scholarly journals Fine-mapping within eQTL credible intervals by expression CROP-seq

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Yidan Pan ◽  
Ruoyu Tian ◽  
Ciaran Lee ◽  
Gang Bao ◽  
Greg Gibson
Keyword(s):  
Single Cell ◽  
Fine Mapping ◽  
Genome Wide Association Study ◽  
Disease Risk ◽  
Myeloid Cell ◽  
Transcript Abundance ◽  
Chromatin Accessibility ◽  
Genome Wide ◽  
Credible Intervals ◽  
Causal Variants

Abstract The majority of genome-wide association study (GWAS)-identified SNPs are located in noncoding regions of genes and are likely to influence disease risk and phenotypes by affecting gene expression. Since credible intervals responsible for genome-wide associations typically consist of ≥100 variants with similar statistical support, experimental methods are needed to fine map causal variants. We report here a moderate-throughput approach to identifying regulatory GWAS variants, expression CROP-seq, which consists of multiplex CRISPR-Cas9 genome editing combined with single-cell RNAseq to measure perturbation in transcript abundance. Mutations were induced in the HL60/S4 myeloid cell line nearby 57 SNPs in three genes, two of which, rs2251039 and rs35675666, significantly altered CISD1 and PARK7 expression, respectively, with strong replication and validation in single-cell clones. The sites overlap with chromatin accessibility peaks and define causal variants for inflammatory bowel disease at the two loci. This relatively inexpensive approach should be scalable for broad surveys and is also implementable for the fine mapping of individual genes.

scholarly journals Pitfalls in Single Clone CRISPR-Cas9 Mutagenesis to Fine-Map Regulatory Intervals

Genes ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 504 ◽  
Author(s):  
Ruoyu Tian ◽  
Yidan Pan ◽  
Thomas H. A. Etheridge ◽  
Harshavardhan Deshmukh ◽  
Dalia Gulick ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Fine Mapping ◽  
Complex Traits ◽  
Transcript Abundance ◽  
Causal Variant ◽  
Single Clone ◽  
Credible Intervals ◽  
Replicated Measurement ◽  
Regulatory Snps

The majority of genetic variants affecting complex traits map to regulatory regions of genes, and typically lie in credible intervals of 100 or more SNPs. Fine mapping of the causal variant(s) at a locus depends on assays that are able to discriminate the effects of polymorphisms or mutations on gene expression. Here, we evaluated a moderate-throughput CRISPR-Cas9 mutagenesis approach, based on replicated measurement of transcript abundance in single-cell clones, by deleting candidate regulatory SNPs, affecting four genes known to be affected by large-effect expression Quantitative Trait Loci (eQTL) in leukocytes, and using Fluidigm qRT-PCR to monitor gene expression in HL60 pro-myeloid human cells. We concluded that there were multiple constraints that rendered the approach generally infeasible for fine mapping. These included the non-targetability of many regulatory SNPs, clonal variability of single-cell derivatives, and expense. Power calculations based on the measured variance attributable to major sources of experimental error indicated that typical eQTL explaining 10% of the variation in expression of a gene would usually require at least eight biological replicates of each clone. Scanning across credible intervals with this approach is not recommended.

scholarly journals High-throughput single-cell chromatin accessibility CRISPR screens enable unbiased identification of regulatory networks in cancer

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Sarah E. Pierce ◽  
Jeffrey M. Granja ◽  
William J. Greenleaf
Keyword(s):  
Transcription Factor ◽  
Single Cell ◽  
Cancer Cells ◽  
High Throughput ◽  
Regulatory Networks ◽  
Temporal Dynamics ◽  
Chromatin Accessibility ◽  
Regulatory Regions ◽  
Factor Binding ◽  
Genome Wide

AbstractChromatin accessibility profiling can identify putative regulatory regions genome wide; however, pooled single-cell methods for assessing the effects of regulatory perturbations on accessibility are limited. Here, we report a modified droplet-based single-cell ATAC-seq protocol for perturbing and evaluating dynamic single-cell epigenetic states. This method (Spear-ATAC) enables simultaneous read-out of chromatin accessibility profiles and integrated sgRNA spacer sequences from thousands of individual cells at once. Spear-ATAC profiling of 104,592 cells representing 414 sgRNA knock-down populations reveals the temporal dynamics of epigenetic responses to regulatory perturbations in cancer cells and the associations between transcription factor binding profiles.

