Using regulatory genomics data to interpret the function of disease variants and prioritise genes from expression studies.
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ABSTRACT:
SUBMITTER: Ferrero E
PROVIDER: S-EPMC5850119 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Recent studies have shown that disease-susceptibility variants frequently lie in cell-type-specific enhancer elements. To identify, interpret, and prioritize such risk variants, we must identify the enhancers active in disease-relevant cell types, their upstream transcription factor (TF) binding, and their downstream target genes. To address this need, we built HACER (http://bioinfo.vanderbilt.edu/AE/HACER/), an atlas of Human ACtive Enhancers to interpret Regulatory variants. The HACER atlas catalogues and annotates in-vivo transcribed cell-type-specific enhancers, as well as placing enhancers within transcriptional regulatory networks by integrating ENCODE TF ChIP-Seq and predicted/validated chromatin interaction data. We demonstrate the utility of HACER in (i) offering a mechanistic hypothesis to explain the association of SNP rs614367 with ER-positive breast cancer risk, (ii) exploring tumor-specific enhancers in selective MYC dysregulation and (iii) prioritizing/annotating non-coding regulatory regions targeting CCND1. HACER provides a valuable resource for studies of GWAS, non-coding variants, and enhancer-mediated regulation.
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Project description:Abdominal aortic aneurysm (AAA) is a common degenerative cardiovascular disease whose pathobiology is not clearly understood. The cellular heterogeneity and cell-type-specific gene regulation of vascular cells in human AAA have not been well-characterized. Here, we performed analysis of whole-genome sequencing data in AAA patients versus controls with the aim of detecting disease-associated variants that may affect gene regulation in human aortic smooth muscle cells (AoSMC) and human aortic endothelial cells (HAEC), two cell types of high relevance to AAA disease. To support this analysis, we generated H3K27ac HiChIP data for these cell types and inferred cell-type-specific gene regulatory networks. We observed that AAA-associated variants were most enriched in regulatory regions in AoSMC, compared with HAEC and CD4+ cells. The cell-type-specific regulation defined by this HiChIP data supported the importance of ERG and the KLF family of transcription factors in AAA disease. The analysis of regulatory elements that contain noncoding variants and also are differentially open between AAA patients and controls revealed the significance of the interleukin-6-mediated signaling pathway. This finding was further validated by including information from the deleteriousness effect of nonsynonymous single-nucleotide variants in AAA patients and additional control data from the Medical Genome Reference Bank dataset. These results shed important insights into AAA pathogenesis and provide a model for cell-type-specific analysis of disease-associated variants.
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Project description:BACKGROUND:Genome-phenome studies have identified thousands of variants that are statistically associated with disease or traits; however, their functional roles are largely unclear. A comprehensive investigation of regulatory mechanisms and the gene regulatory networks between phenome-wide association study (PheWAS) and genome-wide association study (GWAS) is needed to identify novel regulatory variants contributing to risk for human diseases. METHODS:In this study, we developed an integrative functional genomics framework that maps 215,107 significant single nucleotide polymorphism (SNP) traits generated from the PheWAS Catalog and 28,870 genome-wide significant SNP traits collected from the GWAS Catalog into a global human genome regulatory map via incorporating various functional annotation data, including transcription factor (TF)-based motifs, promoters, enhancers, and expression quantitative trait loci (eQTLs) generated from four major functional genomics databases: FANTOM5, ENCODE, NIH Roadmap, and Genotype-Tissue Expression (GTEx). In addition, we performed a tissue-specific regulatory circuit analysis through the integration of the identified regulatory variants and tissue-specific gene expression profiles in 7051 samples across 32 tissues from GTEx. RESULTS:We found that the disease-associated loci in both the PheWAS and GWAS Catalogs were significantly enriched with functional SNPs. The integration of functional annotations significantly improved the power of detecting novel associations in PheWAS, through which we found a number of functional associations with strong regulatory evidence in the PheWAS Catalog. Finally, we constructed tissue-specific regulatory circuits for several complex traits: mental diseases, autoimmune diseases, and cancer, via exploring tissue-specific TF-promoter/enhancer-target gene interaction networks. We uncovered several promising tissue-specific regulatory TFs or genes for Alzheimer's disease (e.g. ZIC1 and STX1B) and asthma (e.g. CSF3 and IL1RL1). CONCLUSIONS:This study offers powerful tools for exploring the functional consequences of variants generated from genome-phenome association studies in terms of their mechanisms on affecting multiple complex diseases and traits.
