{ "question": [ "How do recent advancements in network-based integrative genomics alter our understanding of complex trait architectures?", "What are the latest methodological improvements in evaluating gene-environment interactions using GeneNetwork.org?", "How do multi-omics data integration techniques enhance the prediction accuracy of phenotypic traits in GeneNetwork datasets?", "What are the computational challenges and solutions in analyzing large-scale transcriptomic data within GeneNetwork.org?", "How has the inclusion of data from diverse populations impacted the generalizability of findings on GeneNetwork.org?", "What novel insights have been obtained from GeneNetwork.org regarding the genetic basis of psychiatric disorders?", "How do advancements in machine learning algorithms contribute to the deconvolution of gene expression data in complex tissues?", "What role do enhancer-promoter interactions play in the regulation of gene networks uncovered through GeneNetwork.org?", "How can the integration of ATAC-seq data with RNA-seq data on GeneNetwork.org inform about chromatin accessibility and gene regulation?", "What are the latest strategies for inferring causal relationships within gene networks using data from GeneNetwork.org?", "How do advancements in single-nucleus RNA sequencing provide more granular insights into cell-type-specific gene expression networks?", "What impact have recent discoveries in non-coding RNA regulation had on refining gene interaction maps on GeneNetwork.org?", "How are spatial transcriptomics approaches being integrated into GeneNetwork.org to enhance understanding of tissue architecture and function?", "How do recent developments in quantitative trait locus (QTL) mapping refine our understanding of gene regulatory variants?", "What are the implications of incorporating epigenomic data, such as histone modification maps, into the gene networks on GeneNetwork.org?", "How do recent findings on 3D genome organization contribute to our understanding of functional genomic networks?", "What are the potential applications of artificial intelligence in improving the annotation and interpretation of gene networks?", "How has the study of genetic pleiotropy been advanced by data available on GeneNetwork.org?", "What novel genetic pathways have been identified in GeneNetwork.org studies related to aging and lifespan?", "How do polygenic risk scores (PRS) developed using GeneNetwork.org data enhance the prediction and prevention of complex diseases?" ], "answer": [ "Recent advancements in network-based integrative genomics have significantly enhanced our understanding of complex trait architectures by enabling a more comprehensive analysis of the interactions between genetic and environmental factors. These advancements allow for the integration of gene variants and environmental factors to better understand the networks controlling complex traits [1]. The use of systems genetics approaches facilitates the connection of different trait networks with networks of gene and environmental variants, which helps in identifying the global genetic modulation of complex phenotypes [1]. Moreover, the integration of genotypic, expression, and other data within a Bayesian network framework has been shown to improve our mechanistic understanding of how DNA perturbations can induce changes in gene networks that ultimately affect disease outcomes [2]. This integrative approach provides a more detailed view of the polygenic and pleiotropic networks that modulate complex traits, such as those affecting the nervous system [5]. Overall, these advancements in network-based integrative genomics offer a more nuanced and interconnected view of complex trait architectures, moving beyond single-gene analyses to consider the broader network of interactions that contribute to phenotypic outcomes.", "The latest methodological improvements in evaluating gene-environment interactions using GeneNetwork.org include the addition of data from 10 species, multi-omics analysis, updated code, and new tools, making it a valuable resource for predictive medicine and systems genetics [1]. These enhancements allow for more comprehensive analyses and facilitate the study of correlations between traits and data mining in genomic regions containing candidates for quantitative trait genes [4]. Additionally, GeneNetwork is continuously updated to support systems genetics analyses, which can aid in the discovery, replication, validation, and translation of gene-environment interactions [8].", "Multi-omics data integration techniques enhance the prediction accuracy of phenotypic traits in GeneNetwork datasets by allowing for the construction of biological networks that are predictive of molecular interactions and phenotypic outcomes. When data on DNA variation is combined with gene expression, protein interactions, and DNA-protein binding information, it enables the creation of comprehensive models that can predict complex phenotypes more accurately [6]. This integration facilitates the study of relationships between genes, pathways, and phenotypes, providing a more holistic view of the biological systems involved [2]. Additionally, the use of sophisticated statistical and computational tools in conjunction with multi-omics data allows for the genetic dissection and synthesis of traits, further enhancing predictive accuracy [7].", "Analyzing large-scale transcriptomic data within GeneNetwork.org presents several computational challenges and solutions: 1. **Computational Complexity and Memory Requirements**: The analysis of large networks is computationally complex and requires significant memory resources. Traditional methods may not be ideal for handling such large datasets [5]. 2. **Scalable Algorithms**: To address these challenges, scalable parallel algorithms have been developed. These algorithms are designed to handle fixed-parameter tractable (FPT) problems, which are crucial for managing large-scale data efficiently [6]. 3. **High-Performance Implementations**: Genome-scale algorithms and high-performance implementations are utilized to facilitate the genetic analysis of gene transcription, making it possible to manage and analyze large volumes of data effectively [2]. 4. **Visualization and Biological Validation**: Visualization methods and biological validation are essential to translate complex data into understandable results. These methods help in comparing graphs collected in a systems genetic context, reflecting differences in time, tissue, and treatment effects [3]. 5. **Tools for Large-Scale Network Construction**: Tools like WGCNA, GeneNet, ARACNE, and SPACE are specifically designed to construct gene networks at very large scales. These tools also provide functionalities for analyzing and visualizing resulting networks, including identifying subnetworks [5]. These solutions collectively help in overcoming the computational challenges associated with large-scale transcriptomic data analysis within GeneNetwork.org.", "The inclusion of data from diverse populations on GeneNetwork.org aims to enhance the generalizability of findings by addressing the limitations associated with genetic studies that predominantly focus on less diverse populations. The goal is to increase the diversity of underrepresented populations, with targeted recruitment aimed at over 50% non-European ancestry [9]. This effort is expected to improve the applicability of genetic findings across different populations, thereby enhancing the generalizability of the research outcomes. By incorporating data from a broader range of genetic backgrounds, GeneNetwork.org can provide more comprehensive insights into genetic networks and pathways, which are applicable to a wider array of populations.", "The insights obtained from GeneNetwork.org regarding the genetic basis of psychiatric disorders include the identification of two fundamental yet distinct genetic components shared by major neuropsychiatric disorders. The first component is involved in central nervous system (CNS) development, neural projections, and synaptic transmission [1]. Additionally, the polygenicity of psychiatric illnesses has been highlighted, indicating that psychiatric disorders are influenced by multiple genes, and there is a degree of single nucleotide polymorphism (SNP) sharing among disease cases, which helps estimate the common, inherited portion of these disorders [2]. Furthermore, shared and unique genetic factors have been identified, which highlight key gene sets and molecular processes that may lead to improved diagnosis and treatment of psychiatric disorders [3].", "Advancements in machine learning algorithms contribute to the deconvolution of gene expression data in complex tissues by enabling the prediction of cell-type proportions from bulk genomics data. This computational deconvolution is crucial for understanding the relative abundance of various cell types within a tissue, which is a key step in analyzing gene expression data from complex tissues [1]. Additionally, machine learning methods, such as decision tree methods, are explored to model functional dependencies and predict co-expressed gene profiles, which can further aid in the deconvolution process by identifying regulatory elements and signals that vary with disease status [4]. These advancements allow for more accurate and insightful analysis of gene expression data, facilitating the identification of transcriptional changes and regulatory networks in complex tissues.", "Enhancer-promoter interactions play a significant role in the regulation of gene networks by influencing gene expression levels and patterns. These interactions are crucial for determining cell-specific gene expression, as enhancers can regulate genes over long distances and are involved in complex regulatory networks [4]. Approximately 90,000 enhancer-promoter interactions have been identified, with a majority occurring within the same topologically associating domains (TADs), which suggests a structured and hierarchical organization of these interactions within the genome [3]. Genes with more enhancers tend to have higher expression levels, indicating that enhancers contribute to the regulation of gene expression by interacting with promoters [3]. Additionally, enhancer-promoter interactions can involve long-range interactions, making the prediction of specific enhancer-target relationships challenging [1]. These interactions are part of the broader gene networks that include various regulatory elements and factors, highlighting their importance in the regulation of gene networks as uncovered through platforms like GeneNetwork.org.", "The integration of ATAC-seq data with RNA-seq data can provide valuable insights into chromatin accessibility and gene regulation by combining information about open chromatin regions with gene expression profiles. ATAC-seq is a technique that characterizes accessible chromatin regions, which are often associated with transcriptional activity [1]. This method can simultaneously profile open chromatin, transcription factor-binding footprints, and nucleosome positioning [2]. By integrating this data with RNA-seq, which measures gene expression levels, researchers can relate chromatin accessibility to gene expression patterns. For example, by creating a reference map using single-cell RNA sequencing (scRNA-seq) and assigning cell-type identities, researchers can relate cell-type-resolved accessible chromatin to gene expression [3]. This integration helps in identifying cis-regulatory programs by aggregating reads from cells within each ATAC-seq cluster and linking them to gene expression data. Overall, the integration of ATAC-seq and RNA-seq data allows for a comprehensive understanding of how chromatin accessibility influences gene regulation, providing insights into the regulatory elements that control gene expression in different cellular contexts.", "The latest strategies for inferring causal relationships within gene networks using data from GeneNetwork.org involve several advanced methodologies. One approach is the use of Bayesian network inference, which has been advanced to generate causal networks from observational biological data [2]. This method allows for the modeling of probabilistic relationships between genes and can help infer causality from complex datasets. Additionally, there is a focus on using genetic markers to orient causal inference in genome-wide association studies, which is critical for understanding the genetic basis of phenotypes [5]. This involves identifying genetic variants that can serve as markers to infer causal pathways. Another strategy involves the use of network deconvolution, a general method to infer direct dependencies in networks, which can be applied to gene networks to clarify causal relationships [2]. Furthermore, the integration of multi-omics data and the use of updated tools on platforms like GeneNetwork.org enhance the ability to perform predictive medicine and systems genetics analyses, which are crucial for inferring causal relationships in gene networks [10]. These strategies collectively leverage statistical, computational, and biological insights to improve the inference of causal relationships in gene networks.", "Advancements in single-nucleus RNA sequencing (snRNA-seq) provide more granular insights into cell-type-specific gene expression networks by allowing for the analysis of gene expression within the nuclei of cells, rather than relying on intact cells as in single-cell RNA sequencing (scRNA-seq) [1]. This method is particularly useful for profiling gene expression in complex tissues from frozen samples at the single-cell level, which can be challenging with other techniques [1]. Additionally, snRNA-seq can help clarify cell-type proportions and corresponding transcriptional profiles, as demonstrated in studies involving postmortem human brain tissue [9]. By isolating nuclei and performing snRNA-seq, researchers can achieve finer cell subtype resolution, which is crucial for understanding the heterogeneity within cell populations [7]. This level of detail is essential for constructing accurate cell-type-specific gene expression networks, as it allows for the identification of transcriptional changes and cell-type-specific gene expression patterns that might be obscured in bulk tissue analyses [3]. Overall, snRNA-seq enhances our ability to dissect complex tissues into their constituent cell types and understand the unique gene expression networks within each type, providing a more detailed and nuanced view of cellular function and interaction.", "The context provided does not explicitly mention the impact of recent discoveries in non-coding RNA regulation on refining gene interaction maps on GeneNetwork.org. However, there are some relevant points that can be inferred: 1. GeneNetwork.org has been updated with new tools and data, including multi-omics analysis, which could potentially incorporate non-coding RNA data to enhance gene interaction maps [2]. 