As a large plant-specific gene family, the NAC (NAM, ATAF1/2, and CUC2) transcription factor is related to plant growth, development, and response to abiotic stresses. Although the draft genome of garden asparagus (Asparagus officinalis) has been released, the genome-wide investigation of the NAC gene family is still unavailable. In this study, a total of 85 A. officinalis NAC genes were identified, and a comprehensive analysis of the gene family was performed, including physicochemical properties, phylogenetic relationship, chromosome localization, gene structure, conserved motifs, intron/exon, cis-acting elements, gene duplication, syntenic analysis, and differential gene expression analysis. The phylogenetic analysis demonstrated that there were 14 subgroups in both A. officinalis and Arabidopsis thaliana, and the genes with a similar gene structure and motif distribution were clustered in the same group. The cis-acting regulatory analysis of AoNAC genes indicated four types of cis-acting elements were present in the promoter regions, including light-responsive, hormone-responsive, plant-growth-and-development-related, and stress-responsive elements. The chromosomal localization analysis found that 81 NAC genes in A. officinalis were unevenly distributed on nine chromosomes, and the gene duplication analysis showed three pairs of tandem duplicated genes and five pairs of segmental duplications, suggesting that gene duplication is possibly associated with the amplification of the A. officinalis NAC gene family. The differential gene expression analysis revealed one and three AoNAC genes that were upregulated and downregulated under different types of salinity stress, respectively. This study provides insight into the evolution, diversity, and characterization of NAC genes in garden asparagus and will be helpful for future understanding of their biological roles and molecular mechanisms in plants.

Introduction: Interspecies interactions are a crucial driving force of species evolution. The genes of each coexisting species play a pivotal role in shaping the structure and function within the community, but how to identify them at the genome-wide level has always been challenging.Methods: In this study, we embed the Lotka-Volterra ordinary differential equations in the theory of community ecology into the systems mapping model, so that this model can not only describe how the quantitative trait loci (QTL) of a species directly affects its own phenotype, but also describe the QTL of the species how to indirectly affect the phenotype of its interacting species, and how QTL from different species affects community behavior through epistatic interactions.Results: By designing and implementing a co-culture experiment for 100 pairs of Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus), we mapped 244 significant QTL combinations in the interaction process of the two bacteria using this model, including 69 QTLs from E. coli and 59 QTLs from S. aureus, respectively. Through gene annotation, we obtained 57 genes in E. coli, among which the genes with higher frequency were ypdC, nrfC, yphH, acrE, dcuS, rpnE, and ptsA, while we obtained 43 genes in S. aureus, among which the genes with higher frequency were ebh, SAOUHSC_00172, capF, gdpP, orfX, bsaA, and phnE1.Discussion: By dividing the overall growth into independent growth and interactive growth, we could estimate how QTLs modulate interspecific competition and cooperation. Based on the quantitative genetic model, we can obtain the direct genetic effect, indirect genetic effect, and genome-genome epistatic effect related to interspecific interaction genes, and then further mine the hub genes in the QTL networks, which will be particularly useful for inferring and predicting the genetic mechanisms of community dynamics and evolution. Systems mapping can provide a tool for studying the mechanism of competition and cooperation among bacteria in co-culture, and this framework can lay the foundation for a more comprehensive and systematic study of species interactions.

Growth is the developmental process involving important genetic components. Functional mapping (FunMap) has been used as an approach to map quantitative trait loci (QTLs) governing growth trajectories by incorporating growth equations. FunMap is based on reductionism thinking, with a power to identify a small set of significant QTLs from the whole pool of genome-wide markers. Yet, increasing evidence shows that a complex trait is controlled by all genes the organism may possibly carry. Here, we describe and demonstrate a different mapping approach that encapsulates all markers into genetic interaction networks. This approach, symbolized as FunGraph, combines functional mapping, evolutionary game theory, and prey-predator theory into mathematical graphs, allowing the observed genetic effect of a locus to be decomposed into its independent component (resulting from this locus’ intrinsic capacity) and dependent component (due to extrinsic regulation by other loci). Using FunGraph, we can visualize and trace the roadmap of how each locus interact with every other locus to impact growth. In a population-based association study of Euphrates poplar, we use FunGraph to identify the previously neglected genetic interaction effects that contribute to the genetic architecture of juvenile stem growth. FunGraph could open up a novel gateway to comprehend the global genetic control mechanisms of complex traits.

In genetic studies, permutation tests serve as a cornerstone to estimate P-values. This is because researchers may design new test statistics without a known closed-form distribution, or the assumption of a well-established test may not hold. However, permutation tests require vast number of permutations which is proportional to the magnitude of the actual P-values. When it comes to genome-wide association studies where multiple-test corrections are routinely conducted, the actual P-values are extremely small, requiring a daunting number of permutations that may be beyond the available computational resources. Existing models that reduce the required number of permutations all assume a specific format of the test statistic to exploit its specific statistical properties. We propose Kernel-smoothed permutation which is a model-free method universally applicable to any statistic. Our tool forms the null distribution of test statistics using a kurtosis-driven transformation, followed by a kernel-based density estimation (KDE). We compared our Kernel-smoothed permutation to Naïve permutation using statistics from known closed-form null distributions. Based on three frequently used test statistics in association studies, i.e., t-test, sequence kernel association test (SKAT), and chi-squared test, we demonstrated that our model reduced the required number of permutations by a magnitude with the same or higher accuracy.