scholarly journals Shared activity patterns arising at genetic susceptibility loci reveal underlying genomic and cellular architecture of human disease

2016 ◽  
Author(s):  
J. Kenneth Baillie ◽  
Andrew Bretherick ◽  
Christopher S. Haley ◽  
Sara Clohisey ◽  
Alan Gray ◽  
...  
Keyword(s):  
Transcriptional Regulation ◽  
Fine Mapping ◽  
Genetic Variants ◽  
Complex Traits ◽  
Expression Profiles ◽  
Activity Patterns ◽  
Cell Types ◽  
Regulatory Elements ◽  
Disease Pathogenesis ◽  
Regulatory Regions

AbstractGenetic variants underlying complex traits, including disease susceptibility, are enriched within the transcriptional regulatory elements, promoters and enhancers. There is emerging evidence that regulatory elements associated with particular traits or diseases share patterns of transcriptional regulation. Accordingly, shared transcriptional regulation (coexpression) may help prioritise loci associated with a given trait, and help to identify the biological processes underlying it. Using cap analysis of gene expression (CAGE) profiles of promoter and enhancer-derived RNAs across 1824 human samples, we have quantified coexpression of RNAs originating from trait-associated regulatory regions using a novel analytical method (network density analysis; NDA). For most traits studied, sequence variants in regulatory regions were linked to tightly coexpressed networks that are likely to share important functional characteristics. These networks implicate particular cell types and tissues in disease pathogenesis; for example, variants associated with ulcerative colitis are linked to expression in gut tissue, whereas Crohn’s disease variants are restricted to immune cells. We show that this coexpression signal provides additional independent information for fine mapping likely causative variants. This approach identifies additional genetic variants associated with specific traits, including an association between the regulation of the OCT1 cation transporter and genetic variants underlying circulating cholesterol levels. This approach enables a deeper biological understanding of the causal basis of complex traits.ONE SENTENCE SUMMARYWe discover that variants associated with a specific disease share expression profiles across tissues and cell types, enabling fine mapping and identification of new disease-associated variants, illuminating key cell types involved in disease pathogenesis.

scholarly journals A global overview of pleiotropy and genetic architecture in complex traits

2018 ◽  
Author(s):  
Kyoko Watanabe ◽  
Sven Stringer ◽  
Oleksandr Frei ◽  
Maša Umićević Mirkov ◽  
Tinca J.C. Polderman ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Human Genetics ◽  
Association Studies ◽  
Regulatory Elements ◽  
Genome Wide Association Studies ◽  
Multiple Trait ◽  
Current Availability ◽  
Flanking Regions

ABSTRACTAfter a decade of genome-wide association studies (GWASs), fundamental questions in human genetics are still unanswered, such as the extent of pleiotropy across the genome, the nature of trait-associated genetic variants and the disparate genetic architecture across human traits. The current availability of hundreds of GWAS results provide the unique opportunity to gain insight into these questions. In this study, we harmonized and systematically analysed 4,155 publicly available GWASs. For a subset of well-powered GWAS on 558 unique traits, we provide an extensive overview of pleiotropy and genetic architecture. We show that trait associated loci cover more than half of the genome, and 90% of those loci are associated with multiple trait domains. We further show that potential causal genetic variants are enriched in coding and flanking regions, as well as in regulatory elements, and how trait-polygenicity is related to an estimate of the required sample size to detect 90% of causal genetic variants. Our results provide novel insights into how genetic variation contributes to trait variation. All GWAS results can be queried and visualized at the GWAS ATLAS resource (http://atlas.ctglab.nl).

scholarly journals Integrative analysis of 10,000 epigenomic maps across 800 samples for regulatory genomics and disease dissection

2019 ◽  
Author(s):  
Carles B. Adsera ◽  
Yongjin P. Park ◽  
Wouter Meuleman ◽  
Manolis Kellis
Keyword(s):  
High Resolution ◽  
Genetic Variants ◽  
Complex Traits ◽  
Target Gene ◽  
Activity Patterns ◽  
Complex Trait ◽  
Contributing Factors ◽  
Downstream Target ◽  
Downstream Target Gene ◽  
Upstream Regulators

AbstractTo help elucidate genetic variants underlying complex traits, we develop EpiMap, a compendium of 833 reference epigenomes across 18 uniformly-processed and computationally-completed assays. We define chromatin states, high-resolution enhancers, activity patterns, enhancer modules, upstream regulators, and downstream target gene functions. We annotate 30,247 genetic variants associated with 534 traits, recognize principal and partner tissues underlying each trait, infer trait-tissue, tissue-tissue and trait-trait relationships, and partition multifactorial traits into their tissue-specific contributing factors. Our results demonstrate the importance of dense, rich, and high-resolution epigenomic annotations for complex trait dissection, and yield numerous new insights for understanding the molecular basis of human disease.

