic quantitative trait loci that determine the pattern and process of development. We propose a unifying model that charts the genetic architecture of heterochrony that perceives and responds to environmental perturbations and evolves over geologic time. The new model may potentially enhance our understanding of the adaptive value of heterochrony and its evolutionary origins, providing a useful context for designing new organisms that can best use future resources.

A mapping framework of competition-cooperation QTLs that drive community dynamics

Genes have been thought to affect community ecology and evolution, but their identification at the whole-genome level is challenging. Here, we develop a conceptual framework for the genome-wide mapping of quantitative trait loci (QTLs) that govern interspecific competition and cooperation. This framework integrates the community ecology theory into systems mapping, a statistical model for mapping complex traits as a dynamic system. It can characterize not only how QTLs of one species affect its own phenotype directly, but also how QTLs from this species affect the phenotype of its interacting species indirectly and how QTLs from different species interact epistatically to shape community behavior. We validated the utility of the new mapping framework experimentally by culturing and comparing two bacterial species, Escherichia coli and Staphylococcus aureus, in socialized and socially isolated environments, identifying several QTLs from each species that may act as key drivers of microbial community structure and function.

Mapping genes for drug chronotherapy

Genome-wide association studies have been increasingly used to map and characterize genes that contribute to interindividual variation in drug response. Some studies have integrated the pharmacokinetic (PK) and pharmacodynamic (PD) processes of drug reactions into association mapping, gleaning new insight into how genes determine the dynamic relationship of drug effect and drug dose. Here, we present an evolutionary framework by which two distinct concepts, chronopharmacodynamics and heterochrony (describing variation in the timing and rate of developmental events), are married to comprehend the pharmacogenetic architecture of drug response. The resulting new concept, heterochronopharmacodynamics (HCPD), can better interpret how genes influence drug efficacy and drug toxicity according to the circadian rhythm of the body and changes in drug concentration.

An ecophysiologically based mapping model identifies a major pleiotropic QTL for leaf growth trajectories of Phaseolus vulgaris

Crop modeling, a widely used tool to predict plant growth and development in heterogeneous environments, has been increasingly integrated with genetic information to improve its predictability. This integration can also shed light on the mechanistic path that connects the genotype to a particular phenotype under specific environments. We implemented a bivariate statistical procedure to map and identify quantitative trait loci (QTLs) that can predict the form of plant growth by estimating cultivar-specific growth parameters and incorporating these parameters into a mapping framework. The procedure enables the characterization of how QTLs act differently in response to developmental and environmental cues. We used this procedure to map growth parameters of leaf area and mass in a mapping population of the common bean (Phaseolus vulgaris L.). Different sets of QTLs are responsible for various aspects of growth, including the initiation time of growth, growth rate, inflection point and asymptotic growth. A major QTL of a large effect was identified to pleiotropically affect trait expression in distinct environments and different traits expressed on the same organism. The integration of crop models and QTL mapping through our statistical procedure provides a powerful means of building a more precise predictive model of genotype-phenotype relationships for crops.

Evo-Devo-EpiR: a genome-wide search platform for epistatic control on the evolution of development

Evo-devo is a theory proposed to study how phenotypes evolve by comparing the developmental processes of different organisms or the same organism experiencing changing environments. It has been recognized that nonallelic interactions at different genes or quantitative trait loci, known as epistasis, may play a pivotal role in the evolution of development, but it has proven difficult to quantify and elucidate this role into a coherent picture. We implement a high-dimensional genome-wide association study model into the evo-devo paradigm and pack it into the R-based Evo-Devo-EpiR, aimed at facilitating the genome-wide landscaping of epistasis for the diversification of phenotypic development. By analyzing a high-throughput assay of DNA markers and their pairs simultaneously, Evo-Devo-EpiR is equipped with a capacity to systematically characterize various epistatic interactions that impact on the pattern and timing of development and its evolution. Enabling a global search for all possible genetic interactions for developmental processes throughout the whole genome, Evo-Devo-EpiR provides a computational tool to illustrate a precise genotype-phenotype map at interface between epistasis, development and evolution.

