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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., Graphical Abstract, Highlights • We develop a new theory for complex-trait mapping by integrating behavioral ecology • This theory can characterize how QTL drive cooperation or competition in populations • It can also illustrate how the activation of QTL is driven by ecological interactions • The new theory leverages interdisciplinary studies of genetics, ecology, and evolution, Biological Sciences; Evolutionary Ecology; Mathematical Biosciences

Despite its critical importance to our understanding of plant growth and adaptation, the question of how environment-induced plastic response is affected genetically remains elusive. Previous studies have shown that the reaction norm of an organism across environmental index obeys the allometrical scaling law of part-whole relationships. The implementation of this phenomenon into functional mapping can characterize how quantitative trait loci (QTLs) modulate the phenotypic plasticity of complex traits to heterogeneous environments. Here, we assemble functional mapping and allometry theory through Lokta-Volterra ordinary differential equations (LVODE) into an R-based computing platform, np2 QTL, aimed to map and visualize phenotypic plasticity QTLs. Based on LVODE parameters, np2 QTL constructs a bidirectional, signed and weighted network of QTL-QTL epistasis, whose emergent properties reflect the ecological mechanisms for genotype-environment interactions over any range of environmental change. The utility of np2 QTL was validated by comprehending the genetic architecture of phenotypic plasticity via the reanalysis of published plant height data involving 3502 recombinant inbred lines of maize planted in multiple discrete environments. np2 QTL also provides a tool for constructing a predictive model of phenotypic responses in extreme environments relative to the median environment.

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.

Polyploidy is a common phenomenon among willow species. In this study, genome sequencing was conducted for Salix matsudana Koidz (also named Chinese willow), an important greening and arbor tree species, and the genome of this species was compared with those of four other tree species in Salicaceae. The total genome sequence of S. matsudana was 655.72 Mb in size, with repeated sequences accounting for 45.97% of the total length. In total, 531.43 Mb of the genome sequence could be mapped onto 38 chromosomes using the published genetic map as a reference. The genome of S. matsudana could be divided into two groups, the A and B genomes, through homology analysis with the genome of Populus trichocarpa, and the A and B genomes contained 23,985 and 25,107 genes, respectively. 4DTv combined transposon analysis predicted that allotetraploidy in S. matsudana appeared ~4 million years ago. The results from this study will help reveal the evolutionary history of S. matsudana and lay a genetic basis for its breeding.

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. F ST between the low- and high- altitude population was 0.00748, Q ST 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.

As overfertilization leads to environmental concerns and the cost of N fertilizer increases, the issue of how to select crop cultivars that can produce high yields on N-deficient soils has become crucially important. However, little information is known about the genetic mechanisms by which crops respond to environmental changes induced by N signaling. Here, we dissected the genetic architecture of N-induced phenotypic plasticity in bread wheat (Triticum aestivum L.) by integrating functional mapping and semiautomatic high-throughput phenotyping data of yield-related canopy architecture. We identified a set of quantitative trait loci (QTLs) that determined the pattern and magnitude of how wheat cultivars responded to low N stress from normal N supply throughout the wheat life cycle. This analysis highlighted the phenological landscape of genetic effects exerted by individual QTLs, as well as their interactions with N-induced signals and with canopy measurement angles. This information may shed light on our mechanistic understanding of plant adaptation and provide valuable information for the breeding of N-deficiency tolerant wheat varieties.

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.

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.

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.

Summary The developmental variation in stem height with respect to stem diameter is related to a broad range of ecological and evolutionary phenomena in trees, but the underlying genetic basis of this variation remains elusive. We implement a dynamic statistical model, functional mapping, to formulate a general procedure for the computational identification of quantitative trait loci (QTLs) that control stem height–diameter allometry during development. Functional mapping integrates the biological principles underlying trait formation and development into the association analysis of DNA genotype and endpoint phenotype, thus providing an incentive for understanding the mechanistic interplay between genes and development. Built on the basic tenet of functional mapping, we explore two core ecological scenarios of how stem height and stem diameter covary in response to environmental stimuli: (i) trees pioneer sunlit space by allocating more growth to stem height than diameter and (ii) trees maintain their competitive advantage through an inverse pattern. The model is equipped to characterize ‘pioneering’ QTLs (pi QTLs) and ‘maintaining’ QTLs (mi QTLs) which modulate these two ecological scenarios, respectively. In a practical application to a mapping population of full‐sib hybrids derived from two Populus species, the model has well proven its versatility by identifying several pi QTLs that promote height growth at a cost of diameter growth and several mi QTLs that benefit radial growth at a cost of height growth. Judicious application of functional mapping may lead to improved strategies for studying the genetic control of the formation mechanisms underlying trade‐offs among quantities of assimilates allocated to different growth parts.

