scholarly journals Schizophrenia Risk Alleles Often Affect The Expression of Many Genes and Each Gene May Have a Different Effect On The Risk; A Mediation Analysis

2020 ◽  
Author(s):  
Xi Peng ◽  
Joel S. Bader ◽  
Dimitrios Avramopoulos
Keyword(s):  
Gene Expression ◽  
Mediation Analysis ◽  
Target Genes ◽  
Risk Allele ◽  
Association Studies ◽  
Cell Types ◽  
Developmental Time ◽  
Genome Wide Association Studies ◽  
Expression Change ◽  
Risk Alleles

ABSTRACTVariants identified by genome-wide association studies (GWAS) are often expression quantitative trait loci (eQTLs), suggesting they are proxies or are themselves regulatory. Across many datasets analyses show that variants often affect multiple genes. Lacking data on many tissue types, developmental time points and homogeneous cell types, the extent of this one-to-many relationship is underestimated. This raises questions on whether a disease eQTL target gene explains the genetic association or is a by-stander and puts into question the direction of expression effect of on the risk, since the many variant - regulated genes may have opposing effects, imperfectly balancing each other. We used two brain gene expression datasets (CommonMind and BrainSeq) for mediation analysis of schizophrenia-associated variants. We confirm that eQTL target genes often mediate risk but the direction in which expression affects risk is often different from that in which the risk allele changes expression. Of 38 mediator genes significant in both datasets 33 showed consistent mediation direction (Chi2 test P=6*10−6). One might expect that the expression would correlate with the risk allele in the same direction it correlates with disease. For 15 of these 33 (45%), however, the expression change associated with the risk allele was protective, suggesting the likely presence of other target genes with overriding effects. Our results identify specific risk mediating genes and suggest caution in interpreting the biological consequences of targeted modifications of gene expression, as not all eQTL targets may be relevant to disease while those that are, might have different than expected directions.

scholarly journals EpiRegio: analysis and retrieval of regulatory elements linked to genes

2020 ◽  
Vol 48 (W1) ◽  
pp. W193-W199 ◽  
Author(s):  
Nina Baumgarten ◽  
Dennis Hecker ◽  
Sivarajan Karunanithi ◽  
Florian Schmidt ◽  
Markus List ◽  
...  
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Association Studies ◽  
Web Server ◽  
Cell Types ◽  
Regulatory Elements ◽  
Chromatin Accessibility ◽  
Genome Wide Association Studies ◽  
Coding Regions ◽  
Genomic Regions

Abstract A current challenge in genomics is to interpret non-coding regions and their role in transcriptional regulation of possibly distant target genes. Genome-wide association studies show that a large part of genomic variants are found in those non-coding regions, but their mechanisms of gene regulation are often unknown. An additional challenge is to reliably identify the target genes of the regulatory regions, which is an essential step in understanding their impact on gene expression. Here we present the EpiRegio web server, a resource of regulatory elements (REMs). REMs are genomic regions that exhibit variations in their chromatin accessibility profile associated with changes in expression of their target genes. EpiRegio incorporates both epigenomic and gene expression data for various human primary cell types and tissues, providing an integrated view of REMs in the genome. Our web server allows the analysis of genes and their associated REMs, including the REM’s activity and its estimated cell type-specific contribution to its target gene’s expression. Further, it is possible to explore genomic regions for their regulatory potential, investigate overlapping REMs and by that the dissection of regions of large epigenomic complexity. EpiRegio allows programmatic access through a REST API and is freely available at https://epiregio.de/.

scholarly journals Analysis of chromatin organization and gene expression in T cells identifies functional genes for rheumatoid arthritis

2019 ◽  
Author(s):  
Jing Yang ◽  
Amanda McGovern ◽  
Paul Martin ◽  
Kate Duffus ◽  
Xiangyu Ge ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
T Cells ◽  
Complex Disease ◽  
Target Genes ◽  
Disease Risk ◽  
Association Studies ◽  
Dna Interaction ◽  
Genome Wide Association Studies ◽  
Causal Genes

AbstractGenome-wide association studies have identified genetic variation contributing to complex disease risk. However, assigning causal genes and mechanisms has been more challenging because disease-associated variants are often found in distal regulatory regions with cell-type specific behaviours. Here, we collect ATAC-seq, Hi-C, Capture Hi-C and nuclear RNA-seq data in stimulated CD4+ T-cells over 24 hours, to identify functional enhancers regulating gene expression. We characterise changes in DNA interaction and activity dynamics that correlate with changes gene expression, and find that the strongest correlations are observed within 200 kb of promoters. Using rheumatoid arthritis as an example of T-cell mediated disease, we demonstrate interactions of expression quantitative trait loci with target genes, and confirm assigned genes or show complex interactions for 20% of disease associated loci, including FOXO1, which we confirm using CRISPR/Cas9.

