scholarly journals Functional enrichments of disease variants across thousands of independent loci in eight diseases

2016 ◽  
Author(s):  
Abhishek K. Sarkar ◽  
Lucas D. Ward ◽  
Manolis Kellis
Keyword(s):  
Complex Traits ◽  
Target Genes ◽  
Cell Types ◽  
Biological Pathways ◽  
Joint Analysis ◽  
Genetic Associations ◽  
Narrow Sense Heritability ◽  
Regulatory Regions ◽  
Downstream Target ◽  
Common Diseases

AbstractFor most complex traits, known genetic associations only explain a small fraction of the narrow sense heritability prompting intense debate on the genetic basis of complex traits. Joint analysis of all common variants together explains much of this missing heritability and reveals that large numbers of weakly associated loci are enriched in regulatory regions, but fails to identify specific regions or biological pathways. Here, we use epigenomic annotations across 127 tissues and cell types to investigate weak regulatory associations, the specific enhancers they reside in, their downstream target genes, their upstream regulators, and the biological pathways they disrupt in eight common diseases. We show weak associations are significantly enriched in disease-relevant regulatory regions across thousands of independent loci. We develop methods to control for LD between weak associations and overlap between annotations. We show that weak non-coding associations are additionally enriched in relevant biological pathways implicating additional downstream target genes and upstream disease-specific master regulators. Our results can help guide the discovery of biologically meaningful, but currently undetectable regulatory loci underlying a number of common diseases.

scholarly journals Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms

2021 ◽  
Author(s):  
Milton Pividori ◽  
Sumei Lu ◽  
Binglan Li ◽  
Chun Su ◽  
Matthew E. Johnson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Association Studies ◽  
Expression Patterns ◽  
Cell Types ◽  
Joint Analysis ◽  
Genetic Associations ◽  
Disease Etiology ◽  
Mediating Role ◽  
Gene Modules ◽  
Trait Associations

Understanding how dysregulated transcriptional processes result in tissue-specific pathology requires a mechanistic interpretation of expression regulation across different cell types. It has been shown that this insight is key for the development of new therapies. These mechanisms can be identified with transcriptome-wide association studies (TWAS), which have represented an important step forward to test the mediating role of gene expression in GWAS associations. However, due to pervasive eQTL sharing across tissues, TWAS has not been successful in identifying causal tissues, and other methods generally do not take advantage of the large amounts of RNA-seq data publicly available. Here we introduce a polygenic approach that leverages gene modules (genes with similar co-expression patterns) to project both gene-trait associations and pharmacological perturbation data into a common latent representation for a joint analysis. We observed that diseases were significantly associated with gene modules expressed in relevant cell types, such as hypothyroidism with T cells and thyroid, hypertension and lipids with adipose tissue, and coronary artery disease with cardiomyocytes. Our approach was more accurate in predicting known drug-disease pairs and revealed stable trait clusters, including a complex branch involving lipids with cardiovascular, autoimmune, and neuropsychiatric disorders. Furthermore, using a CRISPR-screen, we show that genes involved in lipid regulation exhibit more consistent trait associations through gene modules than individual genes. Our results suggest that a gene module perspective can contextualize genetic associations and prioritize alternative treatment targets when GWAS hits are not druggable.

scholarly journals Modular Combinatorial Binding among HumanTrans-acting Factors Reveals Direct and Indirect Factor Binding

2015 ◽  
Author(s):  
Yuchun Guo ◽  
David K. Gifford
Keyword(s):  
Topic Model ◽  
Regulatory Region ◽  
Cell Types ◽  
Joint Analysis ◽  
Modular Organization ◽  
Cell Type ◽  
Regulatory Regions ◽  
Regulatory Program ◽  
Cell Type Specific

The combinatorial binding of trans-acting factors (TFs) to regulatory genomic regions is an important basis for the spatial and temporal specificity of gene regulation. We present a new computational approach that reveals how TFs are organized into combinatorial regulatory programs. We define a regulatory program to be a set of TFs that bind together at a regulatory region. Unlike other approaches to characterizing TF binding, we permit a regulatory region to be bound by one or more regulatory programs. We have developed a method called regulatory program discovery (RPD) that produces compact and coherent regulatory programs from in vivo binding data using a topic model. Using RPD we find that the binding of 115 TFs in K562 cells can be organized into 49 interpretable regulatory programs that bind ~140,000 distinct regulatory regions in a modular manner. The discovered regulatory programs recapitulate many published protein-protein physical interactions and have consistent functional annotations of chromatin states. We found that, for certain TFs, direct (motif present) and indirect (motif absent) binding is characterized by distinct sets of binding partners and that the binding of other TFs can predict whether the TF binds directly or indirectly with high accuracy. Joint analysis across two cell types reveals both cell-type-specific and shared regulatory programs and that thousands of regulatory regions use different programs in different cell types. Overall, our results provide comprehensive cell-type-specific combinatorial binding maps and suggest a modular organization of binding programs in regulatory regions.