The genetic architecture of shoot-root covariation during seedling emergence of a desert tree, Populus euphratica

The coordination of shoots and roots is critical for plants to adapt to changing environments by fine-tuning energy production in leaves and the availability of water and nutrients from roots. To understand the genetic architecture of how these two organs covary during developmental ontogeny, we conducted a mapping experiment using Euphrates poplar (Populus euphratica), a so-called hero tree able to grow in the desert. We geminated intraspecific F₁ seeds of Euphrates Poplar individually in a tube to obtain a total of 370 seedlings, whose shoot and taproot lengths were measured repeatedly during the early stage of growth. By fitting a growth equation, we estimated asymptotic growth, relative growth rate, the timing of inflection point and duration of linear growth for both shoot and taproot growth. Treating these heterochronic parameters as phenotypes, a univariate mapping model detected 19 heterochronic quantitative trait loci (hQTLs), of which 15 mediate the forms of shoot growth and four mediate taproot growth. A bivariate mapping model identified 11 pleiotropic hQTLs that determine the covariation of shoot and taproot growth. Most QTLs detected reside within the region of candidate genes with various functions, thus confirming their roles in the biochemical processes underlying plant growth.

A computational framework for mapping the timing of vegetative phase change

Phase change plays a prominent role in determining the form of growth and development. Although considerable attention has been focused on identifying the regulatory control mechanisms of phase change, a detailed understanding of the genetic architecture of this phenomenon is still lacking. We address this issue by deriving a computational model. The model is founded on the framework of functional mapping aimed at characterizing the interplay between quantitative trait loci (QTLs) and development through biologically meaningful mathematical equations. A multiphasic growth equation was implemented into functional mapping, which, via a series of hypothesis tests, allows the quantification of how QTLs regulate the timing and pattern of vegetative phase transition between independently regulated, temporally coordinated processes. The model was applied to analyze stem radial growth data of an interspecific hybrid family derived from two Populus species during the first 24 yr of ontogeny. Several key QTLs related to phase change have been characterized, most of which were observed to be in the adjacent regions of candidate genes. The identification of phase transition QTLs, whose expression is regulated by endogenous and environmental signals, may enhance our understanding of the evolution of development in changing environments.

Integrating Evolutionary Game Theory into Mechanistic Genotype-Phenotype Mapping

Natural selection has shaped the evolution of organisms toward optimizing their structural and functional design. However, how this universal principle can enhance genotype-phenotype mapping of quantitative traits has remained unexplored. Here we show that the integration of this principle and functional mapping through evolutionary game theory gains new insight into the genetic architecture of complex traits. By viewing phenotype formation as an evolutionary system, we formulate mathematical equations to model the ecological mechanisms that drive the interaction and coordination of its constituent components toward population dynamics and stability. Functional mapping provides a procedure for estimating the genetic parameters that specify the dynamic relationship of competition and cooperation and predicting how genes mediate the evolution of this relationship during trait formation.

Plastic expression of heterochrony quantitative trait loci (hQTLs) for leaf growth in the common bean (Phaseolus vulgaris)

Heterochrony, that is, evolutionary changes in the relative timing of developmental events and processes, has emerged as a key concept that links evolution and development. Genes associated with heterochrony encode molecular components of developmental timing mechanisms. However, our understanding of how heterochrony genes alter the expression of heterochrony in response to environmental changes remains very limited. We applied functional mapping to find quantitative trait loci (QTLs) responsible for growth trajectories of leaf area and leaf mass in the common bean (Phaseolus vulgaris) grown in two contrasting environments. We identified three major QTLs pleiotropically expressed under the two environments. Further characterization of the temporal pattern of these QTLs indicates that they are heterochrony QTLs (hQTLs) in terms of their role in influencing four heterochronic parameters: the timing of the inflection point, the timing of maximum acceleration and deceleration, and the duration of linear growth. The pattern of gene action by the hQTLs on each parameter was unique, being environmentally dependent and varying between two allometrically related leaf growth traits. These results provide new insights into the complexity of genetic mechanisms that control trait formation in plants and provide novel findings that will be of use in studying the evolutionary trends.

2HiGWAS: a unifying high-dimensional platform to infer the global genetic architecture of trait development

Whole-genome search of genes is an essential approach to dissecting complex traits, but a marginal one-single-nucleotide polymorphism (SNP)/one-phenotype regression analysis widely used in current genome-wide association studies fails to estimate the net and cumulative effects of SNPs and reveal the developmental pattern of interplay between genes and traits. Here we describe a computational framework, which we refer to as two-side high-dimensional genome-wide association studies (2HiGWAS), to associate an ultrahigh dimension of SNPs with a high dimension of developmental trajectories measured across time and space. The model is implemented with a dual dimension-reduction procedure for both predictors and responses to select a sparse but full set of significant loci from an extremely large pool of SNPs and estimate their net time-varying effects on trait development. The model can not only help geneticists to precisely identify an entire set of genes underlying complex traits but also allow them to elucidate a global picture of how genes control developmental and dynamic processes of trait formation. We investigated the statistical properties of the model via extensive simulation studies. With the increasing availability of GWAS in various organisms, 2HiGWAS will have important implications for genetic studies of developmental compelx traits.