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Assessing the ability of various genomic features to prioritize causal non-coding variants associated with diseases and traits

ABSTRACT: Assessing the ability of various genomic features to prioritize causal non-coding variants associated with diseases and traits

PROVIDER: PRJNA563093 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR10050400.fastq.gz	Fastqsanger.gz
	SRR10050500.fastq.gz	Fastqsanger.gz
	SRR10050600.fastq.gz	Fastqsanger.gz
	SRR10050700.fastq.gz	Fastqsanger.gz
	SRR10050800.fastq.gz	Fastqsanger.gz

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Assessing the ability of various genomic features to prioritize causal non-coding variants associated with diseases and traits [MPRA]

Project description:Assessing the ability of various genomic features to prioritize causal non-coding variants associated with diseases and traits [MPRA]

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Assessing the ability of various genomic features to prioritize causal non-coding variants associated with diseases and traits [ATAC-seq]

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Assessing the ability of various genomic features to prioritize causal non-coding variants associated with diseases and traits [MPRA]

Project description:Genome-wide association studies have associated thousands of genetic variants with complex traits and diseases, but pinpointing the causal variant(s) among those in tight linkage disequilibrium with each associated variant remains a major challenge. Here, we used seven experimental assays to characterize all common variants at the multiple disease-associated TNFAIP3 locus in three disease-relevant immune cell types, based on a set of features related to regulatory potential. Trait/disease-associated variants were enriched among SNPs prioritized based on either: (1) residing within CRISPRi-sensitive regulatory regions, or (2) localizing in a chromatin accessible region while displaying allele-specific reporter activity. Of the 15 trait/disease-associated haplotypes at TNFAIP3, 9 had at least one variant meeting one or both of these criteria, with 3 of these haplotypes having a single prioritized variant. 5 of the 9 prioritized variants were further supported by genetic fine-mapping in our and other studies. Our work provides evidence for the efficacy and limitations of strategies for prioritizing disease- and trait-associated genetic variants.

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Assessing the ability of various genomic features to prioritize causal non-coding variants associated with diseases and traits [CRISPR guide-seq]

Project description:Assessing the ability of various genomic features to prioritize causal non-coding variants associated with diseases and traits [CRISPR guide-seq]

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Assessing the ability of various genomic features to prioritize causal non-coding variants associated with diseases and traits [ATAC-seq]

2020-02-11 | GSE136686 | GEO

Assessing the ability of various genomic features to prioritize causal non-coding variants associated with diseases and traits [CRISPR guide-seq]

2020-02-11 | GSE136693 | GEO

Assessing the ability of various genomic features to prioritize causal non-coding variants associated with diseases and traits

Project description:This SuperSeries is composed of the SubSeries listed below.

2020-02-11 | GSE136703 | GEO

FABIO: TWAS fine-mapping to prioritize causal genes for binary traits.

Project description:Transcriptome-wide association studies (TWAS) have emerged as a powerful tool for identifying gene-trait associations by integrating gene expression mapping studies with genome-wide association studies (GWAS). While most existing TWAS approaches focus on marginal analyses through examining one gene at a time, recent developments in TWAS fine-mapping methods enable the joint modeling of multiple genes to refine the identification of potentially causal ones. However, these fine-mapping methods have primarily focused on modeling quantitative traits and examining local genomic regions, leading to potentially suboptimal performance. Here, we present FABIO, a TWAS fine-mapping method specifically designed for binary traits that is capable of modeling all genes jointly on an entire chromosome. FABIO employs a probit model to directly link the genetically regulated expression (GReX) of genes to binary outcomes while taking into account the GReX correlation among all genes residing on a chromosome. As a result, FABIO effectively controls false discoveries while offering substantial power gains over existing TWAS fine-mapping approaches. We performed extensive simulations to evaluate the performance of FABIO and applied it for in-depth analyses of six binary disease traits in the UK Biobank. In the real datasets, FABIO significantly reduced the size of the causal gene sets by 27.9%-36.9% over existing approaches across traits. Leveraging its improved power, FABIO successfully prioritized multiple potentially causal genes associated with the diseases, including GATA3 for asthma, ABCG2 for gout, and SH2B3 for hypertension. Overall, FABIO represents an effective tool for TWAS fine-mapping of disease traits.

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Mapping short tandem repeats for liver gene expression traits helps prioritize potential causal variants for complex traits in pigs.

Project description:BackgroundShort tandem repeats (STRs) were recently found to have significant impacts on gene expression and diseases in humans, but their roles on gene expression and complex traits in pigs remain unexplored. This study investigates the effects of STRs on gene expression in liver tissues based on the whole-genome sequences and RNA-Seq data of a discovery cohort of 260 F6 individuals and a validation population of 296 F7 individuals from a heterogeneous population generated from crosses among eight pig breeds.ResultsWe identified 5203 and 5868 significantly expression STRs (eSTRs, FDR < 1%) in the F6 and F7 populations, respectively, most of which could be reciprocally validated (π1 = 0.92). The eSTRs explained 27.5% of the cis-heritability of gene expression traits on average. We further identified 235 and 298 fine-mapped STRs through the Bayesian fine-mapping approach in the F6 and F7 pigs, respectively, which were significantly enriched in intron, ATAC peak, compartment A and H3K4me3 regions. We identified 20 fine-mapped STRs located in 100 kb windows upstream and downstream of published complex trait-associated SNPs, which colocalized with epigenetic markers such as H3K27ac and ATAC peaks. These included eSTR of the CLPB, PGLS, PSMD6 and DHDH genes, which are linked with genome-wide association study (GWAS) SNPs for blood-related traits, leg conformation, growth-related traits, and meat quality traits, respectively.ConclusionsThis study provides insights into the effects of STRs on gene expression traits. The identified eSTRs are valuable resources for prioritizing causal STRs for complex traits in pigs.

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Single-cell multiome of the human retina and deep learning nominate causal variants in complex eye diseases

Project description:We present a joint single-cell atlas of gene expression and chromatin accessibility of the adult human retina. We integrate this atlas with a HiChIP enhancer connectome, expression quantitative trait loci (eQTL) data, and base-resolution deep learning models to predict noncoding variants with causal roles in eye disease.

2022-03-09 | GSE196235 | GEO

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