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 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 Comprehensive Multiple eQTL Detection and Its Application to GWAS Interpretation

Genetics ◽  
2019 ◽  
Vol 212 (3) ◽  
pp. 905-918 ◽  
Author(s):  
Biao Zeng ◽  
Luke R. Lloyd-Jones ◽  
Grant W. Montgomery ◽  
Andres Metspalu ◽  
Tonu Esko ◽  
...  
Keyword(s):  
Gene Expression ◽  
Peripheral Blood ◽  
Complex Traits ◽  
Regulation Of Gene Expression ◽  
Credible Interval ◽  
Causal Variant ◽  
Human Phenotype ◽  
Disease States ◽  
Heart Study ◽  
Microarray Studies

Expression QTL (eQTL) detection has emerged as an important tool for unraveling the relationship between genetic risk factors and disease or clinical phenotypes. Most studies are predicated on the assumption that only a single causal variant explains the association signal in each interval. This greatly simplifies the statistical modeling, but is liable to biases in scenarios where multiple local causal-variants are responsible. Here, our primary goal was to address the prevalence of secondary cis-eQTL signals regulating peripheral blood gene expression locally, utilizing two large human cohort studies, each >2500 samples with accompanying whole genome genotypes. The CAGE (Consortium for the Architecture of Gene Expression) dataset is a compendium of Illumina microarray studies, and the Framingham Heart Study is a two-generation Affymetrix dataset. We also describe Bayesian colocalization analysis of the extent of sharing of cis-eQTL detected in both studies as well as with the BIOS RNAseq dataset. Stepwise conditional modeling demonstrates that multiple eQTL signals are present for ∼40% of over 3500 eGenes in both microarray datasets, and that the number of loci with additional signals reduces by approximately two-thirds with each conditioning step. Although <20% of the peak signals across platforms fine map to the same credible interval, the colocalization analysis finds that as many as 50–60% of the primary eQTL are actually shared. Subsequently, colocalization of eQTL signals with GWAS hits detected 1349 genes whose expression in peripheral blood is associated with 591 human phenotype traits or diseases, including enrichment for genes with regulatory functions. At least 10%, and possibly as many as 40%, of eQTL-trait colocalized signals are due to nonprimary cis-eQTL peaks, but just one-quarter of these colocalization signals replicated across the gene expression datasets. Our results are provided as a web-based resource for visualization of multi-site regulation of gene expression and its association with human complex traits and disease states.

scholarly journals Inferring Relevant Cell Types for Complex Traits by Using Single-Cell Gene Expression

2017 ◽  
Vol 101 (5) ◽  
pp. 686-699 ◽  
Author(s):  
Diego Calderon ◽  
Anand Bhaskar ◽  
David A. Knowles ◽  
David Golan ◽  
Towfique Raj ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Complex Traits ◽  
Cell Types ◽  
Cell Gene Expression ◽  
Cell Gene

scholarly journals Using Single Nucleotide Variations in Single-Cell RNA-Seq to Identify Subpopulations and Genotype-phenotype Linkage

2016 ◽  
Author(s):  
Olivier Poirion ◽  
Xun Zhu ◽  
Travers Ching ◽  
Lana X. Garmire
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Cells ◽  
Transcript Abundance ◽  
Rna Seq ◽  
Linear Modeling ◽  
Modeling Framework ◽  
Single Nucleotide ◽  
Single Nucleotide Variations

AbstractDespite its popularity, characterization of subpopulations with transcript abundance is subject to a significant amount of noise. We propose to use effective and expressed nucleotide variations (eeSNVs) from scRNA-seq as alternative features for tumor subpopulation identification. We developed a linear modeling framework, SSrGE, to link eeSNVs associated with gene expression. In all the datasets tested, eeSNVs achieve better accuracies than gene expression for identifying subpopulations. Previously validated cancer-relevant genes are also highly ranked, confirming the significance of the method. Moreover, SSrGE is capable of analyzing coupled DNA-seq and RNA-seq data from the same single cells, demonstrating its value in integrating multi-omics single cell techniques. In summary, SNV features from scRNA-seq data have merits for both subpopulation identification and linkage of genotype-phenotype relationship. The method SSrGE is available at https://github.com/lanagarmire/SSrGE.

scholarly journals Effect sizes of causal variants for gene expression and complex traits differ between populations

2021 ◽  
Author(s):  
Roshni A. Patel ◽  
Shaila A. Musharoff ◽  
Jeffrey P. Spence ◽  
Harold Pimentel ◽  
Catherine Tcheandjieu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Association Studies ◽  
Causal Variant ◽  
Effect Sizes ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Polygenic Scores ◽  
Causal Variants ◽  
Variant Effect

