Sensitive detection of chromatin-altering polymorphisms reveals autoimmune disease mechanisms
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE58852 | GEO | 2015/03/01
SECONDARY ACCESSION(S): PRJNA253718
REPOSITORIES: GEO
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Project description:Although most disease associations detected by GWAS are nongenic, very few have been mapped to causal regulatory variants. Here, we present a method for detecting regulatory QTLs that does not require genotyping or whole-genome sequencing. The method combines deep, long-read ChIP-seq with a new statistical test that simultaneously scores peak height correlation and allelic imbalance: the Genotype-independent Signal Correlation and Imbalance (G-SCI) test. We performed histone acetylation ChIP-seq on 57 human lymphoblastoid cell lines and used the resulting reads to call 500,066 SNPs de novo within regulatory elements. The G-SCI test annotated 8,764 of these as histone acetylation QTLs (haQTLs) - an order of magnitude larger than the set of candidates detected by expression QTL analysis. Lymphoblastoid haQTLs were highly predictive of autoimmune disease mechanisms. Thus, our method facilitates large-scale regulatory variant detection in any moderately-sized cohort for which functional profiling data can be generated, thus simplifying identification of causal variants within GWAS loci. We applied our method, named Regulatory Variant Ascertainment and chromatin Regression by sequencing (RegVAR-seq), to 57 cell lines from a single population group. We used the resulting sequence data for variant calling, and validated calls using an independent platform. We then identified histone acetylation QTLs (haQTLs) using a novel statistical test that requires no prior genotype information and combines peak height and allelic imbalance data across the 57 individuals. Transcription factor binding site analysis was used to independently support the functionality of haQTLs. Finally, we examined the association between haQTLs and SNPs associated with human phenotypes.
2015-03-01 | E-GEOD-58852 | biostudies-arrayexpress
Project description:GWAS have discovered thousands of genomic loci that are associated with disease risk and quantitative traits, but most of the variants responsible for risk remain uncharacterized. The vast majority of GWAS-identified loci contain non-coding SNPs and defining molecular mechanism of risk is challenging. Many non-coding causal SNPs are hypothesized to alter Transcription Factor (TF) binding sites as the mechanism by which they affect organismal phenotypes. We employed an integrative genomics approach to identify candidate TF binding motifs that confer breast cancer-specific phenotypes identified by GWAS. We performed de novo motif analysis of regulatory elements, analyzed evolutionary conservation of identified motifs, and assayed TF footprinting data to identify sequence elements that recruit TFs and maintain chromatin landscape in breast cancer-relevant tissue and cell lines. Regulatory elements for MCF10A were mapped with ATAC-seq.We identified top candidate causal SNPs that are predicted to alter TF binding, within breast cancer-relevant regulatory regions, and in strong linkage disequilibrium with the GWAS SNPs. This integrative analysis pipeline is a general framework to identify candidate causal variants within regulatory regions and TF binding sites that confer phenotypic variation and disease risk.
2016-12-19 | GSE89013 | GEO
Project description:Genome-wide association studies (GWAS) have linked hundreds of loci to cardiac diseases. However, in most loci the causal variants and their target genes remain unknown. We developed a combined experimental and analytical approach that integrates single cell epigenomics with GWAS to prioritize risk variants and genes. We profiled accessible chromatin in single cells obtained from human hearts and leveraged the data to study genetics of Atrial Fibrillation (AF), the most common cardiac arrhythmia. Enrichment analysis of AF risk variants using cell-type-resolved open chromatin regions (OCRs) implicated cardiomyocytes as the main mediator of AF risk. We then performed statistical fine-mapping, leveraging the information in OCRs, and identified putative causal variants in 122 AF-associated loci. Taking advantage of the fine-mapping results, our novel statistical procedure for gene discovery prioritized 45 high-confidence risk genes, highlighting transcription factors and signal transduction pathways important for heart development. We further leveraged our single-cell data to study genetics of gene expression. An unexpected finding from earlier studies is that expression QTLs (eQTLs) are often shared across tissues even though most regulatory elements are cell-type specific. We found that this sharing is largely driven by the limited power of eQTL studies using bulk tissues to detect cell-type-specific regulatory variants. This finding points to an important limitation of using eQTLs to interpret GWAS of complex traits. In summary, our analysis provides a comprehensive map of AF risk variants and genes, and a general framework to integrate single-cell genomics with genetic studies of complex traits.
2023-02-12 | GSE224996 | GEO
Project description:Genome-wide association studies (GWAS) have linked hundreds of loci to cardiac diseases. However, in most loci the causal variants and their target genes remain unknown. We developed a combined experimental and analytical approach that integrates single cell epigenomics with GWAS to prioritize risk variants and genes. We profiled accessible chromatin in single cells obtained from human hearts and leveraged the data to study genetics of Atrial Fibrillation (AF), the most common cardiac arrhythmia. Enrichment analysis of AF risk variants using cell-type-resolved open chromatin regions (OCRs) implicated cardiomyocytes as the main mediator of AF risk. We then performed statistical fine-mapping, leveraging the information in OCRs, and identified putative causal variants in 122 AF-associated loci. Taking advantage of the fine-mapping results, our novel statistical procedure for gene discovery prioritized 45 high-confidence risk genes, highlighting transcription factors and signal transduction pathways important for heart development. We further leveraged our single-cell data to study genetics of gene expression. An unexpected finding from earlier studies is that expression QTLs (eQTLs) are often shared across tissues even though most regulatory elements are cell-type specific. We found that this sharing is largely driven by the limited power of eQTL studies using bulk tissues to detect cell-type-specific regulatory variants. This finding points to an important limitation of using eQTLs to interpret GWAS of complex traits. In summary, our analysis provides a comprehensive map of AF risk variants and genes, and a general framework to integrate single-cell genomics with genetic studies of complex traits.
2023-02-12 | GSE224995 | GEO
Project description:Deciphering the impact of genetic variation on gene regulation is fundamental to understanding common, complex human diseases. In this study, we obtained genome-wide RNA-Seq and ChIP-Seq (for H3K4me3 and H3K27ac histone modifications) data in the human liver. We mapped quantitative trait loci (QTLs) of gene expression levels and histone modification states. We integrated our findings with summary statistics of genome-wide association studies (GWAS) and identified candidate genes, gene regulatory regions, and variants in GWAS loci.
2019-06-13 | GSE128072 | GEO
Project description:Genetic variants that impact gene regulation are important contributors to human phenotypic variation. For this reason, considerable efforts have been made to identify genetic associations with differences in mRNA levels of nearby genes, namely, cis expression quantitative trait loci (eQTLs). The phenotypic consequences of eQTLs are presumably due, in most cases, to their ultimate effects on protein expression levels. Yet, only few studies have quantified the impact of genetic variation on proteins levels directly. It remains unclear how faithfully eQTLs are reflected at the protein level, and whether there is a significant layer of cis regulatory variation acting primarily on translation or steady state protein levels. To address these questions, we measured ribosome occupancy by high-throughput sequencing, and relative protein levels by high-resolution quantitative mass spectrometry, in a panel of lymphoblastoid cell lines (LCLs) in which we had previously measured transcript expression using RNA sequencing. We then mapped genetic variants that are associated with changes in transcript expression (eQTLs), ribosome occupancy (rQTLs), or protein abundance (pQTLs). Most of the QTLs we detected are associated with transcript expression levels, with consequent effects on ribosome and protein levels. However, we found that eQTLs tend to have significantly reduced effect sizes on protein levels, suggesting that their potential impact on downstream phenotypes is often attenuated or buffered. Additionally, we confirmed the presence of a class of cis QTLs that specifically affect protein abundance with little or no effect on mRNA levels; most of these QTLs have little effect on ribosome occupancy, and hence may arise from differences in post-translational regulation.
2014-12-19 | PXD001406 | Pride
Project description:Background: Autism spectrum disorder (ASD) is a severe early onset neurodevelopmental disorder with high heritability but significant heterogeneity. Traditional genome-wide approaches to test for association of common variants with autism susceptibility risk has met with limited success. However, novel methods to identify moderate risk alleles in attainable sample sizes are now gaining momentum. Methods:M-BM- In this study, we utilized publically available GWAS data from the Autism Genome Project (AGP) and annotated the results (p < 0.001) for eQTLs present in the parietal lobe, cerebellum, and lymphoblastoid cell lines. We then performed a test of enrichment by comparing these results to simulated data conditioned on minor allele frequency in order to generate an empirical p-value indicating statistically significant enrichment of eQTLs in top results from the autism GWAS. Results:M-BM- Our findings show a global enrichment of brain eQTLs, but not LCL eQTLs, among top SNPs from an autism GWAS. Additionally, the data implicates individual genesM-BM- SLC25A12,M-BM- PANX1M-BM- andM-BM- PANX2, as well as pathways previously implicated in autism. Conclusions:M-BM- These findings provide supportive rationale for the use of annotation-based approaches to GWAS. We use microarray technology to understand the etiology and pathology of psychiatric diseases at the transcriptomic level. Postmortem human brain samples came from the Stanley Medical Research InstituteM-bM-^@M-^Ys Neuropathology Consortium and Array collections, including schizophrenia, bipolar disorder and control samples.
2012-12-31 | E-GEOD-35974 | biostudies-arrayexpress
Project description:Background: Autism spectrum disorder (ASD) is a severe early onset neurodevelopmental disorder with high heritability but significant heterogeneity. Traditional genome-wide approaches to test for association of common variants with autism susceptibility risk has met with limited success. However, novel methods to identify moderate risk alleles in attainable sample sizes are now gaining momentum. Methods:M-BM- In this study, we utilized publically available GWAS data from the Autism Genome Project (AGP) and annotated the results (p < 0.001) for eQTLs present in the parietal lobe, cerebellum, and lymphoblastoid cell lines. We then performed a test of enrichment by comparing these results to simulated data conditioned on minor allele frequency in order to generate an empirical p-value indicating statistically significant enrichment of eQTLs in top results from the autism GWAS. Results:M-BM- Our findings show a global enrichment of brain eQTLs, but not LCL eQTLs, among top SNPs from an autism GWAS. Additionally, the data implicates individual genesM-BM- SLC25A12,M-BM- PANX1M-BM- andM-BM- PANX2, as well as pathways previously implicated in autism. Conclusions:M-BM- These findings provide supportive rationale for the use of annotation-based approaches to GWAS. We use microarray technology to understand the etiology and pathology of psychiatric diseases at the transcriptomic level. Postmortem human brain samples came from the Stanley Medical Research InstituteM-bM-^@M-^Ys Neuropathology Consortium and Array collections, including schizophrenia, bipolar disorder and control samples.
2012-12-31 | E-GEOD-35977 | biostudies-arrayexpress
Project description:Genome-wide association studies (GWAS) have identified hundreds of genetic risk loci for coronary artery disease (CAD). However, non-European populations are underrepresented in coronary artery disease (CAD). However, non-European populations are underrepresented in GWAS and the causal gene-regulatory mechanisms of these risk loci during atherosclerosis remain unclear. We incorporated local ancestry and haplotype information to identify quantitative trait loci (QTL) for gene expression and splicing in coronary arteries obtained from 138 ancestrally diverse Americans.
2023-11-14 | GSE225650 | GEO
Project description:To date, single-nucleotide polymorphisms (SNPs) have been the most intensively investigated class of polymorphisms in genome wide associations studies (GWAS), however, other classes such as insertion-deletion or multiple nucleotide length polymorphism (MNLPs) may also confer disease risk. Multiple reports have shown that the 5p15.33 prostate cancer (PCa) risk region is a particularly strong expression quantitative trait locus (eQTL) for IRX4 transcripts. Here, we demonstrate using epigenome and genome editing that a biallelic (47bp/21bp) MNLP is the causal variant regulating IRX4 transcript levels. In LNCaP PCa cells (homozygous for the short allele), a single copy knock-in of the long allele potently alters the chromatin state, enabling de novo functional binding of the androgen receptor (AR) associated with increased chromatin accessibility, H3K27 acetylation, and ~3-fold upregulation of IRX4 expression. We further show that an MNLP is amongst the strongest candidate susceptibility variants at two additional PCa risk loci. We estimated that at least 5% of PCa risk loci could be explained by functional non-SNP causal variants, which may have broader implications for other cancers GWAS. More generally, our results underscore the importance of investigating other classes of inherited variation as causal mediators of human traits.
2023-07-11 | GSE231750 | GEO