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Hemizygous genes, which are present on only one of the two homologous chromosomes of diploid organisms, have been mainly studied in the context of sex chromosomes and sex-linked genes. However, these genes can also occur on the autosomes of diploid plants due to structural variants (SVs), such as a deletion/insertion of one allele, and this phenomenon largely unexplored in plants. Here, we investigated the genomic and epigenomic landscapes of hemizygous genes across 22 genomes with varying propagation histories: eleven clonal lineages, seven outcrossed samples, and four inbred and putatively homozygous genomes. We identified SVs leading to genic hemizygosity. As expected, very few genes (0.01 to 1.2%) were hemizygous in the homozygous genomes, representing negative controls. Hemizygosity was appreciable among outcrossed lineages, averaging 8.7% of genes, but consistently elevated for the clonal samples at 13.8% genes, likely reflecting heterozygous SV accumulation during clonal propagation. Compared to diploid genes, hemizygous genes were more often situated in centromeric than telomeric regions and experienced weaker purifying selection. They also had reduced levels of expression, averaging ~20% of the expression levels of diploid genes, violating the evolutionary model of dosage compensation. We also detected higher DNA methylation levels in hemizygous genes and transposable elements, which may contribute to their reduced expression. Finally, expression profiles showed that hemizygous genes were more specifically expressed in contexts related to fruit development, organ differentiation, and stress responses. Overall, hemizygous genes accumulate in clonally propagated lineages and display distinct genetic and epigenetic features compared to diploid genes, shedding unique insights into genetic studies and breeding programs of clonal crops. [ABSTRACT FROM AUTHOR]

Abstract The Ets domain transcription factors direct diverse biological processes throughout all metazoans and are implicated in development as well as in tumor initiation, progression and metastasis. The Drosophila Ets transcription factor Pointed (Pnt) is the downstream effector of the Epidermal growth factor receptor (Egfr) pathway and is required for cell cycle progression, specification, and differentiation of most cell types in the larval eye disc. Despite its critical role in development, very few targets of Pnt have been reported previously. Here, we employed an integrated approach by combining genome-wide single cell and bulk data to identify putative cell type-specific Pnt targets. First, we used chromatin immunoprecipitation with high-throughput sequencing (ChIP-seq) to determine the genome-wide occupancy of Pnt in late larval eye discs. We identified enriched regions that mapped to an average of 6,941 genes, the vast majority of which are novel putative Pnt targets. Next, we integrated ChIP-seq data with two other larval eye single cell genomics datasets (scRNA-seq and snATAC-seq) to reveal 157 putative cell type-specific Pnt targets that may help mediate unique cell type responses upon Egfr-induced differentiation. Finally, our integrated data also predicts cell type-specific functional enhancers that were not reported previously. Together, our study provides a greatly expanded list of putative cell type-specific Pnt targets in the eye and is a resource for future studies that will allow mechanistic insights into complex developmental processes regulated by Egfr signaling.

The Ets domain transcription factors direct diverse biological processes throughout all metazoans and are implicated in development as well as in tumor initiation, progression and metastasis. The Drosophila Ets transcription factor Pointed (Pnt) is the downstream effector of the Epidermal growth factor receptor (Egfr) pathway and is required for cell cycle progression, specification, and differentiation of most cell types in the larval eye disc. Despite its critical role in development, very few targets of Pnt have been reported previously. Here, we employed an integrated approach by combining genome-wide single cell and bulk data to identify putative cell type-specific Pnt targets. First, we used chromatin immunoprecipitation with high-throughput sequencing (ChIP-seq) to determine the genome-wide occupancy of Pnt in late larval eye discs. We identified enriched regions that mapped to an average of 6,941 genes, the vast majority of which are novel putative Pnt targets. Next, we integrated ChIP-seq data with two other larval eye single cell genomics datasets (scRNA-seq and snATAC-seq) to reveal 157 putative cell type-specific Pnt targets that may help mediate unique cell type responses upon Egfr-induced differentiation. Finally, our integrated data also predicts cell type-specific functional enhancers that were not reported previously. Together, our study provides a greatly expanded list of putative cell type-specific Pnt targets in the eye and is a resource for future studies that will allow mechanistic insights into complex developmental processes regulated by Egfr signaling. [ABSTRACT FROM AUTHOR]

Infectious diseases are the leading cause of morbidity and mortality worldwide. Causative pathogenic microbes readily mutate their genome and lead to outbreaks, challenging the healthcare and the medical support. Understanding how certain symptoms manifest clinically is integral for therapeutic decisions and vaccination efficacy/protection. Notably, the interaction between infecting pathogens, host response and co-presence of microbes influence the trajectories of disease progression and clinical outcome. The spectrum of observed symptomatic patients (mild, moderate and severe) and the asymptomatic infections highlight the challenges and the potential for understanding the factors driving protection/susceptibility. With the increasing repertoire of high-throughput tools, such as cutting-edge multi-omics profiling and next-generation sequencing, genetic drivers of factors linked to heterogeneous disease presentations can be investigated in tandem. However, such strategies are not without limits in terms of effectively integrating host–pathogen interactions. Nonetheless, an integrative genomics method (for example, RNA sequencing data) for exploring multiple layers of complexity in host–pathogen interactions could be another way to incorporate findings from high-throughput data. We further propose that a Holo-transcriptome-based technique to capture transcriptionally active microbial units can be used to elucidate functional microbiomes. Thus, we provide holistic perspective on investigative methodologies that can harness the same genomic data to investigate multiple seemingly independent but deeply interconnected functional domains of host–pathogen interaction that modulate disease severity and clinical outcomes. [ABSTRACT FROM AUTHOR]

Psychiatric disorders are complex brain disorders with a high degree of genetic heterogeneity, affecting millions of people worldwide. Despite advances in psychiatric genetics, the underlying pathogenic mechanisms of psychiatric disorders are still largely elusive, which impedes the development of novel rational therapies. There has been accumulating evidence suggesting that the genetics of complex disorders can be viewed through an omnigenic lens, which involves contextualizing genes in highly interconnected networks. Thus, applying network-based multi-omics integration methods could cast new light on the pathophysiology of psychiatric disorders. In this review, we first provide an overview of the recent advances in psychiatric genetics and highlight gaps in translating molecular associations into mechanistic insights. We then present an overview of network methodologies and review previous applications of network methods in the study of psychiatric disorders. Lastly, we describe the potential of such methodologies within a multi-tissue, multi-omics approach, and summarize the future directions in adopting diverse network approaches.

Genome-wide association studies (GWAS) have implicated ~380 genetic loci for plasma lipid regulation. However, these loci only explain 17-27% of the trait variance and a comprehensive understanding of the molecular mechanisms has not been achieved. In this study, we utilized an integrative genomics approach leveraging diverse genomic data from human populations to investigate whether genetic variants associated with various plasma lipid traits, namely total cholesterol (TC), high and low density lipoprotein cholesterol (HDL and LDL), and triglycerides (TG), from GWAS were concentrated on specific parts of tissue-specific gene regulatory networks. In addition to the expected lipid metabolism pathways, gene subnetworks involved in 'interferon signaling', 'autoimmune/immune activation', 'visual transduction', and 'protein catabolism' were significantly associated with all lipid traits. Additionally, we detected trait-specific subnetworks, including cadherin-associated subnetworks for LDL, glutathione metabolism for HDL, valine, leucine and isoleucine biosynthesis for TC, and insulin signaling and complement pathways for TG. Finally, utilizing gene-gene relations revealed by tissue-specific gene regulatory networks, we detected both known (e.g. APOH, APOA4, and ABCA1) and novel (e.g. F2 in adipose tissue) key regulator genes in these lipid-associated subnetworks. Knockdown of the F2 gene (Coagulation Factor II, Thrombin) in 3T3-L1 and C3H10T1/2 adipocytes reduced gene expression of Abcb11, Apoa5, Apof, Fabp1, Lipc, and Cd36, reduced intracellular adipocyte lipid content, and increased extracellular lipid content, supporting a link between adipose thrombin and lipid regulation. Our results shed light on the complex mechanisms underlying lipid metabolism and highlight potential novel targets for lipid regulation and lipid-associated diseases.

