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Prioritizing long range interactions in noncoding regions using GWAS and deletions perturbed TADs.

ABSTRACT: Genome-wide association studies (GWAS) have contributed significantly to predisposing the disease etiology by associating single nucleotide polymorphisms (SNPs) with complex diseases. However, most GWAS-SNPs are in the noncoding regions that may affect distal genes via long range enhancer-promoter interactions. Thus, the common practice on GWAS discoveries cannot fully reveal the molecular mechanisms underpinning complex diseases. It is known that perturbations of topological associated domains (TADs) lead to long range interactions which underlie disease etiology. To identify the probable long range interactions in noncoding regions via GWAS and TADs perturbed by deletions, we integrated datasets from GWAS-SNPs, enhancers, TADs, and deletions. After ranking and clustering, we prioritized 201,132 high confident pairs of GWAS-SNPs and target genes. In this study, we performed a systematic inference on noncoding regions via GWAS-SNPs and deletion-perturbed TADs to boost GWAS discovery power. The high confident pairs of GWAS-SNPs and target genes (SE-Gs) provide the promising candidates to understand the molecular mechanisms underlying complex diseases with emphasis on the three-dimensional genome.

SUBMITTER: Liu X

PROVIDER: S-EPMC7642798 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Prioritizing long range interactions in noncoding regions using GWAS and deletions perturbed TADs.

Liu Xuanshi X Xu Wenjian W Leng Fei F Hao Chanjuan C Kolora Sree Rohit Raj SRR Li Wei W

Computational and structural biotechnology journal 20201021

Genome-wide association studies (GWAS) have contributed significantly to predisposing the disease etiology by associating single nucleotide polymorphisms (SNPs) with complex diseases. However, most GWAS-SNPs are in the noncoding regions that may affect distal genes via long range enhancer-promoter interactions. Thus, the common practice on GWAS discoveries cannot fully reveal the molecular mechanisms underpinning complex diseases. It is known that perturbations of topological associated domains ...[more]

PMID: 33209206

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Project description:Abstract Introduction As new late‐onset Alzheimer's disease (LOAD) genetic risk loci are identified and brain cell–type specific omics data becomes available, there is an unmet need for a bioinformatics framework to prioritize genes and variants for testing in single‐cell molecular profiling experiments and validation using disease models and gene editing technologies. Prior work has characterized and prioritized active enhancers located in LOAD‐genome‐wide association study (GWAS) regions and their potential interactions with candidate genes. The current study extends this work by focusing on single nucleotide polymorphisms (SNPs) within these LOAD enhancers and their impact on altering transcription factor (TF) binding. The proposed bioinformatics pipeline progresses from SNPs located in LOAD‐GWAS regions to a filtered set of candidate regulatory SNPs that have a predicted strong effect on TF binding. Methods Active enhancers within LOAD‐associated regions were identified and SNPs located in the enhancers were catalogued. SNPs that disrupt TF binding sites were prioritized and the respective TFs were filtered to include only those that were expressed in brain tissues relevant to LOAD. The TFs binding to the corresponding sequence was further confirmed by ChIP‐seq signals. Finally, the high‐priority candidate SNPs were evaluated as expression quantitative trait loci (eQTLs) in disease‐relevant tissues. Results We catalogued 61 strong enhancers in LOAD‐GWAS regions encompassing 326 SNPs and 104 TF binding sites. Seventy‐seven and 78 of the TFs were expressed in brain and monocytes, respectively, out of which 19 TF‐binding sites showed ChIP‐seq signals. Eleven SNPs were found to interrupt with TF binding out of which three SNPs were also significant eQTL. Discussion This study provides a framework to catalogue noncoding variations in enhancers located in LOAD‐GWAS loci and characterize their likelihood to perturb TF binding. The approach integrates multiple data types to characterize and prioritize SNPs for putative regulatory function using single‐cell multi‐omics assays and gene editing.

Project description:Chemotaxis enables cells to systematically approach distant targets that emit a diffusible guiding substance. However, the visual observation of an encounter between a cell and a target does not necessarily indicate the presence of a chemotactic approach mechanism, as even a blindly migrating cell can come across a target by chance. To distinguish between the chemotactic approach and blind migration, we present an objective method that is based on the analysis of time-lapse recorded cell migration trajectories: For each movement step of a cell relative to the position of a potential target, we compute a p value that quantifies the likelihood of the movement direction under the null-hypothesis of blind migration. The resulting distribution of p values, pooled over all recorded cell trajectories, is then compared to an ensemble of reference distributions in which the positions of targets are randomized. First, we validate our method with simulated data, demonstrating that it reliably detects the presence or absence of remote cell-cell interactions. In a second step, we apply the method to data from three-dimensional collagen gels, interspersed with highly migratory natural killer (NK) cells that were derived from two different human donors. We find for one of the donors an attractive interaction between the NK cells, pointing to a cooperative behavior of these immune cells. When adding nearly stationary K562 tumor cells to the system, we find a repulsive interaction between K562 and NK cells for one of the donors. By contrast, we find attractive interactions between NK cells and an IL-15-secreting variant of K562 tumor cells. We therefore speculate that NK cells find wild-type tumor cells only by chance, but are programmed to leave a target quickly after a close encounter. We provide a freely available Python implementation of our p value method that can serve as a general tool for detecting long-range interactions in collective systems of self-driven agents.

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Prioritizing long range interactions in noncoding regions using GWAS and deletions perturbed TADs.

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Prioritizing long range interactions in noncoding regions using GWAS and deletions perturbed TADs.

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