Pleiotropy refers to a gene's ability to influence multiple phenotypes or traits. In the context of human genetic diseases, pleiotropy manifests as different pathological effects resulting from mutations in the same gene. This phenomenon plays a crucial role in understanding gene-gene interactions in system-level biological diseases. Previous studies have largely focused on pleiotropy within undirected molecular correlation networks, leaving a gap in examining pleiotropy induced by directed signaling networks, which can better explain dynamic gene-gene interactions. In this study, we utilized a synchronous Boolean network model to explore pleiotropic dynamics induced by various mutations in large-scale networks. We introduced an in-silico Pleiotropic Score (sPS) to quantify the impact of gene mutations and validated the model against observational pleiotropy data from the Human Phenotype Ontology (HPO). Our results indicate a significant correlation between sPS and network structural characteristics, including degree centrality and feedback loop involvement. The highest correlation was observed between closeness centrality and sPS (0.6), suggesting that genes more central in the network exhibit higher pleiotropic potential. Furthermore, genes involved in feedback loops demonstrated higher sPS values (p < 0.0001), supporting the role of feedback loops in amplifying pleiotropic behavior. Our model provides a novel approach for quantifying pleiotropy through directed network dynamics, complementing traditional observational methods.

Epistasis - the interaction between alleles at different genetic loci - plays a fundamental role in biology. However, several recent approaches quantify epistasis using a chimeric formula that measures deviations from a multiplicative fitness model on an additive scale, thus mixing two scales. Here, we show that for pairwise interactions, the chimeric formula yields a different magnitude but the same sign of epistasis compared to the multiplicative formula that measures both fitness and deviations on a multiplicative scale. However, for higher-order interactions, we show that the chimeric formula can have both different magnitude and sign compared to the multiplicative formula. We resolve these inconsistencies by deriving mathematical relationships between the different epistasis formulae and different parametrizations of the multivariate Bernoulli distribution. We argue that the chimeric formula does not appropriately model interactions between the Bernoulli random variables. In simulations, we show that the chimeric formula is less accurate than the classical multiplicative/additive epistasis formulae and may falsely detect higher-order epistasis. Analyzing multi-gene knockouts in yeast, multi-way drug interactions in E. coli, and deep mutational scanning of several proteins, we find that approximately 10% to 60% of inferred higher-order interactions change sign using the multiplicative/additive formula compared to the chimeric formula.

Polygenic risk score (PRS) is a widely used approach for predicting individuals' genetic risk of complex diseases, playing a pivotal role in advancing precision medicine. Traditional PRS methods, predominantly following a linear structure, often fall short in capturing the intricate relationships between genotype and phenotype. In this study, we present PRS-Net, an interpretable geometric deep learning-based framework that effectively models the nonlinearity of biological systems for enhanced disease prediction and biological discovery. PRS-Net begins by deconvoluting the genome-wide PRS at the single-gene resolution and then explicitly encapsulates gene-gene interactions leveraging a graph neural network (GNN) for genetic risk prediction, enabling a systematic characterization of molecular interplay underpinning diseases. An attentive readout module is introduced to facilitate model interpretation. Extensive tests across multiple complex traits and diseases demonstrate the superior prediction performance of PRS-Net compared with a wide range of conventional PRS methods. The interpretability of PRS-Net further enhances the identification of disease-relevant genes and gene programs. PRS-Net provides a potent tool for concurrent genetic risk prediction and biological discovery for complex diseases.

There are still gaps in the study of the miRNA and its SNPs in some diseases such as non-small cell lung cancer (NSCLC). The study aimed to provide useful information on the treatment of NSCLC by investigating the association between miR-21 and its SNPs and NSCLC susceptibility.

The serum of NSCLC patients (n = 205) and cancer-free controls (n = 217) were collected in this study for RNA extraction. The qRT-PCR was used to evaluate the expression of miR-21 and Taqman qPCR was used for genotyping and quantifying miR-21 SNPs (rs1292037, rs6504593). The association of the expression of miR-21, the miR-21 SNPs and their interactions with the susceptibility of NSCLC patients were analysed using logistic regression analysis in this study.

This study showed that the overexpression of miR-21 was related to NSCLC. The C allele and CC genotypes of rs1292037 and rs6504593 were associated with the increased risk of NSCLC susceptibility. Moreover, the interactions of rs1292037 and rs6504593 were also a risk factor for NSCLC. The CC genotypes of rs1292037 and rs6504593 were associated with the increase of miR-21 expression.

The overexpression of miR-21, the miR-21 SNPs rs1292037 and rs6504593 and their interactions were associated with NSCLC susceptibility. MiR-21 and its SNPs have potential for being targets in the therapy of NSCLC. This study provided important information for the treatment of NSCLC.

High-order interactions associated with non-small cell lung cancer (NSCLC) survival may elucidate underlying molecular mechanisms and identify potential therapeutic targets. Our previous work has identified a three-way interaction among pack-year of smoking (the number of packs of cigarettes smoked per day multiplied by the number of years the person has smoked) and two DNA methylation probes (cg05293407TRIM27 and cg00060500KIAA0226). However, whether a four-way interaction exists remains unclear. Therefore, we adopted a two-phase design to identify the four-way gene-smoking interactions by a hill-climbing strategy on the basis of the previously detected three-way interaction. One CpG probe, cg16658473SHISA9, was identified with FDR-q ≤ 0.05 in the discovery phase and P ≤ 0.05 in the validation phase. Meanwhile, the four-way interaction improved the discrimination ability for the prognostic prediction model, as indicated by the area under the receiver operating characteristic curve (AUC) for both 3- and 5-year survival. In summary, we identified a four-way interaction associated with NSCLC survival among pack-year of smoking, cg05293407TRIM27, cg00060500KIAA0226 and g16658473SHISA9, providing novel insights into the complex mechanisms underlying NSCLC progression.