Genome-wide association studies (GWAS) have implicated ~380 genetic loci for plasma lipid regulation. However, these loci only explain 17-27% of the trait variance and a comprehensive understanding of the molecular mechanisms has not been achieved. In this study, we utilized an integrative genomics approach leveraging diverse genomic data from human populations to investigate whether genetic variants associated with various plasma lipid traits, namely total cholesterol (TC), high and low density lipoprotein cholesterol (HDL and LDL), and triglycerides (TG), from GWAS were concentrated on specific parts of tissue-specific gene regulatory networks. In addition to the expected lipid metabolism pathways, gene subnetworks involved in 'interferon signaling', 'autoimmune/immune activation', 'visual transduction', and 'protein catabolism' were significantly associated with all lipid traits. Additionally, we detected trait-specific subnetworks, including cadherin-associated subnetworks for LDL, glutathione metabolism for HDL, valine, leucine and isoleucine biosynthesis for TC, and insulin signaling and complement pathways for TG. Finally, utilizing gene-gene relations revealed by tissue-specific gene regulatory networks, we detected both known (e.g. APOH, APOA4, and ABCA1) and novel (e.g. F2 in adipose tissue) key regulator genes in these lipid-associated subnetworks. Knockdown of the F2 gene (Coagulation Factor II, Thrombin) in 3T3-L1 and C3H10T1/2 adipocytes reduced gene expression of Abcb11, Apoa5, Apof, Fabp1, Lipc, and Cd36, reduced intracellular adipocyte lipid content, and increased extracellular lipid content, supporting a link between adipose thrombin and lipid regulation. Our results shed light on the complex mechanisms underlying lipid metabolism and highlight potential novel targets for lipid regulation and lipid-associated diseases.

BackgroundPsoriasis is a complex multi-factorial disease, involving both genetic susceptibilities and environmental triggers. Genome-wide association studies (GWAS) and epigenome-wide association studies (EWAS) have been carried out to identify genetic and epigenetic variants that are associated with psoriasis. However, these loci cannot fully explain the disease pathogenesis.MethodsTo achieve a comprehensive mechanistic understanding of psoriasis, we conducted a systems biology study, integrating multi-omics datasets including GWAS, EWAS, tissue-specific transcriptome, expression quantitative trait loci (eQTLs), gene networks, and biological pathways to identify the key genes, processes, and networks that are genetically and epigenetically associated with psoriasis risk.ResultsThis integrative genomics study identified both well-characterized (e.g., the IL17 pathway in both GWAS and EWAS) and novel biological processes (e.g., the branched chain amino acid catabolism process in GWAS and the platelet and coagulation pathway in EWAS) involved in psoriasis. Finally, by utilizing tissue-specific gene regulatory networks, we unraveled the interactions among the psoriasis-associated genes and pathways in a tissue-specific manner and detected potential key regulatory genes in the psoriasis networks.ConclusionsThe integration and convergence of multi-omics signals provide deeper and comprehensive insights into the biological mechanisms associated with psoriasis susceptibility.

Blood pressure (BP) is a highly heritable trait and a major cardiovascular disease risk factor. Genome wide association studies (GWAS) have implicated a number of susceptibility loci for systolic (SBP) and diastolic (DBP) blood pressure. However, a large portion of the heritability cannot be explained by the top GWAS loci and a comprehensive understanding of the underlying molecular mechanisms is still lacking. Here, we utilized an integrative genomics approach that leveraged multiple genetic and genomic datasets including (a) GWAS for SBP and DBP from the International Consortium for Blood Pressure (ICBP), (b) expression quantitative trait loci (eQTLs) from genetics of gene expression studies of human tissues related to BP, (c) knowledge-driven biological pathways, and (d) data-driven tissue-specific regulatory gene networks. Integration of these multidimensional datasets revealed tens of pathways and gene subnetworks in vascular tissues, liver, adipose, blood, and brain functionally associated with DBP and SBP. Diverse processes such as platelet production, insulin secretion/signaling, protein catabolism, cell adhesion and junction, immune and inflammation, and cardiac/smooth muscle contraction, were shared between DBP and SBP. Furthermore, "Wnt signaling" and "mammalian target of rapamycin (mTOR) signaling" pathways were found to be unique to SBP, while "cytokine network", and "tryptophan catabolism" to DBP. Incorporation of gene regulatory networks in our analysis informed on key regulator genes that orchestrate tissue-specific subnetworks of genes whose variants together explain ~20% of BP heritability. Our results shed light on the complex mechanisms underlying BP regulation and highlight potential novel targets and pathways for hypertension and cardiovascular diseases.

Non-alcoholic fatty liver disease (NAFLD) is a progressive condition of the liver encompassing a range of pathologies including steatosis, non-alcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular carcinoma. Research into this disease is imperative due to its rapid growth in prevalence, economic burden, and current lack of FDA approved therapies. NAFLD involves a highly complex etiology that calls for multi-tissue multi-omics network approaches to uncover the pathogenic genes and processes, diagnostic biomarkers, and potential therapeutic strategies. In this review, we first present a basic overview of disease pathogenesis, risk factors, and remaining knowledge gaps, followed by discussions of the need and concepts of multi-tissue multi-omics approaches, various network methodologies and application examples in NAFLD research. We highlight the findings that have been uncovered thus far including novel biomarkers, genes, and biological pathways involved in different stages of NAFLD, molecular connections between NAFLD and its comorbidities, mechanisms underpinning sex differences, and druggable targets. Lastly, we outline the future directions of implementing network approaches to further improve our understanding of NAFLD in order to guide diagnosis and therapeutics.

Cancer is a complex collection of diseases that are to some degree unique to each patient. Precision oncology aims to identify the best drug treatment regime using molecular data on tumor samples. While omics-level data is becoming more widely available for tumor specimens, the datasets upon which computational learning methods can be trained vary in coverage from sample to sample and from data type to data type. Methods that can 'connect the dots' to leverage more of the information provided by these studies could offer major advantages for maximizing predictive potential. We introduce a multi-view machinelearning strategy called PLATYPUS that builds 'views' from multiple data sources that are all used as features for predicting patient outcomes. We show that a learning strategy that finds agreement across the views on unlabeled data increases the performance of the learning methods over any single view. We illustrate the power of the approach by deriving signatures for drug sensitivity in a large cancer cell line database. Code and additional information are available from the PLATYPUS website https://sysbiowiki.soe.ucsc.edu/platypus.

