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Tree growth is the consequence of developmental interactions between above- and below-ground compartments. However, a comprehensive view of the genetic architecture of growth as a cohesive whole is poorly understood. We propose a systems biology approach for mapping growth trajectories in genome-wide association studies viewing growth as a complex (phenotypic) system in which above- and below-ground components (or traits) interact with each other to mediate systems behavior. We further assume that trait–trait interactions are controlled by a genetic system composed of many different interactive genes and integrate the Lotka-Volterra predator–prey model to dissect phenotypic and genetic systems into pleiotropic and epistatic interaction components by which the detailed genetic mechanism of above- and below-ground co-growth can be charted. We apply the approach to analyze linkage mapping data of Populus euphratica, which is the only tree species that can grow in the desert, and characterize several loci that govern how above- and below-ground growth is cooperated or competed over development. We reconstruct multilayer and multiplex genetic interactome networks for the developmental trajectories of each trait and their developmental covariation. Many significant loci and epistatic effects detected can be annotated to candidate genes for growth and developmental processes. The results from our model may potentially be useful for marker-assisted selection and genetic editing in applied tree breeding programs. The model provides a general tool to characterize a complete picture of pleiotropic and epistatic genetic architecture in growth traits in forest trees and any other organisms.

IntroductionThe 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.MethodsIn 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.ResultsIn 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.DiscussionOur 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.

Background: Plants have evolved the dual capacity for maximizing light assimilation through stem growth (phototropism) and maximizing water and nutrient absorption through root growth (gravitropism). Previous studies have revealed the physiological and molecular mechanisms of these two processes, but the genetic basis for how gravitropism and phototropism interact and coordinate with one another to determine plant growth remains poorly understood. Methods: We designed a seed germination experiment using a full-sib F1 family of Populus euphratica to simultaneously monitor the gravitropic growth of the radicle and the phototropic growth of the plumule throughout seedling ontogeny. We implemented three functional mapping models to identify quantitative trait loci (QTLs) that regulate gravitropic and phototropic growth. Univariate functional mapping dissected each growth trait separately, bivariate functional mapping mapped two growth traits simultaneously, and composite functional mapping mapped the sum of gravitropic and phototropic growth as a main axis. Results: Bivariate model detected 8 QTLs for gravitropism and phototropism (QWRF, GLUR, F-box, PCFS4, UBQ, TAF12, BHLH95, TMN8), composite model detected 7 QTLs for growth of main axis (ATL8, NEFH, PCFS4, UBQ, SOT16, MOR1, PCMP-H), of which, PCFS4 and UBQ were pleiotropically detected with the both model. Many of these QTLs are situated within the genomic regions of candidate genes. Conclusions: The results from our models provide new insight into the mechanisms of genetic control of gravitropism and phototropism in a desert tree, and will stimulate our understanding of the relationships between gravity and light signal transduction pathways and tree adaptation to arid soil.

Heterophylly is an adaptive strategy used by some plants in response to environmental changes. Due to the lack of representative plants with typical heteromorphic leaves, little is known about the genetic architecture of heterophylly in plants and the genes underlying its control. Here, we investigated the genetic characteristics underlying changes in leaf shape based on the model species, Populus euphratica, which exhibits typical heterophylly. A set of 401,571 single-nucleotide polymorphisms (SNPs) derived from whole-genome sequencing of 860 genotypes were associated with nine leaf traits, which were related to descriptive and shape data using single- and multi-leaf genome-wide association studies (GWAS). Multi-leaf GWAS allows for a more comprehensive understanding of the genetic architecture of heterophylly by considering multiple leaves simultaneously. The single-leaf GWAS detected 140 significant SNPs, whereas the multi-leaf GWAS detected 200 SNP-trait associations. Markers were found across 19 chromosomes, and 21 unique genes were implicated in traits and serve as potential targets for selection. Our results provide novel insights into the genomic architecture of heterophylly, and provide candidate genes for breeding or engineering P. euphratica. Our observations also improve understanding of the intrinsic mechanisms of plant growth, evolution, and adaptation in response to climate change.

