Abstract An important goal of human genetics is to understand the etiology of genetic traits. In the past few years, genome-wide association studies (GWAS) have identified thousands of chromosomal loci associated with hundreds of traits. GWAS provide a list of variants (single nucleotide polymorphisms, SNP) that are statistically associated with a phenotype, but do not offer any direct evidence about the biological mechanism. Most GWAS cancer susceptibility loci are located in non-coding regions suggesting that they function by modifying tracsriptional regulatory elements, such as enhancers and promoters of target genes. Thus, methods to help identify transcription factors that interact with regulatory regions containing risk associated variants can reveal biological mechanisms. We developed a method, SNP-FEMS (FPLC, EMSA, Mass Spec) that combines liquid chromatography (FPLC), electrophoretic mobility shift assays (EMSAs), and mass spectrometry to identify transcription factors interacting with putative regulatory regions at the 8q24 ovarian cancer risk locus. Human embryonic kidney 293FT nuclear extract was subjected to Sepharose 6 10/300 GL size fractionation and all the fractions were screened for allele-specific binding proteins by EMSA using a probe containing a genome-wide significant SNP. Fractions with positive signal were combined, concentrated and further fractionated with an ion exchange column MonoQ 5/10 GL to further reduce the complexity of the fractions. Fractions were screened by EMSA with the same probe, and the positive fractions were combined, concentrated, subjected to PAGE, in-gel trypsin digestion, and analyzed by Mass Spectrometry to identify proteins in the fractions. Bioinformatics analysis predicted different transcription factors binding to the putative regulatory region and the only transcription factor that was also present in our experimental dataset was the Cyclin AMP-dependent transcription factor ATF-1. This data suggests that ATF1 is implicated in ovarian cancer susceptibility and demonstrate the utility of the SNP-FEMS method to identify regulatory factors involved in ovarian cancer risk. This technique is applicable to any predisposition loci mediated by changes in regulatory elements. Citation Format: Gustavo A. Mendoza-Fandino, Nicholas Woods, Rebekah Baskin, Anxhela Gjyshi, Alvaro N. Monteiro. SNP-FEMS: a method to identify DNA binding proteins interacting with enhancer elements. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 841. doi:10.1158/1538-7445.AM2015-841