2. The integration of gene expression data sets, particularly for mouse and rat, into GeneNetwork.org suggests that the platform is equipped to handle complex genetic data, which may include non-coding RNA interactions [7]. 3. There is ongoing research and data collection on non-coding RNAs, as indicated by references to databases like Rfam and cisRED, which could contribute to refining gene interaction maps by providing insights into regulatory networks [9], [10]. While the specific impact of non-coding RNA discoveries on GeneNetwork.org is not detailed, the platform's enhancements and the broader research context suggest that such discoveries could play a role in improving the accuracy and depth of gene interaction maps.", "The provided context does not explicitly mention the integration of spatial transcriptomics approaches into GeneNetwork.org. However, it does describe GeneNetwork as a resource for systems biology and systems genetics, which includes large transcriptome datasets from multiple tissues [2], [9]. The platform is used to study relationships among markers, genes, and phenotypes, and to analyze genetic regulatory commonality and tissue structure and function [3], [4]. While spatial transcriptomics is not directly referenced, the existing capabilities of GeneNetwork in handling multi-omics data and performing systems genetics mapping [1], [5] suggest that it could potentially support spatial transcriptomics approaches to enhance understanding of tissue architecture and function.", "Recent developments in quantitative trait locus (QTL) mapping have significantly refined our understanding of gene regulatory variants in several ways: 1. **Identification of eQTLs**: QTL mapping of gene expression traits allows for the identification of expression quantitative trait loci (eQTLs), which are genomic regions that have a regulatory effect on gene expression traits. These eQTLs can be categorized into local eQTLs, which are located near the gene encoding the transcript, and distant eQTLs, which are located elsewhere in the genome [2]. This distinction helps in understanding the regulatory architecture of the genome. 2. **Increased Resolution and Confidence**: With advancements in DNA sequencing and the availability of whole-genome databases and gene expression data from various tissues, researchers can use bioinformatic tools to identify candidate genes with greater confidence for further functional validations [1]. This enhances the precision of QTL mapping in pinpointing regulatory variants. 3. **Functional Mapping and Hypothesis Generation**: QTL mapping studies, such as those beginning with yeast, have used gene expression as the phenotype to infer regulatory control. Although these studies are not conclusive, they help narrow down potential regulatory candidates, generate hypotheses for further testing, and construct regulatory networks in silico [3]. 4. **Detection of Secondary QTLs and Epistatic Interactions**: Recent developments allow for the identification of large numbers of less strong, secondary QTLs that were previously obscured by background noise. This opens up new possibilities for analyses, such as identifying epistatic interactions, which can reveal pathways of genetic control within the studied tissue [4]. 5. **Integration with Expression Analysis**: The integration of genetic variation in associated loci with expression analysis data through eQTL studies helps define regulatory relationships. This approach provides insights into the physiological consequences of causal variants, aiding in the translation of findings into diagnostic tests and risk evaluation [8]. Overall, these advancements in QTL mapping enhance our understanding of the complex regulatory mechanisms underlying gene expression and trait variation.", "Incorporating epigenomic data, such as histone modification maps, into the gene networks on GeneNetwork.org has several implications: 1. **Enhanced Functional Analysis**: By integrating epigenetic data, the predictive functional analysis of SNPs can be improved. This is because epigenetic data can highlight regions of DNA that are accessible or inaccessible to protein binding by transcription factors and other regulatory proteins, which can affect gene expression and regulation [4]. 2. **Prioritization of Regulatory Variants**: The incorporation of genome-wide histone modification signatures, as revealed by collaborative efforts like the ENCODE Project and Roadmap Epigenomics, allows for the prioritization of functional regulatory variants. This can be particularly useful in mapping studies and databases, enhancing the understanding of regulatory features in various tissues and cell lines [2]. 3. **Understanding Gene Expression Variation**: By studying the genetics of epigenetics, it is possible to reveal genes that directly or indirectly affect epigenetic gene states. This approach can help estimate the percentage of variation in gene expression that can be explained by different epigenetic conformations, thus providing a deeper understanding of gene regulation [3]. 4. **Resource for Predictive Medicine and Systems Genetics**: The integration of multi-omics data, including epigenomic data, makes GeneNetwork.org a valuable resource for predictive medicine and systems genetics. This integration supports more comprehensive analyses and enhances the platform's utility for research and clinical applications [7]. Overall, incorporating epigenomic data into GeneNetwork.org enriches the platform's analytical capabilities, offering deeper insights into gene regulation and expression, and supporting advanced research in genetics and epigenetics.", "Recent findings on 3D genome organization have significantly enhanced our understanding of functional genomic networks in several ways: 1. **Co-regulation through Spatial Organization**: The 3D chromatin structure is known to couple nuclear compartmentalization of chromatin domains with the control of gene activity, which contributes to cell-specific gene expression [1]. This spatial organization within the nucleus suggests that chromosomal and spatial co-localization may indicate co-regulation of genes, thereby influencing functional genomic networks. 2. **Regulation by Distant Elements**: There is a growing awareness that the three-dimensional juxtaposition of DNA regions within nuclei allows genes to be regulated by elements located at a distance from the gene itself [4]. This understanding helps explain how disease-associated SNPs can fall within gene regulatory elements, thus affecting genomic networks and potentially leading to disease. 3. **Integration with Functional Annotations**: Advances in identifying functional genomic elements through various annotations, such as those from the ENCODE project, have been complemented by insights into 3D genome organization. This integration helps in identifying potential regulatory variants and understanding their roles within genomic networks [2]. These findings collectively contribute to a more comprehensive understanding of how genes are regulated within the complex spatial architecture of the genome, thereby enhancing our knowledge of functional genomic networks.", "Artificial intelligence (AI) has several potential applications in improving the annotation and interpretation of gene networks: 1. **Inference of Gene Functions**: AI techniques, such as network inference algorithms, can help infer the putative functions of unknown genes by linking them to genes with known functions that exhibit similar expression patterns. This approach can also prioritize candidate variants and predict disease inheritance modes to some extent [3]. 2. **Network Inference Techniques**: AI-driven network inference techniques can be utilized to infer biological processes and the potential phenotypic impact of variants in genes of unknown function. These techniques can provide powerful approaches to inferring phenotypic information where direct links to phenotype do not exist [4]. 3. **Computational Approaches**: AI, particularly through computational approaches using statistical, machine learning, or soft-computing techniques, serves as a discovery tool for finding gene networks. These approaches can complement literature-based methods that gather published information on genes and their interrelationships [6]. 4. **Pattern Recognition and Predictive Modeling**: Deep learning models, a subset of AI, can be used for pattern recognition in gene sequences to identify potential future illnesses. There is also a demand for explainable AI models that are interpretable in decision-making, which can enhance the understanding and application of genomic data [8]. These applications demonstrate how AI can significantly enhance the annotation and interpretation of gene networks by providing insights into gene functions, biological processes, and potential phenotypic impacts.", "The study of genetic pleiotropy has been advanced by data available on GeneNetwork.org through several key developments: 1. **Multi-Omics Analysis and Data from Multiple Species**: GeneNetwork.org has incorporated data from 10 different species and supports multi-omics analysis, which allows researchers to explore genetic pleiotropy across a wide range of organisms and biological data types. This comprehensive approach provides a richer understanding of how genes can influence multiple traits or diseases [4]. 2. **Systems Genetics Approach**: The platform enables a systems genetics approach, which contrasts with the traditional candidate gene approach. Instead of focusing on single gene mutations, it explores the relationships between diverse genetic and molecular markers and their resulting phenotypes and diseases. This approach is particularly useful for studying pleiotropy, as it considers the complex interactions and shared pathways that can lead to multiple phenotypic effects from a single genetic locus [5]. 3. **Open Web Resource**: GeneNetwork.org is an open web resource, making it accessible to a wide range of researchers. This accessibility facilitates collaborative research and data sharing, which are crucial for advancing the study of pleiotropy by allowing researchers to build on each other's findings and methodologies [8]. These features collectively enhance the ability to study genetic pleiotropy by providing comprehensive data, advanced analytical tools, and a collaborative platform for researchers.", "GeneNetwork.org studies have identified novel genetic pathways related to aging and lifespan through various approaches. One notable method is the use of network identification by regression (NIR), which has been applied to identify novel pathways in the context of aging and lifespan [2]. Additionally, network-based approaches have revealed six pathways and six key genes that might play pivotal roles in regulating longevity, providing new insights into the mechanisms of longevity [6]. These findings highlight the potential of network-based methods to uncover novel genetic pathways associated with aging and lifespan.", "Polygenic risk scores (PRS) developed using GeneNetwork.org data enhance the prediction and prevention of complex diseases by providing a quantitative metric of an individual's inherited risk based on the cumulative impact of many common polymorphisms [7]. These scores aggregate the genetic risk of individual alleles across the genome, which can significantly improve the prediction of future disease occurrence and aid in early diagnosis, intervention, and prevention strategies [5]. PRS can complement established clinical risk factors and intervention paradigms, thereby enhancing early diagnosis and prevention efforts for diseases such as type 2 diabetes (T2D) [6]. Additionally, PRS have emerged as promising biomarkers for predicting disease risk in various areas, including cardiovascular disorders and oncology [8]. By utilizing data from large consortia and genome-wide genotypes, the predictive value of these scores has substantially improved, allowing for a more comprehensive assessment of genetic risk [3]." ], "contexts": [ [ "It is important to integrate the gene variants and environmental factors to the trait to understand the network controlling that trait. In systems genetics approach, different trait networks are related to different networks of gene and environmental variants to find global genetic modulation of the complex phenotype. The availability of genetic reference panels makes it easy to acquire diverse phenotypic data and advanced computational models make it possible to analyse their relationship. 2.2.1.", "Processing Large-Scale, High-Dimension Genetic 325 another. We anticipate these types of networks becoming increasingly important in the human genetics space to gain a mechanistic understanding of how a given DNAperturbation induces changes in one or more genes that go on to affect networks that cause disease. The integration of genotypic and expression and other data have recently been shown, in a Bayesian network framework [76], to enhance the overall", "2. GENETICAL GENOMICS In recent years, there has been growing interest in uniting genetic and genomic approaches to enable more comprehensive dissections of complex traits and their genetic architecture. Jansen and Nap (2001) termed this synthesis genetical ge-", "2. GENETICAL GENOMICS In recent years, there has been growing interest in uniting genetic and genomic approaches to enable more comprehensive dissections of complex traits and their genetic architecture. Jansen and Nap (2001) termed this synthesis genetical ge-", "42.Chesler EJ, et al. 2005. Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system func-tion. Nat. Genet. 37:233242. 43.Iraqi FA, Churchill G, Mott R. 2008. The Collaborative Cross, develop- ing a resource for mammalian systems genetics: a status report of theWellcome Trust cohort. Mamm. Genome 19:379 381. 44.Xiao J, et al. 2010. A novel strategy for genetic dissection of complex traits:", "multiple-SNP analysis of GWAS summary statistics identiesadditional variants inuencing complex traits. Nat Genet 44(369375):S1S3. doi: 10.1038/ng.2213 Yang J, Zaitlen NA, Goddard ME et al (2014) Advantages and pitfalls in the application of mixed-model association methods. NatGenet 46:100106. doi: 10.1038/ng.2876 Yazbek SN, Buchner DA, Geisinger JM et al (2011) Deep congenic", "10. The power of integrating all these genetic and genomic data has now been well documented, offering a glimpse of what the future of com-plex trait genetics will look like. Model systems that are genetically more complex, including extensive eight-strain crosses 11,12 and haplotype association studies using large panels of regular inbred strains of mice, and even humans, are", "tive analysis of omics summary data reveals putative mechanisms underlying complex traits. Nat Commun 9:918 33. Yang J, Hong Lee S, Goddard ME, Visscher PM (2011) GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet 88:7682 34. Zeisel A, Hochgerner H, Lnnerberg P, Johnsson A, Memic F, van der Zwan J etal (2018) Molecular architecture of the mouse nervous system. Cell 174:999.e221014.e22 35. Zhan X, Hu Y, Li B, Abecasis GR, Liu DJ (2016) RVTESTS:", "used to identify molecular traits involved in the p athology of diseases and to eluci- date the networks underlying complex phenotypes. Re cent studies have pushed the genetical genomics concept further towards data int egration and interpretation within and across molecular levels, and have also r evealed remaining challenges. The focus of this review is to discuss these challe nges and their possible solutions in", "2 large populations. The new methods have allowed us to dissect the genetic architecture of complex disorders including the identification of the causal genomic loci, estimation of the disease heritability, estimation of effect sizes of different loci and their non-additive interactions. Linkage analysis The earlier breakthroughs in linking genotype with phenotype involved studies of Mendelian disorders that can be mapped to a single gene and a single mutation. These" ], [ "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", "analytical method, have been used to discover gene- environment interactions; some approaches address similar objectives, whilst others are complementary and can be ap- plied in sequence. Below we describe several of these ap- proaches, and refer the reader to another excellent review of gene-environment interaction methods [ 31]. (a)Established statistical approaches Until 2008, almost all studies of gene-environment interac- tions focused on testing hypotheses based on existing biolog-", "ulated by non-genetic factors. Thus, the once esoteric topic of gene-environment interaction is now becoming mainstream and appealing to investigators across diversedisciplines; this has propelled major methodological in- novations for the discovery, replication, validation and translation of gene-environment interactions. The expo- nentiation of data resources for these purposes has demanded analytical solutions that address data dimen- sionality reduction. Although not yet extensively imple-", "addition to this, GeneNetwork can be used to study correlations between traits and to perform data mining in genomic regions containing candidates for quantitative trait genes (Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods information page that summarizes experimental details relating to the dataset. Databases within GeneNetwork include the transcriptome database, the BXD published", "Eaves LJ 2006 Genotype x environment interaction in psychopathology: fact or artifact? Twin Res Hum Genet 9:18 Hunter DJ 2005 Geneenvironment interactions in human diseases. Nat Rev Genet 6:287298 Ioannidis JP, Ntzani EE, Trikalinos TA, Contopoulos-Ioannidis DG 2001 Replication validity of genetic association studies. Nat Genet 29:306309 Ioannidis JP, Gwinn M, Little J et al 2006 A road map for ef cient and reliable human genome epidemiology. Nat Genet 38:35", "GeneNetwork is an open-access database that collates genomic information of diverse experimental crosses and reference panels as well as phenotypic data from miscellaneous research groups [26]. Statistics Data generation, statistical analysis and graph creation were performed with SPSS Statistics 21 (IBM, Ehningen, Germany). As appropriate, mean and median values were further used for QTLanalysis. Phenotypic robustness for each strain was assessed by the", "NU32CH13-Hu ARI 18 June 2012 13:45 effectively scan the entire genome for interac- tions with environment. Although innovative, the most effective study design and statistical approach for conducting gene-environment- wide interaction studies (GEWIS) remains unresolved (88). The greatest challenge for GEWIS involves nding a balance between rejecting true ndings resulting from stringent multiple-testing correction and reporting false-positive results. Several novel methods", "1 GeneNetwork: a continuously updated tool for systems genetics analyses Pamela M. Watson1, David G. Ashbrook1 1Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA Abstract GeneNetwork and its earlier iteration , WebQTL, have now been an important database and toolkit for quantitative trait genetics research for two decades. Recent improvements to", "13 132. Geneenvironment interaction: overcoming methodological challenges Rudolf Uher MRC Social, Genetic and Developmental Psychiatry Research Centre, Institute of Psychiatry, Kings College London, UK Abstract. While interacting biological effects of genes and environmental exposures (G E) form a natural part of the causal framework underlying disorders of human health, the detection of G E relies on inference from statistical interactions observed at popu-", "A number of recent developments in twin methodology have taken place based on the incorporation of measured genotype information. Thisenables twin models to estimate how much of the genetic variation is dueto variation in a specific gene. Gene-environment interaction studies, link-Copyright National Academy of Sciences. All rights reserved.Cells and Surveys: Should Biological Measures Be Included in Social Science Research? http://www.nap.edu/catalog/9995.html" ], [ "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", "Conclusion GeneNetwork is an excellent tool for exploring complex phenotypes with systems genetics. Here we have used GeneNetwork to explore an inflammatory phenotype, and identified a small number of plausible candidate genes. A similar workflow can be used for any trait on GeneNetwork, or for any phenotype collected by an investigator in a genetically diverse population. GeneNetwork can allow users to study relationships between genes, pathways, and phenotypes in an easy to use format.", "Conclusion GeneNetwork is an excellent tool for exploring complex phenotypes with systems genetics. Here we have used GeneNetwork to explore an inflammatory phenotype, and identified a small number of plausible candidate genes. A similar workflow can be used for any trait on GeneNetwork, or for any phenotype collected by an investigator in a genetically diverse population. GeneNetwork can allow users to study relationships between genes, pathways, and phenotypes in an easy to use format.", "addition to this, GeneNetwork can be used to study correlations between traits and to perform data mining in genomic regions containing candidates for quantitative trait genes (Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods information page that summarizes experimental details relating to the dataset. Databases within GeneNetwork include the transcriptome database, the BXD published", "connect Genotype with Gene2 and Phenotype, knowledge of the Genotype still influences the predicted values of these variables. For example, Genotype = 1 may cause a decrease in Gene1 and this decrease in Gene1 will subsequently cause a reduction in Gene2. 4 Discussion Network modeling of biological datasets is often limited by the number of samples within a dataset, and the available data does not support the construction of precise and reliable large-scale networks", "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to explore systems genetics data. The importance of defining biological networks and predicting molecular interactions has been emphasized by several reports [1,2]. Such studies emphasize that when knowledge about DNA variation within popula- tions is interfaced with data on gene expression, protein interactions and DNA-protein binding, biological networks can be constructed that are predictive of the", "metadata (data about the data) are combined with sophisticated statistical and computation tools for the genetic dissection and synthesis of single traitsor entire systems of traits. One challenge facing investigators in the inter- pretation of the massive data sets on GeneNetworkand elsewhere is deciding how much confidence toplace in QTL extracted from still noisy array andproteomic platforms after having conducted many thousands of statistical tests with poorly understood", "accuracy of predictive networks [40, 5153]. We have also recently demonstrated how this class of network can be used to inform associations identied in GW Astudies [40]. 9 Summary The signicant challenge we face in the post-genome era is deciphering the bio-logical function of individual genes, pathways, and networks that drive complexphenotypes like disease. The availability of low-cost, high-throughput technologies", "members o f pathway modules [78]. Other studies applied gene network modeling algorithms to identify the potential regulators in complex di seases, for example cardiomyopathy [79], hepatic steatosis [80], as well as coronary artery disease [81]. Finally, there are many other integrative approaches available for the analysis of multi -omics data, but have not yet been applied in mouse systems genetics studies. Examples include the transcriptome -wide", "gathered together into an easily accessible format, not siloed into disparate data pools that cannot easily be integrated, valid ated, o r extended. This approach will allow us to make animal models of so called precision medicine, although perhaps more accurately, we want predictive medicine , where a phenotypic outcome (such as disease) can be predicted , and avoided . GeneNetwork (genenetwork.or g; GN) is one tool for systems genetics and predictive medicine," ], [ "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", "Combinatorial Genetic Regulatory Network Analysis Tools for High Throughput Transcriptomic Data Elissa J. Chesler1and Michael A. Langston2 1Life Sciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831-6124, USA 2Department of Computer Science, University of Tennessee, Knoxville, TN 379963450, USA Abstract: A series of genome-scale algorithms and high-performance implementations is described and shown to be useful in the genetic analysis of gene transcription. With", "Combinatorial Genetic Regulatory Network Analysis Tools 163 In addition to expansive volumes of data, there is a growing complexity to the types of research questions that can be asked. We are presently developing approaches to compare graphs collected in a systems gene tic context to reect differences in time, tissue and treatment effects. Visualizatio n methods and compelling biological validation of novel results are essential to translate these methods and deliver them to the broader", "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and statistical analysis of large published phenotype and genome datasets, and includes data from several species (see Supplementary Discussion). GeneNetwork includes extensive phenotype data extracted from the literature and submitted by users, which makes it practical to compare data on drug responses with gene expression patterns. Gene expression", "larger networks well. Because of the computational complexity aswell as the memory requirements, these methods as currentlyimplemented are not the ideal choice for such large networks.WGCNA, GeneNet, ARACNE and SPACE, on the other hand,were designed to construct the gene network at very large scales.Also, it worth mentioning that the WGCNA package providesseveral useful tools to facilitate the analysis and visualization of resulting networks, including tools to identify subnetworks and an", "Proc Natl Acad Sci U S A 100: 94409445. 32. Chesler E, Langston MA (2005) Combinatorial Genetic Regulatory Network Analysis Tools for High Throughput Transcriptomic Data. Proceedings,RECOMB Satellite Workshop on Systems Biology and Regulatory Genomics. 17 p.33. Abu-Khzam F, Langston M, Shanbhag P, Symons C (2006) Scalable Parallel Algorithms for FPT Problems. Algorithmica 45. 34. Langston M, Perkins A, Saxton A, Scharff J, Voy B (2006) Innovative", "computational methods for transcriptomic data analysis. SAC 06: Proceedings of the 2006 ACM symposium on Applied computing. 35. Csardi G, Nepusz T (2006) The igraph software package for complex network research. InterJournal Complex Systems 1695. 36. Chen J, Bardes EE, Aronow BJ, Jegga AG (2009) ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res 37:W305311. 