Presentation Title: High-throughput Reporter Assay Reveals Functional Impact of 3’-UTR SNPs Associated with Neurological and Psychiatric Disorders
Author Name(s): Andy B. Chen1, Kriti Thapa2, Hongyu Gao1, Jill L. Reiter1, Junjie Zhang1, Xiaoling Xuei1, Hongmei Gu2, Yue Wang1, Howard J. Edenberg1,2, Yunlong Liu1
Author Department and School Affiliation: 1Department of Medical and Molecular Genetics, 2Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, United States of America
Abstract: Genome-wide association studies (GWAS) can identify noncoding variants associated with specific traits or phenotypes, but cannot distinguish whether such variants are functional. High-throughput reporter (HTR) assays can be used to experimentally evaluate the impact of genetic variants on gene expression. In this study, our objective was to systematically evaluate the functional activity of 3’-UTR SNPs associated with neurological disorders. We gathered SNPs from the GWAS Catalog that were associated with any neurological disorder trait with p-value < 10-5. For each SNP, we identified the region that was in linkage disequilibrium (r2 > 0.8) and retrieved all the common 3’-UTR SNPs (allele-frequency > 0.05) within that region. We further used an HTR assay to measure the impact of the 3’-UTR variants in SH-SY5Y neuroblastoma cells and a microglial cell line. Of the 13,515 3’-UTR SNPs tested, 400 and 657 significantly impacted gene expression in SH-SY5Y and microglia, respectively. These results were then used to train a deep-learning model to predict the impact of variants and identify features that contribute to the predictions. In conclusion, this study demonstrates that HTR assays combined with advanced machine-learning models can be used to identify causal non-coding variants to further understand the etiology of diseases.