General Information
| Description | A statistical method for generative evaluation of regulatory variants for transcription factor binding |
| Homepage | http://gerv.csail.mit.edu/ |
Publications
GERV: a statistical method for generative evaluation of regulatory variants for transcription factor binding
Motivation:
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.
Results:
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 in breast cancer cell lines. Thus, GERV provides a powerful approach for functionally annotating and prioritizing causal variants for experimental follow-up analysis.
Availability and implementation:
The implementation of GERV and related data are available at http://gerv.csail.mit.edu/.
Contact:
gifford@mit.edu
Supplementary information:
Supplementary data are available at Bioinformatics online.
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