scholarly journals Probing transcription factor combinatorics in different promoter classes and in enhancers

2017 ◽  
Author(s):  
Jimmy Vandel ◽  
Océane Cassan ◽  
Sophie Lèbre ◽  
Charles-Henri Lecellier ◽  
Laurent Bréhélin
Keyword(s):  
Binding Sites ◽  
Target Genes ◽  
Cell Types ◽  
Model Parameters ◽  
Binding Motif ◽  
Cell Type ◽  
Regulatory Regions ◽  
New Approach ◽  
Cell Type Specific ◽  
Common Target

In eukaryotic cells, transcription factors (TFs) are thought to act in a combinatorial way, by competing and collaborating to regulate common target genes. However, several questions remain regarding the conservation of these combina-tions among different gene classes, regulatory regions and cell types. We propose a new approach named TFcoop to infer the TF combinations involved in the binding of a tar-get TF in a particular cell type. TFcoop aims to predict the binding sites of the target TF upon the binding affinity of all identified cooperating TFs. The set of cooperating TFs and model parameters are learned from ChIP-seq data of the target TF. We used TFcoop to investigate the TF combina-tions involved in the binding of 106 TFs on 41 cell types and in four regulatory regions: promoters of mRNAs, lncRNAs and pri-miRNAs, and enhancers. We first assess that TFcoop is accurate and outperforms simple PWM methods for pre-dicting TF binding sites. Next, analysis of the learned models sheds light on important properties of TF combinations in different promoter classes and in enhancers. First, we show that combinations governing TF binding on enhancers are more cell-type specific than that governing binding in pro-moters. Second, for a given TF and cell type, we observe that TF combinations are different between promoters and en-hancers, but similar for promoters of mRNAs, lncRNAs and pri-miRNAs. Analysis of the TFs cooperating with the dif-ferent targets show over-representation of pioneer TFs and a clear preference for TFs with binding motif composition similar to that of the target. Lastly, our models accurately dis-tinguish promoters associated with specific biological processes.

scholarly journals Large-scale multivariate multi-ancestry Interaction analyses point towards different genetic mechanisms by population and exposure

2019 ◽  
Author(s):  
Vincent Laville ◽  
Timothy Majarian ◽  
Yun J Sung ◽  
Karen Schwander ◽  
Mary F Feitosa ◽  
...  
Keyword(s):  
Complex Traits ◽  
Large Scale ◽  
Biological Significance ◽  
Genetic Effect ◽  
Cell Types ◽  
Interaction Effects ◽  
Phenotypic Variance ◽  
Genetic Associations ◽  
Genetic Heritability ◽  
Meta Analyses

AbstractTheCHARGE Gene-Lifestyle Interactions Working Groupis a unique initiative formed to improve our understanding of the role and biological significance of gene-environment interactions in human traits and diseases. The consortium published several multi-ancestry genome-wide interaction studies (GWIS) involving up to 610,475 individuals for three lipids and four blood pressure traits while accounting for interaction effects with drinking and smoking exposures. Here we used GWIS summary statistics from these studies to decipher potential differences in genetic associations and GxE interactions across phenotype-exposure-population trios, and to derive new insights on the potential mechanistic underlying GxE through in-silico functional analyses. Our comparative analysis shows first that interaction effects likely contribute to the commonly reported ancestry-specific genetic effect in complex traits, and second, that some phenotype-exposures pairs are more likely to benefit from a greater detection power when accounting for interactions. It also highlighted a negligible correlation between main and interaction effects, providing material for future methodological development and biological discussions. We also estimated contributions to phenotypic variance, including in particular the genetic heritability conditional on the exposure, and heritability partitioned across a range of functional annotations and cell-types. In these analyses, we found multiple instances of heterogeneity of functional partitions between exposed and unexposed individuals, providing new evidence for likely exposure-specific genetic pathways. Finally, along this work we identified potential biases in methods used to jointly meta-analyses genetic and interaction effects. We performed a series of simulations to characterize these limitations and to provide the community with guideline for future GxE studies.

scholarly journals Induction of Human Liver X Receptor α Gene Expression Via an Autoregulatory Loop Mechanism

2002 ◽  
Vol 16 (3) ◽  
pp. 506-514 ◽  
Author(s):  
Yu Li ◽  
Charles Bolten ◽  
B. Ganesh Bhat ◽  
Jessica Woodring-Dietz ◽  
Suzhen Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Regulatory Element ◽  
Critical Role ◽  
Cell Types ◽  
Plasma Lipoproteins ◽  
Type I ◽  
Type Ii ◽  
Downstream Target ◽  
X Receptors