scholarly journals How genetic disease risks can be misestimated across global populations

2017 ◽  
Author(s):  
Michelle S Kim ◽  
Kane P Patel ◽  
Andrew K Teng ◽  
Ali J Berens ◽  
Joseph Lachance
Keyword(s):  
Genetic Disease ◽  
Risk Allele ◽  
Association Studies ◽  
Allele Frequencies ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Whole Genome ◽  
Risk Alleles ◽  
Disease Associations ◽  
Disease Risks

AbstractBackgroundAccurate assessment of health disparities requires unbiased knowledge of genetic risks in different populations. Unfortunately, most genome-wide association studies use genotyping arrays and European samples. Here, we integrate whole genome sequence data from global populations, results from thousands of GWAS, and extensive computer simulations to identify how genetic disease risks can be misestimated.ResultsIn contrast to null expectations, we find that risk allele frequencies at known disease loci are significantly different for African populations compared to other continents. Strikingly, ancestral risk alleles are found at 9.51% higher frequency in Africa and derived risk alleles are found at 5.40% lower frequency in Africa. By simulating GWAS with different study populations, we find that non-African cohorts yield disease associations that have biased allele frequencies and that African cohorts yield disease associations that are relatively free of bias. We also find empirical evidence that genotyping arrays and SNP ascertainment bias contribute to continental differences in risk allele frequencies. Because of these causes, polygenic risk scores can be grossly misestimated for individuals of African descent. Importantly, continental differences in risk allele frequencies are only moderately reduced if GWAS use whole genome sequences and hundreds of thousands of cases and controls. Finally, comparisons between uncorrected and corrected genetic risk scores reveal the benefits of considering whether risk alleles are ancestral or derived.ConclusionsOur results imply that caution must be taken when extrapolating GWAS results from one population to predict disease risks in another population.

scholarly journals Cascading epigenomic analysis for identifying disease genes from the regulatory landscape of GWAS variants

PLoS Genetics ◽  
2021 ◽  
Vol 17 (11) ◽  
pp. e1009918
Author(s):  
Bernard Ng ◽  
William Casazza ◽  
Nam Hee Kim ◽  
Chendi Wang ◽  
Farnush Farhadi ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Target Genes ◽  
Association Studies ◽  
Disease Genes ◽  
Genome Wide Association Studies ◽  
Cascading Effects ◽  
Risk Alleles ◽  
Gene Sets ◽  
Genome Wide ◽  
Regulatory Landscape

The majority of genetic variants detected in genome wide association studies (GWAS) exert their effects on phenotypes through gene regulation. Motivated by this observation, we propose a multi-omic integration method that models the cascading effects of genetic variants from epigenome to transcriptome and eventually to the phenome in identifying target genes influenced by risk alleles. This cascading epigenomic analysis for GWAS, which we refer to as CEWAS, comprises two types of models: one for linking cis genetic effects to epigenomic variation and another for linking cis epigenomic variation to gene expression. Applying these models in cascade to GWAS summary statistics generates gene level statistics that reflect genetically-driven epigenomic effects. We show on sixteen brain-related GWAS that CEWAS provides higher gene detection rate than related methods, and finds disease relevant genes and gene sets that point toward less explored biological processes. CEWAS thus presents a novel means for exploring the regulatory landscape of GWAS variants in uncovering disease mechanisms.

scholarly journals Genetic regulatory signatures underlying islet gene expression and type 2 diabetes

2017 ◽  
Vol 114 (9) ◽  
pp. 2301-2306 ◽  
Author(s):  
Arushi Varshney ◽  
Laura J. Scott ◽  
Ryan P. Welch ◽  
Michael R. Erdos ◽  
Peter S. Chines ◽  
...  
Keyword(s):  
Gene Expression ◽  
Type 2 Diabetes ◽  
De Novo ◽  
Association Studies ◽  
Regulatory Elements ◽  
Chromatin Accessibility ◽  
Genome Wide Association Studies ◽  
Factor X ◽  
Risk Alleles

