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A genetic Gaussian network of multiple phenotypes, constructed through the inverse matrix of the genetic correlation matrix, is informative for understanding the biological dependencies of the phenotypes. However, its estimation may be challenging because the genetic correlation estimates are biased due to estimation errors and idiosyncratic pleiotropy inherent in GWAS summary statistics. Here, we introduce a novel approach called estimation of genetic graph (EGG), which eliminates the estimation error bias and idiosyncratic pleiotropy bias with the same techniques used in multivariable Mendelian randomization. The genetic network estimated by EGG can be interpreted as shared common biological contributions between phenotypes, conditional on others. We use both simulations and real data to demonstrate the superior efficacy of our novel method in comparison with the traditional network estimators. [ABSTRACT FROM AUTHOR]

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. Summary statement: In this study, we investigated the genetic basis of changes in spike morphology during wheat breeding. We identified candidate regions responsible for consistent alterations in spike morphology traits that led to increased yield. Furthermore, we constructed a genetic network of related genomic regions. Our findings unveil the genetic underpinnings of breeding selection and the interplay of spike morphology traits, offering valuable resources for the regulation of spike morphology in wheat. [ABSTRACT FROM AUTHOR]

Psychiatric disorders frequently co-occur and share common symptoms and genetic backgrounds. Previous research has used genome-wide association studies to identify the interrelationships among psychiatric disorders and identify clusters of disorders; however, these methods have limitations in terms of their ability to examine the relationships among disorders as a network structure and their generalizability to the general population. In this study, we explored the network structure of the polygenic risk score (PRS) for 13 psychiatric disorders in a general population (276,249 participants of European ancestry from the UK Biobank) and identified communities and the centrality of the network. In this network, the nodes represented a PRS for each psychiatric disorder and the edges represented the connections between nodes. The psychiatric disorders comprised four robust communities. The first community included attention-deficit hyperactivity disorder, autism spectrum disorder, major depressive disorder, and anxiety disorder. The second community consisted of bipolar I and II disorders, schizophrenia, and anorexia nervosa. The third group included Tourette's syndrome and obsessive–compulsive disorder. Cannabis use disorder, alcohol use disorder, and post-traumatic stress disorder make up the fourth community. The PRS of schizophrenia had the highest values for the three metrics (strength, betweenness, and closeness) in the network. Our findings provide a comprehensive genetic network of psychiatric disorders and biological evidence for the classification of psychiatric disorders. [ABSTRACT FROM AUTHOR]

Microorganisms often live in complex habitats, where changes in the environment are predictable, providing an opportunity for microorganisms to learn, anticipate the upcoming environmental changes and prepare in advance for better survival and growth. One such environment is the mammalian intestine, where the abundance of different carbon sources is spatially distributed. In this study, we identified seven spatially distributed carbon sources in the mammalian intestine and tested whether Escherichia coli exhibits phenotypes that are consistent with an anticipatory response given their spatial order and abundance within the mammalian intestine. Through RNA-Seq and RT-PCR validation measurements, we found that there was a 67% match in the expression patterns between the measured phenotypes and what would otherwise be expected in the case of anticipatory behavior, while 83% and 0% were in agreement with the homeostatic and random response, respectively. To understand the genetic and phenotypic basis of the discrepancies between the expected and measured anticipatory responses, we thoroughly investigated the discrepancy in D-galactose treatment and the expression of maltose operon in E. coli. Here, the expected anticipatory response, based on the spatial distribution of D-galactose and D-maltose, was that D-galactose should upregulate the maltose operon, but it was the opposite in experimental validation. We performed whole genome random mutagenesis and screening and identified E. coli strains with positive expression of maltose operon in D-galactose. Targeted Sanger sequencing and mutation repair identified that the mutations in the promoter region of malT and in the coding region of the crp gene were the factors responsible for the reversion in the association. Further, to identify why positive association in the D-galactose treatment and the expression of the maltose operon did not evolve naturally, fitness measurements were performed. Fitness experiments demonstrated that the fitness of E. coli strains with a positive association in the D-galactose treatment and the expression of the maltose operon was 12% to 20% lower than that of the wild type strain.

Palatogenesis is a complex and intricate process involving the formation of the palate through various morphogenetic events highly dependent on the surrounding context. These events comprise outgrowth of palatal shelves from embryonic maxillary prominences, their elevation from a vertical to a horizontal position above the tongue, and their subsequent adhesion and fusion at the midline to separate oral and nasal cavities. Disruptions in any of these processes can result in cleft palate, a common congenital abnormality that significantly affects patient's quality of life, despite surgical intervention. Although many genes involved in palatogenesis have been identified through studies on genetically modified mice and human genetics, the precise roles of these genes and their products in signaling networks that regulate palatogenesis remain elusive. Recent investigations have revealed that palatal shelf growth, patterning, adhesion, and fusion are intricately regulated by numerous transcription factors and signaling pathways, including Sonic hedgehog (Shh), bone morphogenetic protein (Bmp), fibroblast growth factor (Fgf), transforming growth factor beta (Tgf-β), Wnt signaling, and others. These studies have also identified a significant number of genes that are essential for palate development. Integrated information from these studies offers novel insights into gene regulatory networks and dynamic cellular processes underlying palatal shelf elevation, contact, and fusion, deepening our understanding of palatogenesis, and facilitating the development of more efficacious treatments for cleft palate. [ABSTRACT FROM AUTHOR]

Abstract 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. Methods and 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.

