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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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Related Experiment Video

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Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration
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Prioritizing long range interactions in noncoding regions using GWAS and deletions perturbed TADs.

Xuanshi Liu1,2,3, Wenjian Xu1,2,3, Fei Leng1,2,3

  • 1Beijing Key Laboratory for Genetics of Birth Defects, Beijing Pediatric Research Institute, Beijing, China.

Computational and Structural Biotechnology Journal
|November 19, 2020
PubMed
Summary

This study integrates genome-wide association study single nucleotide polymorphisms (GWAS-SNPs) with topological associated domains (TADs) to identify noncoding variants linked to complex diseases. The findings prioritize gene-disease associations, enhancing understanding of the three-dimensional genome in disease etiology.

Keywords:
3D, three-dimensionalDEL, deletionDEL-TAD, TAD borders were interrupted by DEL border interrupted by DELDeletionEnhancerGWAS, genome wide association studyGWAS-SNP, SNP significantly associated with diseases or traits in GWASGenome wide association studyNoncoding region interpretationSE-G, a pair of GWAS-SNP and target geneSNP, single nucleotide polymorphismTAD, topological associated domainTopological associated domain

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Area of Science:

  • Genomics
  • Epigenetics
  • Computational Biology

Background:

  • Genome-wide association studies (GWAS) identify single nucleotide polymorphisms (SNPs) associated with complex diseases.
  • Most GWAS-SNPs reside in noncoding regions, complicating mechanistic interpretation.
  • Topological associated domains (TADs) play a role in regulating long-range gene interactions relevant to disease.

Purpose of the Study:

  • To systematically infer long-range interactions between noncoding GWAS-SNPs and target genes.
  • To enhance the discovery power of GWAS by integrating deletion-perturbed TAD data.
  • To identify high-confidence SNP-gene pairs (SE-Gs) for understanding complex disease mechanisms.

Main Methods:

  • Integration of datasets including GWAS-SNPs, enhancers, TADs, and deletion data.
  • Ranking and clustering algorithms to prioritize potential SNP-gene interactions.
  • Analysis focused on noncoding regions and their influence on distal gene regulation.

Main Results:

  • Prioritization of 201,132 high-confidence pairs of GWAS-SNPs and target genes (SE-Gs).
  • Systematic inference of noncoding region functions through GWAS-SNPs and deletion-perturbed TADs.
  • Identification of promising candidates for elucidating complex disease molecular mechanisms.

Conclusions:

  • The study provides a robust framework for linking noncoding variants to disease through 3D genome architecture.
  • The identified SE-Gs offer valuable insights into the molecular underpinnings of complex diseases.
  • This approach enhances GWAS utility by revealing mechanisms in the noncoding genome.