A computational framework for mapping the timing of vegetative phase change

Exploiting light energy utilization strategies in Populus simonii through multitrait-GWAS: insights from stochastic differential models

The photosynthetic phenotype of trees undergoes changes and interactions that reflect their abilities to exploit light energy. Environmental disturbances and genetic factors have been recognized as influencing these changes and interactions, yet our understanding of the underlying biological mechanisms remains limited, particularly in stochastic environments. Here, we developed a high-dimensional stochastic differential framework (HDSD) for the genome-wide mapping of quantitative trait loci (QTLs) that regulate competition or cooperation in environment-dependent phenotypes. The framework incorporates random disturbances into system mapping, a dynamic model that views multiple traits as a system. Not only does this framework describe how QTLs regulate a single phenotype, but also how they regulate multiple phenotypes and how they interact with each other to influence phenotypic variations. To validate the proposed model, we conducted mapping experiments using chlorophyll fluorescence phenotype data from Populus simonii. Through this analysis, we identified several significant QTLs that may play a crucial role in photosynthesis in stochastic environments, in which 76 significant QTLs have already been reported to encode proteins or enzymes involved in photosynthesis through functional annotation. The constructed genetic regulatory network allows for a more comprehensive analysis of the internal genetic interactions of the photosynthesis process by visualizing the relationships between SNPs. This study shows a new way to understand the genetic mechanisms that govern the photosynthetic phenotype of trees, focusing on how environmental stochasticity and genetic variation interact to shape their light energy utilization strategies.

Learning Causes of Functional Dynamic Targets: Screening and Local Methods

This paper addresses the challenge of identifying causes for functional dynamic targets, which are functions of various variables over time. We develop screening and local learning methods to learn the direct causes of the target, as well as all indirect causes up to a given distance. We first discuss the modeling of the functional dynamic target. Then, we propose a screening method to select the variables that are significantly correlated with the target. On this basis, we introduce an algorithm that combines screening and structural learning techniques to uncover the causal structure among the target and its causes. To tackle the distance effect, where long causal paths weaken correlation, we propose a local method to discover the direct causes of the target in these significant variables and further sequentially find all indirect causes up to a given distance. We show theoretically that our proposed methods can learn the causes correctly under some regular assumptions. Experiments based on synthetic data also show that the proposed methods perform well in learning the causes of the target.

Analysis of Growth Trajectories and Verification of Related SNPs in Populus deltoides

As an important timber genus with high economic and ecological values, Populus is a model for dissecting the genetic architecture of growth traits in perennial forest trees. However, the genetic mechanisms of longitudinal growth traits in poplar remain incompletely understood. In this study, we conducted longitudinal genetic analysis of height and diameter at breast height (DBH) in eleven-year poplar clones using ultra-deep sequencing datasets. We compared four S-shaped growth models, including asymptotic, Gompertz, logistic, and Richard, on eleven-year height and DBH records in terms of five metrics. We constructed the best-fitting growth model (Richard) and determined poplar ontogenetic stages by virtue of growth curve fitting and likelihood ratio testing. This study provides some scientific clues for temporal variation of longitudinal growth traits in Populus species.

Natural variation in the prolyl 4-hydroxylase gene PtoP4H9 contributes to perennial stem growth in Populus

Perennial trees must maintain stem growth throughout their entire lifespan to progressively increase in size as they age. The overarching question of the molecular mechanisms that govern stem perennial growth in trees remains largely unanswered. Here we deciphered the genetic architecture that underlies perennial growth trajectories using genome-wide association studies (GWAS) for measures of growth traits across years in a natural population of Populus tomentosa. By analyzing the stem growth trajectory, we identified PtoP4H9, encoding prolyl 4-hydroxylase 9, which is responsible for the natural variation in the growth rate of diameter at breast height (DBH) across years. Quantifying the dynamic genetic contribution of PtoP4H9 loci to stem growth showed that PtoP4H9 played a pivotal role in stem growth regulation. Spatiotemporal expression analysis showed that PtoP4H9 was highly expressed in cambium tissues of poplars of various ages. Overexpression and knockdown of PtoP4H9 revealed that it altered cell expansion to regulate cell wall modification and mechanical characteristics, thereby promoting stem growth in Populus. We showed that natural variation in PtoP4H9 occurred in a BASIC PENTACYSTEINE transcription factor PtoBPC1-binding promoter element controlling PtoP4H9 expression. The geographic distribution of PtoP4H9 allelic variation was consistent with the modes of selection among populations. Altogether, our study provides important genetic insights into dynamic stem growth in Populus, and we confirmed PtoP4H9 as a potential useful marker for breeding or genetic engineering of poplars.

The genetic architecture of trait covariation in Populus euphratica, a desert tree

Introduction

The cooperative strategy of phenotypic traits during the growth of plants reflects how plants allocate photosynthesis products, which is the most favorable decision for them to optimize growth, survival, and reproduction response to changing environment. Up to now, we still know little about why plants make such decision from the perspective of biological genetic mechanisms.

