scholarly journals Genetic effects on promoter usage are highly context-specific and contribute to complex traits

2018 ◽  
Author(s):  
Kaur Alasoo ◽  
Julia Rodrigues ◽  
John Danesh ◽  
Daniel F. Freitag ◽  
Dirk S. Paul ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Complex Traits ◽  
Rna Splicing ◽  
Molecular Mechanisms ◽  
Complex Trait ◽  
Transcript Level ◽  
Cell Types ◽  
Inflammatory Stimuli ◽  
Promoter Usage ◽  
Context Specific

AbstractGenetic variants regulating RNA splicing and transcript usage have been implicated in both common and rare diseases. Although transcript usage quantitative trait loci (tuQTLs) have now been mapped in multiple cell types and conditions, the molecular mechanisms through which these variants exert their effect have remained elusive. Specifically, changes in transcript usage could arise from promoter choice, alternative splicing or 3′ end choice, but current tuQTL studies have not been able to distinguish between them. Here, we performed comprehensive analysis of RNA-seq data from human macrophages exposed to a range of inflammatory stimuli (IFNγ, Salmonella, IFNγ + Salmonella) and a metabolic stimulus (acetylated LDL), obtained from up to 84 individuals. In addition to conventional gene-level and transcript-level analyses, we also developed an analytical approach to directly quantify promoter, internal exon and 3′ end usage. We found that although naive transcript-level analysis often links single genetic variants to multiple coupled changes on the transcriptome, this appears to be an artefact of incomplete transcript annotations. Most of this coupling disappears when promoters, splicing and 3′ end usage are quantified directly. Furthermore, promoter, splicing and 3′ end QTLs are each enriched in distinct genomic features, suggesting that they are predominantly controlled by independent regulatory mechanisms. We also find that promoter usage QTLs are 50% more likely to be context-specific than canonical splicing QTLs and constitute 25% of the transcript-level colocalisations with complex traits. Thus, promoter usage might be a previously underappreciated molecular mechanism mediating complex trait associations in a context-specific manner.

scholarly journals Genetic effects on promoter usage are highly context-specific and contribute to complex traits

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Kaur Alasoo ◽  
Julia Rodrigues ◽  
John Danesh ◽  
Daniel F Freitag ◽  
Dirk S Paul ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Complex Traits ◽  
Rna Splicing ◽  
Molecular Mechanisms ◽  
Complex Trait ◽  
Transcript Level ◽  
Cell Types ◽  
Multiple Cell ◽  
Promoter Usage ◽  
Context Specific

Genetic variants regulating RNA splicing and transcript usage have been implicated in both common and rare diseases. Although transcript usage quantitative trait loci (tuQTLs) have been mapped across multiple cell types and contexts, it is challenging to distinguish between the main molecular mechanisms controlling transcript usage: promoter choice, splicing and 3ʹ end choice. Here, we analysed RNA-seq data from human macrophages exposed to three inflammatory and one metabolic stimulus. In addition to conventional gene-level and transcript-level analyses, we also directly quantified promoter usage, splicing and 3ʹ end usage. We found that promoters, splicing and 3ʹ ends were predominantly controlled by independent genetic variants enriched in distinct genomic features. Promoter usage QTLs were also 50% more likely to be context-specific than other tuQTLs and constituted 25% of the transcript-level colocalisations with complex traits. Thus, promoter usage might be an underappreciated molecular mechanism mediating complex trait associations in a context-specific manner.

scholarly journals The fog of genetics: Known unknowns and unknown unknowns in the genetics of complex traits and diseases

2019 ◽  
Author(s):  
João Pedro de Magalhães ◽  
Jingwei Wang
Keyword(s):  
Genetic Variability ◽  
Genetic Variants ◽  
Genetic Information ◽  
Complex Traits ◽  
Drug Targets ◽  
Molecular Mechanisms ◽  
Genetic Associations ◽  
Missing Heritability ◽  
Genome Space ◽  
Known Unknowns

AbstractAssociating genetic variants with phenotypes is not only important to understand the underlying biology but also to identify potential drug targets for treating diseases. It is widely accepted that for most complex traits many associations remain to be discovered, the so-called “missing heritability.” Yet missing heritability can be estimated, it is a known unknown, and we argue is only a fraction of the unknowns in genetics. The majority of possible genetic variants in the genome space are either too rare to be detected or even entirely absent from populations, and therefore do not contribute to estimates of phenotypic or genetic variability. We call these unknown unknowns in genetics the “fog of genetics.” Using data from the 1000 Genomes Project we then show that larger genes with greater genetic diversity are more likely to be associated with human traits, demonstrating that genetic associations are biased towards particular types of genes and that the genetic information we are lacking about traits and diseases is potentially immense. Our results and model have multiple implications for how genetic variability is perceived to influence complex traits, provide insights on molecular mechanisms of disease and for drug discovery efforts based on genetic information.