Global genetic networks provide additional information for the analysis of human diseases, beyond the traditional analysis that focuses on single genes or local networks. The Gaussian graphical model (GGM) is widely applied to learn genetic networks because it defines an undirected graph decoding the conditional dependence between genes. Many algorithms based on the GGM have been proposed for learning genetic network structures. Because the number of gene variables is typically far more than the number of samples collected, and a real genetic network is typically sparse, the graphical lasso implementation of GGM becomes a popular tool for inferring the conditional interdependence among genes. However, graphical lasso, although showing good performance in low dimensional data sets, is computationally expensive and inefficient or even unable to work directly on genome-wide gene expression data sets. In this study, the method of Monte Carlo Gaussian graphical model (MCGGM) was proposed to learn global genetic networks of genes. This method uses a Monte Carlo approach to sample subnetworks from genome-wide gene expression data and graphical lasso to learn the structures of the subnetworks. The learned subnetworks are then integrated to approximate a global genetic network. The proposed method was evaluated with a relatively small real data set of RNA-seq expression levels. The results indicate the proposed method shows a strong ability of decoding the interactions with high conditional dependences among genes. The method was then applied to genome-wide data sets of RNA-seq expression levels. The gene interactions with high interdependence from the estimated global networks show that most of the predicted gene-gene interactions have been reported in the literatures playing important roles in different human cancers. Also, the results validate the ability and reliability of the proposed method to identify high conditional dependences among genes in large-scale data sets. [ABSTRACT FROM AUTHOR]

Abstract Cancer cells are different from normal cells in regard to phenotypic and functional expression. Cancer is the outcome of aberrant gene expression affecting various cellular signaling pathways. MicroRNAs (MiRs) are small, non-coding RNAs regulating the expression of various protein-coding genes post-transcriptionally and are known to play critical roles in the complicated cellular pathways leading to cell growth, proliferation, development, and apoptosis. MiRs are involved in various cancer-related pathways and function both as tumor suppressor and cancer-causing genes. There is a need for significant biomarkers, and better prognostication of response to a particular treatment and liquid biopsy could be useful to appraise such potential biomarkers. This review has focused on the involvement of anomalous expression of miRs in human pancreatic cancer and the investigation of miR-based biomarkers for disease diagnosis and better therapeutic selection.

Abstract Background Prenatal exposure to ethanol can cause fetal alcohol spectrum disorder (FASD), a prevalent, preventable pediatric disorder. Identifying genetic risk alleles for FASD is challenging since time, dose, and frequency of exposure are often unknown, and manifestations of FASD are diverse and evident long after exposure. Drosophila melanogaster is an excellent model to study the genetic basis of the effects of developmental alcohol exposure since many individuals of the same genotype can be reared under controlled environmental conditions. Results We used 96 sequenced, wild-derived inbred lines from the Drosophila melanogaster Genetic Reference Panel (DGRP) to profile genome-wide transcript abundances in young adult flies that developed on ethanol-supplemented medium or standard culture medium. We found substantial genetic variation in gene expression in response to ethanol with extensive sexual dimorphism. We constructed sex-specific genetic networks associated with alcohol-dependent modulation of gene expression that include protein-coding genes, Novel Transcribed Regions (NTRs, postulated to encode long non-coding RNAs) and female-specific coordinated regulation of snoRNAs that regulate pseudouridylation of ribosomal RNA. We reared DGRP lines which showed extreme upregulation or downregulation of snoRNA expression during developmental alcohol exposure on standard or ethanol supplemented medium and demonstrated that developmental exposure to ethanol has genotype-specific effects on adult locomotor activity and sleep. Conclusions There is significant and sex-specific natural genetic variation in the transcriptional response to developmental exposure to ethanol in Drosophila that comprises networks of genes affecting nervous system development and ethanol metabolism as well as networks of regulatory non-coding RNAs.

In tetrapods, the scapular and pelvic girdles perform the important function of anchoring the limbs to the trunk of the body and facilitating the movement of each appendage. This shared function, however, is one of relatively few similarities between the scapula and pelvis, which have significantly different morphologies, evolutionary histories, embryonic origins, and underlying genetic pathways. The scapula evolved in jawless fish prior to the pelvis, and its embryonic development is unique among bones in that it is derived from multiple progenitor cell populations, including the dermomyotome, somatopleure, and neural crest. Conversely, the pelvis evolved several million years later in jawed fish, and it develops from an embryonic somatopleuric cell population. The genetic networks controlling the formation of the pelvis and scapula also share similarities and differences, with a number of genes shaping only one or the other, while other gene products such as PBX transcription factors act as hierarchical developmental regulators of both girdle structures. Here, we provide a detailed review of the cellular processes and genetic networks underlying pelvis and scapula formation in tetrapods, while also highlighting unanswered questions about girdle evolution and development.

