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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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Comparing Copy Number Variations and SNPs02:26

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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Pleiotropy01:33

Pleiotropy

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Pleiotropy is the phenomenon in which a single gene impacts multiple, seemingly unrelated phenotypic traits. For example, defects in the SOX10 gene cause Waardenburg Syndrome Type 4, or WS4, which can cause defects in pigmentation, hearing impairments, and an absence of intestinal contractions necessary for elimination. This diversity of phenotypes results from the expression pattern of SOX10 in early embryonic and fetal development. SOX10 is found in neural crest cells that form melanocytes,...
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Position-effect Variegation02:32

Position-effect Variegation

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In 1928, a German botanist Emil Heitz observed the moss nuclei with a DNA binding dye. He observed that while some chromatin regions decondense and spread out in the interphase nucleus, others do not. He termed them euchromatin and heterochromatin, respectively. He proposed that the heterochromatin regions reflect a functionally inactive state of the genome. It was later confirmed that heterochromatin is transcriptionally repressed, and euchromatin is transcriptionally active chromatin.
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Related Experiment Video

Updated: May 20, 2025

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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SC-VAR: a computational tool for interpreting polygenic disease risks using single-cell epigenomic data.

Gefei Zhao1,2, Binbin Lai1,2,3,4

  • 1Institute of Medical Technology, Peking University Health Science Center, 38 Xueyuan Rd, Hai Dian Qu, Beijing 100191, China.

Briefings in Bioinformatics
|March 24, 2025
PubMed
Summary

Genome-Wide Association Studies (GWAS) often struggle to interpret noncoding variants. SC-VAR integrates single-cell epigenomic data to improve the identification of disease-associated genes and cell types.

Keywords:
disease relevance scoregenome-wide association studies (GWAS)noncoding variant annotationpolygenic disease riskssingle-cell epigenomics

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Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

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Area of Science:

  • Genomics
  • Computational Biology
  • Systems Biology

Background:

  • Interpreting noncoding variants from Genome-Wide Association Studies (GWAS) is a major challenge.
  • Conventional tools fail to adequately capture the spatial and cell-type specificity of cis-regulatory elements (CREs).
  • Existing methods lack integration of single-cell epigenomic information for comprehensive variant annotation.

Purpose of the Study:

  • To present SC-VAR, a novel computational tool for enhancing the interpretation of disease-associated risks from GWAS.
  • To leverage single-cell epigenomic data for predicting functional outcomes of both coding and noncoding variants.
  • To identify susceptible cell types, CREs, and target genes associated with disease risk.

Main Methods:

  • Development of the SC-VAR computational tool.
  • Integration of single-cell epigenomic data with GWAS data.
  • Prediction of functional outcomes including risk genes, pathways, and cell types.

Main Results:

  • SC-VAR outperforms state-of-the-art methods in predicting validated disease-related genes and pathways.
  • The tool successfully identifies disease-susceptible cell types and their associated CREs and target genes.
  • SC-VAR captures disease risks across human tissues and developmental stages.

Conclusions:

  • SC-VAR significantly enhances the interpretation of GWAS findings by incorporating single-cell epigenomic data.
  • The tool provides a more comprehensive understanding of disease mechanisms by identifying specific cell types and regulatory elements involved.
  • SC-VAR has the potential to advance research in complex diseases across diverse tissues and life stages.