37. Williams RW, Gu J, Qi S, Lu L (2001) The genetic structure of recombinant", "plenary lecture, with a focus on the computational challengesin analyzing large datasets. The type of datasets discussed by Williams included the microarray type outputs first suggestedby Jansen and Nap ( 2001 ) for inclusion in genetical genomics analyses and are now extended to cross-platform datasets (Damerval et al. 1994; Ciobanu et al. 2010 ). A framework for carrying out the genetic analyses was described as being available through the GeneNetwork and WebQTL software", "32. Zhu J, Zhang B, Smith EN, Drees B, Brem RB, Kru glyak L, Bumgarner RE, Schadt EE: Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks . Nat Genet 2008, 40 (7):854-861. 33. Vera G, Jansen RC, Suppi RL: R/parallel--speeding up bioinformatics analysis with R . BMC bioinformatics 2008, 9:390. 34. Alberts R, Terpstra P, Bystrykh LV, de Haan G, Jansen RC: A statistical multiprobe model for analyzing cis and trans genes in genetical", "Processing Large-Scale, High-Dimension Genetic and Gene Expression Data Cliona Molony, Solveig K. Sieberts, and Eric E. Schadt Abstract The now routine generation of large-scale, high-throughput data in mul- tiple dimensions (genotype, gene expression, and so on) provides a signicant challenge to researchers who desire to integrate data across these dimensions in" ], [ "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", "GeneNetwork provided the platform for correlation analysis, principal component generation, and linkage analysis. In general, datasets were queried for gene symbols, downloaded from GeneNetwork, and additional analysis was performed in R whenever necessary. P-values mentioned in relation to Pearsons coecient throughout this paper are based on pair- wise comparisons. All p-values were Bonferroni-adjusted for 36,012 genes, which is equal to the number of genes captured", "GeneNetwork provided the platform for correlation analysis, principal component generation, and linkage analysis. In general, datasets were queried for gene symbols, downloaded from GeneNetwork, and additional analysis was performed in R whenever necessary. P-values mentioned in relation to Pearsons coecient throughout this paper are based on pair- wise comparisons. All p-values were Bonferroni-adjusted for 36,012 genes, which is equal to the number of genes captured", "including correlation and network analysis to compare associations between tissues and between other rodent or human data sets[32] Many of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", "including correlation and network analysis to compare associations between tissues and between other rodent or human data sets[32] Many of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", "network. Cell 9, 12121226 (2014). 12. Hirschhorn, J.N. Genomewide association studiesilluminating biologic pathways. N. Engl. J. Med. 0, 16991701 (2009). 13. Cantor, R.M., Lange, K. & Sinsheimer, J.S. Prioritizing GWAS results: a review of statistical methods and recommendations for their application. Am. J. Hum. Genet. 8, 622 (2010). 14. Lee, I., Date, S.V., Adai, A.T. & Marcotte, E.M. A probabilistic functional network of yeast genes. Science 0, 15551558 (2004).", "addition to this, GeneNetwork can be used to study correlations between traits and to perform data mining in genomic regions containing candidates for quantitative trait genes (Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods information page that summarizes experimental details relating to the dataset. Databases within GeneNetwork include the transcriptome database, the BXD published", "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and statistical analysis of large published phenotype and genome datasets, and includes data from several species (see Supplementary Discussion). GeneNetwork includes extensive phenotype data extracted from the literature and submitted by users, which makes it practical to compare data on drug responses with gene expression patterns. Gene expression", "limit the applicability of genetic ndings in more diversepopulations. In the next phase of the network, the goalis to increase the diversity of underrepresented popula-tions, with targeted recruitment aimed at over 50% non-European ancestry. The lessons from enrollment andRoRs to diverse populations, even limited, will inform our next phase as we continue to strive for a more represen-", "data available across all contributing consortia will facilitate systematic exploration of these correlated phenotypes with more sophisticated statistical methods for joint analysis5254, yielding greater insight into the underlying pathways and genetic networks they represent. As data from human genetic networks accrue, we will be better placed to test whether there is support for the notion of hub genesthat is, genes highly connected with others in the network, proposed by experi" ], [ "Lotan et al. Neuroinformatics of major neuropsychiatric disorders We demonstrated that although these disorders share a rela- tively small set of genes, there are two fundamental yet distinctgenetic components, or vectors, that are both shared by all sixdisorders. While the rst component is involved in CNS develop- ment, neural projections and synaptic transmission, the second", "genetic variation) for any psychiatric disorder (Fig. 1), there is sufficient information to drawsome general conclusions. The polygenicity of psychiatric illness In addition to finding specific genes, molecu- lar genetics can provide information about theheritability of psychiatric disease, an approach that has led to some important insights about the genetic architecture of psychiatric illness.The degree of SNP sharing among disease cases estimates the common, inherited portion of a", "of shared and unique genetic factors highlights key gene sets and molecular processesthat may ultimately translate into improved diagnosis and treatment of these debilitating disorders. Keywords: major neuropsychiatric disorders, neuroinformatics, cross-species, translational, genetic components, genome wide association studies, enrichment INTRODUCTION Common psychiatric disorders including attention-", "6. D. H. Geschwind, J. Flint, Genetics and genomics of psychiatric disease. Science 349, 1489 1494 (2015). doi: 10.1126/science. aaa8954 ; pmid: 26404826 7. S. Cichon et al ., Genomewide association studies: History, rationale, and prospects for psychiatric disorders. Am. J. Psychiatry 166, 540 556 (2009). doi: 10.1176/ appi.ajp.2008.08091354 ; pmid: 19339359 8. A. Battle et al., Genetic effects on gene expression across human tissues. Nature 550, 204 213 (2017). doi: 10.1038/ nature24277 ; pmid: 29022597", "the Psychiatric Genomics Consortium found that the results were highly correlated between methods in a comparison of methods applied across several psychiatric disorders ( Network Pathway Analysis Subgroup of Psychiatric Genomics Consortium 2015 ). A second limitation of pathway-based analysis is that it is still biased by our incomplete prior knowledge of gene function in the etiology of psychiatric illness. Despite these challenges, pathway-based analyses have identified biological pathways", "Lotan et al. Neuroinformatics of major neuropsychiatric disorders GENES FROM THE NHGRI-CROSS-DISORDER SET CLUSTER IN THREE CO-EXPRESSION MODULES WITH DISTINCT SPATIO-TEMPORALEXPRESSION PATTERNS AND FUNCTIONAL BIASES One of the major properties of genes involved in regulation of", "Genet. 2009; 85:847861. [PubMed: 19931040] Brownlee DJ, Fairweather I. Exploring the neurotransmitter labyrinth in nematodes. Trends Neurosci. 1999; 22:1624. [PubMed: 10088995] Bucholz KK, Cadoret R, Cloninger CR, Dinwiddie SH, Hesselbrock VM, Nurnberger JI Jr, Reich T, Schmidt I, Schuckit MA. A new, semi-structured psychiatric interview for use in genetic linkage studies: a report on the reliability of the SSAGA. J Stud Alcohol. 1994; 55:149158. [PubMed: 8189735]", "with shared effects on ve major psychiatric disorders: a genome- wide analysis. Lancet 381(9875):13711379 Davis S, Meltzer P (2007) Geoquery: a bridge between the gene expression omnibus (geo) and bioconductor. Bioinformatics 14:18461847 de Mooij-van Malsen AJG, Vinkers CH, Peterse DP, Olivier B, Kas MJH (2011) Cross-species behavioural genetics: a starting point for unraveling the neurobiology of human psychiatric disorders. Prog Neuropsychopharmacol Biol Psychiatr 35(6):13831390", "systems biology approach based on gene co-expression networks and genotype-gene expression (rather than genotype-disease)associations, these results further validate our methodology to construct polygenic scores linked to the overall biological function of tissue-speci c gene networks. Molecular Psychiatry (2022) 27:27422750; https://doi.org/10.1038/s41380-022-01533-7 INTRODUCTION Several psychiatric disorders of developmental origin are char-", "systems biology approach based on gene co-expression networks and genotype-gene expression (rather than genotype-disease)associations, these results further validate our methodology to construct polygenic scores linked to the overall biological function of tissue-speci c gene networks. Molecular Psychiatry (2022) 27:27422750; https://doi.org/10.1038/s41380-022-01533-7 INTRODUCTION Several psychiatric disorders of developmental origin are char-" ], [ "The method takes as input a large cohort of individuals, wherethe input for each individual includes: (1) genotyping; (2) bulk ex-pression of genes in a certain tissue; (3) the relative abundance(proportions) of the various cell types in the tissue (it is possible to use computational deconvolution methods to predict cell-type proportions from bulk genomics data ( Newman et al. 2015 )). In", "Filtering out the latter class of technical difficulty im-proved the recovery of genuine cis-modulated transcripts and thus to identify genes that are relevant to further down-stream regulation of gene expression and more complex phe-notypes (Ciobanu et al. 2010 ). Williams also discussed the power of a structured mapping population in model organisms and presented the Complex4 Funct Integr Genomics (2012) 12:1 9", "genomic hybridization microarrays (8), can complement RNA expression data and result in novel discoveries. With the evolution and maturation of proteom ics, certainly combining serum- or tissue-based patterns of protein expression with RNA expression holds promise. Finally, other rich sources of complex data such as the literature can be used to complement our analysis of microar ray data (39). These analyses face significant challenges with respect to gene", "data. To model the functional dependence we shall explore machine learning methods16, such as decision tree methods to predict the co-expressed gene profiles. As part of this study and in (E) Future work, see below, we will investigate the benefit of using comparative genomics in helping to lo cate and characterise the regul atory elements and signals. D(d) Integration and Modelling to infer regulato ry systems co-varying with disease status", "derived from complex tissue such as brain show a high level of correspondence24,25. Such structure can be used to inform a new level of neuroscientific investigation that is not possible using standard analysis of differential expression2225. For example, one of the first such studies23 showed that gene networks could be used to provide a unifying method of identifying transcriptional targets of human brain evolution in", "profiling of a multicellular organism,\" Science, vol. 357, no. 6352, pp. 661 -667, 2017. [68] X. Guo, W. Li, and F. Iorio, \"Convolutional neural networks for steady flow approximation,\" in Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining , 2016, pp. 481 -490. [69] V. Ntranos, L. Yi, P. Melsted, and L. Pachter, \"A discriminative learning approach to differentia l expression analysis for single -cell RNA -seq,\" Nature Methods, vol. 16,", "levels can influence the ability to call differential gene expression (Oshlack and Wakefield 2009), we also included, as a feature, the average expression level of the genes in the young samples. All machine-learning algorithms assigned genes to the correct transcriptional change with age 67% 81% of the time on average, significantly above that of a random classification (50%) (Fig. 3B,C; Supplemental Fig. S3B,C ;Supplemental Table S3A,B ). Models de-", "DNA. Microarray technology is helpful in capturing biological genetic information to computer data. Computational techniques can be applied on those large set of genetic data of every individuals with or without disease, so that the genes that are responsible for the disease occurrence can be po inted out. Differentially Expressed Genes (DEG) are identified using many techniques. Machine Learning (ML) algorithms plays a significant role in identifying the distinction between normal", "mapping, several sophisticated analyses will be required to extract full value fromthe enormous amount of collected data, and gain valuable insight into geneticcontrol of gene expression. As recently noted by Ariel Darvasi (2003), I expect thatthe combining of genetic information and gene expression will hasten the day whengenomics delivers on its promise to improve health care. But we must continuestriving to develop and apply sophisticated analytical tools for interpreting the vast,complex data sets that", "mapping, several sophisticated analyses will be required to extract full value fromthe enormous amount of collected data, and gain valuable insight into geneticcontrol of gene expression. As recently noted by Ariel Darvasi (2003), I expect thatthe combining of genetic information and gene expression will hasten the day whengenomics delivers on its promise to improve health care. But we must continuestriving to develop and apply sophisticated analytical tools for interpreting the vast,complex data sets that" ], [ "dynamic16,17, and several studies have proposed that impaired enhancer activation could be at the origin of disease1821. Besides interacting with nearby promoters, enhancers also engage in long-range interactions. Indeed, it is estimated that approximately 3540% of all promoter-enhancer interactions are intervened by at least one gene22, which makes exact enhancer-target prediction challenging. Long-range enhancers interactions can be identi ed by chromosome conformation capture methods23,24.", "motifs found in its promoter (gene-to-sequence). We will referto the ensemble of these inuence interactions as genenetworks. The interaction between two genes in a gene network does not necessarily imply a physical interaction, but can also referto an indirect regulation via proteins, metabolites and ncRNA that have not been measured directly. Inuence interactions include physical interactions, if the two interacting partnersare a transcription factor, and its target, or two proteins in the", "~90,000 enhancer-promoter interactions (fig.S36). As expected, ~75% of enhancer-promoterinteractions occurred within the same TAD, and genes with more enhancers tended to have high- er expression (Fig. 5B and fig. S36). We inte-grated the Hi-C data with QTLs; surprisingly, QTLs involving SNPs distal to eGenes but linked by Hi-C interactions showed significantly stron-ger associations (as indicated by the QTL Pvalue) than those with SNPs directly in the eGene pro- moter or exons (Fig. 5C and fig. S37).", "histone-modifying proteins, and other factors to regulate polymerase-II activity. Such factors can bind in close prox- imity to promoters to influence gene expression. However, there is substantial evidence that additional genetic elements referred to as enhancers play major roles in determining cell- specific patterns of gene expression. 