Over the past century, environmental changes have significantly impacted wheat spike morphology, crucial for adaptation and grain yield. However, the changes in wheat spike modifications during this period remain largely unknown. This study examines 16 spike morphology traits in 830 accessions released from 1900 to 2020. It finds that spike weight, grain number per spike (GN), and thousand kernel weight have significantly increased, while spike length has no significant change. The increase in fertile spikelets is due to fewer degenerated spikelets, resulting in a higher GN. Genome-wide association studies identified 49,994 significant SNPs, grouped into 293 genomic regions. The accumulation of favorable alleles in these genomic regions indicates the genetic basis for modification in spike morphology traits. Genetic network analysis of these genomic regions reveals the genetic basis for phenotypic correlations among spike morphology traits. The haplotypes of the identified genomic regions display obvious geographical differentiation in global accessions and environmental adaptation over the past 120 years. In summary, we reveal the genetic basis of adaptive evolution and the interactions of spike morphology, offering valuable resources for the genetic improvement of spike morphology to enhance environmental adaptation.

Precision medicine, the utilization of targeted treatments to address an individual\'s disease, relies on knowledge about the genetic cause of that individual\'s drug response. Here, we present a functional graph (FunGraph) theory to chart comprehensive pharmacogenetic architecture for each and every patient. FunGraph is the combination of functional mapping - a dynamic model for genetic mapping and evolutionary game theory guiding interactive strategies. It coalesces all pharmacogenetic factors into multilayer and multiplex networks that fully capture bidirectional, signed and weighted epistasis. It can visualize and interrogate how epistasis moves in the cell and how this movement leads to patient- and context-specific genetic architecture in response to organismic physiology. We discuss the future implementation of FunGraph to achieve precision medicine.

BACKGROUND: Indirect interactions between individual solitary mammals, such as the giant panda, are often overlooked because of their nature, yet are important for maintaining the necessary sociality in solitary species.
RESULTS: Here, we determined the genetic identity of all giant panda individuals in a local population and matched these identities with their associations to determine social network of this solitary animal. Total thirty-five giant panda individuals were found in our field survey, and we constructed genetic and social networks for thirty-three individuals who successfully obtained genetic, age and sex information. The results showed that sex had great impact on both social network and genetic network, and age may have the potential to influence the social network of the giant pandas. Adult males, mostly in the central of the social network, which appeared significantly larger network connections than adult females. Due to the female-biased dispersal pattern of wild giant pandas, male-male pairs showed higher relatedness than female-female ones and multi-generational patrilinear assemblages are expected in the study area.
CONCLUSIONS: The relatedness of individuals has an influence on the formation of community social structure of giant pandas, and indirect interactions among solitary giant pandas potentially function to reduce competition for resources and inbreeding.

UNASSIGNED: This study is used to analyze the genetic network of HIV sexual transmission in rural areas of Southwest China after expanding antiretroviral therapy (ART) and to investigate the factors associated with HIV sexual transmission through the genetic network.
UNASSIGNED: This was a longitudinal genetic network study in Guangxi, China. The baseline survey and follow-up study were conducted among patients with HIV in 2015, and among those newly diagnosed from 2016 to 2018, respectively. A generalized estimating equation model was employed to explore the factors associated with HIV transmission through the genetic linkage between newly diagnosed patients with HIV (2016-2018) and those at baseline (2015-2017), respectively.
UNASSIGNED: Of 3,259 identified HIV patient sequences, 2,714 patients were at baseline, and 545 were newly diagnosed patients with HIV at follow-up. A total of 8,691 baseline objectives were observed by repeated measurement analysis. The prevention efficacy in HIV transmission for treated HIV patients was 33% [adjusted odds ratio (AOR): 0.67, 95% confidence interval (CI): 0.48-0.93]. Stratified analyses indicated the prevention efficacy in HIV transmission for treated HIV patients with a viral load (VL) of <50 copies/ml and those treated for 4 years with a VL of <50 copies/ml to be 41 [AOR: 0.59, 95% CI: 0.43-0.82] and 65% [AOR: 0.35, 95% CI: 0.24-0.50], respectively. No significant reduction in HIV transmission occurred among treated HIV patients with VL missing or treated HIV patients on dropout. Some factors were associated with HIV transmission, including over 50 years old, men, Zhuang and other nationalities, with less than secondary schooling, working as a farmer, and heterosexual transmission.
UNASSIGNED: This study reveals the role of ART in reducing HIV transmission, and those older male farmers with less than secondary schooling are at high risk of HIV infection at a population level. Improvements to ART efficacy for patients with HIV and precision intervention on high-risk individuals during the expansion of ART are urgently required.

