scholarly journals A compendium of uniformly processed human gene expression and splicing quantitative trait loci

2021 ◽  
Vol 53 (9) ◽  
pp. 1290-1299
Author(s):  
Nurlan Kerimov ◽  
James D. Hayhurst ◽  
Kateryna Peikova ◽  
Jonathan R. Manning ◽  
Peter Walter ◽  
...  
Keyword(s):  
Gene Expression ◽  
Quantitative Trait ◽  
Target Genes ◽  
Genome Wide Association Study ◽  
Cell Types ◽  
Summary Statistics ◽  
Genome Wide ◽  
Cell Type Specific ◽  
Trait Locus ◽  
Complex Human Traits

AbstractMany gene expression quantitative trait locus (eQTL) studies have published their summary statistics, which can be used to gain insight into complex human traits by downstream analyses, such as fine mapping and co-localization. However, technical differences between these datasets are a barrier to their widespread use. Consequently, target genes for most genome-wide association study (GWAS) signals have still not been identified. In the present study, we present the eQTL Catalogue (https://www.ebi.ac.uk/eqtl), a resource of quality-controlled, uniformly re-computed gene expression and splicing QTLs from 21 studies. We find that, for matching cell types and tissues, the eQTL effect sizes are highly reproducible between studies. Although most QTLs were shared between most bulk tissues, we identified a greater diversity of cell-type-specific QTLs from purified cell types, a subset of which also manifested as new disease co-localizations. Our summary statistics are freely available to enable the systematic interpretation of human GWAS associations across many cell types and tissues.

scholarly journals eQTL Catalogue: a compendium of uniformly processed human gene expression and splicing QTLs

2020 ◽  
Author(s):  
Nurlan Kerimov ◽  
James D Hayhurst ◽  
Kateryna Peikova ◽  
Jonathan R Manning ◽  
Peter Walter ◽  
...  
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Cell Types ◽  
Summary Statistics ◽  
Ensembl Genome Browser ◽  
Cell Type Specific ◽  
Trait Locus ◽  
Rest Api ◽  
Complex Human Traits ◽  
Insight Into

An increasing number of gene expression quantitative trait locus (eQTL) studies have made summary statistics publicly available, which can be used to gain insight into complex human traits by downstream analyses, such as fine mapping and colocalisation. However, differences between these datasets, in their variants tested, allele codings, and in the transcriptional features quantified, are a barrier to their widespread use. Consequently, target genes for most GWAS signals have still not been identified. Here, we present the eQTL Catalogue (https://www.ebi.ac.uk/eqtl/), a resource which contains quality controlled, uniformly re-computed QTLs from 21 eQTL studies. We find that for matching cell types and tissues, the eQTL effect sizes are highly reproducible between studies, enabling the integrative analysis of these data. Although most cis-eQTLs were shared between most bulk tissues, the analysis of purified cell types identified a greater diversity of cell-type-specific eQTLs, a subset of which also manifested as novel disease colocalisations. Our summary statistics can be downloaded by FTP, accessed via a REST API, and visualised on the Ensembl genome browser. New datasets will continuously be added to the eQTL Catalogue, enabling the systematic interpretation of human GWAS associations across many cell types and tissues.

scholarly journals Optimizing expression quantitative trait locus mapping workflows for single-cell studies

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Anna S. E. Cuomo ◽  
Giordano Alvari ◽  
Christina B. Azodi ◽  
Davis J. McCarthy ◽  
Marc Jan Bonder ◽  
...  
Keyword(s):  
Gene Expression ◽  
Quantitative Trait Locus ◽  
Single Cell ◽  
Quantitative Trait ◽  
Cell Types ◽  
Expression Quantitative Trait Locus ◽  
Eqtl Mapping ◽  
Trait Locus ◽  
The Impact