Genome-wide association studies (GWAS) have identified >100 independent SNPs that modulate the risk of type 2 diabetes (T2D) and related traits. However, the pathogenic mechanisms of most of these SNPs remain elusive. Here, we examined genomic, epigenomic, and transcriptomic profiles in human pancreatic islets to understand the links between genetic variation, chromatin landscape, and gene expression in the context of T2D. We first integrated genome and transcriptome variation across 112 islet samples to produce dense cis-expression quantitative trait loci (cis-eQTL) maps. Additional integration with chromatin-state maps for islets and other diverse tissue types revealed that cis-eQTLs for islet-specific genes are specifically and significantly enriched in islet stretch enhancers. High-resolution chromatin accessibility profiling using assay for transposase-accessible chromatin sequencing (ATAC-seq) in two islet samples enabled us to identify specific transcription factor (TF) footprints embedded in active regulatory elements, which are highly enriched for islet cis-eQTL. Aggregate allelic bias signatures in TF footprints enabled us de novo to reconstruct TF binding affinities genetically, which support the high-quality nature of the TF footprint predictions. Interestingly, we found that T2D GWAS loci were strikingly and specifically enriched in islet Regulatory Factor X (RFX) footprints. Remarkably, within and across independent loci, T2D risk alleles that overlap with RFX footprints uniformly disrupt the RFX motifs at high-information content positions. Together, these results suggest that common regulatory variations have shaped islet TF footprints and the transcriptome and that a confluent RFX regulatory grammar plays a significant role in the genetic component of T2D predisposition.

scholarly journals MicroRNA variants and HLA-miRNA interactions are novel rheumatoid arthritis susceptibility factors

2020 ◽  
Author(s):  
Shicheng Guo ◽  
Yehua Jin ◽  
Jieru Zhou ◽  
Qi Zhu ◽  
Ting Jiang ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Target Genes ◽  
Association Studies ◽  
Meta Analysis ◽  
Genome Wide Association Studies ◽  
Risk Alleles ◽  
Novel Mirna ◽  
Chinese Cohort ◽  
Target Binding ◽  
Immune Related Genes

AbstractObjectiveAlthogh Genome-wide association studies have identified >100 variants for rheumatoid arthritis (RA),the reported genetic variants only explain <40% of RA heritability. We conducted a systemic association study between common East-Asian miRNA SNPs with RA in a large Han Chinese cohort to explain missing heritability and identify miRNA epistatic interactions.Methods4 HLA SNPs (HLA-DRB1, HLA-DRB9, HLA-DQB1 and TNFAIP3) and 225 common SNPs located in miRNA which might influence the miRNA target binding or pre-miRNA stability were genotyped in 1,607 rheumatoid arthritis and 1,580 matched normal individuals. A meta-analysis with previous GWAS studies (4,873 RA cases and 17,642 controls) was performed to discovery another novel miRNA RA-associated SNPs.Results2 novel SNPs including rs1414273 (miR-548ac, OR=0.84, P=8.26×10-4) and rs2620381 (miR-627, OR=0.77, P=2.55×10-3) conferred significant association with RA. Individuals carried 8 risk alleles showed 15.38 (95%CI: 4.69-50.49, P<1.0×10-6) times more risk to be affected by RA. In addition, rs5997893 (miR-3928) showed significant epistasis effect with rs4947332 (HLA-DRB1, OR=4.23, P=0.04) and rs2967897 (miR-5695) with rs7752903 (TNFAIP3, OR=4.43, P=0.03). Finally, we demonstrated targets of the significant miRNAs showed enrichment in immune related genes (P=2.0×10-5) and FDA approved drug target genes (P=0.014).Conclusions6 novel miRNA SNPs including rs1414273 (miR-548ac, P=8.26×10-4), rs2620381 (miR-627, P=2.55×10-3), rs4285314 (miR-3135b, P=1.10×10-13), rs28477407 (miR-4308, P=3.44×10-5), rs5997893 (miR-3928, P=5.9×10-3) and rs45596840 (miR-4482, P=6.6×10-3) were confirmed to be significantly associated with RA in a Chinese population. Our study suggests that miRNAs might be interesting targets to accelerate the understanding of the pathogenesis and drug development for rheumatoid arthritis.

scholarly journals Using regulatory genomics data to interpret the function of disease variants and prioritise genes from expression studies

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 121 ◽  
Author(s):  
Enrico Ferrero
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Association Studies ◽  
Cell Types ◽  
Regulatory Elements ◽  
New Drugs ◽  
Genome Wide Association Studies ◽  
Functional Interpretation ◽  
Expression Studies ◽  
Regulatory Genomics

The identification of therapeutic targets is a critical step in the research and developement of new drugs, with several drug discovery programmes failing because of a weak linkage between target and disease. Genome-wide association studies and large-scale gene expression experiments are providing insights into the biology of several common and complex diseases, but the complexity of transcriptional regulation mechanisms often limit our understanding of how genetic variation can influence changes in gene expression. Several initiatives in the field of regulatory genomics are aiming to close this gap by systematically identifying and cataloguing regulatory elements such as promoters and enhacers across different tissues and cell types. In this Bioconductor workflow, we will explore how different types of regulatory genomic data can be used for the functional interpretation of disease-associated variants and for the prioritisation of gene lists from gene expression experiments.

