Explore the vast range of titles available.

Background Common and complex traits are the consequence of the interaction and regulation of multiple genes simultaneously, therefore characterizing the interconnectivity of genes is essential to unravel the underlying biological networks. However, the focus of many studies is on the differential expression of individual genes or on co-expression analysis. Methods Going beyond analysis of one gene at a time, we systematically integrated transcriptomics, genotypes and Hi-C data to identify interconnectivities among individual genes as a causal network. We utilized different machine learning techniques to extract information from the network and identify differential regulatory pattern between cases and controls. We used data from the Allen Brain Atlas for replication. Results Employing the integrative systems approach on the data from CommonMind Consortium showed that gene transcription is controlled by genetic variants proximal to the gene (cis-regulatory factors), and transcribed distal genes (trans-regulatory factors). We identified differential gene regulatory patterns in SCZ-cases versus controls and novel SCZ-associated genes that may play roles in the disorder since some of them are primary expressed in human brain. In addition, we observed genes known associated with SCZ are not likely (OR = 0.59) to have high impacts (degree > 3) on the network. Conclusions Causal networks could reveal underlying patterns and the role of genes individually and as a group. Establishing principles that govern relationships between genes provides a mechanistic understanding of the dysregulated gene transcription patterns in SCZ and creates more efficient experimental designs for further studies. This information cannot be obtained by studying a single gene at the time.

Background Many genome-wide association studies have detected genomic regions associated with traits, yet understanding the functional causes of association often remains elusive. Utilizing systems approaches and focusing on intermediate molecular phenotypes might facilitate biologic understanding. Results The availability of exome sequencing of two populations of African-Americans and European-Americans from the Atherosclerosis Risk in Communities study allowed us to investigate the effects of annotated loss-of-function (LoF) mutations on 122 serum metabolites. To assess the findings, we built metabolomic causal networks for each population separately and utilized structural equation modeling. We then validated our findings with a set of independent samples. By use of methods based on concepts of Mendelian randomization of genetic variants, we showed that some of the affected metabolites are risk predictors in the causal pathway of disease. For example, LoF mutations in the gene KIAA1755 were identified to elevate the levels of eicosapentaenoate ( p -value = 5E-14), an essential fatty acid clinically identified to increase essential hypertension. We showed that this gene is in the pathway to triglycerides, where both triglycerides and essential hypertension are risk factors of metabolomic disorder and heart attack. We also identified that the gene CLDN17, harboring loss-of-function mutations, had pleiotropic actions on metabolites from amino acid and lipid pathways. Conclusion Using systems biology approaches for the analysis of metabolomics and genetic data, we integrated several biological processes, which lead to findings that may functionally connect genetic variants with complex diseases.

Drug-induced liver disease (DILI) represents one of the main problems in the therapeutic field. There are several non-modifiable risk factors, such as age and sex, and all drugs can cause hepatotoxicity of varying degrees, including those for the treatment of inflammatory bowel diseases (IBD). The aim of this review is to illustrate the adverse effects on the liver of the various drugs used in the treatment of IBD, highlighting which drugs are safest to use based on current knowledge. The mechanism by which drugs cause hepatotoxicity is not fully understood. A possible cause is represented by the formation of toxic metabolites, which in some patients may be increased due to alterations in the enzymatic apparatus involved in drug metabolism. Various studies have shown that the drugs that can most frequently cause hepatotoxicity are immunosuppressants, while mesalazine and biological drugs are, for the most part, less associated with such complications. Therefore, it is possible to assume that in the future, biological therapies could become the first line for the treatment of IBD.

Heart failure is a major cause for premature death. Given the heterogeneity of the heart failure syndrome, identifying genetic determinants of cardiac function and structure may provide greater insights into heart failure. Despite progress in understanding the genetic basis of heart failure through genome wide association studies, the heritability of heart failure is not well understood. Gaining further insights into mechanisms that contribute to heart failure requires systematic approaches that go beyond single trait analysis. We integrated a Bayesian multi-trait approach and a Bayesian networks for the analysis of 10 correlated traits of cardiac structure and function measured across 3387 individuals with whole exome sequence data. While using single-trait based approaches did not find any significant genetic variant, applying the integrative Bayesian multi-trait approach, we identified 3 novel variants located in genes, RGS3 , CHD3 , and MRPL38 with significant impact on the cardiac traits such as left ventricular volume index, parasternal long axis interventricular septum thickness, and mean left ventricular wall thickness. Among these, the rare variant NC_000009.11:g.116346115C > A (rs144636307) in RGS3 showed pleiotropic effect on left ventricular mass index, left ventricular volume index and maximal left atrial anterior-posterior diameter while RGS3 can inhibit TGF-beta signaling associated with left ventricle dilation and systolic dysfunction.