scholarly journals CAUSALdb: a database for disease/trait causal variants identified using summary statistics of genome-wide association studies

2019 ◽  
Author(s):  
Jianhua Wang ◽  
Dandan Huang ◽  
Yao Zhou ◽  
Hongcheng Yao ◽  
Huanhuan Liu ◽  
...  
Keyword(s):  
Fine Mapping ◽  
Genetic Variants ◽  
Association Studies ◽  
Complex Trait ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Genome Wide ◽  
Credible Sets ◽  
Causal Variants

Abstract Genome-wide association studies (GWASs) have revolutionized the field of complex trait genetics over the past decade, yet for most of the significant genotype-phenotype associations the true causal variants remain unknown. Identifying and interpreting how causal genetic variants confer disease susceptibility is still a big challenge. Herein we introduce a new database, CAUSALdb, to integrate the most comprehensive GWAS summary statistics to date and identify credible sets of potential causal variants using uniformly processed fine-mapping. The database has six major features: it (i) curates 3052 high-quality, fine-mappable GWAS summary statistics across five human super-populations and 2629 unique traits; (ii) estimates causal probabilities of all genetic variants in GWAS significant loci using three state-of-the-art fine-mapping tools; (iii) maps the reported traits to a powerful ontology MeSH, making it simple for users to browse studies on the trait tree; (iv) incorporates highly interactive Manhattan and LocusZoom-like plots to allow visualization of credible sets in a single web page more efficiently; (v) enables online comparison of causal relations on variant-, gene- and trait-levels among studies with different sample sizes or populations and (vi) offers comprehensive variant annotations by integrating massive base-wise and allele-specific functional annotations. CAUSALdb is freely available at http://mulinlab.org/causaldb.

scholarly journals Prefrontal cortex eQTLs/mQTLs enriched in genetic variants associated with alcohol use disorder and other diseases

Epigenomics ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 789-800
Author(s):  
Honghuang Lin ◽  
Fan Wang ◽  
Andrew J Rosato ◽  
Lindsay A Farrer ◽  
David C Henderson ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Alcohol Use ◽  
Genetic Variants ◽  
Alcohol Use Disorder ◽  
Genome Wide Association Study ◽  
Disease Risk ◽  
Genome Wide ◽  
Morphine Addiction ◽  
Methylation Patterns ◽  
Methylation Quantitative Trait Loci

Aim: This study aimed to investigate the function of genome-wide association study (GWAS)-identified variants associated with alcohol use disorder (AUD)/comorbid psychiatric disorders. Materials & methods: Genome-wide genotype, transcriptome and DNA methylome data were obtained from postmortem prefrontal cortex (PFC) of 48 Caucasians (24 AUD cases/24 controls). Expression/methylation quantitative trait loci (eQTL/mQTL) were identified and their enrichment in GWAS signals for the above disorders were analyzed. Results: PFC cis-eQTLs (923 from cases+controls, 27 from cases and 98 from controls) and cis-mQTLs (9,932 from cases+controls, 264 from cases and 695 from controls) were enriched in GWAS-identified genetic variants for the above disorders. Cis-eQTLs from AUD cases were mapped to morphine addiction-related genes. Conclusion: PFC cis-eQTLs/ cis-mQTLs influence gene expression/DNA methylation patterns, thus increasing the disease risk.

scholarly journals Varmole: a biologically drop-connect deep neural network model for prioritizing disease risk variants and genes

2020 ◽  
Author(s):  
Nam D Nguyen ◽  
Ting Jin ◽  
Daifeng Wang
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Molecular Mechanisms ◽  
Genome Wide Association Study ◽  
Disease Risk ◽  
Supplementary Information ◽  
Neural Network Architecture ◽  
Risk Variants ◽  
Genome Wide

Abstract Summary Population studies such as genome-wide association study have identified a variety of genomic variants associated with human diseases. To further understand potential mechanisms of disease variants, recent statistical methods associate functional omic data (e.g. gene expression) with genotype and phenotype and link variants to individual genes. However, how to interpret molecular mechanisms from such associations, especially across omics, is still challenging. To address this problem, we developed an interpretable deep learning method, Varmole, to simultaneously reveal genomic functions and mechanisms while predicting phenotype from genotype. In particular, Varmole embeds multi-omic networks into a deep neural network architecture and prioritizes variants, genes and regulatory linkages via biological drop-connect without needing prior feature selections. Availability and implementation Varmole is available as a Python tool on GitHub at https://github.com/daifengwanglab/Varmole. Supplementary information Supplementary data are available at Bioinformatics online.