1517 Initially identified >30 years ago, enhancer elements can be located at various distances from promoters, typically between 1 and 50 kilo-", "involved in the regulation of the target genes of both networks, but that the interaction partners through which this regulation is established differs for both target genes.", "variants in epigenomic features using a systematic, data-driven approach. Bioinformatics 31,26012606 (2015). 13. Schug, J. et al. Promoter features related to tissue specicity as measured by Shannon entropy. Genome Biol. 6,R33 (2005).14. He, B., Chen, C., Teng, L. & Tan, K. Global view of enhancer-promoter interactome in human cells. Proc. Natl Acad. Sci. USA 111, E2191E2199 (2014). 15. Parker, S. C. J. et al. Chromatin stretch enhancer states drive cell-specic gene", "regulation and harbor human disease risk variants. Proc. Natl Acad. Sci. USA 110, 1792117926 (2013). 16. Quang, D. X., Erdos, M. R., Parker, S. C. J. & Collins, F. S. Motif signatures in stretch enhancers are enriched for disease-associated genetic variants. Epigenet. Chromatin 8,23 (2015). 17. Whyte, W. A. et al. Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell153, 307319 (2013).", "networks. In fact, several of the higher-order networks we describe below rely on having multiple reliable and interoperable transcriptional activators and repressors for proper functioning. Even so, these engineered transcription factors have not yet been fully characterized, and if they are to be used as building blocks for complex gene networks, then knowledge of their in vivo kinetics and", "BMC Genomics 2008, 9:310 http://www.biomedcen tral.com/1471-2164/9/310 Page 10 of 17 (page number not for citation purposes)A gene regulatory network comprising the regulatory interactions of the significant genes and the significant and enriched TFs is shown in Figure 5. Obvious are the five hubs, the core regulatory circuit derived from [17]. Well-regulated candidates can be identified like Acly and Fabp4 . Target and regulator at the same time is Ipf1. Discussion", "32. Kheradpour P, Ernst J, Melnikov A, Rogov P, Wang L, Zhang X, et al. Systematic dissection of regulatory motifs in 2,000 predicted human enhancers using a massively parallel reporter assay. Genome research. 2013:gr. 144899.112. 33. Rands CM, Meader S, Ponting CP, Lunter G. 8.2% of the human genome is constrained: variation in rates of turnover across functional element classes in the human lineage. PLoS genetics. 2014;10(7):e1004 525." ], [ "high-throughput sequencing (ATAC-seq) allows the characterization of accessible chromatin re- gions,whichcorrespondtoareasoftranscriptionactivity(149).Examiningthethree-dimensional organization of the genome can facilitate the association between regulatory elements and their target genes by dividing the genome into discrete functional blocks, commonly known as topologically associating domains (139). The Encyclopedia of DNA Elements (ENCODE) and", "variants, it is still unclear how multiple independent variants influence gene networks through changes in chromatin states. The Assay for Transpose Accessible Chromatin (ATAC-seq) was recently developed to address the need for sensitive as- says requiring less starting material, which also has the ability to simultaneously profile open chromatin, transcription factor- binding footprints, as well as nucleosome positioning in a single assay [ 57]. Given the limited availability of primary", "Data Fig.4a). To relate cell-type-resolved accessible chromatin to gene expression, we created a single-cell RNA sequencing (scRNA-seq) refer - ence map of peripheral blood and pancreas. We assigned cell-type identi - ties for 90,495 cells to 29 clusters, which identified similar cell types and proportions to snATACseq (Extended Data Fig.5ac). To characterize cis-regulatory programs, we aggregated reads from cells within each snATACseq cluster and identified accessible chroma -", "DNA methylation and ATAC-seq data (Supplementary Fig. 3). Integration across gene- and coordinate-centric views helps users examine genomic events in different chromosome contexts. For example, Xenas Visual Spreadsheet can help elucidate whether a gene amplification is part of a chromosomal arm duplication or a focal amplification (Supplementary Fig. 6).", "matin accessibility assay ATAC-seq has been applied to single cells and has been shown to capture a higher order chromatin structure resembling the profiles generated by Hi-C [ 72]. Additionally, for CAD candidate genes that are transcrip- tion factors (TF), such as TCF21 and STAT3, protein-DNA interactions could be studied on a genome-wide scale using chromatin immunoprecipitation sequencing (ChIP-Seq). Recently, ChIP-Seq performed against TCF21 in human cor-", "seq), Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq), Formaldehyde- Assisted Isolation of Regulatory Elements (FAIRE-seq) and DNase I hypersensitive sites sequencing (DNase-seq). The integration of DNA methylation data (WGBS) and chromatin accessibility data (ATAC-seq) with established ChIP-seq mark ers have provided an opportunity to create high-resolution", "94. Mumbach MR, et al. HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nat Methods. 2016;13:919922. doi: 10.1038/nmeth.3999. 95. Kumasaka N, et al. Fine-mapping cellular QTLs with RASQUAL and ATAC- seq. Nat Genet. 2016;48:206213. doi: 10.1038/ng.3467. 96. Buenrostro JD, et al. ATAC-seq: a method for assaying chromatin acces- sibility genome-wide. Curr Protoc Mol Biol. 2015;109:21.29.121.29.9. doi: 10.1002/0471142727.mb2129s109.", "CpG sites. Single nucleus Assay for Transposase-Accessible Chromatinusing sequencing (snATACseq) was informative of chromatin opennessin various kidney cell types. The RegulomeDB is a database with exten-sive epigenetic annotation for SNPs. The promoter capture HiC (PCHiC) sequencing data identified sequence interaction with gene promoters,", "a method for assaying chromatin accessibility genome-wide. Curr Protoc Mol Biol 109:21.29.2121.29.29. https ://doi.org/10.1002/04711 42727 .mb212 9s109 Bysani M etal (2019) ATAC-seq reveals alterations in open chromatin in pancreatic islets from subjects with type 2 diabetes. Sci Rep 9:7785. https ://doi.org/10.1038/s4159 8-019-44076 -8 Camp JG etal (2015) Human cerebral organoids recapitulate gene expression programs of fetal neocortex development. Proc Natl", "genes are involved with multiple biological features. RNA sequencing has been coupled with protein quantication (DNA barcoded antibodies to quantify protein expression) and ATAC-seq to facilitate the study of genes involved with chromatin accessibility remodeling. their environment [14 , 31 , 88 , 95 , 105] . Advances in multiplexed gene editing and transcriptional programing will also enable CRISPR screens" ], [ "genetic data which are shifting the paradigm of net work inferences by providing statistical evidence to support directed links betw een genes, proteins, metabolites or diseases. In Chapter 6 , different approaches using genetic data for gene network inference that have been proposed are reviewed. Chapter 7 examines the statistical potential of such methods under different realistic settings: varying population sizes and in the presence or absence of hidden factor var iation and suggests ways to", "73. Yu,J., Smith,V.A., Wang,P .P ., Hartemink,A.J. & Jarvis,E.D. Advances to Bayesian network inference for generating causal networks from observational biological data. Bioinformatics 20, 35943603 (2004). 74. Sachs,K., Perez,O., Peer,D., Lauffenburger,D. A. & Nolan,G. P . Causal protein signaling networks derived from multiparameter single cell data. Science 308, 523529 (2005). 75. Feizi,S., Marbach,D., Mdard,M. & Kellis,M. Network deconvolution as a general method to", "Causal Inference of Regulator-Target Pairs by Gene Mapping 97 1.2 Background: Inferring Regula tory Networks from Correlated Gene Expression Independent of the data sets described so far, large collections of gene expres- sion over time course (Spellman et al., 1998) or varying environmental con- ditions (Gasch et al., 2000; Hughes et al., 2000) have been studied to reveal dependent variation among genes and thereby deduce regulatory relationships.", "data, to infer possible pathways and help build a link from the phe-notype back to a causal gene. In many cases, such interaction data are already available in public archives and need not be generated anew by the researcher [ 1 ]. These different sources of interaction data can be collated into network models ( see Note 1 ) which allow analysis using techniques borrowed from graph theory.", "relationships with a causal inference test . BMC Genet 2009, 10 :23. 60. Chaibub Neto E, Ferrara CT, Attie AD, Yandell B S: Inferring causal phenotype networks from segregating populations . Genetics 2008, 179 (2):1089-1100. 61. Li Y, Tesson BM, Churchill GA, Jansen RC: Critical preconditions for causal inference in genome-wide association studies under review 2010. 62. Aten JE, Fuller TF, Lusis AJ, Horvath S: Using genetic markers to orient", "T, Samson L, T I (2006) A systems approach to mapping DNAdamage response pathways. Science 312:10541059 Yu J, Smith V A, Wang PP, Hartemink AJ, Jarvis ED (2004) Advances to bayesian network inference for generating causal networks fromobservational biological data. Bioinformatics 20:35943603How to infer gene networks from expression proles M Bansal et al 10Molecular Systems Biology 2007 &2007 EMBO and Nature Publishing Group", "with the data. To cope with this problem, Siegenthaler et al. proposed a novel assessment procedure that incorporates the inferability of gene regulatory interactions by redening the confusion matrix interms of inferability of the network, i.e., the possibility of the network to be determined from data. The inferability of GRNs was analyzed based on the causal information that could beextracted from experiments. Authors used data from the DREAM", "and can thus be helpful in determining the causal structure of gene networks. Often, such data have already been gathered previously in the form of single-gene experiments and other links can be gleaned from a search of the published literature. In a few cases, a relevant database exists which can be used as a data source. Links of this type will all be directed edges from gene to phenotype (where the phenotype is the same as used as the seed).", "tional methodologies in gene regulatory net-works. IGI Global, Hershey, PA, pp 127 11. Roy S, Das D, Choudhury D, Gohain GG, Sharma R, Bhattacharyya DK (2013) Causality inference techniques for in-silico gene regu-latory network, Mining intelligence and knowl-edge exploration. Springer, New York, pp 432443 12. Olsen C, Meyer PE, Bontempi G (2009) Infer- ring causal relationships using information the-oretic measures. In Proceedings of the 5th Benelux Bioinformatics Conference (BBC09)", "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small" ], [ "On the other hand, single-nucleus RNA-seq (snRNA-seq) provides an alternative method for gene expression proling in complex tissues from frozen samples at single cell levels (Grindberg et al., 2013). Compared to scRNAseq, snRNA-seq analyze gene expression within the nuclei instead of intact cells. It should be noted that there could be potential dierences between the RNA type and expression levels between nucleus and cytosol. As observed in a previous study comparing nuclear", "most genetic and epigenetic mechanisms are yet to be probed with single-cell resolution. To understand the finer details at the level of a singular cell, sophisticated genomic and epigenomic next-generation sequencing (NGS) technologies have increased the potential for research output immensely (see Clark etal. 2018; Clark etal. 2016; Kelsey etal. 2017; Macaulay etal. 2017; Stuart and Satija 2019). These would", "of the disease, profiling gene expression in only bulk tissue sam-ples may obscure biologically relevant cell-type specific changes. While single-cell RNA-seq allows us to evaluate transcriptional changes within cell-types, it is prohibitively costly to executeon large cohorts (i.e. hundreds of individuals). To circumvent this issue, we developed a framework that leverages single-", "2019). The traditional RNA sequencing technology (bulk RNA-seq) is applied to determine gene expression pro les, isoform expression, alternative splicing and single-nucleotide polymorphisms on basis oftissue samples, which contains various cell types ( Kuksin et al., 2021 ). On the contrast, single-cell RNA sequencing (scRNA-seq), a noveltechnology can detect the gene expre ssion patterns for each transcript within single cell and distinguish cell subtypes ( Lhnemann et al., 2020 ).", "sion from smaller amounts of RNA enabled cell typespecific analyses.Specific cell types can beisolated using flow cytometry, for example, using endogenously expressed fluorescent markers, with or without combining with antibodies for cell surface proteins. Transcriptomic analysis by either microarray or bulk RNA sequencing then follows (39,67,68,104,145).Such analyses can 280 Taiberetal. Annu. Rev. Genom. Hum. Genet. 