Between October 2021 and April 2022, 317 outbreaks caused by highly pathogenic avian influenza (HPAI) H5N1 viruses were notified in poultry farms in the northeastern Italian regions. The complete genomes of 214 strains were used to estimate the genetic network based on the similarity of the viruses. An exponential random graph model (ERGM) was used to assess the effect of 'at-risk contacts', 'same owners', 'in-bound/out-bound risk windows overlap', 'genetic differences', 'geographic distances', 'same species', and 'poultry company' on the probability of observing a link within the genetic network, which can be interpreted as the potential propagation of the epidemic via lateral spread or a common source of infection. The variables 'same poultry company' (Est. = 0.548, C.I. = [0.179; 0.918]) and 'risk windows overlap' (Est. = 0.339, C.I. = [0.309; 0.368]) were associated with a higher probability of link formation, while the 'genetic differences' (Est. = −0.563, C.I. = [−0.640; −0.486]) and 'geographic distances' (Est. = −0.058, C.I. = [−0.078; −0.038]) indicated a reduced probability. The integration of epidemiological data with genomic analyses allows us to monitor the epidemic evolution and helps to explain the dynamics of lateral spreads casting light on the potential diffusion routes. The 2021–2022 epidemic stresses the need to further strengthen the biosecurity measures, and to encourage the reorganization of the poultry production sector to minimize the impact of future epidemics. [ABSTRACT FROM AUTHOR]

Global genetic networks provide additional information for the analysis of human diseases, beyond the traditional analysis that focuses on single genes or local networks. The Gaussian graphical model (GGM) is widely applied to learn genetic networks because it defines an undirected graph decoding the conditional dependence between genes. Many algorithms based on the GGM have been proposed for learning genetic network structures. Because the number of gene variables is typically far more than the number of samples collected, and a real genetic network is typically sparse, the graphical lasso implementation of GGM becomes a popular tool for inferring the conditional interdependence among genes. However, graphical lasso, although showing good performance in low dimensional data sets, is computationally expensive and inefficient or even unable to work directly on genome-wide gene expression data sets. In this study, the method of Monte Carlo Gaussian graphical model (MCGGM) was proposed to learn global genetic networks of genes. This method uses a Monte Carlo approach to sample subnetworks from genome-wide gene expression data and graphical lasso to learn the structures of the subnetworks. The learned subnetworks are then integrated to approximate a global genetic network. The proposed method was evaluated with a relatively small real data set of RNA-seq expression levels. The results indicate the proposed method shows a strong ability of decoding the interactions with high conditional dependences among genes. The method was then applied to genome-wide data sets of RNA-seq expression levels. The gene interactions with high interdependence from the estimated global networks show that most of the predicted gene-gene interactions have been reported in the literatures playing important roles in different human cancers. Also, the results validate the ability and reliability of the proposed method to identify high conditional dependences among genes in large-scale data sets. [ABSTRACT FROM AUTHOR]

Background: Prenatal exposure to ethanol can cause fetal alcohol spectrum disorder (FASD), a prevalent, preventable pediatric disorder. Identifying genetic risk alleles for FASD is challenging since time, dose, and frequency of exposure are often unknown, and manifestations of FASD are diverse and evident long after exposure. Drosophila melanogaster is an excellent model to study the genetic basis of the effects of developmental alcohol exposure since many individuals of the same genotype can be reared under controlled environmental conditions. Results: We used 96 sequenced, wild-derived inbred lines from the Drosophila melanogaster Genetic Reference Panel (DGRP) to profile genome-wide transcript abundances in young adult flies that developed on ethanol-supplemented medium or standard culture medium. We found substantial genetic variation in gene expression in response to ethanol with extensive sexual dimorphism. We constructed sex-specific genetic networks associated with alcohol-dependent modulation of gene expression that include protein-coding genes, Novel Transcribed Regions (NTRs, postulated to encode long non-coding RNAs) and female-specific coordinated regulation of snoRNAs that regulate pseudouridylation of ribosomal RNA. We reared DGRP lines which showed extreme upregulation or downregulation of snoRNA expression during developmental alcohol exposure on standard or ethanol supplemented medium and demonstrated that developmental exposure to ethanol has genotype-specific effects on adult locomotor activity and sleep. Conclusions: There is significant and sex-specific natural genetic variation in the transcriptional response to developmental exposure to ethanol in Drosophila that comprises networks of genes affecting nervous system development and ethanol metabolism as well as networks of regulatory non-coding RNAs. [ABSTRACT FROM AUTHOR]

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. Methods and 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. [ABSTRACT FROM AUTHOR]

Background: 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. Materials and methods: 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. Results: 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. Conclusion: 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. [ABSTRACT FROM AUTHOR]

Cancer cells are different from normal cells in regard to phenotypic and functional expression. Cancer is the outcome of aberrant gene expression affecting various cellular signaling pathways. MicroRNAs (MiRs) are small, non-coding RNAs regulating the expression of various protein-coding genes post-transcriptionally and are known to play critical roles in the complicated cellular pathways leading to cell growth, proliferation, development, and apoptosis. MiRs are involved in various cancer-related pathways and function both as tumor suppressor and cancer-causing genes. There is a need for significant biomarkers, and better prognostication of response to a particular treatment and liquid biopsy could be useful to appraise such potential biomarkers. This review has focused on the involvement of anomalous expression of miRs in human pancreatic cancer and the investigation of miR-based biomarkers for disease diagnosis and better therapeutic selection. [ABSTRACT FROM AUTHOR]

