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Activity Number: 46 - Statistical Challenges and Breakthroughs in Diabetes and Obesity Research in the Big Data Era
Type: Topic-Contributed
Date/Time: Sunday, August 8, 2021 : 3:30 PM to 5:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #317422
Title: Prioritization of Causal Genes and Variants in Type 1 Diabetes Using Chromatin Accessibility and Gene Expression Profiling
Author(s): Catherine C Robertson* and Suna Onengut-Gumuscu and Patrick Concannon and Wei-Min Chen and Stephen S Rich
Companies: University of Virginia and University of Virginia and University of Florida and University of Virginia and University of Virginia
Keywords: type 1 diabetes; colocalization; bayesian; posterior probability; genetics; chromatin

Motivation: Genetic association studies have identified over 70 genome regions involved in type 1 diabetes (T1D). However, determining causal variants has been challenging because most variants lie in intergenic regions with regulatory functions that are difficult to decipher. To prioritize causal variants, we built maps of genetic variants influencing chromatin activity in CD4+ T cells and integrated them with T1D associations using Bayesian colocalization. Methods: We generated chromatin accessibility profiles (ATAC-seq) from CD4+ T cells isolated from 279 individuals from the Type 1 Diabetes Genetics Consortium (T1DGC). We tested for association between T1D risk variants and chromatin accessibility (caQTLs). We then used Bayesian colocalization to estimate the probability that observed associations were driven by the same genetic variants. Finally, we used public maps of genetic variation regulating gene expression (eQTLs) to identify potential target genes. Results: Bayesian colocalization identified six regions where data were consistent with the same variant driving association with T1D, chromatin accessibility, and gene expression, highlighting several candidate T1D genes.

Authors who are presenting talks have a * after their name.

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