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Sequencing and genotyping based studies, including Genome-Wide Association Studies (GWAS), candidate locus studies, and deep-sequencing studies, have identified many genetic variants that are statistically associated with a disease, trait, or phenotype. Contrary to early predictions, most of these variants (85-93%) are located in non-coding regions and do not change the amino acid sequence of proteins1,2. Interpreting the function of these non-coding variants and determining the biological mechanisms connecting them to the associated disease, trait, or phenotype has proven challenging3-6. We have developed a general strategy to identify the molecular mechanisms that link variants to an important intermediate phenotype – gene expression. This pipeline is specifically designed to identify modulation of TF binding by genetic variants. This strategy combines computational approaches and molecular biology techniques aimed to predict biological effects of candidate variants in silico, and verify these predictions empirically (Figure 1).

Figure 1: Strategic approach for the analysis of non-coding genetic variants. Steps that are not included in the detailed protocol associated with this manuscript are shaded in grey. Please click here to view a larger version of this figure.
In many cases, it is important to begin by expanding the list of variants to include all those in high linkage-disequilibrium (LD) with each statistically associated variant. LD is a measure of non-random association of alleles at two different chromosomal positions, which can be measured by the r2 statistic7. r2 is a measure of the linkage disequilibrium between two variants, with an r2 = 1 denoting perfect linkage between two variants. Alleles in high LD are found to co-segregate on the chromosome across ancestral populations. Current genotyping arrays do not include all known variants in the human genome. Instead, they exploit the LD within the human genome and include a subset of the known variants that act as proxies for other variants within a particular region of LD8. Thus, a variant without any biological consequence may be associated with a particular disease because it is in LD with the causal variant-the variant with a meaningful biological effect. Procedurally, it is recommended to convert the latest release of the 1,000 genomes project9 variant call files (vcf) into binary files compatible with PLINK10,11, an open-source tool for whole genome association analysis. Subsequently, all other genetic variants with LD r2 >0.8 with each input genetic variant can be identified as candidates. It is important to use the appropriate reference population for this step- e.g., if a variant was identified in subjects of European ancestry, data from subjects of similar ancestry should be used for LD expansion.
LD expansion often results in dozens of candidate variants, and it is likely that only a small fraction of these contribute to disease mechanism. Often, it is infeasible to experimentally examine each of these variants individually. It is therefore useful to leverage the thousands of publically available functional genomic datasets as a filter to prioritize the variants. For example, the ENCODE consortium12 has performed thousands of ChIP-seq experiments describing the binding of TFs and co-factors, and histone marks in a wide range of contexts, along with chromatin accessibility data from technologies such as DNase-seq13, ATAC-seq14, and FAIRE-seq15. Databases and web servers such as the UCSC Genome Browser16, Roadmap Epigenomics17, Blueprint Epigenome18, Cistrome19, and ReMap20 provide free access to data produced by these and other experimental techniques across a wide range of cell types and conditions. When there are too many variants to examine experimentally, these data can be used to prioritize those located within likely regulatory regions in relevant cell and tissue types. Further, in cases where a variant is within a ChIP-seq peak for a specific protein, these data can provide potential leads as to the specific TF(s) or co-factors whose binding might be affecting.
Next, the resulting prioritized variants are screened experimentally to validate predicted genotype-dependent protein binding using EMSA21,22. EMSA measures the change in the migration of the oligo on a non-reducing TBE gel. Fluorescently labeled oligo is incubated with the nuclear lysate, and binding of nuclear factors will retard the movement of the oligo on the gel. In this manner, oligo that has bound more nuclear factors will present as a stronger fluorescent signal upon scanning. Notably, EMSA does not require predictions about the specific proteins whose binding will be affected.
Once variants are identified that are located within predicted regulatory regions and are capable of differentially binding nuclear factors, computational methods are employed to predict the specific TF(s) whose binding they might affect. We prefer to use CIS-BP23,24, RegulomeDB25, UniProbe26, and JASPAR27. Once candidate TFs are identified, these predictions can be specifically tested using antibodies against these TFs (EMSA-supershifts and DAPA-Westerns). An EMSA-supershift involves the addition of a TF-specific antibody to the nuclear lysate and oligo. A positive result in an EMSA-supershift is represented as a further shift in the EMSA band, or a loss of the band (reviewed in reference28). In the complementary DAPA, a 5'-biotinylated oligo duplex containing the variant and the 20 base-pair flanking nucleotides are incubated with nuclear lysate from relevant cell type(s) to capture any nuclear factors specifically binding the oligos. The oligo duplex-nuclear factor complex is immobilized by streptavidin microbeads in a magnetic column. The bound nuclear factors are collected directly through elution29,48. Binding predictions can then be assessed by a Western blot using antibodies specific for the protein. In cases where there are no obvious predictions, or too many predictions, the elutions from variant pull-downs of the DAPA experiments can be sent to a proteomics core to identify candidate TFs using mass-spectrometry, which can subsequently be validated using these previously described methods.
In the remainder of the article, the detailed protocol for EMSA and DAPA analysis of genetic variants is provided.