Developmental modularity, i.e., coherent organization and function of developmentally related traits, is an emergent property of organismic development and evolution. However, knowledge about how modular variation and evolution are driven genetically is still limited. Here, using ornamental plants as an example, we propose a computational framework to map, visualize and annotate the genetic architecture of trait modularity by integrating modularity theory into system mapping, a statistical model for multifaceted genetic mapping of complex traits. A developmental module can be viewed as an ecosystem, in which the constituting components compete for space and resources or cooperate symbiotically to organize its function and behavior. This interactive process is quantified by mathematical models and evolutionarily interpreted by game theory. The proposed framework can test whether and how genes regulate the coordination of different but interconnected traits through their competition or cooperation to downstream developmental modularity.

Abstract Plant adaptation to high altitudes has long been a substantial focus of ecological and evolutionary research. However, the genetic mechanisms underlying such adaptation remain poorly understood. Here, we address this issue by sampling, genotyping, and comparing populations of Tibetan poplar, Populus szechuanica var. tibetica, distributed from low (~2,000 m) to high altitudes (~3,000 m) of Sejila Mountain on the Qinghai–Tibet Plateau. Population structure analyses allow clear classification of two groups according to their altitudinal distributions. However, in contrast to the genetic variation within each population, differences between the two populations only explain a small portion of the total genetic variation (3.64%). We identified asymmetrical gene flow from high‐ to low‐altitude populations. Integrating population genomic and landscape genomic analyses, we detected two hotspot regions, one containing four genes associated with altitudinal variation, and the other containing ten genes associated with response to solar radiation. These genes participate in abiotic stress resistance and regulation of reproductive processes. Our results provide insight into the genetic mechanisms underlying high‐altitude adaptation in Tibetan poplar.

Abstract Background To maximize photosynthetic efficiency, plants have evolved a capacity by which leaf area scales allometrically with leaf mass through interactions with the environment. However, our understanding of genetic control of this allometric relationship remains limited. Results We integrated allometric scaling laws expressed at static and ontogenetic levels into genetic mapping to identify the quantitative trait loci (QTLs) that mediate how leaf area scales with leaf mass and how such leaf allometry, under the control of these QTLs, varies as a response to environment change. A major QTL detected by the static model constantly affects the allometric growth of leaf area vs. leaf mass for the common bean (Phaseolus vulgaris) in two different environments. The ontogenetic model identified this QTL plus a few other QTLs that determine developmental trajectories of leaf allometry, whose expression is contingent heavily upon the environment. Conclusions Our results gain new insight into the genetic mechanisms of how plants program their leaf morphogenesis to adapt to environmental perturbations.

Shade avoidance syndrome (SAS) refers to a set of plant responses that increases light capture in dense stands. This process is crucial for plants in natural and agricultural environments as they compete for resources and avoid suboptimal conditions. Although the molecular, biochemical, and physiological mechanisms underlying the SAS response have been extensively studied, the genetic basis of developmental variation in leaves in regard to leaf area, petiole length, and leaf length (i.e., their allometric relationships) remains unresolved. In this study, with the recombinant inbred line (RIL) population, the developmental traits of leaves of Arabidopsis were investigated under two growth density conditions (high- and low-density plantings). The observed changes were then reconstructed digitally, and their allometric relationships were modelled. Taking the genome-wide association analysis, the SNP genotype and the dynamic phenotype of the leaf from both densities were combined to explore the allometry QTLs. Under different densities, leaf change phenotype was analyzed from two core ecological scenarios: (i) the allometric change of the leaf area with leaf length, and (ii) the change of the leaf length with petiole length. QTLs modulating these two scenarios were characterized as ‘leaf shape QTLs’ and ‘leaf position QTLs’. With functional mapping, results showed a total of 30 and 24 significant SNPs for shapeQTLs and positionQTLs, respectively. By annotation, immune pathway genes, photosensory receptor genes, and phytohormone genes were identified to be involved in the SAS response. Interestingly, genes modulating the immune pathway and salt tolerance, i.e., systemic acquired resistance (SAR) regulatory proteins (MININ-1-related) and salt tolerance homologs (STH), were reported to mediate the SAS response. By dissecting and comparing QTL effects from low- and high-density conditions, our results elucidate the genetic control of leaf formation in the context of the SAS response. The mechanism with leaf development × density interaction can further aid the development of density-tolerant crop varieties for agricultural practices.

