Abstract:
:The majority of genome-wide association study (GWAS) risk variants reside in non-coding DNA sequences. Understanding how these sequence modifications lead to transcriptional alterations and cell-to-cell variability can help unraveling genotype-phenotype relationships. Here, we describe a computational method, dubbed CAPE, which calculates the likelihood of a genetic variant deactivating enhancers by disrupting the binding of transcription factors (TFs) in a given cellular context. CAPE learns sequence signatures associated with putative enhancers originating from large-scale sequencing experiments (such as ChIP-seq or DNase-seq) and models the change in enhancer signature upon a single nucleotide substitution. CAPE accurately identifies causative cis-regulatory variation including expression quantitative trait loci (eQTLs) and DNase I sensitivity quantitative trait loci (dsQTLs) in a tissue-specific manner with precision superior to several currently available methods. The presented method can be trained on any tissue-specific dataset of enhancers and known functional variants and applied to prioritize disease-associated variants in the corresponding tissue.
journal_name
Nucleic Acids Resjournal_title
Nucleic acids researchauthors
Li S,Alvarez RV,Sharan R,Landsman D,Ovcharenko Idoi
10.1093/nar/gkw1263subject
Has Abstractpub_date
2017-03-17 00:00:00pages
2307-2317issue
5eissn
0305-1048issn
1362-4962pii
gkw1263journal_volume
45pub_type
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