Abstract Background The novel approaches to psychiatric classification assume that disorders, contrary to what was previously thought, are not completely separate phenomena. In this regard, in addition to symptom-based criteria, disturbances are also considered on the basis of lower level components. With this viewpoint, identifying common biochemical markers would be beneficial in adopting a comprehensive strategy for prevention, diagnosis and treatment. Main body One of the problematic areas in clinical settings is the coexistence of both obsessive–compulsive disorder (OCD) and bipolar disorder (BD) that is challenging and difficult to manage. In this study, using a system biologic approach we aimed to assess the interconnectedness of OCD and BD at different levels. Gene Set Enrichment Analysis (GSEA) method was used to identify the shared biological network between the two disorders. The results of the analysis revealed 34 common genes between the two disorders, the most important of which were CACNA1C, GRIA1, DRD2, NOS1, SLC18A1, HTR2A and DRD1. Dopaminergic synapse and cAMP signaling pathway as the pathways, dopamine binding and dopamine neurotransmitter receptor activity as the molecular functions, dendrite and axon part as the cellular component and cortex and striatum as the brain regions were the most significant commonalities. Short conclusion The results of this study highlight the role of multiple systems, especially the dopaminergic system in linking OCD and BD. The results can be used to estimate the disease course, prognosis, and treatment choice, particularly in the cases of comorbidity. Such perspectives, going beyond symptomatic level, help to identify common endophenotypes between the disorders and provide diagnostic and therapeutic approaches based on biological in addition to the symptomatic level.

Between October 2021 and April 2022, 317 outbreaks caused by highly pathogenic avian influenza (HPAI) H5N1 viruses were notified in poultry farms in the northeastern Italian regions. The complete genomes of 214 strains were used to estimate the genetic network based on the similarity of the viruses. An exponential random graph model (ERGM) was used to assess the effect of ‘at-risk contacts’, ‘same owners’, ‘in-bound/out-bound risk windows overlap’, ‘genetic differences’, ‘geographic distances’, ‘same species’, and ‘poultry company’ on the probability of observing a link within the genetic network, which can be interpreted as the potential propagation of the epidemic via lateral spread or a common source of infection. The variables ‘same poultry company’ (Est. = 0.548, C.I. = [0.179; 0.918]) and ‘risk windows overlap’ (Est. = 0.339, C.I. = [0.309; 0.368]) were associated with a higher probability of link formation, while the ‘genetic differences’ (Est. = −0.563, C.I. = [−0.640; −0.486]) and ‘geographic distances’ (Est. = −0.058, C.I. = [−0.078; −0.038]) indicated a reduced probability. The integration of epidemiological data with genomic analyses allows us to monitor the epidemic evolution and helps to explain the dynamics of lateral spreads casting light on the potential diffusion routes. The 2021–2022 epidemic stresses the need to further strengthen the biosecurity measures, and to encourage the reorganization of the poultry production sector to minimize the impact of future epidemics.

Abstract Background In human genetic association studies with high-dimensional gene expression data, it has been well known that statistical selection methods utilizing prior biological network knowledge such as genetic pathways and signaling pathways can outperform other methods that ignore genetic network structures in terms of true positive selection. In recent epigenetic research on case-control association studies, relatively many statistical methods have been proposed to identify cancer-related CpG sites and their corresponding genes from high-dimensional DNA methylation array data. However, most of existing methods are not designed to utilize genetic network information although methylation levels between linked genes in the genetic networks tend to be highly correlated with each other. Results We propose new approach that combines data dimension reduction techniques with network-based regularization to identify outcome-related genes for analysis of high-dimensional DNA methylation data. In simulation studies, we demonstrated that the proposed approach overwhelms other statistical methods that do not utilize genetic network information in terms of true positive selection. We also applied it to the 450K DNA methylation array data of the four breast invasive carcinoma cancer subtypes from The Cancer Genome Atlas (TCGA) project. Conclusions The proposed variable selection approach can utilize prior biological network information for analysis of high-dimensional DNA methylation array data. It first captures gene level signals from multiple CpG sites using data a dimension reduction technique and then performs network-based regularization based on biological network graph information. It can select potentially cancer-related genes and genetic pathways that were missed by the existing methods.

Abstract Background Oyster is rich in glycogen and free amino acids and is called “the milk of sea”. To understand the main genetic effects of these traits and the genetic networks underlying their correlation, we have conducted the whole genome resequencing with 427 oysters collected from the world-wide scale. Results After association analysis, 168 clustered significant single nucleotide polymorphism (SNP) loci were identified for glycogen content and 17 SNPs were verified with 288 oyster individuals in another wide populations. These were the most important candidate loci for oyster breeding. Among 24 genes in the 100-kb regions of the leading SNP loci, cytochrome P450 17A1 (CYP17A1) contained a non-synonymous SNP and displayed higher expressions in high glycogen content individuals. This might enhance the gluconeogenesis process by the transcriptionally regulating the expression of phosphoenolpyruvate carboxykinase (PEPCK) and glucose 6-phosphatase (G6Pase). Also, for amino acids content, 417 clustered significant SNPs were identified. After genetic network analysis, three node SNP regions were identified to be associated with glycogen, protein, and Asp content, which might explain their significant correlation. Conclusion Overall, this study provides insights into the genetic correlation among complex traits, which will facilitate future oyster functional studies and breeding through molecular design.

Abstract Background Understanding the genetic networks and their role in chronic diseases (e.g., cancer) is one of the important objectives of biological researchers. In this work, we present a text mining system that constructs a gene-gene-interaction network for the entire human genome and then performs network analysis to identify disease-related genes. We recognize the interacting genes based on their co-occurrence frequency within the biomedical literature and by employing linear and non-linear rare-event classification models. We analyze the constructed network of genes by using different network centrality measures to decide on the importance of each gene. Specifically, we apply betweenness, closeness, eigenvector, and degree centrality metrics to rank the central genes of the network and to identify possible cancer-related genes. Results We evaluated the top 15 ranked genes for different cancer types (i.e., Prostate, Breast, and Lung Cancer). The average precisions for identifying breast, prostate, and lung cancer genes vary between 80-100%. On a prostate case study, the system predicted an average of 80% prostate-related genes. Conclusions The results show that our system has the potential for improving the prediction accuracy of identifying gene-gene interaction and disease-gene associations. We also conduct a prostate cancer case study by using the threshold property in logistic regression, and we compare our approach with some of the state-of-the-art methods.