We propose a novel conditional graphical model - spaceMap - to construct gene regulatory networks from multiple types of high dimensional omic profiles. A motivating application is to characterize the perturbation of DNA copy number alterations (CNAs) on downstream protein levels in tumors. Through a penalized multivariate regression framework, spaceMap jointly models high dimensional protein levels as responses and high dimensional CNAs as predictors. In this setup, spaceMap infers an undirected network among proteins together with a directed network encoding how CNAs perturb the protein network. spaceMap can be applied to learn other types of regulatory relationships from high dimensional molecular profiles, especially those exhibiting hub structures. Simulation studies show spaceMap has greater power in detecting regulatory relationships over competing methods. Additionally, spaceMap includes a network analysis toolkit for biological interpretation of inferred networks. We applies spaceMap to the CNAs, gene expression and proteomics data sets from CPTAC-TCGA breast (n=77) and ovarian (n=174) cancer studies. Each cancer exhibits disruption of 'ion transmembrane transport' and 'regulation from RNA polymerase II promoter' by CNA events unique to each cancer. Moreover, using protein levels as a response yields a more functionally-enriched network than using RNA expressions in both cancer types. The network results also help to pinpoint crucial cancer genes and provide insights on the functional consequences of important CNA in breast and ovarian cancers. The R package spaceMap - including vignettes and documentation - is hosted on https://topherconley.github.io/spacemap.

The past 2 decades have witnessed extraordinary advances in our understanding of the genetic factors influencing inflammatory bowel disease (IBD), providing a foundation for the approaching era of genomic medicine. On behalf of the NIDDK IBD Genetics Consortium, we herein survey 11 grand challenges for the field as it embarks on the next 2 decades of research utilizing integrative genomic and systems biology approaches. These involve elucidation of the genetic architecture of IBD (how it compares across populations, the role of rare variants, and prospects of polygenic risk scores), in-depth cellular and molecular characterization (fine-mapping causal variants, cellular contributions to pathology, molecular pathways, interactions with environmental exposures, and advanced organoid models), and applications in personalized medicine (unmet medical needs, working toward molecular nosology, and precision therapeutics). We review recent advances in each of the 11 areas and pose challenges for the genetics and genomics communities of IBD researchers., (© The Author(s) 2024. Published by Oxford University Press on behalf of Crohn’s & Colitis Foundation. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background: Yoga is a multifaceted spiritual tool that helps in maintaining health, peace of mind, and positive thoughts. In the context of asana, yoga is similar to physical exercise. This study aims to construct a molecular network to find hub genes that play important roles in physical exercise and yoga. Methodology: We combined differentially expressed genes (DEGs) in yoga and exercise using computational bioinformatics from publicly available gene expression omnibus (GEO) datasets and identified the codifferentially expressed mRNAs with GEO2R. The co-DEGs were divided into four different groups and each group was subjected to protein–protein interaction (PPI) network, pathways analysis, and gene ontology. Results: Our study identified immunological modulation as a dominant target of differential expression in yoga and exercise. Yoga predominantly modulated genes affecting the Th1 and NK cells, whereas Cytokines, Macrophage activation, and oxidative stress were affected by exercise. We also observed that while yoga regulated genes for two main physiological functions of the body, namely Circadian Rhythm (BHLHE40) and immunity (LBP, T-box transcription factor 21, CEACAM1), exercise-regulated genes involved in apoptosis (BAG3, protein kinase C alpha), angiogenesis, and cellular adhesion (EPH receptor A1). Conclusion: The dissimilarity in the genetic expression patterns in Yoga and exercise highlights the discrete effect of each in biological systems. The integration and convergences of multi-omics signals can provide deeper and comprehensive insights into the various biological mechanisms through which yoga and exercise exert their beneficial effects and opens up potential newer research areas.

The etiology of non-alcoholic fatty liver disease (NAFLD), the most common form of chronic liver disease, is poorly understood. To understand the causal mechanisms underlying NAFLD, we conducted a multi-omics, multi-tissue integrative study using the Hybrid Mouse Diversity Panel, consisting of ∼100 strains of mice with various degrees of NAFLD. We identified both tissue-specific biological processes and processes that were shared between adipose and liver tissues. We then used gene network modeling to predict candidate regulatory genes of these NAFLD processes, including Fasn, Thrsp, Pklr, and Chchd6. In vivo knockdown experiments of the candidate genes improved both steatosis and insulin resistance. Further in vitro testing demonstrated that downregulation of both Pklr and Chchd6 lowered mitochondrial respiration and led to a shift toward glycolytic metabolism, thus highlighting mitochondria dysfunction as a key mechanistic driver of NAFLD.

The etiology of non-alcoholic fatty liver disease (NAFLD), the most common form of chronic liver disease, is poorly understood. To understand the causal mechanisms underlying NAFLD, we conducted a multi-omics, multi-tissue integrative study using the Hybrid Mouse Diversity Panel, consisting of ∼100 strains of mice with various degrees of NAFLD. We identified both tissue-specific biological processes and processes that were shared between adipose and liver tissues. We then used gene network modeling to predict candidate regulatory genes of these NAFLD processes, including Fasn, Thrsp, Pklr, and Chchd6. In vivo knockdown experiments of the candidate genes improved both steatosis and insulin resistance. Further in vitro testing demonstrated that downregulation of both Pklr and Chchd6 lowered mitochondrial respiration and led to a shift toward glycolytic metabolism, thus highlighting mitochondria dysfunction as a key mechanistic driver of NAFLD.

Interactive analysis notebook environments promise to streamline genomics research through interleaving text, multimedia, and executable code into unified, sharable, reproducible "research narratives." However, current notebook systems require programming knowledge, limiting their wider adoption by the research community. We have developed the GenePattern Notebook environment (http://www.genepattern-notebook.org), to our knowledge the first system to integrate the dynamic capabilities of notebook systems with an investigator-focused, easy-to-use interface that provides access to hundreds of genomic tools without the need to write code.

Abstract Background Bovine TB (bTB), caused by infection with Mycobacterium bovis, is a major endemic disease affecting global cattle production. The key innate immune cell that first encounters the pathogen is the alveolar macrophage, previously shown to be substantially reprogrammed during intracellular infection by the pathogen. Here we use differential expression, and correlation- and interaction-based network approaches to analyse the host response to infection with M. bovis at the transcriptome level to identify core infection response pathways and gene modules. These outputs were then integrated with genome-wide association study (GWAS) data sets to enhance detection of genomic variants for susceptibility/resistance to M. bovis infection. Results The host gene expression data consisted of RNA-seq data from bovine alveolar macrophages (bAM) infected with M. bovis at 24 and 48 h post-infection (hpi) compared to non-infected control bAM. These RNA-seq data were analysed using three distinct computational pipelines to produce six separate gene sets: 1) DE genes filtered using stringent fold-change and P-value thresholds (DEG-24: 378 genes, DEG-48: 390 genes); 2) genes obtained from expression correlation networks (CON-24: 460 genes, CON-48: 416 genes); and 3) genes obtained from differential expression networks (DEN-24: 339 genes, DEN-48: 495 genes). These six gene sets were integrated with three bTB breed GWAS data sets by employing a new genomics data integration tool—gwinteR. Using GWAS summary statistics, this methodology enabled detection of 36, 102 and 921 prioritised SNPs for Charolais, Limousin and Holstein-Friesian, respectively. Conclusions The results from the three parallel analyses showed that the three computational approaches could identify genes significantly enriched for SNPs associated with susceptibility/resistance to M. bovis infection. Results indicate distinct and significant overlap in SNP discovery, demonstrating that network-based integration of biologically relevant transcriptomics data can leverage substantial additional information from GWAS data sets. These analyses also demonstrated significant differences among breeds, with the Holstein-Friesian breed GWAS proving most useful for prioritising SNPS through data integration. Because the functional genomics data were generated using bAM from this population, this suggests that the genomic architecture of bTB resilience traits may be more breed-specific than previously assumed.