Transcriptome- and proteome-wide association studies (TWAS/PWAS) have proven successful in prioritizing genes and proteins whose genetically regulated expression modulates disease risk, but they ignore potential co-expression and interaction effects. To address this limitation, we introduce the co-expression-wide association study (COWAS) method, which can identify pairs of genes or proteins whose genetically regulated co-expression is associated with complex traits. COWAS first trains models to predict expression and co-expression conditional on genetic variation, and then tests for association between imputed co-expression and the trait of interest while also accounting for direct effects from each exposure. We applied our method to plasma proteomic concentrations from the UK Biobank, identifying dozens of interacting protein pairs associated with cholesterol levels, Alzheimer's disease, and Parkinson's disease. Notably, our results demonstrate that co-expression between proteins may affect complex traits even if neither protein is detected to influence the trait when considered on its own. We also show how COWAS can help disentangle direct and interaction effects, providing a richer picture of the molecular networks that mediate genetic effects on disease outcomes.

Dementia causes the loss of functional independence, resulting in a decrease in the quality of life of those who suffer from it.

This study aimed to investigate the interactions influencing susceptibility to the development of dementia through multifactor dimensionality reduction (MDR).

the study population was made up of 221 cases and 534 controls. We performed an MDR analysis as well as a bioinformatic analysis to identify interactions between the genes GSTO1_rs4925, AGER_rs2070600, and ESR1_rs3844508 associated with susceptibility to dementia.

We observed associations between the polymorphism of GSTO1 and risk of dementia for the site rs4925 with the recessive model (OR = 1.720, 95% CI = 1.166-2.537 p = 0.006). Similarly, the site AGER rs2070600 showed risk of dementia with an additive model of inheritance (OR = 7.278, 95% CI = 3.140-16.868; p < 0.001). Furthermore, we identified the best risk model with a high precision of 79.6% that, when combined with three environmental risk factors, did not give an OR = 26.662 95%CI (16.164-43.979) with p < 0.001.

The MDR and bioinformatics results provide new information on the molecular pathogenesis of dementia, allowing identification of possible diagnostic markers and new therapeutic targets.

Cystic fibrosis (CF) is a complex monogenic disorder with a large variability in disease severity. Growing evidence suggests that the variation observed depends not only on variations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene but also on modifier genes. Utilizing five databases (including CINAHL, PubMed, Science Direct, Scopus, and Web of Science), a systematic review was conducted to examine the current literature on the known impacts of genomic variations in modifier genes on the CF disease progression, severity, and therapeutic response. A total of 70 full-text articles describing over 80 gene modifiers associated with CF were selected. The modifier genes included genes associated with the CFTR interactome, the inflammatory response, microbial profiles, and other genes affecting the critical physiological pathways of multiple organ systems, such as the respiratory and gastrointestinal systems. Limitations of the existing literature embrace the lack of clinical studies investigating pharmacogenetic impacts and the significance of gene modifiers on the CF clinical picture, including a limited number of replication and validation studies. Further investigations into other potential gene modifiers using genome-wide association studies are needed to critically explore new therapeutic targets and provide a better understanding of the CF disease phenotype under specific drug treatments.

Under the classification of multicategory survival outcomes of cancer patients, it is crucial to identify biomarkers that affect specific outcome categories. The classification of multicategory survival outcomes from transcriptomic data has been thoroughly investigated in computational biology. Nevertheless, several challenges must be addressed, including the ultra-high-dimensional feature space, feature contamination, and data imbalance, all of which contribute to the instability of the diagnostic model. Furthermore, although most methods achieve accurate predicted performance for binary classification with high-dimensional transcriptomic data, their extension to multi-class classification is not straightforward.

We employ the One-versus-One strategy to transform multi-class classification into multiple binary classification, and utilize the overlapping group screening procedure with binary logistic regression to include pathway information for identifying important genes and gene-gene interactions for multicategory survival outcomes.

A series of simulation studies are conducted to compare the classification accuracy of our proposed approach with some existing machine learning methods. In practical data applications, we utilize the random oversampling procedure to tackle class imbalance issues. We then apply the proposed method to analyze transcriptomic data from various cancers in The Cancer Genome Atlas, such as kidney renal papillary cell carcinoma, lung adenocarcinoma, and head and neck squamous cell carcinoma. Our aim is to establish an accurate microarray-based multicategory cancer diagnosis model. The numerical results illustrate that the new proposal effectively enhances cancer diagnosis compared to approaches that neglect pathway information.

We showcase the effectiveness of the proposed method in terms of class prediction accuracy through evaluations on simulated synthetic datasets as well as real dataset applications. We also identified the cancer-related gene-gene interaction biomarkers and reported the corresponding network structure. According to the identified major genes and gene-gene interactions, we can predict for each patient the probabilities that he/she belongs to each of the survival outcome classes.

The severity of respiratory syncytial virus (RSV) may be linked to host genetic susceptibility. Surfactant protein (SP) genetic variants have been associated with RSV severity, but the impact of single-nucleotide polymorphism (SNP)-SNP interactions remains unexplored. Therefore, we used a novel statistical model to investigate the association of SNP-SNP interactions of SFTP genes with RSV severity in two- and three-interaction models. We analyzed available genotype and clinical data from prospectively enrolled 405 children diagnosed with RSV, categorizing them into moderate or severe RSV groups. Using Wang's statistical model, we studied significant associations of SNP-SNP interactions with RSV severity in a case-control design. We observed, first, association of three interactions with increased risk of severe RSV in a two-SNP model. One intragenic interaction was between SNPs of SFTPA2, and the other two were intergenic, involving SNPs of hydrophilic and hydrophobic SPs alone. We also observed, second, association of 22 interactions with RSV severity in a three-SNP model. Among these, 20 were unique, with 12 and 10 interactions associated with increased or decreased risk of RSV severity, respectively, and included at least one SNP of either SFTPA1 or SFTPA2. All interactions were intergenic except one, among SNPs of SFTPA1. The remaining interactions were either among SNPs of hydrophilic SPs alone (n = 8) or among SNPs of both hydrophilic or hydrophobic SPs (n = 11). Our findings indicate that SNPs of all SFTPs may contribute to genetic susceptibility to RSV severity. However, the predominant involvement of SFTPA1 and/or SFTPA2 SNPs in these interactions underscores their significance in RSV severity.NEW & NOTEWORTHY Although surfactant protein (SP) genetic variants are associated with respiratory syncytial virus (RSV) severity, the impact of single-nucleotide polymorphism (SNP)-SNP interactions of SP genes remained unexplored. Using advanced statistical models, we uncovered 22 SNP-SNP interactions associated with RSV severity, with notable involvement of SFTPA1 and SFTPA2 SNPs. This highlights the comprehensive role of all SPs in genetic susceptibility to RSV severity, shedding light on potential avenues for targeted interventions.

