PT  - JOURNAL ARTICLE
AU  - Haoyang Zeng
AU  - Tatsunori Hashimoto
AU  - Daniel D Kang
AU  - David K Gifford
TI  - GERV: A Statistical Method for Generative Evaluation of Regulatory Variants for Transcription Factor Binding
AID  - 10.1101/017392
DP  - 2015 Jan 01
TA  - bioRxiv
PG  - 017392
4099  - http://biorxiv.org/content/early/2015/07/04/017392.short
4100  - http://biorxiv.org/content/early/2015/07/04/017392.full
AB  - The majority of disease-associated variants identified in genome-wide association studies (GWAS) 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 GWAS 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 as well as associated co-factor motifs. We show that GERV outperforms existing methods in predicting SNPs associated with allele-specific binding. GERV correctly predicts a validated causal variant among linked SNPs, 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 an-notating and prioritizing causal variants for experimental follow-up analysis. The implementation of GERV and related data are available at http://gerv.csail.mit.edu/