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

Chenyang Dong,Yan Li,Sunyoung Shin,Donnie S Stapleton,Mark P Keller,Xiaoxiao Liu,Sündüz Keleş,Shane P Simonett,Leina Lu,Alan D Attie,Kathryn L Schueler,Fulai Jin,Gary A Churchill

doi: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/.

Highlights

Vast majority of disease and complex human trait-associated single nucleotide polymorphisms (SNPs) identified through genome-wide association studies (GWAS) are non-coding [1]
After quality control with transcription start site (TSS) enrichment analysis (Additional file 1: Figure S1) and data processing, we obtained 77.7 ± 4.1 million reads per sample which yielded a total of 51,014 accessible chromatin regions (Additional file 1: Figure S2)
We showed with promoter capture Hi-C data validation that INFIMA, with the current lift-over strategies that we employed, can be a powerful transfer learning approach for exploring susceptibility genes of human GWAS loci

Summary

Introduction

Vast majority of disease and complex human trait-associated single nucleotide polymorphisms (SNPs) identified through genome-wide association studies (GWAS) are non-coding [1]. This creates two key challenges for translation of genetic discoveries into disease mechanisms. Non-coding loci with risk SNPs span broad genomic regions that contain multiple genes [4]. This creates the second challenge of identifying the effector genes through which risk SNPs exert their impact on the phenotype, possibly via long-range chromatin interactions. With the advances in three-dimensional (3D) chromatin structure and interaction profiling, recent studies have successfully shown that a genetic variant is not necessarily causal for the nearest gene

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Results

Discussion

Conclusion