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A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells
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Identifying causal regulatory SNPs in ChIP-seq enhancers.

Di Huang1, Ivan Ovcharenko2

  • 1Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20892, USA.

Nucleic Acids Research
|December 19, 2014
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Identifying disease-causing genetic variants is hard. A new computational model quantifies non-coding single nucleotide polymorphism (SNP) impact by measuring changes in ChIP-seq intensity, revealing SNPs disrupting enhancer activity.

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Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Biology

Background:

  • Thousands of non-coding single nucleotide polymorphisms (SNPs) are associated with human diseases.
  • Quantifying the functional impact of non-coding variation remains a significant challenge in genetic research.

Purpose of the Study:

  • To develop and apply a novel computational model for quantifying the biological role of non-coding SNPs.
  • To identify specific SNPs within enhancers that disrupt their activity due to allelic changes.

Main Methods:

  • Developed a computational model using ChIP-seq intensity variation as a proxy for non-coding SNP impact.
  • Applied the model to HepG2 enhancer regions to detect SNPs affecting enhancer activity.
  • Analyzed the enrichment of identified SNPs in transcription factor binding sites and liver expression quantitative trait loci (eQTLs).

Main Results:

  • Identified 4796 enhancer SNPs capable of disrupting enhancer activity upon allelic change.
  • These SNPs are significantly over-represented in binding sites of HNF4 and FOXA transcription factors and liver eQTLs.
  • Associated SNPs with liver GWAS traits (e.g., type I diabetes) and altered HDL/LDL cholesterol levels.

Conclusions:

  • The developed computational model effectively quantifies the impact of non-coding SNPs on enhancer activity.
  • The findings highlight the role of specific SNPs in liver-related diseases and metabolic traits.
  • The model is broadly applicable for mapping causal regulatory SNPs in any enhancer set.