scholarly journals Glucocorticoids Unmask Silent Non-Coding Genetic Risk Variants for Common Diseases

2021 ◽  
Author(s):  
Thanh Thanh Le Nguyen ◽  
Huanyao Gao ◽  
Duan Liu ◽  
Zhenqing Ye ◽  
Jeong-Heon Lee ◽  
...  
Keyword(s):  
Fine Mapping ◽  
Genetic Variants ◽  
Disease Risk ◽  
Regulatory Elements ◽  
Risk Genes ◽  
3 Dimensional ◽  
Risk Variants ◽  
Expression Of Genes ◽  
Formidable Challenge ◽  
A Genome

AbstractUnderstanding the function of non-coding genetic variants represents a formidable challenge for biomedicine. We previously identified genetic variants that influence gene expression only after exposure to a hormone or drug. Using glucocorticoid signaling as a model system, we have now demonstrated, in a genome-wide manner, that exposure to glucocorticoids triggered disease risk variants with previously unclear function to influence the expression of genes involved in autoimmunity, metabolic and mood disorders, osteoporosis and cancer. Integrating a series of genomic and epigenomic assays, we identified the cis-regulatory elements and 3-dimensional interactions underlying the ligand-dependent associations between those genetic variants and distant risk genes. These observations increase our understanding of mechanisms of non-coding genetic variant-chemical environment interactions and advance the fine-mapping of disease risk and pharmacogenomic loci.One Sentence SummaryGenomic and epigenomic fine-mapping of ligand-dependent genetic variants unmasks novel disease risk genes

scholarly journals Integrative analysis of epigenetics data identifies gene-specific regulatory elements

2019 ◽  
Author(s):  
Florian Schmidt ◽  
Alexander Marx ◽  
Marie Hebel ◽  
Martin Wegner ◽  
Nina Baumgarten ◽  
...  
Keyword(s):  
Transcriptional Regulation ◽  
Cell Types ◽  
Regulatory Elements ◽  
Chromatin Accessibility ◽  
Genome Wide ◽  
A Genome ◽  
Gene Level ◽  
Regulatory Landscape ◽  
Regulatory Sites ◽  
Validation Experiments

AbstractUnderstanding the complexity of transcriptional regulation is a major goal of computational biology. Because experimental linkage of regulatory sites to genes is challenging, computational methods considering epigenomics data have been proposed to create tissue-specific regulatory maps. However, we showed that these approaches are not well suited to account for the variations of the regulatory landscape between cell-types. To overcome these drawbacks, we developed a new method called STITCHIT, that identifies and links putative regulatory sites to genes. Within STITCHIT, we consider the chromatin accessibility signal of all samples jointly to identify regions exhibiting a signal variation related to the expression of a distinct gene. STITCHIToutperforms previous approaches in various validation experiments and was used with a genome-wide CRISPR-Cas9 screen to prioritize novel doxorubicin-resistance genes and their associated non-coding regulatory regions. We believe that our work paves the way for a more refined understanding of transcriptional regulation at the gene-level.

scholarly journals Identifying disease-critical cell types and cellular processes across the human body by integration of single-cell profiles and human genetics

2021 ◽  
Author(s):  
Karthik A. Jagadeesh ◽  
Kushal K Dey ◽  
Daniel T. Montoro ◽  
Steven Gazal ◽  
Jesse M Engreitz ◽  
...  
Keyword(s):  
Single Cell ◽  
Disease Progression ◽  
Genetic Variants ◽  
Complex Traits ◽  
Disease Risk ◽  
Association Studies ◽  
Cell Types ◽  
Genome Wide Association Studies ◽  
Cascade Process ◽  
Cellular Processes

Cellular dysfunction is a hallmark of disease. Genome-wide association studies (GWAS) have provided a powerful means to identify loci and genes contributing to disease risk, but in many cases the related cell types/states through which genes confer disease risk remain unknown. Deciphering such relationships is important both for our understanding of disease, and for developing therapeutic interventions. Here, we introduce a framework for integrating single-cell RNA-seq (scRNA-seq), epigenomic maps and GWAS summary statistics to infer the underlying cell types and processes by which genetic variants influence disease. We analyzed 1.6 million scRNA-seq profiles from 209 individuals spanning 11 tissue types and 6 disease conditions, and constructed gene programs capturing cell types, disease progression in cell types, and cellular processes both within and across cell types. We evaluated these gene programs for disease enrichment by transforming them to SNP annotations with tissue-specific epigenomic maps and computing enrichment scores across 60 diseases and complex traits (average N=297K). The inferred disease enrichments recapitulated known biology and highlighted novel relationships for different conditions, including GABAergic neurons in major depressive disorder (MDD), disease progression programs in M cells in ulcerative colitis, and a disease-specific complement cascade process in multiple sclerosis. Our framework provides a powerful approach for identifying the cell types and cellular processes by which genetic variants influence disease.