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 Investigating tissue-relevant causal molecular mechanisms of complex traits using probabilistic TWAS analysis

2019 ◽  
Author(s):  
Yuhua Zhang ◽  
Corbin Quick ◽  
Ketian Yu ◽  
Alvaro Barbeira ◽  
Francesca Luca ◽  
...  
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Complex Trait ◽  
Causal Effects ◽  
Biological Mechanisms ◽  
Integrative Framework ◽  
Eqtl Data

AbstractTranscriptome-wide association studies (TWAS), an integrative framework using expression quantitative trait loci (eQTLs) to construct proxies for gene expression, have emerged as a promising method to investigate the biological mechanisms underlying associations between genotypes and complex traits. However, challenges remain in interpreting TWAS results, especially regarding their causality implications. In this paper, we describe a new computational framework, probabilistic TWAS (PTWAS), to detect associations and investigate causal relationships between gene expression and complex traits. We use established concepts and principles from instrumental variables (IV) analysis to delineate and address the unique challenges that arise in TWAS. PTWAS utilizes probabilistic eQTL annotations derived from multi-variant Bayesian fine-mapping analysis conferring higher power to detect TWAS associations than existing methods. Additionally, PTWAS provides novel functionalities to evaluate the causal assumptions and estimate tissue- or cell-type specific causal effects of gene expression on complex traits. These features make PTWAS uniquely suited for in-depth investigations of the biological mechanisms that contribute to complex trait variation. Using eQTL data across 49 tissues from GTEx v8, we apply PTWAS to analyze 114 complex traits using GWAS summary statistics from several large-scale projects, including the UK Biobank. Our analysis reveals an abundance of genes with strong evidence of eQTL-mediated causal effects on complex traits and highlights the heterogeneity and tissue-relevance of these effects across complex traits. We distribute software and eQTL annotations to enable users performing rigorous TWAS analysis by leveraging the full potentials of the latest GTEx multi-tissue eQTL data.

scholarly journals Genetic control of age-related gene expression and complex traits in the human brain

2017 ◽  
Author(s):  
Trevor Martin ◽  
Hunter B. Fraser
Keyword(s):  
Gene Expression ◽  
Human Brain ◽  
Neurodegenerative Diseases ◽  
Genetic Variants ◽  
Complex Traits ◽  
Molecular Mechanisms ◽  
Neurological Diseases ◽  
Expression Patterns ◽  
Related Gene ◽  
Age Related

AbstractAge is the primary risk factor for many of the most common human diseases—particularly neurodegenerative diseases—yet we currently have a very limited understanding of how each individual’s genome affects the aging process. Here we introduce a method to map genetic variants associated with age-related gene expression patterns, which we call temporal expression quantitative trait loci (teQTL). We found that these loci are markedly enriched in the human brain and are associated with neurodegenerative diseases such as Alzheimer’s disease and Creutzfeldt-Jakob disease. Examining potential molecular mechanisms, we found that age-related changes in DNA methylation can explain some cis-acting teQTLs, and that trans-acting teQTLs can be mediated by microRNAs. Our results suggest that genetic variants modifying age-related patterns of gene expression, acting through both cis- and trans-acting molecular mechanisms, could play a role in the pathogenesis of diverse neurological diseases.

scholarly journals Microphthalmia Transcription Factor Isoforms in Mast Cells and the Heart

2007 ◽  
Vol 27 (11) ◽  
pp. 3911-3919 ◽  
Author(s):  
Sagi Tshori ◽  
Amir Sonnenblick ◽  
Nurit Yannay-Cohen ◽  
Gillian Kay ◽  
Hovav Nechushtan ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Mast Cells ◽  
Myosin Light Chain ◽  
Physiological Role ◽  
Transcript Level ◽  
Cell Types ◽  
Splice Isoforms ◽  
Ideal System ◽  
Promoter Usage ◽  
Microphthalmia Transcription Factor

