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Thousands of non-coding SNPs have been linked to human diseases in the past. The identification of causal alleles within this pool of disease-associated non-coding SNPs is largely impossible due to the inability to accurately quantify the impact of noncoding variation. To overcome this challenge, we developed a computational model that uses ChIP-seq intensity variation in response to non-coding allelic change as a proxy to the quantification of the biological role of non-coding SNPs. We applieddoi:10.1093/nar/gku1318 pmid:25520196 pmcid:PMC4288203 fatcat:x6s5eugwkrgmrhksv2piaeqina