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.

Age-related macular degeneration (AMD) showed several processes and risk factors in common with neurodegenerative disorders (NDDs). The present work explored the existence of genetic determinants associated with AMD, which may provide insightful clues concerning its relationship with NDDs and their possible application into the clinical practice. In this study, 400 AMD patients were subjected to the genotyping analysis of 120 genetic variants by OpenArray technology. As the reference group, 503 samples representative of the European general population were utilized. Statistical analysis revealed the association of 23 single-nucleotide polymorphisms (SNPs) with AMD risk. The analysis of epistatic effects revealed that ARMS2, IL6, APOE, and IL2RA could contribute to AMD and neurodegenerative processes by synergistic modulation of the expression of disease-relevant genes. In addition, the bioinformatic analysis of the associated miRNA variants highlighted miR-196a, miR-6796, miR-6499, miR-6810, miR-499, and miR-7854 as potential candidates for counteracting AMD and neurodegenerative processes. Finally, this work highlighted the existence of shared disease mechanisms (oxidative stress, immune-inflammatory response, mitochondrial dysfunction, axonal guidance pathway, and synaptogenesis) between AMD and NDDs and described the associated SNPs as candidate biomarkers for developing novel strategies for early diagnosis, monitoring, and treatment of such disorders in a progressive aging population.

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). [ABSTRACT FROM AUTHOR]

Background: Refractive eye development is regulated by optical defocus in a process of emmetropization. Excessive exposure to negative optical defocus often leads to the development of myopia. However, it is still largely unknown how optical defocus is detected by the retina. Methods: Here, we used genome-wide RNA-sequencing to conduct analysis of the retinal gene expression network underlying contrast perception and refractive eye development. Results: We report that the genetic network subserving contrast perception plays an important role in optical defocus detection and emmetropization. Our results demonstrate an interaction between contrast perception, the retinal circadian clock pathway and the signaling pathway underlying optical defocus detection. We also observe that the relative majority of genes causing human myopia are involved in the processing of optical defocus. Conclusions: Together, our results support the hypothesis that optical defocus is perceived by the retina using contrast as a proxy and provide new insights into molecular signaling underlying refractive eye development. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Age-related macular degeneration (AMD) showed several processes and risk factors in common with neurodegenerative disorders (NDDs). The present work explored the existence of genetic determinants associated with AMD, which may provide insightful clues concerning its relationship with NDDs and their possible application into the clinical practice. In this study, 400 AMD patients were subjected to the genotyping analysis of 120 genetic variants by OpenArray technology. As the reference group, 503 samples representative of the European general population were utilized. Statistical analysis revealed the association of 23 single-nucleotide polymorphisms (SNPs) with AMD risk. The analysis of epistatic effects revealed that ARMS2, IL6, APOE , and IL2RA could contribute to AMD and neurodegenerative processes by synergistic modulation of the expression of disease-relevant genes. In addition, the bioinformatic analysis of the associated miRNA variants highlighted miR-196a, miR-6796, miR-6499, miR-6810, miR-499, and miR-7854 as potential candidates for counteracting AMD and neurodegenerative processes. Finally, this work highlighted the existence of shared disease mechanisms (oxidative stress, immune-inflammatory response, mitochondrial dysfunction, axonal guidance pathway, and synaptogenesis) between AMD and NDDs and described the associated SNPs as candidate biomarkers for developing novel strategies for early diagnosis, monitoring, and treatment of such disorders in a progressive aging population. [ABSTRACT FROM AUTHOR]

Phyllosphere microbial communities inhabit the aerial plant parts, such as leaves and flowers, where they form complex molecular interactions with the host plant. Contrary to the relatively well-studied rhizosphere microbiome, scientists are just starting to understand, and potentially utilize, the phyllosphere microbiome. In this article, we summarize the recent studies that have provided novel insights into the mechanism of the host genotype shaping the phyllosphere microbiome and the possibility to select a stable and well-adapted microbiome. We also discuss the most pressing gaps in our knowledge and identify the most promising research directions and tools for understanding the assembly and function of phyllosphere microbiomes – this understanding is necessary if we are to harness phyllosphere microbiomes for improving plant growth and health in managed systems. Plant immunity networks maintain microbial homeostasis in the phyllosphere, which in turn affects the plant health. Plant exudation and volatiles significantly shape the microbiome structure and composition. Various environmental stresses shape the complex interaction between phyllosphere microbiome and plant immunity. Understanding the molecular basis of plant–microbe and microbe–microbe interactions will help elucidate their impact on plant fitness. Recent advances utilizing synthetic microbial community combined with omics tools (such as metagenomics and metabolomics) provide important insights into the physiology and functionality of the phyllosphere microbiome. An integrated knowledge of multiomics combined with synthetic community approach can help determine the individual as well as community level contribution of phyllosphere microbiome in the host fitness. Microbiome engineering can reshape the microbial composition in the phyllosphere, and holds potential for large-scale microbiome research and reconfiguration of phyllosphere microbiome with desired traits to fight plant stresses. [ABSTRACT FROM AUTHOR]

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.

