Understanding the mechanisms of gene regulation is fundamental for both evolution and disease research. The rise of genomics has made it possible to collect a huge amount of data for the study of human polymorphisms and gene regulation. With these data it is common to look for quantitative trait loci (QTLs), polymorphisms in the genome with genotypes that are correlated with a regulatory measurement, most commonly mRNA levels. However, to understand the effects of genetic variation we must look beyond QTLs for mRNA levels in a single tissue. Gene regulation may vary across tissues and polymorphisms may take effect at many stages including chromatin, transcription, translation, or degradation levels. However, studying all of these can introduce challenges as these experiments are often expensive and samples hard to acquire. Moreover, to fully understand gene regulation variation we must look for patterns in local sequence context to explain why some polymorphism are QTLs and why others are not. I will present WASP, a set of tools designed to (i) remove experimental artefacts from QTL studies, (ii) account for the many sources of variation in sequencing data, and (iii) maximize power to detect QTLs in small sample sizes. I will then describe how I extended WASP to look for consistent effects across polymorphisms with similar contexts. I will demonstrate how I applied WASP to discover QTLs for four different histone modifications in the human genome. These modifications are important markers of function and chromatin state and were measured in 10 unrelated human lymphoblastoid cell lines. Even with this limited sample size, I was able to identify hundreds of QTLs using WASP. I found that polymorphisms interrupting transcription factor binding sites consistently alter local histone modifications. Finally, I found that variants impacting chromatin at distal regulatory sites frequently also direct changes in chromatin and gene expression at associated promoters.