Sai Zhang, Tobias Moll, Jasper Rubin-Sigler, Sharon Tu, Shuya Li, Enming Yuan, Menghui Liu, Afreen Butt, Calum Harvey, Sarah Gornall, Elham Alhalthli, Allan Shaw, Cleide Dos Santos Souza, Laura Ferraiuolo, Eran Hornstein, Tatyana Shelkovnikova, Charlotte H van Dijk, Ilia S Timpanaro, Kevin P Kenna, Jianyang Zeng, Philip S Tsao, Pamela J Shaw, Justin K Ichida, Johnathan Cooper-Knock, Michael P Snyder
Amyotrophic lateral sclerosis (ALS) is a fatal and incurable neurodegenerative disease caused by the selective and progressive death of motor neurons (MNs). Understanding the genetic and molecular factors influencing ALS survival is crucial for disease management and therapeutics. In this study, we introduce a deep learning-powered genetic analysis framework to link rare noncoding genetic variants to ALS survival. Using data from human induced pluripotent stem cell (iPSC)-derived MNs, this method prioritizes functional noncoding variants using deep learning, links cis-regulatory elements (CREs) to target genes using epigenomics data, and integrates these data through gene-level burden tests to identify survival-modifying variants, CREs, and genes...
April 1, 2024: medRxiv