Methods for fine-mapping with chromatin and expression data

PLoS Genet. 2018 Feb 26;14(2):e1007240. doi: 10.1371/journal.pgen.1007240. eCollection 2018 Feb.


Recent studies have identified thousands of regions in the genome associated with chromatin modifications, which may in turn be affecting gene expression. Existing works have used heuristic methods to investigate the relationships between genome, epigenome, and gene expression, but, to our knowledge, none have explicitly modeled the chain of causality whereby genetic variants impact chromatin, which impacts gene expression. In this work we introduce a new hierarchical fine-mapping framework that integrates information across all three levels of data to better identify the causal variant and chromatin mark that are concordantly influencing gene expression. In simulations we show that our method is more accurate than existing approaches at identifying the causal mark influencing expression. We analyze empirical genetic, chromatin, and gene expression data from 65 African-ancestry and 47 European-ancestry individuals and show that many of the paths prioritized by our method are consistent with the proposed causal model and often lie in likely functional regions.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • African Continental Ancestry Group / genetics
  • Chromatin / genetics*
  • Chromatin / metabolism
  • Chromosome Mapping / methods*
  • Databases, Genetic
  • European Continental Ancestry Group / genetics
  • Gene Expression*
  • Genetic Loci
  • Genetic Markers
  • Genetic Predisposition to Disease
  • Genetic Variation
  • Genome-Wide Association Study
  • Humans
  • Linkage Disequilibrium
  • Models, Genetic
  • Polymorphism, Single Nucleotide
  • Quantitative Trait Loci
  • Statistics as Topic / methods


  • Chromatin
  • Genetic Markers