INFIMA leverages multi-omics model organism data to identify effector genes of human GWAS variants

Genome Biol. 2021 Aug 23;22(1):241. doi: 10.1186/s13059-021-02450-8.


Genome-wide association studies reveal many non-coding variants associated with complex traits. However, model organism studies largely remain as an untapped resource for unveiling the effector genes of non-coding variants. We develop INFIMA, Integrative Fine-Mapping, to pinpoint causal SNPs for diversity outbred (DO) mice eQTL by integrating founder mice multi-omics data including ATAC-seq, RNA-seq, footprinting, and in silico mutation analysis. We demonstrate INFIMA's superior performance compared to alternatives with human and mouse chromatin conformation capture datasets. We apply INFIMA to identify novel effector genes for GWAS variants associated with diabetes. The results of the application are available at .

Keywords: ATAC-seq; Diversity outbred mouse; Fine-mapping; Generative probabilistic modeling; Genome-wide association studies; Molecular quantitative trait loci; Pancreatic islets; Transfer learning.

Publication types

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

MeSH terms

  • Animals
  • Base Sequence
  • Chromatin / metabolism
  • Chromatin Immunoprecipitation Sequencing
  • Computer Simulation
  • Genetic Predisposition to Disease
  • Genetic Variation*
  • Genome-Wide Association Study*
  • Genomics
  • Humans
  • Mice
  • Physical Chromosome Mapping*
  • Polymorphism, Single Nucleotide / genetics
  • Quantitative Trait Loci / genetics
  • RNA-Seq
  • Statistics as Topic
  • Transcriptome / genetics


  • Chromatin