Exploring the dynamic variations of viral genomes utilizing with a phylogenetic analysis is vital to control the pandemic and stop its waves. Genetic network can be applied to depict the complicated evolution relationships of viral genomes. However, current phylogenetic methods cannot handle the cases with deletions effectively. Therefore, the k-mer natural vector is employed to characterize the compositions and distribution features of k-mers occurring in a viral genome, and construct a one-to-one relationship between a viral genome and its k-mer natural vector. Utilizing the k-mer natural vector, we proposed a novel genetic network to investigate the variations of viral genomes in transmission among humans. With the assistance of genetic network, we identified the super-spreaders that were responsible for the pandemic outbreaks all over the world and chose the parental strains to evaluate the effectiveness of diagnostics, therapeutics, and vaccines. The obtaining results fully demonstrated that our genetic network can truly describe the relationships of viral genomes, effectively simulate virus spread tendency, and trace the transmission routes precisely. In addition, this work indicated that the k-mer natural vector has the ability to capture established hotspots of diversities existing in the viral genomes and understand how genomic contents change over time.

The interaction between the microbial communities in aquatic animals and those in the ambient environment is important for both healthy aquatic animals and the ecological balance of aquatic environment. Crayfish (Procambarus clarkii), with their high commercial value, have become the highest-yield freshwater shrimp in China. The traditional cultivation in ponds (i.e., monoculture, MC) and emerging cultivation in rice co-culture fields (i.e., rice-crayfish co-culture, RC) are the two main breeding modes for crayfish, and the integrated RC is considered to be a successful rice-livestock integration practice in eco-agricultural systems. This study explored the ecological interactions between the microbial communities in crayfish intestine and the ambient environment, which have not been fully described to date. The bacterial communities in crayfish intestine, the surrounding water, and sediment in the two main crayfish breeding modes were analyzed with MiSeq sequencing and genetic networks. In total, 53 phyla and 1,206 genera were identified, among which Proteobacteria, Actinobacteria, Tenericutes, Firmicutes, Cyanobacteria, Chloroflexi, Bacteroidetes, Acidobacteria, RsaHF231, and Nitrospirae were the dominant phyla. The microbiota composition significantly differed between the water, sediment, and crayfish intestine, while it did not between the two breeding modes. We also generated a co-occurrence correlation network based on the high-confidence interactions with Spearman correlation ρ ≥ 0.75. In the genera co-correlation network, 95 nodes and 1,158 edges were identified, indicating significant genera interactions between crayfish intestine and the environment. Furthermore, the genera clustered into three modules, based on the different environments. Additionally, Candidatus_Bacilloplasma, g_norank_f_Steroidobacteraceae, Dinghuibacter, Hydrogenophaga, Methyloparacoccus, and Defluviicoccus had the highest betweenness centrality and might be important in the interaction between crayfish and the ambient environment. Overall, this study enhances our understanding of the characteristics of the microbiota in crayfish and their surrounding environment. Moreover, our findings provide insights into the microecological balance in crayfish eco-agricultural systems and theoretical reference for the development of such systems.

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.

Substantial diversity exists for both the size and shape of the leaf, the main photosynthetic organ of flowering plants. The two major forms of leaf are simple leaves, in which the leaf blade is undivided, and compound leaves, which comprise several leaflets. Leaves form at the shoot apical meristem from a group of undifferentiated cells, which first establish polarity, then grow and differentiate. Each of these processes is controlled by a combination of transcriptional regulators, microRNAs and phytohormones. The present review documents recent advances in our understanding of how these various factors modulate the development of both simple leaves (focusing mainly on the model plant Arabidopsis thaliana) and compound leaves (focusing mainly on the model legume species Medicago truncatula).

Rice and maize are the principal food crop species worldwide. The mechanism of gene regulation for the yield of rice and maize is still the research focus at present. Seed size, weight and shape are important traits of crop yield in rice and maize. Most members of three gene families, APETALA2/ethylene response factor, auxin response factors and MADS, were identified to be involved in yield traits in rice and maize. Analysis of molecular regulation mechanisms related to yield traits provides theoretical support for the improvement of crop yield. Genetic regulatory network analysis can provide new insights into gene families with the improvement of sequencing technology. Here, we analyzed the evolutionary relationships and the genetic regulatory network for the gene family members to predicted genes that may be involved in yield-related traits in rice and maize. The results may provide some theoretical and application guidelines for future investigations of molecular biology, which may be helpful for developing new rice and maize varieties with high yield traits.

Callus differentiation is a key developmental process in plant regeneration from cells. A better understanding of the genetic architecture of callus differentiation timing can help improve tissue transformation and the efficiency of artificial propagation. In this study, we investigated genotypic variation in callus differentiation capacity among 297 diverse P. euphratica trees sampled from a natural population. We employed a genome-wide association study (GWAS) of binary and growth-based parameters to identify loci and characterize the genetic architecture and genetic network underlying regulation of callus differentiation in P. euphratica. The results of this GWAS experiment suggested potential associations controlling whether the callus could differentiate and the process of callus differentiation. We identified multiple significant quantitative trait loci (QTLs), including the genes LOG1 and LOG7 and a locus containing WOX1. We reconstructed a genetic network that visualizes how each QTL interacts uniquely with other variants, and several core QTLs were detected that are involved in the degree of callus differentiation, providing potential targets for selection. This study represents one of the first to identify genetic variants affecting callus differentiation in a forest tree. Our results suggest that callus differentiation may be a typical qualitative-quantitative trait controlled by a major gene as well as polygenes across the genome of P. euphratica. This GWAS will help to design more complex and specific molecular tools for systematically manipulating organ regeneration.