The causes of Alzheimer's disease (AD) are poorly understood, although many genes are known to be involved in this pathology. To gain insights into the underlying molecular mechanisms, it is essential to identify the relationships between individual AD genes. Previous work has shown that the splice variant E of KLC1 (KLC1_vE) promotes AD, and that the CELF1 gene, which encodes an RNA-binding protein involved in splicing regulation, is at a risk locus for AD. Here, we identified a functional link between CELF1 and KLC1 in AD pathogenesis. Transcriptomic data from human samples from different ethnic groups revealed that CELF1 mRNA levels are low in AD brains, and the splicing pattern of KLC1 is strongly correlated with CELF1 expression levels. Specifically, KLC1_vE is negatively correlated with CELF1. Depletion and overexpression experiments in cultured cells demonstrated that the CELF1 protein down-regulates KLC1_vE. In a cross-linking and immunoprecipitation sequencing (CLIP-seq) database, CELF1 directly binds to KLC1 RNA, following which it likely modulates terminal exon usage, hence KLC1_vE formation. These findings reveal a new pathogenic pathway where a risk allele of CELF1 is associated with reduced CELF1 expression, which up-regulates KLC1_vE to promote AD.

Chronic kidney disease (CKD) is a heterogeneous disorder that affects the kidney structure and function. This study investigated the effect of the interaction between genetic factors and dietary pattern on kidney dysfunction in Korean adults.

Baseline data were obtained from the Ansan and Ansung Study of the Korean Genome and Epidemiology Study involving 8230 participants aged 40-69 years. Kidney dysfunction was defined as an estimated glomerular filtration rate < 90 mL/minute/1.73 m2. Genomic DNAs genotyped on the Affymetrix® Genome-Wide Human SNP array 5.0 were isolated from peripheral blood. A genome-wide association study using a generalized linear model was performed on 1,590,162 single-nucleotide polymorphisms (SNPs). To select significant SNPs, the threshold criterion was set at P-value < 5 × 10-8. Linkage disequilibrium clumping was performed based on the R2 value, and 94 SNPs had a significant effect. Participants were divided into two groups based on their generic risk score (GRS): the low-GR group had GRS > 0, while the high-GR group had GRS ≤ 0.

Three distinct dietary patterns were extracted, namely, the "prudent pattern," "flour-based and animal food pattern," and "white rice pattern," to analyze the effect of dietary pattern on kidney function. In the "flour-based and animal food pattern," higher pattern scores were associated with a higher prevalence of kidney dysfunction in both the low and high GR groups (P for trend < 0.0001 in the low-, high-GR groups of model 1; 0.0050 and 0.0065 in the low-, high-GR groups of model 2, respectively).

The results highlight a significant association between the 'flour-based and animal food pattern' and higher kidney dysfunction prevalence in individuals with both low and high GR. These findings suggest that personalized nutritional interventions based on GR profiles may become the basis for presenting GR-based individual dietary patterns for kidney dysfunction.

Single nucleotide polymorphism (SNP) interactions are the key to improving polygenic risk scores. Previous studies reported several significant SNP-SNP interaction pairs that shared a common SNP to form a cluster, but some identified pairs might be false positives. This study aims to identify factors associated with the cluster effect of false positivity and develop strategies to enhance the accuracy of SNP-SNP interactions. The results showed the cluster effect is a major cause of false-positive findings of SNP-SNP interactions. This cluster effect is due to high correlations between a causal pair and null pairs in a cluster. The clusters with a hub SNP with a significant main effect and a large minor allele frequency (MAF) tended to have a higher false-positive rate. In addition, peripheral null SNPs in a cluster with a small MAF tended to enhance false positivity. We also demonstrated that using the modified significance criterion based on the 3 p-value rules and the bootstrap approach (3pRule + bootstrap) can reduce false positivity and maintain high true positivity. In addition, our results also showed that a pair without a significant main effect tends to have weak or no interaction. This study identified the cluster effect and suggested using the 3pRule + bootstrap approach to enhance SNP-SNP interaction detection accuracy.

The supervised machine learning method is often used for biomedical relationship extraction. The disadvantage is that it requires much time and money to manually establish an annotated dataset. Based on distant supervision, the knowledge base is combined with the corpus, thus, the training corpus can be automatically annotated. As many biomedical databases provide knowledge bases for study with a limited number of annotated corpora, this method is practical in biomedicine. The clinical significance of each patient's genetic makeup can be understood based on the healthcare provider's genetic database. Unfortunately, the lack of previous biomedical relationship extraction studies focuses on gene-gene interaction. The main purpose of this study is to develop extraction methods for gene-gene interactions that can help explain the heritability of human complex diseases. This study referred to the information on gene-gene interactions in the KEGG PATHWAY database, the abstracts in PubMed were adopted to generate the training sample set, and the graph kernel method was adopted to extract gene-gene interactions. The best assessment result was an F1-score of 0.79. Our developed distant supervision method automatically finds sentences through the corpus without manual labeling for extracting gene-gene interactions, which can effectively reduce the time cost for manual annotation data; moreover, the relationship extraction method based on a graph kernel can be successfully applied to extract gene-gene interactions. In this way, the results of this study are expected to help achieve precision medicine.