scholarly journals Cell type-specific enhancer-promoter connectivity maps in the human brain and disease risk association

2019 ◽  
Author(s):  
Alexi Nott ◽  
Inge R. Holtman ◽  
Nicole G. Coufal ◽  
Johannes C.M. Schlachetzki ◽  
Miao Yu ◽  
...  
Keyword(s):  
Human Brain ◽  
Psychiatric Disorders ◽  
Complex Traits ◽  
Disease Risk ◽  
Cell Types ◽  
Regulatory Elements ◽  
Gene Promoters ◽  
Cell Type ◽  
Risk Variants ◽  
Cell Type Specific

AbstractUnique cell type-specific patterns of activated enhancers can be leveraged to interpret non-coding genetic variation associated with complex traits and diseases such as neurological and psychiatric disorders. Here, we have defined active promoters and enhancers for major cell types of the human brain. Whereas psychiatric disorders were primarily associated with regulatory regions in neurons, idiopathic Alzheimer’s disease (AD) variants were largely confined to microglia enhancers. Interactome maps connecting GWAS variants in cell type-specific enhancers to gene promoters revealed an extended microglia gene network in AD. Deletion of a microglia-specific enhancer harboring AD-risk variants ablated BIN1 expression in microglia but not in neurons or astrocytes. These findings revise and expand the genes likely to be influenced by non-coding variants in AD and suggest the probable brain cell types in which they function.One Sentence SummaryIdentification of cell type-specific regulatory elements in the human brain enables interpretation of non-coding GWAS risk variants.

scholarly journals A Review of Statistical Methods for Identifying Trait-Relevant Tissues and Cell Types

2021 ◽  
Vol 11 ◽  
Author(s):  
Huanhuan Zhu ◽  
Lulu Shang ◽  
Xiang Zhou
Keyword(s):  
Statistical Methods ◽  
Genetic Variants ◽  
Statistical Models ◽  
Complex Traits ◽  
Association Studies ◽  
Cell Types ◽  
Genome Wide Association Studies ◽  
Genomic Information ◽  
Biological Mechanisms ◽  
Regulated Gene Expression

Genome-wide association studies (GWASs) have identified and replicated many genetic variants that are associated with diseases and disease-related complex traits. However, the biological mechanisms underlying these identified associations remain largely elusive. Exploring the biological mechanisms underlying these associations requires identifying trait-relevant tissues and cell types, as genetic variants likely influence complex traits in a tissue- and cell type-specific manner. Recently, several statistical methods have been developed to integrate genomic data with GWASs for identifying trait-relevant tissues and cell types. These methods often rely on different genomic information and use different statistical models for trait-tissue relevance inference. Here, we present a comprehensive technical review to summarize ten existing methods for trait-tissue relevance inference. These methods make use of different genomic information that include functional annotation information, expression quantitative trait loci information, genetically regulated gene expression information, as well as gene co-expression network information. These methods also use different statistical models that range from linear mixed models to covariance network models. We hope that this review can serve as a useful reference both for methodologists who develop methods and for applied analysts who apply these methods for identifying trait relevant tissues and cell types.

scholarly journals Genetic determinants of chromatin accessibility in T cell activation across humans

2016 ◽  
Author(s):  
Rachel E. Gate ◽  
Christine S. Cheng ◽  
Aviva P. Aiden ◽  
Atsede Siba ◽  
Marcin Tabaka ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
T Cell ◽  
T Cell Activation ◽  
Genetic Variants ◽  
Cell Activation ◽  
Cell Types ◽  
Regulatory Elements ◽  
Specific Cell ◽  
Accessible Chromatin

AbstractOver 90% of genetic variants associated with complex human traits map to non-coding regions, but little is understood about how they modulate gene regulation in health and disease. One possible mechanism is that genetic variants affect the activity of one or more cis-regulatory elements leading to gene expression variation in specific cell types. To identify such cases, we analyzed Assay for Transposase-Accessible Chromatin sequencing (ATAC-seq) and RNA-seq profiles from activated CD4+ T cells of up to 105 healthy donors. We found that regions of accessible chromatin (ATAC-peaks) are co-accessible at kilobase and megabase resolution, in patterns consistent with the 3D organization of chromosomes measured by in situ Hi-C in T cells. 15% of genetic variants located within ATAC-peaks affected the accessibility of the corresponding peak through disrupting binding sites for transcription factors important for T cell differentiation and activation. These ATAC quantitative trait nucleotides (ATAC-QTNs) have the largest effects on co-accessible peaks, are associated with gene expression from the same aliquot of cells, are rarely affecting core binding motifs, and are enriched for autoimmune disease variants. Our results provide insights into how natural genetic variants modulate cis- regulatory elements, in isolation or in concert, to influence gene expression in primary immune cells that play a key role in many human diseases.