Methods

In this study, we construct an analytical mapping framework to explore the genetic mechanism regulating the interaction of two complex traits. The framework describes the dynamic growth of two traits and their interaction as Differential Interaction Regulatory Equations (DIRE), then DIRE is embedded into QTL mapping model to identify the key quantitative trait loci (QTLs) that regulate this interaction and clarify the genetic effect, genetic contribution and genetic network structure of these key QTLs. Computer simulation experiment proves the reliability and practicability of our framework.

Results

In order to verify that our framework is universal and flexible, we applied it to two sets of data from Populus euphratica, namely, aboveground stem length - underground taproot length, underground root number - underground root length, which represent relationships of phenotypic traits in two spatial dimensions of plant architecture. The analytical result shows that our model is well applicable to datasets of two dimensions.

Discussion

Our model helps to better illustrate the cooperation-competition patterns between phenotypic traits, and understand the decisions that plants make in a specific environment that are most conducive to their growth from the genetic perspective.

A Multilayer Interactome Network Constructed in a Forest Poplar Population Mediates the Pleiotropic Control of Complex Traits

The effects of genes on physiological and biochemical processes are interrelated and interdependent; it is common for genes to express pleiotropic control of complex traits. However, the study of gene expression and participating pathways in vivo at the whole-genome level is challenging. Here, we develop a coupled regulatory interaction differential equation to assess overall and independent genetic effects on trait growth. Based on evolutionary game theory and developmental modularity theory, we constructed multilayer, omnigenic networks of bidirectional, weighted, and positive or negative epistatic interactions using a forest poplar tree mapping population, which were organized into metagalactic, intergalactic, and local interstellar networks that describe layers of structure between modules, submodules, and individual single nucleotide polymorphisms, respectively. These multilayer interactomes enable the exploration of complex interactions between genes, and the analysis of not only differential expression of quantitative trait loci but also previously uncharacterized determinant SNPs, which are negatively regulated by other SNPs, based on the deconstruction of genetic effects to their component parts. Our research framework provides a tool to comprehend the pleiotropic control of complex traits and explores the inherent directional connections between genes in the structure of omnigenic networks.

Genetic Architecture of Multiphasic Growth Covariation as Revealed by a Nonlinear Mixed Mapping Framework

Trait covariation during multiphasic growth is of crucial significance to optimal survival and reproduction during the entire life cycle. However, current analyses are mainly focused on the study of individual traits, but exploring how genes determine trait interdependence spanning multiphasic growth processes remains challenging. In this study, we constructed a nonlinear mixed mapping framework to explore the genetic mechanisms that regulate multiphasic growth changes between two complex traits and used this framework to study stem diameter and stem height in forest trees. The multiphasic nonlinear mixed mapping framework was implemented in system mapping, by which several key quantitative trait loci were found to interpret the process and pattern of stem wood growth by regulating the ecological interactions of stem apical and lateral growth. We quantified the timing and pattern of the vegetative phase transition between independently regulated, temporally coordinated processes. Furthermore, we visualized the genetic machinery of significant loci, including genetic effects, genetic contribution analysis, and the regulatory relationship between these markers in the network structure. We validated the utility of the new mapping framework experimentally via computer simulations. The results may improve our understanding of the evolution of development in changing environments.

Genomic insight into the divergence and adaptive potential of a forgotten landrace G. hirsutum L. purpurascens

Wild progenitors are an excellent source for strengthening the genetic basis and accumulation of desirable variation lost because of directional selection and adaptation in modern cultivars. Here, we re-evaluate a landrace of Gossypium hirsutum, formerly known as Gossypium purpurascens. Our study seeks to understand the genomic structure, variation, and breeding potential of this landrace, providing potential insights into the biogeographic history and genomic changes likely associated with domestication. A core set of accessions, including current varieties, obsolete accessions, G. purpurascens, and other geographical landraces, are subjected to genotyping along with multilocation phenotyping. Population fixation statistics suggests a marked differentiation between G. purpurascens and three other groups, emphasizing the divergent genomic behavior of G. purpurascens. Phylogenetic analysis establishes the primitive nature of G. purpurascens, identifying it as a vital source of functional variation, the inclusion of which in the upland cotton (cultivated G. hirsutum) gene pool may broaden the genetic basis of modern cultivars. Genome-wide association results indicate multiple loci associated with domestication regions corresponding to flowering and fiber quality. Moreover, the conserved nature of G. purpurascens can also provide insights into the evolutionary process of G. hirsutum.