Background: Neonatal respiratory distress syndrome (RDS), due to surfactant deficiency in preterm infants, is the most common cause of respiratory morbidity. The surfactant proteins (SFTP) genetic variants have been well-studied in association with RDS; however, the impact of SNP-SNP (single nucleotide polymorphism) interactions on RDS has not been addressed. Therefore, this study utilizes a newer statistical model to determine the association of SFTP single SNP model and SNP-SNP interactions in a two and a three SNP interaction model with RDS susceptibility.Methods: This study used available genotype and clinical data in the Floros biobank at Penn State University. The patients consisted of 848 preterm infants, born

Phenotypic plasticity is the exhibition of various phenotypic traits produced by a single genotype in response to environmental changes, enabling organisms to adapt to environmental changes by maintaining growth and reproduction. Despite its significance in evolutionary studies, we still know little about the genetic control of phenotypic plasticity. In this study, we designed and conducted a genome-wide association study (GWAS) to reveal genetic architecture of how Staphylococcus aureus strains respond to increasing concentrations of vancomycin (0, 2, 4, and 6 μg/mL) in a time course. We implemented functional mapping, a dynamic model for genetic mapping using longitudinal data, to map specific loci that mediate the growth trajectories of abundance of vancomycin-exposed S. aureus strains. 78 significant single nucleotide polymorphisms were identified following analysis of the whole growth and development process, and seven genes might play a pivotal role in governing phenotypic plasticity to the pressure of vancomycin. These seven genes, SAOUHSC_00020 (walR), SAOUHSC_00176, SAOUHSC_00544 (sdrC), SAOUHSC_02998, SAOUHSC_00025, SAOUHSC_00169, and SAOUHSC_02023, were found to help S. aureus regulate antibiotic pressure. Our dynamic gene mapping technique provides a tool for dissecting the phenotypic plasticity mechanisms of S. aureus under vancomycin pressure, emphasizing the feasibility and potential of functional mapping in the study of bacterial phenotypic plasticity.

Summary: Community ecology theory suggests that an individual's phenotype is determined by the phenotypes of its coexisting members to the extent at which this process can shape community evolution. Here, we develop a mapping theory to identify interaction quantitative trait loci (QTL) governing inter-individual dependence. We mathematically formulate the decision-making strategy of interacting individuals. We integrate these mathematical descriptors into a statistical procedure, enabling the joint characterization of how QTL drive the strengths of ecological interactions and how the genetic architecture of QTL is driven by ecological networks. In three fish full-sib mapping experiments, we identify a set of genome-wide QTL that control a range of societal behaviors, including mutualism, altruism, aggression, and antagonism, and find that these intraspecific interactions increase the genetic variation of body mass by about 50%. We showcase how the interaction QTL can be used as editors to reconstruct and engineer new social networks for ecological communities. : Biological Sciences; Evolutionary Ecology; Mathematical Biosciences Subject Areas: Biological Sciences, Evolutionary Ecology, Mathematical Biosciences

Heterophylly, or leaf morphological changes along plant shoot axes, is an important indicator of plant eco-adaptation to heterogeneous microenvironments. Despite extensive studies on the genetic control of leaf shape, the genetic architecture of heterophylly remains elusive. To identify genes related to heterophylly and their associations with plant saline tolerance, we conducted a leaf shape mapping experiment using leaves from a natural population of Populus euphratica. We included 106 genotypes grown under salt stress and salt-free (control) conditions using clonal seedling replicates. We developed a shape tracking method to monitor and analyze the leaf shape using principal component (PC) analysis. PC1 explained 42.18% of the shape variation, indicating that shape variation is mainly determined by the leaf length. Using leaf length along shoot axes as a dynamic trait, we implemented a functional mapping-assisted genome-wide association study (GWAS) for heterophylly. We identified 171 and 134 significant quantitative trait loci (QTLs) in control and stressed plants, respectively, which were annotated as candidate genes for stress resistance, auxin, shape, and disease resistance. Functions of the stress resistance genes ABSCISIC ACIS-INSENSITIVE 5-like (ABI5), WRKY72, and MAPK3 were found to be related to many tolerance responses. The detection of AUXIN RESPONSE FACTOR17-LIKE (ARF17) suggests a balance between auxin-regulated leaf growth and stress resistance within the genome, which led to the development of heterophylly via evolution. Differentially expressed genes between control and stressed plants included several factors with similar functions affecting stress-mediated heterophylly, such as the stress-related genes ABC transporter C family member 2 (ABCC2) and ABC transporter F family member (ABCF), and the stomata-regulating and reactive oxygen species (ROS) signaling gene RESPIRATORY BURST OXIDASE HOMOLOG (RBOH). A comparison of the genetic architecture of control and salt-stressed plants revealed a potential link between heterophylly and saline tolerance in P. euphratica, which will provide new avenues for research on saline resistance-related genetic mechanisms.

