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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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Capturing Chromosome Conformation Across Length Scales
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Extending partial haplotypes to full genome haplotypes using chromosome conformation capture data.

Shay Ben-Elazar1, Benny Chor2, Zohar Yakhini3

  • 1Department of Computer Science, Tel-Aviv University, Israel Microsoft R&D, HerzlyiaIsrael.

Bioinformatics (Oxford, England)
|September 3, 2016
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Summary
This summary is machine-generated.

This study introduces a new computational framework to accurately phase whole-genome haplotypes and create phased Hi-C maps. It integrates short-range haplotypes and Hi-C data for improved genomic analysis.

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Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.
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Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Biology

Background:

  • Allelic interactions influence inherited traits and disease risk.
  • Current sequencing technologies have limitations in determining long-range haplotypes.
  • Hi-C techniques provide insights into genome 3D structure but struggle with phased homologous chromosome data.

Purpose of the Study:

  • To develop a robust algorithmic framework for constructing full-genome haplotypes and phased diploid Hi-C maps.
  • To bridge the gap between short-range and long-range haplotype phasing.
  • To enable accurate inference of homologous chromosome-specific 3D genome organization.

Main Methods:

  • Integration of raw Hi-C data with partial short-range haplotypes.
  • Development of a computational framework to jointly analyze these datasets.
  • Validation using simulated and measured biological data, assessing performance against noise and data depth.

Main Results:

  • Successful recovery of ground truth haplotypes with high accuracy.
  • Generation of phased diploid Hi-C maps from un-phased data.
  • Demonstration of the method's robustness to varying data quality and length.

Conclusions:

  • The developed framework effectively combines complementary genomic data for comprehensive haplotype and 3D genome structure analysis.
  • This approach overcomes limitations of existing technologies, enabling more precise genetic and epigenetic studies.
  • Inferred 3D genome structures can reveal insights into functional genomics, such as transcription factor co-localization.