Abstract Background Single-cell RNA sequencing (scRNA-seq) has enabled the unbiased, high-throughput quantification of gene expression specific to cell types and states. With the cost of scRNA-seq decreasing and techniques for sample multiplexing improving, population-scale scRNA-seq, and thus single-cell expression quantitative trait locus (sc-eQTL) mapping, is increasingly feasible. Mapping of sc-eQTL provides additional resolution to study the regulatory role of common genetic variants on gene expression across a plethora of cell types and states and promises to improve our understanding of genetic regulation across tissues in both health and disease. Results While previously established methods for bulk eQTL mapping can, in principle, be applied to sc-eQTL mapping, there are a number of open questions about how best to process scRNA-seq data and adapt bulk methods to optimize sc-eQTL mapping. Here, we evaluate the role of different normalization and aggregation strategies, covariate adjustment techniques, and multiple testing correction methods to establish best practice guidelines. We use both real and simulated datasets across single-cell technologies to systematically assess the impact of these different statistical approaches. Conclusion We provide recommendations for future single-cell eQTL studies that can yield up to twice as many eQTL discoveries as default approaches ported from bulk studies.

scholarly journals HCR-FlowFISH: A flexible CRISPR screening method to identify cis-regulatory elements and their target genes

2020 ◽  
Author(s):  
SK Reilly ◽  
SJ Gosai ◽  
A Gutierrez ◽  
JC Ulirsch ◽  
M Kanai ◽  
...  
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Screening Method ◽  
Cell Types ◽  
Regulatory Elements ◽  
Hybridization Chain Reaction ◽  
Genome Wide ◽  
Wide Range ◽  
Causal Variants ◽  
Endogenous Loci

AbstractCRISPR screens for cis-regulatory elements (CREs) have shown unprecedented power to endogenously characterize the non-coding genome. To characterize CREs we developed HCR-FlowFISH (Hybridization Chain Reaction Fluorescent In-Situ Hybridization coupled with Flow Cytometry), which directly quantifies native transcripts within their endogenous loci following CRISPR perturbations of regulatory elements, eliminating the need for restrictive phenotypic assays such as growth or transcript-tagging. HCR-FlowFISH accurately quantifies gene expression across a wide range of transcript levels and cell types. We also developed CASA (CRISPR Activity Screen Analysis), a hierarchical Bayesian model to identify and quantify CRE activity. Using >270,000 perturbations, we identified CREs for GATA1, HDAC6, ERP29, LMO2, MEF2C, CD164, NMU, FEN1 and the FADS gene cluster. Our methods detect subtle gene expression changes and identify CREs regulating multiple genes, sometimes at different magnitudes and directions. We demonstrate the power of HCR-FlowFISH to parse genome-wide association signals by nominating causal variants and target genes.

scholarly journals Cell-type Specific Expression Quantitative Trait Loci Associated with Alzheimer Disease in Blood and Brain Tissue

2020 ◽  
Author(s):  
Devanshi Patel ◽  
Xiaoling Zhang ◽  
John J. Farrell ◽  
Jaeyoon Chung ◽  
Thor D. Stein ◽  
...  
Keyword(s):  
Gene Expression ◽  
Quantitative Trait ◽  
Expression Patterns ◽  
Regulation Of Gene Expression ◽  
Cell Types ◽  
Eqtl Analysis ◽  
Cell Type ◽  
Specific Expression ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

ABSTRACTBecause regulation of gene expression is heritable and context-dependent, we investigated AD-related gene expression patterns in cell-types in blood and brain. Cis-expression quantitative trait locus (eQTL) mapping was performed genome-wide in blood from 5,257 Framingham Heart Study (FHS) participants and in brain donated by 475 Religious Orders Study/Memory & Aging Project (ROSMAP) participants. The association of gene expression with genotypes for all cis SNPs within 1Mb of genes was evaluated using linear regression models for unrelated subjects and linear mixed models for related subjects. Cell type-specific eQTL (ct-eQTL) models included an interaction term for expression of “proxy” genes that discriminate particular cell type. Ct-eQTL analysis identified 11,649 and 2,533 additional significant gene-SNP eQTL pairs in brain and blood, respectively, that were not detected in generic eQTL analysis. Of note, 386 unique target eGenes of significant eQTLs shared between blood and brain were enriched in apoptosis and Wnt signaling pathways. Five of these shared genes are established AD loci. The potential importance and relevance to AD of significant results in myeloid cell-types is supported by the observation that a large portion of GWS ct-eQTLs map within 1Mb of established AD loci and 58% (23/40) of the most significant eGenes in these eQTLs have previously been implicated in AD. This study identified cell-type specific expression patterns for established and potentially novel AD genes, found additional evidence for the role of myeloid cells in AD risk, and discovered potential novel blood and brain AD biomarkers that highlight the importance of cell-type specific analysis.