Abstract Background Sexual precocity is a common biological phenomenon in animal species. A large number of precocity individuals were identified in Chinese mitten crab Eriocheir sinensis, which caused huge economic loss annually. However, the underlying genetic basis of precocity in E. sinensis remains unclear to date. Results In this study, morphological and histological observation and comparative transcriptome analysis were conducted among different stages of precocious one-year-old and normal two-year-old sexually mature E. sinensis. The expression profiles of the ovary, hepatopancreas, and eyestalk tissues were presented and compared. Genes associated with lipid metabolic process, lipid transport, vitelline membrane formation, vitelline synthesis, and neuropeptide hormone-related genes were upregulated in the ovary, hepatopancreas, and eyestalk of precocious E. sinensis. Our results indicated that the eyestalk was involved in the neuroendocrine system providing neuropeptide hormones that may induce vitellogenesis in the hepatopancreas and further stimulate ovary development. The hepatopancreas is a site for energy storage and vitellogenin synthesis, and it may assist oogenesis through lipid transport in precocious E. sinensis. Conclusion We provided not only an effective and convenient phenotype measurement method for the identification of potential precocious E. sinensis detection but also valuable genetic resources and novel insights into the molecular mechanism of precocity in E. sinensis. The genetic basis of precocity in E. sinensis is an integrated gene regulatory network of eyestalk, hepatopancreas, and ovary tissues.

Abstract Background Forty-two percent of patients experience disease comorbidity, contributing substantially to mortality rates and increased healthcare costs. Yet, the possibility of underlying shared mechanisms for diseases remains not well established, and few studies have confirmed their molecular predictions with clinical datasets. Methods In this work, we integrated genome-wide association study (GWAS) associating diseases and single nucleotide polymorphisms (SNPs) with transcript regulatory activity from expression quantitative trait loci (eQTL). This allowed novel mechanistic insights for noncoding and intergenic regions. We then analyzed pairs of SNPs across diseases to identify shared molecular effectors robust to multiple test correction (False Discovery Rate FDReRNA 1.5, FDRcomorbidity

Abstract Background The endomembrane system, known as secretory pathway, is responsible for the synthesis and transport of protein molecules in cells. Therefore, genes involved in the secretory pathway are essential for the cellular development and function. Recent scientific investigations show that ER and Golgi apparatus may provide a convenient drug target for cancer therapy. On the other hand, it is known that abundantly expressed genes in different cellular organelles share interconnected pathways and co-regulate each other activities. The cross-talks among these genes play an important role in signaling pathways, associated to the regulation of intracellular protein transport. Results In the present study, we device an integrated approach to understand these complex interactions. We analyze gene perturbation expression profiles, reconstruct a directed gene interaction network and decipher the regulatory interactions among genes involved in protein transport signaling. In particular, we focus on expression signatures of genes involved in the secretory pathway of MCF7 breast cancer cell line. Furthermore, network biology analysis delineates these gene-centric cross-talks at the level of specific modules/sub-networks, corresponding to different signaling pathways. Conclusions We elucidate the regulatory connections between genes constituting signaling pathways such as PI3K-Akt, Ras, Rap1, calcium, JAK-STAT, EFGR and FGFR signaling. Interestingly, we determine some key regulatory cross-talks between signaling pathways (PI3K-Akt signaling and Ras signaling pathway) and intracellular protein transport.

Bistability, i.e., the existence of just two stable equilibria, is known to play an important role in biological systems, e.g., cellular differentiation and apoptosis. In this paper, we consider the bistability but as a structural property of a class of network systems, that is, the bistability under the assumption that the information on the network structure is available but the information on the components is not available. First, we introduce Boolean networks as a model of biological network systems and give the notion of structural bistability. We next focus on the systems with a flower-shaped network structure and present a necessary and sufficient condition based on three characteristics of the network topology. Finally, the result is extended to the Boolean networks with a chain-shaped network structure. [ABSTRACT FROM AUTHOR]

Migraine is the most common neurological disorder worldwide and it has been shown to have complex polygenic origins with a heritability of estimated 40-70%. Both common and rare genetic variants are believed to underlie the pathophysiology of the prevalent types of migraine, migraine with typical aura and migraine without aura. However, only common variants have been identified so far. Here we identify for the first time a gene module with rare mutations through a systems genetics approach integrating RNA sequencing data from brain and vascular tissues likely to be involved in migraine pathology in combination with whole genome sequencing of 117 migraine families. We found a gene module in the visual cortex, based on single nuclei RNA sequencing data, that had increased rare mutations in the migraine families and replicated this in a second independent cohort of 1930 patients. This module was mainly expressed by interneurons, pyramidal CA1, and pyramidal SS cells, and pathway analysis showed association with hormonal signalling (thyrotropin-releasing hormone receptor and oxytocin receptor signalling pathways), Alzheimer's disease pathway, serotonin receptor pathway and general heterotrimeric G-protein signalling pathways. Our results demonstrate that rare functional gene variants are strongly implicated in the pathophysiology of migraine. Furthermore, we anticipate that the results can be used to explain the critical mechanisms behind migraine and potentially improving the treatment regime for migraine patients. [ABSTRACT FROM AUTHOR]