Epistasis, or interactions in which alleles at one locus modify the fitness effects of alleles at other loci, plays a fundamental role in genetics, protein evolution, and many other areas of biology. Epistasis is typically quantified by computing the deviation from the expected fitness under an additive or multiplicative model using one of several formulae. However, these formulae are not all equivalent. Importantly, one widely used formula - which we call the chimeric formula - measures deviations from a multiplicative fitness model on an additive scale, thus mixing two measurement scales. We show that for pairwise interactions, the chimeric formula yields a different magnitude, but the same sign (synergistic vs. antagonistic) of epistasis compared to the multiplicative formula that measures both fitness and deviations on a multiplicative scale. However, for higher-order interactions, we show that the chimeric formula can have both different magnitude and sign compared to the multiplicative formula - thus confusing negative epistatic interactions with positive interactions, and vice versa. We resolve these inconsistencies by deriving fundamental connections between the different epistasis formulae and the parameters of the multivariate Bernoulli distribution . Our results demonstrate that the additive and multiplicative epistasis formulae are more mathematically sound than the chimeric formula. Moreover, we demonstrate that the mathematical issues with the chimeric epistasis formula lead to markedly different biological interpretations of real data. Analyzing multi-gene knockout data in yeast, multi-way drug interactions in E. coli , and deep mutational scanning (DMS) of several proteins, we find that 10 - 60% of higher-order interactions have a change in sign with the multiplicative or additive epistasis formula. These sign changes result in qualitatively different findings on functional divergence in the yeast genome, synergistic vs. antagonistic drug interactions, and and epistasis between protein mutations. In particular, in the yeast data, the more appropriate multiplicative formula identifies nearly 500 additional negative three-way interactions, thus extending the trigenic interaction network by 25%.

Epileptogenesis is the process by which a normal brain becomes hyperexcitable and capable of generating spontaneous recurrent seizures. The extensive dysregulation of gene expression associated with epileptogenesis is shaped, in part, by microRNAs (miRNAs) - short, non-coding RNAs that negatively regulate protein levels. Functional miRNA-mediated regulation can, however, be difficult to elucidate due to the complexity of miRNA-mRNA interactions. Here, we integrated miRNA and mRNA expression profiles sampled over multiple time-points during and after epileptogenesis in rats, and applied bi-clustering and Bayesian modelling to construct temporal miRNA-mRNA-mRNA interaction networks. Network analysis and enrichment of network inference with sequence- and human disease-specific information identified key regulatory miRNAs with the strongest influence on the mRNA landscape, and miRNA-mRNA interactions closely associated with epileptogenesis and subsequent epilepsy. Our findings underscore the complexity of miRNA-mRNA regulation, can be used to prioritise miRNA targets in specific systems, and offer insights into key regulatory processes in epileptogenesis with therapeutic potential for further investigation.

The DDR1 locus is associated with the diagnosis of schizophrenia and with processing speed in patients with schizophrenia and first-episode psychosis. Here, we investigated whether DDR1 variants are associated with bipolar disorder (BD) features. First, we performed a case‒control association study comparing DDR1 variants between patients with BD and healthy controls. Second, we performed linear regression analyses to assess the associations of DDR1 variants with neurocognitive domains and psychosocial functioning. Third, we conducted a mediation analysis to explore whether neurocognitive impairment mediated the association between DDR1 variants and psychosocial functioning in patients with BD. Finally, we studied the association between DDR1 variants and white matter microstructure. We did not find any statistically significant associations in the case‒control association study; however, we found that the combined genotypes rs1264323AA-rs2267641AC/CC were associated with worse neurocognitive performance in patients with BD with psychotic symptoms. In addition, the combined genotypes rs1264323AA-rs2267641AC/CC were associated with worse psychosocial functioning through processing speed. We did not find correlations between white matter microstructure abnormalities and the neurocognitive domains associated with the combined genotypes rs1264323AA-rs2267641AC/CC. Overall, the results suggest that DDR1 may be a marker of worse neurocognitive performance and psychosocial functioning in patients with BD, specifically those with psychotic symptoms.

The genome-wide association study (GWAS) aims to detect associations between individual single nucleotide polymorphisms (SNPs) or SNP interactions and phenotypes to decipher the genetic mechanism. Existing GWAS analysis tools have different focuses and advantages, but suffer a series of tedious and heterogeneous configurations for computation. It is inconvenient for researchers to simply choose and apply these tools, statistically and biologically analyze their results for different usages. To address these issues, we develop a user friendly web pipeline GWASTool for detecting associations, which includes simulation data generation, associated loci detection, result visualization, analysis and comparison. GWASTool provides a unified and plugin-able framework to encapsulate the heterogeneity of GWAS algorithms, simplifies the analysis steps and energizes GWAS tasks. GWASTool is implemented in Java and is freely available for public use at http://www.sdu-idea.cn/GWASTool. The website hosts a comprehensive collection of resources, including a user manual, description of integrated algorithms, data examples and standalone version for download.

Genome-wide association studies and candidate gene studies have identified several genetic variants that might play a role in achieving longevity. This study investigates interactions between pairs of those single nucleotide polymorphisms (SNPs) and their effect on survival above the age of 85 in a sample of 327 Croatian individuals. Although none of the SNPs individually showed a significant effect on survival in this sample, 14 of the 359 interactions tested (between SNPs not in LD) reached the level of nominal significance (p<0.05), showing a potential effect on late-life survival. Notably, SH2B3 rs3184504 interacted with different SNPs near TERC, TP53 rs1042522 with different SNPs located near the CDKN2B gene, and CDKN2B rs1333049 with different SNPs in FOXO3, as well as with LINC02227 rs2149954. The other interaction pairs with a possible effect on survival were FOXO3 rs2802292 and ERCC2 rs50871, IL6 rs1800795 and GHRHR rs2267723, LINC02227 rs2149954 and PARK7 rs225119, as well as PARK7 rs225119 and PTPN1 rs6067484. These interactions remained significant when tested together with a set of health-related variables that also had a significant effect on survival above 85 years. In conclusion, our results confirm the central role of genetic regulation of insulin signalling and cell cycle control in longevity.