As an important functional organ of plants, leaves alter their shapes in response to a changing environment. The variation of leaf shape has long been an important evolutionary and developmental force in plants. Despite an increasing amount of investigations into the genetic controls of leaf morphology, few have systematically studied the genetic architecture controlling shape differences among distinct altitudes. Altitude denotes a comprehensive complex of environmental factors affecting plant growth in many aspects, e.g., UV-light radiation, temperature, and humidity. To reveal how plants alter ecological adaptation to altitude through genes, we used Populus szechuanica var. tibetica growing on the Qinghai-Tibetan plateau. FST between the low- and high- altitude population was 0.00748, QST for leaf width, length and area were 0.00924, 0.1108, 0.00964 respectively. With the Elliptic Fourier-based morphometric model, association study of leaf shape was allowed, the dissection of the pleiotropic expression of genes mediating altitude-derived leaf shape variation was performed. For high and low altitudes, 130 and 131 significant single-nucleotide polymorphisms (SNPs) were identified. QTLs that affected leaf axis length and leaf width were expressed in both-altitude population, while QTLs regulating “leaf tip” and “leaf base” were expressed in low-altitude population. Pkinase and PRR2 were common significant genes in both types of populations. Auxin-related and differentiation-related genes included PIN1, CDK-like, and CAK1AT at high altitude, whereas they included NAP5, PIN-LIKES, and SCL1 at low altitude. The presence of Stress-antifung gene, CIPK3 and CRPK1 in high-altitude population suggested an interaction between genes and harsh environment in mediating leaf shape, while the senescence repression-related genes (EIN2 and JMJ18) and JMT in jasmonic acid pathway in low-altitude population suggested their crucial roles in ecological adaptability. These data provide new information that strengthens the understanding of genetic control with respect to leaf shape and constitute an entirely novel perspective regarding leaf adaptation and development in plants.

Genetic variation from coexisting species influences interspecific interactions in a community. Here, the authors develop a framework for identifying quantitative trait loci (QTLs) underlying community dynamics and validate the tool using data from co-culturing of two bacterial species.

Mei (Prunus mume) is a woody tree that produces ornamental blossoms which symbolize spring in East Asia. Here, Zhang et al. resequence wild and domesticated mei to reveal considerable admixture and introgression from other Prunus species and identify loci associated with floral traits.

Crossover (CO) interference is a universal phenomenon by which the occurrence of one CO event inhibits the simultaneous occurrence of other COs along a chromosome. Because of its critical role in the evolution of genome structure and organization, the cytological and molecular mechanisms underlying CO interference have been extensively investigated. However, the genome-wide distribution of CO interference and its interplay with sex-, stress-, and age-induced differentiation remain poorly understood. Multi-point linkage analysis has proven to be a powerful tool for landscaping CO interference, especially within species for which CO mutants are rarely available. We implemented four-point linkage analysis to landscape a detailed picture of how CO interference is distributed through the entire genome of Populus euphratica, the only forest tree that can survive and grow in saline desert. We identified an extensive occurrence of CO interference, and found that its strength depends on the length of chromosomes and the genomic locations within the chromosome. We detected high-order CO interference, possibly suggesting a highly complex mechanism crucial for P. euphratica to grow, reproduce, and evolve in its harsh environment.