2022.23:275-299. Downloaded from www.annualreviews.org", "Recent applications Single-cell RNA sequencing has had a profound impact on our understanding of neuronal and hematopoietic cell types, as well as the immune system. Examples of novel insights in immunity include a window on to an unexpected plethora of dendritic cells in mouse immun- ity [25] and new regulators and subpopulations of CD4+ T cells [26 28]. In hematopoiesis, much single-cell tran- scriptomics work has focused on hematopoetic stem cells and the single-cell perspective has provided reso-", "single- nucleus RNAseq makes them a valuable complement to the find- ings published by Orozco, Chen et al. (Orozco et al., 2020 ). Furthermore, Yan et al. (2020) used cell sorting to enrich for cell types with a high degree of heterogeneity, resulting in finer cell subtype resolution for non-photoreceptor cell types such as RGCs. In addition to neural retina, our understanding of the choroidal", "using sequencing (ATAC-seq),95,96 that can map chro- matin interactions and accessibility with higher resolu-tion than previous methods will improve our ability to disentangle GWAS loci; while single-cell RNA sequenc- ing 97,98 and CRISPR-based pooled gene perturbation methods99103 provide unprecedented opportunities for studies of how RNA expression patterns differ between cells within tissues and how those tissues and cells react to perturbation of multiple genes in parallel.", "cell RNA-seq data from a smaller cohort in conjunction withco-expression network analysis in order to estimate cell-typespecific transcriptomic changes in large, bulk tissue RNA-seq datasets. We isolated nuclei and performed single-nuclei RNA-seq (snRNA-seq, n= 27 321 nuclei) on postmortem human brain tissue from aged, neurologically healthy controls ( n=5 ,6 7t o9 0 + years old, PFC, Supplementary Material, Table S1 ) to clarify cell- type proportions and the corresponding transcriptional profiles", "without the biases of probe sequence selection and hybridization reactions. The second innovation is cell-specific RNA profiling methods [79] that make it practical to generate comparatively accurate expression data for individual cell types in genetically engineered lines of mice. We can soon expect far more comprehensive and specific lists of genes for several important cell and tissue types that can be used to assemble multicellular expression networks in eye.ACKNOWLEDGMENTS Dr. Eldon E." ], [ "52.Zhu J et al. (2007) Increasing the power to detect causal associations by combining genotypicand expression data in segregating populations. PLoS Comput Biol 3:e69 53.Zhu J et al. (2008) Integrating large-scale functional genomic data to dissect the complexity ofyeast regulatory networks. Nat Genet 40:854861 54.Kim JK et al. (2005) Functional genomic analysis of RNA interference in C. elegans. Science308:11641167", "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", "expression and its effect on disease . Nature 2008, 452 (7186):423-428. 12. Chen LS, Emmert-Streib F, Storey JD: Harnessing naturally randomized transcription to infer regulatory relationships amo ng genes . Genome Biol 2007, 8(10):R219. 13. Aten JE, Fuller TF, Lusis AJ, Horvath S: Using genetic markers to orient the edges in quantitative trait networks: the NEO s oftware . BMC Syst Biol 2008, 2:34. 14. Millstein J, Zhang B, Zhu J, Schadt EE: Disentangling molecular", "and unknown function by large-scale coexpression analysis. Plant Physiol 2008, 147:41-57. 98. Wolfe CJ, Kohane IS, Butte AJ: Systematic survey reveals gen- eral applicability of \"guilt-by-a ssociation\" within gene coex- pression networks. BMC Bioinformatics 2005, 6:227. 99. Lee NH: Genomic approaches for reconstructing gene net- works. Pharmacogenomics 2005, 6:245-58. 100. Goutsias J, Lee NH: Computational and experimental approaches for modeling ge ne regulatory networks. Curr", "the discovery of interface genes. These mRNA transcripts regulate expression of genes in those structures, and thereby couple multiple networks a nd biological processes. The detection of these transcripts and the analysis of their gen es regulatory polymorphisms 37", "Rev. Genet 2007;8:437449. [PubMed: 17510664] A review of theory and approaches to mapping genetic interaction networks. 16. Bork P, et al. Protein interaction networks from yeast to human. Curr. Opin. Struct. Biol 2004;14:292 299. [PubMed: 15193308] 17. Ewing B, Hillier L, Wendl MC, Green P. Base-calling of automated sequencer traces using phred. I. Accuracy assessment. Genome Res 1998;8:175185. [PubMed: 9521921]", "CC represents a dramatic improvement over existinggenetic resources for mammalian systems biology appli- cations (Adam et al. 2007 ; Chesler et al. 2008 ). A number of gene expression data sets from microarray experiments,particularly those for mouse and rat, have been integrated into GeneNetwork ( http://www.genenetwork.org ), which is essentially a web knowledgebase in which the entire dataset and relevant metadata (data about the data) are com- bined with sophisticated statistical and computation tools", "gene, and the first f unctional anti -sense miRNA, Lastly, we have used comparative genomics to infer regulatory networks based on individual conserved instances of regulatory motifs, which show functional enrichments similar and sometimes higher to genome -scale experimental met hods such as ChIP -chip. As part of the ENCODE and modENCODE projects, we are now studying dynamics of developmental and cell -differentiation networks in", "(ncRNAs) from the Rfam database (Grifths-Jones et al. , 2005) and predicted regu- latory sites from the cisRED database (Robertson et al. , 2006). There is much to do in both of these emerging areas but even preliminary data have already given new insights into mammalian biology: it seems there is high lineage specic expansion of some ncRNA classes relative to protein-coding genes (Birney et al. , 2006). Another growing area of activity is in cataloguing the genetic variation present in human", "(ncRNAs) from the Rfam database (Grifths-Jones et al. , 2005) and predicted regu- latory sites from the cisRED database (Robertson et al. , 2006). There is much to do in both of these emerging areas but even preliminary data have already given new insights into mammalian biology: it seems there is high lineage specic expansion of some ncRNA classes relative to protein-coding genes (Birney et al. , 2006). Another growing area of activity is in cataloguing the genetic variation present in human" ], [ "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", "of importance in the emergence of precision medicine ( Curtis, 2015 ; Desautels et al., 2014 ; Glade Bender et al., 2015 ; Jorgensen, 2015 ; Kummar et al., 2015 ; Marquet et al., 2015 ; Rubin, 2014 ) wherein therapeutic strategies need to be aligned with specific properties of tumors. Methods GeneNetwork and WebGestalt GeneNetwork is an open access, online data analysis resource for systems biology and systems genetics. It contains a large number of microarray datasets from multiple tissues of", "GeneNetwork, a public web source used to study relations amongmarkers, genes, and phenotypes. We made use of large transcriptomedata sets for the amygdala, hippocampus, ventral tegmental area", "ject to mapping analysis. We examine the connectivity among these sets and analyze the molecular, biochemical and genetic regulatory commonality of connected genes us-ing novel and existing bioinformatics tools. We also develop data-driven hypotheses to explain the mechanisms of genetic perturbations and variation as a means of dening global consequences of individual differences on tissue structure and function. Much of our work is motivated by prior studies of brain gene expression and mRNA", "including correlation and network analysis to compare associations between tissues and between other rodent or human data sets[32] Many of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", "including correlation and network analysis to compare associations between tissues and between other rodent or human data sets[32] Many of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", "weighted gene co-expression network are described in[54]. Consensus network analysis was carried out with Rfunction blockwiseConsensusModules in the WGCNA R package [54]. Our online R software tutorial easily permits the user to identify tissue-specific age related modules and CpGs. Gene ontology enrichment analysis", "approach employed in the construction of large expression data sets, such as those provided by GeneNetwork,39treats gene expression as a continuous variable across RI strains, rather than asa categorical one (knockout model). Hence, we believe that using these complementary, yet conceptually distinct, approaches enhanced our ability to propose mechanistic insights. A limitation of the current study relates to the non-trivial relationship between structural and functional brain connectivity.4", "GeneNetwork ( http://www.genenetwork.org ; Williams and Mulligan, 2012)). These databases 180 represent transcriptome datasets for different tissues of recombinant inbred mice. If several probes 181 for the same gene were available, probes with higher maximum likelihood ratio statistic (LRS, a 182 measurement of the association or linkage between differences in traits and differences in particular 183 genotype markers values) were used. 184", "pathways.TheGeneNetworkdatabaseisauniqueresourceforco-expressionanalysisusingdatafromavarietyof tissues acrossgeneticallydistinctinbredmice.However,extractionofbiologicallymeaningfulco-expressedgenesets ischallengingduetovariabilityinmicroarrayplatforms,probequality,normalizationmethods,andconfounding biologicalfactors.Inthisstudy,wetestedwhetherliteraturederivedfunctionalcohesioncouldbeusedasanobjectivemetricinlieuofgroundtruthtoevaluatethequalityofprobesandmicroarraydatasets." ], [ "to as quantitative trait loc us (QTL) mapping study. QTL studies inform us region s on the chromosome where existing polymorphisms or SNPs are highly correlated with variation of the trait of interest. With the advancement in DNA sequencing, whole genome database of several mouse strains as well as gene expression data from several tiss ues are available. This allows us to use bioinformatic tools to identify candidate genes with greater confidence for further functional validations .", "differences, allows for a far more comprehensive understanding of the genetic regulatory links underlying this variation. QTL mapping of gene expression traits allows us to identify eQTLs; genomic regions that have a regulatory effect on those expression traits. Two types of eQTLs can be distinguished, i.e., those that map near (less than 10 Mb from) the gene which encodes the transcript (local ) and those that map elsewhere in the genome ( distant ). 18 Together, local", "simultaneously. Beginning with a study in yeast (Brem et al. 2002), QTL mapping has been done with gene expression as the phenotype. In such a study, the genomic loci responsible for variation in gene expression can be used to infer regulatory control. While such a study is not conclusive, it can be used to narrow the potential regulatory candidates, generate hypotheses for further testing and construct regulatory networks in s ilico.", "is that one can now identify large numbers of less strong, second-ary QTLs which were previously lost to background noise, and this information opens up a whole new range of possible analy-ses, such as the identi cation of epistatic interactions ( Figure 5), that promise to uncover pathways of genetic control within the tissue studied. Traditionally, QTL mapping starts with a phenotype of inter-", "and quantitative trait loci (QTL) regulatory models. A major goal is to identify which,among a set of candidate genes, are the most likely regulators of trait variation. These methods are applied in an effort to identify multiple-QTL regulatory models for large groups of genetically co-expressed genes, and to extrapolate the consequences of thisgenetic variation on phenotypes observed across levels of biological scale through the", "distal regions into even finer regulatory loci. This influence on gene expression may be the reason why so many classical QTLs have been mapped to Qrr1 . The complexity highlighted by Qrr1 may very well be the rule rather than the exception for loci that modulate complex traits. Efforts to fine -map a single QTL have often been confronted by clusters of multiple small effect QTLs within the original interval (Legare et al., 2000; Demarest et al., 2001) . This poses a serious challenge, and", "genotypes, availing of genetic markers across the whole genome, and allow the identication of QTLs with signi- cant effects on the disease (Darvasi 1998 ; Manolio 2010 ). QTLs are genetic regions closely linked to a gene with a quantitative effect on the phenotype. QTL mapping is based on the concept that phenotypic differences between inbred mouse strains can be used to demonstrate theimportance of genetic effects on complex phenotypes (Andreux et al. 2012 ; Hillebrandt et al. 2002 ). The standard", "of the variants within associated loci through expression-quantitative trait locus (eQTL) studies will combine the genetic variation in associate d loci with expression analysis data to define regulatory relationships. Studies designed to understand the functional effect of any causal variants in relevant cell systems and an imal models will give insight to physiological consequence. These advances will underpin efforts to translate the findings through development of diagnostic tests, ris k evaluation and", "illustrating the potential of functional mapping for effici ently establishing associations between existing QTL, as well as for novel QTL discovery. References 1. Damerval C, Maurice A, Josse JM, De Vienne D: Quantitative trait loci underlying gene product va riation: a novel perspective for analyzing regulation of genome expression. Genetics 1994, 137:289-301. 