Loss of gene function is common throughout evolution, even though it often leads to reduced fitness. In this study, we systematically evaluated how an organism adapts after deleting genes that are important for growth under oxidative stress. By evolving, sequencing, and phenotyping over 200 yeast lineages, we found that gene loss can enhance an organism's capacity to evolve and adapt. Although gene loss often led to an immediate decrease in fitness, many mutants rapidly acquired suppressor mutations that restored fitness. Depending on the strain's genotype, some ultimately even attained higher fitness levels than similarly adapted wild-type cells. Further, cells with deletions in different modules of the genetic network followed distinct and predictable mutational trajectories. Finally, losing highly connected genes increased evolvability by facilitating the emergence of a more diverse array of phenotypes after adaptation. Together, our findings show that loss of specific parts of a genetic network can facilitate adaptation by opening alternative evolutionary paths. [ABSTRACT FROM AUTHOR]

Key Message: The mutation of FAX1 (Fatty Acid Export 1) disrupts ROS homeostasis and suppresses transcription activity of DYT1-TDF1-AMS-MS188 genetic network, leading to atypical tapetum PCD and defective pollen formation in Arabidopsis. Fatty acids (FAs) have multiple important biological functions and exert diverse cellular effects through modulating Reactive Oxygen Species (ROS) homeostasis. Arabidopsis FAX1 (Fatty Acid Export 1) mediates the export of de novo synthesized FA from chloroplast and loss of function of FAX1 impairs male fertility. However, mechanisms underlying the association of FAX1-mediated FA export with male sterility remain enigmatic. In this study, by using an integrated approach that included morphological, cytological, histological, and molecular analyses, we revealed that loss of function of FAX1 breaks cellular FA/lipid homeostasis, which disrupts ROS homeostasis and suppresses transcriptional activation of the DYT1-TDF1-AMS-MS188 genetic network of anther development, impairing tapetum development and pollen wall formation, and resulting in male sterility. This study provides new insights into the regulatory network for male reproduction in plants, highlighting an important role of FA export-mediated ROS homeostasis in the process. [ABSTRACT FROM AUTHOR]

SIX homeoproteins were first described in Drosophila, where they participate in the Pax-Six-Eya-Dach (PSED) network with eyeless , eyes absent and dachsund to drive synergistically eye development through genetic and biochemical interactions. The role of the PSED network and SIX proteins in muscle formation in vertebrates was subsequently identified. Evolutionary conserved interactions with EYA and DACH proteins underlie the activity of SIX transcriptional complexes (STC) both during embryogenesis and in adult myofibers. Six genes are expressed throughout muscle development, in embryonic and adult proliferating myogenic stem cells and in fetal and adult post-mitotic myofibers, where SIX proteins regulate the expression of various categories of genes. In vivo, SIX proteins control many steps of muscle development, acting through feedforward mechanisms: in the embryo for myogenic fate acquisition through the direct control of Myogenic Regulatory Factors; in adult myofibers for their contraction/relaxation and fatigability properties through the control of genes involved in metabolism, sarcomeric organization and calcium homeostasis. Furthermore, during development and in the adult, SIX homeoproteins participate in the genesis and the maintenance of myofibers diversity. [ABSTRACT FROM AUTHOR]

Background: The novel approaches to psychiatric classification assume that disorders, contrary to what was previously thought, are not completely separate phenomena. In this regard, in addition to symptom-based criteria, disturbances are also considered on the basis of lower level components. With this viewpoint, identifying common biochemical markers would be beneficial in adopting a comprehensive strategy for prevention, diagnosis and treatment. Main body: One of the problematic areas in clinical settings is the coexistence of both obsessive–compulsive disorder (OCD) and bipolar disorder (BD) that is challenging and difficult to manage. In this study, using a system biologic approach we aimed to assess the interconnectedness of OCD and BD at different levels. Gene Set Enrichment Analysis (GSEA) method was used to identify the shared biological network between the two disorders. The results of the analysis revealed 34 common genes between the two disorders, the most important of which were CACNA1C, GRIA1, DRD2, NOS1, SLC18A1, HTR2A and DRD1. Dopaminergic synapse and cAMP signaling pathway as the pathways, dopamine binding and dopamine neurotransmitter receptor activity as the molecular functions, dendrite and axon part as the cellular component and cortex and striatum as the brain regions were the most significant commonalities. Short conclusion: The results of this study highlight the role of multiple systems, especially the dopaminergic system in linking OCD and BD. The results can be used to estimate the disease course, prognosis, and treatment choice, particularly in the cases of comorbidity. Such perspectives, going beyond symptomatic level, help to identify common endophenotypes between the disorders and provide diagnostic and therapeutic approaches based on biological in addition to the symptomatic level. [ABSTRACT FROM AUTHOR]

Long‐distance dispersal (LDD) is a pivotal process for plants determining their range of distribution and promoting gene flow among distant populations. Most fleshy‐fruited species rely on frugivorous vertebrates to disperse their seeds across the landscape. While LDD events are difficult to record, a few ecological studies have shown that birds move a sizeable number of ingested seeds across geographic barriers, such as sea straits. The foraging movements of migrant frugivores across distant populations, including those separated by geographic barriers, creates a constant flow of propagules that in turn shapes the spatial distributions of the genetic variation in populations. Here, we have analysed the genetic diversity and structure of 74 populations of Pistacia lentiscus, a fleshy‐fruited shrub widely distributed in the Mediterranean Basin, to elucidate whether the Mediterranean Sea acts as a geographic barrier or alternatively whether migratory frugivorous birds promote gene flow among populations located on both sides of the sea. Our results show reduced genetic distances among populations, including intercontinental populations, and they show a significant genetic structure across an eastern‐western axis. These findings are consistent with known bird migratory routes that connect the European and African continents following a north‐southwards direction during the fruiting season of many fleshy‐fruited plants. Further, approximate Bayesian analysis failed to explain the observed patterns as a result of historical population migrations at the end of Last Glacial Maximum. Therefore, anthropic and/or climatic changes that would disrupt the migratory routes of frugivorous birds might have genetic consequences for the plant species they feed upon. [ABSTRACT FROM AUTHOR]