Phenotypic plasticity enables individuals to develop different phenotypes in a changing environment and promotes adaptive evolution. Genome-wide association study (GWAS) facilitates the study of the genetic basis of bacterial phenotypes, and provides a new opportunity for bacterial phenotypic plasticity research. To investigate the relationship between growth plasticity and genotype in bacteria, 41 Staphylococcus aureus strains, including 29 vancomycin-intermediate S. aureus (VISA) strains, were inoculated in the absence or presence of vancomycin for 48 h. Growth curves and maximum growth rates revealed that strains with the same minimum inhibitory concentration (MIC) showed different levels of plasticity in response to vancomycin. A bivariate GWAS was performed to map single-nucleotide polymorphisms (SNPs) associated with growth plasticity. In total, 227 SNPs were identified from 14 time points, while 15 high-frequency SNPs were mapped to different annotated genes. The P-values and growth variations between the two cultures suggest that non-coding region (SNP 738836), ebh (SNP 1394043), drug transporter (SNP 264897), and pepV (SNP 1775112) play important roles in the growth plasticity of S. aureus. Our study provides an alternative strategy for dissecting the adaptive growth of S. aureus in vancomycin and highlights the feasibility of bivariate GWAS in bacterial phenotypic plasticity research.

Surfactant proteins (SP) are involved in surfactant function and innate immunity in the human lung. Both lung function and innate immunity are altered in CF, and altered SP levels and genetic association are observed in Cystic Fibrosis (CF). We hypothesized that single nucleotide polymorphisms (SNPs) within the SP genes associate with CF or severity subgroups, either through single SNP or via SNP-SNP interactions between two SNPs of a given gene (intragenic) and/or between two genes (intergenic). We genotyped a total of 17 SP SNPs from 72 case-trio pedigree (SFTPA1 (5), SFTPA2 (4), SFTPB (4), SFTPC (2), and SFTPD (2)), and identified SP SNP associations by applying quantitative genetic principles. The results showed (a) Two SNPs, SFTPB rs7316 (p = 0.0083) and SFTPC rs1124 (p = 0.0154), each associated with CF. (b) Three intragenic SNP-SNP interactions, SFTPB (rs2077079, rs3024798), and SFTPA1 (rs1136451, rs1059057 and rs4253527), associated with CF. (c) A total of 34 intergenic SNP-SNP interactions among the 4 SP genes to be associated with CF. (d) No SNP-SNP interaction was observed between SFTPA1 or SFTPA2 and SFTPD. (e) Equal number of SNP-SNP interactions were observed between SFTPB and SFTPA1/SFTPA2 (n = 7) and SP-B and SFTPD (n = 7). (f) SFTPC exhibited significant SNP-SNP interactions with SFTPA1/SFTPA2 (n = 11), SFTPB (n = 4) and SFTPD (n = 3). (g) A single SFTPB SNP was associated with mild CF after Bonferroni correction, and several intergenic interactions that are associated (p < 0.01) with either mild or moderate/severe CF were observed. These collectively indicate that complex SNP-SNP interactions of the SP genes may contribute to the pulmonary disease in CF patients. We speculate that SPs may serve as modifiers for the varied progression of pulmonary disease in CF and/or its severity.

How a species responds to such a biotic environment in the community, ultimately leading to its evolution, has been a topic of intense interest to ecological evolutionary biologists. Until recently, limited knowledge was available regarding the genotypic changes that underlie phenotypic changes. Our study implemented GWAS (Genome-Wide Association Studies) to illustrate the genetic architecture of ecological interactions that take place in microbial populations. By choosing 45 such interspecific pairs of Escherichia coli and Staphylococcus aureus strains that were all genotyped throughout the entire genome, we employed Q-ROADTRIPS to analyze the association between single SNPs and microbial abundance measured at each time point for bacterial populations reared in monoculture and co-culture, respectively. We identified a large number of SNPs and indels across the genomes (35.69 G clean data of E. coli and 50.41 G of S. aureus). We reported 66 and 111 SNPs that were associated with interaction in E. coli and S. aureus, respectively. 23 out of 66 polymorphic changes resulted in amino acid alterations.12 significant genes, such as murE, treA, argS, and relA, which were also identified in previous evolutionary studies. In S. aureus, 111 SNPs detected in coding sequences could be divided into 35 non-synonymous and 76 synonymous SNPs. Our study illustrated the potential of genome-wide association methods for studying rapidly evolving traits in bacteria. Genetic association study methods will facilitate the identification of genetic elements likely to cause phenotypes of interest and provide targets for further laboratory investigation.