2. Brem RB, Yvert G, C linton R, Kruglyak L: Genetic dissection of transcriptional regulation in budding yeast. Science 2002, 296:752-755.", "over a decade ago, using new genometypes for the BXD family of murine strains, as well as new statistical tools, showing that we can identify new quantitative trait loci (QTLs), resulting in highly plausible candidate genes. Quantitative trait locus (QTL) mapping has been carried out in numerous species to associate regions of the genome to phenotypes even before the structure of the genome was well understood (e.g., [ 3]). Rodents, especially mice, have been the species most prominently used for biomedi-" ], [ "frequent usage of terms like epigenetic or chromatin land-scape. New methods for high-throughput mapping ofgenome-wide histone modifications and protein-DNA inter- actions were developed over the last few years (Blecher-Gonen et al., 2013; Garber et al., 2012). Histone Modifications Associated with Gene EnhancersChromatin can be modulated by covalent histone modifica-", "orative efforts of the ENCODE Project [ 42] and Roadmap Epigenomics [ 43] consortia have already revealed a compendia of genome-wide histone modification signatures for various regulatory features in multiple primary tissues and cell lines. These datasets have been applied to global mapping studies and databases to prioritize functional regula- tory variants [ 44,45]. While these assays have been employed extensively in LCLs, and tumor cell lines to follow-up auto-", "genetical genomics) and the genetics of epigeneticscould be studied simultaneously, thus revealing genes that directly or indirectly affect epigenetic gene states. An additional issue that could be addressed by such anapproach is to estimate the percentage of variation in gene expression that can be explained by different epigenetic conformations. The level of complexity could be further increased by including different cell types in the analysis, such as the", "Incorporating epigenetics into genetic analysis can also enhance the predictive functional analysis of SNPs by highlighting regions of DNA that are accessible or inaccessible to protein binding by transcription factors and other regulatory pro- teins. SNPs may also lead to loss or gain of cytosineguanine dinucleotide (CpG) methylation sites. Rakyan et al. (2004) suggested that such an event might affect the overall methylation prole of a locus and, consequently, promoter activity and gene", "Incorporating epigenetics into genetic analysis can also enhance the predictive functional analysis of SNPs by highlighting regions of DNA that are accessible or inaccessible to protein binding by transcription factors and other regulatory pro- teins. SNPs may also lead to loss or gain of cytosineguanine dinucleotide (CpG) methylation sites. Rakyan et al. (2004) suggested that such an event might affect the overall methylation prole of a locus and, consequently, promoter activity and gene", "Incorporating epigenetics into genetic analysis can also enhance the predictive functional analysis of SNPs by highlighting regions of DNA that are accessible or inaccessible to protein binding by transcription factors and other regulatory pro- teins. SNPs may also lead to loss or gain of cytosineguanine dinucleotide (CpG) methylation sites. Rakyan et al. (2004) suggested that such an event might affect the overall methylation prole of a locus and, consequently, promoter activity and gene", "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", "374. Bernstein, B.E., Stamatoyannopoulos, J.A., Costello, J.F ., Ren, B. et al. (2010), The NIH Roadmap Epigenomics Mapping Consortium, Nat. Biotechnol. V ol. 28, pp. 10451048. 375. Portela, A. and Esteller, M. (2010), Epigenetic modications and human disease, Nat. Biotechnol. V ol. 28, pp. 10571068. 376. Esteller, M. (2007), Cancer epigenomics: DNA methylomes and histone-modication maps, Nat. Rev . Genet. V ol. 8, pp. 286298. 377. Gilad, Y ., Rifkin, S.A. and Pritchard, J.K. (2008), Revealing the archi-", "likely to be part of regulatory elements. Our global map of histone marks will serve as an important resource forunderstanding the epigenetic basis of type 2 diabetes. [Supplemental material is available online at http:/ /www.genome.org. The ChIP-seq and gene expression data from this study have been submitted to ArrayExpress (http:/ /www.ebi.ac.uk/microarray-as/ae/) under accession nos. E-MTAB-189 and E-MTAB-191, respectively.] Genetic and epigenetic factors determine cell fate and function.", "these with other epigenetic mechanisms. This section will describe each method and provide the reader with technologies and recommendations to aide in the design and implementation of an epigenetic study . Histone Modifi cation Analysis Histone modi cation signals can be captured with chromatin immunoprecipita- tion (ChIP), which provides modi cation position approximation on the genome" ], [ "genomes. Hence, chromosomal and spatial co-localization in the nucleus may indicate co-regulation. It was previously shown that 3D chromatin structure couples nuclear compartmentaliza-tion of chromatin domains with the control of gene activity ( Gue- len et al., 2008 ) and thus contributes to cell-specic gene expression ( Zullo et al., 2012 ). In this context, it is noteworthy that cellular senescence is associated with modications of theglobal chromatin interaction network ( Chandra et al., 2015 ). To", "2 Introduction Recent scientific advances have enabled the identification of functional genomic elements through a diverse set of functional annotations, including proteins functional scores (1, 2) , evolutionary conservation scores (3-5), and epigenetics scores from the Encyclopedia of DNA Elements (ENCODE) (6). Other initiatives such as the R oadmap Epigenomics project (7) and FANTOM5 project (8, 9) also provide evidence for potential regulatory v ariants in the human", "accuracy of predictive networks [40, 5153]. We have also recently demonstrated how this class of network can be used to inform associations identied in GW Astudies [40]. 9 Summary The signicant challenge we face in the post-genome era is deciphering the bio-logical function of individual genes, pathways, and networks that drive complexphenotypes like disease. The availability of low-cost, high-throughput technologies", "a growing awareness that the three-dimensional juxtaposition of DNAregions within nuclei means that genes can be regulated by regulatory elements that are located at some distance from the gene ( Fig. 5 ) (Javierre et al., 2016 ;Kadauke and Blobel, 2009 ). As a result of this, disease associated SNPs have been shown to fall in gene regulatory elements ( Chen and Tian, 2016; Fadason et al., 2017; Farh et al., 2014; Lee et al., 2014; Schierding et al., 2015 ).", "network. Cell 9, 12121226 (2014). 12. Hirschhorn, J.N. Genomewide association studiesilluminating biologic pathways. N. Engl. J. Med. 0, 16991701 (2009). 13. Cantor, R.M., Lange, K. & Sinsheimer, J.S. Prioritizing GWAS results: a review of statistical methods and recommendations for their application. Am. J. Hum. Genet. 8, 622 (2010). 14. Lee, I., Date, S.V., Adai, A.T. & Marcotte, E.M. A probabilistic functional network of yeast genes. Science 0, 15551558 (2004).", "Processing Large-Scale, High-Dimension Genetic 325 another. We anticipate these types of networks becoming increasingly important in the human genetics space to gain a mechanistic understanding of how a given DNAperturbation induces changes in one or more genes that go on to affect networks that cause disease. The integration of genotypic and expression and other data have recently been shown, in a Bayesian network framework [76], to enhance the overall", "regions correlated with functional noncoding elements, including enhancers, better than did regions identified solely on the basis of nucleotide sequence. These results support the idea that the molecular shape of DNA is under selection and can identify evolutionary history. Genomic sequences that code for proteinsare relatively well understood but make up only ~2% of the human genome ( 1). Many functions are encoded in the remaining ~98% noncoding portion of the genome, but little", "gene, and the first f unctional anti -sense miRNA, Lastly, we have used comparative genomics to infer regulatory networks based on individual conserved instances of regulatory motifs, which show functional enrichments similar and sometimes higher to genome -scale experimental met hods such as ChIP -chip. As part of the ENCODE and modENCODE projects, we are now studying dynamics of developmental and cell -differentiation networks in", "References 1. Cremer T, Cremer M, Dietzel S, Muller S, Solovei I, Fakan S. Chromosome territoriesa function-al nuclear landscape. Curr Opin Cell Biol 2006; 18:307-16. 2. Misteli T. Beyond the sequence: cellular organization of genome function. Cell 2007; 128:787-800. 3. Schneider R, Grosschedl R. Dynamics and interplay of nuclear architecture, genome organization and gene expression. Genes Dev 2007; 21:3027-43.", "enhancers in the control of cell identity and disease. Cell(2013) 155:934 47. doi: 10.1016/j.cell.2013.09.053 45. de Wit E, de Laat W. A decade of 3C technologies: insights into nuclear organization. Genes Dev (2012) 26:11 24. doi: 10.1101/gad.179804.111 46. Schmitt AD, Hu M, Ren B. Genome-wide mapping and analysis of chromosome architecture. Nat Rev Mol Cell Biol (2016) 17:743 55. doi: 10.1038/nrm.2016.104 47. Javierre BM, Burren OS, Wilder SP, Kreuzhuber R, Hill SM, Sewitz S, et al." ], [ "[111], and for generation of networks based on known gene interactions such as GeneMania [112] and Cytoscape [113], as well as for identifying cross-species orthology relation-ships [114], network-based thinking has been increasingly applied to the study of aging and lifespan [115-118]. Re-cently, the novel computational method of network identifi- cation by regression (NIR) [119] has been used to identify", "Here we will focus on gene network inference algorithms (the inuence approach). A description of other methods based on the physical approach and more details oncomputational aspects can be found in (Beer and Tavazoie,2004; Tadesse et al, 2004; Faith and Gardner, 2005; Prakash and Tompa, 2005; Ambesi and di Bernardo, 2006; Foat et al, 2006). We will also briey describe two improper reverse-engineering tools (MNI and TSNI), whose main focus is not", "NIA[360] may help to infer a putative function by linking unkn own genes to genes known from previous studies to show a similar e xpres- sion pattern. We can also characterize unknown genes by thei r evolu- tionary, loss-of-function and network interaction proper ties to prioritize candidate variants[184] and even predict disease inherita nce mode to a certain degree[153]. Taking this approach a step further, GeneNetwork[99] is con structed", "network inference techniques can be utilized to infer biologicalprocess and the potential phenotypic impact of variants in genes of unknown function [71 78]. Thus, pathway and network based annotation approaches can be powerful approaches to inferring phenotypic information where direct links to phenotype do not exist. 2.12. De novo association analyses involving multiple genomes In the absence of prior information one might leverage to annotate", "interaction may be difficult to quantify. Conversely the directions and signs that accompany signalling or regula- tory pathways are generally known, but their incorpora- tion requires more work. It could nevertheless lead to important advances for the interpretation of microarray data in cancer studies, for example. Conclusion We have presented a general framework to analyse gene expression data when a gene network is known a priori . The approach involves the attenuation of the high-fre-", "A number of techniques have been proposed for network inference. Existing techniques for nding gene networks can be broadly cate-gorized as (i) computational approaches, and (ii) literature-based approaches. The computational approach mainly uses statistical, machine learning, or soft-computing techniques [ 14,15] as discov- ery tools. On the other hand, a literature-based approach gathers relevant published information on genes and their interrelation-", "addition, data from linkage or association studies (e.g. GWAS), or from high -throughput genetic screening experiments (e.g. CRISPR screening), or from animal gain -or-loss- of function studies, or from the gene -drug interactions, can also be exploited to predict potential gene functions. Integration of GeneBridge with data from these sources will further enhance the performance for gene function prediction, as is done in STRING [253], GeneMANIA [254] and Mitocarta [190, 255].", "include the deep learning-driven pattern recognition models for analyzing the gene se- quences for identifying the possible future illness and developing mobile applications that can generalize the information from the genomic data. However, there is great demand for explainable Articial Intelligence models that are interpretable in decision-making. Author Contributions: The authors contributions are as follows, Conceptualization of the study,", "Gene network inference algorithms are becoming accurate enough to be practically useful, at least when steady-state gene expression data are available, but efforts must be directedin assessing algorithm performances. In a few years, gene network inference will become as common as clustering for microarray data analysis. These algorithms will become moreTable IV Results of the application of network inference algorithms on the experiment data sets Data sets ARACNE BANJO NIR Clustering Random", "accuracy of predictive networks [40, 5153]. We have also recently demonstrated how this class of network can be used to inform associations identied in GW Astudies [40]. 