Prioritization of regulatory variants with tissue-specific function in the non-coding regions of human genome.
Ontology highlight
ABSTRACT:
SUBMITTER: Dong S
PROVIDER: S-EPMC8754628 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Over a decade of genome-wide association, studies have made great strides toward the detection of genes and genetic mechanisms underlying complex traits. However, the majority of associated loci reside in non-coding regions that are functionally uncharacterized in general. Now, the availability of large-scale tissue and cell type-specific transcriptome and epigenome data enables us to elucidate how non-coding genetic variants can affect gene expressions and are associated with phenotypic changes. Here, we provide an overview of this emerging field in human genomics, summarizing available data resources and state-of-the-art analytic methods to facilitate in-silico prioritization of non-coding regulatory mutations. We also highlight the limitations of current approaches and discuss the direction of much-needed future research.
| S-EPMC5892192 | biostudies-literature
Project description:MotivationModel-based estimates of general deleteriousness, like CADD, DANN or PolyPhen, have become indispensable tools in the interpretation of genetic variants. However, these approaches say little about the tissues in which the effects of deleterious variants will be most meaningful. Tissue-specific annotations have been recently inferred for dozens of tissues/cell types from large collections of cross-tissue epigenomic data, and have demonstrated sensitivity in predicting affected tissues in complex traits. It remains unclear, however, whether including additional genome-scale data specific to the tissue of interest would appreciably improve functional annotations.ResultsHerein, we introduce TiSAn, a tool that integrates multiple genome-scale data sources, defined by expert knowledge. TiSAn uses machine learning to discriminate variants relevant to a tissue from those with no bearing on the function of that tissue. Predictions are made genome-wide, and can be used to contextualize and filter variants of interest in whole genome sequencing or genome-wide association studies. We demonstrate the accuracy and flexibility of TiSAn by producing predictive models for human heart and brain, and detecting tissue-relevant variations in large cohorts for autism spectrum disorder (TiSAn-brain) and coronary artery disease (TiSAn-heart). We find the multiomics TiSAn model is better able to prioritize genetic variants according to their tissue-specific action than the current state-of-the-art method, GenoSkyLine.Availability and implementationSoftware and vignettes are available at http://github.com/kevinVervier/TiSAn.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC6137979 | biostudies-literature
Project description:Personalized medicine approaches are increasingly sought for diseases with a heritable component. Systemic lupus erythematosus (SLE) is the prototypic autoimmune disease resulting from loss of immunologic tolerance, but the genetic basis of SLE remains incompletely understood. Genome wide association studies (GWAS) identify regions associated with disease, based on common single nucleotide polymorphisms (SNPs) within them, but these SNPs may simply be markers in linkage disequilibrium with other, causative mutations. Here we use an hierarchical screening approach for prediction and testing of true functional variants within regions identified in GWAS; this involved bioinformatic identification of putative regulatory elements within close proximity to SLE SNPs, screening those regions for potentially causative mutations by high resolution melt analysis, and functional validation using reporter assays. Using this approach, we screened 15 SLE associated loci in 143 SLE patients, identifying 7 new variants including 5 SNPs and 2 insertions. Reporter assays revealed that the 5 SNPs were functional, altering enhancer activity. One novel variant was linked to the relatively well characterized rs9888739 SNP at the ITGAM locus, and may explain some of the SLE heritability at this site. Our study demonstrates that non-coding regulatory elements can contain private sequence variants affecting gene expression, which may explain part of the heritability of SLE.
| S-EPMC6817816 | biostudies-literature
Project description:Identifying the underlying causes of disease requires accurate interpretation of genetic variants. Current methods ineffectively capture pathogenic non-coding variants in genic regions, resulting in overlooking synonymous and intronic variants when searching for disease risk. Here we present the Transcript-inferred Pathogenicity (TraP) score, which uses sequence context alterations to reliably identify non-coding variation that causes disease. High TraP scores single out extremely rare variants with lower minor allele frequencies than missense variants. TraP accurately distinguishes known pathogenic and benign variants in synonymous (AUC?=?0.88) and intronic (AUC?=?0.83) public datasets, dismissing benign variants with exceptionally high specificity. TraP analysis of 843 exomes from epilepsy family trios identifies synonymous variants in known epilepsy genes, thus pinpointing risk factors of disease from non-coding sequence data. TraP outperforms leading methods in identifying non-coding variants that are pathogenic and is therefore a valuable tool for use in gene discovery and the interpretation of personal genomes.While non-coding synonymous and intronic variants are often not under strong selective constraint, they can be pathogenic through affecting splicing or transcription. Here, the authors develop a score that uses sequence context alterations to predict pathogenicity of synonymous and non-coding genetic variants, and provide a web server of pre-computed scores.
| S-EPMC5550444 | biostudies-literature
Project description:Mutations in non-coding DNA regions are increasingly recognized as cancer drivers. These mutations can modify gene expression in cis or by inducing high-order chormatin structure modifications with long-range effects. Previous analysis reported the detection of recurrent and functional non-coding DNA mutations in the chronic lymphocytic leukemia (CLL) genome, such as those in the 3' untranslated region of NOTCH1 and in the PAX5 super-enhancer. In this report, we used whole genome sequencing data produced by the International Cancer Genome Consortium in order to analyze regions with previously reported regulatory activity. This approach enabled the identification of numerous recurrently mutated regions that were frequently positioned in the proximity of genes involved in immune and oncogenic pathways. By correlating these mutations with expression of their nearest genes, we detected significant transcriptional changes in genes such as PHF2 and S1PR2. More research is needed to clarify the function of these mutations in CLL, particularly those found in intergenic regions.