Identification of ST3AGL4, MFHAS1, CSNK2A2 and CD226 as loci associated with systemic lupus erythematosus (SLE) and evaluation of SLE genetics in drug repositioning

2018 ◽  
Vol 77 (7) ◽  
pp. 1078-1084 ◽  
Author(s):  
Yong-Fei Wang ◽  
Yan Zhang ◽  
Zhengwei Zhu ◽  
Ting-You Wang ◽  
David L Morris ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Fine Mapping ◽  
Lupus Erythematosus ◽  
Drug Repositioning ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Systemic Lupus ◽  
Genome Wide ◽  
Causal Variants

ObjectivesSystemic lupus erythematosus (SLE) is a prototype autoimmune disease with a strong genetic component in its pathogenesis. Through genome-wide association studies (GWAS), we recently identified 10 novel loci associated with SLE and uncovered a number of suggestive loci requiring further validation. This study aimed to validate those loci in independent cohorts and evaluate the role of SLE genetics in drug repositioning.MethodsWe conducted GWAS and replication studies involving 12 280 SLE cases and 18 828 controls, and performed fine-mapping analyses to identify likely causal variants within the newly identified loci. We further scanned drug target databases to evaluate the role of SLE genetics in drug repositioning.ResultsWe identified three novel loci that surpassed genome-wide significance, including ST3AGL4 (rs13238909, pmeta=4.40E-08), MFHAS1 (rs2428, pmeta=1.17E-08) and CSNK2A2 (rs2731783, pmeta=1.08E-09). We also confirmed the association of CD226 locus with SLE (rs763361, pmeta=2.45E-08). Fine-mapping and functional analyses indicated that the putative causal variants in CSNK2A2 locus reside in an enhancer and are associated with expression of CSNK2A2 in B-lymphocytes, suggesting a potential mechanism of association. In addition, we demonstrated that SLE risk genes were more likely to be interacting proteins with targets of approved SLE drugs (OR=2.41, p=1.50E-03) which supports the role of genetic studies to repurpose drugs approved for other diseases for the treatment of SLE.ConclusionThis study identified three novel loci associated with SLE and demonstrated the role of SLE GWAS findings in drug repositioning.

scholarly journals Combined genetic analysis of juvenile idiopathic arthritis clinical subtypes identifies novel risk loci, target genes and key regulatory mechanisms

2020 ◽  
pp. annrheumdis-2020-218481 ◽  
Author(s):  
Elena López-Isac ◽  
Samantha L Smith ◽  
Miranda C Marion ◽  
Abigail Wood ◽  
Marc Sudman ◽  
...  
Keyword(s):  
Juvenile Idiopathic Arthritis ◽  
Fine Mapping ◽  
Target Genes ◽  
Genome Wide Association Study ◽  
Disease Risk ◽  
Enrichment Analysis ◽  
Joint Analysis ◽  
Integrative Approach ◽  
Nucleotide Polymorphisms ◽  
A Genome

ObjectivesJuvenile idiopathic arthritis (JIA) is the most prevalent form of juvenile rheumatic disease. Our understanding of the genetic risk factors for this disease is limited due to low disease prevalence and extensive clinical heterogeneity. The objective of this research is to identify novel JIA susceptibility variants and link these variants to target genes, which is essential to facilitate the translation of genetic discoveries to clinical benefit.MethodsWe performed a genome-wide association study (GWAS) in 3305 patients and 9196 healthy controls, and used a Bayesian model selection approach to systematically investigate specificity and sharing of associated loci across JIA clinical subtypes. Suggestive signals were followed-up for meta-analysis with a previous GWAS (2751 cases/15 886 controls). We tested for enrichment of association signals in a broad range of functional annotations, and integrated statistical fine-mapping and experimental data to identify target genes.ResultsOur analysis provides evidence to support joint analysis of all JIA subtypes with the identification of five novel significant loci. Fine-mapping nominated causal single nucleotide polymorphisms with posterior inclusion probabilities ≥50% in five JIA loci. Enrichment analysis identified RELA and EBF1 as key transcription factors contributing to disease risk. Our integrative approach provided compelling evidence to prioritise target genes at six loci, highlighting mechanistic insights for the disease biology and IL6ST as a potential drug target.ConclusionsIn a large JIA GWAS, we identify five novel risk loci and describe potential function of JIA association signals that will be informative for future experimental works and therapeutic strategies.

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