scholarly journals Addiction-associated genetic variants implicate brain cell type- and region-specific cis-regulatory elements in addiction neurobiology

2020 ◽  
Author(s):  
Chaitanya Srinivasan ◽  
BaDoi N. Phan ◽  
Alyssa J. Lawler ◽  
Easwaran Ramamurthy ◽  
Michael Kleyman ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Cell Types ◽  
Regulatory Elements ◽  
Brain Regions ◽  
Open Chromatin ◽  
Genome Wide Association Studies ◽  
Cell Type ◽  
Coding Regions ◽  
Cell Type Specific ◽  
The Impact

ABSTRACTRecent large genome-wide association studies (GWAS) have identified multiple confident risk loci linked to addiction-associated behavioral traits. Genetic variants linked to addiction-associated traits lie largely in non-coding regions of the genome, likely disrupting cis-regulatory element (CRE) function. CREs tend to be highly cell type-specific and may contribute to the functional development of the neural circuits underlying addiction. Yet, a systematic approach for predicting the impact of risk variants on the CREs of specific cell populations is lacking. To dissect the cell types and brain regions underlying addiction-associated traits, we applied LD score regression to compare GWAS to genomic regions collected from human and mouse assays for open chromatin, which is associated with CRE activity. We found enrichment of addiction-associated variants in putative regulatory elements marked by open chromatin in neuronal (NeuN+) nuclei collected from multiple prefrontal cortical areas and striatal regions known to play major roles in reward and addiction. To further dissect the cell type-specific basis of addiction-associated traits, we also identified enrichments in human orthologs of open chromatin regions of mouse neuron subtypes: cortical excitatory, PV, D1, and D2. Lastly, we developed machine learning models from mouse cell type-specific regions of open chromatin to further dissect human NeuN+ open chromatin regions into cortical excitatory or striatal D1 and D2 neurons and predict the functional impact of addiction-associated genetic variants. Our results suggest that different neuron subtypes within the reward system play distinct roles in the variety of traits that contribute to addiction.Significance StatementOur study on cell types and brain regions contributing to heritability of addiction-associated traits suggests that the conserved non-coding regions within cortical excitatory and striatal medium spiny neurons contribute to genetic predisposition for nicotine, alcohol, and cannabis use behaviors. This computational framework can flexibly integrate epigenomic data across species to screen for putative causal variants in a cell type- and tissue-specific manner across numerous complex traits.

Functional Genomics Analysis to Disentangle the Role of Genetic Variants in Major Depression

2022 ◽  
Author(s):  
Judith Pérez-Granado ◽  
Janet Piñero ◽  
Alejandra Medina-Rivera ◽  
Laura I. Furlong
Keyword(s):  
Transcription Factor ◽  
Major Depression ◽  
Binding Site ◽  
Fine Mapping ◽  
Genetic Variants ◽  
Gene Expression Regulation ◽  
Transcription Factor Binding ◽  
Disease Pathogenesis ◽  
Factor Binding

Abstract Background: Major Depression is the leading cause of impairment worldwide. The understanding of its molecular underpinnings is key to identifying new potential biomarkers and drug targets to alleviate its burden in society. Leveraging available GWAS data and functional genomic tools to assess regulatory variation could help explain the role of Major Depression associated genetic variants in disease pathogenesis. We have conducted a fine-mapping analysis of genetic variants associated with Major Depression and applied a pipeline focused on gene expression regulation by using two complementary approaches: cis-eQTL colocalization analysis using GTEx data and alteration of transcription factor binding sites with pattern matching approaches and chromatin accessibility data.Results: The fine-mapping of major depression genetic variants uncovered putative causally associated variants whose proximal genes were linked with Major Depression pathophysiology. Four genetic variants altering the expression of 5 genes were found by colocalization analysis, highlighting the role of SLC12A5, involved in chlorine homeostasis in neurons, and MYRF, related with central nervous system myelination and oligodendrocyte differentiation. The transcription factor binding analysis revealed the potential role of the genomic variant rs62259947 in modulating the expression of P4HTM through the alteration of YY1 binding site, altogether regulating hypoxia response.Conclusions: The combination of GWAS signals, cis-eQTL, transcription factor binding site information and active regulatory regions in the chromatin, enabled the prioritization of putative causal genetic variants in Major Depression. Importantly, our pipeline can be applied when only index genetic variants are available. Finally, the presented approach enabled the proposal of mechanistic hypotheses of these genetic variants and their role in disease pathogenesis.

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