scholarly journals Evaluating the contribution of cell-type specific alternative splicing to variation in lipid levels

2019 ◽  
Author(s):  
K.A.B. Gawronski ◽  
W. Bone ◽  
Y. Park ◽  
E. Pashos ◽  
X. Wang ◽  
...  
Keyword(s):  
Alternative Splicing ◽  
Quantitative Trait ◽  
Cell Types ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Lipid Levels ◽  
Cell Type ◽  
Genome Wide ◽  
Genetic Mechanisms ◽  
Cell Type Specific

AbstractBackgroundGenome-wide association studies have identified 150+ loci associated with lipid levels. However, the genetic mechanisms underlying most of these loci are not well-understood. Recent work indicates that changes in the abundance of alternatively spliced transcripts contributes to complex trait variation. Consequently, identifying genetic loci that associate with alternative splicing in disease-relevant cell types and determining the degree to which these loci are informative for lipid biology is of broad interest.Methods and ResultsWe analyze gene splicing in 83 sample-matched induced pluripotent stem cell (iPSC) and hepatocyte-like cell (HLC) lines (n=166), as well as in an independent collection of primary liver tissues (n=96). We observe that transcript splicing is highly cell-type specific, and the genes that are differentially spliced between iPSCs and HLCs are enriched for metabolism pathway annotations. We identify 1,381 HLC splicing quantitative trait loci (sQTLs) and 1,462 iPSC sQTLs and find that sQTLs are often shared across cell types. To evaluate the contribution of sQTLs to variation in lipid levels, we conduct colocalization analysis using lipid genome-wide association data. We identify 19 lipid-associated loci that colocalize either with an HLC expression quantitative trait locus (eQTL) or sQTL. Only one locus colocalizes with both an sQTL and eQTL, indicating that sQTLs contribute information about GWAS loci that cannot be obtained by analysis of steady-state gene expression alone.ConclusionsThese results provide an important foundation for future efforts that use iPSC and iPSC-derived cells to evaluate genetic mechanisms influencing both cardiovascular disease risk and complex traits in general.

scholarly journals Optimising expression quantitative trait locus mapping workflows for single-cell studies

2021 ◽  
Author(s):  
Anna S.E. Cuomo ◽  
Giordano Alvari ◽  
Christina B. Azodi ◽  
Davis J. McCarthy ◽  
Marc Jan Bonder ◽  
...  
Keyword(s):  
Gene Expression ◽  
Quantitative Trait Locus ◽  
Single Cell ◽  
Quantitative Trait ◽  
Cell Types ◽  
Expression Quantitative Trait Locus ◽  
Eqtl Mapping ◽  
Trait Locus ◽  
The Impact

