The gut microbiota may play an important role in affecting human health. To explore the genetic mechanisms underlying microbiota-host relationships, many genome-wide association studies have begun to identify host genes that shape the microbial composition of the gut. It is becoming increasingly clear that the gut microbiota impacts host processes not only through the action of individual microbes but also their interaction networks. However, a systematic characterization of microbial interactions that occur in densely packed aggregates of the gut bacteria has proven to be extremely difficult. We develop a computational rule of thumb for addressing this issue by integrating ecological behavioral theory and genetic mapping theory. We introduce behavioral ecology theory to derive mathematical descriptors of how each microbe interacts with every other microbe through a web of cooperation and competition. We estimate the emergent properties of gut-microbiota networks reconstructed from these descriptors and map host-driven mutualism, antagonism, aggression, and altruism QTLs. We further integrate path analysis and mapping theory to detect and visualize how host genetic variants affect human diseases by perturbing the internal workings of the gut microbiota. As the proof of concept, we apply our model to analyze a published dataset of the gut microbiota, showing its usefulness and potential to gain new insight into how microbes are organized in human guts. The new model provides an analytical tool for revealing the “endophenotype” role of microbial networks in linking genotype to end-point phenotypes.

Identifying general biological rules that underlie the complexity and heterogeneity of microbial communities has proven to be highly challenging. We present a rule-of-thumb framework for studying and characterizing how microbes interact with each other across different taxa to determine community behavior and dynamics. This framework is computationally simple but conceptually meaningful, and it can provide a starting point to generate novel biological hypotheses about microbial interactions and explore internal workings of microbial community assembly in depth.

Covariation between organ growth and biomass accumulation plays an important role in plants. Plant to capture optimal fitness in nature, which depend coordinate and interact for distinct organs such as leaves, stems, and roots. Although many studies have focused on plant growth or biomass allocation, detailed information on the genetic mechanism of coordinated variation is lacking. Here, we expand a new mapping model based on functional mapping to detect covariation quantitative trait loci (QTLs) that govern development of plant organs and whole biomass, which, via a series of hypothesis tests, allows quantification of how QTLs regulate covariation between organ growth and biomass accumulation. The model was implemented to analyze leaf number data and the whole dry weight of recombinant inbred lines (RILs) of Arabidopsis . Two key QTLs related to growth and biomass allocation that reside within biologically meaningful genes, CRA1 and HIPP25 , are characterized. These two genes may control covariation between two traits. The new model will enable the elucidation of the genetic architecture underlying growth and biomass accumulation, which may enhance our understanding of fitness development in plants.

Despite its importance in understanding the emergent property of plant communities and ecosystems, the question of how genes govern species coexistence has proven very difficult to answer. In a plant community that behaves like a network game, each coexisting plant strives to maximize its fitness by pursuing a “rational self‐interest” strategy in a way that affects the decisive reaction of other plants. We integrated this principle founding game theory into a quantitative trait locus (QTL) mapping paradigm, on which to derive a game mapping model for the genetic landscaping of how plants coexist. The new mapping model dissolves the phenotype of each plant in a community into two components, autonomous phenotype, characteristic of the plant's intrinsic ability expected to be expressed in isolation, and social phenotype, determined by game theory‐guided interactions between the plant and other members. We implemented the new model into a competition experiment by pairwise growing 116 recombinant inbred lines of Arabidopsis. Most QTLs detected from this experiment reside within biologically meaningful genes, including SCL6, CAR6, CLPB1, ALDH5F1, and EMB2217, which may mediate competitive interactions in unique ways. The new model can chart more detailed genetic architecture of plant community structure and diversity by extracting the genetic effects of QTLs on social phenotypes. Our model lays the groundwork for predicting and managing dynamic relationships between biodiversity and ecosystem functioning from co‐species genotypes.

How one trait developmentally varies as a function of others shapes a spectrum of biological phenomena. Despite its importance to trait dissection, the understanding of whether and how genes mediate such developmental covariation is poorly understood. We integrate developmental allometry equations into the functional mapping framework to map specific QTLs that govern the correlated development of different traits. Based on evolutionary game theory, we assemble and contextualize these QTLs into an intricate but organized network coded by bidirectional, signed, and weighted QTL-QTL interactions. We use this approach to map shoot height-diameter allometry QTLs in an ornamental woody species, mei (Prunus mume). We detect "pioneering" QTLs (piQTLs) and "maintaining" QTLs (miQTLs) that determine how shoot height varies with diameter and how shoot diameter varies with height, respectively. The QTL networks inferred can visualize how each piQTL regulates others to promote height growth at a cost of diameter growth, how miQTL regulates others to benefit radial growth at a cost of height growth, and how piQTLs and miQTLs regulate each other to form a pleiotropic web of primary and secondary growth in trees. Our approach provides a unique gateway to explore the genetic architecture of developmental covariation, a widespread phenomenon in nature.