Functional QTL mapping and genomic prediction of canopy height in wheat measured using a robotic field phenotyping platform

Genetic studies increasingly rely on high-throughput phenotyping, but the resulting longitudinal data pose analytical challenges. We used canopy height data from an automated field phenotyping platform to compare several approaches to scanning for quantitative trait loci (QTLs) and performing genomic prediction in a wheat recombinant inbred line mapping population based on up to 26 sampled time points (TPs). We detected four persistent QTLs (i.e. expressed for most of the growing season), with both empirical and simulation analyses demonstrating superior statistical power of detecting such QTLs through functional mapping approaches compared with conventional individual TP analyses. In contrast, even very simple individual TP approaches (e.g. interval mapping) had superior detection power for transient QTLs (i.e. expressed during very short periods). Using spline-smoothed phenotypic data resulted in improved genomic predictive abilities (5-8% higher than individual TP prediction), while the effect of including significant QTLs in prediction models was relatively minor (<1-4% improvement). Finally, although QTL detection power and predictive ability generally increased with the number of TPs analysed, gains beyond five or 10 TPs chosen based on phenological information had little practical significance. These results will inform the development of an integrated, semi-automated analytical pipeline, which will be more broadly applicable to similar data sets in wheat and other crops.

A dissection model for mapping complex traits

Many quantitative traits are composites of other traits that contribute differentially to genetic variation. Quantitative trait locus (QTL) mapping of these composite traits can benefit by incorporating the mechanistic process of how their formation is mediated by the underlying components. We propose a dissection model by which to map these interconnected components traits under a joint likelihood setting. The model can test how a composite trait is determined by pleiotropic QTLs for its component traits or jointly by different sets of QTLs each responsible for a different component. The model can visualize the pattern of time-varying genetic effects for individual components and their impacts on composite traits. The dissection model was used to map two composite traits, stemwood volume growth decomposed into its stem height, stem diameter and stem form components for Euramerican poplar adult trees, and total lateral root length constituted by its average lateral root length and lateral root number components for Euphrates poplar seedlings. We found the pattern of how QTLs for different components contribute to phenotypic variation in composite traits. The detailed understanding of the genetic machineries of composite traits will not only help in the design of molecular breeding in plants and animals, but also shed light on the evolutionary processes of quantitative traits under natural selection.

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.

Bayesian estimation and use of high-throughput remote sensing indices for quantitative genetic analyses of leaf growth

We develop Bayesian function-valued trait models that mathematically isolate genetic mechanisms underlying leaf growth trajectories by factoring out genotype-specific differences in photosynthesis. Remote sensing data can be used instead of leaf-level physiological measurements. Characterizing the genetic basis of traits that vary during ontogeny and affect plant performance is a major goal in evolutionary biology and agronomy. Describing genetic programs that specifically regulate morphological traits can be complicated by genotypic differences in physiological traits. We describe the growth trajectories of leaves using novel Bayesian function-valued trait (FVT) modeling approaches in Brassica rapa recombinant inbred lines raised in heterogeneous field settings. While frequentist approaches estimate parameter values by treating each experimental replicate discretely, Bayesian models can utilize information in the global dataset, potentially leading to more robust trait estimation. We illustrate this principle by estimating growth asymptotes in the face of missing data and comparing heritabilities of growth trajectory parameters estimated by Bayesian and frequentist approaches. Using pseudo-Bayes factors, we compare the performance of an initial Bayesian logistic growth model and a model that incorporates carbon assimilation (A _max) as a cofactor, thus statistically accounting for genotypic differences in carbon resources. We further evaluate two remotely sensed spectroradiometric indices, photochemical reflectance (pri2) and MERIS Terrestrial Chlorophyll Index (mtci) as covariates in lieu of A _max, because these two indices were genetically correlated with A _max across years and treatments yet allow much higher throughput compared to direct leaf-level gas-exchange measurements. For leaf lengths in uncrowded settings, including A _max improves model fit over the initial model. The mtci and pri2 indices also outperform direct A _max measurements. Of particular importance for evolutionary biologists and plant breeders, hierarchical Bayesian models estimating FVT parameters improve heritabilities compared to frequentist approaches.

Two-stage identification of SNP effects on dynamic poplar growth

This project proposes an approach to identify significant single nucleotide polymorphism (SNP) effects, both additive and dominant, on the dynamic growth of poplar in diameter and height. The annual changes in yearly phenotypes based on regular observation periods are considered to represent multiple responses. In total 156,362 candidate SNPs are studied, and the phenotypes of 64 poplar trees are recorded. To address this ultrahigh dimensionality issue, this paper adopts a two-stage approach. First, the conventional genome-wide association studies (GWAS) and the distance correlation sure independence screening (DC-SIS) methods (Li et al., 2012) were combined to reduce the model dimensions at the sample size; second, a grouped penalized regression was applied to further refine the model and choose the final sparse SNPs. The multiple response issue was also carefully addressed. The SNP effects on the dynamic diameter and height growth patterns of poplar were systematically analyzed. In addition, a series of intensive simulation studies was performed to validate the proposed approach.