|
Activity Number:
|
43
- Statistical Genetics II – New Models for Complex Study Designs
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Monday, August 3, 2020 : 10:00 AM to 2:00 PM
|
|
Sponsor:
|
Section on Statistics in Genomics and Genetics
|
|
Abstract #311078
|
|
|
Title:
|
A Statistical Integration Framework for Model Organism Multi-Omics Data to Identify Target Genes of Human GWAS Variants
|
|
Author(s):
|
Chenyang Dong* and Mark Keller and Shane Simonett and Sunyoung Shin and Donnie Stapleton and Gary Churchill and Alan Attie and Sunduz Keles
|
|
Companies:
|
University of Wisconsin-Madison and University of Wisconsin-Madison and University of Wisconsin-Madison and University of Texas at Dallas and University of Wisconsin-Madison and The Jackson Laboratory and University of Wisconsin-Madison and University of Wisconsin, Madison
|
|
Keywords:
|
Fine-mapping;
Molecular Quantitative Trait Loci;
GWAS;
Model organism;
ATAC-seq;
Generative probablistic modeling
|
|
Abstract:
|
Genome-wide association studies (GWAS) revealed single nucleotide variants (SNV) that are statistically associated with diseases. However, target genes at most GWAS risk loci remain unknown. While transcriptome-wide association studies elucidate candidate genes, model organism studies remain as an untapped resource for unveiling susceptibility genes and investigating the findings of human GWAS functionally. A recent eQTL study in Diversity Outbred (DO) mice islet cells identified hundreds of eQTL genes; however, it lacked the resolution to illuminate SNV deriving the eQTL signals. To address this bottleneck, we developed a framework named INFIMA for Integrative Fine-Mapping with Model Organism Multi-Omics Data. In addition to leveraging ATAC-seq and RNA-seq data from DO mice founder strains, INFIMA exploited footprinting and in silico mutation analysis of variants for fine-mapping published DO mice eQTL. We utilized the fine-mapped eQTL and identified novel susceptibility genes for the human diabetic traits GWAS loci. Validation of INFIMA results with chromatin capture data revealed INFIMA as a robust method for fine-mapping and transferring the results to human genome.
|
Authors who are presenting talks have a * after their name.