Background The human exposome is composed of diverse metabolites and small chemical compounds originated from endogenous and exogenous sources, respectively. Genetic and environmental factors influence metabolite levels, while the extent of genetic contributions across metabolic pathways is not yet known. Untargeted profiling of human metabolome using high-resolution mass spectrometry (HRMS) combined with genome-wide genotyping allows comprehensive identification of genetically influenced metabolites. As such previous studies of adults discovered and replicated genotype–metabotype associations. However, these associations have not been characterized in children. Results We conducted the largest genome by metabolome-wide association study to date of children ( N  = 441) using 619,688 common genetic variants and 14,342 features measured by HRMS. Narrow-sense heritability ( h 2 ) estimates of plasma metabolite concentrations using genomic relatedness matrix restricted maximum likelihood (GREML) method showed a bimodal distribution with high h 2 (> 0.8) for 15.9% of features and low h 2 (< 0.2) for most of features (62.0%). The features with high h 2 were enriched for amino acid and nucleic acid metabolism, while carbohydrate and lipid concentrations showed low h 2 . For each feature, a metabolite quantitative trait loci (mQTL) analysis was performed to identify genetic variants that were potentially associated with plasma levels. Fifty-four associations among 29 features and 43 genetic variants were identified at a genome-wide significance threshold p  < 3.5 × 10 –12 (= 5 × 10 –8 /14,342 features). Previously reported associations such as UGT1A1 and bilirubin; PYROXD2 and methyl lysine; and ACADS and butyrylcarnitine were successfully replicated in our pediatric cohort. We found potential candidates for novel associations including CSMD1 and a monostearyl alcohol triglyceride ( m/z 781.7483, retention time (RT) 89.3 s); CALN1 and Tridecanol ( m/z 283.2741, RT 27.6). A gene-level enrichment analysis using MAGMA revealed highly interconnected modules for dADP biosynthesis, sterol synthesis, and long-chain fatty acid transport in the gene-feature network. Conclusion Comprehensive profiling of plasma metabolome across age groups combined with genome-wide genotyping revealed a wide range of genetic influence on diverse chemical species and metabolic pathways. The developmental trajectory of a biological system is shaped by gene–environment interaction especially in early life. Therefore, continuous efforts on generating metabolomics data in diverse human tissue types across age groups are required to understand gene–environment interaction toward healthy aging trajectories.

Background Gene signatures derived from transcriptomic-causal networks offer potential for tailoring clinical care in cancer treatment by identifying predictive and prognostic biomarkers. This study aimed to uncover such signatures in metastatic colorectal cancer (CRC) patients to aid treatment decisions. Methods We constructed transcriptomic-causal networks and integrated gene interconnectivity into overall survival (OS) analysis to control for confounding genes. This integrative approach involved germline genotype and tumor RNA-seq data from 1165 metastatic CRC patients. The patients were enrolled in a randomized clinical trial receiving either cetuximab or bevacizumab in combination with chemotherapy. An external cohort of paired CRC normal and tumor samples, along with protein-protein interaction databases, was used for replication. Results We identify promising predictive and prognostic gene signatures from pre-treatment gene expression profiles. Our study discerns sets of genes, each forming a signature that collectively contribute to define patient subgroups with different prognosis and response to the therapies. Using an external cohort, we show that the genes influencing OS within the signatures, such as FANCI and PRC1 , are upregulated in CRC tumor vs. normal tissue. These signatures are highly associated with immune features, including macrophages, cytotoxicity, and wound healing. Furthermore, the corresponding proteins encoded by the genes within the signatures interact with each other and are functionally related. Conclusions This study underscores the utility of gene signatures derived from transcriptomic-causal networks in patient stratification for effective therapies. The interpretability of the findings, supported by replication, highlights the potential of these signatures to identify patients likely to benefit from cetuximab or bevacizumab. Plain language summary Response to cancer treatment varies greatly among patients. To improve outcomes, it is crucial to identify patients who are more likely to respond well to particular treatments. Changes in gene expression within cancer cells lead to alterations in cellular behavior, which can influence the response to treatment. In this study, we investigated how these changes affect patient outcomes. We identified specific gene expression patterns in patients who benefited from certain treatments. These findings could help guide treatment decisions, enabling personalized therapies that are more likely to be effective and improve patient outcomes. Yazdani et al. identify gene signatures by integrating transcriptomic-causal networks into overall survival analysis, providing interpretable biomarkers for patient stratification. Gene signatures are found in metastatic colorectal cancer that indicate patients with specific profiles might benefit from cetuximab or bevacizumab.