AbstractSingle-cell RNA-sequencing (scRNA-seq) has enabled the unbiased, high-throughput quantification of gene expression specific to cell types and states. With the cost of scRNA-seq decreasing and techniques for sample multiplexing improving, population-scale scRNA-seq, and thus single-cell expression quantitative trait locus (sc-eQTL) mapping, is increasingly feasible. Mapping of sc-eQTL provides additional resolution to study the regulatory role of common genetic variants on gene expression across a plethora of cell types and states, and promises to improve our understanding of genetic regulation across tissues in both health and disease. While previously established methods for bulk eQTL mapping can, in principle, be applied to sc-eQTL mapping, there are a number of open questions about how best to process scRNA-seq data and adapt bulk methods to optimise sc-eQTL mapping. Here, we evaluate the role of different normalisation and aggregation strategies, covariate adjustment techniques, and multiple testing correction methods to establish best practice guidelines. We use both real and simulated datasets across single-cell technologies to systematically assess the impact of these different statistical approaches and provide recommendations for future single-cell eQTL studies that can yield up to twice as many eQTL discoveries as default approaches ported from bulk studies.

scholarly journals CT-FOCS: a novel method for inferring cell type-specific enhancer-promoter maps

2019 ◽  
Author(s):  
Tom Aharon Hait ◽  
Ran Elkon ◽  
Ron Shamir
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Expression Patterns ◽  
Cell Types ◽  
Specific Gene ◽  
Cell Type ◽  
Chromatin Interactions ◽  
Cell Type Specific ◽  
Novel Method ◽  
Validation Scheme

AbstractSpatiotemporal gene expression patterns are governed to a large extent by enhancer elements, typically located distally from their target genes. Identification of enhancer-promoter (EP) links that are specific and functional in individual cell types is a key challenge in understanding gene regulation. We introduce CT-FOCS, a new statistical inference method that utilizes multiple replicates per cell type to infer cell type-specific EP links. Computationally predicted EP links are usually benchmarked against experimentally determined chromatin interactions measured by ChIA-PET and promoter-capture HiC techniques. We expand this validation scheme by using also loops that overlap in their anchor sites. In analyzing 1,366 samples from ENCODE, Roadmap epigenomics and FANTOM5, CT-FOCS inferred highly cell type-specific EP links more accurately than state-of-the-art methods. We illustrate how our inferred EP links drive cell type-specific gene expression and regulation.

scholarly journals Genome-wide heritability analysis of severe malaria susceptibility and resistance reveals evidence of polygenic inheritance

2019 ◽  
Author(s):  
Delesa Damena ◽  
Emile R. Chimusa
Keyword(s):  
Severe Malaria ◽  
Genome Wide Association Study ◽  
Summary Statistics ◽  
Cell Type ◽  
Protein Coding ◽  
Polygenic Inheritance ◽  
Genome Wide ◽  
Cell Type Specific ◽  
Malaria Susceptibility ◽  
Susceptibility And Resistance

ABSTRACTObjectiveEstimating SNP-heritability (h2g) of severe malaria/resistance and its distribution across the genome might shed new light in to the underlying biology.MethodWe investigated h2g of severe malaria susceptibility and resistance from genome-wide association study (GWAS) dataset (sample size =11, 657). We partitioned the h2g in to chromosomes, allele frequencies and annotations. We further examined none-cell type specific and cell type specific enrichments from GWAS-summary statistics.ResultsWe estimated the h2g of severe malaria at 0.21 (se=0.05, p=2.7×10−5), 0.20 (se =0.05, p=7.5×10−5) and 0.17 (se =0.05, p= 7.2×10−4) in Gambian, Kenyan and Malawi populations, respectively. The h2g attributed to the GWAS significant SNPs and the well-known sickle cell (HbS) variant was approximately 0.07 and 0.03, respectively. We prepared African population reference panel and obtained comparable h2g estimate (0.21 (se = 0.02, p< 1×10−5)) from GWAS-summary statistics meta-analysed across the three populations. Partitioning analysis from raw genotype data showed significant enrichment of h2g in protein coding genic SNPs while summary statistics analysis suggests pattern of enrichment in multiple categories.ConclusionWe report for the first time that the heritability of malaria susceptibility and resistance is largely ascribed by common SNPs and the causal variants are overrepresented in protein coding regions of the genome. Overall, our results suggest that malaria susceptibility and resistance is a polygenic trait. Further studies with larger sample sizes are needed to better understand the underpinning genetics of resistance and susceptibility to severe malaria.

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