Cholangiocarcinoma (CCA) is an aggressive malignancy of the bile ducts, with poor prognosis and limited treatment options. Here, we describe the integrated analysis of somatic mutations, RNA expression, copy number, and DNA methylation by The Cancer Genome Atlas of a set of predominantly intrahepatic CCA cases and propose a molecular classification scheme. We identified an IDH mutant-enriched subtype with distinct molecular features including low expression of chromatin modifiers, elevated expression of mitochondrial genes, and increased mitochondrial DNA copy number. Leveraging the multi-platform data, we observed that ARID1A exhibited DNA hypermethylation and decreased expression in the IDH mutant subtype. More broadly, we found that IDH mutations are associated with an expanded histological spectrum of liver tumors with molecular features that stratify with CCA. Our studies reveal insights into the molecular pathogenesis and heterogeneity of cholangiocarcinoma and provide classification information of potential therapeutic significance.

Elucidating the mechanisms of complex diseases such as cardiovascular disease (CVD) remains a significant challenge due to multidimensional alterations at molecular, cellular, tissue, and organ levels. To better understand CVD and offer insights into the underlying mechanisms and potential therapeutic strategies, data from multiple omics types (genomics, epigenomics, transcriptomics, metabolomics, proteomics, microbiomics) from both humans and model organisms have become available. However, individual omics data types capture only a fraction of the molecular mechanisms. To address this challenge, there have been numerous efforts to develop integrative genomics methods that can leverage multidimensional information from diverse data types to derive comprehensive molecular insights. In this review, we summarize recent methodological advances in multidimensional omics integration, exemplify their applications in cardiovascular research, and pinpoint challenges and future directions in this incipient field.

BackgroundComplex diseases are characterized by multiple subtle perturbations to biological processes. New omics platforms can detect these perturbations, but translating the diverse molecular and statistical information into testable mechanistic hypotheses is challenging. Therefore, we set out to create a public tool that integrates these data across multiple datasets, platforms, study designs and species in order to detect the most promising targets for further mechanistic studies.ResultsWe developed Mergeomics, a computational pipeline consisting of independent modules that 1) leverage multi-omics association data to identify biological processes that are perturbed in disease, and 2) overlay the disease-associated processes onto molecular interaction networks to pinpoint hubs as potential key regulators. Unlike existing tools that are mostly dedicated to specific data type or settings, the Mergeomics pipeline accepts and integrates datasets across platforms, data types and species. We optimized and evaluated the performance of Mergeomics using simulation and multiple independent datasets, and benchmarked the results against alternative methods. We also demonstrate the versatility of Mergeomics in two case studies that include genome-wide, epigenome-wide and transcriptome-wide datasets from human and mouse studies of total cholesterol and fasting glucose. In both cases, the Mergeomics pipeline provided statistical and contextual evidence to prioritize further investigations in the wet lab. The software implementation of Mergeomics is freely available as a Bioconductor R package.ConclusionMergeomics is a flexible and robust computational pipeline for multidimensional data integration. It outperforms existing tools, and is easily applicable to datasets from different studies, species and omics data types for the study of complex traits.

It is well known that different racial groups have significantly different incidence and mortality rates for certain cancers. It has been suggested that biological factors play a major role in these cancer racial disparities. Previous studies on the biological factors contributing to cancer racial disparity have generated a very large number of candidate factors, although there is modest agreement among the results of the different studies. Here, we performed an integrative analysis using genomic data of 21 cancer types from TCGA, GTEx, and the 1000 Genomes Project to identify biological factors contributing to racial disparity in cancer. We also built a companion website with additional results for cancer researchers to freely mine. Our study identified genes, gene families, and pathways displaying similar differential expression patterns between different racial groups across multiple cancer types. Among them, XKR9 gene expression was found to be significantly associated with overall survival for all cancers combined as well as for several individual cancers. Our results point to the interesting hypothesis that XKR9 could be a novel drug target for cancer immunotherapy. Bayesian network modeling showed that XKR9 is linked to important cancer‐related genes, including FOXM1, cyclin B1, and RB1CC1 (RB1 regulator). In addition, metabolic pathways, neural signaling pathways, and several cancer‐related gene families were found to be significantly associated with cancer racial disparities for multiple cancer types. Single nucleotide polymorphisms (SNPs) discovered through integrating data from the TCGA, GTEx, and 1000 Genomes databases provide biologists the opportunity to test highly promising, targeted hypotheses to gain a deeper understanding of the genetic drivers of cancer racial disparity and cancer biology in general.

Abstract To provide a comprehensive mechanistic interpretation of how known trait-associated SNPs affect complex traits, we propose a method, Primo, for integrative analysis of GWAS summary statistics with multiple sets of omics QTL summary statistics from different cellular conditions or studies. Primo examines association patterns of SNPs to complex and omics traits. In gene regions harboring known susceptibility loci, Primo performs conditional association analysis to account for linkage disequilibrium. Primo allows for unknown study heterogeneity and sample correlations. We show two applications using Primo to examine the molecular mechanisms of known susceptibility loci and to detect and interpret pleiotropic effects.

There has been a rapid increase in human genome sequencing in the past two decades, resulting in the identification of millions of previously unknown genetic variants. However, African populations are under-represented in sequencing efforts. Additional sequencing from diverse African populations and the construction of African-specific reference genomes is needed to better characterize the full spectrum of variation in humans. However, sequencing alone is insufficient to address the molecular and cellular mechanisms underlying variable phenotypes and disease risks. Determining functional consequences of genetic variation using multi-omics approaches is a fundamental post-genomic challenge. We discuss approaches to close the knowledge gaps about African genomic diversity and review advances in African integrative genomic studies and their implications for precision medicine. Africans harbor great genetic, phenotypic, cultural and linguistic diversity. Disparity remains in genetic studies representing African ancestries. Whole-genome sequencing (WGS) for African populations lags behind European and Asian ancestries. More WGS efforts to capture genetic variation in Africa are warranted. The human reference genome (HRG) lacks diversity, which can bias downstream analysis. Population-specific reference genomes for African populations would enable the detection of more complex variation and variation that would be difficult to map to the current HRG. Combining genomic data with intermediate phenotypes is required to understand the biological mechanisms underlying phenotype and disease in the post-genomic era. African integrative genomics is in its infancy. The scientific rewards and translational health benefits of dramatically expanded integrative omics studies in African populations is high. [ABSTRACT FROM AUTHOR]