| S-EPMC7015923 | biostudies-literature
Project description:A gene is considered essential if loss of function results in loss of viability, fitness or in disease. This concept is well established for coding genes; however, non-coding regions are thought less likely to be determinants of critical functions. Here we train a machine learning model using functional, mutational and structural features, including new genome essentiality metrics, 3D genome organization and enhancer reporter data to identify deleterious variants in non-coding regions. We assess the model for functional correlates by using data from tiling-deletion-based and CRISPR interference screens of activity of cis-regulatory elements in over 3 Mb of genome sequence. Finally, we explore two user cases that involve indels and the disruption of enhancers associated with a developmental disease. We rank variants in the non-coding genome according to their predicted deleteriousness. The model prioritizes non-coding regions associated with regulation of important genes and with cell viability, an in vitro surrogate of essentiality.
| S-EPMC6868241 | biostudies-literature
Project description:BACKGROUND: In livestock, as in humans, the number of genetic variants that can be tested for association with complex quantitative traits, or used in genomic predictions, is increasing exponentially as whole genome sequencing becomes more common. The power to identify variants associated with traits, particularly those of small effects, could be increased if certain regions of the genome were known a priori to be enriched for associations. Here, we investigate whether twelve genomic annotation classes were enriched or depleted for significant associations in genome wide association studies for complex traits in beef and dairy cattle. We also describe a variance component approach to determine the proportion of genetic variance captured by each annotation class. RESULTS: P-values from large GWAS using 700K SNP in both dairy and beef cattle were available for 11 and 10 traits respectively. We found significant enrichment for trait associated variants (SNP significant in the GWAS) in the missense class along with regions 5 kilobases upstream and downstream of coding genes. We found that the non-coding conserved regions (across mammals) were not enriched for trait associated variants. The results from the enrichment or depletion analysis were not in complete agreement with the results from variance component analysis, where the missense and synonymous classes gave the greatest increase in variance explained, while the upstream and downstream classes showed a more modest increase in the variance explained. CONCLUSION: Our results indicate that functional annotations could assist in prioritization of variants to a subset more likely to be associated with complex traits; including missense variants, and upstream and downstream regions. The differences in two sets of results (GWAS enrichment depletion versus variance component approaches) might be explained by the fact that the variance component approach has greater power to capture the cumulative effect of mutations of small effect, while the enrichment or depletion approach only captures the variants that are significant in GWAS, which is restricted to a limited number of common variants of moderate effects.
| S-EPMC4070550 | biostudies-literature
Project description:De novo emergence of genes is the most fundamental form of genetic diversity that is attracting the attention of the scientific community. Identification of short open reading frames (sORFs) from the non-coding regions of different genomes has been leading this thought recently. The coding potential of these newly identified sORFs have been investigated through experimental and computational approaches in recent studies. In the present work we have tried to make peptides from intergenic sequences of D. melanogaster genome leading to therapeutic applications. Towards this goal of making novel peptides from non-coding genome, we have found strong computational evidence of 145 peptides with conformational stability from the intergenic sequences of D. melanogaster. The structure of these completely unique peptides was predicted using ab initio method. The function annotation of these peptides was carried out using this structural information. The newly generated proteins were categorised as DNA/Protein/ion binding proteins, electron transporters and a very few as enzymes too. Experimental studies can certainly provide validations to these preliminary findings. This work provides further evidence of untapped potential of non-coding genome.
| S-EPMC5267965 | biostudies-literature
Project description:Predicting the functional or pathogenic regulatory variants in the human non-coding genome facilitates the interpretation of disease causation. While numerous prediction methods are available, their performance is inconsistent or restricted to specific tasks, which raises the demand of developing comprehensive integration for those methods. Here, we compile whole genome base-wise aggregations, regBase, that incorporate largest prediction scores. Building on different assumptions of causality, we train three composite models to score functional, pathogenic and cancer driver non-coding regulatory variants respectively. We demonstrate the superior and stable performance of our models using independent benchmarks and show great success to fine-map causal regulatory variants on specific locus or at base-wise resolution. We believe that regBase database together with three composite models will be useful in different areas of human genetic studies, such as annotation-based casual variant fine-mapping, pathogenic variant discovery as well as cancer driver mutation identification. regBase is freely available at https://github.com/mulinlab/regBase.
| S-EPMC6868349 | biostudies-literature
Project description:Histone variants play further important roles in DNA packaging and controlling gene expression. However, our understanding about their composition and their functions is limited.Integrating proteomic and genomic approaches, we performed a comprehensive analysis of the epigenetic landscapes containing the four histone variants H3.1, H3.3, H2A.Z, and macroH2A. These histones were FLAG-tagged in HeLa cells and purified using chromatin immunoprecipitation (ChIP). By adopting ChIP followed by mass spectrometry (ChIP-MS), we quantified histone post-translational modifications (PTMs) and histone variant nucleosomal ratios in highly purified mononucleosomes. Subsequent ChIP followed by next-generation sequencing (ChIP-seq) was used to map the genome-wide localization of the analyzed histone variants and define their chromatin domains. Finally, we included in our study large datasets contained in the ENCODE database. We newly identified a group of regulatory regions enriched in H3.1 and the histone variant associated with repressive marks macroH2A. Systematic analysis identified both symmetric and asymmetric patterns of histone variant occupancies at intergenic regulatory regions. Strikingly, these directional patterns were associated with RNA polymerase II (PolII). These asymmetric patterns correlated with the enhancer activities measured using global run-on sequencing (GRO-seq) data.Our studies show that H2A.Z and H3.3 delineate the orientation of transcription at enhancers as observed at promoters. We also showed that enhancers with skewed histone variant patterns well facilitate enhancer activity. Collectively, our study indicates that histone variants are deposited at regulatory regions to assist gene regulation.
| S-EPMC4397702 | biostudies-literature