Cell type-specific gene expression in humans involves complex interactions 
between regulatory factors and DNA at enhancers and promoters. Mapping studies 
for expression quantitative trait loci (eQTLs), transcription factors (TFs) and 
chromatin markers have become widely used tools for identifying gene regulatory 
elements, but prediction of target genes remains a major challenge. Here, we 
integrate genome-wide data on TF-binding sites, chromatin markers and functional 
annotations to predict genes associated with human eQTLs. Using the random 
forest classifier, we found that genomic proximity plus five TF and chromatin 
features are able to predict >90% of target genes within 1 megabase of eQTLs. 
Despite being regularly used to map target genes, proximity is not a good 
indicator of eQTL targets for genes 150 kilobases away, but insulators, TF 
co-occurrence, open chromatin and functional similarities between TFs and genes 
are better indicators. Using all six features in the classifier achieved an area 
under the specificity and sensitivity curve of 0.91, much better compared with 
at most 0.75 for using any single feature. We hope this study will not only 
provide validation of eQTL-mapping studies, but also provide insight into the 
molecular mechanisms explaining how genetic variation can influence gene 
expression.