Oct 20, 2015

GERV: a statistical method for generative evaluation of regulatory variants for transcription factor binding

Bioinformatics
Haoyang ZengDavid K Gifford

Abstract

The majority of disease-associated variants identified in genome-wide association studies reside in noncoding regions of the genome with regulatory roles. Thus being able to interpret the functional consequence of a variant is essential for identifying causal variants in the analysis of genome-wide association studies. We present GERV (generative evaluation of regulatory variants), a novel computational method for predicting regulatory variants that affect transcription factor binding. GERV learns a k-mer-based generative model of transcription factor binding from ChIP-seq and DNase-seq data, and scores variants by computing the change of predicted ChIP-seq reads between the reference and alternate allele. The k-mers learned by GERV capture more sequence determinants of transcription factor binding than a motif-based approach alone, including both a transcription factor's canonical motif and associated co-factor motifs. We show that GERV outperforms existing methods in predicting single-nucleotide polymorphisms associated with allele-specific binding. GERV correctly predicts a validated causal variant among linked single-nucleotide polymorphisms and prioritizes the variants previously reported to modulate the binding of FOXA1 i...Continue Reading

  • References29
  • Citations5

References

Mentioned in this Paper

Severe Acute Respiratory Syndrome
Genome-Wide Association Study
Deoxyribonuclease I
Follow-up
Genome
Menopause
Bio-Informatics
Chromatin Immunoprecipitation
Plasma Protein Binding Capacity
Evaluation

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