We studied associations of persistent respiratory morbidity (PRM) at 6 and 12 months after acute respiratory failure (ARF) in previously healthy children with single-nucleotide polymorphisms (SNPs) of surfactant protein (SP) genes. Of the 250 enrolled subjects, 155 and 127 were followed at 6 and 12 months after an ARF episode, respectively. Logistic regression analysis and SNP–SNP interaction models were used. We found that 1) in the multivariate analysis, an increased risk at 6 and 12 months was associated with rs1124_A and rs4715_A of SFTPC , respectively; 2) in a single SNP model, increased and decreased risks of PRM at both timepoints were associated with rs1124 of SFTPC and rs721917 of SFTPD , respectively; an increased risk at 6 months was associated with rs1130866 of SFTPB and rs4715 of SFTPC , and increased and decreased risks at 12 months were associated with rs17886395 of SFTPA2 and rs2243639 of SFTPD , respectively; 3) in a two-SNP model, PRM susceptibility at both timepoints was associated with a number of intergenic interactions between SNPs of the studied SP genes. An increased risk at 12 months was associated with one intragenic (rs1965708 and rs113645 of SFTPA2) interaction; 4) in a three-SNP model, decreased and increased risks at 6 and 12 months, respectively, were associated with an interaction among rs1130866 of SFTPB , rs721917 of SFTPD , and rs1059046 of SFTPA2. A decreased risk at 6 months was associated with an interaction among the same SNPs of SFTPB and SFTPD and the rs1136450 of SFTPA1. The findings revealed that SNPs of all SFTP s appear to play a role in long-term outcomes of ARF survivors and may serve as markers for disease susceptibility. [ABSTRACT FROM AUTHOR]

Allopolyploids are a group of polyploids with more than two sets of chromosomes derived from different species. Previous linkage analysis of allopolyploids is based on the assumption that different chromosomes pair randomly during meiosis. Amore sophisticated model to relax this assumption has been developed for allotetraploids by incorporating the preferential pairing behavior of homologous over homoeologous chromosomes. Here, we show that the basic principle of this model can be extended to perform linkage analysis of higher-ploidy allohexaploids, where multiple preferential pairing factors are used to characterize chromosomal-pairing meiotic features between different constituent species. We implemented the extended model into an R package, called AlloMap6, allowing the recombination fractions and preferential pairing factors to be estimated simultaneously. Allomap6 has two major functionalities, computer simulation and real-data analysis. By analyzing a real data from a full-sib family of allohexaploid persimmon, we tested and validated the usefulness and utility of this package. AlloMap6 lays a foundation for allohexaploid genetic mapping and provides a new horizon to explore the chromosomal kinship of allohexaploids. [ABSTRACT FROM AUTHOR]

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 genomewide 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 highthroughput 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. [ABSTRACT FROM AUTHOR]

Linkage analysis has played an important role in understanding genome structure and evolution. However, two-point linkage analysis widely used for genetic map construction can rarely chart a detailed picture of genome organization because it fails to identify the dependence of crossovers distributed along the length of a chromosome, a phenomenon known as crossover interference. Multi-point analysis, proven to be more advantageous in gene ordering and genetic distance estimation for dominant markers than two-point analysis, is equipped with a capacity to discern and quantify crossover interference. Here, we review a statistical model for four-point analysis, which, beyond three-point analysis, can characterize crossover interference that takes place not only between two adjacent chromosomal intervals, but also over multiple successive intervals. This procedure provides an analytical tool to elucidate the detailed landscape of crossover interference over the genome and further infer the evolution of genome structure and organization. [ABSTRACT FROM AUTHOR]