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.

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.

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.

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.

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.

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.

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., 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.

De novo shoot regeneration is one of the important manifestations of cell totipotency in organogenesis, which reflects a survival strategy organism evolved when facing natural selection. Compared with tissue regeneration, and somatic embryogenesis, de novo shoot regeneration denotes a shoot regeneration process directly from detatched or injured tissues of plant. Studies on plant shoot regeneration had identified key genes mediating shoot regeneration. However, knowledge was derived from Arabidopsis, the regeneration capacity is hugely distinct among species. To achieve a comprehensive understanding of the shoot regeneration mechanism from tree species, we select four genetic lines of Populus euphratica from a natural population to be sequenced at transcriptome level. On the basis of the large difference of differentiation capacity, between the highly differentiated (HD) and low differentiated (LD) groups, the analysis of differential expression identified 4920 differentially expressed genes (DEGs), which were revealed in five groups of expression patterns by clustering analysis. Enrichment showed crucial pathways involved in regulation of regeneration difference, including &ldquo, plant hormone signal transduction&rdquo, &ldquo, cell differentiation&rdquo, "cellular response to auxin stimulus", and &ldquo, auxin-activated signaling pathway&rdquo, The expression of nine genes reported to be associated with shoot regeneration was validated using quantitative real-time PCR (qRT-PCR). For the specificity of regeneration mechanism with P. euphratica, large amount of DEGs involved in "plant-pathogen interaction", ubiquitin-26S proteosome mediated proteolysis pathway, stress-responsive DEGs, and senescence-associated DEGs were summarized to possibly account for the differentiation difference with distinct genotypes of P. euphratica. The result in this study helps screening of key regulators in mediating the shoot differentiation. The transcriptomic characteristic in P. euphratica further enhances our understanding of key processes affecting the regeneration capacity of de novo shoots among distinct species.

Vegetative phase changes in plants describes the transition between juvenile and adult phases of vegetative growth before flowering. It is one of the most fundamental mechanisms for plants to sense developmental signals, presenting a complex process involving many still-unknown determinants. Several studies in annual and perennial plants have identified the conservative roles of miR156 and its targets, SBP/SPL genes, in guiding the switch of plant growth from juvenile to adult phases. Here, we review recent progress in understanding the regulation of miR156 expression and how miR156-SPLs mediated plant age affect other processes in Arabidopsis. Powerful high-throughput sequencing techniques have provided rich data to systematically study the regulatory mechanisms of miR156 regulation network. From this data, we draw an expanded miR156-regulated network that links plant developmental transition and other fundamental biological processes, gaining novel and broad insight into the molecular mechanisms of plant-age-related processes in Arabidopsis.

Mei (Prunus mume) is an ornamental woody plant that has been domesticated in East Asia for thousands of years. High diversity in floral traits, along with its recent genome sequence, makes mei an ideal model system for studying the evolution of woody plants. Here, we investigate the genetic architecture of floral traits in mei and its domestication history by sampling and resequencing a total of 351 samples including 348 mei accessions and three other Prunus species at an average sequencing depth of 19.3×. Highly-admixed population structure and introgression from Prunus species are identified in mei accessions. Through a genome-wide association study (GWAS), we identify significant quantitative traits locus (QTLs) and genomic regions where several genes, such as MYB108, are positively associated with petal color, stigma color, calyx color, and bud color. Results from this study shed light on the genetic basis of domestication in flowering plants, particularly woody plants., 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.

Summary 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.

Abscisic acid (ABA) plays a fundamental role in plant response and adaptation to abiotic stresses, such as drought, high salinity and low temperature. Populus hopeiensis exhibits exceptional tolerance to water-deficit environments and is therefore an excellent choice for studying drought tolerance in trees. This study provides a global view of transcriptome dynamics in P. hopeiensis in response to exogenous ABA using Illumina RNA-sequencing. Endogenous ABA content increased and reached a peak at 8 h after ABA treatment and then significantly decreased at latter time points. Differential expression analysis and Gene ontology enrichment revealed that the number of transcripts exhibited significant increase during the first 8 hours after ABA treatment, which then significantly decreased at 12 and 24 h. Transcription factors (TFs) analysis showed that six different patterns were observed based on the expression of the six TFs families (AP2/ERF, NAC, MYB, MYB-related, bZIP and WRKY) and the majority of differentially expressed TFs increased rapidly after ABA treatment. This study provides a robust resource for investigating the functions of genes induced by ABA and will help to develop a better understanding of the molecular regulatory mechanism in response to drought in poplar.

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.

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 metabolism of an organism. Our model provides a tool to detect pleiotropy and epistasis that cause the size- and temperature-dependent change of metabolic rate.