Background Availability of affordable and accessible whole genome sequencing for biomedical applications poses a number of statistical challenges and opportunities, particularly related to the analysis of rare variants and sparseness of the data. Although efforts have been devoted to address these challenges, the performance of statistical methods for rare variants analysis still needs further consideration. Result We introduce a new approach that applies restricted principal component analysis with convex penalization and then selects the best predictors of a phenotype by a concave penalized regression model, while estimating the impact of each genomic region on the phenotype. Using simulated data, we show that the proposed method maintains good power for association testing while keeping the false discovery rate low under a verity of genetic architectures. Illustrative data analyses reveal encouraging result of this method in comparison with other commonly applied methods for rare variants analysis. Conclusion By taking into account linkage disequilibrium and sparseness of the data, the proposed method improves power and controls the false discovery rate compared to other commonly applied methods for rare variant analyses.

Common and complex traits are the consequence of the interaction and regulation of multiple genes simultaneously, therefore characterizing the interconnectivity of genes is essential to unravel the underlying biological networks. However, the focus of many studies is on the differential expression of individual genes or on co-expression analysis. Going beyond analysis of one gene at a time, we systematically integrated transcriptomics, genotypes and Hi-C data to identify interconnectivities among individual genes as a causal network. We utilized different machine learning techniques to extract information from the network and identify differential regulatory pattern between cases and controls. We used data from the Allen Brain Atlas for replication. Employing the integrative systems approach on the data from CommonMind Consortium showed that gene transcription is controlled by genetic variants proximal to the gene (cis-regulatory factors), and transcribed distal genes (trans-regulatory factors). We identified differential gene regulatory patterns in SCZ-cases versus controls and novel SCZ-associated genes that may play roles in the disorder since some of them are primary expressed in human brain. In addition, we observed genes known associated with SCZ are not likely (OR = 0.59) to have high impacts (degree > 3) on the network. Causal networks could reveal underlying patterns and the role of genes individually and as a group. Establishing principles that govern relationships between genes provides a mechanistic understanding of the dysregulated gene transcription patterns in SCZ and creates more efficient experimental designs for further studies. This information cannot be obtained by studying a single gene at the time.

Availability of affordable and accessible whole genome sequencing for biomedical applications poses a number of statistical challenges and opportunities, particularly related to the analysis of rare variants and sparseness of the data. Although efforts have been devoted to address these challenges, the performance of statistical methods for rare variants analysis still needs further consideration. We introduce a new approach that applies restricted principal component analysis with convex penalization and then selects the best predictors of a phenotype by a concave penalized regression model, while estimating the impact of each genomic region on the phenotype. Using simulated data, we show that the proposed method maintains good power for association testing while keeping the false discovery rate low under a verity of genetic architectures. Illustrative data analyses reveal encouraging result of this method in comparison with other commonly applied methods for rare variants analysis. By taking into account linkage disequilibrium and sparseness of the data, the proposed method improves power and controls the false discovery rate compared to other commonly applied methods for rare variant analyses.

Hepatitis-splenomegaly syndrome is caused by avian hepatitis E virus (aHEV), a nonenveloped, single-stranded RNA virus. The economic importance of this disease in the poultry industry is due to the decline in egg production (10%–40%) and the rise in mortality (1%–4%). In the present study, 1540 serum samples from 33 broiler breeder flocks were analyzed by an enzyme-linked immunosorbent assay for the presence of an anti-aHEV antibody. In addition, a diagnostic nested reverse transcriptase-PCR was done on all farm samples. In the serologic study, 66.7% (22/33) of the flocks and 28.5% (439/1540) of the chickens were positive. The molecular study showed that three farms were positive, and PCR products were observed for the conserved regions of the aHEV helicase and capsid virus genes as 386 bp and 242 bp, respectively. It should be noted that clinical and pathologic symptoms including decreased egg production, enlarged livers and spleens, and a slight rise in mortality rate were observed in eight farms. To our knowledge, this is the first documented study on the aHEV identification and its antibody detection in broiler breeder farms in Iran.