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It is normally assumed that the genetic variant with the highest association is functional; however, that is usually hard to prove due to linkage disequilibrium (LD) involving SNPs (The 1000 Genomes Project. 2012). Only 15 with the anticipated functional variants are situated in coding regions, and many are believed to act via the regulation of gene expression, which can be in agreement together with the notion that gene activity is largely genetically controlled (Schadt et al. 2008; Lappalainen et al. 2013). Identifying the SNP with all the strongest association to gene expression (eSNP) on a haplotype was proposed as a suggests to obtaining the variant driving the association to illness, and consequently the NIH started the Genotype Tissue Expression project (GTEX) to correlate a person’s genotype with gene expression in many tissues. Having said that, eSNPs are also frequently in LD with other SNPs leaving the question of directScience for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden Science for Life Laboratory, Division of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden Department of Biosciences and Nutrition, Center for Biosciences, Karolinska Institute, Novum, Huddinge, Sweden Science for Life Laboratory, Division of Healthcare Sciences, Uppsala University, Uppsala, Sweden Broad Institute of MIT and Harvard, Cambridge, MA, USAHum Genet (2016) 135:485functionality generally unanswered. The Encyclopedia of DNA Components (ENCODE) project (The ENCODE Project Consortium 2012) was initiated with the aim of locating all functional components inside the genome. Information on chromatin and transcribed genes have been generated in cell lines and tissues and, according to these, candidate regulatory components were suggested. These regulatory components have already been shown to harbor an enrichment of GWAS-SNPs (The ENCODE Project Consortium 2012), but regardless of all efforts, to our knowledge, the functionality of around 20 SNPs have been described, e.g., the ones regulating SORT1 (Musunuru et al. 2010), RFX6 (Huang et al. 2014) and TOX3 (Cowper-Sal et al. 2012). In these cases, a typical feature is the fact that the functional SNP is located inside a motif for any transcription issue where the alleles differ in their ability to bind the transcription aspect (TF) and thus their capacity to regulate a single or extra genes. Immediately after our initial discovery that signals in DNA enriched by chromatin immunoprecipitation (ChIP) could differ between alleles (Ameur et al. 2009), the strategy has been applied genome wide employing next-generation sequencing (ChIP-seq) data generated by us (Motallebipour et al. 2009; Wallerman et al. 2009) and others (purchase SIS3 Kasowski et al. 2010; Rozowsky et al. 2011; Reddy et al. 2012). SNPs with allele-specific (AS) TF binding are most likely to become functional and in this project we systematically searched for them. We have characterized AS-SNPs in four big ENCODE cell lines and made functional validations. This has resulted within a collection of 9962 candidate functional SNPs.Genomic capabilities AS-SNPs collections were intersected and filtered with quite a few publicly accessible databases: NHGRI GWAS catalog (Jan 2014), collection of signal artifact blacklisted ENCODE regions (The ENCODE Project Consortium 2012), 1000 Genomes SNPs collection (1000 Genomes project, phase1_release_v3.20101123) and lymphoblastoid cell lines eQTLs collections (Lappalainen et al. 2013). Information from the 1000 Genomes project (The 1000 Genomes Project 2012) was utilised to.