Background: Type 2 diabetes mellitus (T2DM) is a metabolic disorder with severe comorbidities. A multiomics approach can facilitate the identification of novel therapeutic targets and biomarkers with proper validation of potential microRNA (miRNA) interactions. Objective: The aim of this study was to identify significant differentially expressed common target genes in various tissues and their regulating miRNAs from publicly available Gene Expression Omnibus (GEO) data sets of patients with T2DM using in silico analysis. Methods: Using differentially expressed genes (DEGs) identified from 5 publicly available T2DM data sets, we performed functional enrichment, coexpression, and network analyses to identify pathways, protein-protein interactions, and miRNA-mRNA interactions involved in T2DM. Results: We extracted 2852, 8631, 5501, 3662, and 3753 DEGs from the expression profiles of GEO data sets GSE38642, GSE25724, GSE20966, GSE26887, and GSE23343, respectively. DEG analysis showed that 16 common genes were enriched in insulin secretion, endocrine resistance, and other T2DM-related pathways. Four DEGs, MAML3, EEF1D, NRG1, and CDK5RAP2, were important in the cluster network regulated by commonly targeted miRNAs (hsa-let-7b-5p, hsa-mir-155-5p, hsa-mir-124-3p, hsa-mir-1-3p), which are involved in the advanced glycation end products (AGE)-receptor for advanced glycation end products (RAGE) signaling pathway, culminating in diabetic complications and endocrine resistance. Conclusions: This study identified tissue-specific DEGs in T2DM, especially pertaining to the heart, liver, and pancreas. We identified a total of 16 common DEGs and the top four common targeting miRNAs (hsa-let-7b-5p, hsa-miR-124-3p, hsa-miR-1-3p, and has-miR-155-5p). The miRNAs identified are involved in regulating various pathways, including the phosphatidylinositol-3-kinase-protein kinase B, endocrine resistance, and AGE-RAGE signaling pathways. [ABSTRACT FROM AUTHOR]

Background: Yoga is a multifaceted spiritual tool that helps in maintaining health, peace of mind, and positive thoughts. In the context of asana, yoga is similar to physical exercise. This study aims to construct a molecular network to find hub genes that play important roles in physical exercise and yoga. Methodology: We combined differentially expressed genes (DEGs) in yoga and exercise using computational bioinformatics from publicly available gene expression omnibus (GEO) datasets and identified the codifferentially expressed mRNAs with GEO2R. The co-DEGs were divided into four different groups and each group was subjected to protein–protein interaction (PPI) network, pathways analysis, and gene ontology. Results: Our study identified immunological modulation as a dominant target of differential expression in yoga and exercise. Yoga predominantly modulated genes affecting the Th1 and NK cells, whereas Cytokines, Macrophage activation, and oxidative stress were affected by exercise. We also observed that while yoga regulated genes for two main physiological functions of the body, namely Circadian Rhythm (BHLHE40) and immunity (LBP, T-box transcription factor 21, CEACAM1), exercise-regulated genes involved in apoptosis (BAG3, protein kinase C alpha), angiogenesis, and cellular adhesion (EPH receptor A1). Conclusion: The dissimilarity in the genetic expression patterns in Yoga and exercise highlights the discrete effect of each in biological systems. The integration and convergences of multi-omics signals can provide deeper and comprehensive insights into the various biological mechanisms through which yoga and exercise exert their beneficial effects and opens up potential newer research areas. [ABSTRACT FROM AUTHOR]

Mycobacterium tuberculosis , the causative agent of human tuberculosis (hTB), is a close evolutionary relative of Mycobacterium bovis , which causes bovine tuberculosis (bTB), one of the most damaging infectious diseases to livestock agriculture. Previous studies have shown that the pathogenesis of bTB disease is comparable to hTB disease, and that the bovine and human alveolar macrophage (bAM and hAM, respectively) transcriptomes are extensively reprogrammed in response to infection with these intracellular mycobacterial pathogens. In this study, a multi-omics integrative approach was applied with functional genomics and GWAS data sets across the two primary hosts (Bos taurus and Homo sapiens) and both pathogens (M. bovis and M. tuberculosis). Four different experimental infection groups were used: 1) bAM infected with M. bovis , 2) bAM infected with M. tuberculosis , 3) hAM infected with M. tuberculosis , and 4) human monocyte-derived macrophages (hMDM) infected with M. tuberculosis. RNA-seq data from these experiments 24 h post-infection (24 hpi) was analysed using three computational pipelines: 1) differentially expressed genes, 2) differential gene expression interaction networks, and 3) combined pathway analysis. The results were integrated with high-resolution bovine and human GWAS data sets to detect novel quantitative trait loci (QTLs) for resistance to mycobacterial infection and resilience to disease. This revealed common and unique response macrophage pathways for both pathogens and identified 32 genes (12 bovine and 20 human) significantly enriched for SNPs associated with disease resistance, the majority of which encode key components of the NF-κB signalling pathway and that also drive formation of the granuloma. [ABSTRACT FROM AUTHOR]

BackgroundBoth genetic and epigenetic factors influence the development and progression of epithelial ovarian cancer (EOC). However, there is an incomplete understanding of the interrelationship between these factors and the extent to which they interact to impact disease risk. In the present study, we aimed to gain insight into this relationship by identifying DNA methylation marks that are candidate mediators of ovarian cancer genetic risk.MethodsWe used 214 cases and 214 age-matched controls from the Mayo Clinic Ovarian Cancer Study. Pretreatment, blood-derived DNA was profiled for genome-wide methylation (Illumina Infinium HumanMethylation27 BeadArray) and single nucleotide polymorphisms (SNPs, Illumina Infinium HD Human610-Quad BeadArray). The Causal Inference Test (CIT) was implemented to distinguish CpG sites that mediate genetic risk, from those that are consequential or independently acted on by genotype.ResultsControlling for the estimated distribution of immune cells and other key covariates, our initial epigenome-wide association analysis revealed 1,993 significantly differentially methylated CpGs that between cases and controls (FDR, q < 0.05). The relationship between methylation and case-control status for these 1,993 CpGs was found to be highly consistent with the results of previously published, independent study that consisted of peripheral blood DNA methylation signatures in 131 pretreatment cases and 274 controls. Implementation of the CIT test revealed 17 CpG/SNP pairs, comprising 13 unique CpGs and 17 unique SNPs, which represent potential methylation-mediated relationships between genotype and EOC risk. Of these 13 CpGs, several are associated with immune related genes and genes that have been previously shown to exhibit altered expression in the context of cancer.ConclusionsThese findings provide additional insight into EOC etiology and may serve as novel biomarkers for EOC susceptibility.

The recent obesity epidemic has imposed significant health, economical, and societal concerns. However, effective preventive and therapeutic strategies are currently lacking, primarily due to a lack of comprehensive understanding of the underlying molecular mechanisms. Recent genome-wide scans of genetic variants, transcriptome, and epigenome have uncovered >50 genetic loci that predispose individuals to obesity and revealed hundreds of genes with altered transcriptional activity and/or epigenetic variations in obesity-related tissues upon various environmental challenges such as high caloric diets, lack of physical activity, and environmental chemicals. These discoveries highlight the importance of genes involved in the control of energy homeostasis and food intake by the central nervous system, as well as genes contributing to lipid metabolism, adipogenesis, fat cell differentiation, and immune response in peripheral tissues, in obesity development. Future studies that are directed to obtain a more comprehensive, systems-level understanding of disease mechanisms and that test novel therapeutic strategies aiming at systems-level normalization of the obesity-related molecular alterations are warranted.

