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

• Main Authors: Chenyang Dong, Shane P. Simonett, Sunyoung Shin, Donnie S. Stapleton, Kathryn L. Schueler, Gary A. Churchill, Leina Lu, Xiaoxiao Liu, Fulai Jin, Yan Li, Alan D. Attie, Mark P. Keller, Sündüz Keleş
• Format: Article
• Language: English
• Published: BMC 2021-08-01
• Series: Genome Biology
• Subjects: Fine-mapping; Molecular quantitative trait loci; Genome-wide association studies; Pancreatic islets; Diversity outbred mouse; ATAC-seq
• Online Access: https://doi.org/10.1186/s13059-021-02450-8

Abstract 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/ .