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

Autor: Dong C; Department of Statistics, University of Wisconsin-Madison, Madison, WI, USA., Simonett SP; Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA., Shin S; Department of Mathematical Sciences, University of Texas at Dallas, Richardson, TX, USA., Stapleton DS; Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA., Schueler KL; Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA., Churchill GA; The Jackson Laboratory, Bar Harbor, ME, USA., Lu L; Case Western University, Cleveland, OH, USA., Liu X; Case Western University, Cleveland, OH, USA., Jin F; Case Western University, Cleveland, OH, USA., Li Y; Case Western University, Cleveland, OH, USA., Attie AD; Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA., Keller MP; Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA. mark.keller@wisc.edu., Keleş S; Department of Statistics, University of Wisconsin-Madison, Madison, WI, USA. keles@stat.wisc.edu.; Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA. keles@stat.wisc.edu.
Jazyk: angličtina
Zdroj: Genome biology [Genome Biol] 2021 Aug 23; Vol. 22 (1), pp. 241. Date of Electronic Publication: 2021 Aug 23.
DOI: 10.1186/s13059-021-02450-8
Abstrakt: 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 http://www.statlab.wisc.edu/shiny/INFIMA/ .
(© 2021. The Author(s).)
Databáze: MEDLINE
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