scholarly journals Colocalization of GWAS and eQTL Signals Detects Target Genes

2016 ◽  
Author(s):  
Farhad Hormozdiari ◽  
Martijn van de Bunt ◽  
Ayellet V. Segrè ◽  
Xiao Li ◽  
Jong Wha J Joo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Disease Risk ◽  
Association Studies ◽  
Causal Variant ◽  
Genome Wide Association Studies ◽  
Specific Tissue ◽  
The Individual ◽  
Eqtl Data ◽  
Different Tissues

AbstractThe vast majority of genome-wide association studies (GWAS) risk loci fall in non-coding regions of the genome. One possible hypothesis is that these GWAS risk loci alter the individual’s disease risk through their effect on gene expression in different tissues. In order to understand the mechanisms driving a GWAS risk locus, it is helpful to determine which gene is affected in specific tissue types. For example, the relevant gene and tissue may play a role in the disease mechanism if the same variant responsible for a GWAS locus also affects gene expression. Identifying whether or not the same variant is causal in both GWAS and eQTL studies is challenging due to the uncertainty induced by linkage disequilibrium (LD) and the fact that some loci harbor multiple causal variants. However, current methods that address this problem assume that each locus contains a single causal variant. In this paper, we present a new method, eCAVIAR, that is capable of accounting for LD while computing the quantity we refer to as the colocalization posterior probability (CLPP). The CLPP is the probability that the same variant is responsible for both the GWAS and eQTL signal. eCAVIAR has several key advantages. First, our method can account for more than one causal variant in any loci. Second, it can leverage summary statistics without accessing the individual genotype data. We use both simulated and real datasets to demonstrate the utility of our method. Utilizing publicly available eQTL data on 45 different tissues, we demonstrate that computing CLPP can prioritize likely relevant tissues and target genes for a set of Glucose and Insulin-related traits loci. eCAVIAR is available at http://genetics.cs.ucla.edu/caviar/

scholarly journals Functional genomics atlas of synovial fibroblasts defining rheumatoid arthritis heritability

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Xiangyu Ge ◽  
Mojca Frank-Bertoncelj ◽  
Kerstin Klein ◽  
Amanda McGovern ◽  
Tadeja Kuret ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
Cellular Proliferation ◽  
Immune Cell ◽  
Association Studies ◽  
Synovial Fibroblasts ◽  
Cell Types ◽  
Joint Involvement ◽  
Genome Wide Association Studies ◽  
Causal Genes

Abstract Background Genome-wide association studies have reported more than 100 risk loci for rheumatoid arthritis (RA). These loci are shown to be enriched in immune cell-specific enhancers, but the analysis so far has excluded stromal cells, such as synovial fibroblasts (FLS), despite their crucial involvement in the pathogenesis of RA. Here we integrate DNA architecture, 3D chromatin interactions, DNA accessibility, and gene expression in FLS, B cells, and T cells with genetic fine mapping of RA loci. Results We identify putative causal variants, enhancers, genes, and cell types for 30–60% of RA loci and demonstrate that FLS account for up to 24% of RA heritability. TNF stimulation of FLS alters the organization of topologically associating domains, chromatin state, and the expression of putative causal genes such as TNFAIP3 and IFNAR1. Several putative causal genes constitute RA-relevant functional networks in FLS with roles in cellular proliferation and activation. Finally, we demonstrate that risk variants can have joint-specific effects on target gene expression in RA FLS, which may contribute to the development of the characteristic pattern of joint involvement in RA. Conclusion Overall, our research provides the first direct evidence for a causal role of FLS in the genetic susceptibility for RA accounting for up to a quarter of RA heritability.

scholarly journals Mapping genomic loci prioritises genes and implicates synaptic biology in schizophrenia

2020 ◽  
Author(s):  
◽  
Stephan Ripke ◽  
James TR Walters ◽  
Michael C O'Donovan
Keyword(s):  
Developmental Disorder ◽  
Association Studies ◽  
Cell Types ◽  
Genome Wide Association Studies ◽  
Biological Processes ◽  
Neuronal Function ◽  
Protein Coding ◽  
Risk Alleles ◽  
Genome Wide ◽  
Coding Variants

Schizophrenia is a psychiatric disorder whose pathophysiology is largely unknown. It has a heritability of 60-80%, much of which is attributable to common risk alleles, suggesting genome-wide association studies can inform our understanding of aetiology. Here, in 69,369 people with schizophrenia and 236,642 controls, we report common variant associations at 270 distinct loci. Using fine-mapping and functional genomic data, we prioritise 19 genes based on protein-coding or UTR variation, and 130 genes in total as likely to explain these associations. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in autism and developmental disorder. Associations were concentrated in genes expressed in CNS neurons, both excitatory and inhibitory, but not other tissues or cell types, and implicated fundamental processes related to neuronal function, particularly synaptic organisation, differentiation and transmission. We identify biological processes of pathophysiological relevance to schizophrenia, show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders, and provide a rich resource of priority genes and variants to advance mechanistic studies.

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