Providing access to quantitative genomic data is key to ensure large-scale data validation and promote new discoveries. TheCellMap.org serves as a central repository for storing and analyzing quantitative genetic interaction data produced by genome-scale Synthetic Genetic Array (SGA) experiments with the budding yeast Saccharomyces cerevisiae. In particular, TheCellMap.org allows users to easily access, visualize, explore, and functionally annotate genetic interactions, or to extract and reorganize subnetworks, using data-driven network layouts in an intuitive and interactive manner.

Deciphering the underlying genetic basis behind pancreatic cancer (PC) and its associated multimorbidities will enhance our knowledge toward PC control. The study investigated the common genetic background of PC and different morbidities through a computational approach and further evaluated the less explored association between PC and autoimmune diseases (AIDs) through an epidemiological analysis. Gene‐disease associations (GDAs) of 26 morbidities of interest and PC were obtained using the DisGeNET public discovery platform. The association between AIDs and PC pointed by the computational analysis was confirmed through multivariable logistic regression models in the PanGen European case–control study population of 1,705 PC cases and 1,084 controls. Fifteen morbidities shared at least one gene with PC in the DisGeNET database. Based on common genes, several AIDs were genetically associated with PC pointing to a potential link between them. An epidemiologic analysis confirmed that having any of the nine AIDs studied was significantly associated with a reduced risk of PC (Odds Ratio (OR) = 0.74, 95% confidence interval (CI) 0.58–0.93) which decreased in subjects having ≥2 AIDs (OR = 0.39, 95%CI 0.21–0.73). In independent analyses, polymyalgia rheumatica, and rheumatoid arthritis were significantly associated with low PC risk (OR = 0.40, 95%CI 0.19–0.89, and OR = 0.73, 95%CI 0.53–1.00, respectively). Several inflammatory‐related morbidities shared a common genetic component with PC based on public databases. These molecular links could shed light into the molecular mechanisms underlying PC development and simultaneously generate novel hypotheses. In our study, we report sound findings pointing to an association between AIDs and a reduced risk of PC. What's new? Deaths from pancreatic cancer are increasing making it a public health emergency to define the molecular causes of this deadly disease. Here the authors show that autoimmune diseases share genetic components with pancreatic cancer and further corroborate this association in a case‐control study. This could bring new mechanistic understanding of pancreatic cancer, potentially impacting its prevention and treatment in the future. [ABSTRACT FROM AUTHOR]

Background: Understanding the genetic networks and their role in chronic diseases (e.g., cancer) is one of the important objectives of biological researchers. In this work, we present a text mining system that constructs a gene-gene-interaction network for the entire human genome and then performs network analysis to identify disease-related genes. We recognize the interacting genes based on their co-occurrence frequency within the biomedical literature and by employing linear and non-linear rare-event classification models. We analyze the constructed network of genes by using different network centrality measures to decide on the importance of each gene. Specifically, we apply betweenness, closeness, eigenvector, and degree centrality metrics to rank the central genes of the network and to identify possible cancer-related genes. Results: We evaluated the top 15 ranked genes for different cancer types (i.e., Prostate, Breast, and Lung Cancer). The average precisions for identifying breast, prostate, and lung cancer genes vary between 80-100%. On a prostate case study, the system predicted an average of 80% prostate-related genes. Conclusions: The results show that our system has the potential for improving the prediction accuracy of identifying gene-gene interaction and disease-gene associations. We also conduct a prostate cancer case study by using the threshold property in logistic regression, and we compare our approach with some of the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Coronary artery disease (CAD) is a complex human disease, involving multiple genes and their nonlinear interactions, which often act in a modular fashion. Genome-wide single nucleotide polymorphism (SNP) profiling provides an effective technique to unravel these underlying genetic interplays or their functional involvements for CAD. This study aimed to identify the susceptible pathways and modules for CAD based on SNP omics. First, the Wellcome Trust Case Control Consortium (WTCCC) SNP datasets of CAD and control samples were used to assess the joint effect of multiple genetic variants at the pathway level, using logistic kernel machine regression model. Then, an expanded genetic network was constructed by integrating statistical gene–gene interactions involved in these susceptible pathways with their protein–protein interaction (PPI) knowledge. Finally, risk functional modules were identified by decomposition of the network. Of 276 KEGG pathways analyzed, 6 pathways were found to have a significant effect on CAD. Other than glycerolipid metabolism, glycosaminoglycan biosynthesis, and cardiac muscle contraction pathways, three pathways related to other diseases were also revealed, including Alzheimer’s disease, non-alcoholic fatty liver disease, and Huntington’s disease. A genetic epistatic network of 95 genes was further constructed using the abovementioned integrative approach. Of 10 functional modules derived from the network, 6 have been annotated to phospholipase C activity and cell adhesion molecule binding, which also have known functional involvement in Alzheimer’s disease. These findings indicate an overlap of the underlying molecular mechanisms between CAD and Alzheimer’s disease, thus providing new insights into the molecular basis for CAD and its molecular relationships with other diseases.