Despite the growing number of genome-wide association studies (GWAS) for complex traits, it remains unclear whether effect sizes of causal genetic variants differ between populations. In principle, effect sizes of causal variants could differ between populations due to gene-by-gene or gene-by-environment interactions. However, comparing causal variant effect sizes is challenging: it is difficult to know which variants are causal, and comparisons of variant effect sizes are confounded by differences in linkage disequilibrium (LD) structure between ancestries. Here, we develop a method to assess causal variant effect size differences that overcomes these limitations. Specifically, we leverage the fact that segments of European ancestry shared between European-American and admixed African-American individuals have similar LD structure, allowing for unbiased comparisons of variant effect sizes in European ancestry segments. We apply our method to two types of traits: gene expression and low-density lipoprotein cholesterol (LDL-C). We find that causal variant effect sizes for gene expression are significantly different between European-Americans and African-Americans; for LDL-C, we observe a similar point estimate although this is not significant, likely due to lower statistical power. Cross-population differences in variant effect sizes highlight the role of genetic interactions in trait architecture and will contribute to the poor portability of polygenic scores across populations, reinforcing the importance of conducting GWAS on individuals of diverse ancestries and environments.

scholarly journals Scalable unified framework of total and allele-specific counts for cis-QTL, fine-mapping, and prediction

2020 ◽  
Author(s):  
Yanyu Liang ◽  
François Aguet ◽  
Alvaro Barbeira ◽  
Kristin Ardlie ◽  
Hae Kyung Im
Keyword(s):  
Gene Expression ◽  
Fine Mapping ◽  
Complex Traits ◽  
Association Studies ◽  
Average Power ◽  
Specific Gene ◽  
Genome Wide Association Studies ◽  
Unified Framework ◽  
Causal Genes ◽  
Allele Specific

AbstractGenome-wide association studies (GWAS) have been highly successful in identifying genomic loci associated with complex traits. However, identification of the causal genes that mediate these associations remains challenging, and many approaches integrating transcriptomic data with GWAS have been proposed. However, there currently exist no computationally scalable methods that integrate total and allele-specific gene expression to maximize power to detect genetic effects on gene expression. Here, we describe a unified framework that is scalable to studies with thousands of samples. Using simulations and data from GTEx, we demonstrate an average power gain equivalent to a 29% increase in sample size for genes with sufficient allele-specific read coverage. We provide a suite of freely available tools, mixQTL, mixFine, and mixPred, that apply this framework for mapping of quantitative trait loci, fine-mapping, and prediction.

scholarly journals Improved methods for multi-trait fine mapping of pleiotropic risk loci

2016 ◽  
Author(s):  
Gleb Kichaev ◽  
Megan Roytman ◽  
Ruth Johnson ◽  
Eleazar Eskin ◽  
Sara Lindstroem ◽  
...  
Keyword(s):  
Fine Mapping ◽  
Complex Traits ◽  
Functional Annotation ◽  
Large Scale ◽  
Association Studies ◽  
Real Data ◽  
Causal Variant ◽  
Genome Wide Association Studies ◽  
Annotation Data ◽  
Association Data

AbstractGenome-wide association studies (GWAS) have identified thousands of regions in the genome that contain genetic variants that increase risk for complex traits and diseases. However, the variants uncovered in GWAS are typically not biologicaly causal, but rather, correlated to the true causal variant through linkage disequilibrium (LD). To discern the true causal variant(s), a variety of statistical fine-mapping methods have been proposed to prioritize variants for functional validation. In this work we introduce a new approach, fastPAINTOR, that leverages evidence across correlated traits, as well as functional annotation data, to improve fine-mapping accuracy at pleiotropic risk loci. To improve computational efficiency, we describe an new importance sampling scheme to perform model inference. First, we demonstrate in simulations that by leveraging functional annotation data, fastPAINTOR increases fine-mapping resolution relative to existing methods. Next, we show that jointly modeling pleiotropic risk regions improves fine-mapping resolution relative to standard single trait and pleiotropic fine mapping strategies. We report a reduction in the number of SNPs required for follow-up in order to capture 90% of the causal variants from 23 SNPs per locus using a single trait to 12 SNPs when fine-mapping two traits simultaneously. Finally, we analyze summary association data from a large-scale GWAS of lipids and show that these improvements are largely sustained in real data.

scholarly journals Leveraging allele-specific expression to refine fine-mapping for eQTL studies

2018 ◽  
Author(s):  
Jennifer Zou ◽  
Farhad Hormozdiari ◽  
Brandon Jew ◽  
Jason Ernst ◽  
Jae Hoon Sul ◽  
...  
Keyword(s):  
Gene Expression ◽  
Fine Mapping ◽  
Complex Traits ◽  
Association Studies ◽  
Reduction Rate ◽  
Genome Wide Association Studies ◽  
Specific Expression ◽  
Allele Specific Expression ◽  
Allele Specific ◽  
Causal Variants

