software : we used the r statistical computing software ( v3.2.2 ) and bioconductor ( v3.2 ) packages on os x 10.11.4 . we obtained 397 tfbs location data files ( derived from chip - seq analysis of 133 tfs in 68 human cell lines ) from the public repository of the encode project7 via the university of california santa cruz genome browser portal . additionally , we obtained data files containing esnps and associated eqtl p values from online supplementary data for two large - cohort monocyte eqtl studies involving 1,773 unrelated individuals.10,11 we carried out all genome analyses using coordinates from the grch37/hg19 human reference genome assembly . we identified mouse orthologs expressed in monocytes and other immune cell types of the top tfs using the immunological genome ( immgen ) project database.32 we used the gene annotation portal , biogps,33 to obtain human microarray - based gene expression measurements for the top - ranked tfs . we mapped genomic intervals containing snps that are in ld with each esnp using the snp annotation and proxy ( snap ) tool34 using haplotype data from the 1,000 genomes project,35 including only snps with r  1.0 and distance 10 kbp . we created a set of allowed regions for random eqtl ld block placement ( for use as our background model for tfbs / eqtl overlap counts ) by combining peak regions from all 397 tf chip - seq data sets with the monocyte eqtls and merging together any regions whose edges were within 1.9 kbp of one another ( selected so that the resulting set of allowed regions covered ~25% of the genome ) . as a background model for the counts of tfbs / eqtl overlaps , we placed all eqtl ld blocks into random nonoverlapping locations within the set of allowed regions . for each tf chip - seq data set , the count of peak center coordinates that overlapped with an eqtl ld block was computed , for both the human eqtl ld blocks and ( as a background model ) 1,000 iterations of the randomly placed ld blocks . for each chip - seq data set , we computed z - scores for the overlap counts using the mean and standard deviation of the background model - based counts ( where z > > 0 indicates that the count of regions of overlap between the eqtls and the chip - seq peaks is higher than would be expected based on the overlap count distribution of the null model where the eqtl ld blocks are randomly placed ) . for scoring the enrichment of cell types among chip - seq data sets ranked by z ( in descending order ) as well as scoring the enrichment of monocyte - regulating tfs among tfs ranked by adjusted p value ( padj ) ( in ascending order ) , we used the gene set enrichment analysis ( gsea ) statistical test17 with p = 0 ( unweighted ) to obtain an enrichment score ( e score ) . for testing cell types among chip - seq data sets , a higher e score means that the chip - seq experiments corresponding to the cell type or tf have lower ranks for their z - scores ( ie , higher z - scores ) than would be expected by chance . for testing monocyte - regulating tfs among all tfs ranked by padj , a higher e score means that the monocyte - regulating tfs have lower padj value ranks than would be expected by chance . to test for e 0 significance , we used a permutation - based approach36 with at least 1,000 iterations . for filtering tfs , we computed the average ( over the 1,000 iterations ) fraction of chip - seq peaks whose centers overlap one of the randomly placed ld blocks ; the probability was used in a one - tailed binomial test . hochberg false discovery rate method.37 we used genemania38 ( 4/16/2016 release ) to construct the interaction network for the top 27 tfs ( by p value ) . the software source code for this method is available under a free - software , open - source ( apache 2.0 ) license at github.com/ramseylab/eqtlchiptest . we obtained 397 tfbs location data files ( derived from chip - seq analysis of 133 tfs in 68 human cell lines ) from the public repository of the encode project7 via the university of california santa cruz genome browser portal . additionally , we obtained data files containing esnps and associated eqtl p values from online supplementary data for two large - cohort monocyte eqtl studies involving 1,773 unrelated individuals.10,11 we carried out all genome analyses using coordinates from the grch37/hg19 human reference genome assembly . we identified mouse orthologs expressed in monocytes and other immune cell types of the top tfs using the immunological genome ( immgen ) project database.32 we used the gene annotation portal , biogps,33 to obtain human microarray - based gene expression measurements for the top - ranked tfs . we mapped genomic intervals containing snps that are in ld with each esnp using the snp annotation and proxy ( snap ) tool34 using haplotype data from the 1,000 genomes project,35 including only snps with r  1.0 and distance 10 kbp . we created a set of allowed regions for random eqtl ld block placement ( for use as our background model for tfbs / eqtl overlap counts ) by combining peak regions from all 397 tf chip - seq data sets with the monocyte eqtls and merging together any regions whose edges were within 1.9 kbp of one another ( selected so that the resulting set of allowed regions covered ~25% of the genome ) . as a background model for the counts of tfbs / eqtl overlaps , we placed all eqtl ld blocks into random nonoverlapping locations within the set of allowed regions . for each tf chip - seq data set , the count of peak center coordinates that overlapped with an eqtl ld block was computed , for both the human eqtl ld blocks and ( as a background model ) 1,000 iterations of the randomly placed ld blocks . for each chip - seq data set , we computed z - scores for the overlap counts using the mean and standard deviation of the background model - based counts ( where z > > 0 indicates that the count of regions of overlap between the eqtls and the chip - seq peaks is higher than would be expected based on the overlap count distribution of the null model where the eqtl ld blocks are randomly placed ) . for scoring the enrichment of cell types among chip - seq data sets ranked by z ( in descending order ) as well as scoring the enrichment of monocyte - regulating tfs among tfs ranked by adjusted p value ( padj ) ( in ascending order ) , we used the gene set enrichment analysis ( gsea ) statistical test17 with p = 0 ( unweighted ) to obtain an enrichment score ( e score ) . for testing cell types among chip - seq data sets , a higher e score means that the chip - seq experiments corresponding to the cell type or tf have lower ranks for their z - scores ( ie , higher z - scores ) than would be expected by chance . for testing monocyte - regulating tfs among all tfs ranked by padj , a higher e score means that the monocyte - regulating tfs have lower padj value ranks than would be expected by chance . to test for e 0 significance , we used a permutation - based approach36 with at least 1,000 iterations . for filtering tfs , we computed the average ( over the 1,000 iterations ) fraction of chip - seq peaks whose centers overlap one of the randomly placed ld blocks ; the probability was used in a one - tailed binomial test . we used genemania38 ( 4/16/2016 release ) to construct the interaction network for the top 27 tfs ( by p value ) . the software source code for this method is available under a free - software , open - source ( apache 2.0 ) license at github.com/ramseylab/eqtlchiptest .
we describe a novel computational approach to identify transcription factors ( tfs ) that are candidate regulators in a human cell type of interest . 
 our approach involves integrating cell type - specific expression quantitative trait locus ( eqtl ) data and tf data from chromatin immunoprecipitation - to - tag - sequencing ( chip - seq ) experiments in cell lines . to test the method 
 , we used eqtl data from human monocytes in order to screen for tfs . using a list of known monocyte - regulating tfs 
 , we tested the hypothesis that the binding sites of cell type - specific tf regulators would be concentrated in the vicinity of monocyte eqtls . 
 for each of 397 chip - seq data sets , we obtained an enrichment ratio for the number of chip - seq peaks that are located within monocyte eqtls . 
 we ranked chip - seq data sets according to their statistical significances for eqtl overlap , and from this ranking , we observed that monocyte - regulating tfs are more highly ranked than would be expected by chance . 
 we identified 27 tfs that had significant monocyte enrichment scores and mapped them into a protein interaction network . 
 our analysis uncovered two novel candidate monocyte - regulating tfs , bclaf1 and sin3a . 
 our approach is an efficient method to identify candidate tfs that can be used for any cell / tissue type for which eqtl data are available .