Abstract The liver X receptors (LXRs), members of the nuclear receptor superfamily, play an important role in controlling lipid homeostasis by activating several genes involved in reverse cholesterol transport. These include members of the ATP binding cassette (ABC) superfamily of transporter proteins ABCA1 and ABCG1, surface constituents of plasma lipoproteins like apolipoprotein E, and cholesterol ester transport protein. They also play an important role in fatty acid metabolism by activating the sterol regulatory element-binding protein 1c gene. Here, we identify human LXRα (hLXRα) as an autoinducible gene. Induction in response to LXR ligands is observed in multiple human cell types including macrophages and occurs within 2–4 h. Analysis of the hLXRα promoter revealed three LXR response elements (LXREs); one exhibits strong affinity for both LXRα:RXR and LXRβ:RXR (a type I LXRE), and deletion and mutational studies indicate it plays a critical role in LXR-mediated induction. The other two LXREs are identical to each other, exist within highly conserved Alu repeats, and exhibit selective binding to LXRα:RXR (type II LXREs). In transfections, the type I LXRE acts as a strong mediator of both LXRα and LXRβ activity, whereas the type II LXRE acts as a weaker and selective mediator of LXRα activity. Our data suggest a model in which LXR ligands trigger an autoregulatory loop leading to selective induction of hLXRα gene expression. This would lead to increased hLXRα levels and transcription of its downstream target genes such as ABCA1, providing a simple yet exquisite mechanism for cells to respond to LXR ligands and cholesterol loading.

scholarly journals Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function

2016 ◽  
Vol 7 (1) ◽  
Author(s):  
Cristian Pattaro ◽  
◽  
Alexander Teumer ◽  
Mathias Gorski ◽  
Audrey Y. Chu ◽  
...  
Keyword(s):  
Kidney Function ◽  
Cell Types ◽  
Biological Pathways ◽  
Genetic Associations

scholarly journals An Effector Index to Predict Target Genes at GWAS Loci

2021 ◽  
Author(s):  
Vincenzo Forgetta ◽  
Lai Jiang ◽  
Nicholas Vulpescu ◽  
Meganq Hogan ◽  
Siyuan Chen ◽  
...  
Keyword(s):  
Drug Development ◽  
Target Genes ◽  
Association Studies ◽  
Predictive Performance ◽  
Positive Control ◽  
Open Chromatin ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Single Nucleotide Variants ◽  
Common Diseases

Abstract Drug development and biological discovery require effective strategies to map existing genetic associations to causal genes. To approach this problem, we selected 12 common diseases and quantitative traits for which highly powered genome-wide association studies (GWAS) were available. For each disease or trait, we systematically curated positive control gene sets from Mendelian forms of the disease and from targets of medicines used for disease treatment. We found that these positive control genes were highly enriched in proximity of GWAS-associated single nucleotide variants (SNVs). We then performed quantitative assessment of the contribution of commonly used genomic features, including open chromatin maps, expression quantitative trait loci (eQTL), and chromatin conformation data. Using these features, we trained and validated an Effector Index (Ei), to map target genes for these 12 common diseases and traits. Ei demonstrated high predictive performance, both with cross-validation on the training set, and an independently derived set for type 2 diabetes. Key predictive features included coding or transcript altering SNVs, distance to gene, and open chromatin-based metrics. This work outlines a simple, understandable approach to prioritize genes at GWAS loci for functional follow-up and drug development, and provides a systematic strategy for prioritization of GWAS target genes.

scholarly journals Genomic features of BRDT binding sites in gene units suggest transcriptional partners and specific epigenetic landscapes to regulate transcriptional activity during spermatogenesis

2019 ◽  
Author(s):  
Li Wang ◽  
Iouri Chepelev ◽  
Yoon Ra Her ◽  
Marcia Manterola ◽  
Binyamin Berkovits ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Binding Sites ◽  
Target Genes ◽  
Germ Line ◽  
Cell Types ◽  
Histone Variants ◽  
Wide Distribution ◽  
Male Meiosis ◽  
Downstream Target ◽  
Pachytene Spermatocytes

AbstractBRDT, a member of the BET family of double bromodomain-containing proteins, is expressed uniquely in the male germ line, is essential for spermatogenesis in the mouse, and binds to acetylated transcription start sites of genes expressed in meiosis and spermiogenesis. It has thus been postulated to be a key regulator of transcription in meiotic and post-meiotic cells. To understand the function of BRDT in regulating gene expression, we characterized its genome-wide distribution, in particular the features of the BRDT binding sites within gene units, by ChIP-Seq analysis of enriched fractions of spermatocytes and spermatids. In both cell types, BRDT binding sites were mainly located in promoters, first exon, and introns of genes that are highly transcribed during meiosis and spermiogenesis. Furthermore, in promoters, BRDT binding sites overlapped with several histone modifications and histone variants associated with active transcription, and were also enriched for consensus sequences for specific transcription factors, including MYB, RFX, ETS and ELF1 in pachytene spermatocytes, and JunD, c-Jun, CRE and RFX in round spermatids. Our analysis further revealed that BRDT-bound genes play key roles in diverse biological processes that are essential for proper spermatogenesis. Taken together, our data suggest that BRDT is involved in the recruitment of different transcription factors to distinctive chromatin regions within gene units to regulate diverse downstream target genes that function in male meiosis and spermiogenesis.

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