9 Summary The signicant challenge we face in the post-genome era is deciphering the bio-logical function of individual genes, pathways, and networks that drive complexphenotypes like disease. The availability of low-cost, high-throughput technologies" ], [ "920 Diabetologia. 2020;63: 977986. doi:10.1007/s00125-020-05101-y 921 9. Stearns FW. One hundred years of pleiotropy: A retrospective. Genetics. Genetics; 922 2010. pp. 767773. doi:10.1534/genetics.110.122549 923 10. Geiler-Samerotte KA, Li S, Lazaris C, Taylor A, Ziv N, Ramjeawan C, et al. Extent and 924 context dependence of pleiotropy revealed by high-throughput single-cell phenotyping. 925 PLoS Biol. 2020;18. doi:10.1371/journal.pbio.3000836", "920 Diabetologia. 2020;63: 977986. doi:10.1007/s00125-020-05101-y 921 9. Stearns FW. One hundred years of pleiotropy: A retrospective. Genetics. Genetics; 922 2010. pp. 767773. doi:10.1534/genetics.110.122549 923 10. Geiler-Samerotte KA, Li S, Lazaris C, Taylor A, Ziv N, Ramjeawan C, et al. Extent and 924 context dependence of pleiotropy revealed by high-throughput single-cell phenotyping. 925 PLoS Biol. 2020;18. doi:10.1371/journal.pbio.3000836", "advances, the more examples become known which canbe explained only under the assumption of pleiotropy (Plate 1910, quoted from M cKusick 1976, pp. 301302). His assertion of the extent and importance of pleiotropyhas been a central theme that has been challenged andstrengthened throughout the past 100 years as the way inwhich we study pleiotropy has changed. DEVELOPMENT OF PLEIOTROPIC RESEARCH One of the rst experimental studies of the mecha-", "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics analysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for predictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most common functions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", "users can take advantage of a systems genetics approach (Rosen et al., 2003, 2007). While the candidate gene approach asks which one gene mutation causes a particular disease, the systems genetics approach explores which phenotypes and diseases result from diverse sets of genetic and molecular markers (Rosen et al., 2003, 2007). The majority of data sets in GeneNetwork are collected from GRPs consisting of hundreds of diverse, inbred strains of", "34. Pyeritz, R.E. (1989) Pleiotropy revisited: molecular explanations of a classic concept. Am. J. Med. Genet. ,34, 124134. 35. Gruneberg, H. (1938) An analysis of the pleiotropic effects of a lethal mutation in the rat. Proc. R. Soc. Lond. B. ,125, 123144. 36. Wagner, G.P. and Zhang, J. (2011) The pleiotropic structure of the genotypephenotype map: the evolvability of complex organisms. Nat. Rev. Genet. ,12, 204213. 37. Solovieff, N., Cotsapas, C., Lee, P.H., Purcell, S.M. and Smoller, J.W.", "21. Byars, S. G. et al. Genetic loci associated with coronary artery disease harbor evidence of selection and antagonistic pleiotropy. PLoS Genet. 13, e1006328 (2017). 22. Rodrguez, J. A. et al. Antagonistic pleiotropy and mutation accumulation inuence human senescence and disease. Nat. Ecol. Evol. 1, 0055 (2017). 23. Institute for Health Metrics and Evaluation. Findings from the Global Burden of Disease Study 2017 (IHME, 2018).", "traits can be due to shared molecular mechanisms and processes (true gene pleiotropy)or covariance can be due to statistical error or to linkage of neighboring, but mechanis-tically independent gene variants. This latter effect is particularly serious and is described in more length by Gerlai 4and in Wang5in the context of RI strains. GeneNetwork GeneNetwork (GN, www.genenetwork.org ) is an open web resource that enables", "2019;20 .https://doi.or g/10.118 6/s13059 -019-1628-0 PMID: 30678704 19. Chesmo reK,Bartlett J,Williams SM.Theubiquity ofpleiotropy inhuman disease. Hum Genet. 2018; 137: 3944. https://doi.or g/10.100 7/s00439 -017-1854 -zPMID: 29164333 20. Bulik-Sulli vanB,Finucane HK,Anttila V,Gusev A,DayFR,LohPR,etal.Anatlas ofgenetic correla- tions across human diseases andtraits. NatGenet 2015 4711. 2015; 47:12361241. https://doi.or g/ 10.1038 /ng.3406 PMID: 26414676", "2019;20 .https://doi.or g/10.118 6/s13059 -019-1628-0 PMID: 30678704 19. Chesmo reK,Bartlett J,Williams SM.Theubiquity ofpleiotropy inhuman disease. Hum Genet. 2018; 137: 3944. https://doi.or g/10.100 7/s00439 -017-1854 -zPMID: 29164333 20. Bulik-Sulli vanB,Finucane HK,Anttila V,Gusev A,DayFR,LohPR,etal.Anatlas ofgenetic correla- tions across human diseases andtraits. NatGenet 2015 4711. 2015; 47:12361241. https://doi.or g/ 10.1038 /ng.3406 PMID: 26414676" ], [ "the different pathways linked with aging and even study genenetworks. In such works, GenAge is an adequate resource asit provides a framework for the functional genomics of aging.For example, Xue et al . (2007) used GenAge to construct a modular network of aging and obtain insights into aging, including thefact that genes connecting different modules are more likely toaffect longevity and/or aging, an hypothesis the authors validatedexperimentally in worms (Xue et al", "[111], and for generation of networks based on known gene interactions such as GeneMania [112] and Cytoscape [113], as well as for identifying cross-species orthology relation-ships [114], network-based thinking has been increasingly applied to the study of aging and lifespan [115-118]. Re-cently, the novel computational method of network identifi- cation by regression (NIR) [119] has been used to identify", "network analysis is a useful approach toward identifying genetic determinants of longevity . PLoS One , 2008 , 3(11), e3802. [38] Bell, R.; Hubbard, A.; Che ttier, R.; Chen, D.; Miller, J.P.; Kapahi, P.; Tarnopolsky, M.; Sahasrabuhde, S.; Melov, S.; Hughes, R.E. A human protein interaction network shows conservation of aging processes between human and invertebrate species . PLoS Genet , 2009 , 5(3), e1000414. [39] Budovsky, A.; Abramovich, A.; Cohen, R.; Chalifa-Caspi, V.;", "genes (http://genomics.senescence.info/genes/), more than700 genes have been identified that regulate lifespan inmodel organisms (de Magalha es et al., 2009a). Many ofthese genes and their associated pathwayssuch as theinsulin/IGF1/GH pathwayhave been shown to affect lon-gevity across different model organisms (Kenyon, 2010).Therefore, at least some mechanisms of aging are evolu-tionarily conserved and may have potential therapeuticapplications (Baur et al., 2006). For example, evidencesuggests the use of", "30. Vartiainen, S., Aarnio, V., Lakso, M. & Wong, G. Increased lifespan in transgenic Caenorhabditis elegans overexpressing human -synuclein. Exp. Gerontol. 41, 871 876 (2006). 31. Lpez-Otn, C. et al. The hallmarks of aging. Cell153, 1194 1217 (2013). 32. Kenyon, C. J. The genetics of ageing. Nature 464, 504 512 (2010). 33. Liberzon, A. et al. The molecular signatures database hallmark gene set collection. Cell Syst. 1, 417 425 (2015).", "1118 compared to young ones. Overall, our results revealed that six pathways and six key genes might play pivotal roles in regulating longevity, and three interacting genes might be implicated in longevity. The results will not only provide new insight into the mechanisms of longevity, but also provide novel ideas for network-based approaches for longevity-related research. Keywords Drosophila melanogaster Longevity Gene Pathway Network Introduction", "During the past century, remarkable progress has been made in unveiling the mechanisms of aging. Genetic and molecular pathways that regulate healthspan and lifespan have been identified in various model organisms, provid-ing a rich knowledge base (Longo etal. 2015; Lopez-Otin etal. 2013, 2016; Singh etal. 2019). However, the focus on", "In addition to aging- and CR-related genes, another source of candidate genes and pathways for drug designare human longevity-associated genes (Barzilai andShuldiner, 2001; Browner et al., 2004; Kenyon, 2010).Dozens of genes have now been associated with humanlongevity (de Magalha es et al., 2009a), although only ahandful of genes have been shown to have consistenteffects across populations. Many longevity-associated genes are related to spe-", "been associated with human longevity in genetic asso-ciation studies. The parallel emergence of networkapproaches offers prospects to develop multitargetdrugs and combinatorial therapies. Understandinghow the environment modulates aging-related genesmay lead to human applications and disease therapiesthrough diet, lifestyle, or pharmacological interven-tions. Unlocking the capacity to manipulate humanaging would result in unprecedented health benefits. I. Introduction", "Network approaches are instrumental in discerning global properties of aging/lifespan regulators, making com- putational predictions and inferring the modularity and rela-tionships of various aging regulators. However, they should be applied with great caution as to avoid bias introduced by the literature, the lack of spatial and temporal information, or the limited coverage of the network [44]. 4. EPIGENETIC REGULATION OF AGING In addition to gene expression changes, the states of epi-" ], [ "in advance. Polygenic Risk Scores (PRS) were proposed by Duncan L. et al. [ 8] for risk analysis using the sum of the weight of each risk-associated locus of genomic sequence obtained from the corresponding evidence. These weights are assessed from the regression coefcient associated with each locus. These combined genetics features and correlation matrices would signicantly assist the entire eld of genomics study [ 9]. These studies on", "Owing to their small effect sizes, SNP associations have very little clinical applicability for risk prediction. A polygenic risk score (PRS) attempts to estimate the combined risk from multiple SNPs that have been associated with a certain trait with genome-wide sig-nificance. By accounting for a large proportion of the genetic variance underlying a trait, the overall effect size", "of genome-wide genotypes and publicly available data from large consortia, GRSs with a larger number of vari- ants are being used, and the predictive value of these genome-wide polygenic risk scores (PRSs) has substantially improved 50,51. PRSs can be derived using different approaches, however, these require both summary statistics from an exter -", "use for estimation of polygenic risk scores (PRS) has grownin recent years. PRS screening may be used to determine therisk of common complex diseases for individuals and theiroffspring, and although it is not widely clinically availablenow, there is an ongoing interest in increasing its utility. Useof GWAS data from European populations for PRS esti-mation would subsequently impose a bias in favor of in- dividuals with similar ancestry, whereas limited bene ti s", "(GWAS) in diverse populations have identified hundreds of genetic loci associated with T2D [79]. Polygenic risk scores (PRS), which aggregate the genetic risk of individ - ual alleles across the genome, are thus promising to pre - dict future T2D occurrence and improve early diagnosis, intervention, and prevention of T2D [1015]. However, to date, T2D PRS were most widely developed and vali - dated in individuals of European descent. Given that the predictive performance of PRS often attenuates in non-", "(GWAS), polygenic risk scores (PRS) have shown promise to complement established clinical risk factors and inter vention paradigms, and improve early diagnosis and prevention of T2D. However, to date, T2D PRS have been most widely developed and validated in individuals of European descent. Comprehensive assessment of T2D PRS in non European populations is critical for equitable deployment of PRS to clinical practice that benefits global populations.", "Letters NATure GeNeTicsMethods Polygenic score derivation. Polygenic scores provide a quantitative metric of an individuals inherited risk based on the cumulative impact of many common polymorphisms. Weights are generally assigned to each genetic variant according to the strength of their association with disease risk (effect estimate). Individuals are scored based on how many risk alleles they have for each variant (for example, zero, one, or two copies) included in the polygenic score.", "(Fig. 1B ). Polygenic risk scores (PRS) have emerged as promising biomarkers for the prediction of disease risk, not only in the area of cardiovascular disorders, but also oncology (21). These risk scores also have become increasingly available for a multitude of phenotypes and are systematically curated in a free online database (22). It has been shown that certain preexisting autoimmune diseases as well as the occurrence of imAE upon treatment are associated with", "eases identify individuals with risk equivalent to monogenicmutations. Nat. Genet. ,50, 12191224. 13. Euesden, J., Lewis, C.M. and OReilly, P.F. (2015) PRSice: poly- genic risk score software. Bioinformatics ,31, 14661468. 14. Belsky, D.W., Moffitt, T.E., Sugden, K., Williams, B., Houts, R., McCarthy, J. and Caspi, A. (2013) Development and evalu- ation of a genetic risk score for obesity. Biodemography Soc. Biol.,59, 85100. 15. De Jager, P.L., Chibnik, L.B., Cui, J., Reischl, J., Lehr, S., Simon,", "in tissue-specic regions or use gene co-expression information may provide a more comprehensive view of a specic gene or a gene networks role in modulating an individuals response to environmental variations, compared to that provided by the single candidate gene approach (Gamazon et al., 2015; Barth et al., 2020). Expression-based polygenic risk scores (ePRS) oer one such approach to understand the underlying genetic background linked to behavioral outcomes (Hari Dass" ] ], "task_id": [1,2,3,4,5,6,7,8,9,10,1,2,3,4,5,6,7,8,9,10] }