ABSTRACT The microphthalmia transcription factor (Mitf) is critical for the survival and differentiation of a variety of cell types. While on the transcript level it has been noted that melanocytes and cardiomyocytes express specific Mitf isoforms, mast cells express several isoforms, mainly Mitf-H and Mitf-MC, whose function has not been thoroughly investigated. We found that in mast cells the expression of the specific Mitf isoforms is dependent on physiological stimuli that cause a major shifting of promoter usage and internal splicing. For example, activation of the c-kit signaling pathway almost totally abolished one of the main splice isoforms. Since cardiomyocytes express only the Mitf-H isoform, they were an ideal system to determine this isoform's physiological role. We identified that the expression of myosin light-chain 1a (MLC-1a) is regulated by Mitf-H. Interestingly, the transactivation of MLC-1a by Mitf-H in cardiomyocytes is decreased by overexpression of the splice form with exon 6a. In conclusion, we found that there is physiological switching of Mitf isoforms and that the promoter context and the cell context have a combined influence on gene expression programs.

scholarly journals Alcohol causes lasting differential transcription in Drosophila mushroom body neurons

2019 ◽  
Author(s):  
Emily Petruccelli ◽  
Nicolas Ledru ◽  
Karla R. Kaun
Keyword(s):  
Mushroom Body ◽  
Molecular Mechanisms ◽  
Neuronal Cell ◽  
Cell Types ◽  
Memory Encoding ◽  
Sensory Cues ◽  
New Methods ◽  
Specific Regulation ◽  
Context Specific ◽  
Differential Transcription

AbstractRepeated alcohol experiences can produce long-lasting memories for sensory cues associated with intoxication. These memories can ultimately trigger relapse in individuals recovering from alcohol use disorder (AUD). The molecular mechanisms by which alcohol changes memories to become long-lasting and inflexible remain unclear. New methods to analyze gene expression within precise neuronal cell-types can provide further insight towards AUD prevention and treatment. Here, we employed genetic tools in Drosophila melanogaster to investigate the lasting consequences of ethanol on transcription in memory-encoding neurons. Drosophila rely on mushroom body (MB) neurons to make associative memories, including memories of ethanol-associated sensory cues. Differential expression analyses found that distinct transcripts, but not genes, in the MB were associated with experiencing ethanol alone compared to forming a memory of an odor cue associated with ethanol. These findings reveal the dynamic and highly context-specific regulation of splicing associated with encoding behavioral experiences. Our data thus demonstrate that alcohol can have lasting effects on transcription and RNA processing during memory formation, and identify new transcript targets for future AUD and addiction investigation.

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 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 Core gene identification using gene expression

2020 ◽  
Author(s):  
◽  
Annique Claringbould
Keyword(s):  
Gene Expression ◽  
Genetic Variation ◽  
Genetic Code ◽  
Genetic Variants ◽  
Complex Traits ◽  
Drug Targets ◽  
Molecular Mechanisms ◽  
Expression Patterns ◽  
Altered Level ◽  
Common Genetic Variants

While humans share most of their genetic code with one another, small differences in the DNA can have an impact on an individual’s risk of disease. Common genetic variants exert individually small effects on the development of a disease, but their combined impact is substantial. Although recent research has identified thousands of variants that are associated to complex traits, our understanding of the molecular mechanisms that eventually lead to disease is limited. One way to dive into the molecular changes that result from genetic variation, is to look at changes in gene activity (‘gene expression’). Each cell contains the same genetic code, but genes are only expressed when and where they are required. Research has shown that many disease-associated genetic variants also affect gene expression. Such a change in the expression of a gene can lead to an altered level of the protein it encodes, which in turn can be the start of a dysregulation in the system that can eventually develop into a disease. This thesis describes how gene expression patterns can be used to prioritise and describe the function of trait-relevant genes. The first chapters evaluate methodological considerations for doing gene expression research. Another study covers the systematic linking of genetic variation to gene expression in blood and the last research chapter describes a method for gene prioritisation that leverages the idea that multiple genetic variants converge onto disease-causing genes. These insights can be used to better understand disease and to identify potential drug targets.

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