Conducting integrative analyses of multiple data sets is an important strategy in data analysis. It is becoming increasingly popular in genomics, which enjoys a wealth of publicly available data sets that can be compared, contrasted, and combined in order to extract novel scientific insights. This study examines a stylized example of data integration for a classical statistical problem: leveraging side information to estimate a vector of normal means. We formulate this task as a compound decision problem, derive an oracle integrative decision rule, and propose a data-driven estimate of this rule based on minimizing an unbiased estimate of its risk. The data-driven rule is shown to asymptotically achieve the minimum possible risk among all separable decision rules, and it can outperform existing methods in terms of numerical properties. The proposed procedure leads naturally to an integrative high-dimensional classification procedure, which is illustrated by combining data from two independent gene expression profiling studies. [ABSTRACT FROM AUTHOR]

Background: Bovine TB (bTB), caused by infection with Mycobacterium bovis, is a major endemic disease affecting global cattle production. The key innate immune cell that first encounters the pathogen is the alveolar macrophage, previously shown to be substantially reprogrammed during intracellular infection by the pathogen. Here we use differential expression, and correlation- and interaction-based network approaches to analyse the host response to infection with M. bovis at the transcriptome level to identify core infection response pathways and gene modules. These outputs were then integrated with genome-wide association study (GWAS) data sets to enhance detection of genomic variants for susceptibility/resistance to M. bovis infection. Results: The host gene expression data consisted of RNA-seq data from bovine alveolar macrophages (bAM) infected with M. bovis at 24 and 48 h post-infection (hpi) compared to non-infected control bAM. These RNA-seq data were analysed using three distinct computational pipelines to produce six separate gene sets: 1) DE genes filtered using stringent fold-change and P-value thresholds (DEG-24: 378 genes, DEG-48: 390 genes); 2) genes obtained from expression correlation networks (CON-24: 460 genes, CON-48: 416 genes); and 3) genes obtained from differential expression networks (DEN-24: 339 genes, DEN-48: 495 genes). These six gene sets were integrated with three bTB breed GWAS data sets by employing a new genomics data integration tool—gwinteR. Using GWAS summary statistics, this methodology enabled detection of 36, 102 and 921 prioritised SNPs for Charolais, Limousin and Holstein-Friesian, respectively. Conclusions: The results from the three parallel analyses showed that the three computational approaches could identify genes significantly enriched for SNPs associated with susceptibility/resistance to M. bovis infection. Results indicate distinct and significant overlap in SNP discovery, demonstrating that network-based integration of biologically relevant transcriptomics data can leverage substantial additional information from GWAS data sets. These analyses also demonstrated significant differences among breeds, with the Holstein-Friesian breed GWAS proving most useful for prioritising SNPS through data integration. Because the functional genomics data were generated using bAM from this population, this suggests that the genomic architecture of bTB resilience traits may be more breed-specific than previously assumed. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWASs) have implicated ∼380 genetic loci for plasma lipid regulation. However, these loci only explain 17–27% of the trait variance, and a comprehensive understanding of the molecular mechanisms has not been achieved. In this study, we utilized an integrative genomics approach leveraging diverse genomic data from human populations to investigate whether genetic variants associated with various plasma lipid traits, namely, total cholesterol, high and low density lipoprotein cholesterol (HDL and LDL), and triglycerides, from GWASs were concentrated on specific parts of tissue-specific gene regulatory networks. In addition to the expected lipid metabolism pathways, gene subnetworks involved in “interferon signaling,” “autoimmune/immune activation,” “visual transduction,” and “protein catabolism” were significantly associated with all lipid traits. In addition, we detected trait-specific subnetworks, including cadherin-associated subnetworks for LDL; glutathione metabolism for HDL; valine, leucine, and isoleucine biosynthesis for total cholesterol; and insulin signaling and complement pathways for triglyceride. Finally, by using gene-gene relations revealed by tissue-specific gene regulatory networks, we detected both known (e.g., APOH, APOA4, and ABCA1) and novel (e.g., F2 in adipose tissue) key regulator genes in these lipid-associated subnetworks. Knockdown of the F2 gene (coagulation factor II, thrombin) in 3T3-L1 and C3H10T1/2 adipocytes altered gene expression of Abcb11, Apoa5, Apof, Fabp1, Lipc, and Cd36; reduced intracellular adipocyte lipid content; and increased extracellular lipid content, supporting a link between adipose thrombin and lipid regulation. Our results shed light on the complex mechanisms underlying lipid metabolism and highlight potential novel targets for lipid regulation and lipid-associated diseases.

Abstract Background The cellular response to ionizing radiation involves activation of p53-dependent pathways and activation of the atypical NF-κB pathway. The crosstalk between these two transcriptional networks include (co)regulation of common gene targets. Here we looked for novel genes potentially (co)regulated by p53 and NF-κB using integrative genomics screening in human osteosarcoma U2-OS cells irradiated with a high dose (4 and 10 Gy). Radiation-induced expression in cells with silenced TP53 or RELA (coding the p65 NF-κB subunit) genes was analyzed by RNA-Seq while radiation-enhanced binding of p53 and RelA in putative regulatory regions was analyzed by ChIP-Seq, then selected candidates were validated by qPCR. Results We identified a subset of radiation-modulated genes whose expression was affected by silencing of both TP53 and RELA, and a subset of radiation-upregulated genes where radiation stimulated binding of both p53 and RelA. For three genes, namely IL4I1, SERPINE1, and CDKN1A, an antagonistic effect of the TP53 and RELA silencing was consistent with radiation-enhanced binding of both p53 and RelA. This suggested the possibility of a direct antagonistic (co)regulation by both factors: activation by NF-κB and inhibition by p53 of IL4I1, and activation by p53 and inhibition by NF-κB of CDKN1A and SERPINE1. On the other hand, radiation-enhanced binding of both p53 and RelA was observed in a putative regulatory region of the RRAD gene whose expression was downregulated both by TP53 and RELA silencing, which suggested a possibility of direct (co)activation by both factors. Conclusions Four new candidates for genes directly co-regulated by NF-κB and p53 were revealed.

Abstract Background The integration of high-quality, genome-wide analyses offers a robust approach to elucidating genetic factors involved in complex human diseases. Even though several methods exist to integrate heterogeneous omics data, most biologists still manually select candidate genes by examining the intersection of lists of candidates stemming from analyses of different types of omics data that have been generated by imposing hard (strict) thresholds on quantitative variables, such as P-values and fold changes, increasing the chance of missing potentially important candidates. Methods To better facilitate the unbiased integration of heterogeneous omics data collected from diverse platforms and samples, we propose a desirability function framework for identifying candidate genes with strong evidence across data types as targets for follow-up functional analysis. Our approach is targeted towards disease systems with sparse, heterogeneous omics data, so we tested it on one such pathology: spontaneous preterm birth (sPTB). Results We developed the software integRATE, which uses desirability functions to rank genes both within and across studies, identifying well-supported candidate genes according to the cumulative weight of biological evidence rather than based on imposition of hard thresholds of key variables. Integrating 10 sPTB omics studies identified both genes in pathways previously suspected to be involved in sPTB as well as novel genes never before linked to this syndrome. integRATE is available as an R package on GitHub (https://github.com/haleyeidem/integRATE). Conclusions Desirability-based data integration is a solution most applicable in biological research areas where omics data is especially heterogeneous and sparse, allowing for the prioritization of candidate genes that can be used to inform more targeted downstream functional analyses.