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Project description:BACKGROUND: Transcriptional regulation plays an important role in the control of many biological processes. Transcription factor binding sites (TFBSs) are the functional elements that determine transcriptional activity and are organized into separable cis-regulatory modules, each defining the cooperation of several transcription factors required for a specific spatio-temporal expression pattern. Consequently, the discovery of novel TFBSs in promoter sequences is an important step to improve our understanding of gene regulation. RESULTS: Here, we applied a detection strategy that combines features of classic motif overrepresentation approaches in co-regulated genes with general comparative footprinting principles for the identification of biologically relevant regulatory elements and modules in Arabidopsis thaliana, a model system for plant biology. In total, we identified 80 TFBSs and 139 regulatory modules, most of which are novel, and primarily consist of two or three regulatory elements that could be linked to different important biological processes, such as protein biosynthesis, cell cycle control, photosynthesis and embryonic development. Moreover, studying the physical properties of some specific regulatory modules revealed that Arabidopsis promoters have a compact nature, with cooperative TFBSs located in close proximity of each other. CONCLUSION: These results create a starting point to unravel regulatory networks in plants and to study the regulation of biological processes from a systems biology point of view.
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Project description:Context:Variants of uncertain significance (VUSs) lack sufficient evidence, in terms of statistical power or experimental studies, to allow unequivocal determination of their damaging effect. VUSs are a major burden in performing genetic analysis. Although in silico prediction tools are widely used, their specificity is low, thus urgently calling for methods for prioritizing and characterizing variants. Objective:To assess the frequency of VUSs in genes causing endocrine and metabolic disorders, the concordance rate of predictions from different in silico methods, and the added value of three-dimensional protein structure analysis in discerning and prioritizing damaging variants. Results:A total of 12,266 missense variants reported in 641 genes causing endocrine and metabolic disorders were analyzed. Among these, 4123 (33.7%) were VUSs, of which 2010 (48.8%) were predicted to be damaging and 1452 (35.2%) were predicted to be tolerated according to in silico tools. A total of 5383 (87.7%) of 6133 disease-causing variants and 823 (55.8%) of 1474 benign variants were correctly predicted. In silico predictions were noninformative in 5.7%, 14.4%, and 16% of damaging, benign, and VUSs, respectively. A damaging effect on 3D protein structure was present in 240 (30.9%) of predicted damaging and 40 (9.7%) of predicted tolerated VUSs (P < 0.001). An in-depth analysis of nine VUSs occurring in TSHR, LDLR, CASR, and APOE showed that they greatly affect protein stability and are therefore strong candidates for disease. Conclusions:In our dataset, we confirmed the high sensitivity but low specificity of in silico predictions tools. 3D protein structural analysis is a compelling tool for characterizing and prioritizing VUSs and should be a part of genetic variant analysis.
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Project description:Whole exome sequencing (WES) accelerates disease gene discovery using rare genetic variants, but further statistical and functional evidence is required to avoid false-discovery. To complement variant-driven disease gene discovery, here we present function-driven disease gene discovery in zebrafish (Danio rerio), a promising human disease model owing to its high anatomical and genomic similarity to humans. To facilitate zebrafish-based function-driven disease gene discovery, we developed a genome-scale co-functional network of zebrafish genes, DanioNet (www.inetbio.org/danionet), which was constructed by Bayesian integration of genomics big data. Rigorous statistical assessment confirmed the high prediction capacity of DanioNet for a wide variety of human diseases. To demonstrate the feasibility of the function-driven disease gene discovery using DanioNet, we predicted genes for ciliopathies and performed experimental validation for eight candidate genes. We also validated the existence of heterozygous rare variants in the candidate genes of individuals with ciliopathies yet not in controls derived from the UK10K consortium, suggesting that these variants are potentially involved in enhancing the risk of ciliopathies. These results showed that an integrated genomics big data for a model animal of diseases can expand our opportunity for harnessing WES data in disease gene discovery.
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Project description:Identifying and understanding the impact of gene regulatory variation is of considerable importance in evolutionary and medical genetics; such variants are thought to be responsible for human-specific adaptation and to have an important role in genetic disease. Regulatory variation in cis is readily detected in individuals showing uneven expression of a transcript from its two allelic copies, an observation referred to as allelic imbalance (AI). Identifying individuals exhibiting AI allows mapping of regulatory DNA regions and the potential to identify the underlying causal genetic variant(s). However, existing mapping methods require knowledge of the haplotypes, which make them sensitive to phasing errors. In this study, we introduce a genotype-based mapping test that does not require haplotype-phase inference to locate regulatory regions. The test relies on partitioning genotypes of individuals exhibiting AI and those not expressing AI in a 2×3 contingency table. The performance of this test to detect linkage disequilibrium (LD) between a potential regulatory site and a SNP located in this region was examined by analyzing the simulated and the empirical AI datasets. In simulation experiments, the genotype-based test outperforms the haplotype-based tests with the increasing distance separating the regulatory region from its regulated transcript. The genotype-based test performed equally well with the experimental AI datasets, either from genome-wide cDNA hybridization arrays or from RNA sequencing. By avoiding the need of haplotype inference, the genotype-based test will suit AI analyses in population samples of unknown haplotype structure and will additionally facilitate the identification of cis-regulatory variants that are located far away from the regulated transcript.
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Project description:Analysis of genomic data is often complicated by the presence of missing values, which may arise due to cost or other reasons. The prevailing approach of single imputation is generally invalid if the imputation model is misspecified. In this paper, we propose a robust score statistic based on imputed data for testing the association between a phenotype and a genomic variable with (partially) missing values. We fit a semiparametric regression model for the genomic variable against an arbitrary function of the linear predictor in the phenotype model and impute each missing value by its estimated posterior expectation. We show that the score statistic with such imputed values is asymptotically unbiased under general missing-data mechanisms, even when the imputation model is misspecified. We develop a spline-based method to estimate the semiparametric imputation model and derive the asymptotic distribution of the corresponding score statistic with a consistent variance estimator using sieve approximation theory and empirical process theory. The proposed test is computationally feasible regardless of the number of independent variables in the imputation model. We demonstrate the advantages of the proposed method over existing methods through extensive simulation studies and provide an application to a major cancer genomics study.
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Project description:BackgroundChildhood-onset asthma is highly affected by genetic components. In recent years, many genome-wide association studies (GWAS) have reported a large group of genetic variants and susceptible genes associated with asthma-related phenotypes including childhood-onset asthma. However, the regulatory mechanisms of these genetic variants for childhood-onset asthma susceptibility remain largely unknown.MethodsIn the current investigation, we conducted a two-stage designed Sherlock-based integrative genomics analysis to explore the cis- and/or trans-regulatory effects of genome-wide SNPs on gene expression as well as childhood-onset asthma risk through incorporating a large-scale GWAS data (N?=?314,633) and two independent expression quantitative trait loci (eQTL) datasets (N?=?1890). Furthermore, we applied various bioinformatics analyses, including MAGMA gene-based analysis, pathway enrichment analysis, drug/disease-based enrichment analysis, computer-based permutation analysis, PPI network analysis, gene co-expression analysis and differential gene expression analysis, to prioritize susceptible genes associated with childhood-onset asthma.ResultsBased on comprehensive genomics analyses, we found 31 genes with multiple eSNPs to be convincing candidates for childhood-onset asthma risk; such as, PSMB9 (cis-rs4148882 and cis-rs2071534) and TAP2 (cis-rs9267798, cis-rs4148882, cis-rs241456, and trans-10,447,456). These 31 genes were functionally interacted with each other in our PPI network analysis. Our pathway enrichment analysis showed that numerous KEGG pathways including antigen processing and presentation, type I diabetes mellitus, and asthma were significantly enriched to involve in childhood-onset asthma risk. The co-expression patterns among 31 genes were remarkably altered according to asthma status, and 25 of 31 genes (25/31?=?80.65%) showed significantly or suggestively differential expression between asthma group and control group.ConclusionsWe provide strong evidence to highlight 31 candidate genes for childhood-onset asthma risk, and offer a new insight into the genetic pathogenesis of childhood-onset asthma.
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