Primary biliary cholangitis (PBC) and primary sclerosing cholangitis (PSC) are autoimmune disorders characterized by progressive and chronic damage to the bile ducts, presenting clinicians with significant challenges. The objective of this study is to identify potential druggable targets to offer new avenues for treatment. A Mendelian randomization analysis was performed to identify druggable targets for PBC and PSC. This involved obtaining Cis-protein quantitative trait loci (Cis-pQTL) data from the deCODE database to serve as exposure. Outcome data for PBC (557 cases and 281,127 controls) and PSC (1,715 cases and 330,903 controls) were obtained from the FINNGEN database. Colocalization analysis was conducted to determine whether these features share the same associated SNPs. Validation of the expression level of druggable targets was done using the GSE119600 dataset and immunohistochemistry for clinical samples. Lastly, the DRUGBANK database was used to predict potential drugs. The MR analysis identified eight druggable targets each for PBC and PSC. Subsequent summary-data-based MR and colocalization analyses showed that LEFTY2 had strong evidence as a therapeutic candidate for PBC, while HSPB1 had moderate evidence. For PSC, only FCGR3B showed strong evidence as a therapeutic candidate. Additionally, upregulated expression of these genes was validated in PBC and PSC groups by GEO dataset and clinical samples. This study identifies two novel druggable targets with strong evidence for therapeutic candidates for PBC (LEFTY2 and HSPB1) and one for PSC (FCGR3B). These targets offer new therapeutic opportunities to address the challenging nature of PBC and PSC treatment.

In Bayesian Network Regression models, networks are considered the predictors of continuous responses. These models have been successfully used in brain research to identify regions in the brain that are associated with specific human traits, yet their potential to elucidate microbial drivers in biological phenotypes for microbiome research remains unknown. In particular, microbial networks are challenging due to their high dimension and high sparsity compared to brain networks. Furthermore, unlike in brain connectome research, in microbiome research, it is usually expected that the presence of microbes has an effect on the response (main effects), not just the interactions. Here, we develop the first thorough investigation of whether Bayesian Network Regression models are suitable for microbial datasets on a variety of synthetic and real data under diverse biological scenarios. We test whether the Bayesian Network Regression model that accounts only for interaction effects (edges in the network) is able to identify key drivers (microbes) in phenotypic variability. We show that this model is indeed able to identify influential nodes and edges in the microbial networks that drive changes in the phenotype for most biological settings, but we also identify scenarios where this method performs poorly which allows us to provide practical advice for domain scientists aiming to apply these tools to their datasets. BNR models provide a framework for microbiome researchers to identify connections between microbes and measured phenotypes. We allow the use of this statistical model by providing an easy-to-use implementation which is publicly available Julia package at https://github.com/solislemuslab/BayesianNetworkRegression.jl.

Single-nucleotide polymorphism (SNP) codes for multiple amino acids, impacting protein functions and disease prognosis. Runt-related transcription factor-2 (RUNX2), a transcription factor linked to osteoblast differentiation, regulates cell proliferation in endothelium and osteoblastic cells. Understanding Runx2's role in nonosseous tissues is rapidly advancing. This study aims to identify harmful SNPs of the RUNX2 gene that may alter disease susceptibility using computational techniques.

The study uses various in silico methods to identify nonsynonymous SNPs (nsSNPs) of the RUNX2 gene, which could potentially alter protein structure and functions, with further analyses by I-Mutant, ConSurf, Netsurf 3.0, GeneMANIA, and Have (y)Our Protein Explained.

Six missense nsSNPs were identified as potentially harmful, disease-causing, and damaging. Four were found to be unstable, while five were conserved. All six nsSNPs had a coiled secondary structure. Five nsSNPs were found to be destabilized.

The RUNX2 gene's deleterious missense nsSNPs were identified by this study, and they may be exploited in future experimental studies. These high-risk nsSNPs might be considered target molecules in therapeutic and diagnostic therapies in teeth and bone development.

Genetic interactions have the potential to modulate phenotypes, including human disease. In principle, genome-wide association studies (GWAS) provide a platform for detecting genetic interactions; however, traditional methods for identifying them, which tend to focus on testing individual variant pairs, lack statistical power. In this protocol, we describe a novel computational approach, called Bridging Gene sets with Epistasis (BridGE), for discovering genetic interactions between biological pathways from GWAS data. We present a Python-based implementation of BridGE along with instructions for its application to a typical human GWAS cohort. The major stages include initial data processing and quality control, construction of a variant-level genetic interaction network, measurement of pathway-level genetic interactions, evaluation of statistical significance using sample permutations and generation of results in a standardized output format. The BridGE software pipeline includes options for running the analysis on multiple cores and multiple nodes for users who have access to computing clusters or a cloud computing environment. In a cluster computing environment with 10 nodes and 100 GB of memory per node, the method can be run in less than 24 h for typical human GWAS cohorts. Using BridGE requires knowledge of running Python programs and basic shell script programming experience.

