Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Enhancer scanning to locate regulatory regions in genomic loci


This protocol provides a rapid, streamlined and scalable strategy to systematically scan genomic regions for the presence of transcriptional regulatory regions that are active in a specific cell type. It creates genomic tiles spanning a region of interest that are subsequently cloned by recombination into a luciferase reporter vector containing the simian virus 40 promoter. Tiling clones are transfected into specific cell types to test for the presence of transcriptional regulatory regions. The protocol includes testing of different single-nucleotide polymorphism (SNP) alleles to determine their effect on regulatory activity. This procedure provides a systematic framework for identifying candidate functional SNPs within a locus during functional analysis of genome-wide association studies. This protocol adapts and combines previous well-established molecular biology methods to provide a streamlined strategy, based on automated primer design and recombinational cloning, allowing one to rapidly go from a genomic locus to a set of candidate functional SNPs in 8 weeks.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type



Prices may be subject to local taxes which are calculated during checkout

Figure 1
Figure 2: Strategies for analyzing regions containing candidate functional SNPs.
Figure 3: Vector maps.
Figure 4: Guide for transfections in a 96-well format.
Figure 5: Typical anticipated results.


  1. Manolio, T.A. Genomewide association studies and assessment of the risk of disease. N. Engl. J. Med. 363, 166–176 (2010).

    Article  CAS  Google Scholar 

  2. Lewis, C.M. & Knight, J. Introduction to genetic association studies. Cold Spring Harb. Protoc. 2012, 297–306 (2012).

    Article  Google Scholar 

  3. Freedman, M.L. et al. Principles for the post-GWAS functional characterization of cancer risk loci. Nat. Genet. 43, 513–518 (2011).

    Article  CAS  Google Scholar 

  4. Edwards, S.L., Beesley, J., French, J.D. & Dunning, A.M. Beyond GWASs: illuminating the dark road from association to function. Am. J. Hum. Genet. 93, 779–797 (2013).

    Article  CAS  Google Scholar 

  5. Monteiro, A.N. & Freedman, M.L. Lessons from postgenome-wide association studies: functional analysis of cancer predisposition loci. J. Intern. Med. 274, 414–424 (2013).

    Article  CAS  Google Scholar 

  6. Tang, W. et al. Mapping of the UGT1A locus identifies an uncommon coding variant that affects mRNA expression and protects from bladder cancer. Hum. Mol. Genet. 21, 1918–1930 (2012).

    Article  CAS  Google Scholar 

  7. Maurano, M.T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).

    Article  CAS  Google Scholar 

  8. Sakoda, L.C., Jorgenson, E. & Witte, J.S. Turning of COGS moves forward findings for hormonally mediated cancers. Nat. Genet. 45, 345–348 (2013).

    Article  CAS  Google Scholar 

  9. Chung, C.C., Magalhaes, W.C., Gonzalez-Bosquet, J. & Chanock, S.J. Genome-wide association studies in cancer—current and future directions. Carcinogenesis 31, 111–120 (2010).

    Article  CAS  Google Scholar 

  10. Carey, M. & Smale, S.T. in Transcriptional Regulation in Eukaryotes: Concepts, Strategies and Techniques (Cold Spring Harbor Laboratory Press, 1999).

  11. Pharoah, P.D. et al. GWAS meta-analysis and replication identifies three new susceptibility loci for ovarian cancer. Nat. Genet. 45, 362–370 (2013).

    Article  CAS  Google Scholar 

  12. Baskin, R. et al. Functional analysis of the 11q23.3 glioma susceptibility locus implicates PHLDB1 and DDX6 in glioma susceptibility. Sci. Rep. 5, 17367 (2015).

    Article  CAS  Google Scholar 

  13. Neph, S. et al. An expansive human regulatory lexicon encoded in transcription factor footprints. Nature 489, 83–90 (2012).

    Article  CAS  Google Scholar 

  14. Thurman, R.E. et al. The accessible chromatin landscape of the human genome. Nature 489, 75–82 (2012).

    Article  CAS  Google Scholar 

  15. Dunham, I. et al. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

    Article  CAS  Google Scholar 

  16. Sanyal, A., Lajoie, B.R., Jain, G. & Dekker, J. The long-range interaction landscape of gene promoters. Nature 489, 109–113 (2012).

    Article  CAS  Google Scholar 

  17. Djebali, S. et al. Landscape of transcription in human cells. Nature 489, 101–108 (2012).

    Article  CAS  Google Scholar 

  18. Gerstein, M.B. et al. Architecture of the human regulatory network derived from ENCODE data. Nature 489, 91–100 (2012).

    Article  CAS  Google Scholar 

  19. Andersson, R. et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455–461 (2014).

    Article  CAS  Google Scholar 

  20. FANTOM Consortium and the RIKEN PMI and CLST (DGT). A promoter-level mammalian expression atlas. Nature 507, 462–470 (2014).

  21. Stergachis, A.B. et al. Exonic transcription factor binding directs codon choice and affects protein evolution. Science 342, 1367–1372 (2013).

    Article  CAS  Google Scholar 

  22. Mansour, M.R. et al. Oncogene regulation. An oncogenic super-enhancer formed through somatic mutation of a noncoding intergenic element. Science 346, 1373–1377 (2014).