Leiomyosarcoma (LMS) is a soft tissue tumor with a significant degree of morphologic and molecular heterogeneity. We used integrative molecular profiling to discover and characterize molecular subtypes of LMS. Gene expression profiling was performed on 51 LMS samples. Unsupervised clustering showed three reproducible LMS clusters. Array comparative genomic hybridization (aCGH) was performed on 20 LMS samples and showed that the molecular subtypes defined by gene expression showed distinct genomic changes. Tumors from the 'muscle-enriched' cluster showed significantly increased copy number changes (P=0.04). A majority of the muscle-enriched cases showed loss at 16q24, which contains Fanconi anemia, complementation group A, known to have an important role in DNA repair, and loss at 1p36, which contains PRDM16, of which loss promotes muscle differentiation. Immunohistochemistry (IHC) was performed on LMS tissue microarrays (n=377) for five markers with high levels of messenger RNA in the muscle-enriched cluster (ACTG2, CASQ2, SLMAP, CFL2 and MYLK) and showed significantly correlated expression of the five proteins (all pairwise P

Interrogation of disease-relevant cellular and molecular traits exhibited by genetically diverse cell populations enables in vitro systems genetics approaches for uncovering the basic properties of cellular function and identity. Primary cells, stem cells, and organoids derived from genetically diverse mouse strains, such as Collaborative Cross and Diversity Outbred populations, offer the opportunity for parallel in vitro / in vivo screening. These panels provide genetic resolution for variant discovery and functional characterization, as well as disease modeling and in vivo validation capabilities. Here we review mouse cellular systems genetics approaches for characterizing the influence of genetic variation on signaling networks and phenotypic diversity, and we discuss approaches for data integration and cross-species validation. Genetically diverse mice, such as Diversity Outbred (DO) and Collaborative Cross (CC) panels, can be used to model human genetic diversity and map genetic modifiers underlying phenotypic variation at high resolution and under controlled environmental conditions. Cell lines derived from DO and CC mice can be used to study systems genetics in a variety of cell culture applications, to understand the influence of genetics on cell state transitions and cellular function in a variety of cell types and in toxicology and pharmacogenetic screening. DO and CC cell lines enable integrated in vitro and in vivo screening capabilities across the same genotypes for multidimensional systems genetics studies and validation. Integrative genomics facilitates the identification of orthologous genes and cellular pathways that regulate human diseases and traits. [ABSTRACT FROM AUTHOR]

Data integration, or the strategic analysis of multiple sources of data simultaneously, can often lead to discoveries that may be hidden in individualistic analyses of a single data source. We develop a new unsupervised data integration method named Integrated Principal Components Analysis (iPCA), which is a model-based generalization of PCA and serves as a practical tool to nd and visualize common patterns that occur in multiple data sets. The key idea driving iPCA is the matrix-variate normal model, whose Kronecker product covariance structure captures both individual patterns within each data set and joint patterns shared by multiple data sets. Building upon this model, we develop several penalized (sparse and non-sparse) covariance estimators for iPCA, and using geodesic convexity, we prove that our non-sparse iPCA estimator converges to the global solution of a non-convex problem. We also demonstrate the practical advantages of iPCA through extensive simulations and a case study application to integrative genomics for Alzheimer's disease. In particular, we show that the joint patterns extracted via iPCA are highly predictive of a patient's cognition and Alzheimer's diagnosis. [ABSTRACT FROM AUTHOR]

It is well known that different racial groups have significantly different incidence and mortality rates for certain cancers. It has been suggested that biological factors play a major role in these cancer racial disparities. Previous studies on the biological factors contributing to cancer racial disparity have generated a very large number of candidate factors, although there is modest agreement among the results of the different studies. Here, we performed an integrative analysis using genomic data of 21 cancer types from TCGA, GTEx, and the 1000 Genomes Project to identify biological factors contributing to racial disparity in cancer. We also built a companion website with additional results for cancer researchers to freely mine. Our study identified genes, gene families, and pathways displaying similar differential expression patterns between different racial groups across multiple cancer types. Among them, XKR9 gene expression was found to be significantly associated with overall survival for all cancers combined as well as for several individual cancers. Our results point to the interesting hypothesis that XKR9 could be a novel drug target for cancer immunotherapy. Bayesian network modeling showed that XKR9 is linked to important cancer‐related genes, including FOXM1, cyclin B1, and RB1CC1 (RB1 regulator). In addition, metabolic pathways, neural signaling pathways, and several cancer‐related gene families were found to be significantly associated with cancer racial disparities for multiple cancer types. Single nucleotide polymorphisms (SNPs) discovered through integrating data from the TCGA, GTEx, and 1000 Genomes databases provide biologists the opportunity to test highly promising, targeted hypotheses to gain a deeper understanding of the genetic drivers of cancer racial disparity and cancer biology in general. [ABSTRACT FROM AUTHOR]

Abstract Background Genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) significantly associated with chronic obstructive pulmonary disease (COPD). However, many genetic variants show suggestive evidence for association but do not meet the strict threshold for genome-wide significance. Integrative analysis of multiple omics datasets has the potential to identify novel genes involved in disease pathogenesis by leveraging these variants in a functional, regulatory context. Results We performed expression quantitative trait locus (eQTL) analysis using genome-wide SNP genotyping and gene expression profiling of lung tissue samples from 86 COPD cases and 31 controls, testing for SNPs associated with gene expression levels. These results were integrated with a prior COPD GWAS using an ensemble statistical and network methods approach to identify relevant genes and observe them in the context of overall genetic control of gene expression to highlight co-regulated genes and disease pathways. We identified 250,312 unique SNPs and 4997 genes in the cis(local)-eQTL analysis (5% false discovery rate). The top gene from the integrative analysis was MAPT, a gene recently identified in an independent GWAS of lung function. The genes HNRNPAB and PCBP2 with RNA binding activity and the gene ACVR1B were identified in network communities with validated disease relevance. Conclusions The integration of lung tissue gene expression with genome-wide SNP genotyping and subsequent intersection with prior GWAS and omics studies highlighted candidate genes within COPD loci and in communities harboring known COPD genes. This integration also identified novel disease genes in sub-threshold regions that would otherwise have been missed through GWAS.

Abstract Background Burgeoning interest in integrative analyses has produced a rise in studies which incorporate data from multiple genomic platforms. Literature for conducting formal hypothesis testing on an integrative gene set level is considerably sparse. This paper is biologically motivated by our interest in the joint effects of epigenetic methylation loci and their associated mRNA gene expressions on lung cancer survival status. Results We provide an efficient screening approach across multiplatform genomic data on the level of biologically related sets of genes, and our methods are applicable to various disease models regardless whether the underlying true model is known (iTEGS) or unknown (iNOTE). Our proposed testing procedure dominated two competing methods. Using our methods, we identified a total of 28 gene sets with significant joint epigenomic and transcriptomic effects on one-year lung cancer survival. Conclusions We propose efficient variance component-based testing procedures to facilitate the joint testing of multiplatform genomic data across an entire gene set. The testing procedure for the gene set is self-contained, and can easily be extended to include more or different genetic platforms. iTEGS and iNOTE implemented in R are freely available through the inote package at https://cran.r-project.org// .

Bovine tuberculosis is caused by infection with Mycobacterium bovis, which can also cause disease in a range of other mammals, including humans. Alveolar macrophages are the key immune effector cells that first encounter M. bovis and how the macrophage epigenome responds to mycobacterial pathogens is currently not well understood. Here, we have used chromatin immunoprecipitation sequencing (ChIP-seq), RNA-seq and miRNA-seq to examine the effect of M. bovis infection on the bovine alveolar macrophage (bAM) epigenome. We show that H3K4me3 is more prevalent, at a genome-wide level, in chromatin from M. bovis-infected bAM compared to control non-infected bAM; this was particularly evident at the transcriptional start sites of genes that determine programmed macrophage responses to mycobacterial infection (e.g. M1/M2 macrophage polarisation). This pattern was also supported by the distribution of RNA Polymerase II (Pol II) ChIP-seq results, which highlighted significantly increased transcriptional activity at genes demarcated by permissive chromatin. Identification of these genes enabled integration of high-density genome-wide association study (GWAS) data, which revealed genomic regions associated with resilience to infection with M. bovis in cattle. Through integration of these data, we show that bAM transcriptional reprogramming occurs through differential distribution of H3K4me3 and Pol II at key immune genes. Furthermore, this subset of genes can be used to prioritise genomic variants from a relevant GWAS data set.

Fatty acid (FA) content affects the sensorial and nutritional value of meat and plays a significant role in biological processes such as adipogenesis and immune response. It is well known that, in beef, the main FAs associated with these biological processes are oleic acid (C18:1 cis9, OA) and conjugated linoleic acid (CLA-c9t11), which may have beneficial effects on metabolic diseases such as type 2 diabetes and obesity. Here, we performed differential expression and co-expression analyses, weighted gene co-expression network analysis (WGCNA) and partial correlation with information theory (PCIT), to uncover the complex interactions between miRNAs and mRNAs expressed in skeletal muscle associated with FA content. miRNA and mRNA expression data were obtained from skeletal muscle of Nelore cattle that had extreme genomic breeding values for OA and CLA. Insulin and MAPK signaling pathways were identified by WGCNA as central pathways associated with both of these fatty acids. Co-expression network analysis identified bta-miR-33a/b, bta-miR-100, bta-miR-204, bta-miR-365-5p, bta-miR-660, bta-miR-411a, bta-miR-136, bta-miR-30-5p, bta-miR-146b, bta-let-7a-5p, bta-let-7f, bta-let-7, bta-miR 339, bta-miR-10b, bta-miR 486, and the genes ACTA1 and ALDOA as potential regulators of fatty acid synthesis. This study provides evidence and insights into the molecular mechanisms and potential target genes involved in fatty acid content differences in Nelore beef cattle, revealing new candidate pathways of phenotype modulation that could positively benefit beef production and human consumption.

Alopecia areata (AA) is a highly prevalent autoimmune disease that attacks the hair follicle and leads to hair loss that can range from small patches to complete loss of scalp and body hair. Our previous linkage and genome‐wide association studies (GWAS) generated strong evidence for aetiological contributions from inherited genetic variants at different population frequencies, including both rare mutations and common polymorphisms. Additionally, we conducted gene expression (GE) studies on scalp biopsies of 96 patients and controls to establish signatures of active disease. In this study, we performed an integrative analysis on these two datasets to test the hypothesis that rare CNVs in patients with AA could be leveraged to identify drivers of disease in our AA GE signatures. We analysed copy number variants (CNVs) in a case‐control cohort of 673 patients with AA and 16 311 controls independent of the case‐control cohort of 96 research participants used in our GE study. Using an integrative computational analysis, we identified 14 genes whose expression levels were altered by CNVs in a consistent direction of effect, corresponding to gene expression changes in lesional skin of patients. Four of these genes were affected by CNVs in three or more unrelated patients with AA, including ATG4B and SMARCA2, which are involved in autophagy and chromatin remodelling, respectively. Our findings identified new classes of genes with potential contributions to AA pathogenesis. [ABSTRACT FROM AUTHOR]

Bovine tuberculosis is caused by infection with Mycobacterium bovis , which can also cause disease in a range of other mammals, including humans. Alveolar macrophages are the key immune effector cells that first encounter M. bovis and how the macrophage epigenome responds to mycobacterial pathogens is currently not well understood. Here, we have used chromatin immunoprecipitation sequencing (ChIP-seq), RNA-seq and miRNA-seq to examine the effect of M. bovis infection on the bovine alveolar macrophage (bAM) epigenome. We show that H3K4me3 is more prevalent, at a genome-wide level, in chromatin from M. bovis -infected bAM compared to control non-infected bAM; this was particularly evident at the transcriptional start sites of genes that determine programmed macrophage responses to mycobacterial infection (e.g. M1/M2 macrophage polarisation). This pattern was also supported by the distribution of RNA Polymerase II (Pol II) ChIP-seq results, which highlighted significantly increased transcriptional activity at genes demarcated by permissive chromatin. Identification of these genes enabled integration of high-density genome-wide association study (GWAS) data, which revealed genomic regions associated with resilience to infection with M. bovis in cattle. Through integration of these data, we show that bAM transcriptional reprogramming occurs through differential distribution of H3K4me3 and Pol II at key immune genes. Furthermore, this subset of genes can be used to prioritise genomic variants from a relevant GWAS data set. [ABSTRACT FROM AUTHOR]

Fatty acid (FA) content affects the sensorial and nutritional value of meat and plays a significant role in biological processes such as adipogenesis and immune response. It is well known that, in beef, the main FAs associated with these biological processes are oleic acid (C18:1 cis9, OA) and conjugated linoleic acid (CLA-c9t11), which may have beneficial effects on metabolic diseases such as type 2 diabetes and obesity. Here, we performed differential expression and co-expression analyses, weighted gene co-expression network analysis (WGCNA) and partial correlation with information theory (PCIT), to uncover the complex interactions between miRNAs and mRNAs expressed in skeletal muscle associated with FA content. miRNA and mRNA expression data were obtained from skeletal muscle of Nelore cattle that had extreme genomic breeding values for OA and CLA. Insulin and MAPK signaling pathways were identified by WGCNA as central pathways associated with both of these fatty acids. Co-expression network analysis identified bta-miR-33a/b, bta-miR-100, bta-miR-204, bta-miR-365-5p, bta-miR-660, bta-miR-411a, bta-miR-136, bta-miR-30-5p, bta-miR-146b, bta-let-7a-5p, bta-let-7f, bta-let-7, bta-miR 339, bta-miR-10b, bta-miR 486, and the genes ACTA1 and ALDOA as potential regulators of fatty acid synthesis. This study provides evidence and insights into the molecular mechanisms and potential target genes involved in fatty acid content differences in Nelore beef cattle, revealing new candidate pathways of phenotype modulation that could positively benefit beef production and human consumption. [ABSTRACT FROM AUTHOR]

In solving the gene prioritization problem, ranking candidate genes from most to least promising is attempted before further experimental validation. Integrating the results of various data sources and methods tends to result in a better performance when solving the gene prioritization problem. Therefore, a wide range of datasets and algorithms was investigated; these included topological features of protein networks, physicochemical characteristics and blast similarity scores of protein sequences, gene ontology, biological pathways, and tissue-based data sources. The novelty of this study lies in how the best-performing methods and reliable multi-genomic data sources were applied in an efficient two-step approach. In the first step, various multi-genomic data sources and algorithms were evaluated and seven best-performing rankers were then applied to prioritize candidate genes in different ways. In the second step, global prioritization was obtained by aggregating several scoring schemes. The results showed that protein networks, functional linkage networks, gene ontology, and biological pathway data sources have a significant impact on the quality of the gene prioritization approach. The findings also demonstrated a direct relationship between the degree of genes and the ranking quality of the evaluated tools. This approach outperformed previously published algorithms (e.g., DIR, GPEC, GeneDistiller, and Endeavour) in all evaluation metrices and led to the development of GPS software. Its user-friendly interface and accuracy makes GPS a powerful tool for the identification of human disease genes. GPS is available at http://gpsranker.com and http://LBB.ut.ac.ir. • We developed a web-based tool by fusing multi-genomic data sources. • In our evaluation, GPS is the least affected by degree distribution of genes. • Integrating the outputs of local-rankers in a two-step approach leads to high efficiency. • The test set should contain genes with various degrees for an unbiased evaluation. [ABSTRACT FROM AUTHOR]