AbstractMany disease risk loci identified in genome-wide association studies are present in non-coding regions of the genome. It is hypothesized that these variants affect complex traits by acting as expression quantitative trait loci (eQTLs) that influence expression of nearby genes. This indicates that many causal variants for complex traits are likely to be causal variants for gene expression. Hence, identifying causal variants for gene expression is important for elucidating the genetic basis of not only gene expression but also complex traits. However, detecting causal variants is challenging due to complex genetic correlation among variants known as linkage disequilibrium (LD) and the presence of multiple causal variants within a locus. Although several fine-mapping approaches have been developed to overcome these challenges, they may produce large sets of putative causal variants when true causal variants are in high LD with many non-causal variants. In eQTL studies, there is an additional source of information that can be used to improve fine-mapping called allele-specific expression (ASE) that measures imbalance in gene expression due to different alleles. In this work, we develop a novel statistical method that leverages both ASE and eQTL information to detect causal variants that regulate gene expression. We illustrate through simulations and application to the Genotype-Tissue Expression (GTEx) dataset that our method identifies the true causal variants with higher specificity than an approach that uses only eQTL information. In the GTEx dataset, our method achieves the median reduction rate of 11% in the number of putative causal [email protected], [email protected]

scholarly journals Inferring relevant cell types for complex traits using single-cell gene expression

2017 ◽  
Author(s):  
Diego Calderon ◽  
Anand Bhaskar ◽  
David A. Knowles ◽  
David Golan ◽  
Towfique Raj ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Complex Traits ◽  
Late Onset ◽  
Cell Types ◽  
Rna Seq ◽  
Cortical Cells ◽  
Functional Regions ◽  
Cell Gene Expression ◽  
Cell Gene

AbstractPrevious studies have prioritized trait-relevant cell types by looking for an enrichment of GWAS signal within functional regions. However, these studies are limited in cell resolution by the lack of functional annotations from difficult-to-characterize or rare cell populations. Measurement of single-cell gene expression has become a popular method for characterizing novel cell types, and yet, hardly any work exists linking single-cell RNA-seq to phenotypes of interest. To address this deficiency, we present RolyPoly, a regression-based polygenic model that can prioritize trait-relevant cell types and genes from GWAS summary statistics and single-cell RNA-seq. We demonstrate RolyPoly’s accuracy through simulation and validate previously known tissue-trait associations. We discover a significant association between microglia and late-onset Alzheimer’s disease, and an association between oligodendrocytes and replicating fetal cortical cells with schizophrenia. Additionally, RolyPoly computes a trait-relevance score for each gene which reflects the importance of expression specific to a cell type. We found that differentially expressed genes in the prefrontal cortex of Alzheimer’s patients were significantly enriched for highly ranked genes by RolyPoly gene scores. Overall, our method represents a powerful framework for understanding the effect of common variants on cell types contributing to complex traits.

scholarly journals High-throughput imaging of mRNA at the single-cell level in human primary immune cells

2020 ◽  
Author(s):  
Manasi Gadkari ◽  
Jing Sun ◽  
Adrian Carcamo ◽  
Hugh Alessi ◽  
Zonghui Hu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Immune Cells ◽  
Single Cells ◽  
Transcript Abundance ◽  
Hybridization Chain Reaction ◽  
Single Cell Level ◽  
Cell Level ◽  
Experimental Conditions ◽  
General Applicability

AbstractMeasurement of gene expression at the single-cell level has led to important advances in the study of transcriptional regulation programs in healthy and disease states. In particular, single-cell gene expression approaches have shed light on the high level of transcriptional heterogeneity of individual cells, both at baseline and in response to experimental or environmental perturbations. We have developed a method for High-Content Imaging (HCI)-based quantification of transcript abundance at the single-cell level in primary human immune cells and have validated its performance under multiple experimental conditions to demonstrate its general applicability. This method, which we abbreviate as hcHCR, combines the high sensitivity of the hybridization chain reaction (HCR) for the visualization of mRNA molecules in single cells, with the speed, scalability, and technical reproducibility of HCI. We first tested eight microscopy-compatible attachment substrates for short-term culture of primary human B cells, T cells, monocytes, or neutrophils. We then miniaturized HCR in a 384-well format and documented the ability of the method to detect increased or decreased transcript abundance at the single-cell level in thousands of cells for each experimental condition by HCI. Furthermore, we demonstrated the feasibility of multiplexing gene expression measurements by simultaneously assaying the abundance of two transcripts per cell, both at baseline and in response to an experimental stimulus. Finally, we tested the robustness of the assay to technical and biological variation. We anticipate that hcHCR will be a suitable and cost-effective assay for low- to medium-throughput chemical, genetic or functional genomic screens in primary human cells, with the possibility of performing personalized screens or screens on cells obtained from patients with a specific disease.

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