Mar 18, 2017

cepip: context-dependent epigenomic weighting for prioritization of regulatory variants and disease-associated genes

Genome Biology
Mulin Jun LiJunwen Wang

Abstract

It remains challenging to predict regulatory variants in particular tissues or cell types due to highly context-specific gene regulation. By connecting large-scale epigenomic profiles to expression quantitative trait loci (eQTLs) in a wide range of human tissues/cell types, we identify critical chromatin features that predict variant regulatory potential. We present cepip, a joint likelihood framework, for estimating a variant's regulatory probability in a context-dependent manner. Our method exhibits significant GWAS signal enrichment and is superior to existing cell type-specific methods. Furthermore, using phenotypically relevant epigenomes to weight the GWAS single-nucleotide polymorphisms, we improve the statistical power of the gene-based association test.

Mentioned in this Paper

Genome-Wide Association Study
Quantitative Trait Loci
Genome
Genes
Tissue Specificity
Weighing Patient
Gene Expression
Gene Delivery Systems
Nucleotides
Cell Type

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