Artificial intelligence (AI) is a thriving research field with many successful applications in areas such as computer vision and speech recognition. Machine learning methods, such as artificial neural networks (ANN), play a central role in modern AI technology. While ANN also holds great promise for human genetic research, the high-dimensional genetic data and complex genetic structure bring tremendous challenges. The vast majority of genetic variants on the genome have small or no effects on diseases, and fitting ANN on a large number of variants without considering the underlying genetic structure (e.g., linkage disequilibrium) could bring a serious overfitting issue. Furthermore, while a single disease phenotype is often studied in a classic genetic study, in emerging research fields (e.g., imaging genetics), researchers need to deal with different types of disease phenotypes. To address these challenges, we propose a functional neural networks (FNN) method. FNN uses a series of basis functions to model high-dimensional genetic data and a variety of phenotype data and further builds a multi-layer functional neural network to capture the complex relationships between genetic variants and disease phenotypes. Through simulations, we demonstrate the advantages of FNN for high-dimensional genetic data analysis in terms of robustness and accuracy. The real data applications also showed that FNN attained higher accuracy than the existing methods.

Statistical epistasis, or "gene-gene interaction" in genetic association studies, means the nonadditive effects between the polymorphic sites on two different genes affecting the same phenotype. In the genetic association analysis of complex traits, nevertheless, the researchers haven't found enough clues of statistical epistasis so far.

We developed a statistical model where the statistical epistasis was presented as an extra linkage disequilibrium between the polymorphic sites of different risk genes. The power of statistical test for identifying the gene-gene interaction was calculated and then compared in different hypothesis scenarios.

Our results show the statistical power increases with the increasing of interaction coefficient, relative risk, and linkage disequilibrium with genetic markers. However, the power of interaction discovery is much lower than that of regular single-site association test. When rigorous criteria were employed in statistical tests, the identification of gene-gene interaction became a very difficult task. Since the criterion of significance was given to be p-value ≤ 5.0 × 10-8, the same as that of many genome-wide association studies, there is little chance to identify the gene-gene interaction in all kind of circumstances.

The lack of epistasis tends to be an inevitable result caused by the statistical principles of methods in the genetic association studies and therefore is the inherent characteristic of the research itself.

The role of gene-environment (GxE) interaction in disease and complex trait architectures is widely hypothesized, but currently unknown. Here, we apply three statistical approaches to quantify and distinguish three different types of GxE interaction for a given trait and E variable. First, we detect locus-specific GxE interaction by testing for genetic correlation r g < 1 across E bins. Second, we detect genome-wide effects of the E variable on genetic variance by leveraging polygenic risk scores (PRS) to test for significant PRSxE in a regression of phenotypes on PRS, E, and PRSxE, together with differences in SNP-heritability across E bins. Third, we detect genome-wide proportional amplification of genetic and environmental effects as a function of the E variable by testing for significant PRSxE with no differences in SNP-heritability across E bins. Simulations show that these approaches achieve high sensitivity and specificity in distinguishing these three GxE scenarios. We applied our framework to 33 UK Biobank traits (25 quantitative traits and 8 diseases; average N = 325 K ) and 10 E variables spanning lifestyle, diet, and other environmental exposures. First, we identified 19 trait-E pairs with r g significantly < 1 (FDR<5%) (average r g = 0.95 ); for example, white blood cell count had r g = 0.95 (s.e. 0.01) between smokers and non-smokers. Second, we identified 28 trait-E pairs with significant PRSxE and significant SNP-heritability differences across E bins; for example, BMI had a significant PRSxE for physical activity (P=4.6e-5) with 5% larger SNP-heritability in the largest versus smallest quintiles of physical activity (P=7e-4). Third, we identified 15 trait-E pairs with significant PRSxE with no SNP-heritability differences across E bins; for example, waist-hip ratio adjusted for BMI had a significant PRSxE effect for time spent watching television (P=5e-3) with no SNP-heritability differences. Across the three scenarios, 8 of the trait-E pairs involved disease traits, whose interpretation is complicated by scale effects. Analyses using biological sex as the E variable produced additional significant findings in each of the three scenarios. Overall, we infer a significant contribution of GxE and GxSex effects to complex trait and disease variance.

Detecting epistatic drivers of human phenotypes is a considerable challenge. Traditional approaches use regression to sequentially test multiplicative interaction terms involving pairs of genetic variants. For higher-order interactions and genome-wide large-scale data, this strategy is computationally intractable. Moreover, multiplicative terms used in regression modeling may not capture the form of biological interactions. Building on the Predictability, Computability, Stability (PCS) framework, we introduce the epiTree pipeline to extract higher-order interactions from genomic data using tree-based models. The epiTree pipeline first selects a set of variants derived from tissue-specific estimates of gene expression. Next, it uses iterative random forests (iRF) to search training data for candidate Boolean interactions (pairwise and higher-order). We derive significance tests for interactions, based on a stabilized likelihood ratio test, by simulating Boolean tree-structured null (no epistasis) and alternative (epistasis) distributions on hold-out test data. Finally, our pipeline computes PCS epistasis p-values that probabilisticly quantify improvement in prediction accuracy via bootstrap sampling on the test set. We validate the epiTree pipeline in two case studies using data from the UK Biobank: predicting red hair and multiple sclerosis (MS). In the case of predicting red hair, epiTree recovers known epistatic interactions surrounding MC1R and novel interactions, representing non-linearities not captured by logistic regression models. In the case of predicting MS, a more complex phenotype than red hair, epiTree rankings prioritize novel interactions surrounding HLA-DRB1, a variant previously associated with MS in several populations. Taken together, these results highlight the potential for epiTree rankings to help reduce the design space for follow up experiments.

Despite the importance of gene-environment interactions (GxEs) in improving and operationalizing genetic discovery, interpretation of any GxEs that are discovered can be surprisingly difficult. There are many potential biological and statistical explanations for a statistically significant finding and, likewise, it is not always clear what can be claimed based on a null result. A better understanding of the possible underlying mechanisms leading to a detected GxE can help investigators decide which are and which are not relevant to their hypothesis. Here, we provide a detailed explanation of five "phenomena," or data-generating mechanisms, that can lead to nonzero interaction estimates, as well as a discussion of specific instances in which they might be relevant. We hope that, given this framework, investigators can design more targeted experiments and provide cleaner interpretations of the associated results.

Unraveling the genetic sources of gene expression variation is essential to better understand the origins of phenotypic diversity in natural populations. Genome-wide association studies identified thousands of variants involved in gene expression variation, however, variants detected only explain part of the heritability. In fact, variants such as low-frequency and structural variants (SVs) are poorly captured in association studies. To assess the impact of these variants on gene expression variation, we explored a half-diallel panel composed of 323 hybrids originated from pairwise crosses of 26 natural Saccharomyces cerevisiae isolates. Using short- and long-read sequencing strategies, we established an exhaustive catalog of single nucleotide polymorphisms (SNPs) and SVs for this panel. Combining this dataset with the transcriptomes of all hybrids, we comprehensively mapped SNPs and SVs associated with gene expression variation. While SVs impact gene expression variation, SNPs exhibit a higher effect size with an overrepresentation of low-frequency variants compared to common ones. These results reinforce the importance of dissecting the heritability of complex traits with a comprehensive catalog of genetic variants at the population level.

Migraine is a severe, debilitating neurovascular disorder. Hemiplegic migraine (HM) is a rare and debilitating neurological condition with a strong genetic basis. Sequencing technologies have improved the diagnosis and our understanding of the molecular pathophysiology of HM. Linkage analysis and sequencing studies in HM families have identified pathogenic variants in ion channels and related genes, including CACNA1A, ATP1A2, and SCN1A, that cause HM. However, approximately 75% of HM patients are negative for these mutations, indicating there are other genes involved in disease causation. In this review, we explored our current understanding of the genetics of HM. The evidence presented herein summarises the current knowledge of the genetics of HM, which can be expanded further to explain the remaining heritability of this debilitating condition. Innovative bioinformatics and computational strategies to cover the entire genetic spectrum of HM are also discussed in this review.

The problem of missing heritability requires the consideration of genetic interactions among different loci, called epistasis. Current GWAS statistical models require years to assess the entire combinatorial epistatic space for a single phenotype. We propose Next-Gen GWAS (NGG) that evaluates over 60 billion single nucleotide polymorphism combinatorial first-order interactions within hours. We apply NGG to Arabidopsis thaliana providing two-dimensional epistatic maps at gene resolution. We demonstrate on several phenotypes that a large proportion of the missing heritability can be retrieved, that it indeed lies in epistatic interactions, and that it can be used to improve phenotype prediction.

Cardiometabolic diseases (CMDs) are complex disorders with a heterogenous phenotype, which are caused by multiple factors including genetic factors. Single nucleotide polymorphisms (SNPs) rs45539933 (p.Ala64Thr), rs10011540 (c.-112A>C), rs3811791 (c.-1766A>G), and rs1800592 (c.-3826A>G) in the UCP1 gene have been analyzed for association with CMDs in many studies providing controversial results. However, previous studies only considered individual UCP1 SNPs and did not evaluate them in an integrated manner, which is a more powerful approach to uncover genetic component of complex diseases. This study aimed to investigate associations between UCP1 genotype combinations and CMDs or CMD risk factors in the context of non-genetic factors. We performed multiple logistic regression analysis and proposed new methodology of testing different combinations of SNP genotypes. We found that probability of CMDs increased in presence of the three-SNP combination of genotypes with minor alleles of c.-3826A>G and p.Ala64Thr and wild allele of c.-112A>C, with increasing age, body mass index (BMI), body fat percentage (BF%) and may differ between sexes and between countries. The combination of genotypes with c.-3826A>G minor allele and wild homozygotes of c.-112A>C and p.Ala64Thr was associated with increased probability of diabetes. While combination of genotypes with minor alleles of all three SNPs reduced the CMD probability. The present results suggest that age, BMI, sex, and UCP1 three-SNP combinations of genotypes significantly contribute to CMD probability. Varying of c.-112A>C alleles in the genotype combination with minor alleles of c.-3826A>G and p.Ala64Thr markedly changes CMD probability.

Plants synthesize specialized metabolites to facilitate environmental and ecological interactions. During evolution, plants diversified in their potential to synthesize these metabolites. Quantitative differences in metabolite levels of natural Arabidopsis (Arabidopsis thaliana) accessions can be employed to unravel the genetic basis for metabolic traits using genome-wide association studies (GWAS). Here, we performed metabolic GWAS on seeds of a panel of 315 A. thaliana natural accessions, including the reference genotypes C24 and Col-0, for polar and semi-polar seed metabolites using untargeted ultra-performance liquid chromatography-mass spectrometry. As a complementary approach, we performed quantitative trait locus (QTL) mapping of near-isogenic introgression lines between C24 and Col-0 for specific seed specialized metabolites. Besides common QTL between seeds and leaves, GWAS revealed seed-specific QTL for specialized metabolites, indicating differences in the genetic architecture of seeds and leaves. In seeds, aliphatic methylsulfinylalkyl and methylthioalkyl glucosinolates associated with the ALKENYL HYDROXYALKYL PRODUCING loci (GS-ALK and GS-OHP) on chromosome 4 containing alkenyl hydroxyalkyl producing 2 (AOP2) and 3 (AOP3) or with the GS-ELONG locus on chromosome 5 containing methylthioalkyl malate synthase (MAM1) and MAM3. We detected two unknown sulfur-containing compounds that were also mapped to these loci. In GWAS, some of the annotated flavonoids (kaempferol 3-O-rhamnoside-7-O-rhamnoside, quercetin 3-O-rhamnoside-7-O-rhamnoside) were mapped to transparent testa 7 (AT5G07990), encoding a cytochrome P450 75B1 monooxygenase. Three additional mass signals corresponding to quercetin-containing flavonols were mapped to UGT78D2 (AT5G17050). The association of the loci and associating metabolic features were functionally verified in knockdown mutant lines. By performing GWAS and QTL mapping, we were able to leverage variation of natural populations and parental lines to study seed specialized metabolism. The GWAS data set generated here is a high-quality resource that can be investigated in further studies.

Pharmacogenetics currently supports clinical decision-making on the basis of a limited number of variants in a few genes and may benefit paediatric prescribing where there is a need for more precise dosing. Integrating genomic information such as methylation into pharmacogenetic models holds the potential to improve their accuracy and consequently prescribing decisions. Cytochrome P450 2D6 (CYP2D6) is a highly polymorphic gene conventionally associated with the metabolism of commonly used drugs and endogenous substrates. We thus sought to predict epigenetic loci from single nucleotide polymorphisms (SNPs) related to CYP2D6 in children from the GUSTO cohort.

Buffy coat DNA methylation was quantified using the Illumina Infinium Methylation EPIC beadchip. CpG sites associated with CYP2D6 were used as outcome variables in Linear Regression, Elastic Net and XGBoost models. We compared feature selection of SNPs from GWAS mQTLs, GTEx eQTLs and SNPs within 2 MB of the CYP2D6 gene and the impact of adding demographic data. The samples were split into training (75%) sets and test (25%) sets for validation. In Elastic Net model and XGBoost models, optimal hyperparameter search was done using 10-fold cross validation. Root Mean Square Error and R-squared values were obtained to investigate each models' performance. When GWAS was performed to determine SNPs associated with CpG sites, a total of 15 SNPs were identified where several SNPs appeared to influence multiple CpG sites.

Overall, Elastic Net models of genetic features appeared to perform marginally better than heritability estimates and substantially better than Linear Regression and XGBoost models. The addition of nongenetic features appeared to improve performance for some but not all feature sets and probes. The best feature set and Machine Learning (ML) approach differed substantially between CpG sites and a number of top variables were identified for each model.

The development of SNP-based prediction models for CYP2D6 CpG methylation in Singaporean children of varying ethnicities in this study has clinical application. With further validation, they may add to the set of tools available to improve precision medicine and pharmacogenetics-based dosing.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia in the elderly worldwide. The exact etiology of AD, particularly its genetic mechanisms, remains incompletely understood. Traditional genome-wide association studies (GWAS), which primarily focus on single-nucleotide polymorphisms (SNPs) with main effects, provide limited explanations for the "missing heritability" of AD, while there is growing evidence supporting the important role of epistasis. In this study, we performed a genome-wide SNP-SNP interaction detection using a linear regression model and employed multiple GPUs for parallel computing, significantly enhancing the speed of whole-genome analysis. The cerebrospinal fluid (CSF) phosphorylated tau (P-tau)/amyloid-[Formula: see text] (A[Formula: see text]) ratio was used as a quantitative trait (QT) to enhance statistical power. Age, gender, and clinical diagnosis were included as covariates to control for potential non-genetic factors influencing AD. We identified 961 pairs of statistically significant SNP-SNP interactions, explaining a high-level variance of P-tau/A[Formula: see text] level, all of which exhibited marginal main effects. Additionally, we replicated 432 previously reported AD-related genes and found 11 gene-gene interaction pairs overlapping with the protein-protein interaction (PPI) network. Our findings may contribute to partially explain the "missing heritability" of AD. The identified subnetwork may be associated with synaptic dysfunction, Wnt signaling pathway, oligodendrocytes, inflammation, hippocampus, and neuronal cells.

Psoriasis and vitiligo are inflammatory autoimmune skin disorders with remarkable genetic involvement. Mannose-binding lectin (MBL) represents a significant immune molecule with one of its gene variants strongly linked to autoimmune diseases. Therefore, in this study, we investigated the role of the MBL variant, rs1800450, in psoriasis and vitiligo disease susceptibility.

The study comprised performing in silico analysis, performing an observational study regarding psoriasis patients, and performing an observational study regarding vitiligo patients. Various in silico tools were used to investigate the impact of the selected mutation on the function, stability, post-translational modifications (PTMs), and secondary structures of the protein. In addition, a total of 489 subjects were enrolled in this study, including their demographic and clinicopathological data. Genotyping analysis was performed using real-time PCR for the single nucleotide polymorphism (SNP) rs1800450 on codon 54 of the MBL gene, utilizing TaqMan genotyping technology. In addition, implications of the studied variant on disease susceptibility and various clinicopathological data were analyzed.

Computational analysis demonstrated the anticipated effects of the mutation on MBL protein. Furthermore, regarding the observational studies, rs1800450 SNP on codon 54 displayed comparable results in our population relative to global frequencies reported via the 1,000 Genomes Project. This SNP showed no significant association with either psoriasis or vitiligo disease risk in all genetic association models. Furthermore, rs1800450 SNP did not significantly correlate with any of the demographic or clinicopathological features of both psoriasis and vitiligo.

Our findings highlighted that the rs1800450 SNP on the MBL2 gene has no role in the disease susceptibility to autoimmune skin diseases, such as psoriasis and vitiligo, among Egyptian patients. In addition, our analysis advocated the notion of the redundancy of MBL and revealed the lack of significant impact on both psoriasis and vitiligo disorders.

Neurodegeneration is a protracted process involving progressive changes in myriad cell types that ultimately results in the death of vulnerable neuronal populations. To dissect how individual cell types within a heterogeneous tissue contribute to the pathogenesis and progression of a neurodegenerative disorder, we performed longitudinal single-nucleus RNA sequencing of mouse and human spinocerebellar ataxia type 1 (SCA1) cerebellar tissue, establishing continuous dynamic trajectories of each cell population. Importantly, we defined the precise transcriptional changes that precede loss of Purkinje cells and, for the first time, identified robust early transcriptional dysregulation in unipolar brush cells and oligodendroglia. Finally, we applied a deep learning method to predict disease state accurately and identified specific features that enable accurate distinction of wild-type and SCA1 cells. Together, this work reveals new roles for diverse cerebellar cell types in SCA1 and provides a generalizable analysis framework for studying neurodegeneration.