Whole-organism metabolic rate co-varies allometrically with body mass, and is also affected by temperature through different biochemical mechanisms. Here we implement a computational platform to map specific quantitative trait loci (QTLs) that govern the dependence of metabolic rate on size and temperature. The model was formulated within settings of genetic mapping or genome-wide association studies through a mapping population genotyped by a set of molecular markers throughout the genome and phenotyped for metabolic parameters over a range of temperature. The model, estimated by a maximum-likelihood approach, allows a genome-wide search for the underlying metabolic QTLs and the estimation of genotype-specific parameters that specify the metabolismof an organism. Our model provides a tool to detect pleiotropy and epistasis that cause the size- and temperature-dependent change of metabolic rate. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Unlike annuals, all perennial plants undergo seasonal transitions during ontogeny . As an adaptive response to seasonal changes in climate, the seasonal pattern of growth is likely to be under genetic control, although its underlying genetic basis remains unknown. Here, we develop a computational model that can map specific quantitative trait loci (QTLs) responsible for seasonal transitions of growth in perennials. The model is founded on functional mapping, a statistical framework to map developmental dynamics, which is reformed to integrate a seasonally adjusted growth function. The new model is equipped with a capacity to characterize the genetic effects of QTLs on seasonal alternation at different ages and then to better elucidate the genetic architecture of development. The model is implemented with a series of testing procedures, including (i) how a QTL controls an overall ontogenetic growth curve, (ii) how the QTL determines seasonal trajectories of growth within years and (iii) how it determines the dynamic nature of age-specific season response. The model was validated through computer simulation. The extension of season adjustment to other types of biological curves is statistically straightforward, facilitating a wider variety of genetic studies into ontogenetic growth and development in perennial plants. [ABSTRACT FROM AUTHOR]

Dynamic changes of gene expression reflect an intrinsic mechanism of how an organism responds to developmental and environmental signals. With the increasing availability of expression data across a time-space scale by RNA-seq, the classification of genes as per their biological function using RNA-seq data has become one of the most significant challenges in contemporary biology. Here we develop a clustering mixture model to discover distinct groups of genes expressed during a period of organ development. By integrating the density function of multivariate Poisson distribution, the model accommodates the discrete property of read counts characteristic of RNA-seq data. The temporal dependence of gene expression is modeled by the first-order autoregressive process. The model is implemented with the Expectation-Maximization algorithm and model selection to determine the optimal number of gene clusters and obtain the estimates of Poisson parameters that describe the pattern of time-dependent expression of genes from each cluster. The model has been demonstrated by analyzing a real data from an experiment aimed to link the pattern of gene expression to catkin development in white poplar. The usefulness of the model has been validated through computer simulation. The model provides a valuable tool for clustering RNA-seq data, facilitating our global view of expression dynamics and understanding of gene regulation mechanisms. [ABSTRACT FROM AUTHOR]

MADS-box gene, designated PtAP3, was isolated from a floral bud cDNA library derived from Populus tomentosa. Analysis by multiple alignments of both nucleotide and amino acid sequences, together with phylogenetic analysis, revealed that PtAP3 is an ortholog of Arabidopsis AP3. Analysis of RNA extracts from vegetative and reproductive tissues of P. tomentosa by RT-PCR indicated that PtAP3 is expressed in roots, stems, leaves and vegetative and floral buds. Notably, the expression of PtAP3 fluctuated during floral bud development between September and February with differences between male and female buds. In the former, a gradual down-regulation during this period, interrupted by a slight up-regulation in December, was followed by a sharper up-regulation on February. In developing female floral buds, expression was stable from September to November, sharply up-regulated in December, and then gradually down-regulated until February. The functional role of PtAP3 was investigated in transgenic tobacco plants. Of 25 transformants, nine displayed an earlier flowering phenotype compared with the wild type plants. Furthermore, transgenic tobacco had faster growth and more leaves than untransformed controls. The traits proved to be heritable between the T0 and T1 generations. Our results demonstrate a regulatory role of the PtAP3 gene during plant flowering and growth and suggest that the gene may be an interesting target for genetic modification to induce early flowering in plants. [ABSTRACT FROM AUTHOR]