    Article  CAS  Google Scholar 

  23. Melnikov, A. et al. Systematic dissection and optimization of inducible enhancers in human cells using a massively parallel reporter assay. Nat. Biotechnol. 30, 271–277 (2012).

    Article  CAS  Google Scholar 

  24. Arnold, C.D. et al. Genome-wide quantitative enhancer activity maps identified by STARR-seq. Science 339, 1074–1077 (2013).

    Article  CAS  Google Scholar 

  25. Plank, J.L. & Dean, A. Enhancer function: mechanistic and genome-wide insights come together. Mol. Cell 55, 5–14 (2014).

    Article  CAS  Google Scholar 

  26. van Arensbergen, J., van Steensel, B. & Bussemaker, H.J. In search of the determinants of enhancer-promoter interaction specificity. Trends Cell Biol. 24, 695–702 (2014).

    Article  CAS  Google Scholar 

  27. Carlson, C.S. et al. Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium. Am. J. Hum. Genet. 74, 106–120 (2004).

    Article  CAS  Google Scholar 

  28. Hazelett, D.J. et al. Comprehensive functional annotation of 77 prostate cancer risk loci. PLoS Genet. 10, e1004102 (2014).

    Article  Google Scholar 

  29. French, J.D. et al. Functional variants at the 11q13 risk locus for breast cancer regulate cyclin D1 expression through long-range enhancers. Am. J. Hum. Genet. 92, 489–503 (2013).

    Article  CAS  Google Scholar 

  30. Boyle, A.P. et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 22, 1790–1797 (2012).

    Article  CAS  Google Scholar 

  31. Coetzee, S.G., Rhie, S.K., Berman, B.P., Coetzee, G.A. & Noushmehr, H. FunciSNP: an R/Bioconductor tool integrating functional non-coding data sets with genetic association studies to identify candidate regulatory SNPs. Nucleic Acids Res. 40, e139 (2012).

    Article  CAS  Google Scholar 

  32. Blackwood, E.M. & Kadonaga, J.T. Going the distance: a current view of enhancer action. Science 281, 60–63 (1998).

    Article  CAS  Google Scholar 

  33. Birney, E. et al. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 447, 799–816 (2007).

    Article  CAS  Google Scholar 

  34. Khoury, G. & Gruss, P. Enhancer elements. Cell 33, 313–314 (1983).

    Article  CAS  Google Scholar 

  35. Braman, J., Papworth, C. & Greener, A. Site-directed mutagenesis using double-stranded plasmid DNA templates. Methods Mol. Biol. 57, 31–44 (1996).

    CAS  PubMed  Google Scholar 

  36. Machiela, M.J. & Chanock, S.J. LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants. Bioinformatics 31, 3555–3557 (2015).

    Article  CAS  Google Scholar 

  37. Millot, G.A. et al. A guide for functional analysis of BRCA1 variants of uncertain significance. Hum. Mutat. 33, 1526–1537 (2012).

    Article  CAS  Google Scholar 

  38. Ikram, M.K. et al. Four novel loci (19q13, 6q24, 12q24 and 5q14) influence the microcirculation in vivo. PLoS Genet. 6, e1001184 (2010).

    Article  Google Scholar 

Download references


This work was supported by the US National Institutes of Health (NIH) Genetic Association and Mechanisms in Oncology (GAME-ON) through the National Cancer Institute (NCI) U19 award (no. CA148112), the Ovarian Cancer Research Foundation (no. 258807), the Phi Beta Psi Foundation and in part by the Molecular Genomics Core Facilities at the Moffitt Cancer Center through its NCI Cancer Center Support Grant (grant no. P30-CA76292). M.B. is an ARCS (Achievement Rewards for College Scientists) fellow and a recipient of the Ruth L. Kirschstein National Research Service Award (no. F31 CA165528). R.B. is a trainee on an NIH R25T award (no. CA147832). R.S.C. is supported by a fellowship from the Brazilian National Council for Scientific and Technological Development (CNPq). We thank A. Valle, P. Cilas Jr., B. Reid and X. Li for technical assistance.

Author information

Authors and Affiliations



M.B., A.G., R.S.C., N.T.W. and A.N.A.M. conceived the project and designed the experiments. M.B., G.M.-F., A.G., R.B. and R.S.C. performed the experiments. M.B., G.M.-F., A.G., R.B., R.S.C., M.A.C., N.T.W. and A.N.A.M. performed the analysis and contributed to the discussion and overall data interpretation. M.B., G.M.-F., A.G. and A.N.A.M. wrote the paper. All authors provided intellectual input and approved the final version of the manuscript.

Corresponding author

Correspondence to Alvaro N A Monteiro.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Table 1

Steps to generate a BED file containing SNP coordinates from LDlink or SNAP Proxy. Contains step-by-step examples of data generated from LDlink or SNA Proxy and formatted for uploading onto the UCSC Human Genome Browser. (XLSX 718 kb)

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Buckley, M., Gjyshi, A., Mendoza-Fandiño, G. et al. Enhancer scanning to locate regulatory regions in genomic loci. Nat Protoc 11, 46–60 (2016).

Download citation

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing