Functional fine-mapping of noncoding risk variants in amyotrophic lateral sclerosis utilizing convolutional neural network

Ali Yousefian-Jazi, Min Kyung Sung, Taeyeop Lee, Yoon Ho Hong, Jung Kyoon Choi, Jinwook Choi

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Recent large-scale genome-wide association studies have identified common genetic variations that may contribute to the risk of amyotrophic lateral sclerosis (ALS). However, pinpointing the risk variants in noncoding regions and underlying biological mechanisms remains a major challenge. Here, we constructed a convolutional neural network model with a large-scale GWAS meta-analysis dataset to unravel functional noncoding variants associated with ALS based on their epigenetic features. After filtering and prioritizing of candidates, we fine-mapped two new risk variants, rs2370964 and rs3093720, on chromosome 3 and 17, respectively. Further analysis revealed that these polymorphisms are associated with the expression level of CX3CR1 and TNFAIP1, and affect the transcription factor binding sites for CTCF, NFATc1 and NR3C1. Our results may provide new insights for ALS pathogenesis, and the proposed research methodology can be applied for other complex diseases as well.

Original languageEnglish
Article number12872
JournalScientific Reports
Volume10
Issue number1
DOIs
StatePublished - 1 Dec 2020

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